From 90ca058c4414060c6399ddd5efc1aea39690d6bd Mon Sep 17 00:00:00 2001 From: Jan Pipek Date: Fri, 10 Jan 2020 19:02:30 +0100 Subject: [PATCH 01/15] EDA2: before applying comments --- lessons/pydata/pandas_types/index.ipynb | 6941 +++++++++++++++++ .../pydata/visualization_basics/index.ipynb | 4922 ++++++++++++ 2 files changed, 11863 insertions(+) create mode 100644 lessons/pydata/pandas_types/index.ipynb create mode 100644 lessons/pydata/visualization_basics/index.ipynb diff --git a/lessons/pydata/pandas_types/index.ipynb b/lessons/pydata/pandas_types/index.ipynb new file mode 100644 index 0000000000..bb925d07bd --- /dev/null +++ b/lessons/pydata/pandas_types/index.ipynb @@ -0,0 +1,6941 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas - datové typy a manipulace se sloupci\n", + "\n", + "V minulé lekci jsme si představili knihovnu pandas a její základní třídy: `Series`, `DataFrame` a `Index`. Brali jsme je ovšem jako statické objekty, na které jsme se pouze dívali.\n", + "\n", + "V této lekci začneme upravovat existující tabulky. Ukážeme si:\n", + "\n", + "* jak přidat či ubrat sloupce a řádky\n", + "* jak změnit hodnotu konkrétní buňky\n", + "* jaké datové typy se hodí pro který účel\n", + "* aritmetické a logické operace, které lze se sloupci provádět\n", + "* filtrování a řazení řádků\n", + "\n", + "A jelikož o výsledky práce určitě nechceš přijít, přijde nakonec vhod i ukládání výsledků do externích souborů." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Manipulace s DataFrames\n", + "\n", + "Pro rozehřátí budeme pracovat s malou tabulkou obsahující několik základních informací o planetách, které snadno najdeš např. na [wikipedii](https://en.wikipedia.org/wiki/Planet)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_doba
jmeno
Merkur☿0.390.24
Venuše♀0.720.62
Země⊕1.001.00
Mars♂1.521.88
Jupiter♃5.2011.86
Saturn♄9.5429.46
Uran♅19.2284.01
Neptun♆30.06164.80
\n", + "
" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba\n", + "jmeno \n", + "Merkur ☿ 0.39 0.24\n", + "Venuše ♀ 0.72 0.62\n", + "Země ⊕ 1.00 1.00\n", + "Mars ♂ 1.52 1.88\n", + "Jupiter ♃ 5.20 11.86\n", + "Saturn ♄ 9.54 29.46\n", + "Uran ♅ 19.22 84.01\n", + "Neptun ♆ 30.06 164.80" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety = pd.DataFrame({\n", + " \"jmeno\": [\"Merkur\", \"Venuše\", \"Země\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uran\", \"Neptun\"],\n", + " \"symbol\": [\"☿\", \"♀\", \"⊕\", \"♂\", \"♃\", \"♄\", \"♅\", \"♆\"],\n", + " \"obezna_poloosa\": [0.39, 0.72, 1.00, 1.52, 5.20, 9.54, 19.22, 30.06],\n", + " \"obezna_doba\": [0.24, 0.62, 1, 1.88, 11.86, 29.46, 84.01, 164.8],\n", + "})\n", + "planety = planety.set_index(\"jmeno\") # S jmenným indexem se ti bude snáze pracovat\n", + "planety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Přidání nového sloupce\n", + "\n", + "Když chceme přidat nový sloupec (`Series`), přiřadíme ho do `DataFrame` jako hodnotu do slovníku - tedy v hranatých závorkách s názvem sloupce. Dobrá zpráva je, že stejně jako v konstruktoru, `pandas` si \"poradí\" jak se `Series`, tak s obyčejným seznamem.\n", + "\n", + "V našem konkrétním případě si najdeme a přidáme počet známých měsíců (velkých i malých)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesice
jmeno
Merkur☿0.390.240
Venuše♀0.720.620
Země⊕1.001.001
Mars♂1.521.882
Jupiter♃5.2011.8679
Saturn♄9.5429.4682
Uran♅19.2284.0127
Neptun♆30.06164.8014
\n", + "
" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice\n", + "jmeno \n", + "Merkur ☿ 0.39 0.24 0\n", + "Venuše ♀ 0.72 0.62 0\n", + "Země ⊕ 1.00 1.00 1\n", + "Mars ♂ 1.52 1.88 2\n", + "Jupiter ♃ 5.20 11.86 79\n", + "Saturn ♄ 9.54 29.46 82\n", + "Uran ♅ 19.22 84.01 27\n", + "Neptun ♆ 30.06 164.80 14" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mesice = [0, 0, 1, 2, 79, 82, 27, 14] # Alternativně mesice = pd.Series([...])\n", + "planety[\"mesice\"] = mesice\n", + "planety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "💡 V tomto případě jsme přímo upravili existující `DataFrame`. Většina metod / operací v `pandas` (už znáš např. `set_index`) ve výchozím nastavení vždy vrací nový objekt - je to dobrým zvykem, který budeme dodržovat. Přiřazování sloupců je jednou z výjimek tohoto jinak uznávaného pravidla (tou druhou je pohodlnost).\n", + "\n", + "
TODO: \n", + " Jak to píšu, tak mi to zase tak samozřejmé nepřijde. Nějak bych tohle chtěl zformulovat líp.
\n", + " \n", + "`DataFrame` nabízí ještě metodu `assign`, která nemění tabulku, ale vytváří její kopii s přidanými (nebo nahrazenými) sloupci:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesiceje_stavebnicema_vztah_k_vestonicim
jmeno
Merkur☿0.390.240TrueFalse
Venuše♀0.720.620FalseTrue
Země⊕1.001.001FalseFalse
Mars♂1.521.882FalseFalse
Jupiter♃5.2011.8679FalseFalse
Saturn♄9.5429.4682FalseFalse
Uran♅19.2284.0127FalseFalse
Neptun♆30.06164.8014FalseFalse
\n", + "
" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice je_stavebnice \\\n", + "jmeno \n", + "Merkur ☿ 0.39 0.24 0 True \n", + "Venuše ♀ 0.72 0.62 0 False \n", + "Země ⊕ 1.00 1.00 1 False \n", + "Mars ♂ 1.52 1.88 2 False \n", + "Jupiter ♃ 5.20 11.86 79 False \n", + "Saturn ♄ 9.54 29.46 82 False \n", + "Uran ♅ 19.22 84.01 27 False \n", + "Neptun ♆ 30.06 164.80 14 False \n", + "\n", + " ma_vztah_k_vestonicim \n", + "jmeno \n", + "Merkur False \n", + "Venuše True \n", + "Země False \n", + "Mars False \n", + "Jupiter False \n", + "Saturn False \n", + "Uran False \n", + "Neptun False " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Nový dočasný DataFrame\n", + "planety.assign(\n", + " je_stavebnice=[True, False, False, False, False, False, False, False],\n", + " ma_vztah_k_vestonicim=[False, True, False, False, False, False, False, False],\n", + ")\n", + "\n", + "# Objekt `planety` zůstal nezměněn." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol**: Zkus (jedním či druhým způsobem) přidat sloupec s rokem objevu (`\"objeveno\"`). Údaje najdeš např. zde: https://cs.wikipedia.org/wiki/Slune%C4%8Dn%C3%AD_soustava." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Není to zase tak často praktické, ale pro hodnoty nového sloupce lze použít i jednu skalární hodnotu:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "planety[\"je_planeta\"] = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Přidání nového řádku\n", + "\n", + "Když se strojem času vrátíme do dětství (nebo rané dospělosti) autorů těchto materiálů, tedy před rok 2006, kdy se v Praze konal astronomický kongres, který definoval pojem \"planeta\" (ale ne před rok 1930!), přibude nám nová planeta: Pluto.\n", + "\n", + "Do naší tabulky ho vložíme pomocí indexeru `loc`, který jsme již dříve používali pro \"koukání\" do tabulky:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesiceje_planeta
jmeno
Merkur☿0.390.240True
Venuše♀0.720.620True
Země⊕1.001.001True
Mars♂1.521.882True
Jupiter♃5.2011.8679True
Saturn♄9.5429.4682True
Uran♅19.2284.0127True
Neptun♆30.06164.8014True
Pluto♇39.48247.945True
\n", + "
" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice je_planeta\n", + "jmeno \n", + "Merkur ☿ 0.39 0.24 0 True\n", + "Venuše ♀ 0.72 0.62 0 True\n", + "Země ⊕ 1.00 1.00 1 True\n", + "Mars ♂ 1.52 1.88 2 True\n", + "Jupiter ♃ 5.20 11.86 79 True\n", + "Saturn ♄ 9.54 29.46 82 True\n", + "Uran ♅ 19.22 84.01 27 True\n", + "Neptun ♆ 30.06 164.80 14 True\n", + "Pluto ♇ 39.48 247.94 5 True" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety.loc[\"Pluto\"] = [\"♇\", 39.48, 247.94, 5, True] # Seznam hodnot v řádku\n", + "planety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Změna hodnoty buňky\n", + "\n", + "\"Indexery\" `.loc` a `.iloc` se dvěma argumenty v hranatých závorkách odkazují přímo na konkrétní buňku, a přiřazením do nich (opět, podobně jako ve slovníku) se hodnota na příslušné místo zapíše. Jen je třeba zachovat pořadí (řádek, sloupec). \n", + "\n", + "Vrátíme se opět do současnosti a Pluto zbavíme jeho privilegií:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesiceje_planeta
jmeno
Merkur☿0.390.240True
Venuše♀0.720.620True
Země⊕1.001.001True
Mars♂1.521.882True
Jupiter♃5.2011.8679True
Saturn♄9.5429.4682True
Uran♅19.2284.0127True
Neptun♆30.06164.8014True
Pluto♇39.48247.945False
\n", + "
" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice je_planeta\n", + "jmeno \n", + "Merkur ☿ 0.39 0.24 0 True\n", + "Venuše ♀ 0.72 0.62 0 True\n", + "Země ⊕ 1.00 1.00 1 True\n", + "Mars ♂ 1.52 1.88 2 True\n", + "Jupiter ♃ 5.20 11.86 79 True\n", + "Saturn ♄ 9.54 29.46 82 True\n", + "Uran ♅ 19.22 84.01 27 True\n", + "Neptun ♆ 30.06 164.80 14 True\n", + "Pluto ♇ 39.48 247.94 5 False" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety.loc[\"Pluto\", \"je_planeta\"] = False\n", + "planety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**⚠ Pozor:** Podobně jako ve slovníku, ale možná poněkud neintuitivně, je možné zapsat hodnotu do řádku i sloupce, které neexistují!" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesiceje_planetaplaneta
jmeno
Merkur☿0.390.240.0TrueNaN
Venuše♀0.720.620.0TrueNaN
Země⊕1.001.001.0TrueNaN
Mars♂1.521.882.0TrueNaN
Jupiter♃5.2011.8679.0TrueNaN
Saturn♄9.5429.4682.0TrueNaN
Uran♅19.2284.0127.0TrueNaN
Neptun♆30.06164.8014.0TrueNaN
Pluto♇39.48247.945.0FalseNaN
ZemeNaNNaNNaNNaNNaNTrue
\n", + "
" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice je_planeta planeta\n", + "jmeno \n", + "Merkur ☿ 0.39 0.24 0.0 True NaN\n", + "Venuše ♀ 0.72 0.62 0.0 True NaN\n", + "Země ⊕ 1.00 1.00 1.0 True NaN\n", + "Mars ♂ 1.52 1.88 2.0 True NaN\n", + "Jupiter ♃ 5.20 11.86 79.0 True NaN\n", + "Saturn ♄ 9.54 29.46 82.0 True NaN\n", + "Uran ♅ 19.22 84.01 27.0 True NaN\n", + "Neptun ♆ 30.06 164.80 14.0 True NaN\n", + "Pluto ♇ 39.48 247.94 5.0 False NaN\n", + "Zeme NaN NaN NaN NaN NaN True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety_bad = planety.copy() # Pro jistotu si uděláme kopii\n", + "\n", + "planety_bad.loc[\"Zeme\", \"planeta\"] = True\n", + "planety_bad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "💡 Jistě se ptáš, co znamená **NaN** v tabulce. Tato hodnota, více slovy \"not a number\", označuje chybějící, neplatnou nebo neznámou hodnotu (v našem případě jsme ji nezadali, a tedy se není co divit). O problematice chybějících hodnot (a jejich napravování) si budeme povídat někdy příště, prozatím se jimi nenech znervóznit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Přiřazovat je možné i do rozsahů v indexech - jen je potřeba hlídat, aby přiřazovaná hodnota či hodnoty byly buď skalárem, nebo měly stejný tvar jako oblast, do které přiřazujeme:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesiceje_planetaje_obr
jmeno
Merkur☿0.390.240TrueFalse
Venuše♀0.720.620TrueFalse
Země⊕1.001.001TrueFalse
Mars♂1.521.882TrueFalse
Jupiter♃5.2011.8679TrueTrue
Saturn♄9.5429.4682TrueTrue
Uran♅19.2284.0127TrueTrue
Neptun♆30.06164.8014TrueTrue
Pluto♇39.48247.945FalseNaN
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" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice je_planeta je_obr\n", + "jmeno \n", + "Merkur ☿ 0.39 0.24 0 True False\n", + "Venuše ♀ 0.72 0.62 0 True False\n", + "Země ⊕ 1.00 1.00 1 True False\n", + "Mars ♂ 1.52 1.88 2 True False\n", + "Jupiter ♃ 5.20 11.86 79 True True\n", + "Saturn ♄ 9.54 29.46 82 True True\n", + "Uran ♅ 19.22 84.01 27 True True\n", + "Neptun ♆ 30.06 164.80 14 True True\n", + "Pluto ♇ 39.48 247.94 5 False NaN" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety.loc[\"Merkur\":\"Mars\", \"je_obr\"] = False\n", + "planety.loc[\"Jupiter\":\"Neptun\", \"je_obr\"] = [True, True, True, True]\n", + "planety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "**Úkol:** Shodou okolností (nebo jde o astronomickou nevyhnutelnost?) mají všichni planetární obři alespoň nějaký prstenec. Dokážeš jednoduše vytvořit sloupec `\"ma_prstenec\"`?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Odstranění řádku\n", + "\n", + "Pro odebrání sloupce či řádku z DataFrame slouží metoda `drop`. Její první argument očekává označení (index) jednoho nebo více řádků či sloupců, které chceš odebrat. Argument axis označuje, ve které dimenzi se operace má aplikovat (0 či 1). Číslo je intuitivní a odpovídá pořadí, ve kterém se uvádějí klíče při odkazování na buňky.\n", + "\n", + "Osa (axis):\n", + "\n", + "- 0 = řádky\n", + "- 1 = sloupce\n", + "(Tento argument používají i četné další metody a funkce, proto se ujisti, že mu rozumíš).\n", + "\n", + "Když už jsme se vrátili do budoucnosti (resp. současnosti), vypořádejme se nemilosrdně s Plutem (pro metodu `drop` je výchozí hodnotou argumentu `axis` 0, a tedy to nemusíme psát):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesiceje_planetaje_obr
jmeno
Merkur☿0.390.240TrueFalse
Venuše♀0.720.620TrueFalse
Země⊕1.001.001TrueFalse
Mars♂1.521.882TrueFalse
Jupiter♃5.2011.8679TrueTrue
Saturn♄9.5429.4682TrueTrue
Uran♅19.2284.0127TrueTrue
Neptun♆30.06164.8014TrueTrue
\n", + "
" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice je_planeta je_obr\n", + "jmeno \n", + "Merkur ☿ 0.39 0.24 0 True False\n", + "Venuše ♀ 0.72 0.62 0 True False\n", + "Země ⊕ 1.00 1.00 1 True False\n", + "Mars ♂ 1.52 1.88 2 True False\n", + "Jupiter ♃ 5.20 11.86 79 True True\n", + "Saturn ♄ 9.54 29.46 82 True True\n", + "Uran ♅ 19.22 84.01 27 True True\n", + "Neptun ♆ 30.06 164.80 14 True True" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety = planety.drop(\"Pluto\") # Přidej axis=0, chceš-li být explicitní\n", + "planety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Odstranění sloupce\n", + "\n", + "U sloupce funguje metoda `drop` velmi podobně, jen tentokrát argument `axis` uvést musíme.\n", + "\n", + "Odstraňme zbytečný sloupec s informační hodnotou na úrovni \"stěrače stírají, klakson troubí\"..." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesiceje_obr
jmeno
Merkur☿0.390.240False
Venuše♀0.720.620False
Země⊕1.001.001False
Mars♂1.521.882False
Jupiter♃5.2011.8679True
Saturn♄9.5429.4682True
Uran♅19.2284.0127True
Neptun♆30.06164.8014True
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" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice je_obr\n", + "jmeno \n", + "Merkur ☿ 0.39 0.24 0 False\n", + "Venuše ♀ 0.72 0.62 0 False\n", + "Země ⊕ 1.00 1.00 1 False\n", + "Mars ♂ 1.52 1.88 2 False\n", + "Jupiter ♃ 5.20 11.86 79 True\n", + "Saturn ♄ 9.54 29.46 82 True\n", + "Uran ♅ 19.22 84.01 27 True\n", + "Neptun ♆ 30.06 164.80 14 True" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety = planety.drop(\"je_planeta\", axis=1) \n", + "planety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "⛧ Metoda `drop`, v souladu s výše zmíněnou konvencí, vrací nový `DataFrame` (a proto výsledek operace musíme přiřadit do `planety`). Pokud chceš operovat rovnou na tabulce, můžeš použít příkaz `del` (funguje stejně jako u slovníku) nebo poprosit pandí bohy (a autory těchto materiálů) o odpuštění a přidat argument `inplace=True`:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Alternativa 1)\n", + "# del planety[\"je_planeta\"]\n", + "\n", + "# Alternativa 2)\n", + "# planety.drop(\"je_planeta\", axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Datové typy\n", + "\n", + "Jak už jsme předeslali, datové typy v pandas se trochu liší od typů v Pythonu a nejsou to v pravém slova smyslu třídy, ale naštěstí konverze mezi nimi je často automatická a \"chovající se dle očekávání\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Příprava dat\n", + "\n", + "V datovém kurzu budeme využívat různých datových sad (obvykle větších - takových, kde není praktické je celé zapsat v konstruktoru). Nyní opustíme planety a podíváme se na některé zajímavé charakteristiky zemí kolem světa (ježto definice toho, co je to země, je poněkud vágní, bereme v potaz členy OSN), zachycené k jednomu konkrétnímu roku uplynulé dekády (protože ne vždy jsou všechny údaje k dispozici, bereme poslední rok, kde je známo dost ukazatelů). Data pocházejí povětšinou z projektu [Gapminder](https://www.gapminder.org/), doplnili jsme je jen o několik dalších informací z wikipedie.\n", + "\n", + "
TODO: Upravit URL podle toho, kde nakonec data budou.
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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isoworld_6regionworld_4regionincome_groupsis_euis_oecdeu_accessionyearareapopulationalcohol_adultsbmi_menbmi_womencar_deaths_per_100000_peoplecalories_per_dayinfant_mortalitylife_expectancylife_expectancy_femalelife_expectancy_maleun_accession
name
AfghanistanAFGsouth_asiaasialow_incomeFalseFalseNaN2018652860.034500000.00.0320.6221.07NaN2090.066.358.6965.81263.1011946-11-19
AlbaniaALBeurope_central_asiaeuropeupper_middle_incomeFalseFalseNaN201828750.03238000.07.2926.4525.665.9783193.012.578.0180.73776.6931955-12-14
AlgeriaDZAmiddle_east_north_africaafricaupper_middle_incomeFalseFalseNaN20182381740.036980000.00.6924.6026.37NaN3296.021.977.8677.78475.2791962-10-08
AndorraANDeurope_central_asiaeuropehigh_incomeFalseFalseNaN2017470.088910.010.1727.6326.43NaNNaN2.182.55NaNNaN1993-07-28
AngolaAGOsub_saharan_africaafricaupper_middle_incomeFalseFalseNaN20181246700.020710000.05.5722.2523.48NaN2473.096.065.1964.93959.2131976-12-01
...............................................................
VenezuelaVENamericaamericasupper_middle_incomeFalseFalseNaN2018912050.030340000.07.6027.4528.137.3322631.012.975.9179.07970.9501945-11-15
VietnamVNMeast_asia_pacificasialower_middle_incomeFalseFalseNaN2018330967.090660000.03.9120.9221.07NaN2745.017.374.8881.20372.0031977-09-20
YemenYEMmiddle_east_north_africaasialower_middle_incomeFalseFalseNaN2018527970.026360000.00.2024.4426.11NaN2223.033.867.1466.87163.8751947-09-30
ZambiaZMBsub_saharan_africaafricalower_middle_incomeFalseFalseNaN2018752610.014310000.03.5620.6823.0511.2601930.043.359.4565.36259.8451964-12-01
ZimbabweZWEsub_saharan_africaafricalow_incomeFalseFalseNaN2018390760.013330000.04.9622.0324.6520.8502110.046.660.1863.94460.1201980-08-25
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193 rows × 20 columns

\n", + "
" + ], + "text/plain": [ + " iso world_6region world_4region income_groups \\\n", + "name \n", + "Afghanistan AFG south_asia asia low_income \n", + "Albania ALB europe_central_asia europe upper_middle_income \n", + "Algeria DZA middle_east_north_africa africa upper_middle_income \n", + "Andorra AND europe_central_asia europe high_income \n", + "Angola AGO sub_saharan_africa africa upper_middle_income \n", + "... ... ... ... ... \n", + "Venezuela VEN america americas upper_middle_income \n", + "Vietnam VNM east_asia_pacific asia lower_middle_income \n", + "Yemen YEM middle_east_north_africa asia lower_middle_income \n", + "Zambia ZMB sub_saharan_africa africa lower_middle_income \n", + "Zimbabwe ZWE sub_saharan_africa africa low_income \n", + "\n", + " is_eu is_oecd eu_accession year area population \\\n", + "name \n", + "Afghanistan False False NaN 2018 652860.0 34500000.0 \n", + "Albania False False NaN 2018 28750.0 3238000.0 \n", + "Algeria False False NaN 2018 2381740.0 36980000.0 \n", + "Andorra False False NaN 2017 470.0 88910.0 \n", + "Angola False False NaN 2018 1246700.0 20710000.0 \n", + "... ... ... ... ... ... ... \n", + "Venezuela False False NaN 2018 912050.0 30340000.0 \n", + "Vietnam False False NaN 2018 330967.0 90660000.0 \n", + "Yemen False False NaN 2018 527970.0 26360000.0 \n", + "Zambia False False NaN 2018 752610.0 14310000.0 \n", + "Zimbabwe False False NaN 2018 390760.0 13330000.0 \n", + "\n", + " alcohol_adults bmi_men bmi_women car_deaths_per_100000_people \\\n", + "name \n", + "Afghanistan 0.03 20.62 21.07 NaN \n", + "Albania 7.29 26.45 25.66 5.978 \n", + "Algeria 0.69 24.60 26.37 NaN \n", + "Andorra 10.17 27.63 26.43 NaN \n", + "Angola 5.57 22.25 23.48 NaN \n", + "... ... ... ... ... \n", + "Venezuela 7.60 27.45 28.13 7.332 \n", + "Vietnam 3.91 20.92 21.07 NaN \n", + "Yemen 0.20 24.44 26.11 NaN \n", + "Zambia 3.56 20.68 23.05 11.260 \n", + "Zimbabwe 4.96 22.03 24.65 20.850 \n", + "\n", + " calories_per_day infant_mortality life_expectancy \\\n", + "name \n", + "Afghanistan 2090.0 66.3 58.69 \n", + "Albania 3193.0 12.5 78.01 \n", + "Algeria 3296.0 21.9 77.86 \n", + "Andorra NaN 2.1 82.55 \n", + "Angola 2473.0 96.0 65.19 \n", + "... ... ... ... \n", + "Venezuela 2631.0 12.9 75.91 \n", + "Vietnam 2745.0 17.3 74.88 \n", + "Yemen 2223.0 33.8 67.14 \n", + "Zambia 1930.0 43.3 59.45 \n", + "Zimbabwe 2110.0 46.6 60.18 \n", + "\n", + " life_expectancy_female life_expectancy_male un_accession \n", + "name \n", + "Afghanistan 65.812 63.101 1946-11-19 \n", + "Albania 80.737 76.693 1955-12-14 \n", + "Algeria 77.784 75.279 1962-10-08 \n", + "Andorra NaN NaN 1993-07-28 \n", + "Angola 64.939 59.213 1976-12-01 \n", + "... ... ... ... \n", + "Venezuela 79.079 70.950 1945-11-15 \n", + "Vietnam 81.203 72.003 1977-09-20 \n", + "Yemen 66.871 63.875 1947-09-30 \n", + "Zambia 65.362 59.845 1964-12-01 \n", + "Zimbabwe 63.944 60.120 1980-08-25 \n", + "\n", + "[193 rows x 20 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = \"https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/countries.csv\"\n", + "countries = pd.read_csv(url, index_col=\"name\") # Místo `set_index`\n", + "countries = countries.sort_index()\n", + "countries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Namátkou si vybereme nějakou zemi a podíváme se, jaké údaje o ní v tabulce máme." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "iso CZE\n", + "world_6region europe_central_asia\n", + "world_4region europe\n", + "income_groups high_income\n", + "is_eu True\n", + "is_oecd True\n", + "eu_accession 2004-05-01\n", + "year 2018\n", + "area 78870\n", + "population 1.059e+07\n", + "alcohol_adults 16.47\n", + "bmi_men 27.91\n", + "bmi_women 26.51\n", + "car_deaths_per_100000_people 5.72\n", + "calories_per_day 3256\n", + "infant_mortality 2.8\n", + "life_expectancy 79.37\n", + "life_expectancy_female 81.858\n", + "life_expectancy_male 76.148\n", + "un_accession 1993-01-19\n", + "Name: Czechia, dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries.loc[\"Czechia\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Už na první pohled je každé pole jiného typu. Ale jakého? Na to nám odpoví vlastnost `dtypes` naší tabulky (u `Series` použiješ `dtype`, resp. raději `dtype.name`, pokud chceš stejně pěknou řetězcovou reprezentaci)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "iso object\n", + "world_6region object\n", + "world_4region object\n", + "income_groups object\n", + "is_eu bool\n", + "is_oecd bool\n", + "eu_accession object\n", + "year int64\n", + "area float64\n", + "population float64\n", + "alcohol_adults float64\n", + "bmi_men float64\n", + "bmi_women float64\n", + "car_deaths_per_100000_people float64\n", + "calories_per_day float64\n", + "infant_mortality float64\n", + "life_expectancy float64\n", + "life_expectancy_female float64\n", + "life_expectancy_male float64\n", + "un_accession object\n", + "dtype: object" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Typy v pandas vycházejí z toho, jak je definuje knihovna `numpy` (obecně užitečná pro práci s numerickými poli a poskytující vektorové operace s rychlostí řádově rychlejší než v Pythonu jako takovém). Ta potřebuje především vědět, jak alokovat pole pro prvky daného typu - na to, aby mohly být seřazeny efektivně jeden za druhým, a tedy i kolik bajtů paměti každý zabírá. Kopíruje přitom \"nativní\" datové typy, jako je můžeš znát, pokud už máš takovou zkušenost, např. z jazyka C. Umístění paměti je něco, co v Pythonu obvykle neřešíme, ale rychlé počítání se bez toho neobejde. My nepůjdeme do detailů, ale požadavek na rychlost se nám tu a tam vynoří a my budeme klást důraz na to, aby se operace dělaly \"vektorově\", řešily \"na úrovni numpy\".\n", + "\n", + "Poněkud kryptický systém typů v `numpy` (popsaný v [dokumentaci](https://docs.scipy.org/doc/numpy/user/basics.types.html)) je naštěstí v `pandas` (mírně) zjednodušen a nabízí jen několik užitečných základních (rodin) typů, které si teď představíme." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Celá čísla (integers)\n", + "\n", + "V Pythonu je pro celá čísla vyhrazen přesně jeden typ: `int`, který možňuje pracovat s libovolně velkými celými čísly (0, -58 nebo třeba 123456789012345678901234567890). V `pandas` se můžeš setkat s `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32` a `uint64` - všechny mají stejné základní vlastnosti a každý z nich má jen určitý rozsah čísel, která do něj lze uložit. Liší se velikostí paměti, kterou jedno číslo zabere (číslovka v názvu vyjadřuje počet bitů), a tím, zda jsou podporována i záporná čísla (předpona `u` znamená, že počítáme pouze s nulou a kladnými čísly). \n", + "\n", + "Rozsahy:\n", + "\n", + "- `int8`: -128 až 127 \n", + "- `uint8`: 0 až 255\n", + "- `int16`: -32 768 až 32 767\n", + "- `uint16`: 0 až 65 535\n", + "- `int32`: -2 147 483 647 až 2 147 483 647 (tedy +/- ~2 miliardy)\n", + "- `uint32`: 0 až 4 294 967 295 (tedy až ~4 miliardy)\n", + "- `int64`: -9 223 372 036 854 775 808 až 9 223 372 036 854 775 807 (tedy +/- ~9 trilionů)\n", + "- `uint64`: 0 až 18 446 744 073 709 551 615 (tedy až ~18 trilionů)\n", + "\n", + "💡 Aby toho nebylo málo, ke každému `intX` / `uintY` typu existuje ještě jeho alternativa, která umožňuje ve sloupci použít chybějící hodnoty, t.j. `NaN`. Místo malého `i`, případně `u` v názvu se použije písmeno velké. Tato vlastnost (tzv. \"nullable integer types\") je relativně užitečná, ale je dosud poněkud experimentální. My ji nebudeme v kurzu využívat.\n", + "\n", + "Detailní vysvětlení toho, jak jsou celá čísla v paměti počítače reprezentována, najdeš třeba ve [wikipedii](https://cs.wikipedia.org/wiki/Integer)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "V `pandas` je výchozí celočíselný typ `int64`, a pokud neřekneš jinak, automaticky se pro celá čísla použije (ve většině případů to bude vhodná volba):" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan 2018\n", + "Albania 2018\n", + "Algeria 2018\n", + "Andorra 2017\n", + "Angola 2018\n", + " ... \n", + "Venezuela 2018\n", + "Vietnam 2018\n", + "Yemen 2018\n", + "Zambia 2018\n", + "Zimbabwe 2018\n", + "Name: year, Length: 193, dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[\"year\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 123\n", + "2 12345\n", + "dtype: int64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([0, 123, 12345])\n", + "\n", + "# pd.Series([0, 123, 12345], dtype=\"int64\") # totéž" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pomocí argumentu `dtype` můžeš ovšem přesně specifikovat, který typ celých čísel chceš:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 123\n", + "2 12345\n", + "dtype: int16" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([0, 123, 12345], dtype=\"int16\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**⚠ Pozor:** Když vybíráš konkrétní celočíselný typ, musíš si dát pozor na rozsahy, protože `pandas` tě nebude varovat, pokud se nějaká z tvých hodnot do rozsahu \"nevleze\" a vesele zahodí tu část binární reprezentace, která je navíc:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 123\n", + "2 57\n", + "dtype: int8" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([0, 123, 12345], dtype=\"int8\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Toto naštěstí neplatí pro typ s nejširším rozsahem (`int64`). Zkusme do něj vložit veliké číslo (třeba 123456789012345678901234567890) a uvidíme, co se stane:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 123\n", + "2 123456789012345678901234567890\n", + "dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Toto vyhodí výjimku:\n", + "# pd.Series([0, 123, 123456789012345678901234567890], dtype=\"int64\")\n", + "\n", + "# Toto ano, ale už to není int64:\n", + "pd.Series([0, 123, 123456789012345678901234567890])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Když ho budeme explicitně požadovat, vyhodí se výjimka.\n", + "- Když `pandas` necháme dělat jeho práci, použije se obecný typ `object` a přijdeme o jistou část výhod: sloupec nám zabere násobně více paměti a aritmetické operace s ním jsou o řád až dva pomalejší. Dokud není výkon na předním místě našich priorit, není to zase takový problém.\n", + "\n", + "Obecně proto doporučujeme držet se `int64`, resp. nechat `pandas`, aby jej za nás automaticky použil. Teprve v případě, že si to budou žádat přísné paměťové nároky, se ti vyplatí hledat ten \"nejvíce růžový\" typ." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Čísla s plovoucí desetinnou čárkou (floats)\n", + "\n", + "Podobně jako u celočíselných hodnot, i jednomu typu v Python (`float`) odpovídá několik typů v `pandas`: `float16`, `float32`, `float64`. Součástí názvu je opět počet bitů, které jedno číslo potřebuje ke svému uložení. Naštěstí v tomto případě `float64` přesně odpovídá svým chováním `float` z Pythonu, zbylé dva typy nejsou tak přesné a mají menší rozsah - kromě optimalizace paměťových nároků u specifického druhu dat je nejspíš nepoužiješ.\n", + "\n", + "Více teoretického čtení o reprezentaci čísel s desetinnou čárkou najdeš na [wiki](https://cs.wikipedia.org/wiki/Pohybliv%C3%A1_%C5%99%C3%A1dov%C3%A1_%C4%8D%C3%A1rka)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan 20.62\n", + "Albania 26.45\n", + "Algeria 24.60\n", + "Andorra 27.63\n", + "Angola 22.25\n", + " ... \n", + "Venezuela 27.45\n", + "Vietnam 20.92\n", + "Yemen 24.44\n", + "Zambia 20.68\n", + "Zimbabwe 22.03\n", + "Name: bmi_men, Length: 193, dtype: float64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[\"bmi_men\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3.141593\n", + "dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Docela přesné pí\n", + "pd.Series([3.14159265])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3.140625\n", + "dtype: float16" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Ne už tak přesné pí\n", + "pd.Series([3.14159265], dtype=\"float16\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logické hodnoty (booleans)\n", + "\n", + "Toto je asi nejméně překvapivý datový typ. Chová se v zásadě stejně jako typ `bool` v Pythonu. Nabírá hodnot `True` a `False` (které lze též pokládat za 1 a 0 v některých operacích). Má ještě jednu skvělou vlastnost - objekty `Series` i `DataFrame` jde filtrovat právě pomocí sloupce logického typu (o tom viz níže)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Slovakia True\n", + "Slovenia True\n", + "Solomon Islands False\n", + "Somalia False\n", + "South Africa False\n", + "South Korea False\n", + "South Sudan False\n", + "Spain True\n", + "Sri Lanka False\n", + "Sudan False\n", + "Suriname False\n", + "Swaziland False\n", + "Sweden True\n", + "Switzerland False\n", + "Syria False\n", + "Name: is_eu, dtype: bool" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[\"is_eu\"][\"Slovakia\":\"Syria\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 True\n", + "1 False\n", + "2 False\n", + "dtype: bool" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([True, False, False])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Jde to ovšem i takto:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 True\n", + "1 False\n", + "2 False\n", + "dtype: bool" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([1, 0, 0], dtype=\"bool\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Objekty a řetězce (objects)\n", + "\n", + "Toto tě pravděpodobně překvapí: `pandas` nemá zvláštní datový typ pro řetězce! Spadá společně s dalšími neurčenými nebo nerozpoznanými hodnotami do kategorie `object`, která umožňuje v daném sloupci mít cokoliv, co znáš z Pythonu, a chová se tak do značné míry jako obyčejný seznam s výhodami (žádné podivné konverze, sledování rozsahů, ...) i nevýhodami (je to pomalejší, než by mohlo; nikdo ti nezaručí, že ve sloupci budou jen řetězce). " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan AFG\n", + "Albania ALB\n", + "Algeria DZA\n", + "Andorra AND\n", + "Angola AGO\n", + " ... \n", + "Venezuela VEN\n", + "Vietnam VNM\n", + "Yemen YEM\n", + "Zambia ZMB\n", + "Zimbabwe ZWE\n", + "Name: iso, Length: 193, dtype: object" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[\"iso\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 pes\n", + "1 kočka\n", + "2 křeček\n", + "3 tarantule\n", + "4 hroznýš\n", + "dtype: object" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Domácí mazlíčci\n", + "pd.Series([\"pes\", \"kočka\", \"křeček\", \"tarantule\", \"hroznýš\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 dvě\n", + "2 3\n", + "dtype: object" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([1, \"dvě\", 3.0]) # Řetězec a další \"smetí\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pozor, třeba i takový seznam může být hodnotou v sloupci typu `object`:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Eva [řízek, brambory, cola]\n", + "Evelína [smažák, hranolky]\n", + "Evženie [sodovka]\n", + "dtype: object" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Objednávky\n", + "pd.Series(\n", + " [[\"řízek\", \"brambory\", \"cola\"], [\"smažák\", \"hranolky\"], [\"sodovka\"]],\n", + " index=[\"Eva\", \"Evelína\", \"Evženie\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Datum / čas (datetime)\n", + "\n", + "Časovými daty se blíže zabývá jedna z následujících lekcí, nicméně nějaká v tabulce zemí už máme, a tak alespoň pro úplnost uvedeme, co v tomto směru `pandas` nabízí:\n", + "\n", + "- Časové či datumové údaje (*datetime*) jakožto body na časové ose.\n", + "\n", + "- Časové údaje s označením časové zóny (*datetimes with time zone*).\n", + "\n", + "- Časové úseky (*timedeltas*) jakožto určení délky nějakého úseku (počítáno v nanosekundách)\n", + "\n", + "- Období (*periods*) udávají nějak určená časová období (třeba \"únor 2020\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "💡 Pro převod z nejrůznějších formátů na datum / čas slouží funkce `to_datetime`, kterou použijeme pro následující ukázku:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan 1946-11-19\n", + "Albania 1955-12-14\n", + "Algeria 1962-10-08\n", + "Andorra 1993-07-28\n", + "Angola 1976-12-01\n", + " ... \n", + "Venezuela 1945-11-15\n", + "Vietnam 1977-09-20\n", + "Yemen 1947-09-30\n", + "Zambia 1964-12-01\n", + "Zimbabwe 1980-08-25\n", + "Name: un_accession, Length: 193, dtype: datetime64[ns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.to_datetime(countries[\"un_accession\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Kategorické (category)\n", + "\n", + "Pokud chceme být efektivní při práci se sloupci, kde se často opakují hodnoty (zejména řetězcové), můžeme je zakódovat do kategorií. Tím mnohdy ušetříme zabrané místo a urychlíme některé operace. Při takové konverzi `pandas` najde všechny unikátní hodnoty v daném sloupci, uloží si je do zvláštního seznamu a do sloupce uloží jenom indexy z tohoto seznamu. Vše se chová transparentně a při používání tak většinou ani nepoznáte, jestli máte sloupec typu `object` nebo `category`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "💡 Pro převod mezi různými datovými typy slouží metoda `astype`, která jako svůj argument akceptuje jméno dtype, na který chceme převést:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan low_income\n", + "Albania upper_middle_income\n", + "Algeria upper_middle_income\n", + "Andorra high_income\n", + "Angola upper_middle_income\n", + " ... \n", + "Venezuela upper_middle_income\n", + "Vietnam lower_middle_income\n", + "Yemen lower_middle_income\n", + "Zambia lower_middle_income\n", + "Zimbabwe low_income\n", + "Name: income_groups, Length: 193, dtype: category\n", + "Categories (4, object): [high_income, low_income, lower_middle_income, upper_middle_income]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[\"income_groups\"].astype(\"category\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol:** Napadne tě, které sloupce z tabulky `countries` bychom měli překonvertovat na nějaký jiný typ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matematika\n", + "\n", + "Počítání se `Series` v `pandas` je navrženo tak, aby co nejméně překvapilo. Jednotlivé sloupce se tak můžou stát součástí aritmetických výrazů společně se skalárními hodnotami, s jinými sloupci, `numpy` poli příslušného tvaru, a dokonce i seznamy." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan 21421.85\n", + "Albania 28473.65\n", + "Algeria 28418.90\n", + "Andorra 30130.75\n", + "Angola 23794.35\n", + " ... \n", + "Venezuela 27707.15\n", + "Vietnam 27331.20\n", + "Yemen 24506.10\n", + "Zambia 21699.25\n", + "Zimbabwe 21965.70\n", + "Name: life_expectancy, Length: 193, dtype: float64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Očekávaná doba života ve dnech\n", + "countries[\"life_expectancy\"] * 365" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan 52.844408\n", + "Albania 112.626087\n", + "Algeria 15.526464\n", + "Andorra 189.170213\n", + "Angola 16.611855\n", + " ... \n", + "Venezuela 33.265720\n", + "Vietnam 273.924591\n", + "Yemen 49.927079\n", + "Zambia 19.013832\n", + "Zimbabwe 34.113011\n", + "Length: 193, dtype: float64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Hustota obyvatelstva\n", + "countries[\"population\"] / countries[\"area\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "řízek 129.9\n", + "smažák 109.9\n", + "dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Jak nám zdražili obědy\n", + "pd.Series([109, 99], index=[\"řízek\", \"smažák\"]) + [20.9, 10.9] # sčítání se seznamem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol**: Spočti celkový počet mrtvých v automobilových haváriích v jednotlivých zemích (použij sloupce \"population\" a \"car_deaths_per_100000_people\" a jednoduchou aritmetiku). Sedí výsledek pro ČR?" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan 26700 days 00:54:33.011664\n", + "Albania 23388 days 00:54:33.011664\n", + "Algeria 20898 days 00:54:33.011664\n", + "Andorra 9647 days 00:54:33.011664\n", + "Angola 15730 days 00:54:33.011664\n", + " ... \n", + "Venezuela 27069 days 00:54:33.011664\n", + "Vietnam 15437 days 00:54:33.011664\n", + "Yemen 26385 days 00:54:33.011664\n", + "Zambia 20113 days 00:54:33.011664\n", + "Zimbabwe 14367 days 00:54:33.011664\n", + "Name: un_accession, Length: 193, dtype: timedelta64[ns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Jak dlouho jsou v OSN?\n", + "from datetime import datetime\n", + "datetime.now() - pd.to_datetime(countries[\"un_accession\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "💡 Čísla s plouvoucí desetinnou čárkou mohou obsahovat i speciální hodnoty \"not a number\" a plus nebo mínus nekonečno. Vzniknou např. při nevhodném dělení nulou:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 NaN\n", + "1 -inf\n", + "2 inf\n", + "dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([0, -1, 1]) / pd.Series([0, 0, 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Varování:** Nabádáme tě k opatrnosti při práci s omezenými celočíselnými typy. Podobně jako při jejich nevhodné konverzi, i tady může výsledek \"přetéct\" a ukazovat pochybné výsledky. O důvod víc, proč se držet `int64`." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 14\n", + "1 28\n", + "2 42\n", + "dtype: int8" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([7, 14, 149], dtype=\"int8\") * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Porovnávání" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pro `Series` lze použít nejen operátory početní, ale také logické. Výsledkem pak není jedna logická hodnota, ale sloupec logických hodnot." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan False\n", + "Albania False\n", + "Algeria False\n", + "Andorra False\n", + "Angola False\n", + " ... \n", + "Venezuela False\n", + "Vietnam False\n", + "Yemen False\n", + "Zambia False\n", + "Zimbabwe False\n", + "Name: alcohol_adults, Length: 193, dtype: bool" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 15 litrů čistého alkoholu budeme považovat za hranici nadměrného pití (nekonzultováno s adiktology!)\n", + "# Kde se hodně pije?\n", + "countries[\"alcohol_adults\"] > 15" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Skoro nikde. A jak jsme na tom u nás?\n", + "(countries[\"alcohol_adults\"] > 15).loc[\"Czechia\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan False\n", + "Albania True\n", + "Algeria False\n", + "Andorra True\n", + "Angola False\n", + " ... \n", + "Venezuela False\n", + "Vietnam False\n", + "Yemen False\n", + "Zambia False\n", + "Zimbabwe False\n", + "Length: 193, dtype: bool" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Jsou muži v jednotlivých zemích tlustší než ženy?\n", + "countries[\"bmi_men\"] > countries[\"bmi_women\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol**: Zjistěte, jestli se v jednotlivých zemích dožívají více muži nebo ženy." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan False\n", + "Albania False\n", + "Algeria True\n", + "Andorra False\n", + "Angola True\n", + " ... \n", + "Venezuela False\n", + "Vietnam False\n", + "Yemen False\n", + "Zambia True\n", + "Zimbabwe True\n", + "Name: world_4region, Length: 193, dtype: bool" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Leží země v Africe?\n", + "countries[\"world_4region\"] == \"africa\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podobně jako v Pythonu lze podmínky kombinovat pomocí operátorů. Vzhledem k jistým syntaktickým požadavkům Pythonu je ale potřeba použít místo vám známých logických operátorů jejich alternativy: `&` (místo `and`), `|` (místo `or`) a `~` (místo `not`). Protože mají jiné priority než jejich klasičtí břatříčci, bude lepší, když při kombinaci s jinýmim operátory vždycky použiješ závorky." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan False\n", + "Albania True\n", + "Algeria True\n", + "Andorra False\n", + "Angola False\n", + " ... \n", + "Venezuela False\n", + "Vietnam False\n", + "Yemen False\n", + "Zambia False\n", + "Zimbabwe False\n", + "Length: 193, dtype: bool" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Kde se ženy i muži dožívají přes 75 let?\n", + "(countries[\"life_expectancy_male\"] > 75) & (countries[\"life_expectancy_female\"] > 75)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filtrování\n", + "\n", + "Pokud chceš z tabulky vybrat řádky, které splňují nějaké kritérium, musíš (není to vždy těžké :-)) toto kritérium převést do podoby sloupce logických hodnot. Potom tento sloupec (sloupec samotný, nikoliv jeho název!) vložíš do hranatých závorek jako index `DataFrame`.\n", + "\n", + "Když budeš například chtít informace jen o členech EU, můžeš k tomu přímo použít sloupec \"is_eu\", který logické hodnoty obsahuje:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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isoworld_6regionworld_4regionincome_groupsis_euis_oecdeu_accessionyearareapopulationalcohol_adultsbmi_menbmi_womencar_deaths_per_100000_peoplecalories_per_dayinfant_mortalitylife_expectancylife_expectancy_femalelife_expectancy_maleun_accession
name
AustriaAUTeurope_central_asiaeuropehigh_incomeTrueTrue1995-01-01201883879.08441000.012.4026.4725.093.5413768.02.981.8484.24979.5851955-12-14
BelgiumBELeurope_central_asiaeuropehigh_incomeTrueTrue1952-07-23201830530.010820000.010.4126.7625.145.4273733.03.381.2383.75179.1311945-12-27
BulgariaBGReurope_central_asiaeuropeupper_middle_incomeTrueFalse2007-01-012018111000.07349000.011.4026.5425.529.6622829.09.375.3278.48571.6181955-12-14
CroatiaHRVeurope_central_asiaeuropehigh_incomeTrueFalse2013-01-01201856590.04379000.015.0026.6025.186.4343059.03.677.6681.16774.7011992-05-22
CyprusCYPeurope_central_asiaeuropehigh_incomeTrueFalse2004-05-0120189250.01141000.08.8427.4225.936.4192649.02.580.7982.91878.7341960-09-20
CzechiaCZEeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201878870.010590000.016.4727.9126.515.7203256.02.879.3781.85876.1481993-01-19
DenmarkDNKeurope_central_asiaeuropehigh_incomeTrueTrue1973-01-01201842922.05611000.012.0226.1325.113.4813367.02.981.1082.87879.1301945-10-24
EstoniaESTeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201845230.01339000.017.2426.2625.195.8963253.02.377.6682.11173.2011991-09-17
FinlandFINeurope_central_asiaeuropehigh_incomeTrueTrue1995-01-012018338420.05419000.013.1026.7325.583.6153368.01.982.0684.42378.9341955-12-14
FranceFRAeurope_central_asiaeuropehigh_incomeTrueTrue1952-07-232018549087.063780000.012.4825.8524.832.4913482.03.582.6285.74779.9911945-10-24
GermanyDEUeurope_central_asiaeuropehigh_incomeTrueTrue1952-07-232018357380.081800000.012.1427.1725.743.2803499.03.181.2583.63279.0601973-09-18
GreeceGRCeurope_central_asiaeuropehigh_incomeTrueTrue1981-01-012018131960.011450000.011.0126.3424.929.1753400.03.681.3484.07179.1291945-10-25
HungaryHUNeurope_central_asiaeuropeupper_middle_incomeTrueTrue2004-05-01201893030.09934000.016.1227.1225.985.2343037.05.375.9079.55772.6101955-12-14
IrelandIRLeurope_central_asiaeuropehigh_incomeTrueTrue1973-01-01201870280.04631000.014.9227.6526.623.7683600.03.081.4983.73779.8851955-12-14
ItalyITAeurope_central_asiaeuropehigh_incomeTrueTrue1952-07-232018301340.061090000.09.7226.4824.793.7783579.02.982.6285.43581.1461955-12-14
LatviaLVAeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201864490.02226000.013.4526.4625.628.2753174.06.975.1379.49869.8821991-09-17
LithuaniaLTUeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201865286.03278000.016.3026.8626.018.0903417.03.375.3180.06069.5541991-09-17
LuxembourgLUXeurope_central_asiaeuropehigh_incomeTrueTrue1952-07-2320182590.0530000.012.8427.4326.095.9713539.01.582.3984.22779.9811945-10-24
MaltaMLTeurope_central_asiaeuropehigh_incomeTrueFalse2004-05-012018320.0420600.04.1027.6827.052.2283378.05.181.7582.72479.5701964-12-01
NetherlandsNLDeurope_central_asiaeuropehigh_incomeTrueTrue1952-07-23201841540.016760000.09.7526.0225.472.2373228.03.281.9283.84180.4401945-12-10
PolandPOLeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-012018312680.038330000.014.4326.6725.927.6753451.04.578.1981.73274.0431945-10-24
PortugalPRTeurope_central_asiaeuropehigh_incomeTrueTrue1986-01-01201892225.010700000.013.8926.6826.185.0783477.03.081.3084.37278.6851955-12-14
RomaniaROUeurope_central_asiaeuropeupper_middle_incomeTrueFalse2007-01-012018238390.021340000.016.1525.4125.228.8083358.09.775.5379.15872.2651955-12-14
SlovakiaSVKeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201849035.05489000.013.3126.9326.326.7462944.05.877.1680.51173.5891993-01-19
SloveniaSVNeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201820270.02045000.014.9427.4426.585.3153168.02.181.1284.01778.4991992-05-22
SpainESPeurope_central_asiaeuropehigh_incomeTrueTrue1986-01-012018505940.047040000.011.8327.5026.315.1463174.03.583.2386.11980.6941955-12-14
SwedenSWEeurope_central_asiaeuropehigh_incomeTrueTrue1995-01-012018447420.09546000.09.5026.3825.152.7373179.02.482.3784.44381.1261946-11-19
United KingdomGBReurope_central_asiaeuropehigh_incomeTrueTrue1973-01-012018243610.063180000.013.2427.3926.943.3773424.03.581.1983.55880.1271945-10-24
\n", + "
" + ], + "text/plain": [ + " iso world_6region world_4region income_groups \\\n", + "name \n", + "Austria AUT europe_central_asia europe high_income \n", + "Belgium BEL europe_central_asia europe high_income \n", + "Bulgaria BGR europe_central_asia europe upper_middle_income \n", + "Croatia HRV europe_central_asia europe high_income \n", + "Cyprus CYP europe_central_asia europe high_income \n", + "Czechia CZE europe_central_asia europe high_income \n", + "Denmark DNK europe_central_asia europe high_income \n", + "Estonia EST europe_central_asia europe high_income \n", + "Finland FIN europe_central_asia europe high_income \n", + "France FRA europe_central_asia europe high_income \n", + "Germany DEU europe_central_asia europe high_income \n", + "Greece GRC europe_central_asia europe high_income \n", + "Hungary HUN europe_central_asia europe upper_middle_income \n", + "Ireland IRL europe_central_asia europe high_income \n", + "Italy ITA europe_central_asia europe high_income \n", + "Latvia LVA europe_central_asia europe high_income \n", + "Lithuania LTU europe_central_asia europe high_income \n", + "Luxembourg LUX europe_central_asia europe high_income \n", + "Malta MLT europe_central_asia europe high_income \n", + "Netherlands NLD europe_central_asia europe high_income \n", + "Poland POL europe_central_asia europe high_income \n", + "Portugal PRT europe_central_asia europe high_income \n", + "Romania ROU europe_central_asia europe upper_middle_income \n", + "Slovakia SVK europe_central_asia europe high_income \n", + "Slovenia SVN europe_central_asia europe high_income \n", + "Spain ESP europe_central_asia europe high_income \n", + "Sweden SWE europe_central_asia europe high_income \n", + "United Kingdom GBR europe_central_asia europe high_income \n", + "\n", + " is_eu is_oecd eu_accession year area population \\\n", + "name \n", + "Austria True True 1995-01-01 2018 83879.0 8441000.0 \n", + "Belgium True True 1952-07-23 2018 30530.0 10820000.0 \n", + "Bulgaria True False 2007-01-01 2018 111000.0 7349000.0 \n", + "Croatia True False 2013-01-01 2018 56590.0 4379000.0 \n", + "Cyprus True False 2004-05-01 2018 9250.0 1141000.0 \n", + "Czechia True True 2004-05-01 2018 78870.0 10590000.0 \n", + "Denmark True True 1973-01-01 2018 42922.0 5611000.0 \n", + "Estonia True True 2004-05-01 2018 45230.0 1339000.0 \n", + "Finland True True 1995-01-01 2018 338420.0 5419000.0 \n", + "France True True 1952-07-23 2018 549087.0 63780000.0 \n", + "Germany True True 1952-07-23 2018 357380.0 81800000.0 \n", + "Greece True True 1981-01-01 2018 131960.0 11450000.0 \n", + "Hungary True True 2004-05-01 2018 93030.0 9934000.0 \n", + "Ireland True True 1973-01-01 2018 70280.0 4631000.0 \n", + "Italy True True 1952-07-23 2018 301340.0 61090000.0 \n", + "Latvia True True 2004-05-01 2018 64490.0 2226000.0 \n", + "Lithuania True True 2004-05-01 2018 65286.0 3278000.0 \n", + "Luxembourg True True 1952-07-23 2018 2590.0 530000.0 \n", + "Malta True False 2004-05-01 2018 320.0 420600.0 \n", + "Netherlands True True 1952-07-23 2018 41540.0 16760000.0 \n", + "Poland True True 2004-05-01 2018 312680.0 38330000.0 \n", + "Portugal True True 1986-01-01 2018 92225.0 10700000.0 \n", + "Romania True False 2007-01-01 2018 238390.0 21340000.0 \n", + "Slovakia True True 2004-05-01 2018 49035.0 5489000.0 \n", + "Slovenia True True 2004-05-01 2018 20270.0 2045000.0 \n", + "Spain True True 1986-01-01 2018 505940.0 47040000.0 \n", + "Sweden True True 1995-01-01 2018 447420.0 9546000.0 \n", + "United Kingdom True True 1973-01-01 2018 243610.0 63180000.0 \n", + "\n", + " alcohol_adults bmi_men bmi_women \\\n", + "name \n", + "Austria 12.40 26.47 25.09 \n", + "Belgium 10.41 26.76 25.14 \n", + "Bulgaria 11.40 26.54 25.52 \n", + "Croatia 15.00 26.60 25.18 \n", + "Cyprus 8.84 27.42 25.93 \n", + "Czechia 16.47 27.91 26.51 \n", + "Denmark 12.02 26.13 25.11 \n", + "Estonia 17.24 26.26 25.19 \n", + "Finland 13.10 26.73 25.58 \n", + "France 12.48 25.85 24.83 \n", + "Germany 12.14 27.17 25.74 \n", + "Greece 11.01 26.34 24.92 \n", + "Hungary 16.12 27.12 25.98 \n", + "Ireland 14.92 27.65 26.62 \n", + "Italy 9.72 26.48 24.79 \n", + "Latvia 13.45 26.46 25.62 \n", + "Lithuania 16.30 26.86 26.01 \n", + "Luxembourg 12.84 27.43 26.09 \n", + "Malta 4.10 27.68 27.05 \n", + "Netherlands 9.75 26.02 25.47 \n", + "Poland 14.43 26.67 25.92 \n", + "Portugal 13.89 26.68 26.18 \n", + "Romania 16.15 25.41 25.22 \n", + "Slovakia 13.31 26.93 26.32 \n", + "Slovenia 14.94 27.44 26.58 \n", + "Spain 11.83 27.50 26.31 \n", + "Sweden 9.50 26.38 25.15 \n", + "United Kingdom 13.24 27.39 26.94 \n", + "\n", + " car_deaths_per_100000_people calories_per_day \\\n", + "name \n", + "Austria 3.541 3768.0 \n", + "Belgium 5.427 3733.0 \n", + "Bulgaria 9.662 2829.0 \n", + "Croatia 6.434 3059.0 \n", + "Cyprus 6.419 2649.0 \n", + "Czechia 5.720 3256.0 \n", + "Denmark 3.481 3367.0 \n", + "Estonia 5.896 3253.0 \n", + "Finland 3.615 3368.0 \n", + "France 2.491 3482.0 \n", + "Germany 3.280 3499.0 \n", + "Greece 9.175 3400.0 \n", + "Hungary 5.234 3037.0 \n", + "Ireland 3.768 3600.0 \n", + "Italy 3.778 3579.0 \n", + "Latvia 8.275 3174.0 \n", + "Lithuania 8.090 3417.0 \n", + "Luxembourg 5.971 3539.0 \n", + "Malta 2.228 3378.0 \n", + "Netherlands 2.237 3228.0 \n", + "Poland 7.675 3451.0 \n", + "Portugal 5.078 3477.0 \n", + "Romania 8.808 3358.0 \n", + "Slovakia 6.746 2944.0 \n", + "Slovenia 5.315 3168.0 \n", + "Spain 5.146 3174.0 \n", + "Sweden 2.737 3179.0 \n", + "United Kingdom 3.377 3424.0 \n", + "\n", + " infant_mortality life_expectancy life_expectancy_female \\\n", + "name \n", + "Austria 2.9 81.84 84.249 \n", + "Belgium 3.3 81.23 83.751 \n", + "Bulgaria 9.3 75.32 78.485 \n", + "Croatia 3.6 77.66 81.167 \n", + "Cyprus 2.5 80.79 82.918 \n", + "Czechia 2.8 79.37 81.858 \n", + "Denmark 2.9 81.10 82.878 \n", + "Estonia 2.3 77.66 82.111 \n", + "Finland 1.9 82.06 84.423 \n", + "France 3.5 82.62 85.747 \n", + "Germany 3.1 81.25 83.632 \n", + "Greece 3.6 81.34 84.071 \n", + "Hungary 5.3 75.90 79.557 \n", + "Ireland 3.0 81.49 83.737 \n", + "Italy 2.9 82.62 85.435 \n", + "Latvia 6.9 75.13 79.498 \n", + "Lithuania 3.3 75.31 80.060 \n", + "Luxembourg 1.5 82.39 84.227 \n", + "Malta 5.1 81.75 82.724 \n", + "Netherlands 3.2 81.92 83.841 \n", + "Poland 4.5 78.19 81.732 \n", + "Portugal 3.0 81.30 84.372 \n", + "Romania 9.7 75.53 79.158 \n", + "Slovakia 5.8 77.16 80.511 \n", + "Slovenia 2.1 81.12 84.017 \n", + "Spain 3.5 83.23 86.119 \n", + "Sweden 2.4 82.37 84.443 \n", + "United Kingdom 3.5 81.19 83.558 \n", + "\n", + " life_expectancy_male un_accession \n", + "name \n", + "Austria 79.585 1955-12-14 \n", + "Belgium 79.131 1945-12-27 \n", + "Bulgaria 71.618 1955-12-14 \n", + "Croatia 74.701 1992-05-22 \n", + "Cyprus 78.734 1960-09-20 \n", + "Czechia 76.148 1993-01-19 \n", + "Denmark 79.130 1945-10-24 \n", + "Estonia 73.201 1991-09-17 \n", + "Finland 78.934 1955-12-14 \n", + "France 79.991 1945-10-24 \n", + "Germany 79.060 1973-09-18 \n", + "Greece 79.129 1945-10-25 \n", + "Hungary 72.610 1955-12-14 \n", + "Ireland 79.885 1955-12-14 \n", + "Italy 81.146 1955-12-14 \n", + "Latvia 69.882 1991-09-17 \n", + "Lithuania 69.554 1991-09-17 \n", + "Luxembourg 79.981 1945-10-24 \n", + "Malta 79.570 1964-12-01 \n", + "Netherlands 80.440 1945-12-10 \n", + "Poland 74.043 1945-10-24 \n", + "Portugal 78.685 1955-12-14 \n", + "Romania 72.265 1955-12-14 \n", + "Slovakia 73.589 1993-01-19 \n", + "Slovenia 78.499 1992-05-22 \n", + "Spain 80.694 1955-12-14 \n", + "Sweden 81.126 1946-11-19 \n", + "United Kingdom 80.127 1945-10-24 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[countries[\"is_eu\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nemusíš použít existující sloupec v tabulce, ale i jakoukoliv vypočítanou hodnotu stejného tvaru:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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isoworld_6regionworld_4regionincome_groupsis_euis_oecdeu_accessionyearareapopulationalcohol_adultsbmi_menbmi_womencar_deaths_per_100000_peoplecalories_per_dayinfant_mortalitylife_expectancylife_expectancy_femalelife_expectancy_maleun_accession
name
AndorraANDeurope_central_asiaeuropehigh_incomeFalseFalseNaN2017470.088910.010.1727.6326.43NaNNaN2.1082.55NaNNaN1993-07-28
Antigua and BarbudaATGamericaamericashigh_incomeFalseFalseNaN2018440.091400.08.1725.7727.51NaN2417.05.8077.6079.02874.1541981-11-11
DominicaDMAamericaamericasupper_middle_incomeFalseFalseNaN2017750.067700.08.6824.5728.78NaN2931.019.6073.01NaNNaN1978-12-18
LiechtensteinLIEeurope_central_asiaeuropehigh_incomeFalseFalseNaN2017160.036870.0NaNNaNNaNNaNNaN1.76NaNNaNNaN1990-09-18
Marshall IslandsMHLeast_asia_pacificasiaupper_middle_incomeFalseFalseNaN2017180.056690.0NaN29.3731.391.800NaN29.6065.00NaNNaN1991-09-17
MonacoMCOeurope_central_asiaeuropehigh_incomeFalseFalseNaN20172.035460.0NaNNaNNaNNaNNaN2.80NaNNaNNaN1993-05-28
NauruNRUeast_asia_pacificasiaNaNFalseFalseNaN201520.010440.04.8133.9035.02NaNNaN29.10NaNNaNNaN1999-09-14
PalauPLWeast_asia_pacificasiaupper_middle_incomeFalseFalseNaN2017460.020920.09.8630.3831.8510.730NaN14.20NaNNaNNaN1994-12-15
Saint Kitts and NevisKNAamericaamericashigh_incomeFalseFalseNaN2017260.054340.010.6228.2330.51NaN2492.08.40NaNNaNNaN1983-09-23
San MarinoSMReurope_central_asiaeuropehigh_incomeFalseFalseNaN201760.032160.0NaNNaNNaN5.946NaN2.60NaNNaNNaN1992-03-02
SeychellesSYCsub_saharan_africaafricaupper_middle_incomeFalseFalseNaN2018460.087420.012.1125.5627.9711.700NaN11.7074.2378.73069.6931976-09-21
TuvaluTUVeast_asia_pacificasiaupper_middle_incomeFalseFalseNaN201730.09888.02.14NaNNaNNaNNaN22.80NaNNaNNaN2000-09-05
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" + ], + "text/plain": [ + " iso world_6region world_4region \\\n", + "name \n", + "Andorra AND europe_central_asia europe \n", + "Antigua and Barbuda ATG america americas \n", + "Dominica DMA america americas \n", + "Liechtenstein LIE europe_central_asia europe \n", + "Marshall Islands MHL east_asia_pacific asia \n", + "Monaco MCO europe_central_asia europe \n", + "Nauru NRU east_asia_pacific asia \n", + "Palau PLW east_asia_pacific asia \n", + "Saint Kitts and Nevis KNA america americas \n", + "San Marino SMR europe_central_asia europe \n", + "Seychelles SYC sub_saharan_africa africa \n", + "Tuvalu TUV east_asia_pacific asia \n", + "\n", + " income_groups is_eu is_oecd eu_accession year \\\n", + "name \n", + "Andorra high_income False False NaN 2017 \n", + "Antigua and Barbuda high_income False False NaN 2018 \n", + "Dominica upper_middle_income False False NaN 2017 \n", + "Liechtenstein high_income False False NaN 2017 \n", + "Marshall Islands upper_middle_income False False NaN 2017 \n", + "Monaco high_income False False NaN 2017 \n", + "Nauru NaN False False NaN 2015 \n", + "Palau upper_middle_income False False NaN 2017 \n", + "Saint Kitts and Nevis high_income False False NaN 2017 \n", + "San Marino high_income False False NaN 2017 \n", + "Seychelles upper_middle_income False False NaN 2018 \n", + "Tuvalu upper_middle_income False False NaN 2017 \n", + "\n", + " area population alcohol_adults bmi_men bmi_women \\\n", + "name \n", + "Andorra 470.0 88910.0 10.17 27.63 26.43 \n", + "Antigua and Barbuda 440.0 91400.0 8.17 25.77 27.51 \n", + "Dominica 750.0 67700.0 8.68 24.57 28.78 \n", + "Liechtenstein 160.0 36870.0 NaN NaN NaN \n", + "Marshall Islands 180.0 56690.0 NaN 29.37 31.39 \n", + "Monaco 2.0 35460.0 NaN NaN NaN \n", + "Nauru 20.0 10440.0 4.81 33.90 35.02 \n", + "Palau 460.0 20920.0 9.86 30.38 31.85 \n", + "Saint Kitts and Nevis 260.0 54340.0 10.62 28.23 30.51 \n", + "San Marino 60.0 32160.0 NaN NaN NaN \n", + "Seychelles 460.0 87420.0 12.11 25.56 27.97 \n", + "Tuvalu 30.0 9888.0 2.14 NaN NaN \n", + "\n", + " car_deaths_per_100000_people calories_per_day \\\n", + "name \n", + "Andorra NaN NaN \n", + "Antigua and Barbuda NaN 2417.0 \n", + "Dominica NaN 2931.0 \n", + "Liechtenstein NaN NaN \n", + "Marshall Islands 1.800 NaN \n", + "Monaco NaN NaN \n", + "Nauru NaN NaN \n", + "Palau 10.730 NaN \n", + "Saint Kitts and Nevis NaN 2492.0 \n", + "San Marino 5.946 NaN \n", + "Seychelles 11.700 NaN \n", + "Tuvalu NaN NaN \n", + "\n", + " infant_mortality life_expectancy \\\n", + "name \n", + "Andorra 2.10 82.55 \n", + "Antigua and Barbuda 5.80 77.60 \n", + "Dominica 19.60 73.01 \n", + "Liechtenstein 1.76 NaN \n", + "Marshall Islands 29.60 65.00 \n", + "Monaco 2.80 NaN \n", + "Nauru 29.10 NaN \n", + "Palau 14.20 NaN \n", + "Saint Kitts and Nevis 8.40 NaN \n", + "San Marino 2.60 NaN \n", + "Seychelles 11.70 74.23 \n", + "Tuvalu 22.80 NaN \n", + "\n", + " life_expectancy_female life_expectancy_male \\\n", + "name \n", + "Andorra NaN NaN \n", + "Antigua and Barbuda 79.028 74.154 \n", + "Dominica NaN NaN \n", + "Liechtenstein NaN NaN \n", + "Marshall Islands NaN NaN \n", + "Monaco NaN NaN \n", + "Nauru NaN NaN \n", + "Palau NaN NaN \n", + "Saint Kitts and Nevis NaN NaN \n", + "San Marino NaN NaN \n", + "Seychelles 78.730 69.693 \n", + "Tuvalu NaN NaN \n", + "\n", + " un_accession \n", + "name \n", + "Andorra 1993-07-28 \n", + "Antigua and Barbuda 1981-11-11 \n", + "Dominica 1978-12-18 \n", + "Liechtenstein 1990-09-18 \n", + "Marshall Islands 1991-09-17 \n", + "Monaco 1993-05-28 \n", + "Nauru 1999-09-14 \n", + "Palau 1994-12-15 \n", + "Saint Kitts and Nevis 1983-09-23 \n", + "San Marino 1992-03-02 \n", + "Seychelles 1976-09-21 \n", + "Tuvalu 2000-09-05 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Prťavé země\n", + "countries[countries[\"population\"] < 100_000]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "...a samozřejmě kombinace:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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isoworld_6regionworld_4regionincome_groupsis_euis_oecdeu_accessionyearareapopulationalcohol_adultsbmi_menbmi_womencar_deaths_per_100000_peoplecalories_per_dayinfant_mortalitylife_expectancylife_expectancy_femalelife_expectancy_maleun_accession
name
BulgariaBGReurope_central_asiaeuropeupper_middle_incomeTrueFalse2007-01-012018111000.07349000.011.4026.5425.529.6622829.09.375.3278.48571.6181955-12-14
HungaryHUNeurope_central_asiaeuropeupper_middle_incomeTrueTrue2004-05-01201893030.09934000.016.1227.1225.985.2343037.05.375.9079.55772.6101955-12-14
RomaniaROUeurope_central_asiaeuropeupper_middle_incomeTrueFalse2007-01-012018238390.021340000.016.1525.4125.228.8083358.09.775.5379.15872.2651955-12-14
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" + ], + "text/plain": [ + " iso world_6region world_4region income_groups is_eu \\\n", + "name \n", + "Bulgaria BGR europe_central_asia europe upper_middle_income True \n", + "Hungary HUN europe_central_asia europe upper_middle_income True \n", + "Romania ROU europe_central_asia europe upper_middle_income True \n", + "\n", + " is_oecd eu_accession year area population alcohol_adults \\\n", + "name \n", + "Bulgaria False 2007-01-01 2018 111000.0 7349000.0 11.40 \n", + "Hungary True 2004-05-01 2018 93030.0 9934000.0 16.12 \n", + "Romania False 2007-01-01 2018 238390.0 21340000.0 16.15 \n", + "\n", + " bmi_men bmi_women car_deaths_per_100000_people calories_per_day \\\n", + "name \n", + "Bulgaria 26.54 25.52 9.662 2829.0 \n", + "Hungary 27.12 25.98 5.234 3037.0 \n", + "Romania 25.41 25.22 8.808 3358.0 \n", + "\n", + " infant_mortality life_expectancy life_expectancy_female \\\n", + "name \n", + "Bulgaria 9.3 75.32 78.485 \n", + "Hungary 5.3 75.90 79.557 \n", + "Romania 9.7 75.53 79.158 \n", + "\n", + " life_expectancy_male un_accession \n", + "name \n", + "Bulgaria 71.618 1955-12-14 \n", + "Hungary 72.610 1955-12-14 \n", + "Romania 72.265 1955-12-14 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Chudší země EU\n", + "countries[countries[\"is_eu\"] & (countries[\"income_groups\"] != \"high_income\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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isoworld_6regionworld_4regionincome_groupsis_euis_oecdeu_accessionyearareapopulationalcohol_adultsbmi_menbmi_womencar_deaths_per_100000_peoplecalories_per_dayinfant_mortalitylife_expectancylife_expectancy_femalelife_expectancy_maleun_accession
name
EstoniaESTeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201845230.01339000.017.2426.2625.195.8963253.02.377.6682.11173.2011991-09-17
HungaryHUNeurope_central_asiaeuropeupper_middle_incomeTrueTrue2004-05-01201893030.09934000.016.1227.1225.985.2343037.05.375.9079.55772.6101955-12-14
LatviaLVAeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201864490.02226000.013.4526.4625.628.2753174.06.975.1379.49869.8821991-09-17
LithuaniaLTUeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201865286.03278000.016.3026.8626.018.0903417.03.375.3180.06069.5541991-09-17
MexicoMEXamericaamericasupper_middle_incomeFalseTrueNaN20181964380.0117500000.08.5527.4228.749.4683072.011.376.7879.88075.1201945-11-07
SlovakiaSVKeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201849035.05489000.013.3126.9326.326.7462944.05.877.1680.51173.5891993-01-19
\n", + "
" + ], + "text/plain": [ + " iso world_6region world_4region income_groups is_eu \\\n", + "name \n", + "Estonia EST europe_central_asia europe high_income True \n", + "Hungary HUN europe_central_asia europe upper_middle_income True \n", + "Latvia LVA europe_central_asia europe high_income True \n", + "Lithuania LTU europe_central_asia europe high_income True \n", + "Mexico MEX america americas upper_middle_income False \n", + "Slovakia SVK europe_central_asia europe high_income True \n", + "\n", + " is_oecd eu_accession year area population alcohol_adults \\\n", + "name \n", + "Estonia True 2004-05-01 2018 45230.0 1339000.0 17.24 \n", + "Hungary True 2004-05-01 2018 93030.0 9934000.0 16.12 \n", + "Latvia True 2004-05-01 2018 64490.0 2226000.0 13.45 \n", + "Lithuania True 2004-05-01 2018 65286.0 3278000.0 16.30 \n", + "Mexico True NaN 2018 1964380.0 117500000.0 8.55 \n", + "Slovakia True 2004-05-01 2018 49035.0 5489000.0 13.31 \n", + "\n", + " bmi_men bmi_women car_deaths_per_100000_people calories_per_day \\\n", + "name \n", + "Estonia 26.26 25.19 5.896 3253.0 \n", + "Hungary 27.12 25.98 5.234 3037.0 \n", + "Latvia 26.46 25.62 8.275 3174.0 \n", + "Lithuania 26.86 26.01 8.090 3417.0 \n", + "Mexico 27.42 28.74 9.468 3072.0 \n", + "Slovakia 26.93 26.32 6.746 2944.0 \n", + "\n", + " infant_mortality life_expectancy life_expectancy_female \\\n", + "name \n", + "Estonia 2.3 77.66 82.111 \n", + "Hungary 5.3 75.90 79.557 \n", + "Latvia 6.9 75.13 79.498 \n", + "Lithuania 3.3 75.31 80.060 \n", + "Mexico 11.3 76.78 79.880 \n", + "Slovakia 5.8 77.16 80.511 \n", + "\n", + " life_expectancy_male un_accession \n", + "name \n", + "Estonia 73.201 1991-09-17 \n", + "Hungary 72.610 1955-12-14 \n", + "Latvia 69.882 1991-09-17 \n", + "Lithuania 69.554 1991-09-17 \n", + "Mexico 75.120 1945-11-07 \n", + "Slovakia 73.589 1993-01-19 " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Které země OECD mají očekávanou dobu dožití méně 78 let?\n", + "countries[countries[\"is_oecd\"] & (countries[\"life_expectancy\"] < 78)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Protože tento způsob filtrování je poněkud nešikovný, existuje ještě metoda `query`, která umožňuje vybírat řádky na základě řetězce, který popisuje nějakou nerovnost z názvů sloupců a číselných hodnot (což poměrně často jde, někdy ovšem nemusí)." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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isoworld_6regionworld_4regionincome_groupsis_euis_oecdeu_accessionyearareapopulationalcohol_adultsbmi_menbmi_womencar_deaths_per_100000_peoplecalories_per_dayinfant_mortalitylife_expectancylife_expectancy_femalelife_expectancy_maleun_accession
name
BangladeshBGDsouth_asiaasialow_incomeFalseFalseNaN2018147630.01.544000e+080.1720.4020.554.4012450.030.773.4174.93771.4841974-09-17
BrazilBRAamericaamericasupper_middle_incomeFalseFalseNaN20188515770.02.001000e+0810.0825.7925.991.8723263.014.675.7079.52772.3401945-10-24
ChinaCHNeast_asia_pacificasiaupper_middle_incomeFalseFalseNaN20189562911.01.359000e+095.5622.9222.913.5903108.09.276.9278.16375.0961945-10-24
IndiaINDsouth_asiaasialower_middle_incomeFalseFalseNaN20183287259.01.275000e+092.6920.9621.313.0342459.037.969.1070.67867.5381945-10-30
IndonesiaIDNeast_asia_pacificasialower_middle_incomeFalseFalseNaN20181910931.02.472000e+080.5621.8622.991.2322777.022.872.0371.74267.4261950-09-28
JapanJPNeast_asia_pacificasiahigh_incomeFalseTrueNaN2018377962.01.263000e+087.7923.5021.871.3812726.02.084.1787.24480.8031956-12-18
MexicoMEXamericaamericasupper_middle_incomeFalseTrueNaN20181964380.01.175000e+088.5527.4228.749.4683072.011.376.7879.88075.1201945-11-07
NigeriaNGAsub_saharan_africaafricalower_middle_incomeFalseFalseNaN2018923770.01.709000e+0812.7223.0323.67NaN2700.069.466.1455.15853.5121960-10-07
PakistanPAKsouth_asiaasialower_middle_incomeFalseFalseNaN2018796100.01.832000e+080.0522.3023.45NaN2440.065.867.9667.86965.7501947-09-30
RussiaRUSeurope_central_asiaeuropehigh_incomeFalseFalseNaN201817098250.01.426000e+0816.2326.0127.2114.3803361.08.271.0776.88265.7711945-10-24
United StatesUSAamericaamericashigh_incomeFalseTrueNaN20189831510.03.185000e+089.7028.4628.349.5233682.05.679.1481.94277.4291945-10-24
\n", + "
" + ], + "text/plain": [ + " iso world_6region world_4region income_groups \\\n", + "name \n", + "Bangladesh BGD south_asia asia low_income \n", + "Brazil BRA america americas upper_middle_income \n", + "China CHN east_asia_pacific asia upper_middle_income \n", + "India IND south_asia asia lower_middle_income \n", + "Indonesia IDN east_asia_pacific asia lower_middle_income \n", + "Japan JPN east_asia_pacific asia high_income \n", + "Mexico MEX america americas upper_middle_income \n", + "Nigeria NGA sub_saharan_africa africa lower_middle_income \n", + "Pakistan PAK south_asia asia lower_middle_income \n", + "Russia RUS europe_central_asia europe high_income \n", + "United States USA america americas high_income \n", + "\n", + " is_eu is_oecd eu_accession year area population \\\n", + "name \n", + "Bangladesh False False NaN 2018 147630.0 1.544000e+08 \n", + "Brazil False False NaN 2018 8515770.0 2.001000e+08 \n", + "China False False NaN 2018 9562911.0 1.359000e+09 \n", + "India False False NaN 2018 3287259.0 1.275000e+09 \n", + "Indonesia False False NaN 2018 1910931.0 2.472000e+08 \n", + "Japan False True NaN 2018 377962.0 1.263000e+08 \n", + "Mexico False True NaN 2018 1964380.0 1.175000e+08 \n", + "Nigeria False False NaN 2018 923770.0 1.709000e+08 \n", + "Pakistan False False NaN 2018 796100.0 1.832000e+08 \n", + "Russia False False NaN 2018 17098250.0 1.426000e+08 \n", + "United States False True NaN 2018 9831510.0 3.185000e+08 \n", + "\n", + " alcohol_adults bmi_men bmi_women \\\n", + "name \n", + "Bangladesh 0.17 20.40 20.55 \n", + "Brazil 10.08 25.79 25.99 \n", + "China 5.56 22.92 22.91 \n", + "India 2.69 20.96 21.31 \n", + "Indonesia 0.56 21.86 22.99 \n", + "Japan 7.79 23.50 21.87 \n", + "Mexico 8.55 27.42 28.74 \n", + "Nigeria 12.72 23.03 23.67 \n", + "Pakistan 0.05 22.30 23.45 \n", + "Russia 16.23 26.01 27.21 \n", + "United States 9.70 28.46 28.34 \n", + "\n", + " car_deaths_per_100000_people calories_per_day \\\n", + "name \n", + "Bangladesh 4.401 2450.0 \n", + "Brazil 1.872 3263.0 \n", + "China 3.590 3108.0 \n", + "India 3.034 2459.0 \n", + "Indonesia 1.232 2777.0 \n", + "Japan 1.381 2726.0 \n", + "Mexico 9.468 3072.0 \n", + "Nigeria NaN 2700.0 \n", + "Pakistan NaN 2440.0 \n", + "Russia 14.380 3361.0 \n", + "United States 9.523 3682.0 \n", + "\n", + " infant_mortality life_expectancy life_expectancy_female \\\n", + "name \n", + "Bangladesh 30.7 73.41 74.937 \n", + "Brazil 14.6 75.70 79.527 \n", + "China 9.2 76.92 78.163 \n", + "India 37.9 69.10 70.678 \n", + "Indonesia 22.8 72.03 71.742 \n", + "Japan 2.0 84.17 87.244 \n", + "Mexico 11.3 76.78 79.880 \n", + "Nigeria 69.4 66.14 55.158 \n", + "Pakistan 65.8 67.96 67.869 \n", + "Russia 8.2 71.07 76.882 \n", + "United States 5.6 79.14 81.942 \n", + "\n", + " life_expectancy_male un_accession \n", + "name \n", + "Bangladesh 71.484 1974-09-17 \n", + "Brazil 72.340 1945-10-24 \n", + "China 75.096 1945-10-24 \n", + "India 67.538 1945-10-30 \n", + "Indonesia 67.426 1950-09-28 \n", + "Japan 80.803 1956-12-18 \n", + "Mexico 75.120 1945-11-07 \n", + "Nigeria 53.512 1960-10-07 \n", + "Pakistan 65.750 1947-09-30 \n", + "Russia 65.771 1945-10-24 \n", + "United States 77.429 1945-10-24 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Opravdu veliké země (počet obyvatel nad 100 milionů)\n", + "countries.query(\"population > 100_000_000\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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isoworld_6regionworld_4regionincome_groupsis_euis_oecdeu_accessionyearareapopulationalcohol_adultsbmi_menbmi_womencar_deaths_per_100000_peoplecalories_per_dayinfant_mortalitylife_expectancylife_expectancy_femalelife_expectancy_maleun_accession
name
AustriaAUTeurope_central_asiaeuropehigh_incomeTrueTrue1995-01-01201883879.08441000.012.4026.4725.093.5413768.02.981.8484.24979.5851955-12-14
BelgiumBELeurope_central_asiaeuropehigh_incomeTrueTrue1952-07-23201830530.010820000.010.4126.7625.145.4273733.03.381.2383.75179.1311945-12-27
IrelandIRLeurope_central_asiaeuropehigh_incomeTrueTrue1973-01-01201870280.04631000.014.9227.6526.623.7683600.03.081.4983.73779.8851955-12-14
ItalyITAeurope_central_asiaeuropehigh_incomeTrueTrue1952-07-232018301340.061090000.09.7226.4824.793.7783579.02.982.6285.43581.1461955-12-14
LuxembourgLUXeurope_central_asiaeuropehigh_incomeTrueTrue1952-07-2320182590.0530000.012.8427.4326.095.9713539.01.582.3984.22779.9811945-10-24
\n", + "
" + ], + "text/plain": [ + " iso world_6region world_4region income_groups is_eu \\\n", + "name \n", + "Austria AUT europe_central_asia europe high_income True \n", + "Belgium BEL europe_central_asia europe high_income True \n", + "Ireland IRL europe_central_asia europe high_income True \n", + "Italy ITA europe_central_asia europe high_income True \n", + "Luxembourg LUX europe_central_asia europe high_income True \n", + "\n", + " is_oecd eu_accession year area population alcohol_adults \\\n", + "name \n", + "Austria True 1995-01-01 2018 83879.0 8441000.0 12.40 \n", + "Belgium True 1952-07-23 2018 30530.0 10820000.0 10.41 \n", + "Ireland True 1973-01-01 2018 70280.0 4631000.0 14.92 \n", + "Italy True 1952-07-23 2018 301340.0 61090000.0 9.72 \n", + "Luxembourg True 1952-07-23 2018 2590.0 530000.0 12.84 \n", + "\n", + " bmi_men bmi_women car_deaths_per_100000_people \\\n", + "name \n", + "Austria 26.47 25.09 3.541 \n", + "Belgium 26.76 25.14 5.427 \n", + "Ireland 27.65 26.62 3.768 \n", + "Italy 26.48 24.79 3.778 \n", + "Luxembourg 27.43 26.09 5.971 \n", + "\n", + " calories_per_day infant_mortality life_expectancy \\\n", + "name \n", + "Austria 3768.0 2.9 81.84 \n", + "Belgium 3733.0 3.3 81.23 \n", + "Ireland 3600.0 3.0 81.49 \n", + "Italy 3579.0 2.9 82.62 \n", + "Luxembourg 3539.0 1.5 82.39 \n", + "\n", + " life_expectancy_female life_expectancy_male un_accession \n", + "name \n", + "Austria 84.249 79.585 1955-12-14 \n", + "Belgium 83.751 79.131 1945-12-27 \n", + "Ireland 83.737 79.885 1955-12-14 \n", + "Italy 85.435 81.146 1955-12-14 \n", + "Luxembourg 84.227 79.981 1945-10-24 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# V kterých zemích EU se hodně jí?\n", + "countries.query(\"is_eu & (calories_per_day > 3500)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol**: Která jediná země Afriky patří do skupiny s vysokými příjmy?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol**: Ve kterých zemích se pije opravdu hodně (použij výše uvedené nebo jakékoliv jiné kritérium)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Řazení" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "V úvodní lekci `pandas` jsme si již ukázali, jak pomocí metody `sort_index` seřadit řádky podle indexu. Jelikož `countries` už jsou srovnané, vyzkoušíme si to ještě jednou na planetách:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesiceje_obr
jmeno
Jupiter♃5.2011.8679True
Mars♂1.521.882False
Merkur☿0.390.240False
Neptun♆30.06164.8014True
Saturn♄9.5429.4682True
Uran♅19.2284.0127True
Venuše♀0.720.620False
Země⊕1.001.001False
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" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice je_obr\n", + "jmeno \n", + "Jupiter ♃ 5.20 11.86 79 True\n", + "Mars ♂ 1.52 1.88 2 False\n", + "Merkur ☿ 0.39 0.24 0 False\n", + "Neptun ♆ 30.06 164.80 14 True\n", + "Saturn ♄ 9.54 29.46 82 True\n", + "Uran ♅ 19.22 84.01 27 True\n", + "Venuše ♀ 0.72 0.62 0 False\n", + "Země ⊕ 1.00 1.00 1 False" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety.sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pro řazení hodnot v `Series` se použije metoda `sort_values`:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Tuvalu 9888.0\n", + "Nauru 10440.0\n", + "Palau 20920.0\n", + "San Marino 32160.0\n", + "Monaco 35460.0\n", + "Liechtenstein 36870.0\n", + "Saint Kitts and Nevis 54340.0\n", + "Marshall Islands 56690.0\n", + "Dominica 67700.0\n", + "Seychelles 87420.0\n", + "Name: population, dtype: float64" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 10 zemí s nejmenším počtem obyvatel\n", + "countries[\"population\"].sort_values().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nepovinný argument `ascending` říká, kterým směrem máme řadit. Výchozí hodnota je `True`, změnou na `False` tedy budeme řadit od největšího k nejmenšímu:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Russia 17098250.0\n", + "Canada 9984670.0\n", + "United States 9831510.0\n", + "China 9562911.0\n", + "Brazil 8515770.0\n", + "Australia 7741220.0\n", + "India 3287259.0\n", + "Argentina 2780400.0\n", + "Kazakhstan 2724902.0\n", + "Algeria 2381740.0\n", + "Name: area, dtype: float64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Největších 10 zemí podle rozlohy\n", + "countries[\"area\"].sort_values(ascending=False).head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "V případě tabulky je třeba jako první argument uvést jméno sloupce (nebo sloupců), podle kterých chceme řadit:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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isoworld_6regionworld_4regionincome_groupsis_euis_oecdeu_accessionyearareapopulationalcohol_adultsbmi_menbmi_womencar_deaths_per_100000_peoplecalories_per_dayinfant_mortalitylife_expectancylife_expectancy_femalelife_expectancy_maleun_accession
name
MoldovaMDAeurope_central_asiaeuropelower_middle_incomeFalseFalseNaN201833850.03496000.023.0124.2427.065.5292714.013.672.4176.09067.5441992-03-02
South KoreaKOReast_asia_pacificasiahigh_incomeFalseTrueNaN2018100280.048770000.019.1523.9923.334.3193334.02.981.3585.46779.4561991-09-17
BelarusBLReurope_central_asiaeuropeupper_middle_incomeFalseFalseNaN2018207600.09498000.018.8526.1626.648.4543250.03.473.7678.58367.6931945-10-24
North KoreaPRKeast_asia_pacificasialow_incomeFalseFalseNaN2018120540.024650000.018.2822.0221.25NaN2094.019.771.1375.51268.4501991-09-17
UkraineUKReurope_central_asiaeuropelower_middle_incomeFalseFalseNaN2018603550.044700000.017.4725.4226.238.7713138.07.772.2977.06767.2461945-10-24
EstoniaESTeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201845230.01339000.017.2426.2625.195.8963253.02.377.6682.11173.2011991-09-17
CzechiaCZEeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201878870.010590000.016.4727.9126.515.7203256.02.879.3781.85876.1481993-01-19
UgandaUGAsub_saharan_africaafricalow_incomeFalseFalseNaN2018241550.036760000.016.4022.3622.4813.6902130.037.762.8662.66758.2521962-10-25
LithuaniaLTUeurope_central_asiaeuropehigh_incomeTrueTrue2004-05-01201865286.03278000.016.3026.8626.018.0903417.03.375.3180.06069.5541991-09-17
RussiaRUSeurope_central_asiaeuropehigh_incomeFalseFalseNaN201817098250.0142600000.016.2326.0127.2114.3803361.08.271.0776.88265.7711945-10-24
\n", + "
" + ], + "text/plain": [ + " iso world_6region world_4region income_groups \\\n", + "name \n", + "Moldova MDA europe_central_asia europe lower_middle_income \n", + "South Korea KOR east_asia_pacific asia high_income \n", + "Belarus BLR europe_central_asia europe upper_middle_income \n", + "North Korea PRK east_asia_pacific asia low_income \n", + "Ukraine UKR europe_central_asia europe lower_middle_income \n", + "Estonia EST europe_central_asia europe high_income \n", + "Czechia CZE europe_central_asia europe high_income \n", + "Uganda UGA sub_saharan_africa africa low_income \n", + "Lithuania LTU europe_central_asia europe high_income \n", + "Russia RUS europe_central_asia europe high_income \n", + "\n", + " is_eu is_oecd eu_accession year area population \\\n", + "name \n", + "Moldova False False NaN 2018 33850.0 3496000.0 \n", + "South Korea False True NaN 2018 100280.0 48770000.0 \n", + "Belarus False False NaN 2018 207600.0 9498000.0 \n", + "North Korea False False NaN 2018 120540.0 24650000.0 \n", + "Ukraine False False NaN 2018 603550.0 44700000.0 \n", + "Estonia True True 2004-05-01 2018 45230.0 1339000.0 \n", + "Czechia True True 2004-05-01 2018 78870.0 10590000.0 \n", + "Uganda False False NaN 2018 241550.0 36760000.0 \n", + "Lithuania True True 2004-05-01 2018 65286.0 3278000.0 \n", + "Russia False False NaN 2018 17098250.0 142600000.0 \n", + "\n", + " alcohol_adults bmi_men bmi_women car_deaths_per_100000_people \\\n", + "name \n", + "Moldova 23.01 24.24 27.06 5.529 \n", + "South Korea 19.15 23.99 23.33 4.319 \n", + "Belarus 18.85 26.16 26.64 8.454 \n", + "North Korea 18.28 22.02 21.25 NaN \n", + "Ukraine 17.47 25.42 26.23 8.771 \n", + "Estonia 17.24 26.26 25.19 5.896 \n", + "Czechia 16.47 27.91 26.51 5.720 \n", + "Uganda 16.40 22.36 22.48 13.690 \n", + "Lithuania 16.30 26.86 26.01 8.090 \n", + "Russia 16.23 26.01 27.21 14.380 \n", + "\n", + " calories_per_day infant_mortality life_expectancy \\\n", + "name \n", + "Moldova 2714.0 13.6 72.41 \n", + "South Korea 3334.0 2.9 81.35 \n", + "Belarus 3250.0 3.4 73.76 \n", + "North Korea 2094.0 19.7 71.13 \n", + "Ukraine 3138.0 7.7 72.29 \n", + "Estonia 3253.0 2.3 77.66 \n", + "Czechia 3256.0 2.8 79.37 \n", + "Uganda 2130.0 37.7 62.86 \n", + "Lithuania 3417.0 3.3 75.31 \n", + "Russia 3361.0 8.2 71.07 \n", + "\n", + " life_expectancy_female life_expectancy_male un_accession \n", + "name \n", + "Moldova 76.090 67.544 1992-03-02 \n", + "South Korea 85.467 79.456 1991-09-17 \n", + "Belarus 78.583 67.693 1945-10-24 \n", + "North Korea 75.512 68.450 1991-09-17 \n", + "Ukraine 77.067 67.246 1945-10-24 \n", + "Estonia 82.111 73.201 1991-09-17 \n", + "Czechia 81.858 76.148 1993-01-19 \n", + "Uganda 62.667 58.252 1962-10-25 \n", + "Lithuania 80.060 69.554 1991-09-17 \n", + "Russia 76.882 65.771 1945-10-24 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 10 zemí s největší spotřebou alkoholu na jednoho obyvatele\n", + "countries.sort_values(\"alcohol_adults\", ascending=False).head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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eu_accessionun_accession
name
France1952-07-231945-10-24
Luxembourg1952-07-231945-10-24
Netherlands1952-07-231945-12-10
Belgium1952-07-231945-12-27
Italy1952-07-231955-12-14
Germany1952-07-231973-09-18
Denmark1973-01-011945-10-24
United Kingdom1973-01-011945-10-24
Ireland1973-01-011955-12-14
Greece1981-01-011945-10-25
Portugal1986-01-011955-12-14
Spain1986-01-011955-12-14
Sweden1995-01-011946-11-19
Austria1995-01-011955-12-14
Finland1995-01-011955-12-14
Poland2004-05-011945-10-24
Hungary2004-05-011955-12-14
Cyprus2004-05-011960-09-20
Malta2004-05-011964-12-01
Estonia2004-05-011991-09-17
Latvia2004-05-011991-09-17
Lithuania2004-05-011991-09-17
Slovenia2004-05-011992-05-22
Czechia2004-05-011993-01-19
Slovakia2004-05-011993-01-19
Bulgaria2007-01-011955-12-14
Romania2007-01-011955-12-14
Croatia2013-01-011992-05-22
\n", + "
" + ], + "text/plain": [ + " eu_accession un_accession\n", + "name \n", + "France 1952-07-23 1945-10-24\n", + "Luxembourg 1952-07-23 1945-10-24\n", + "Netherlands 1952-07-23 1945-12-10\n", + "Belgium 1952-07-23 1945-12-27\n", + "Italy 1952-07-23 1955-12-14\n", + "Germany 1952-07-23 1973-09-18\n", + "Denmark 1973-01-01 1945-10-24\n", + "United Kingdom 1973-01-01 1945-10-24\n", + "Ireland 1973-01-01 1955-12-14\n", + "Greece 1981-01-01 1945-10-25\n", + "Portugal 1986-01-01 1955-12-14\n", + "Spain 1986-01-01 1955-12-14\n", + "Sweden 1995-01-01 1946-11-19\n", + "Austria 1995-01-01 1955-12-14\n", + "Finland 1995-01-01 1955-12-14\n", + "Poland 2004-05-01 1945-10-24\n", + "Hungary 2004-05-01 1955-12-14\n", + "Cyprus 2004-05-01 1960-09-20\n", + "Malta 2004-05-01 1964-12-01\n", + "Estonia 2004-05-01 1991-09-17\n", + "Latvia 2004-05-01 1991-09-17\n", + "Lithuania 2004-05-01 1991-09-17\n", + "Slovenia 2004-05-01 1992-05-22\n", + "Czechia 2004-05-01 1993-01-19\n", + "Slovakia 2004-05-01 1993-01-19\n", + "Bulgaria 2007-01-01 1955-12-14\n", + "Romania 2007-01-01 1955-12-14\n", + "Croatia 2013-01-01 1992-05-22" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " # Uvažuj jenom EU\n", + " countries[countries[\"is_eu\"]]\n", + " \n", + " # Seřaď nejdřív podle data vstupu do EU, pak podle vstupu do OSN\n", + " .sort_values([\"eu_accession\", \"un_accession\"])\n", + "\n", + " # Zobraz si jen ty dva sloupce\n", + " [[\"eu_accession\", \"un_accession\"]]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "💡 Ostatně je možné řadit nejen řádky, ale i sloupce. Následující příklad rovná sloupce podle jejich názvu (indexu). Poslouží k tomu (podobně jako v jiných podobných případech) argument `axis`." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcohol_adultsareabmi_menbmi_womencalories_per_daycar_deaths_per_100000_peopleeu_accessionincome_groupsinfant_mortalityis_euis_oecdisolife_expectancylife_expectancy_femalelife_expectancy_malepopulationun_accessionworld_4regionworld_6regionyear
name
Afghanistan0.03652860.020.6221.072090.0NaNNaNlow_income66.3FalseFalseAFG58.6965.81263.10134500000.01946-11-19asiasouth_asia2018
Albania7.2928750.026.4525.663193.05.978NaNupper_middle_income12.5FalseFalseALB78.0180.73776.6933238000.01955-12-14europeeurope_central_asia2018
Algeria0.692381740.024.6026.373296.0NaNNaNupper_middle_income21.9FalseFalseDZA77.8677.78475.27936980000.01962-10-08africamiddle_east_north_africa2018
Andorra10.17470.027.6326.43NaNNaNNaNhigh_income2.1FalseFalseAND82.55NaNNaN88910.01993-07-28europeeurope_central_asia2017
Angola5.571246700.022.2523.482473.0NaNNaNupper_middle_income96.0FalseFalseAGO65.1964.93959.21320710000.01976-12-01africasub_saharan_africa2018
...............................................................
Venezuela7.60912050.027.4528.132631.07.332NaNupper_middle_income12.9FalseFalseVEN75.9179.07970.95030340000.01945-11-15americasamerica2018
Vietnam3.91330967.020.9221.072745.0NaNNaNlower_middle_income17.3FalseFalseVNM74.8881.20372.00390660000.01977-09-20asiaeast_asia_pacific2018
Yemen0.20527970.024.4426.112223.0NaNNaNlower_middle_income33.8FalseFalseYEM67.1466.87163.87526360000.01947-09-30asiamiddle_east_north_africa2018
Zambia3.56752610.020.6823.051930.011.260NaNlower_middle_income43.3FalseFalseZMB59.4565.36259.84514310000.01964-12-01africasub_saharan_africa2018
Zimbabwe4.96390760.022.0324.652110.020.850NaNlow_income46.6FalseFalseZWE60.1863.94460.12013330000.01980-08-25africasub_saharan_africa2018
\n", + "

193 rows × 20 columns

\n", + "
" + ], + "text/plain": [ + " alcohol_adults area bmi_men bmi_women calories_per_day \\\n", + "name \n", + "Afghanistan 0.03 652860.0 20.62 21.07 2090.0 \n", + "Albania 7.29 28750.0 26.45 25.66 3193.0 \n", + "Algeria 0.69 2381740.0 24.60 26.37 3296.0 \n", + "Andorra 10.17 470.0 27.63 26.43 NaN \n", + "Angola 5.57 1246700.0 22.25 23.48 2473.0 \n", + "... ... ... ... ... ... \n", + "Venezuela 7.60 912050.0 27.45 28.13 2631.0 \n", + "Vietnam 3.91 330967.0 20.92 21.07 2745.0 \n", + "Yemen 0.20 527970.0 24.44 26.11 2223.0 \n", + "Zambia 3.56 752610.0 20.68 23.05 1930.0 \n", + "Zimbabwe 4.96 390760.0 22.03 24.65 2110.0 \n", + "\n", + " car_deaths_per_100000_people eu_accession income_groups \\\n", + "name \n", + "Afghanistan NaN NaN low_income \n", + "Albania 5.978 NaN upper_middle_income \n", + "Algeria NaN NaN upper_middle_income \n", + "Andorra NaN NaN high_income \n", + "Angola NaN NaN upper_middle_income \n", + "... ... ... ... \n", + "Venezuela 7.332 NaN upper_middle_income \n", + "Vietnam NaN NaN lower_middle_income \n", + "Yemen NaN NaN lower_middle_income \n", + "Zambia 11.260 NaN lower_middle_income \n", + "Zimbabwe 20.850 NaN low_income \n", + "\n", + " infant_mortality is_eu is_oecd iso life_expectancy \\\n", + "name \n", + "Afghanistan 66.3 False False AFG 58.69 \n", + "Albania 12.5 False False ALB 78.01 \n", + "Algeria 21.9 False False DZA 77.86 \n", + "Andorra 2.1 False False AND 82.55 \n", + "Angola 96.0 False False AGO 65.19 \n", + "... ... ... ... ... ... \n", + "Venezuela 12.9 False False VEN 75.91 \n", + "Vietnam 17.3 False False VNM 74.88 \n", + "Yemen 33.8 False False YEM 67.14 \n", + "Zambia 43.3 False False ZMB 59.45 \n", + "Zimbabwe 46.6 False False ZWE 60.18 \n", + "\n", + " life_expectancy_female life_expectancy_male population \\\n", + "name \n", + "Afghanistan 65.812 63.101 34500000.0 \n", + "Albania 80.737 76.693 3238000.0 \n", + "Algeria 77.784 75.279 36980000.0 \n", + "Andorra NaN NaN 88910.0 \n", + "Angola 64.939 59.213 20710000.0 \n", + "... ... ... ... \n", + "Venezuela 79.079 70.950 30340000.0 \n", + "Vietnam 81.203 72.003 90660000.0 \n", + "Yemen 66.871 63.875 26360000.0 \n", + "Zambia 65.362 59.845 14310000.0 \n", + "Zimbabwe 63.944 60.120 13330000.0 \n", + "\n", + " un_accession world_4region world_6region year \n", + "name \n", + "Afghanistan 1946-11-19 asia south_asia 2018 \n", + "Albania 1955-12-14 europe europe_central_asia 2018 \n", + "Algeria 1962-10-08 africa middle_east_north_africa 2018 \n", + "Andorra 1993-07-28 europe europe_central_asia 2017 \n", + "Angola 1976-12-01 africa sub_saharan_africa 2018 \n", + "... ... ... ... ... \n", + "Venezuela 1945-11-15 americas america 2018 \n", + "Vietnam 1977-09-20 asia east_asia_pacific 2018 \n", + "Yemen 1947-09-30 asia middle_east_north_africa 2018 \n", + "Zambia 1964-12-01 africa sub_saharan_africa 2018 \n", + "Zimbabwe 1980-08-25 africa sub_saharan_africa 2018 \n", + "\n", + "[193 rows x 20 columns]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries.sort_index(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol:** Seřaď země světa podle hustoty obyvatel." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol:** Které země mají problémy s nadváhou (průměrné BMI mužů a žen je přes 25)?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol:** V kterých 20 zemích umře absolutně nejvíc lidí při automobilových haváriích?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ulož výsledky!\n", + "\n", + "A tím už pomalu končíme. Jenže jsme udělali (skoro) netriviální množství práce a ta bude do příště ztracená. Naštěstí zapsat `DataFrame` do externího souboru v některém z typických formátů není vůbec komplikované. K sadě funkcí `pd.read_XXX` existují jejich protějšky `DataFrame.to_XXX`. Liší se různými parametry, ale základní použití je velmi jednoduché:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "planety.to_csv(\"planety.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "planety.to_excel(\"planety.xlsx\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Jednou z možností je i vytvoření HTML tabulky (které lze dodat i různé formátování, což ovšem nechme raději na jindy nebo na doma, viz [dokumentace \"Styling\"](https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html)). Výchozí `to_html` si bohužel neporadí s \"nezápadními\" symboly (což je třeba ☿), a tak mu (v našem konkrétním případě) musíme předat korektně otevřený soubor:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# planety.to_html(\"planety.html\") # To nefunguje :-(\n", + "\n", + "with open(\"planety.html\", \"w\", encoding=\"utf-8\") as out:\n", + " planety.to_html(out)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "countries.to_html(\"countries.html\") # Žádné exotické symboly :-)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol**: Podívej se, co ve výstupních souborech najdeš." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol**: Podívej se na seznam možných výstupních formátů a zkus si planety nebo země zapsat do nějakého z nich: https://pandas.pydata.org/pandas-docs/stable/reference/frame.html#serialization-io-conversion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A to už je opravdu všechno. 👋" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/pydata/visualization_basics/index.ipynb b/lessons/pydata/visualization_basics/index.ipynb new file mode 100644 index 0000000000..454a83e0e5 --- /dev/null +++ b/lessons/pydata/visualization_basics/index.ipynb @@ -0,0 +1,4922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Základy vizualizace - v pandas a pro pandas\n", + "\n", + "Jeden obrázek (či graf) někdy dokáže říci více než tisíc slov. U (explorativní) datové analýzy to platí dvojnásob (A jako umí být manipulativní článek o tisíci slovech, o to manipulativnější umí být \"vhodně\" připravený graf).\n", + "\n", + "V této lekci si ukážeme, jak z dat, která už umíš načíst a se kterými provádíš mnohé aritmetické operace, vykreslíš některé základní typy grafů (sloupcový, spojnicový a bodový)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rozmanitý svět vizualizačních knihoven v Pythonu\n", + "\n", + "Zatímco ohledně knihovny pro běžné zpracování tabulkových dat panuje shoda a při zkoumání malých až středně velkých dat nepříliš exotického typu téměř vždy analytici běžně sahají po `pandas`, knihoven pro vizualizaci dat existuje nepřeberné množství - každá má svoje výhody i nevýhody. My si během lekcí EDA zmíníme tyto tři (a budeme se soustředit především na to, jak je použít společně s pandas):\n", + "\n", + "- `matplotlib` - Toto je asi nejrozšířenější a v mnoha ohledech nejflexibilnější knihovna. Představuje výchozí volbu, pokud potřebuješ dobře vyhlížející statické grafy, které budou fungovat skoro všude. Značná flexibilita je vyvážena někdy ne zcela intuitivními jmény funkcí a argumentů. Pandas ji využívá interně (takže s trochou snahy můžeš předstírat, že o její existenci nevíš). Viz https://matplotlib.org/.\n", + "\n", + "- `seaborn` - Cílem této knihovny je pomoci zejména se statistickými grafy. Staví na matplotlibu, ale překrývá ho \"lidskou\" tváří. My s ním budeme pracovat při vizualizaci složitějších vztahů mezi více proměnnými. Viz https://seaborn.pydata.org/.\n", + "\n", + "- `plotly` (a zejména její podmnožina `plotly.express`) - Po této knihovně zejména sáhneš, budeš-li chtít do své vizualizace vložit interaktivitu. Ta se samozřejmě obtížně tiskne na papír, ale zejména při práci v Jupyter notebooku umožní vše zkoumat výrazně rychleji. Viz https://plot.ly/python/.\n", + "\n", + "Pro zájemce o bližší vysvětlení doporučujeme podívat se na (již poněkud starší) video od J. Vanderplase: Python Visualizations' Landscape (https://www.youtube.com/watch?v=FytuB8nFHPQ), které shrnuje základní vlastnosti jednotlivých knihoven.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "# Co to má znamenat!?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Jestli ses dosud tvářil/a, že nevíš o existenci matplotlibu, teď už nemůžeš :-). Tato mysteriózní řádka (ve skutečnosti \"IPython magic command\") říká, že všechny grafy se automaticky vykreslí přímo do notebooku (to vůbec není samozřejmé a lekcdy to ani nechceme - třeba když chceme grafy ukládat rovnou do souboru nebo interaktivně mimo notebook).\n", + "\n", + "Více viz https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-matplotlib.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Příprava - zdroj dat\n", + "\n", + "Nejdříve si načteme nám již známá data se zeměmi světa. Přidáme k tomu i tabulku s vývojem některých ukazatelů v čase pro Českou republiku (a hned se na ně podíváme)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearpopulationalcohol_adultsbmi_menbmi_womencar_deaths_per_100000_peoplecalories_per_dayinfant_mortalitylife_expectancylife_expectancy_femalelife_expectancy_male
0198010260000.0NaN26.3126.80NaNNaN17.070.6074.28967.153
1198110290000.0NaN26.3526.80NaNNaN16.470.7174.34167.181
2198210300000.0NaN26.4126.80NaNNaN16.070.7474.40367.216
3198310300000.0NaN26.4526.80NaNNaN15.670.8074.48367.269
4198410300000.0NaN26.5026.79NaNNaN15.470.9274.58767.345
5198510300000.0NaN26.5426.77NaNNaN15.171.1374.71667.446
6198610300000.0NaN26.5826.76NaNNaN14.771.3074.86867.567
7198710300000.0NaN26.6226.74NaNNaN14.371.5075.03767.703
8198810300000.0NaN26.6726.71NaNNaN13.771.6675.21867.852
9198910300000.0NaN26.7226.69NaNNaN13.371.7575.41168.018
10199010300000.0NaN26.7726.66NaNNaN12.771.8275.61968.208
11199110310000.0NaN26.8126.61NaNNaN12.272.0375.84568.434
12199210310000.0NaN26.8526.56NaNNaN11.572.3776.09268.703
13199310320000.0NaN26.9026.50NaN3037.010.772.7076.35769.013
14199410320000.0NaN26.9326.466.1442978.09.772.9976.63669.359
15199510320000.0NaN26.9826.457.8373209.08.873.3476.92369.731
16199610310000.0NaN27.0426.457.6413319.07.973.7677.20870.112
17199710300000.0NaN27.1026.467.5113236.07.274.1077.48470.489
18199810280000.0NaN27.1626.476.4103269.06.574.4477.74770.848
19199910260000.0NaN27.2226.466.2223118.06.074.7177.99571.185
20200010240000.0NaN27.2826.456.6003079.05.674.9978.23171.500
21200110230000.0NaN27.3526.466.0993170.05.375.2378.46271.800
22200210210000.0NaN27.4126.476.2083243.05.175.3878.69572.096
23200310200000.0NaN27.4826.476.3803319.04.875.6078.93672.396
24200410200000.0NaN27.5626.485.9103322.04.675.8879.18672.702
25200510220000.016.4527.6326.486.1233318.04.476.1979.44273.013
26200610260000.0NaN27.7326.514.9783279.04.276.5279.70373.327
27200710310000.0NaN27.8126.516.2123261.03.976.8279.96173.639
28200810380000.016.4727.9126.515.7203268.03.877.0980.21273.942
29200910440000.0NaNNaNNaNNaN3276.03.677.2480.45074.234
30201010490000.0NaNNaNNaNNaN3276.03.477.4780.67274.511
31201110530000.0NaNNaNNaNNaN3251.03.277.7580.87374.768
32201210570000.0NaNNaNNaNNaN3243.03.278.0081.05575.006
33201310590000.0NaNNaNNaNNaN3256.03.078.2781.21975.225
\n", + "
" + ], + "text/plain": [ + " year population alcohol_adults bmi_men bmi_women \\\n", + "0 1980 10260000.0 NaN 26.31 26.80 \n", + "1 1981 10290000.0 NaN 26.35 26.80 \n", + "2 1982 10300000.0 NaN 26.41 26.80 \n", + "3 1983 10300000.0 NaN 26.45 26.80 \n", + "4 1984 10300000.0 NaN 26.50 26.79 \n", + "5 1985 10300000.0 NaN 26.54 26.77 \n", + "6 1986 10300000.0 NaN 26.58 26.76 \n", + "7 1987 10300000.0 NaN 26.62 26.74 \n", + "8 1988 10300000.0 NaN 26.67 26.71 \n", + "9 1989 10300000.0 NaN 26.72 26.69 \n", + "10 1990 10300000.0 NaN 26.77 26.66 \n", + "11 1991 10310000.0 NaN 26.81 26.61 \n", + "12 1992 10310000.0 NaN 26.85 26.56 \n", + "13 1993 10320000.0 NaN 26.90 26.50 \n", + "14 1994 10320000.0 NaN 26.93 26.46 \n", + "15 1995 10320000.0 NaN 26.98 26.45 \n", + "16 1996 10310000.0 NaN 27.04 26.45 \n", + "17 1997 10300000.0 NaN 27.10 26.46 \n", + "18 1998 10280000.0 NaN 27.16 26.47 \n", + "19 1999 10260000.0 NaN 27.22 26.46 \n", + "20 2000 10240000.0 NaN 27.28 26.45 \n", + "21 2001 10230000.0 NaN 27.35 26.46 \n", + "22 2002 10210000.0 NaN 27.41 26.47 \n", + "23 2003 10200000.0 NaN 27.48 26.47 \n", + "24 2004 10200000.0 NaN 27.56 26.48 \n", + "25 2005 10220000.0 16.45 27.63 26.48 \n", + "26 2006 10260000.0 NaN 27.73 26.51 \n", + "27 2007 10310000.0 NaN 27.81 26.51 \n", + "28 2008 10380000.0 16.47 27.91 26.51 \n", + "29 2009 10440000.0 NaN NaN NaN \n", + "30 2010 10490000.0 NaN NaN NaN \n", + "31 2011 10530000.0 NaN NaN NaN \n", + "32 2012 10570000.0 NaN NaN NaN \n", + "33 2013 10590000.0 NaN NaN NaN \n", + "\n", + " car_deaths_per_100000_people calories_per_day infant_mortality \\\n", + "0 NaN NaN 17.0 \n", + "1 NaN NaN 16.4 \n", + "2 NaN NaN 16.0 \n", + "3 NaN NaN 15.6 \n", + "4 NaN NaN 15.4 \n", + "5 NaN NaN 15.1 \n", + "6 NaN NaN 14.7 \n", + "7 NaN NaN 14.3 \n", + "8 NaN NaN 13.7 \n", + "9 NaN NaN 13.3 \n", + "10 NaN NaN 12.7 \n", + "11 NaN NaN 12.2 \n", + "12 NaN NaN 11.5 \n", + "13 NaN 3037.0 10.7 \n", + "14 6.144 2978.0 9.7 \n", + "15 7.837 3209.0 8.8 \n", + "16 7.641 3319.0 7.9 \n", + "17 7.511 3236.0 7.2 \n", + "18 6.410 3269.0 6.5 \n", + "19 6.222 3118.0 6.0 \n", + "20 6.600 3079.0 5.6 \n", + "21 6.099 3170.0 5.3 \n", + "22 6.208 3243.0 5.1 \n", + "23 6.380 3319.0 4.8 \n", + "24 5.910 3322.0 4.6 \n", + "25 6.123 3318.0 4.4 \n", + "26 4.978 3279.0 4.2 \n", + "27 6.212 3261.0 3.9 \n", + "28 5.720 3268.0 3.8 \n", + "29 NaN 3276.0 3.6 \n", + "30 NaN 3276.0 3.4 \n", + "31 NaN 3251.0 3.2 \n", + "32 NaN 3243.0 3.2 \n", + "33 NaN 3256.0 3.0 \n", + "\n", + " life_expectancy life_expectancy_female life_expectancy_male \n", + "0 70.60 74.289 67.153 \n", + "1 70.71 74.341 67.181 \n", + "2 70.74 74.403 67.216 \n", + "3 70.80 74.483 67.269 \n", + "4 70.92 74.587 67.345 \n", + "5 71.13 74.716 67.446 \n", + "6 71.30 74.868 67.567 \n", + "7 71.50 75.037 67.703 \n", + "8 71.66 75.218 67.852 \n", + "9 71.75 75.411 68.018 \n", + "10 71.82 75.619 68.208 \n", + "11 72.03 75.845 68.434 \n", + "12 72.37 76.092 68.703 \n", + "13 72.70 76.357 69.013 \n", + "14 72.99 76.636 69.359 \n", + "15 73.34 76.923 69.731 \n", + "16 73.76 77.208 70.112 \n", + "17 74.10 77.484 70.489 \n", + "18 74.44 77.747 70.848 \n", + "19 74.71 77.995 71.185 \n", + "20 74.99 78.231 71.500 \n", + "21 75.23 78.462 71.800 \n", + "22 75.38 78.695 72.096 \n", + "23 75.60 78.936 72.396 \n", + "24 75.88 79.186 72.702 \n", + "25 76.19 79.442 73.013 \n", + "26 76.52 79.703 73.327 \n", + "27 76.82 79.961 73.639 \n", + "28 77.09 80.212 73.942 \n", + "29 77.24 80.450 74.234 \n", + "30 77.47 80.672 74.511 \n", + "31 77.75 80.873 74.768 \n", + "32 78.00 81.055 75.006 \n", + "33 78.27 81.219 75.225 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# TODO: opravit podle toho, jak to bude\n", + "\n", + "# Světová data\n", + "url = \"https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/countries.csv\"\n", + "countries = pd.read_csv(url).set_index(\"name\")\n", + "\n", + "# Česká data\n", + "url = \"https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/cze.csv\"\n", + "czech = pd.read_csv(url)\n", + "czech" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sloupcový graf (bar plot)\n", + "\n", + "Úplně nejjednodušší graf, který můžeš vytvořit, je **sloupcový**. Vedle sebe postupně zobrazíš sloupečky vysoké podle vlastnosti, která tě zajímá. Ukazuje hodnoty jedné proměnné, aniž by je jakýmkoliv způsobem statisticky zpracovával nebo porovnával s proměnnou jinou.\n", + "\n", + "V `pandas` se k funkcím pro kreslení grafů přistupuje pomocí tzv. **accessoru** `.plot`. To je hybridní objekt, který lze volat jako metodu (`Series.plot()` - použije výchozí typ grafu), anebo lze pomocí další tečky odkazovat na jeho vlastní metody, které kreslí různé typy grafů. Z \"pedagogických důvodů\" (které bývají leckdy nepochopitelné) chceme začít od sloupcového grafu, který výchozí není, a tak voláme `Series.plot.bar()`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "countries[\"life_expectancy\"].plot.bar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Uf, to nevypadá úplně nejpřehledněji. Zkusme totéž, jen pro země Evropské Unie (kterých bylo v době psaní materiálu i zahájení kurzu stále ještě 28)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eu_countries = countries.query(\"is_eu\") # Filtrování -> výsledek je opět DataFrame\n", + "eu_countries[\"life_expectancy\"].plot.bar();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To se neporovnává úplně snadno - dožívají se lidé více ve Spojeném Království nebo v Německu? Co kdybychom (opakování z minula) hodnoty seřadili a teprve pak zobrazili?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eu_countries[\"life_expectancy\"].sort_values(ascending=False).plot.bar();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A vlastně musíme kroutit hlavou, když chceme najit svoji (nebo někoho jiného domovinu). Můžeme zkusit horizontální sloupcový graf, `.plot.barh`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eu_countries[\"life_expectancy\"].sort_values(ascending=False).plot.barh();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Funkce pro kreslení grafů nabízejí spoustu parametrů, které nejsou úplně dobře zdokumentované a jsou dost úzce svázány s tím, jak funguje knihovna `matplotlib`. Budeme si je postupně ukazovat, když nám přijdou vhod. Náš graf by se nám hodilo trošku zvětšit na výšku. Také se hodnoty od sebe příliš neliší a nastavení vlastního rozsahu na ose x by pomohlo rozdíly zvýraznit. Plus si přidáme trošku formátování.\n", + "\n", + "- `figsize` specifikuje velikost grafu jako n-tici (tuple) velikosti v palcích v pořadí (šířka, výška). Při volbě ideální hodnoty si prostě v notebooku zaexperimentuj.\n", + "- `xlim` specifikuje rozsah hodnot na ose x v podobně ntice (minimum, maximum)\n", + "- `color` specifikuje barvu: může jít o název či o hexadecimální RGB zápis\n", + "- `edgecolor` říká, jakou barvou mají být sloupce ohraničeny\n", + "- `title` nastavuje titulek celého grafu" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eu_countries[\"life_expectancy\"].sort_values().plot.barh(\n", + " figsize=(6, 8),\n", + " xlim=(75, 85),\n", + " color=\"yellow\",\n", + " edgecolor=\"#888888\", # střední šeď\n", + " title=\"Očekávaná doba dožití (roky)\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "💡 Začínat sloupcové (ale i mnohé další) grafy jinde než u nuly ti pomůže všimnout si i nepatrných rozdílů, a proto v explorativní fázi je to určitě dobrý nápad. Ovšem při prezentaci výsledků mohou zvýrazněné rozdíly mást publikum a budit dojem, že nějaký efekt je výrazně silnější než ve skutečnosti. Manipulační efekt je tím silnější, čím méně intuitivní jsou prezentovaná data. V tomto případě by asi málokdo uvěřil, že ve Španělsku žijí lidé šedesátkrát déle než v Lotyšsku, protože to neodpovídá běžnému očekávání, ale i tak na první pohled situace vypadá velice dramaticky (necháváme ti na posouzení, jestli rozdíl mezi 75 a 83, neboli cca 10 % je obrovský či nikoliv). Novináři takto matou poměrně často - ať už úmyslně, nebo omylem." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "V grafu ovšem můžeme velice snadno zobrazit více veličin, pokud jej nevytváříme skrze `Series`, ale `DataFrame`. Stačí místo jednoho sloupce dodat sloupců více (například výběrem z `DataFrame`) a pro každý řádek se nám zobrazí více sloupečků pod sebou.\n", + "\n", + "V našem případě se podíváme na to, kolika let se dožívají muži a ženy zvlášť. Zvolíme genderově stereotypní barvy (ono je to někdy přehlednější), ale ty si je samozřejmě můžeš upravit podle libosti." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eu_countries.sort_values(\"life_expectancy\")[[\"life_expectancy_male\", \"life_expectancy_female\"]].plot.barh(\n", + " figsize=(8, 10),\n", + " xlim=(68, 88), # rozsah osy\n", + " color=[\"blue\", \"red\"], # dvě různé barvy pro dva sloupce\n", + " edgecolor=\"#888888\", # střední šeď\n", + " title=\"Očekávaná doba dožití (roky)\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol:** Zkus si nakreslit sloupcový graf některé z dalších charakteristik (\"sloupců\") zemí (ať už evropských, nebo filtrováním přes nějaký region) a zamysli se nad tím, jakou výpovědní hodnotu takový graf má (někdy prachbídnou)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bodový graf (scatter plot)\n", + "\n", + "Bodový graf je nejjednodušším způsobem, jak porovnat dvě různé veličiny. V soustavě souřadníc, jak se používá v matematice, každému řádku odpovídá jeden bod (nakreslený jako symbol, nejčastěji kolečko), hodnoty dvou sloupců pak kódují souřadnici `x` a `y`. To se odráží i ve způsobu, jak bodový graf v `pandas` vytváříme.\n", + "\n", + "Zavoláme metodu `plot.scatter` naší tabulky (poznámka: bodový graf nelze jednoduše vytvořit ze `Series`) a dodáme jí coby argumenty `x` a `y` jména sloupců, která se pro souřadnice mají použít:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Souvislost mezi pitím a střední dobou života\n", + "countries.plot.scatter(\n", + " x=\"life_expectancy\",\n", + " y=\"alcohol_adults\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "💡 O kauzalitách, korelacích a souvislostech mezi veličinami si budeme povídat jindy, ale taky se nemůžeš ubránit dojmu, že čím více se někde pije, tím déle se tam žije?\n", + "\n", + "I bez matematické rigoróznosti ovšem asi poznáme, kde bude zakopaný pes. Zkusme si obarvit jednotlivé regiony světa různými (stereotypními?) barvami. Naučíme se u toho šikovnou funkci `map`, která hodnoty v `Series` nahradí podle slovníku od->do (a vrátí novou instanci `Series`). Sloupec `world_4region` obsahuje přesně 4 různé oblasti (\"kontinenty\"), tak nám bude stačit velice jednoduchý slovník.\n", + "\n", + "Ukážeme si několik dalších argumentů (jež jsou vlastně spíše argumenty použité v knihovně `matplotlib`, a tak nemůžeme jednoduše použít jméno sloupce :-( ):\n", + "- `s` vyjadřuje druhou mocninu velikosti symbolu v bodech (může být jedna hodnota nebo sloupec/pole hodnot)\n", + "- `marker` značí tvar symbolu, většinou pomocí jednoho písmene, viz [seznam možností](https://matplotlib.org/3.1.1/api/markers_api.html)\n", + "- `alpha` vyjadřuje neprůhlednost symbolu (0 = naprosto průhledný a není vidět, 1 = neprůhledný, intenzivní, schovává vše \"za\" ním). Hodí se, když máme velké množství symbolů v grafu a chceme jim dovolit, aby se překrývaly." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Souvislost mezi pitím a střední dobou života\n", + "import numpy as np\n", + "\n", + "barvy_kontinentu = {\n", + " \"europe\": \"blue\",\n", + " \"asia\": \"yellow\",\n", + " \"africa\": \"black\",\n", + " \"americas\": \"red\"\n", + "}\n", + "barva = countries[\"world_4region\"].map(barvy_kontinentu) \n", + "# barva obsahuje sloupec plný barev\n", + "\n", + "countries.plot.scatter(\n", + " figsize=(7, 7),\n", + " x=\"life_expectancy\",\n", + " y=\"alcohol_adults\",\n", + " marker=\"h\", # Tvar symbolu: šestiúhelník - (h)exagon\n", + " color=barva, # Bohužel nejde použít jen jméno sloupce, musíme dát celé \"pole\" hodnot \n", + " s=countries[\"population\"] / 1e6, # Velikost symbolu (na druhou) podle populace\n", + " edgecolor=\"black\", # Barva okraje\n", + " alpha=0.5 # Poloprůhledné symboly\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A tak to vlastně vypadá, že v Asii se obecně pije málo, v Americe tak středně, v Africe se lidé dožívají menšího věku, ale na první pohled v těchto skupinách zemí nevidíme žádný trend. Jediný kontinent, který se vymyká, je Evropa, kde se jak hodně pije, tak dlouho žije, ale obojí je nejspíš důsledkem moderního způsobu života. No a při bližším pohledu se naopak zdá, že v rámci Evropy větší pití znamená kratší život. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Často se stane, že jsou hodnoty obtížně souměřitelné. Například co do rozlohy či počtu obyvatelstva se na světě vyskytují země miniaturní a naopak gigantické, kde rozdíly činí několik řádů:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "countries.plot.scatter(\n", + " x=\"area\",\n", + " y=\"population\",\n", + " figsize=(6,6)\n", + ") \n", + "# Tady úmyslně není středník" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No nic moc - odděleně vidíme cca 7 až 20 bodů a zbytek splývá v jednu velikou \"kaňku\". V takovém případě se hodí opustit běžné, **lineární měřítko**. Místo něj použijeme **logaritmické měřítko**.\n", + "\n", + "To bohužel nejde udělat v `pandas` přímo, a tak se budeme chtě nechtě (ale určitě chtě, protože jsme zvídaví!) dotknout objektů knihovny `matplotlib`. Všimni se, že volání `plot` nám vrátilo jakýsi `matplotlib.axes._subplots.AxesSubplot`. To je třída reprezentující samotný graf, která má další metody, umožňující graf dále upravit. Pro změnu měřítka se používají funkce `set_xscale` a `set_yscale`:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = countries.plot.scatter(\n", + " x=\"area\",\n", + " y=\"population\",\n", + " color=\"black\",\n", + " alpha=0.5,\n", + " figsize=(6, 6)\n", + ") \n", + "# ax obsahuje objekt \"grafu\", přesněji instanci třídy `AxesSubplot`\n", + "\n", + "# Pomocí metod objektu `AxesSubplot` nastavíme měřítko obou os na logaritmické\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol:** Vyzkoušej si zobrazení některých dalších dvojic veličin. Které z nich ukazují zajímavé výsledky?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spojnicový graf (line plot)\n", + "\n", + "Tento druh grafu má smysl zejména tehdy, pokud se nějaká proměnná vyvíjí spojitě v závislosti na proměnné jiné. Časové řady jsou pro to skvělým příkladem (ať už pro vztah mezi časem a veličinou, anebo dvěma veličinami, které se obě vyvíjí ve stejném čase).\n", + "\n", + "Spojnicový graf vytvoříš pomocí funkce `plot.line`. Shodou okolností je to také výchozí typ grafů pro `pandas`, a tak vlastně postačí `plot` zavolat jako metodu. Parametry má podobné jako `scatter` (bodový graf).\n", + "\n", + "Pojďme se například podívat na vývoj očekávané doby života v Česku, jak se vyvíjela s časem od začátku 80. let:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "czech.plot(x=\"year\", y=[\"life_expectancy_female\", \"life_expectancy_male\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pro čárové grafy existuje několik zajímavých argumentů:\n", + "\n", + "- `lw` udává tloušťku čáry v bodech\n", + "- `style` je styl čáry: \"-\" je plná, \":\" tečkovaná, \"--\" přerušovaná, \"-.\" čerchovaná\n", + "- `markersize` je velikost symbolu, který může volitelně čáru doprovázet" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "czech.plot.line(\n", + " x=\"year\",\n", + " y=[\"bmi_men\", \"bmi_women\"],\n", + " lw=1,\n", + " style=\"--\",\n", + " marker=\"o\", # Přidáme kulaté body pro hodnoty z tabulky\n", + " markersize=3);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Moc smysl čárový graf používat v případě, že na sobě dvě proměnné nejsou přímo závislé, nebo se nevyvíjí společně. Zkusme například nakreslit čárový graf vztahu mezi pitím alkoholu a dobou života v jednotlivých zemích:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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life_expectancyalcohol_adults
name
Lesotho51.125.56
Central African Republic51.583.17
Somalia58.030.50
Swaziland58.645.05
Afghanistan58.690.03
.........
NauruNaN4.81
PalauNaN9.86
San MarinoNaNNaN
Saint Kitts and NevisNaN10.62
TuvaluNaN2.14
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193 rows × 2 columns

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" + ], + "text/plain": [ + " life_expectancy alcohol_adults\n", + "name \n", + "Lesotho 51.12 5.56\n", + "Central African Republic 51.58 3.17\n", + "Somalia 58.03 0.50\n", + "Swaziland 58.64 5.05\n", + "Afghanistan 58.69 0.03\n", + "... ... ...\n", + "Nauru NaN 4.81\n", + "Palau NaN 9.86\n", + "San Marino NaN NaN\n", + "Saint Kitts and Nevis NaN 10.62\n", + "Tuvalu NaN 2.14\n", + "\n", + "[193 rows x 2 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_countries = countries.sort_values(\"life_expectancy\")\n", + "sorted_countries.plot.line(x=\"life_expectancy\", y=\"alcohol_adults\")\n", + "sorted_countries[[\"life_expectancy\", \"alcohol_adults\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dává to smysl? Čára sice nelítá napříč celým grafem, \"jen\" zdola nahoru, ale i tak je to nesmysl, protože žádné \"přirozené\" uspořádání zemí neexistuje a nemá smysl se ho snažit lámáním přes koleno sestavit. V tomto případě byl bodový graf mnohem lepší volbou." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus: Jak kreslit pomocí jiných knihoven?\n", + "\n", + "A to je ze základů vizualizace vlastně všechno, další typy grafů si ukážeme jindy.\n", + "\n", + "Pokud ti to ještě nestačilo, ještě si ukážeme, jak by se bodový graf vztahu mezi očekávanou délkou života a množstvím vypitého čistého alkoholu vytvořil ve třech jiných vizualizačních knihovnách. Nebudeme to však již příliš komentovat." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bonus 1: \"čistý\" matplotlib\n", + "\n", + "Protože výchozí kreslení grafů v `pandas` staví na knihovně `matplotlib` a jen jednotlivé funkce obaluje a zpříjemňuje práci se sloupci, budou parametry funkcí povětšinou podobné (hlavní rozdíl je v tom, že neberou názvy sloupců, musíš předat sloupec jako takový)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(figsize=(7,7))\n", + "\n", + "# TODO: změnit\n", + "ax.scatter(\n", + " countries[\"life_expectancy\"],\n", + " countries[\"alcohol_adults\"],\n", + " s=countries[\"population\"] / 1e6,\n", + " color=countries[\"world_4region\"].map({\"europe\": \"blue\", \"asia\": \"yellow\", \"africa\": \"black\", \"americas\": \"red\"}),\n", + " edgecolor=\"black\"\n", + ");\n", + "\n", + "# Popisky os musíme doplnit ručně\n", + "ax.set_xlabel(\"alcohol_adults\")\n", + "ax.set_ylabel(\"life_expectancy\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Galerie ukázkových příkladů `matplotlib` je nepřeberná: https://matplotlib.org/3.1.1/gallery/index.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bonus 2: seaborn\n", + "\n", + "Seaborn je vhodný především pro složitější statistické grafy. Ale obsahuje též vlastní funkce, které obalují volání `matplotlib`u." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "fig, ax = plt.subplots(figsize=(8,8))\n", + "\n", + "sns.scatterplot(\n", + " data=countries, # Pracuje s DataFrame\n", + " x=\"life_expectancy\", # Rozumí názvům sloupců :-)\n", + " y=\"alcohol_adults\",\n", + " size=\"population\", # Velikost podle sloupce (nepříliš vhodná)\n", + " hue=\"world_4region\", # Umí přiřadit barvičky podle nějaké kategorie\n", + " marker=\"h\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mnoho ukázkových vizualací najdeš na stránkách samotného projektu: https://seaborn.pydata.org/examples/index.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bonus 3: plotly.(express)\n", + "\n", + "`plotly` se vymyká, protože umožňuje přímo do notebooku zobrazit interaktivní grafy, ve kterých jde libovolně zoomovat, navíc při najetí na nějaký bod ukazují užitečné doplňují tooltipy. Od verze 4.0 navíc pomocí velice elegantně designovaných funkcí v integrovaném balíčku `plotly.express`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverlabel": { + "namelength": 0 + }, + "hovertemplate": "%{hovertext}

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\n", + " \n", + " \n", + "
\n", + " \n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "px.scatter(\n", + " countries.reset_index(),\n", + " x=\"life_expectancy\",\n", + " y=\"alcohol_adults\",\n", + " size=\"population\",\n", + " color=\"world_4region\",\n", + " hover_name=\"name\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A co by řekl/a na mapu světa se zeměmi vybarvenými podle očekávané délky života?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "coloraxis": "coloraxis", + "geo": "geo", + "hoverlabel": { + "namelength": 0 + }, + "hovertemplate": "%{hovertext}

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true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + }, + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "px.choropleth(countries.reset_index(), locations=\"iso\", color=\"life_expectancy\", hover_name=\"name\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mnoho ukázek, včetně několika se zeměmi světa, najdeš na stránkách projektu: https://plot.ly/python/plotly-express/" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f673888a00e5ffc6ce371549c21e72a2d5394788 Mon Sep 17 00:00:00 2001 From: Jan Pipek Date: Fri, 10 Jan 2020 19:31:40 +0100 Subject: [PATCH 02/15] =?UTF-8?q?Zapracov=C3=A1n=C3=AD=20koment=C3=A1?= =?UTF-8?q?=C5=99=C5=AF=20=20z=20#23?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lessons/pydata/pandas_types/index.ipynb | 78 ++++++++++++++++--------- 1 file changed, 51 insertions(+), 27 deletions(-) diff --git a/lessons/pydata/pandas_types/index.ipynb b/lessons/pydata/pandas_types/index.ipynb index bb925d07bd..3316e01313 100644 --- a/lessons/pydata/pandas_types/index.ipynb +++ b/lessons/pydata/pandas_types/index.ipynb @@ -295,12 +295,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "💡 V tomto případě jsme přímo upravili existující `DataFrame`. Většina metod / operací v `pandas` (už znáš např. `set_index`) ve výchozím nastavení vždy vrací nový objekt - je to dobrým zvykem, který budeme dodržovat. Přiřazování sloupců je jednou z výjimek tohoto jinak uznávaného pravidla (tou druhou je pohodlnost).\n", - "\n", - "
TODO: \n", - " Jak to píšu, tak mi to zase tak samozřejmé nepřijde. Nějak bych tohle chtěl zformulovat líp.
\n", + "💡 V tomto případě jsme přímo upravili existující `DataFrame`. Většina metod / operací v `pandas` (už znáš např. `set_index`) ve výchozím nastavení vždy vrací nový objekt - je to dobrým zvykem, který budeme dodržovat. Přiřazování sloupců je jednou z akceptovaných výjimek tohoto jinak uznávaného pravidla, zejména když se tabulka upravuje jen v úzkém rozsahu řádků kódů.\n", " \n", - "`DataFrame` nabízí ještě metodu `assign`, která nemění tabulku, ale vytváří její kopii s přidanými (nebo nahrazenými) sloupci:" + "`DataFrame` však nabízí ještě metodu `assign`, která nemění tabulku, ale vytváří její kopii s přidanými (nebo nahrazenými) sloupci. Pokud se chceš vyhnout nepříjemnému sledování, kterou tabulku jsi změnil/a či nikoliv, `assign` ti můžeme jen doporučit." ] }, { @@ -473,7 +470,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Není to zase tak často praktické, ale pro hodnoty nového sloupce lze použít i jednu skalární hodnotu:" + "Pro hodnoty nového sloupce lze použít i jednu skalární hodnotu (v praxi se ale s touto potřebou nepotkáme tak často):" ] }, { @@ -1146,12 +1143,13 @@ "source": [ "### Odstranění řádku\n", "\n", - "Pro odebrání sloupce či řádku z DataFrame slouží metoda `drop`. Její první argument očekává označení (index) jednoho nebo více řádků či sloupců, které chceš odebrat. Argument axis označuje, ve které dimenzi se operace má aplikovat (0 či 1). Číslo je intuitivní a odpovídá pořadí, ve kterém se uvádějí klíče při odkazování na buňky.\n", + "Pro odebrání sloupce či řádku z DataFrame slouží metoda `drop`. Její první argument očekává označení (index) jednoho nebo více řádků či sloupců, které chceš odebrat. Argument axis označuje, ve které dimenzi se operace má aplikovat - můžeš použít buď číslo 0 či 1 (odpovídá pořadí od nuly, ve kterém se uvádějí klíče při odkazování na buňky), anebo pojmenování dané dimenze:\n", "\n", "Osa (axis):\n", "\n", - "- 0 = řádky\n", - "- 1 = sloupce\n", + "- 0 nebo \"rows\" nebo \"index\" = řádky\n", + "- 1 nebo \"columns\" = sloupce\n", + "\n", "(Tento argument používají i četné další metody a funkce, proto se ujisti, že mu rozumíš).\n", "\n", "Když už jsme se vrátili do budoucnosti (resp. současnosti), vypořádejme se nemilosrdně s Plutem (pro metodu `drop` je výchozí hodnotou argumentu `axis` 0, a tedy to nemusíme psát):" @@ -1296,7 +1294,7 @@ } ], "source": [ - "planety = planety.drop(\"Pluto\") # Přidej axis=0, chceš-li být explicitní\n", + "planety = planety.drop(\"Pluto\") # Přidej axis=\"rows\", chceš-li být explicitní\n", "planety" ] }, @@ -1440,7 +1438,7 @@ } ], "source": [ - "planety = planety.drop(\"je_planeta\", axis=1) \n", + "planety = planety.drop(\"je_planeta\", axis=\"columns\") \n", "planety" ] }, @@ -1464,6 +1462,13 @@ "# planety.drop(\"je_planeta\", axis=1, inplace=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ale opravdu to nedělej!" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1895,7 +1900,10 @@ ], "source": [ "url = \"https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/countries.csv\"\n", - "countries = pd.read_csv(url, index_col=\"name\") # Místo `set_index`\n", + "\n", + "# Místo `set_index` vybereme index rovnou při načítání\n", + "countries = pd.read_csv(url, index_col=\"name\")\n", + "\n", "countries = countries.sort_index()\n", "countries" ] @@ -1998,7 +2006,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Typy v pandas vycházejí z toho, jak je definuje knihovna `numpy` (obecně užitečná pro práci s numerickými poli a poskytující vektorové operace s rychlostí řádově rychlejší než v Pythonu jako takovém). Ta potřebuje především vědět, jak alokovat pole pro prvky daného typu - na to, aby mohly být seřazeny efektivně jeden za druhým, a tedy i kolik bajtů paměti každý zabírá. Kopíruje přitom \"nativní\" datové typy, jako je můžeš znát, pokud už máš takovou zkušenost, např. z jazyka C. Umístění paměti je něco, co v Pythonu obvykle neřešíme, ale rychlé počítání se bez toho neobejde. My nepůjdeme do detailů, ale požadavek na rychlost se nám tu a tam vynoří a my budeme klást důraz na to, aby se operace dělaly \"vektorově\", řešily \"na úrovni numpy\".\n", + "Typy v pandas vycházejí z toho, jak je definuje knihovna `numpy` (obecně užitečná pro práci s numerickými poli a poskytující vektorové operace s rychlostí řádově vyšší než v Pythonu jako takovém). Ta potřebuje především vědět, jak alokovat pole pro prvky daného typu - na to, aby mohly být seřazeny efektivně jeden za druhým, a tedy i kolik bajtů paměti každý zabírá. Kopíruje přitom \"nativní\" datové typy, jako je můžeš znát, pokud už máš takovou zkušenost, např. z jazyka C. Umístění paměti je něco, co v Pythonu obvykle neřešíme, ale rychlé počítání se bez toho neobejde. My nepůjdeme do detailů, ale požadavek na rychlost se nám tu a tam vynoří a my budeme klást důraz na to, aby se operace dělaly \"vektorově\", řešily \"na úrovni numpy\".\n", "\n", "Poněkud kryptický systém typů v `numpy` (popsaný v [dokumentaci](https://docs.scipy.org/doc/numpy/user/basics.types.html)) je naštěstí v `pandas` (mírně) zjednodušen a nabízí jen několik užitečných základních (rodin) typů, které si teď představíme." ] @@ -2729,17 +2737,17 @@ "data": { "text/plain": [ "name\n", - "Afghanistan 26700 days 00:54:33.011664\n", - "Albania 23388 days 00:54:33.011664\n", - "Algeria 20898 days 00:54:33.011664\n", - "Andorra 9647 days 00:54:33.011664\n", - "Angola 15730 days 00:54:33.011664\n", + "Afghanistan 26715 days 19:30:33.070854\n", + "Albania 23403 days 19:30:33.070854\n", + "Algeria 20913 days 19:30:33.070854\n", + "Andorra 9662 days 19:30:33.070854\n", + "Angola 15745 days 19:30:33.070854\n", " ... \n", - "Venezuela 27069 days 00:54:33.011664\n", - "Vietnam 15437 days 00:54:33.011664\n", - "Yemen 26385 days 00:54:33.011664\n", - "Zambia 20113 days 00:54:33.011664\n", - "Zimbabwe 14367 days 00:54:33.011664\n", + "Venezuela 27084 days 19:30:33.070854\n", + "Vietnam 15452 days 19:30:33.070854\n", + "Yemen 26400 days 19:30:33.070854\n", + "Zambia 20128 days 19:30:33.070854\n", + "Zimbabwe 14382 days 19:30:33.070854\n", "Name: un_accession, Length: 193, dtype: timedelta64[ns]" ] }, @@ -6816,7 +6824,7 @@ } ], "source": [ - "countries.sort_index(axis=1)" + "countries.sort_index(axis=\"columns\")" ] }, { @@ -6871,7 +6879,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Jednou z možností je i vytvoření HTML tabulky (které lze dodat i různé formátování, což ovšem nechme raději na jindy nebo na doma, viz [dokumentace \"Styling\"](https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html)). Výchozí `to_html` si bohužel neporadí s \"nezápadními\" symboly (což je třeba ☿), a tak mu (v našem konkrétním případě) musíme předat korektně otevřený soubor:" + "Excel ani CSV nejsou formáty pro ukládání velikých dat zcela vhodné - první je vázaný na jeden konkrétní kancelářský balík, druhý zase v textové reprezentaci ztrácí informace o typech, nemluvě o výkonu a datové náročnosti. Z jiných formátů můžeš vyzkoušet například [feather](https://github.com/wesm/feather) nebo [parquet](https://en.wikipedia.org/wiki/Apache_Parquet)." ] }, { @@ -6880,7 +6888,23 @@ "metadata": {}, "outputs": [], "source": [ - "# planety.to_html(\"planety.html\") # To nefunguje :-(\n", + "countries.reset_index().to_feather(\"countries.feather\") # Pozor: feather neukládá index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Jednou z možností je i vytvoření HTML tabulky (které lze dodat i různé formátování, což ovšem nechme raději na jindy nebo na doma, viz [dokumentace \"Styling\"](https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html)). Výchozí `to_html` si bohužel neporadí s \"nezápadními\" symboly (což je třeba ☿), a tak mu (v našem konkrétním případě) musíme předat korektně otevřený soubor:" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# planety.to_html(\"planety.html\") # To (zatím) nefunguje :-(\n", "\n", "with open(\"planety.html\", \"w\", encoding=\"utf-8\") as out:\n", " planety.to_html(out)" @@ -6888,7 +6912,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ From 8e7bba2ec411990a8c8feaf4da4a97c448c427f5 Mon Sep 17 00:00:00 2001 From: Jan Pipek Date: Sat, 11 Jan 2020 10:32:14 +0100 Subject: [PATCH 03/15] =?UTF-8?q?Zapracov=C3=A1n=C3=AD=20pozn=C3=A1mek=20k?= =?UTF-8?q?=20typ=C5=AFm?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lessons/pydata/pandas_types/index.ipynb | 139 ++++++++++++++---------- 1 file changed, 82 insertions(+), 57 deletions(-) diff --git a/lessons/pydata/pandas_types/index.ipynb b/lessons/pydata/pandas_types/index.ipynb index 3316e01313..23bdc8527e 100644 --- a/lessons/pydata/pandas_types/index.ipynb +++ b/lessons/pydata/pandas_types/index.ipynb @@ -6,7 +6,7 @@ "source": [ "# Pandas - datové typy a manipulace se sloupci\n", "\n", - "V minulé lekci jsme si představili knihovnu pandas a její základní třídy: `Series`, `DataFrame` a `Index`. Brali jsme je ovšem jako statické objekty, na které jsme se pouze dívali.\n", + "V minulé lekci jsme si představili knihovnu pandas a její základní třídy: `Series`, `DataFrame` a `Index`. Brali jsme je ovšem jako statické objekty, které jsme si pouze prohlíželi.\n", "\n", "V této lekci začneme upravovat existující tabulky. Ukážeme si:\n", "\n", @@ -25,6 +25,7 @@ "metadata": {}, "outputs": [], "source": [ + "# Obligátní import\n", "import pandas as pd" ] }, @@ -162,7 +163,7 @@ "source": [ "### Přidání nového sloupce\n", "\n", - "Když chceme přidat nový sloupec (`Series`), přiřadíme ho do `DataFrame` jako hodnotu do slovníku - tedy v hranatých závorkách s názvem sloupce. Dobrá zpráva je, že stejně jako v konstruktoru, `pandas` si \"poradí\" jak se `Series`, tak s obyčejným seznamem.\n", + "Když chceme přidat nový sloupec (`Series`), přiřadíme ho do `DataFrame` jako hodnotu do slovníku - tedy v hranatých závorkách s názvem sloupce. Dobrá zpráva je, že stejně jako v konstruktoru si `pandas` \"poradí\" jak se `Series`, tak s obyčejným seznamem.\n", "\n", "V našem konkrétním případě si najdeme a přidáme počet známých měsíců (velkých i malých)." ] @@ -490,7 +491,7 @@ "\n", "Když se strojem času vrátíme do dětství (nebo rané dospělosti) autorů těchto materiálů, tedy před rok 2006, kdy se v Praze konal astronomický kongres, který definoval pojem \"planeta\" (ale ne před rok 1930!), přibude nám nová planeta: Pluto.\n", "\n", - "Do naší tabulky ho vložíme pomocí indexeru `loc`, který jsme již dříve používali pro \"koukání\" do tabulky:" + "Do naší tabulky ho coby nový řádek vložíme pomocí indexeru `loc`, který jsme již dříve používali pro \"koukání\" do tabulky:" ] }, { @@ -643,7 +644,7 @@ "\n", "\"Indexery\" `.loc` a `.iloc` se dvěma argumenty v hranatých závorkách odkazují přímo na konkrétní buňku, a přiřazením do nich (opět, podobně jako ve slovníku) se hodnota na příslušné místo zapíše. Jen je třeba zachovat pořadí (řádek, sloupec). \n", "\n", - "Vrátíme se opět do současnosti a Pluto zbavíme jeho privilegií:" + "Vrátíme se opět do současnosti a Pluto zbavíme jeho statutu:" ] }, { @@ -792,7 +793,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**⚠ Pozor:** Podobně jako ve slovníku, ale možná poněkud neintuitivně, je možné zapsat hodnotu do řádku i sloupce, které neexistují!" + "**⚠ Pozor:** Podobně jako u slovníku, ale možná poněkud neintuitivně, je možné zapsat hodnotu do řádku i sloupce, které neexistují!" ] }, { @@ -971,7 +972,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Přiřazovat je možné i do rozsahů v indexech - jen je potřeba hlídat, aby přiřazovaná hodnota či hodnoty byly buď skalárem, nebo měly stejný tvar jako oblast, do které přiřazujeme:" + "Přiřazovat je možné i do rozsahů v indexech - jen je potřeba hlídat, aby přiřazovaná hodnota či hodnoty byly buď skalárem, nebo měly stejný tvar (počet řádků a sloupců) jako oblast, do které přiřazujeme:" ] }, { @@ -1143,12 +1144,12 @@ "source": [ "### Odstranění řádku\n", "\n", - "Pro odebrání sloupce či řádku z DataFrame slouží metoda `drop`. Její první argument očekává označení (index) jednoho nebo více řádků či sloupců, které chceš odebrat. Argument axis označuje, ve které dimenzi se operace má aplikovat - můžeš použít buď číslo 0 či 1 (odpovídá pořadí od nuly, ve kterém se uvádějí klíče při odkazování na buňky), anebo pojmenování dané dimenze:\n", + "Pro odebrání sloupce či řádku z DataFrame slouží metoda `drop`. Její první argument očekává označení (index) jednoho nebo více řádků či sloupců, které chceš odebrat. Argument `axis` označuje, ve které dimenzi se operace má aplikovat - můžeš použít buď číslo 0 či 1 (odpovídá pořadí od nuly, ve kterém se uvádějí klíče při odkazování na buňky), anebo pojmenování dané dimenze:\n", "\n", "Osa (axis):\n", "\n", - "- 0 nebo \"rows\" nebo \"index\" = řádky\n", - "- 1 nebo \"columns\" = sloupce\n", + "- 0 nebo \"rows\" nebo \"index\" → řádky\n", + "- 1 nebo \"columns\" → sloupce\n", "\n", "(Tento argument používají i četné další metody a funkce, proto se ujisti, že mu rozumíš).\n", "\n", @@ -1446,7 +1447,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "⛧ Metoda `drop`, v souladu s výše zmíněnou konvencí, vrací nový `DataFrame` (a proto výsledek operace musíme přiřadit do `planety`). Pokud chceš operovat rovnou na tabulce, můžeš použít příkaz `del` (funguje stejně jako u slovníku) nebo poprosit pandí bohy (a autory těchto materiálů) o odpuštění a přidat argument `inplace=True`:" + "⛧ Metoda `drop`, v souladu s výše zmíněnou konvencí, vrací nový `DataFrame` (a proto výsledek operace musíme přiřadit do `planety`). Pokud chceš operovat rovnou na tabulce, můžeš použít příkaz `del` (funguje stejně jako u slovníku) nebo poprosit pandí bohy (a autory těchto materiálů) o odpuštění a přidat argument `inplace=True` (tento argument lze, bohužel, použít i mnoha dalších operací):" ] }, { @@ -1455,6 +1456,8 @@ "metadata": {}, "outputs": [], "source": [ + "# Jen na vlastní nebezpečí\n", + "\n", "# Alternativa 1)\n", "# del planety[\"je_planeta\"]\n", "\n", @@ -1484,9 +1487,7 @@ "source": [ "#### Příprava dat\n", "\n", - "V datovém kurzu budeme využívat různých datových sad (obvykle větších - takových, kde není praktické je celé zapsat v konstruktoru). Nyní opustíme planety a podíváme se na některé zajímavé charakteristiky zemí kolem světa (ježto definice toho, co je to země, je poněkud vágní, bereme v potaz členy OSN), zachycené k jednomu konkrétnímu roku uplynulé dekády (protože ne vždy jsou všechny údaje k dispozici, bereme poslední rok, kde je známo dost ukazatelů). Data pocházejí povětšinou z projektu [Gapminder](https://www.gapminder.org/), doplnili jsme je jen o několik dalších informací z wikipedie.\n", - "\n", - "
TODO: Upravit URL podle toho, kde nakonec data budou.
" + "V datovém kurzu budeme využívat různých datových sad (obvykle větších - takových, kde není praktické je celé zapsat v konstruktoru). Nyní opustíme planety a podíváme se na některé zajímavé charakteristiky zemí kolem světa (ježto definice toho, co je to země, je poněkud vágní, bereme v potaz členy OSN), zachycené k jednomu konkrétnímu roku uplynulé dekády (protože ne vždy jsou všechny údaje k dispozici, bereme poslední rok, kde je známo dost ukazatelů). Data pocházejí povětšinou z projektu [Gapminder](https://www.gapminder.org/), doplnili jsme je jen o několik dalších informací z wikipedie." ] }, { @@ -2006,9 +2007,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Typy v pandas vycházejí z toho, jak je definuje knihovna `numpy` (obecně užitečná pro práci s numerickými poli a poskytující vektorové operace s rychlostí řádově vyšší než v Pythonu jako takovém). Ta potřebuje především vědět, jak alokovat pole pro prvky daného typu - na to, aby mohly být seřazeny efektivně jeden za druhým, a tedy i kolik bajtů paměti každý zabírá. Kopíruje přitom \"nativní\" datové typy, jako je můžeš znát, pokud už máš takovou zkušenost, např. z jazyka C. Umístění paměti je něco, co v Pythonu obvykle neřešíme, ale rychlé počítání se bez toho neobejde. My nepůjdeme do detailů, ale požadavek na rychlost se nám tu a tam vynoří a my budeme klást důraz na to, aby se operace dělaly \"vektorově\", řešily \"na úrovni numpy\".\n", + "Typy v pandas vycházejí z toho, jak je definuje knihovna `numpy` (obecně užitečná pro práci s numerickými poli a poskytující vektorové operace s rychlostí řádově vyšší než v Pythonu jako takovém). Ta potřebuje především vědět, jak alokovat pole pro prvky daného typu - na to, aby mohly být seřazeny efektivně jeden za druhým, a tedy i kolik bajtů paměti každý zabírá. Kopíruje přitom \"nativní\" datové typy, které už můžeš znát z jiných jazyků, např. [C](https://cs.wikipedia.org/wiki/C_(programovac%C3%AD_jazyk)). Umístění paměti je něco, co v Pythonu obvykle neřešíme, ale rychlé počítání se bez toho neobejde. My nepůjdeme do detailů, ale požadavek na rychlost se nám tu a tam vynoří a my budeme klást důraz na to, aby se operace dělaly \"vektorově\", řešily \"na úrovni numpy\".\n", "\n", - "Poněkud kryptický systém typů v `numpy` (popsaný v [dokumentaci](https://docs.scipy.org/doc/numpy/user/basics.types.html)) je naštěstí v `pandas` (mírně) zjednodušen a nabízí jen několik užitečných základních (rodin) typů, které si teď představíme." + "Poněkud tajuplný systém typů v `numpy` (popsaný v [dokumentaci](https://docs.scipy.org/doc/numpy/user/basics.types.html)) je naštěstí v `pandas` (mírně) zjednodušen a nabízí jen několik užitečných základních (rodin) typů, které si teď představíme." ] }, { @@ -2030,7 +2031,7 @@ "- `int64`: -9 223 372 036 854 775 808 až 9 223 372 036 854 775 807 (tedy +/- ~9 trilionů)\n", "- `uint64`: 0 až 18 446 744 073 709 551 615 (tedy až ~18 trilionů)\n", "\n", - "💡 Aby toho nebylo málo, ke každému `intX` / `uintY` typu existuje ještě jeho alternativa, která umožňuje ve sloupci použít chybějící hodnoty, t.j. `NaN`. Místo malého `i`, případně `u` v názvu se použije písmeno velké. Tato vlastnost (tzv. \"nullable integer types\") je relativně užitečná, ale je dosud poněkud experimentální. My ji nebudeme v kurzu využívat.\n", + "💡 Aby toho nebylo málo, ke každému `int?` / `uint?` typu existuje ještě jeho alternativa, která umožňuje ve sloupci použít chybějící hodnoty, t.j. `NaN`. Místo malého `i`, případně `u` v názvu se použije písmeno velké. Tato vlastnost (tzv. \"nullable integer types\") je relativně užitečná, ale je dosud poněkud experimentální. My ji nebudeme v kurzu využívat.\n", "\n", "Detailní vysvětlení toho, jak jsou celá čísla v paměti počítače reprezentována, najdeš třeba ve [wikipedii](https://cs.wikipedia.org/wiki/Integer)." ] @@ -2133,7 +2134,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**⚠ Pozor:** Když vybíráš konkrétní celočíselný typ, musíš si dát pozor na rozsahy, protože `pandas` tě nebude varovat, pokud se nějaká z tvých hodnot do rozsahu \"nevleze\" a vesele zahodí tu část binární reprezentace, která je navíc:" + "**⚠ Pozor:** Když vybíráš konkrétní celočíselný typ, musíš si dát pozor na rozsahy, protože `pandas` tě nebude varovat, pokud se nějaká z tvých hodnot do rozsahu \"nevleze\" a vesele zahodí tu část binární reprezentace, která je navíc (a dostaneš mnohem menší číslo, než jsi čekal/a):" ] }, { @@ -2168,7 +2169,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -2180,7 +2181,7 @@ "dtype: object" ] }, - "execution_count": 20, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -2189,7 +2190,7 @@ "# Toto vyhodí výjimku:\n", "# pd.Series([0, 123, 123456789012345678901234567890], dtype=\"int64\")\n", "\n", - "# Toto ano, ale už to není int64:\n", + "# Toto projde, ale už to není int64:\n", "pd.Series([0, 123, 123456789012345678901234567890])" ] }, @@ -2198,7 +2199,7 @@ "metadata": {}, "source": [ "- Když ho budeme explicitně požadovat, vyhodí se výjimka.\n", - "- Když `pandas` necháme dělat jeho práci, použije se obecný typ `object` a přijdeme o jistou část výhod: sloupec nám zabere násobně více paměti a aritmetické operace s ním jsou o řád až dva pomalejší. Dokud není výkon na předním místě našich priorit, není to zase takový problém.\n", + "- Když `pandas` necháme dělat jeho práci, použije se obecný typ `object` a přijdeme o jistou část výhod: sloupec nám zabere násobně více paměti a aritmetické operace s ním jsou o řád až dva pomalejší. Dokud naší prioritou, není to zase takový problém.\n", "\n", "Obecně proto doporučujeme držet se `int64`, resp. nechat `pandas`, aby jej za nás automaticky použil. Teprve v případě, že si to budou žádat přísné paměťové nároky, se ti vyplatí hledat ten \"nejvíce růžový\" typ." ] @@ -2301,43 +2302,48 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", - "Slovakia True\n", - "Slovenia True\n", - "Solomon Islands False\n", - "Somalia False\n", - "South Africa False\n", - "South Korea False\n", - "South Sudan False\n", - "Spain True\n", - "Sri Lanka False\n", - "Sudan False\n", - "Suriname False\n", - "Swaziland False\n", - "Sweden True\n", - "Switzerland False\n", - "Syria False\n", - "Name: is_eu, dtype: bool" + "Afghanistan False\n", + "Albania False\n", + "Algeria False\n", + "Andorra False\n", + "Angola False\n", + "Antigua and Barbuda False\n", + "Argentina False\n", + "Armenia False\n", + "Australia True\n", + "Austria True\n", + "Azerbaijan False\n", + "Bahamas False\n", + "Bahrain False\n", + "Bangladesh False\n", + "Barbados False\n", + "Belarus False\n", + "Belgium True\n", + "Belize False\n", + "Benin False\n", + "Bhutan False\n", + "Name: is_oecd, dtype: bool" ] }, - "execution_count": 24, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "countries[\"is_eu\"][\"Slovakia\":\"Syria\"]" + "countries[\"is_oecd\"].iloc[:20]" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -2349,12 +2355,13 @@ "dtype: bool" ] }, - "execution_count": 25, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Vytvoření nového sloupce\n", "pd.Series([True, False, False])" ] }, @@ -2394,7 +2401,9 @@ "source": [ "### Objekty a řetězce (objects)\n", "\n", - "Toto tě pravděpodobně překvapí: `pandas` nemá zvláštní datový typ pro řetězce! Spadá společně s dalšími neurčenými nebo nerozpoznanými hodnotami do kategorie `object`, která umožňuje v daném sloupci mít cokoliv, co znáš z Pythonu, a chová se tak do značné míry jako obyčejný seznam s výhodami (žádné podivné konverze, sledování rozsahů, ...) i nevýhodami (je to pomalejší, než by mohlo; nikdo ti nezaručí, že ve sloupci budou jen řetězce). " + "Toto tě pravděpodobně překvapí: `pandas` nemá zvláštní datový typ pro řetězce! Spadá společně s dalšími neurčenými nebo nerozpoznanými hodnotami do kategorie `object`, která umožňuje v daném sloupci mít cokoliv, co znáš z Pythonu, a chová se tak do značné míry jako obyčejný seznam s výhodami (žádné podivné konverze, sledování rozsahů, ...) i nevýhodami (je to pomalejší, než by mohlo; nikdo ti nezaručí, že ve sloupci budou jen řetězce).\n", + "\n", + "*Poznámka: V době psaní těchto materiálů se připravuje `pandas` verze 1.0, která speciální typ pro řetězce zavádí.*" ] }, { @@ -2737,17 +2746,17 @@ "data": { "text/plain": [ "name\n", - "Afghanistan 26715 days 19:30:33.070854\n", - "Albania 23403 days 19:30:33.070854\n", - "Algeria 20913 days 19:30:33.070854\n", - "Andorra 9662 days 19:30:33.070854\n", - "Angola 15745 days 19:30:33.070854\n", + "Afghanistan 26716 days 10:25:30.265721\n", + "Albania 23404 days 10:25:30.265721\n", + "Algeria 20914 days 10:25:30.265721\n", + "Andorra 9663 days 10:25:30.265721\n", + "Angola 15746 days 10:25:30.265721\n", " ... \n", - "Venezuela 27084 days 19:30:33.070854\n", - "Vietnam 15452 days 19:30:33.070854\n", - "Yemen 26400 days 19:30:33.070854\n", - "Zambia 20128 days 19:30:33.070854\n", - "Zimbabwe 14382 days 19:30:33.070854\n", + "Venezuela 27085 days 10:25:30.265721\n", + "Vietnam 15453 days 10:25:30.265721\n", + "Yemen 26401 days 10:25:30.265721\n", + "Zambia 20129 days 10:25:30.265721\n", + "Zimbabwe 14383 days 10:25:30.265721\n", "Name: un_accession, Length: 193, dtype: timedelta64[ns]" ] }, @@ -2865,7 +2874,9 @@ } ], "source": [ - "# 15 litrů čistého alkoholu budeme považovat za hranici nadměrného pití (nekonzultováno s adiktology!)\n", + "# 15 litrů čistého alkoholu na osobu na rok budeme považovat za hranici nadměrného pití\n", + "# (nekonzultováno s adiktology!)\n", + "\n", "# Kde se hodně pije?\n", "countries[\"alcohol_adults\"] > 15" ] @@ -2968,7 +2979,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Podobně jako v Pythonu lze podmínky kombinovat pomocí operátorů. Vzhledem k jistým syntaktickým požadavkům Pythonu je ale potřeba použít místo vám známých logických operátorů jejich alternativy: `&` (místo `and`), `|` (místo `or`) a `~` (místo `not`). Protože mají jiné priority než jejich klasičtí břatříčci, bude lepší, když při kombinaci s jinýmim operátory vždycky použiješ závorky." + "Podobně jako v Pythonu lze podmínky kombinovat pomocí operátorů. Vzhledem k jistým syntaktickým požadavkům Pythonu je ale potřeba použít místo vám známých logických operátorů jejich alternativy: `&` (místo `and`), `|` (místo `or`) a `~` (místo `not`). Protože mají jiné priority než jejich klasičtí bratříčci, bude lepší, když při kombinaci s jinými operátory vždycky použiješ závorky." ] }, { @@ -4405,7 +4416,7 @@ ], "source": [ "# Prťavé země\n", - "countries[countries[\"population\"] < 100_000]" + "countries[countries[\"population\"] < 100_000] # Podtržítko pomáhá oddělit tisíce vizuálně" ] }, { @@ -4877,7 +4888,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Protože tento způsob filtrování je poněkud nešikovný, existuje ještě metoda `query`, která umožňuje vybírat řádky na základě řetězce, který popisuje nějakou nerovnost z názvů sloupců a číselných hodnot (což poměrně často jde, někdy ovšem nemusí)." + "Protože tento způsob filtrování je poněkud nešikovný, existuje ještě metoda `query`, která umožňuje vybírat řádky na základě řetězce, který popisuje nějakou (ne)rovnost z názvů sloupců a číselných hodnot (což poměrně často jde, někdy ovšem nemusí)." ] }, { @@ -6176,6 +6187,13 @@ "countries.sort_values(\"alcohol_adults\", ascending=False).head(10)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "💡 V následující buňce je celý kód uzavřen do závorky. Umožnili jsme si tím roztáhnout jeden výraz na více řádků, abychom jeho části mohli náležitě okomentovat." + ] + }, { "cell_type": "code", "execution_count": 54, @@ -6407,6 +6425,13 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, From 63a5cba31186d5098018b19e7da81fc6e649c418 Mon Sep 17 00:00:00 2001 From: Jan Pipek Date: Sat, 11 Jan 2020 10:50:52 +0100 Subject: [PATCH 04/15] =?UTF-8?q?Zapracov=C3=A1n=C3=AD=20pozn=C3=A1mek=20k?= =?UTF-8?q?=20vizualizac=C3=ADm?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../pydata/visualization_basics/index.ipynb | 660 +++--------------- 1 file changed, 83 insertions(+), 577 deletions(-) diff --git a/lessons/pydata/visualization_basics/index.ipynb b/lessons/pydata/visualization_basics/index.ipynb index 454a83e0e5..f2f1041fa7 100644 --- a/lessons/pydata/visualization_basics/index.ipynb +++ b/lessons/pydata/visualization_basics/index.ipynb @@ -19,13 +19,13 @@ "\n", "Zatímco ohledně knihovny pro běžné zpracování tabulkových dat panuje shoda a při zkoumání malých až středně velkých dat nepříliš exotického typu téměř vždy analytici běžně sahají po `pandas`, knihoven pro vizualizaci dat existuje nepřeberné množství - každá má svoje výhody i nevýhody. My si během lekcí EDA zmíníme tyto tři (a budeme se soustředit především na to, jak je použít společně s pandas):\n", "\n", - "- `matplotlib` - Toto je asi nejrozšířenější a v mnoha ohledech nejflexibilnější knihovna. Představuje výchozí volbu, pokud potřebuješ dobře vyhlížející statické grafy, které budou fungovat skoro všude. Značná flexibilita je vyvážena někdy ne zcela intuitivními jmény funkcí a argumentů. Pandas ji využívá interně (takže s trochou snahy můžeš předstírat, že o její existenci nevíš). Viz https://matplotlib.org/.\n", + "- `matplotlib` - Toto je asi nejrozšířenější a v mnoha ohledech nejflexibilnější knihovna. Představuje výchozí volbu, pokud potřebuješ dobře vyhlížející statické grafy, které budou fungovat skoro všude. Značná flexibilita je vyvážena někdy ne zcela intuitivními jmény funkcí a argumentů. Pandas ji využívá interně (proto se s ní nemusíš seznámit tak detailně). Viz https://matplotlib.org/.\n", "\n", "- `seaborn` - Cílem této knihovny je pomoci zejména se statistickými grafy. Staví na matplotlibu, ale překrývá ho \"lidskou\" tváří. My s ním budeme pracovat při vizualizaci složitějších vztahů mezi více proměnnými. Viz https://seaborn.pydata.org/.\n", "\n", "- `plotly` (a zejména její podmnožina `plotly.express`) - Po této knihovně zejména sáhneš, budeš-li chtít do své vizualizace vložit interaktivitu. Ta se samozřejmě obtížně tiskne na papír, ale zejména při práci v Jupyter notebooku umožní vše zkoumat výrazně rychleji. Viz https://plot.ly/python/.\n", "\n", - "Pro zájemce o bližší vysvětlení doporučujeme podívat se na (již poněkud starší) video od J. Vanderplase: Python Visualizations' Landscape (https://www.youtube.com/watch?v=FytuB8nFHPQ), které shrnuje základní vlastnosti jednotlivých knihoven.\n" + "Pro zájemce o bližší vysvětlení doporučujeme podívat se na (již poněkud starší) video od Jakea Vanderplase: Python Visualizations' Landscape (https://www.youtube.com/watch?v=FytuB8nFHPQ), které shrnuje základní vlastnosti jednotlivých knihoven.\n" ] }, { @@ -43,7 +43,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Jestli ses dosud tvářil/a, že nevíš o existenci matplotlibu, teď už nemůžeš :-). Tato mysteriózní řádka (ve skutečnosti \"IPython magic command\") říká, že všechny grafy se automaticky vykreslí přímo do notebooku (to vůbec není samozřejmé a lekcdy to ani nechceme - třeba když chceme grafy ukládat rovnou do souboru nebo interaktivně mimo notebook).\n", + "Jestli ses dosud tvářil/a, že nevíš o existenci matplotlibu, teď už nemůžeš :-). Tato mysteriózní řádka (ve skutečnosti \"IPython magic command\") říká, že všechny grafy se automaticky vykreslí přímo do notebooku (to vůbec není samozřejmé a leckdy to ani nechceme - třeba když chceme grafy ukládat rovnou do souboru nebo interaktivně mimo notebook).\n", "\n", "Více viz https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-matplotlib.\n" ] @@ -54,7 +54,7 @@ "source": [ "## Příprava - zdroj dat\n", "\n", - "Nejdříve si načteme nám již známá data se zeměmi světa. Přidáme k tomu i tabulku s vývojem některých ukazatelů v čase pro Českou republiku (a hned se na ně podíváme)." + "Nejdříve si načteme data se zeměmi světa, použitá již v lekci o typech. Přidáme k tomu i tabulku s vývojem některých ukazatelů v čase pro Českou republiku (a hned se na ně podíváme)." ] }, { @@ -98,412 +98,6 @@ " \n", " \n", " \n", - " 0\n", - " 1980\n", - " 10260000.0\n", - " NaN\n", - " 26.31\n", - " 26.80\n", - " NaN\n", - " NaN\n", - " 17.0\n", - " 70.60\n", - " 74.289\n", - " 67.153\n", - " \n", - " \n", - " 1\n", - " 1981\n", - " 10290000.0\n", - " NaN\n", - " 26.35\n", - " 26.80\n", - " NaN\n", - " NaN\n", - " 16.4\n", - " 70.71\n", - " 74.341\n", - " 67.181\n", - " \n", - " \n", - " 2\n", - " 1982\n", - " 10300000.0\n", - " NaN\n", - " 26.41\n", - " 26.80\n", - " NaN\n", - " NaN\n", - " 16.0\n", - " 70.74\n", - " 74.403\n", - " 67.216\n", - " \n", - " \n", - " 3\n", - " 1983\n", - " 10300000.0\n", - " NaN\n", - " 26.45\n", - " 26.80\n", - " NaN\n", - " NaN\n", - " 15.6\n", - " 70.80\n", - " 74.483\n", - " 67.269\n", - " \n", - " \n", - " 4\n", - " 1984\n", - " 10300000.0\n", - " NaN\n", - " 26.50\n", - " 26.79\n", - " NaN\n", - " NaN\n", - " 15.4\n", - " 70.92\n", - " 74.587\n", - " 67.345\n", - " \n", - " \n", - " 5\n", - " 1985\n", - " 10300000.0\n", - " NaN\n", - " 26.54\n", - " 26.77\n", - " NaN\n", - " NaN\n", - " 15.1\n", - " 71.13\n", - " 74.716\n", - " 67.446\n", - " \n", - " \n", - " 6\n", - " 1986\n", - " 10300000.0\n", - " NaN\n", - " 26.58\n", - " 26.76\n", - " NaN\n", - " NaN\n", - " 14.7\n", - " 71.30\n", - " 74.868\n", - " 67.567\n", - " \n", - " \n", - " 7\n", - " 1987\n", - " 10300000.0\n", - " NaN\n", - " 26.62\n", - " 26.74\n", - " NaN\n", - " NaN\n", - " 14.3\n", - " 71.50\n", - " 75.037\n", - " 67.703\n", - " \n", - " \n", - " 8\n", - " 1988\n", - " 10300000.0\n", - " NaN\n", - " 26.67\n", - " 26.71\n", - " NaN\n", - " NaN\n", - " 13.7\n", - " 71.66\n", - " 75.218\n", - " 67.852\n", - " \n", - " \n", - " 9\n", - " 1989\n", - " 10300000.0\n", - " NaN\n", - " 26.72\n", - " 26.69\n", - " NaN\n", - " NaN\n", - " 13.3\n", - " 71.75\n", - " 75.411\n", - " 68.018\n", - " \n", - " \n", - " 10\n", - " 1990\n", - " 10300000.0\n", - " NaN\n", - " 26.77\n", - " 26.66\n", - " NaN\n", - " NaN\n", - " 12.7\n", - " 71.82\n", - " 75.619\n", - " 68.208\n", - " \n", - " \n", - " 11\n", - " 1991\n", - " 10310000.0\n", - " NaN\n", - " 26.81\n", - " 26.61\n", - " NaN\n", - " NaN\n", - " 12.2\n", - " 72.03\n", - " 75.845\n", - " 68.434\n", - " \n", - " \n", - " 12\n", - " 1992\n", - " 10310000.0\n", - " NaN\n", - " 26.85\n", - " 26.56\n", - " NaN\n", - " NaN\n", - " 11.5\n", - " 72.37\n", - " 76.092\n", - " 68.703\n", - " \n", - " \n", - " 13\n", - " 1993\n", - " 10320000.0\n", - " NaN\n", - " 26.90\n", - " 26.50\n", - " NaN\n", - " 3037.0\n", - " 10.7\n", - " 72.70\n", - " 76.357\n", - " 69.013\n", - " \n", - " \n", - " 14\n", - " 1994\n", - " 10320000.0\n", - " NaN\n", - " 26.93\n", - " 26.46\n", - " 6.144\n", - " 2978.0\n", - " 9.7\n", - " 72.99\n", - " 76.636\n", - " 69.359\n", - " \n", - " \n", - " 15\n", - " 1995\n", - " 10320000.0\n", - " NaN\n", - " 26.98\n", - " 26.45\n", - " 7.837\n", - " 3209.0\n", - " 8.8\n", - " 73.34\n", - " 76.923\n", - " 69.731\n", - " \n", - " \n", - " 16\n", - " 1996\n", - " 10310000.0\n", - " NaN\n", - " 27.04\n", - " 26.45\n", - " 7.641\n", - " 3319.0\n", - " 7.9\n", - " 73.76\n", - " 77.208\n", - " 70.112\n", - " \n", - " \n", - " 17\n", - " 1997\n", - " 10300000.0\n", - " NaN\n", - " 27.10\n", - " 26.46\n", - " 7.511\n", - " 3236.0\n", - " 7.2\n", - " 74.10\n", - " 77.484\n", - " 70.489\n", - " \n", - " \n", - " 18\n", - " 1998\n", - " 10280000.0\n", - " NaN\n", - " 27.16\n", - " 26.47\n", - " 6.410\n", - " 3269.0\n", - " 6.5\n", - " 74.44\n", - " 77.747\n", - " 70.848\n", - " \n", - " \n", - " 19\n", - " 1999\n", - " 10260000.0\n", - " NaN\n", - " 27.22\n", - " 26.46\n", - " 6.222\n", - " 3118.0\n", - " 6.0\n", - " 74.71\n", - " 77.995\n", - " 71.185\n", - " \n", - " \n", - " 20\n", - " 2000\n", - " 10240000.0\n", - " NaN\n", - " 27.28\n", - " 26.45\n", - " 6.600\n", - " 3079.0\n", - " 5.6\n", - " 74.99\n", - " 78.231\n", - " 71.500\n", - " \n", - " \n", - " 21\n", - " 2001\n", - " 10230000.0\n", - " NaN\n", - " 27.35\n", - " 26.46\n", - " 6.099\n", - " 3170.0\n", - " 5.3\n", - " 75.23\n", - " 78.462\n", - " 71.800\n", - " \n", - " \n", - " 22\n", - " 2002\n", - " 10210000.0\n", - " NaN\n", - " 27.41\n", - " 26.47\n", - " 6.208\n", - " 3243.0\n", - " 5.1\n", - " 75.38\n", - " 78.695\n", - " 72.096\n", - " \n", - " \n", - " 23\n", - " 2003\n", - " 10200000.0\n", - " NaN\n", - " 27.48\n", - " 26.47\n", - " 6.380\n", - " 3319.0\n", - " 4.8\n", - " 75.60\n", - " 78.936\n", - " 72.396\n", - " \n", - " \n", - " 24\n", - " 2004\n", - " 10200000.0\n", - " NaN\n", - " 27.56\n", - " 26.48\n", - " 5.910\n", - " 3322.0\n", - " 4.6\n", - " 75.88\n", - " 79.186\n", - " 72.702\n", - " \n", - " \n", - " 25\n", - " 2005\n", - " 10220000.0\n", - " 16.45\n", - " 27.63\n", - " 26.48\n", - " 6.123\n", - " 3318.0\n", - " 4.4\n", - " 76.19\n", - " 79.442\n", - " 73.013\n", - " \n", - " \n", - " 26\n", - " 2006\n", - " 10260000.0\n", - " NaN\n", - " 27.73\n", - " 26.51\n", - " 4.978\n", - " 3279.0\n", - " 4.2\n", - " 76.52\n", - " 79.703\n", - " 73.327\n", - " \n", - " \n", - " 27\n", - " 2007\n", - " 10310000.0\n", - " NaN\n", - " 27.81\n", - " 26.51\n", - " 6.212\n", - " 3261.0\n", - " 3.9\n", - " 76.82\n", - " 79.961\n", - " 73.639\n", - " \n", - " \n", - " 28\n", - " 2008\n", - " 10380000.0\n", - " 16.47\n", - " 27.91\n", - " 26.51\n", - " 5.720\n", - " 3268.0\n", - " 3.8\n", - " 77.09\n", - " 80.212\n", - " 73.942\n", - " \n", - " \n", " 29\n", " 2009\n", " 10440000.0\n", @@ -579,35 +173,6 @@ ], "text/plain": [ " year population alcohol_adults bmi_men bmi_women \\\n", - "0 1980 10260000.0 NaN 26.31 26.80 \n", - "1 1981 10290000.0 NaN 26.35 26.80 \n", - "2 1982 10300000.0 NaN 26.41 26.80 \n", - "3 1983 10300000.0 NaN 26.45 26.80 \n", - "4 1984 10300000.0 NaN 26.50 26.79 \n", - "5 1985 10300000.0 NaN 26.54 26.77 \n", - "6 1986 10300000.0 NaN 26.58 26.76 \n", - "7 1987 10300000.0 NaN 26.62 26.74 \n", - "8 1988 10300000.0 NaN 26.67 26.71 \n", - "9 1989 10300000.0 NaN 26.72 26.69 \n", - "10 1990 10300000.0 NaN 26.77 26.66 \n", - "11 1991 10310000.0 NaN 26.81 26.61 \n", - "12 1992 10310000.0 NaN 26.85 26.56 \n", - "13 1993 10320000.0 NaN 26.90 26.50 \n", - "14 1994 10320000.0 NaN 26.93 26.46 \n", - "15 1995 10320000.0 NaN 26.98 26.45 \n", - "16 1996 10310000.0 NaN 27.04 26.45 \n", - "17 1997 10300000.0 NaN 27.10 26.46 \n", - "18 1998 10280000.0 NaN 27.16 26.47 \n", - "19 1999 10260000.0 NaN 27.22 26.46 \n", - "20 2000 10240000.0 NaN 27.28 26.45 \n", - "21 2001 10230000.0 NaN 27.35 26.46 \n", - "22 2002 10210000.0 NaN 27.41 26.47 \n", - "23 2003 10200000.0 NaN 27.48 26.47 \n", - "24 2004 10200000.0 NaN 27.56 26.48 \n", - "25 2005 10220000.0 16.45 27.63 26.48 \n", - "26 2006 10260000.0 NaN 27.73 26.51 \n", - "27 2007 10310000.0 NaN 27.81 26.51 \n", - "28 2008 10380000.0 16.47 27.91 26.51 \n", "29 2009 10440000.0 NaN NaN NaN \n", "30 2010 10490000.0 NaN NaN NaN \n", "31 2011 10530000.0 NaN NaN NaN \n", @@ -615,35 +180,6 @@ "33 2013 10590000.0 NaN NaN NaN \n", "\n", " car_deaths_per_100000_people calories_per_day infant_mortality \\\n", - "0 NaN NaN 17.0 \n", - "1 NaN NaN 16.4 \n", - "2 NaN NaN 16.0 \n", - "3 NaN NaN 15.6 \n", - "4 NaN NaN 15.4 \n", - "5 NaN NaN 15.1 \n", - "6 NaN NaN 14.7 \n", - "7 NaN NaN 14.3 \n", - "8 NaN NaN 13.7 \n", - "9 NaN NaN 13.3 \n", - "10 NaN NaN 12.7 \n", - "11 NaN NaN 12.2 \n", - "12 NaN NaN 11.5 \n", - "13 NaN 3037.0 10.7 \n", - "14 6.144 2978.0 9.7 \n", - "15 7.837 3209.0 8.8 \n", - "16 7.641 3319.0 7.9 \n", - "17 7.511 3236.0 7.2 \n", - "18 6.410 3269.0 6.5 \n", - "19 6.222 3118.0 6.0 \n", - "20 6.600 3079.0 5.6 \n", - "21 6.099 3170.0 5.3 \n", - "22 6.208 3243.0 5.1 \n", - "23 6.380 3319.0 4.8 \n", - "24 5.910 3322.0 4.6 \n", - "25 6.123 3318.0 4.4 \n", - "26 4.978 3279.0 4.2 \n", - "27 6.212 3261.0 3.9 \n", - "28 5.720 3268.0 3.8 \n", "29 NaN 3276.0 3.6 \n", "30 NaN 3276.0 3.4 \n", "31 NaN 3251.0 3.2 \n", @@ -651,35 +187,6 @@ "33 NaN 3256.0 3.0 \n", "\n", " life_expectancy life_expectancy_female life_expectancy_male \n", - "0 70.60 74.289 67.153 \n", - "1 70.71 74.341 67.181 \n", - "2 70.74 74.403 67.216 \n", - "3 70.80 74.483 67.269 \n", - "4 70.92 74.587 67.345 \n", - "5 71.13 74.716 67.446 \n", - "6 71.30 74.868 67.567 \n", - "7 71.50 75.037 67.703 \n", - "8 71.66 75.218 67.852 \n", - "9 71.75 75.411 68.018 \n", - "10 71.82 75.619 68.208 \n", - "11 72.03 75.845 68.434 \n", - "12 72.37 76.092 68.703 \n", - "13 72.70 76.357 69.013 \n", - "14 72.99 76.636 69.359 \n", - "15 73.34 76.923 69.731 \n", - "16 73.76 77.208 70.112 \n", - "17 74.10 77.484 70.489 \n", - "18 74.44 77.747 70.848 \n", - "19 74.71 77.995 71.185 \n", - "20 74.99 78.231 71.500 \n", - "21 75.23 78.462 71.800 \n", - "22 75.38 78.695 72.096 \n", - "23 75.60 78.936 72.396 \n", - "24 75.88 79.186 72.702 \n", - "25 76.19 79.442 73.013 \n", - "26 76.52 79.703 73.327 \n", - "27 76.82 79.961 73.639 \n", - "28 77.09 80.212 73.942 \n", "29 77.24 80.450 74.234 \n", "30 77.47 80.672 74.511 \n", "31 77.75 80.873 74.768 \n", @@ -695,8 +202,6 @@ "source": [ "import pandas as pd\n", "\n", - "# TODO: opravit podle toho, jak to bude\n", - "\n", "# Světová data\n", "url = \"https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/countries.csv\"\n", "countries = pd.read_csv(url).set_index(\"name\")\n", @@ -704,7 +209,7 @@ "# Česká data\n", "url = \"https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/cze.csv\"\n", "czech = pd.read_csv(url)\n", - "czech" + "czech.tail()" ] }, { @@ -728,7 +233,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -786,7 +291,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To se neporovnává úplně snadno - dožívají se lidé více ve Spojeném Království nebo v Německu? Co kdybychom (opakování z minula) hodnoty seřadili a teprve pak zobrazili?" + "To se neporovnává úplně snadno - dožívají se lidé více ve Spojeném Království, nebo v Německu? Co kdybychom (opakování z minula) hodnoty seřadili a teprve pak zobrazili?" ] }, { @@ -815,7 +320,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A vlastně musíme kroutit hlavou, když chceme najit svoji (nebo někoho jiného domovinu). Můžeme zkusit horizontální sloupcový graf, `.plot.barh`:" + "A vlastně musíme kroutit hlavou, když chceme najit svoji (nebo někoho jiného) domovinu. Můžeme zkusit horizontální sloupcový graf, `.plot.barh`:" ] }, { @@ -825,7 +330,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -837,7 +342,7 @@ } ], "source": [ - "eu_countries[\"life_expectancy\"].sort_values(ascending=False).plot.barh();" + "eu_countries[\"life_expectancy\"].sort_values().plot.barh();" ] }, { @@ -846,9 +351,9 @@ "source": [ "Funkce pro kreslení grafů nabízejí spoustu parametrů, které nejsou úplně dobře zdokumentované a jsou dost úzce svázány s tím, jak funguje knihovna `matplotlib`. Budeme si je postupně ukazovat, když nám přijdou vhod. Náš graf by se nám hodilo trošku zvětšit na výšku. Také se hodnoty od sebe příliš neliší a nastavení vlastního rozsahu na ose x by pomohlo rozdíly zvýraznit. Plus si přidáme trošku formátování.\n", "\n", - "- `figsize` specifikuje velikost grafu jako n-tici (tuple) velikosti v palcích v pořadí (šířka, výška). Při volbě ideální hodnoty si prostě v notebooku zaexperimentuj.\n", - "- `xlim` specifikuje rozsah hodnot na ose x v podobně ntice (minimum, maximum)\n", - "- `color` specifikuje barvu: může jít o název či o hexadecimální RGB zápis\n", + "- `figsize` specifikuje velikost grafu jako dvojici (tuple) velikosti v palcích v pořadí (šířka, výška). Pro volbu ideální hodnoty si prostě v notebooku zaexperimentuj.\n", + "- `xlim` specifikuje rozsah hodnot na ose x v podobně dvojice (minimum, maximum)\n", + "- `color` specifikuje barvu výplně: může jít o název či o hexadecimální RGB zápis\n", "- `edgecolor` říká, jakou barvou mají být sloupce ohraničeny\n", "- `title` nastavuje titulek celého grafu" ] @@ -894,7 +399,7 @@ "source": [ "V grafu ovšem můžeme velice snadno zobrazit více veličin, pokud jej nevytváříme skrze `Series`, ale `DataFrame`. Stačí místo jednoho sloupce dodat sloupců více (například výběrem z `DataFrame`) a pro každý řádek se nám zobrazí více sloupečků pod sebou.\n", "\n", - "V našem případě se podíváme na to, kolika let se dožívají muži a ženy zvlášť. Zvolíme genderově stereotypní barvy (ono je to někdy přehlednější), ale ty si je samozřejmě můžeš upravit podle libosti." + "V našem případě se podíváme na to, kolika let se dožívají muži a ženy. Zvolíme genderově stereotypní barvy (ono je to někdy intuitivnější), ale ty si je samozřejmě můžeš upravit podle libosti." ] }, { @@ -974,12 +479,12 @@ "source": [ "💡 O kauzalitách, korelacích a souvislostech mezi veličinami si budeme povídat jindy, ale taky se nemůžeš ubránit dojmu, že čím více se někde pije, tím déle se tam žije?\n", "\n", - "I bez matematické rigoróznosti ovšem asi poznáme, kde bude zakopaný pes. Zkusme si obarvit jednotlivé regiony světa různými (stereotypními?) barvami. Naučíme se u toho šikovnou funkci `map`, která hodnoty v `Series` nahradí podle slovníku od->do (a vrátí novou instanci `Series`). Sloupec `world_4region` obsahuje přesně 4 různé oblasti (\"kontinenty\"), tak nám bude stačit velice jednoduchý slovník.\n", + "I bez matematické rigoróznosti ovšem asi poznáme, kde bude zakopaný pes. Zkusme si obarvit jednotlivé regiony světa různými (stereotypními?) barvami. Naučíme se u toho šikovnou funkci `map`, která hodnoty v `Series` nahradí podle slovníku z->do (a vrátí novou instanci `Series`). Sloupec `world_4region` obsahuje přesně 4 různé oblasti (\"kontinenty\"), tak nám bude stačit velice jednoduchý slovník.\n", "\n", "Ukážeme si několik dalších argumentů (jež jsou vlastně spíše argumenty použité v knihovně `matplotlib`, a tak nemůžeme jednoduše použít jméno sloupce :-( ):\n", - "- `s` vyjadřuje druhou mocninu velikosti symbolu v bodech (může být jedna hodnota nebo sloupec/pole hodnot)\n", + "- `s` vyjadřuje velikost (resp. přibližně plochu) symbolu v bodech (může být jedna hodnota nebo sloupec/pole hodnot)\n", "- `marker` značí tvar symbolu, většinou pomocí jednoho písmene, viz [seznam možností](https://matplotlib.org/3.1.1/api/markers_api.html)\n", - "- `alpha` vyjadřuje neprůhlednost symbolu (0 = naprosto průhledný a není vidět, 1 = neprůhledný, intenzivní, schovává vše \"za\" ním). Hodí se, když máme velké množství symbolů v grafu a chceme jim dovolit, aby se překrývaly." + "- `alpha` vyjadřuje neprůhlednost symbolu (0 = naprosto průhledný a není vidět, 1 = neprůhledný, intenzivní, schovává vše \"za sebou\"). Hodí se, když máme velké množství symbolů v grafu a chceme jim dovolit, aby se překrývaly." ] }, { @@ -1036,7 +541,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Často se stane, že jsou hodnoty obtížně souměřitelné. Například co do rozlohy či počtu obyvatelstva se na světě vyskytují země miniaturní a naopak gigantické, kde rozdíly činí několik řádů:" + "Často se stane, že jsou hodnoty obtížně souměřitelné. Například co do rozlohy či počtu obyvatelstva se na světě vyskytují země miniaturní a naopak gigantické, rozdíly jsou řádové:" ] }, { @@ -1047,7 +552,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -1092,7 +597,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1108,7 +613,6 @@ " x=\"area\",\n", " y=\"population\",\n", " color=\"black\",\n", - " alpha=0.5,\n", " figsize=(6, 6)\n", ") \n", "# ax obsahuje objekt \"grafu\", přesněji instanci třídy `AxesSubplot`\n", @@ -1146,7 +650,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -1185,7 +689,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -1252,7 +756,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Moc smysl čárový graf používat v případě, že na sobě dvě proměnné nejsou přímo závislé, nebo se nevyvíjí společně. Zkusme například nakreslit čárový graf vztahu mezi pitím alkoholu a dobou života v jednotlivých zemích:" + "Moc nemá smysl čárový graf používat v případě, že na sobě dvě proměnné nejsou přímo závislé, nebo se nevyvíjí společně. Zkusme například nakreslit čárový graf vztahu mezi pitím alkoholu a dobou života v jednotlivých zemích:" ] }, { @@ -1263,7 +767,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -1272,7 +776,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1284,14 +788,14 @@ } ], "source": [ - "countries.plot.line(x=\"life_expectancy\", y=\"alcohol_adults\")" + "countries.plot.line(x=\"alcohol_adults\", y=\"life_expectancy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Dostali jsme čáranici, ze které nelze vyčíst vůbec nic. Můžeš namítnout, že hodnoty nejsou seřazené, a že by situace byla lepší, kdybychom třeba země seřadili podle očekávané doby dožití. No pojďme to zkusit:" + "Dostali jsme čáranici, ze které nelze vyčíst vůbec nic. Můžeš namítnout, že hodnoty nejsou seřazené, a že by situace byla lepší, kdybychom třeba země seřadili podle spotřeby alkoholu. No pojďme to zkusit:" ] }, { @@ -1320,8 +824,8 @@ " \n", " \n", " \n", - " life_expectancy\n", " alcohol_adults\n", + " life_expectancy\n", " \n", " \n", " name\n", @@ -1331,29 +835,29 @@ " \n", " \n", " \n", - " Lesotho\n", - " 51.12\n", - " 5.56\n", + " Afghanistan\n", + " 0.03\n", + " 58.69\n", " \n", " \n", - " Central African Republic\n", - " 51.58\n", - " 3.17\n", + " Pakistan\n", + " 0.05\n", + " 67.96\n", " \n", " \n", - " Somalia\n", - " 58.03\n", - " 0.50\n", + " Kuwait\n", + " 0.10\n", + " 79.96\n", " \n", " \n", - " Swaziland\n", - " 58.64\n", - " 5.05\n", + " Libya\n", + " 0.10\n", + " 75.47\n", " \n", " \n", - " Afghanistan\n", - " 58.69\n", - " 0.03\n", + " Mauritania\n", + " 0.11\n", + " 70.57\n", " \n", " \n", " ...\n", @@ -1361,29 +865,29 @@ " ...\n", " \n", " \n", - " Nauru\n", + " Marshall Islands\n", " NaN\n", - " 4.81\n", + " 65.00\n", " \n", " \n", - " Palau\n", + " Monaco\n", + " NaN\n", " NaN\n", - " 9.86\n", " \n", " \n", - " San Marino\n", - " NaN\n", + " Montenegro\n", " NaN\n", + " 77.35\n", " \n", " \n", - " Saint Kitts and Nevis\n", + " San Marino\n", + " NaN\n", " NaN\n", - " 10.62\n", " \n", " \n", - " Tuvalu\n", + " South Sudan\n", " NaN\n", - " 2.14\n", + " 60.72\n", " \n", " \n", "\n", @@ -1391,19 +895,19 @@ "" ], "text/plain": [ - " life_expectancy alcohol_adults\n", - "name \n", - "Lesotho 51.12 5.56\n", - "Central African Republic 51.58 3.17\n", - "Somalia 58.03 0.50\n", - "Swaziland 58.64 5.05\n", - "Afghanistan 58.69 0.03\n", - "... ... ...\n", - "Nauru NaN 4.81\n", - "Palau NaN 9.86\n", - "San Marino NaN NaN\n", - "Saint Kitts and Nevis NaN 10.62\n", - "Tuvalu NaN 2.14\n", + " alcohol_adults life_expectancy\n", + "name \n", + "Afghanistan 0.03 58.69\n", + "Pakistan 0.05 67.96\n", + "Kuwait 0.10 79.96\n", + "Libya 0.10 75.47\n", + "Mauritania 0.11 70.57\n", + "... ... ...\n", + "Marshall Islands NaN 65.00\n", + "Monaco NaN NaN\n", + "Montenegro NaN 77.35\n", + "San Marino NaN NaN\n", + "South Sudan NaN 60.72\n", "\n", "[193 rows x 2 columns]" ] @@ -1414,7 +918,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1426,9 +930,9 @@ } ], "source": [ - "sorted_countries = countries.sort_values(\"life_expectancy\")\n", - "sorted_countries.plot.line(x=\"life_expectancy\", y=\"alcohol_adults\")\n", - "sorted_countries[[\"life_expectancy\", \"alcohol_adults\"]]" + "sorted_countries = countries.sort_values(\"alcohol_adults\")\n", + "sorted_countries.plot.line(x=\"alcohol_adults\", y=\"life_expectancy\")\n", + "sorted_countries[[\"alcohol_adults\", \"life_expectancy\"]]" ] }, { @@ -1465,7 +969,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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R0TySFDchhGgGJpOJAQP60bNnKmfPniU5ORmTSQ6euYoUNyGEaEb+/v507tzZ6BgeTz42CCFEK1RZWenRMzVl5CaEEK2Iw+Fg6dIVLFy4i4SENjzyyGSP7JQiIzchhGhFLly4wCeffEV09E84dSqNFSu2GB3JJRpc3JRSnyilbldKSUEUQgg3ZTKZUMqOxVKGzVaJ2exZO3B/rTGF6h/AvUC2UuqPSqmuLsokhBDCRcLDw/nBD0Zgtb7PkCHFTJjgma2/GnzOTWu9ClillAoFpgMrlVJngLeB2Vprq4syCiFEs9m+fTcffbSOqVNHMGBAX6PjOJ1SioED+zNwYH+jo7hUow4xKqXCgQeAR4A9wN+BPsBKpycTQggDfPHFPo4eDWP9+n1GRxE3ocEjN6XUAqArMAu447LdtOcppXa6IpwQQjS3u+8eTdu2WxgzZqDRUcRNaMxSgNe01muudoXWOsNJeYQQwmXy8/PZtGkHw4cPJCIi4qq3iY+P5+GH45s5WfM5ffo0a9duY+DAnqSkpFzapcDTNKa4dVNK7dZaXwRQSoUB07XWb7gmmhDCE1VXV7N8+TrKyqq4445RhIWFNdtzL1u2gTlzjlNTY+Peeyc32/O2BKWlpSxatJI1a05jMvVnzZqV9O+/nbvuyqRdu3ZGx3O6xhS3R7XWr3/9jda6RCn1KCDFTQjRYLNmLWTjRl+8vNpy5Mh7vPjiT5rtuW+5pR8WSy3DhzvvYJPVamXevCXs23eKBx7IpEeP7k57bGc5e/YsL744h9ra/nToMBGz2QeHYxB79+5g1653+PnPJ9C9e8vLfTMaM6HEpC4bvyqlzICP8yMJITxZWZmFoKAuhIenUlpqadbnTkhI4PHH7yUuLs5pj5mXl8eKFeeoqBjDggUbnfa4zlRYWIjF0oUOHW7BbK572zaZzLRvPxCHoz/nz+cbnND5GlPclgPzlVKjlVKjgA+BLNfEEkJ4qszM/nh5fU5p6X+YOnWo0XFuWps2bYiKslBVtZJu3ZxXNJuPnHN7Fngc+CF1/xorgH+5IpQQnkhrTXZ2Nr6+vq16D6/U1O786U8dsdlsBAUFGR3npoWEhPD73z9FUVERsbGxRsdxitLSUj79dCWhoQHcdttofH19jY7UaI1ZxO2grkvJP1wXRwjPdP78eebNm8euXbtQSjFy5EgmT57crJMpWhI/Pz+jIziVr69viy5sZrOZ2tp8amou4ufX5tLlVmsVNTVnMJu/PTt09eqNrFjhDeQTH3+Avn3dbzF7Y9a5DQGeBxLq76cArbXu6JpoQri/yspKli1bxmeffYa3tzdJSUk4HA42bdrEli1bmDJlCqNGjcLHR05fC9fp3r07DzxQzEcf/RObrT8xMYPIz9+L3b6ee+5JY8CAb3crCQ0Nxmz+Ei+varcdXSutdcNuqNRh4BlgF3BpEyCtdZFron1bRkaG3rlT1ooL96C1ZtOmTXz44YdUVVURGxuLt7f3t25jsVjIy8sjIiKC++67j169ehmUVrQWXy8HWL36EP37J15zGcDXh9ADAgKcOvnG2ZRSu661zroxxW2b1nqAU5M1ghQ34U5yc3P55S9/SWRkJIGBgde9bVlZGRcvXuTVV1+94W2FcIbKykoCAgIatYC7tra2xR1huF5xa8xsybVKqT8rpQYppfp8/cdJGYXwKA6HA29v7wYVq5CQkEv3EaI5BAYGNqqwHT9+nGee+QP5+e6zZKAxsyW/HrVdXiU1MMp5cYQQQrQ00dHR3HnnSMLCwigsLMTHx4fQ0FCjY11XY2ZLeuamP0IIIa4rMDCQAQP6sXBhFsuWHcHLy8G0af0YOXJoiztU+bXGjNxQSt0O9AAuzePVWv/W2aGEEEK0HJs3b2POnC+oru5F+/Y/wmaz8MEHq1ix4jUefng83bp1MzriFRp8zk0p9SZwN/Aj6pYBTKNuWYAQQggPZbPZeOut5QQGPkR8/Di8vPzw8wslMfFOLJYJvPPOcqMjXlVjJpQM1lrfD5RorV8ABgEdXBNLCPemlKK2thaL5ca9EysrK3E4HB679YhwH6WlpaxZs46iom9WeNXNqDcREHDlFkGBgZHY7Q2bcd/cGlPcquv/rlJKxQJWIMn5kYRwf7GxscyYMYOioiLOnDmD3W6/4jZWq5WcnByqq6t5/PHH3XaxrHAvdrudLVu2MW/eYi5evHjp8osXL/KrX73JO+9c5Je//Bd5eXnXeZSWrzHn3JYqpdoAfwZ2UzdTUnpLCnEVJpOJcePG0b9/fxYuXMi6desIDAy8tGD2/PnzWK1WJkyYQGZmpqxvE83i2LFjzJ69nJMnQzCb27Nq1T8vTQypqKigoiKEjh0ncvr0bMrKyoiJiQHqRm9aO1Dq2+Ohuq6MLVNjFnH7aq0tX39N3aSSmq8vczVZxC3c2alTp5g9ezZHjx5FKUWfPn24++67iY6ONjqauIbjx4+zdOlG+vZNYdCg/pjNZqMjNZnVauXddz9m48ZCgoPHER5etwN3TU0p58+vIiIih4cfvo3PPtvIvn0ldOkSxNNPzyAkJAStNW++OYctW8oICxtPWFgSWmsKCg5QXb2KiRO7MmXKeENel7M6lOzWWve50WWuIsVNuDuHw8G+ffvw8/Oja9euco6tBbtw4QLPPvtvvLzGUl6+iyee6MbQoUOMjtVkubm5/OpXnxIf/wNMpisP2J05s5lRowq4995J5OXlER0djcn0zShNa83hw4eZNWsFublRaF1Jt242ZswYT3x8/BWP11yuV9xueFhSKRUNtAf8lVLpfLP5TwgQ4LSUQniYmpoarFYrwcHBQN2hyvT0dINTiYawWCzY7YG0b59GRcX5Bk0MaunMZp+rFjYAb++6t3Kl1FV3N1BK0a1bN377285s3bqDgAA/0tN7t+gPaA055zYOeACIA17mm+JWBvzSNbGEcG82m43nnnuOsrIyXnjhBSIjI42O5PG2b99OREQEHTve/EYlkZGRDBkSzoYNL9OxYwA9ekwDuDSrtSW/qV+PxWIhN/cckZHtmjSBycvLi6FDB7kgmfPdsLhprd8D3lNK3am1/qQZMgnh9rTW2O12HA4HDT30L25OfHy801pCeXt78+ij05k0qYDw8PBL59tWLV2Kb2AgI0aPdsrzNLdNm/Zw8WIoPj57GDt2gNO6i9TW1rJmzQZWrtzLhAn9GDp00BW7YDS3xsyW7KuUWq21vgiglAoDfqa1/rVrognhvry9vXnhhRewWq20adPmxncQN80Vk3O+O+LuN2wYXl6NauzUIvj5+WEyFVNaehSlhmG3c+lDV3V1MRUV+wgKimrSY3/55QHee28FFy4kEhZ2F+++u4Xly1/nwQfH06VLF2e+jEZpzISSPVrr9O9cJhNKhBDCDZw+fZo335zH+vXldOo0jbi4FM6dW4+f3x7uuWcIgwcPaFLhfuSR3xIc/CChod/09CguPobWn/Dqq8868yVc4aYmlFzG/J3lAP6ArzMCCiGEcK34+HhefPHn7Nmzl9mzV3D27OdkZnZlwoQnbqqBgM3mICTk2xuahoTEUVRk7Bq4xhS32cBqpdQ71C3gfgh4zyWphBBCOF3dGst0evToTkVFBeHh4UZHcpnGbHnzJ6XUfmAMdTMmf6e1bpkdM4UQQlyTr68vvr7OOfAWGOhNQcF+IiN7opRCawcFBV8SEuI+E0oADgE2rfUqpVSAUipYa13uimBCCCFavl/84n7mzFnGV1/tICCgH1VVW+jd24fp02cYmqvBxU0p9SjwGNAWSKZuYfebgHvOiRVCCHHTOnTowLPPPsrevftYu3Y3Y8cOpUePHoavBWzMyO1JoD+wDUBrna2UkpWpQgi3Y7FYOHLkCCkpKfj5+d34DuK6lFKkp/cmPb03FouFdes20K5dW0OLXGOKm0VrXft1UKWUF3UTS4QQwm3U1NTw/PNvcO5cGyIjV/D88z+Q7Yac6NVXZ7F/fygOx5c8/ngZw4YNNiRHY/Zz+0Ip9UvqekzeCnwELHFNLCGEcI3KykrOn1ckJn6fCxf8KSsrMzqSR8nPryA8fACQTHl5hWE5GlPcfgEUAl8CjwOfA9KdRAjhVkJDQ+nbty2nTv2Jnj39PXo6vBHuums4tbUfkJJygr59exmWo8EdSgCUUj5AV+oORx7RWte6Kth3SYcSIdxXfn4+Bw7so2/f/i2iHZnWmqKiIsLDww2f+OCJrFYrZrP5W9vmuML1OpQ0+JmVUrcDx4FXgNeAY0opY3aoE0K4jfXrV/H++/+PyspZ/POf/82ePTuMjoRSioiICClsLuLt7e3ywnYjjZlQ8jJwi9b6GIBSKhn4DFjmimBCCM9w4MAX3HtvG9q3DyE5uYB9+7aTnt7P6FitWkVFBUuXrubs2SKmTRtNQkKC0ZGcrjGlteDrwlbvBFDg5DxCCI+jqKqyAlz6WxjrP//5hGXLzBw/nsGLL35Aebnn9eJozMjtoFLqc2A+defcpgE7lFJTALTWC1yQTwjh5oYPn8rChf8mLOw4FRXtmThRzmYYrayslrCwNEJDO3D69GqsVs/70NGY4uYH5AMj6r8vpK5byR3UFTspbkKIK6Sm9iQ5+Y8cO3aMrl27Gr6JpYDBg7sxe/Y8iouDSU8PJTg42OhITteo2ZJX3Fkpn+aaMSmzJYUQwnkKCgooKSkhJSXFbSfWOGu25DqlVOJl3/cDjJ/2JIQQotEiIyPp0qWL2xa2G2nMhJKXgCyl1BNKqReBt4AHXRNLCOEpioqKWLNsGTdzlEiIxmrMfm7LlVI/AFYCF4B0rfV5lyUTQniEkJAQ4pKTPXaEIFqmxhyW/A3wKjAceB5YV7+wWwghrsnb25uUlBSjY1yX1prc3FzsdrvRUYSTNGa2ZATQX2tdDWxRSmUB/6JuIbcQQrilvLw8subNo2jXLnwTExl333107tzZY0eaZWVlLF26BpNJcdtttxASEmJ0JJdo9GxJpVSg1rrSRXmuSWZLCiGcqbKykjWff87hzz/nFm9v+sTEcLy4mOVlZbQZOJBxU6fSrl07o2M63a9//XfOneuB1g46dDjMb3/7tNGRmux6syUbsxP3IODfQBAQr5TqBTyutX7COTGFaD42m42NGzeyePFiRo4cya233oq/v7/RsUQzsNvtbN+yhQ0ffkjPqiqeio3Fv37tXefwcDqGhbFj927e2bmTnpMmMcLDfjZKSmqIihoAaEpK9l7zdqdPn2bF3LmYzGbG3XMP7du3/9b15eXl7Nq1g27dehAVFeXi1I3X4JGbUmobMBVYrLVOr7/sgNY61YX5LpGRm3CWo0eP8u6775Kbm0t4eDglJSWEhIQwc+ZMMjIyPPZwlKg7t/bmSy8RfOQImVFRRAQEXPO2lbW1rM3L41BYGI899xyhoaHNmLSus/6RI0fo1KmTU3cLnz9/KZ9/ng1obr+9C9OmfXvqhNaahR98wMmVKxnj749Da1ZbLKSMH8+EadNQSrF160bWr59DSkoF2dnedO8+gfHjJzd7s2SnjNwAtNZnvvOLL2dfhVvRWvPnP/8Zf39/kpKSgLrZfBUVFbz++uv85S9/ISIiwuCUwlUcDgeFR4/yw/r/++sJ9PFhQkIC+WfOUFpa2mzFTWvNwYMHWfn++wTn5ZEVHs4tM2eS3qePU4rHXXdNYPDgswDExcVdcX11dTWHV67kZx064GM2A9DNZuPPn3/OuMmT8fHxYcWKd3jiiXAiIqKorrbyz38uok+fQcTExNx0PmdpTHE7o5QaDOj6fd2eBg5d7w5Kqf8AE6hrupxaf1lbYB6QCJwC7tJalzQ+uhBNY7VarzjEEhQURElJicyWE4Y6d+4cWXPnYt27lylt25KQnExeeTnLXnmFHV26kDljBomJiTf9PFcrapczm0yXChuAr5cXCsjNzaW83AJowsLqRpP+/t74+TVqnNQsGvMx4AfAk0B74CzQu/7763kXyPzOZb8AVmutOwOr678XQtykoqIi1q1bR1lZmdFRRCNVVFSweP58PvjVr+h95AiPJiWRUL+pa0xwMA8mJTEsN5dPn3uOj957j4sXL7o0j81up7L2m86K5RYLDq3x9/cnONgPf/9w1q/PxWKxsWfPeUpKzE49dLwdH6AAACAASURBVOoMjVnEfQGYca3rlVL/T2v90nfus/7yll31JgEj679+D1gHPNvQHELcLJPJRGVlJYGBgZcus1gs2Gw2tz3f9tVXX/HXv/6Vmpoa5s2bx7PPPuuUT/jCtex2O9s2b2bj3Ln0rq7mqfbt8fO68m1ZKUWPyEhS7HY2b9jAW5s302/qVIaMGIGPj49TsuTm5vLZZ+8RGdmR9ClTeH3JEoYphR3YDAyZPp2oqCiUUjz22K9ZuXIRf/7zamJienL//fcQFhbmlBzO4syx5DTqWnTdSJTWOg9Aa52nlIq81g2VUo8BjwHEx8c7JaRo3ZRS/PCHP+T999/nwoULREdHU1hYiFKK6dOnu+3U77Nnz2Kz2UhOTiYnJ4f8/Hwpbm7g808+oWTJEh6OiSG8AT973mYzI+Li6F1TQ9b77/PJyZNMf+wxp2TZsWMdSUmHOHjwENOm/ZGMwYNZ9emnmMxmHvne92jbti3V1dWsWbORwEB/Jk++l6qqyQQHB7fID4XOLG5Of3Va67eo62FJRkaGNKYTTtGvXz969OhBVlYWS5cuZciQIUyZMqXFffJsjODgYKxWK+fOncNut39rVCparqqSEvqFhBB+nVmbVxPq58fgiAiWX7jgtCzJyT1YsmQdwcFxVFVVERsbe6lwOhwOtm3bwZw56ygv74bDUcyKFa9z331j6dq1q9MyOJMzi1tDi0++UiqmftQWg+zmLQwQEBDAlClTuOOOOzxif7GBAwcSFBTEhg0bGDNmDJ07dzY6UstlMnGuvJzYBuxhVlpTQ7nV2uxT3I2QltYbs/kZZs9ewW9/u5j09DBmzpxAZGQkf//7u+zZYyIy8n4SEurWtJWUnOCll7K47bZs7rlnosHpr+TM/7GGjtwWA9+v//r7wCInZhAewmq1UlLi+km0nlDYoO5wa1paGk888YRb78/lamazmUlPP80HDgeLTp2iovbq21Fa7XbWnTnDm8XF9LrvPmJjY5s5qTFmz17FxYtj6NjxGfbujWfp0i8AOHQoj4SEewkK+maxdlhYR9q1m8yRI3lGxb0uZ47cPvruBUqpD6mbPBKhlDoLPAf8EZivlHoYOE3duTohLjlz5gyvvfYa+fn5TJgwgTvvvFPerIXT9Ordm67durF+1SreWLiQIVozIDYWL5Opbo1ZYSErKyuJGzWKxydNok39rMXWwGbTBAfHopSJ4OBYrNaiy6692u9gy/29bEz7rRTgH9RNCElVSvUEJmqtfw+gtf7Dd++jtZ5+jYcb3ZSwonXYt28f58+fp0OHDixZsoTJkyfjdZUZZEI0la+vL7fefjt9Bg5kxYIF7Nq4kSG+vuyrqcHatStTZswgISHBac+3a9de5s9fy223DWDEiMFOe1xn69EjjtWr5+Ht3RO7fRspKQOMjtRkjXnHeBv4L+CfAFrr/UqpD4DfuyKY8Cw5OTkopRo06zUoKAi73X6pPZaM2oSrhIeHM/3RRzk+ahSbP/+c3oMH0zs9vcHn2IqKijh//jzdunW77n2ysnZw+nQyixZtZcSIwTgcDo4fP06t1eqsl+IU998/hX79DrFt2wHGjJlGhw4dKCkp4fjxI2zZ8gsGDpxO+/Z13a7sditFRQeIiWmZh/YbU9wCtNbbv/NGY3NyHuGBNm7cyNtvvw3AY489xpAhQ657+xEjRhAaGsrJkycZPXo05ss6JbQ2drud3bt3k5iY6LbLFNxBcnIyyT/6UYNvX1NTw/pVq9i7cCFhtbWsT0lh/MyZ11x+cccdg6iuXstttw2loKCAT956C8eRI+zOzSUpMhJayP+tUoru3bvTvXv3S5etXr2J0NAJ2GxtWLv272Rm/hqt7VRUrGDkyFgmT/6egYmvrTHF7YJSKpn6WZFKqalAyzyTKFqU8+fP43A4ACgouPHkWKUU6enppKenuzpai5ednc2LL77IyJEj+clPfmJ0HEHdB45//O53JOfm8kRsLIHe3hzKy+PT555j+I9+RN+MK/v49uyZSs+edT3mV3z2GUmHDzMuOZlXz57l3PHj0EKn0wOYTAp/f1/S07vQtm0UPj4Lads2hBkzJl3qz9oSNaa4PUndmrOuSqlc4CTX6VgixNeGDh3K4cOHUUoxeHDLPd/QEiUkJDBz5kx69epldBRRz263U5Wby8TLRmnd27WjuLqakqKia9/xMt4mE0opggIC2FVUxIiKCqKCghqcobi6mvVFRQSnun5TltGjh1JcvIwjRz7huefuo1evngAt/nTBDYubUurHWuu/AzFa6zFKqUDApLUud3084Qmio6P59a9/bXQMt+Tv78/UqVONjiGcqHuvXny8YQNnjh8nPy6O7lOm8P6+fXTLyeGWmBgCr9NOy2Kzsf7cOfb4+jL44YcZeIND/M4QGhrKI4/c4/LncbaGjNweBP4OvAr0MWIXbiGEaCmUUjiUoqiq6lJnEYfWXKiupiFjr7i4OJ783e84ePAgUzp2JCQkhOrqatYtX87rixcz3GymX0wM5ssmqGit2Zufz2qLhc7jxvHD228nuAGL0FuzG25WWr9WbRDQDjh++VWA1lr3dF28b8hmpUKIlmLrpk2snzOH3jU1JAQEsLq8nMCMDCbOnHlTbdwKCwvJmj+f0u3byQwNpVPbtpwuLWVZURFevXqReZUdsVuz621W2qCduJVS0cBy4IoeK1rrnJtO2ABS3IQQLUlFRQWrly4l98gRRt19N126dHHKeSitNdnZ2WS9/z5eOTnUREdz6/33k5qa2uLPczW3my5uLYEUNyGEuyguLmbd55+T0qsXqWlpTXoMu93OsWPHSEpKctq2Np7mesXthisVlVLz6//+Uim1/7I/Xyql9js7rBBCuIrNZmPu3EVkZa1x6fNsXLkSy6JFfPrqq01+DLPZTJcuXW6qsFVWVlJ7jf6Znq4hE0p+XP/3BFcGEUIIVysoKGDRogP4+9sYM2Z4k9q6aa05cuQIBXl59B806Ko7UHfq0YOFX3xBtwHGtK+yWq1sWLuW7R9/jHdwMCOnT7/q+jtPJoclhRCthtaaTZu2EhISRM+ejT9cWFBQQNb8+ZRv306MUpwIC2PUffddtWWXw+EwbKucHTt2sP+vf2VaQgIfHzzGW0cKmLX0Xx63FdL1Dks2ZJ1bOVffq+3r2ZIhN5lPCCGahVKKoUMHNfp+VVVVrFu+nINLlzLCbCYjKQmTUpwrLyfr739nR7duZN5777eaLRu5B5zNZqO9ry8hvr50Dg8jIqC4VexJd7kbvlqtdbDWOuQqf4KlsAlhvIKCAv723HOcO3fO6ChOU1hYiM3WclrXvvn736MXL+bJ6Gj6x8Ziqp+1GBsczINJSQw5c4aPn3+eo0ePGpy0TkhICF9pzcGCAjA7GDq4a6vrTdq6SrkQHshut1NZUYHdbjc6itPkHDtGYWFhg2579uxZ/vSrX1FRUeGyPOV5edwWH0/AVTa3VUqRGhlJilKUl7eMxk09evTge889x+b4eM4NGsTjv/89ISGtaywim2QJ4eZiYmL41csvGx3DqTIGNfzQYdu2bRk0bhz+/v4uTOR+kpKSePQXvzA6hmGkuAkh3FpAQADDhg83OoZoYeSwpBCi0ex2O++//z75+flGRxHNwG63c+bMGdxldj3IyE0I0QRms5mePXvSpk0bo6M0myqr9Zod+7XWVLWwXbWdRWvNX/7yLw4cqGDw4Gh++EP32OlMRm5CiCbp3bs3vr6+RsdoFkOmTuX18+fZmpuLvX7j3a/llpXx75MnKe3Rg+TkZIMSuo7WmsOHzxMUdCuHD7vPjFwZuQkhxA2Muf12evXvT9b8+eys79gfFRjIqrw8TrRty+hnnqFX794e2djYZDLxgx/cxsqVm5k0abzRcRpMOpQIIUQDXd6xvzI/n35TpjBs1KirjmDP5OSgtSb+sh27Rd35u+PHj5OYmHjTDaFvqkOJEKLhtNacOXOGmJgYvK+yJkq4N6UUKSkpJL/wAhaLhYD6zUpFw2RnZ7N8+SxMppPU1EQxZsx9pKX1dMmIV4qbaJFsNhtbtmyhrKyMUaNGucUapnPnzjF37lz27NlDTEwM999/Pz169PDIQ1WtndlsvmFh63BZK67W7sKFCyxf/jHFxVvIzAyhc+dkzpwpJSvrZbZvTyMzczpxcXFOfU45LClanKKiIv76179y5swZTCYTwcHBPP3003Tq1MnoaNe0ZMkSFixYgI+PD9HR0ZSWllJcXEyfPn148sknZRRnkFOnTrHm449JSE1lxJgxTdoFQNyczZvXsXHjbIYNU/TvH4PZ/M08Rq01+/bls3p1Dd27T2T8+CmNeuyb2s9NiOZ2+vRpzp49S1JSEgkJCVRUVLB/f8veOnDVqlVERkYSExODUoo2bdqQlJTEvn37KCkpMTpeq7Xg9dfpefQo2bNnc+TIEaPjtEoHDmzg7ruDGDSo/bcKG9Qd5u3dO5rHHoth375VTn1eKW6iRbq8g7m7dDP/bk6llNtk91TeAQFcsFioMZlk9Gwgb2/zda/38nL+74mM0UWLExUVRXBwMDk5OZjNZpRSHrl+SLjefc88w6Y1axiTktKgvcxqamrYsn49bSIi6NW7t3w4cWNS3ESLExsby0svvURWVhalpaVMnDiR8PBwo2NdV2xsLF999RWxsbH4+vpit9s5d+4c/v7+bjEZxlO1adOG26c07DzO3j17WPXee3QqKeGE1mzv1o07vv99YmNjXZxSuIIUN9EiBQQEMKWBb0otwTPPPMPatWv5+OOPqa2tBWDkyJFMnjyZ4OBgg9OJG7Hb7Sx+9VUeDQsjJikJrTWbs7NZOW8e33/mGaPjiSaQ4iaEE/j4+DBu3Dj69+/Pxo0bSUtLI1EW77oVpTUx9R9ElFLEBgeT7UF75BnFz68N27ZlM3asL4GBVy7arq21s2lTHn5+MU59XjmgLIQThYWFcccdd0hhc0NaayrqR90A5Zd9LZpu2rRH8PObwuuvn2fr1lzs9rrenFpr9u/P57XXzlBWNoqHHnrWqc8rIzfhFmpra1mzZg1nz55l0qRJtGvXzuhIwoOYzWaG3nMPb3z8MYOBIpuN7LAwJtx+u9HR3J6/vz/jx08mI2MIWVnz2blzO4MH+7F7dzUOR3emTZtBhw4dnP68sohbtHjV1dW88MILnD9/Hi8vLxwOBz/96U9JTU01OprwMEVFRaxZvJg2kZEMHzPG6bse1NTUYLFY8PLyIjAwsMH3s1gsnDt3jsTEREM73jgcDt54Yzbjxw9p0gzmr3tzbt2aRc+ew+jV6+aaTUtvSeHWKioqKCwsvHSo79SpU5w/f16Km3C68PBwpj34oEse+913P2LevE/p2rWcmJh4hg69hwEDBmM2X38NGMCbL76IPnmS7tOmMXbiRJfkawilFImJ0U2eJPV1b86UlBQnJ7uSnHMTLZ5SCpvNhsViweFwUFNTY3Qk0Qy01pw+fZrKykqjo9wUrTW7du1k8eLX6dLlNFOnevHQQ4GcOvUv3njjObKzs2/4GNVlZYTabFRVVDRD4mtTSjFhwlgiIyMNzdEQclhStHhaa5YvX86CBQuoqalhwIABzJgxo1XtAt3alJWVsWjWLIq2baM2OJhR999PxoABRsdqkvPnzzN79rNMmuSHUlXExbXHz88PgGPHipk7t5pnn33juh1UCgsLOX7sGOl9+rSaDWIbQg5LCremlCIzM5MBAwZQWlpKQkKCdNr3cAcOHMBn0yZ+1KkThVVV/Ps//3Hb4ma32wkN9aZz5yununfq1BaTqW7ft+tp166dTKJqJCluwm2EhYURFhZmdAyX0lpjsVgufbK/lvLychYtmk1JSS633/4QHTt2bKaEzSfQywuzyUSIry+UlRkdR7gZOecmRAsyZ84cnnrqKY4dO3bd2x08eBAvrw0MGlTAF1980kzpmk9SUhLZ0dF8ePw4b+bm0mvMGKMjCTcjIzchWpDKykpqa2uxWCzXvV1oaCi5ub5UVlYTFhbdTOmaT0xMDE+9+CK7d+1iaPv2LlkH1Vz8/PwoKjKxZ895eveOunRI3Wq1s2VLHiZTiDRodgGZUCJEC2Kz2bh48SIRERE3vO2ZM2coLi4mLS1N3hxbuNzcXLKy5mK37yMzM5yKilpWrKggJmY4Y8dOISAgAKUUPj5XtqcS13a9CSVS3IQQognKy8vZunUDSUkpDdolXmvNgQMHWLnyffz9Q8jMnEl8fDw7tm1j/QcfYPLyYtR999E7PV0+rDSQFDchhMfLy8tjyZJ19O7dmf79r/p+5xQ2m42tWzeyefM8unev4sQJTUTEEMaNm9qgrZkcDgdKKfLz8/n4H/8g9NQpxkVGYnM4WFZYiK1bN6Y+9lijtnnSWnP48GFsViupaWmtZjaxLAUQbkdrTVlZGaGhoUZHEW5iwYI1bN0awebNWaSl9XD6Pnpaa44cOcLy5e8TFXWORx6Jom3bKOx2B9u27eTf/95B796TGT58zHVnu349Ktu/ezedjx9nbHLypWL0UFAQi778kkOHDjF06NAG5crPz+fzDz/Esns3XsCW7t25beZM4uLibvo1uzMpbqLFyc/PZ+7cuezatYthw4Zx55130rZtW6NjiUbQWrPpiy9I7NSp2d5kU1Li2L59M506tXX6uauCggKysuZTXr6dCRPakJyceOk6s9nE4MHt6dmzljVrPua111Zxyy0zSU/vc/3Di1oT5OPzrVGWUoqgRi7S/vTtt0k7eZJBSUkoYM/Jk8x/5RV++qc/NfJVehYpbqJFsdvt/O53v8NqtZKQkMC2bds4fvw4f/zjH42OJhpBa82qBQu4ZeLEZitut946gr590wgNDW1Qv8aGqKqqYt265Rw8uJQRI8xkZCRhMl39kF9QkA8TJyaSl1fOsmWvsGNHF8aPn0lCQoJTslyLvaaGlLZtMdUXyS7h4aw2uE1XSyDFTbQoDoeDioqKS02So6OjKSoqMjaUaDSTycT//O1vzX7ux1kjfLvdzs6d21m//gN69KjgySdjCQj4dnus0tJSfH19rzgEGRMTzIMPBnHwYC4LFvwPcXGjuPXWSVe0i/MPDOSgxUJyRQXRQUEAnCkt5YjVysBGHlK1XLapqkU2WAWkuIkWRimFl5cXJSUltGnThuLiYuml1wKUl5ezevVSTp7cy4gRd9G7941n9LnrjL/jx4/z+efvcaFgO/37hzF2bGe8vK58LaeOHiU0IoLEpKQrrlNKkZoaSZcudjZtWs9bb22mX7+pDBky4tIh0yHDhxMQFMTs2bNJOXUKK5ATEcGY//5v0tLSGpw3/dZb+WD2bAaXl+OtFOvtdvpOndrk1+8pZLakaHEOHz7Mu+++S25uLp06deL+++8n6SpvIML1bDYbW7ZsZMuWefTpU0vnzsGsWlWMzdaNzMx7XX7IrTkVFxezfPknFBZupDr/BO3PlGECLH1iePAnA6+4/dfvnQ0ZnZaW1rBq1XlOn45kzJj7SE1NvXS/mpoaNqxejZe3N0NGjmzS+cLi4mJWLVyI1WLh1jvvdIuu/c4gSwGE27FarZw+fZqkpCS3HQG4u7y8PObPf5WoqHOMHRtF27Z1h8rq1msVsGpVNQkJo5k8eYZb/x9ZLBbWr1/Fnj0LGTJEM2BALK8+u5IHvc2YFLxdY+Nnf810ynOdPl1KVlYxZnNPxo+fTmxsrFMet7W6XnFz359I4dG8vb1JTk526zfNq3E4HDfsAN9SHD16lJSU09xzT+KlwgZ1I5W0tCiefLIDR4+uprq62qU5CgsLef+VVzhx/LjTH/vw4UO89tovqKr6iCeeaMeQIXF4eZmIS43kw3PlfJBbToe0KKc9X3x8KI8+mkifPtl8+OGvWLRoLg6Hw2mPL74h59yEaCZ2u51nnnmGkSNHMmXKFKPjNIif37XfInx8zJjNrv/wkZeXx4lNm2ifkkLH5GSnPvaWLUsYO7aGtLTEb10+9YHeHBnUAa01XbveuBVaYyilSE+Ppnt3G2+8sYwLF0a1msOIzUmKmxANoLWmqKiI8PDwJs8ANJlMDB48WM4fNlJaWhptX36ZmJgr90NzhpCQKycsKaWcXtS+y9fXCx8feQt2FfmXFeIGKioq+OSddzi3YwftevZk6iOPNGkXcKUU99xzjwsSejalVKvvtiEaz7NOaAjhAidOnMC0fTv/nZhI6J49HD582OhIzaa01HLNc4QVFbXYbE0/X+RwOHjnb38jOzu7yY9hs9mw2WxNvn9rp7V2m3PAjSUjNyFuwMvLizKtOV1aykWtSfJqHb82qampLFiQzr/+dYDx4yOIiwsBqO+lmMfGjZqMjGkEBAQ06fGVUsR369bk3dUPHz7Msv/8B4fNxrgHHyS1EWvDBFy4cIG5r76KrbaWu59+2mWHfY3SOn5LhbgJXbp0ofQHP2Dx55/Te9IkevfubXSkZhEeHs4jj/wX+/fvY968WSQlnaJzZ3/WrasiPHwwDz10Z4P2nbsWpRSjx41r8v2Xv/MOt1VV4WM289Hbb5P6yitNfqzW6MSJE7Q7dgw/h4OjR49KcROitTGbzQwaOpRB1+jSXllZycaNG+nYsSOdO3du5nSupZSiV6/edOvWnQ0b1rBt2z4yM6e0iNepzGZqbDY0YJJNPhstKSmJrUlJ2GtruasB+9G5GyluQtyEvXv38vbbb1NR36h20KBBPPTQQx63o7KPjw+jR2cCzlnM7AwTH3uMz959F7vNxuQHHjA6jttp164dT/32t4D7tkq7nlZT3E6dOsVbb73FuHHjGDFihNFxhIdYvXo1UPcp2OFwsHXrViZOnCidJ5pBYmIiTzz3HNCwFljiSp5Y1L7mua/sO06dOsWXX37Jnj17jI4iPIxX/QQTk8nktK1WRMMopaSwiatqNSO3wYMHExgY2CLOFQjP0aVLF/bv309tbS3V1dVERkYSEhJidCwhWr1WU9x8fHzo16+f0TGEh5kwYQLdu3dnwYIFdO/endGjR8sWPUK0AK2muAnhKh07duTnP/+50TGEm7NarTgcDvlw5CSt5pybEEYoLCxk164tRscQl6mtrcVisRgd41tycnJ45dlneeXnPyf76NErri8sLJROLI0kIzchXMjHxwdf30CjY4h6FRUVvPn882iHg0f/53+MjnNJ9uHD9LpwAX8vLw7v3UvnlBSgrhAvnDOHk+vW4de+PZMff9yjNoh1JcNGbkqpU0qpL5VSe5VSsgupm5BPj9emtWbVqlX86U9/4vTp0wCEhoaSmtrT4GTiaxaLBdvFi+iLF6mpqTE6ziWJHTuyOyiIjd7edOzW7dLlubm5FK1dy0/j48k4f56da9camNK9GD1yu0VrfcHgDKKBNm7cyHvvvcdvfvMb4uPjG33/Y8eOMWfOHIKDg7nnnns8bi1YZWUls2bNAmDx4sU89dRTBidynj17drFp0yL69BnD4MHDjY7TZOHh4Uz/zW9wOBxER0cbHeeSTp0789Rf/oLD4SAoKOjS5WazGQtQXF1NidWKqZX0NXUGOecmGszf3x9/f3+8vb0bfd/S0lJeeuklCgsLOXr0KC+++KLHdSP39/cnLS0Nk8lEz56eNVrLyvo3Y8YUs2rVf7BarU16jJycHPZscf75x8WLF7N06dIG3z4hIaFF7qkXEBDwrcIGEBcXR79HHuE/NTVUjhzJLRMmGJTO/Rj5MUADK5RSGvin1vqt795AKfUY8BjQpJGCcK6+ffvSt2/fJt3XarWitSYiIgKtNadOnXJuuBbAbDbzs5/9jKqqKgIDPes8W2xsV5Yt20G7dt2avFA9NDQUu93u5GQ3JyCgLVu2HKBNGz9CQ/2a7XkdDs3OnXmUlXnj53ft5zWZTAwZPpzBw4bJYvVGUkZ9elZKxWqtzymlIoGVwI+01uuvdfuMjAy9c6ecmnNXFouFP/zhD5w6dQqtNX369OHHP/6x/MIaxGq1kpubS0JCQoP+DxwOB2fOnCEuLs6jurDU1tayadMXbN/+MQMG2BgyJBZvb9e+vhMnSsjKukhgYAaZmXcRFRXl0ufzZEqpXVrrjKte1xIODSmlngcqtNZ/udZtpLi5P5vNxvbt2wkICKBXr15S2Axy/Phxlix5G8glIKAH3/vew7Rr184pj11dXc369Svx8vJh2LBRbtNA+uLFi6xatZgzZ9Zw660B9OjRzuk/n8XF1axYcZ78/PaMG3c/Xbp0kd+Bm9TiiptSKhAwaa3L679eCfxWa511rftIcRPCOd599y/07JlNeno0WVnHgLsYP36SUx571aosCgreo7YWUlJ+xODBg53yuM0lJyeHrKwP8PY+RGZmO2Jjg2/6MS0WGxs2nGP3bl8GD76bgQOHXupHKm7O9YqbUf/CUcCn9Z9avIAPrlfYhBDO5e/vhVIKf39vqqud9wHXx8eXigoTVqtym1Hb5RISEnj00WfZu3cPH3wwi86dTzF6dCxBQY1/LVpr9u3LZ/VqC8nJY/nhDycQHHzzxVI0TIs4LNkQMnITwjn2799DVta/aNeujIsXY7jzzqecNmHL4XCwf/8+vLy86dGjh1sfdqupqWH9+lXs3buQIUNgwIBYvLwaNsH8zJlSli0rwmRKY/z4e2nfvr2L07ZOLe6wZFNIcfM8DoeDnJwc4uLimrS8QDRdRUUFx44do0ePHob92x8+fBBvb1+Sk6/cBTo7O5uzp04xYMgQAgICDEj3jaKiIpYv/4QLFzYxblwwKSnh1yzaZWUWVq7MIycngjFj7iMtradbF/iWToqbaHGKi4v5xz/+weHDh2nfvj1PPPGELPdoZfbs2Ya3ty+pqb2/dfnevXv54v/+j0TgRIcOPPPii4bk+65jx46xfPlsQkJOkpkZSbt23yz3sFrtbN58jq1bTfTrN5WhQ29xy8Oy7qYlnnMTrVx2djaHDh0iOTmZnJwctm/f3qqK29mzZ5k/fz6BgYFMmTLF75UAWQAAIABJREFUabMV3Ul6+oCrXl5eXk6S3c4t8fHsy8tr5lTX1qlTJ5KSfsPOndt55505pKUVMXJkDCdOlLByZRWxsSN4/PHv0aZNG6OjCqS4CYOYTCYcDge1tbXU1tZ61Nqp6ykvL2fJkiWsWLECPz8/7HY727dvZ9KkSdx66634+/sbHdFwXbt25aMuXXglJ4ehd91ldJxvMZvNDBgwiLS0Xqxdm8X//d8SwsM7MXnyDBITE42OJy4jhyWFISwWC8uWLWPJkiUMGzaMKVOmePwO1l9++SWvv/46VquVmJiYS9PBa2trycvLIyQkhGeeeUbeJKk7H1tdXe20Ti8WiwVvb29MJud2HKyoqCAgIACTyUR5ebnMhmxmclhStDi+vr5MnjyZCRMmtJo1P3v37sXhcNChQ4dvXe7j40NCQgInTpzg2LFj/7+9O4+PsroXP/45mZlM9pBAgCRkIxv7koSdIGsSFtkEBYFifakUt6q91d7e/qzt1aqot7cV1HJtVdCCVpFFJQHCEpEQ2QIBlDUQICGEYPZtMnN+f0ySBsi+zUxy3q8XL5KZZ57ne/Ik852zq+SGuWbfFonNYDBw4EAS3333KR4efsTFLW3TdSWr14I8cuQI27b9mXvvfbbFS9QpbatrvKsoVqurJLZqDdUc6nuusLAQrVarmiybQUrJqVOn2LlzPX36ZPP4473JzLzOli0v4e09gZiY+Xh4eLTZ9dzd3enWrQ/u7u5tds6OZjQa+e67A0RGRnSKtVG71juLotiQiooKdu/ezRdffIFer+fBBx9k1KhRXaZ/sjU2b/6E7OwdzJvnWVMT7tbNgbCw7iQnJ7N2bQr33/+fNbW48vJydm/fTn5ODlPnzaNHjx7Nul5ISAjPPPNaWxejQxUVFfHFF4n4+/up5KYoSvvIysrirbfe4ubNm3h7e1NZWcnf/vY3EhIS+NWvftXp+ydbKyvrDPfd15NevW7fQkartSM6ug8FBZfJycmpSW6pqalkf/opvvb2JFRUsOSJJywRtkW5u7vzl7+8ZOkw2ozaz01pF0ePHuXNN9/k5k21F201Z2dnCgsLKS8vv+u5kpISysvL0ev1AFy6dImcnBwCAwPR6/U4OzsTGBhIRkYGWRYeHn/27CkyMi5bNIbWunNitb29PUUaDblGI/YWnjSutA1Vc1PaxYkTJ0hJSSEuLq7ZTTyd1cyZM3FwcODLL79ESomPjw8mk4nMzEwcHBxYuXIlY8aMqTn+zj44IUSrRvtduHCBffu+xN9/INHRk2sSaXPpdA7odJ1rgvKwYcPQ/PrX5OXmMnr8eEuHo7QBldyUdrFo0SKio6Pp27evpUOxGnq9nhkzZjB69Gg2bdrEt99+i52dHbGxscyaNeuuXZjrmqbTkqk7t27dIiHhC3Jy9jNpkhPnz59k9eqdTJmyjKFDhzVpeSiDwUBq6jF8fHwJCgpudgzWTgjBkKFDLR2G0oZUclPahYODA8HBLXsTrB7pdu1aOuPGTbrrTd/WeXp68sgjjzBt2jTs7e3x9va+6xgfHx+cnZ3JyMjAx8cHo9FIVlYWPXr0aPJqJuXl5SQl7eLYsc2MGydZuDAArdaOwYPh6tUC4uP/zKFDg4iLW3zX9ITafvjhBxISPsLL6xpJSVqCg2OYNm22VQ86cHb2Yv/+w8TE6HB1vb2Gev78Lc6ercTfXzU/dmZqErfSZjIzM/n000/Jz89nyZIlhIaGtug8x48fJynpTYKC7Lh0qS9PPvn7Jr2uuLiY+Ph4NBoNMTExFl9wt7WKior46quvSEhIQKvVMm/ePKZMmYJer0dKidForHMqhZSS1NRj7N69npCQn+rdskVKSVraDXbtKiUwcCpTp86+a6CKwWDg9ddXsmSJM0FBHpSXV7J160Xc3ZcQEzOz3creWuXl5Xz7bSJHj37J2LEwerQ3+fllJCRkc/OmH3FxPyM0NFQtamzj1CRupd0ZDAZeeeUVKisrsbe359VXX+XVV1+lV69ezT5XWVkZnp6VhIX14Mcff2r0eKPRSHJyMhs2bKCkpASAxMREHnzwQUaOHGmzQ+ddXFxYtGgRkydPRqfT3TYva+3atZw6dYoXX3zxtj7NjIwM4uP/iUZzmsWLvfDxCaz3/EIIhgzpRb9+Rvbv38O77+5nzJgHGDNmfM1OAVJK7OyMBAWZr63Xa/Hzc+KnnwztU+g2otfrmTp1BhERo9mxYxNvvbUbo9GViRMf4YEHxtrs74TSdCq5KW3CZDJRUlJCQEAAAKWlpVRUVLToXIMGDeLy5Ri++eYMM2Ysa/DYsrIyXn/9dS5evEivXr1qmuyKi4t59913SUxM5Pnnn7fpFdp79ux512P5+fmUlpZSWVlZ8/3OnVvIyEhk2jQnBg0KanKtxN5ew+TJfgwfXsrOnR+yZk0C06YtY8CAAQCYTJKiogpcXOyRUlJYWA7YRo3H09OTefOW8cgj+/Dz687YsdGWDknpICq5KW1Cq9Xi7+9Peno6dnZ2uLu7t3gulrOzM/ff//MmHZufn8/ly5fvWlLJ2dmZoKAgLl68SGFhId27d29RLNbqmWeeobS0FCcnJ/bu3UVKyr8YObKS2bP9sbc310rOnLnJmTPZ3HNPMO7uDo2e08PDkfvvD+LSpTy2b3+d77+PIC7uAUaPvp933tnEqFEmLl82UlgYzPz5I1sU940bN0hI+BQnJ3diYuZ1yFqMWq2W6OjoFrUiKLZLJTelTWg0Gn73u9+RlJREQUEBMTExHTbgoL4aSmfuT9HpdJw9e5adO9fRp082K1Z4063b7Qls27bDuLjc4uBBHbGxYU0+d2BgN1ascOfo0dN8/PFv6ddvBg888P84ciSJ8PAQoqJa3tQbH78Bf/9j5OZWcOCAJ7Gxs1p0nubQaDQ89thj7X4dxbqo5Ka0GZ1Ox5QpUywdRqeXmZlJfPxGDIZU5s/3JCCg7oWAIyKCOXXKnn797m7WbIydnSAqyoeBAw3s2/cNn366l+joxa1KbAAajT35+SaKi6FbN/X201xSypoRtGr3+oZ16d8uk8nElStX8PX17XIL+HYmRqMRk8l01wRnk8mEyWSyUFRNk5eXx9atW8nPz2fBggV1TguoLScnh3Xrfk9MjB3DhgVhZ1d/7XTy5FAmT27ZiNVqjo464uICiIoq4euv/8bNm5nce2/L91ibM2cZSUm98Pd3Y9y4e1oVW1dz5coVNmyI5+TJYnx84Gc/i6F///6duoWiNbr0O/r777/Pt99+S79+/fjtb3+rfklskKenJ6NGjeLgwYO4ubnRvXt3pJTk5uZSWFjI+PHjrXZn5NTUVN555x2MRiMajYajR4+yaNEiYmNj631NSUkJPXuaiIjw7cBIoUcPJ0aNcic19VarzuPi4sKMGfPbKKquobCwkM2bd5CYeBlHx6kEBQ0mP/8yr722nYiIFBYvnqH6E+vQpZPbpUuX0Gq1pKenWzoUpYV0Oh2/+MUvmDJlCuvXr6+5l0FBQTz77LMtnkjeEU6dOgVQM4G6oKCAgwcPNpjclK5n8+adJCRoCQp6Eo3GPOq3W7dA3N1XkJaWTH7+F/z+949bOErr06WT22OPPcb27duZMGGCqrXZMCEEYWFhvPTSS6SkpKDRaIiKirKJuUy1m1LV76BSl4oKI25u4TWJrZoQdnTv3o+SkiMWisy6denkFhgYyMqVKy0dhtJGNBoNY8eOtXQYTda9e3eKi4vJz89Hp9ORk5NDeHi4pcNSlE6hSyc3RbGk2NhYfH19WbduHUVFRTz66KO37QqgKErLqeSmKBYihGDw4MH86U9/wmg04uDQ+ERrpevR67UUFp7D0zMErfbfi0BLaSI39wf8/dXbeF3UZqWK0k6klKSkHCQ7O7vB43Q6XbslNilli7bJUazH3LnTmDEDrl1bzfXrx5BScuvWBdLT32X48IusXLnA0iFaJZXylU7h1q1baLXaFi/51R4KCgrYvv1vRERMZ/bsls8Na6nMzEI2bjzJ5ct5LF06hMhInwbnxSnWycXFhcWL5zJhwjU2bozn+PE99Omj4fHHYwkPD1cDkeqhkpvSKVy+fB6dzoFBg4ZYOpQa7u7uPPzwHyyyE/mWLT+yefOP6PUanJ3tefvt7wkL684zz4yuc/sbxfr5+vry3HMPc/XqVby9vdXCE41QPx2lUxg+vGUL+bY3f39/i1w3MTGdXr1ccHAw/4m7uNhz4cItrl8vIiTEs12vnZGRwYEDOxg4cBSDBg1SNYs2JIRocGPZ9nL+/HmSkjYTEhLJ2LHRNpFYrT9CRVFapHYTpBCi3Zsky8vL2bbtU65c2c3o0RqSk7/l+++HMHfuQ51uV4aupLKyko0bX2f2bC0HDqTi4dGDwYMHWzqsRqkBJYrVkVKSl5enBkK0ghBQVlZZ873RaKKiwtiu17xw4QJ5eTt44gk/xozx49FHA+nV6whHjx5q1+sq7cv8dyjRau0QwnYGKKnkpliVGzdusGbNGp566inee+89cnJyLB2STVq2bCgVFUYuX87n+vUirlwpICYmGH9/93a9rqurvmY/OSEEHh4OgG28GXYmBQUFbZaEdDodCxb8B/v3+xIaurxmE1trp5olFauRn5/Piy++iMlkIjAwkKNHj5KWlsaqVatwcXGxdHg2JSrKh/79exAff56MjHzuu29Auyc2gOLiCiorTWi1dkgpyc8vR+3M0rESE78mOfkz+vSJYPnyp9ukz7Nfv3706/fbNoiu46jkpliNsrIyDAZDTYe5r68vV65coaysTCW3FnB2tue++zruU3ZQUBAnTtzDmjXfMm6cnhMnyjAYBjF/flSHxaBAevoJoqMFe/emIqXssgN6VHJTrErtvdlMJhNGY/v2E7VGRUUFFy9eJDg4WG0cCTg6OrJo0SNcuDCJ5OR4hg0bzbBhw+/aZ09pX1OmLGLv3i+ZOXN8l/7Zq+SmWA1PT09Gjx5NcnIyWq2WyspKoqOjrXI/tpycHN544w2ysrLo06cPzz//PB4eHpYOyyoEBwcTHPyEpcPosoKCgggKes7SYVicSm6K1ai9N1tSUhITJ0602v3YMjMzycnJITg4mIyMDK5fv94hyc3V1ZUbN/QkJV1lzBhvdLqO2dbnypV89u0rICCgd4dcT1FaSyU3xeqEhoYSGhrapuc0mUwcOnSIrKwspk6d2uo+PDc3N+zs7EhPT8fe3h5XV9c2irRhnp6erFjxCjt2bGLNmiRiYlzo379Hu/Wr5OeXsWvXdS5f9mLq1P+wiflNigIgbGXOQlRUlDx8+LClw1BsVGpqKm+++SZCCEaMGMHTTz/d6nNeu3aNQ4cOMXr0aHr3btsajclkoqysDCcnp3qPSU9PJz7+ExwdzxIX15Pevdtu0I3BYOTAgSwOHrRjxIgFjB8/EXt7tWyXYl2EEEeklHWOWOq6vY1Kl2IymZBSYmdn12aDVHx9fZk7d26bJzaAL7/8kmeffZbCwsJ6jwkKCmLFit8yaNCTrF9v4KuvLlNcXNGq60opOXXqBqtXZ3DjxnhWrFjF5MkxKrE1UXZ2NmvXvkVxcbGlQ+nyVLOk0iUMGTKEhx9+mGvXrjFr1ixLh9Oofv36UVJSgqOjY4PH2dnZERU1koEDB7Nv307WrNnChAkaRozojUbTvM+uWVmFxMffpLw8jPnzlxIQENCaInRJOp0OV1cPNJqO6QtV6qeaJRWliTIyMtBoNPj6+lo6lHrl5OSQkPA5eXkHiYtzb9IiycXFFSQmZnL2bDcmTVrK8OERXXoIuWI7GmqWVDU3RWmC5ORk3nvvPYQQPPfccwwZYj1b69Tm5eXFkiW/4Ny5KWzfvh5Pz4vExvamR4+7++6MRhMpKVns3y8ZOnQBjz8+mby8vC476VfpXFRyU6xOaWkpRqPRqlYluXHjBpWVlRiNRm7dumXpcBokhCAsLIzg4JdISTnAP/6xkaFDb3LPPT44OGiRUnLu3C0SEgro3n0sDz98H4WFhXz00VsUFZ3Fw2MI06c/aNU1VMX2lJSUEB+/ifLyEqZPv7/d56+q5KZYDSkl3333HRs2bMBgMLBw4UImT55sFf0X48eP59KlS9jb2zN8+HBLh9MkGo2GsWOjGTo0gt27v2H16u2MG2fHhQsG8vICiYt7mtDQUAwGA3/7238xb56W/v37cuzYeT7++A1eeOF/LV0EpRNJS0ujpORrXF0FKSk+xMa2b9+3Sm6K1cjOzub999/Hy8sLjUbDRx99REBAAGFhYW16HSklZ8+eZc+ePUycOJHw8PBGm+K6d+/OL3/5yzaNo6M4Oztz770LiYoaT1LS14SEDGDEiFE1HxpMJhNCVDBggDcAgwf35Jtvrlsy5A4jpawZRau0Lw8PD7KynMnJqSQ62qvdr6eSm2I1TCYTGo0GZ2dnAPR6PSaTqU2vIaVk7dq1HDhwAHt7e/bu3YufXx9ee+11q6ghtidvb28eeOCRux43r+Op5cyZm4SFdef06Ry02oZHado6c9PsOdavX09lZSXLli2jf//+qr+xHYWFhbF8+Z8wGAwd0uStkptiNdzc3PDw8ODSpUvY2dnh7OyMp2fjo/2aw2AwkJycTEBAAHZ2dly9epWUlG3k5j5Pz5492/RatkKn07F48W/Yvn0dX3+djovLAJYsWWLpsNrVhx9+yN69e2tWmnnttdcYO3YsK1asqDPBSSlJT0/HxcXFpn5PMjMzSU09xLhxE3F3b/8tjxrTkT87ldwUq+Hi4sLLL79MYmIiZWVlxMbGttugkuo3MF9fX8rKRnTY8lnWKjg4mJUrXyQzM5M+ffp0+hpMcnIyfn5+aLXmt8Bu3bpx8OBBHnvssbvKnpOTQ3z8Z+TlfU9ZmZaBA2cxcWJsg6vHWIukpK+4cOFfODq6MGnSFEuH06FUclOsioODAzNnzmy382u1WsLCwjhz5gzu7u7k5eURHh6uVuDAPAClei+9rqB2EqsrmZeXl7N793bS0rZyzz1aoqICKC83snfvVtas2c099yxhxIhRVv1BYNy46Tg4uDFsWISlQ+lwKrkpnV52djZZWVkMHjwYjUbDCy+8QHJyMjt27GDhwoWMHTu20/e3KbdzcHAgNzcXLy8vhBDk5ube9QHn5MmTZGV9xhNPBOLsbH7OycmOGTMCiIws4qOP3iM4OJTu3bs3er1z587x8ccf07NnT+6//368vNp/QAWAn58ffn6LOuRa1kYlN6XTklKyZcsWtm7disFgIDAwkMcffxxvb2/Gjx/P+PHjLR1il1FeXk5S0i5ycq4wadIcvL29LRrPc889x7p16zh//jwAAQEBLF++vKYf1sXFBZPJRM+e+prEVluvXi44Of3UpAFPBoOBVatW4eDgQEZGBsXFxTz//PNtXibldiq5KZ2W0Whk8+bN+Pr6otPpOH/+PGlpac16Y62oqKC0tNQqOuNtkZSS1NRjJCauJyTkJ0JCdHzyyfeEh09n8uQZNSNjO1pgYCC/+93vOHz4MJWVlYwaNYr8/Hw2bnyfrKxvqaiwx9V1MN7erV9kW0qJwWDAy8uLioqKBhfDVtqOSm6KzTKZTI3OT6o9h6klc5kOHjzAxYtpPPTQUy2KsSvLyMggPv6faDSnWby4B76+gQAMGVLJvn3bWbNmL9HRixk5coxFmoXt7OwYOXIkUD1F5L8ZN66QBQsCKCkxsH59PEeOFDBvXt8WX6OgoIAdOzZjNGaTkpJBSckNRo0aRmFhYZcfxNTeVHJTbE5RURGbNm1i//79zJkzh9jY2JpRb7VpNBqmTZvGrl27MBqN9OrVi379+jXrWmPGjGP48Mi2Cr1LyM/PZ9euLVy+vJtp0xwZNCjotkEXDg5aYmP9iYwsISHh/zh8OIG4uJ+1+Qa1zVVWdovo6CAA3Nz0RES48+GH2eTkFOPldXsN89y5XIqKBDqdrt7zlZaW8s47/4/Q0KvMn5/HwYOlXLyopXdvJwwGQ7uWRVHJTbFBKSkpfPPNNwQEBLBhwwaCg4PrTFpCCJYuXUp0dDRXrlxh5MiRzR4VqdPpGnwD6+yuX79OUtI3BAT0JypqZKM1rLKyMt5770VGjizi3nv9sLev//gePZxYsiSIc+dy2b79vyko+CWRkXd/kKisrKSwsBAPD49Wl6cxUsqaROzp6UmfPuP54INSBg++ycSJPhQVVZCQcIOffvLnvvuebnB9xNzcXC5cSCE8vBS9Xs/QoSaOHDEQEhLT5vM3lbup5KbYnOo3oOqk09i2TQEBAWpvsmYqLi5m9+7tnDnzDWPHCs6c2cfhwzuIi1tKcHBwva8rLy/H3r6ISZOaPqUgNLQ7164V1tsXtXXrBk6c2MMTT7zerqMMAwMjeP/9I8TGdic3t5Tdu8uZOHEJERFj2LMnnrff/prKSns8PCKZPHkKISEhDZ4vIyOD69dz0Ou9cXLSkp8vcHFxYM+ePSxYsKDdyqGYqeSm2JzIyEhOnz7N0aNHmTlzJn37trxPxNrUrjk05dizZ89SUJBHRERUm/ZbffbZWnr0SOXJJ/1wcNAyZozk7NkcvvjiFZYte6VDRzv27dufkpLCdt0lQgjB8uVPc+LEcf71r4/p1i2URYsW1SwTNWvWfYwdO4lNmz7A1fV7du06gpS/ZtCgQfWe083NDZOpL+vW3aRvXyNnzjji5TWk0Q9jSttQyU25TWlpKUlJSfTs2ZNhw4ZZ5QRVDw8PnnrqKUpKSiw22q6tlZeXs3PnTrZt28aUKVOYOXNmg2W7ceMG8fGfUVh4CHd3ycGDAcTGLmuzRabLyvIYMcILBwfzW4QQgvDwHvTseYWysrI2uQaY++dOnjzJyZM/0atXTp3HDBsWRUBAMKWlpY3uTF4XKSWVlZWNNi8LIRg6dBhDhgyt+b42T09PTKYyAgIcKCkp5tixo6SmJjFu3HSCgoLqPJ+Xly9eXsPIzs6lXz9vTCYT+fn5zS6D0nwquSk1TCYTf/zjH8nMzERKyezZs622+UQI0WkSG8B3333HJ598gp+fH1u2bMFkMrFoUf2Tbz/++C1Gj85lxAh/dDoN587l8sUXr/PYY2/YRH9OeXk5P/74I+np6Wi1WgoLC/nqq79jMBi49957a0YSlpWVkZS0i9TUzQAMHTqHe+6ZhoODQ5OvtXfvDvbt28Lzz7/ZpCWzGvpAFxe3jB07NlBerqOiIoHoaGc2bDjG00//+a6aZc+ePXF3dycrK4vevXtz8+ZNKioqmDt3bpNjV1pOJTelhtFoJCsri8DAQG7dukVWVpalQ2oW85yqVE6dOkVMTIxNLXBrMBhwcHDAyckJV1fXRmtHxcU/kZl5i9deO0ZkZAgTJ4bg7l7SpqPwiooqbvu+stJEWVllq8+bm5vLgQMHMJlMNQsXOzpW4uXlRWJiIklJSfzqV7+isLCA3bvXExaWx+OP+yAE7N69idWrdzFx4lIiIiKbNL3D3z+YyMiJ6PX6Vsfu7+/PI4+8QFpaGikpR/D1dUWjycVo/Pd8OCklJ0+eZOfOdYSG5uLo6EBm5g2GDRvG4sWLLT6BvatQyU2podFoGDJkCKmpqWg0mgb7E6zRX//6V44cOYJWq2XPnj2sXLmSqKgoS4fVJIGBgej1ei5cuIBer2fAgAFVgzPsb6tJGI1Gvv8+mVOnkomIsOPJJ11ITj7DmjWXyMnpedubbGuMGzePTZveZ9Cgy0yc6MOlS3ns2FGEt/eEVm9Xkp+fj9FovGukYfXalidPnuS991YxcGA5S5Z44e0dWHPMvfcGEBVVSHz82xw+HM6MGcvw9/dv8HrBwSEEBzc8+KO5wsLCSE+fw7p13zFu3EO4ubnVPHfq1Cn27n2DBQu64+cXzLlzt9iyxciCBQtUYutAKrkpNezs7Hj22Wf54Ycf6NatGz4+PpYOqVlSU1MJCjLPqcrMzOT8+fM2k9zCw8NZtWoVR44cITQ0lNOnU3njjSfo02cwcXEP0Lt3b86dO0d8/Do8PTN49NHueHn9hFZrZMoUZzw98/jii0I2blzDnDkPNziisSmGDBlGSMgq9uyJ589/3oaHRxCzZ/+qQwbvXLlylpiYAH7+84g6mwi9vV156CEXUlMz2LTpHZ555rV2j+lOer2e2bPvZ/bs++96rqysjKAgLf7+5lVtwsK64+1dQnl5eUeH2aWp5KbcRgjBgAEDLB1GiwghKCkpwcnJidLSUpvbXdnNzY0JEybw17++SN++13j6aW/OnDnN+vW/w8VlAJWVx4mLcyM0NAiTKYBr166wbdvXODrqGDhwHC+/HEx6ej5fffUHQkLuZebMha2Kx8nJiZkz5zNhwjScnZ2b9PO0t7envNyZvXuvMG6cDzpd4yM4r10rJS3NVDMH0WQy4ePj0mDflxCC4GBP9uyxzoRhMskGv1fan2399SsWdfXqVT744H/YsGEtubm5lg7nLitXrqSsrIz09HSGDBnCpEmTLB1SsxmNRkpLM5kzJxA3Nz0jRnjTr5+Jc+eOsnRpb0JDzSvQ29nZ4ecXwKhRsURGxhIWNgC9Xk+/fj148MFepKcfa7OYXF1daxJbcXFxg8c6Ojryi1/8kZycCaxefYWTJ2/UO/Q9P9/AN9/8xJdf2uPoOMEiTXYFBQVkZ2e36Tl9fHw4c8adbdsukZlZyGefpXPrVq8O2wlAMbNYzU0IEQf8BdAA70spO75tQWkSk8nEli0buXhxB1OmOFBSYuTvf/+eESMWMmlSrKXDqzFixAgGDhxIVlYWffv2tcppDE0hpbxtvlt1bqirPHU1P7ZXuTdt2sSWLVuYNWsWCxfWXyvs1q0bCxcu5/LlicTH/5Pvv/+BuDgvfHzMIyCl1HDoUCEnTpjo2TOSAQP8apKn+UOTxMWl/ffXu3jxIqtWraKsrIwHH3yQmJiYNjmvj48PTz75Kvv27eTjj3czevRDzJs3vl1WujlwYB/Hj+9l+vTlBAYGtvk4muyhAAALoUlEQVT5bZlFkpsQQgOsAaYBV4FDQoitUsrTlohHaVhhYSHnz+/gl7/893JKAweW8fbbn1tVcgNzU1pr+5ssSavV0qvXID744ASTJ3vyww/5/PijC66ulu9BOH36NGVlZZw8ebLB5FYtICCARx99gdTUY/zzn+sJDb2Ej489e/eCn99TGAyZZGdnU1ZWhpSSGzduEBAQQEzMGIYObXyPtNbKzMykuLgYd3d30tLS2iy5gbkGGxc3m7i42W12zrocPLgVF5ezpKX1V8ntDpb6ixkJnJdSXgQQQmwE5gAquVkprVZz2zqBde1xpbSenZ0dDz/8HKmpx9i8+V+EhETzxBNxrF37oqVD46GHHmLfvn1MmDChya+xs7MjIiKSAQMGkpS0i7NnM1iyZC4+Pj5UVlZy4MABPv30UwAeffRRxowZw9tv/6a9inCb4OBgfHx8yMvLa1aZrEls7M85eTKF0aNtrwm+vVkqufkCV2p9fxUYdedBQojHgMeARof7Ku3LYKiktNSAo6O5aaWw0Do78juD6oQQEWFduxH06dOHJUuWtOi1Dg4OxMTMuu0xrVbLhAkTiIqKQghRs/qIs7MXSUmnmDrVt94PUSUlBvbuzcLJqeUrsnh7e/Pyyy9TWVnZopVPrMHAgYMYONC2pux0FEsNKKmrU+CuXmcp5VopZZSUMkp1xlqOm5sbAwfOYfXqLA4dymTfvqusXfsTEye27I1OUWpzcnK6LbksXfoUev19rFmTTXLyNYzGf+92bTSaSEnJZPXqLHS6uSxf/myrrq3T6Ww2sSkNs1TN7SpQe9nwPkCmhWJRGiGEYObM+URGjmXnzs9xdHRlxYo5DW73obQtvd6dQ4fSueeePg1uI1NeXsnhwzfQ6/t3YHRtq7q/KjJyDAkJn3PkyEFiY92wsxPEx+fj5jaShx5aaFMr0CgdT1hihWohhBY4C0wBrgGHgAellKfqe01UVJQ8fPhwB0WoKNbFvAHoVjIyEpk61ZFBg3reNipSSsnx49kkJpYTHBzDlCmzOsVOz1JKzp07R0LCeqQ0ERv7M8LCwmx2JKzStoQQR6SUda7UYJHkBiCEmAH8L+apAP+QUr7S0PEquSmKeY+w7ds/Qav9genTzcPrr1zJZ/v2XOzsBjN9+oOtXh7LGlW/T6mkptRmlcmtuVRyUxQz8wLRx0hMXE+3bjcpKOjF1KnLGDx4iHrzV7qUhpKb5SfPKIrSLEIIhg+PYMCAgZw9e5bw8PCapasURTFTyU1RbJRer2fw4MGWDkNRrJJaW1JRFEXpdFRyUxRFUTodldwURVGUTkclN0VRFKXTUclNURRF6XRUclMURVE6HZXcFEVRlE5HJTdFURSl01HJTVEURel0bGZtSSFEDnC5jqd6ADc7OJz21hnLBKpctqQzlglUuWxJU8oUIKWsc7NPm0lu9RFCHK5v4Uxb1RnLBKpctqQzlglUuWxJa8ukmiUVRVGUTkclN0VRFKXT6QzJba2lA2gHnbFMoMplSzpjmUCVy5a0qkw23+emKIqiKHfqDDU3RVEURbmNSm6KoihKp2NTyU0IcUkIkSaESBVCHK56zFMIsVMIca7qfw9Lx9lc9ZTrJSHEtarHUoUQMywdZ3MIIboJIT4XQvwohPhBCDGmk9yruspl6/cqvFbsqUKIAiHEM7Z+vxool63fr2eFEKeEECeFEBuEEA5CiCAhRErVvfpUCGFv6Tibq55yfSiESK91r4Y1+Xy21OcmhLgEREkpb9Z6bBVwS0r5mhDiN4CHlPIFS8XYEvWU6yWgSEr5pqXiag0hxEfAt1LK96v+0JyA32L796qucj2DDd+r2oQQGuAaMAp4Ahu/X9XuKNfPsdH7JYTwBfYDA6SUpUKIz4BvgBnAJinlRiHEe8BxKeW7loy1ORoo10TgKynl5809p03V3OoxB/io6uuPgLkWjEUBhBBuwATg7wBSygopZR42fq8aKFdnMgW4IKW8jI3frzvULpet0wKOQggt5g9XWcBkoDoB2Oq9urNcma05ma0lNwnsEEIcEUI8VvVYLyllFkDV/z0tFl3L1VUugCeFECeEEP+wsSahvkAO8IEQ4pgQ4n0hhDO2f6/qKxfY7r260yJgQ9XXtn6/aqtdLrDR+yWlvAa8CWRgTmr5wBEgT0pZWXXYVcDXMhG2TF3lklLuqHr6lap79WchhL6p57S15DZOShkBTAeeEEJMsHRAbaSucr0LBAPDMN/stywYX3NpgQjgXSnlcKAY+I1lQ2oT9ZXLlu9Vjapm1tnAvywdS1uqo1w2e7+qEvEcIAjwAZwxv2/cyXb6m6i7XEKIpcB/Av2AEYAn0ORmcZtKblLKzKr/bwBfAiOBbCGEN0DV/zcsF2HL1FUuKWW2lNIopTQB/4e5rLbiKnBVSplS9f3nmJOCrd+rOstl4/eqtunAUSlldtX3tn6/qt1WLhu/X1OBdClljpTSAGwCxgLdqprzAPrQyiY9C6izXFLKLGlWDnxAM+6VzSQ3IYSzEMK1+msgBjgJbAWWVx22HNhimQhbpr5yVb+pVJmHuaw2QUp5HbgihAivemgKcBobv1f1lcuW79UdFnN7051N369abiuXjd+vDGC0EMJJCCH499/WHmBB1TG2eK/qKtcPtT5cCcz9iE2+VzYzWlII0RdzrQbMzUP/lFK+IoToDnwG+GP+AS2UUt6yUJjN1kC51mNuNpHAJWBFdf+HLagasvs+YA9cxDxCzQ4bvldQb7n+ig3fKwAhhBNwBegrpcyvesym/7ag3nLZ+t/WH4AHgErgGPAI5j62jZib7o4BS6tqOzajnnJtB7wAAaQCv5BSFjXpfLaS3BRFURSlqWymWVJRFEVRmkolN0VRFKXTUclNURRF6XRUclMURVE6HZXcFEVRlE5HJTdFURSl01HJTVFaQZi3K+rRgtd9KIRY0PiRNccHCiFaPdm4KdcVQjwkhFhd9fVcIcSA1l5XUTqaSm6KojRkLqCSm2JzVHJTlCYSQmyu2rnh1B27N1Q//7Oq1cuPV62CgRAiQAiRWPV4ohDCv9ZLJgghDgghLlbXpoTZG8K8YWOaEOKBJsYWKIT4VghxtOrf2FrnWy2EOC2E+JpaK/vXrnUKIaKEEHvvOOdYzAsOvyHMG0UGCyGerjrXCSHExmb9ABWlA2kbP0RRlCoPSylvCSEcgUNCiC+qnxBCDAT+C/MODzeFEJ5VT60G1kkpPxJCPIx5qa7qvba8gfGYVz3finkh5vmYl4YaCvSouk5SE2K7AUyTUpYJIUIxr6UYhXntxHBgMNAL8zqE/2hKYaWUB4QQW6m1WaQwb1oaJKUsF0J0a8p5FMUSVM1NUZruaSHEceAg4AeE1npuMvB59W7qtdZgHAP8s+rr9ZiTWbXNUkqTlPI05sRD1fMbqlatzwb2Yd7uozE64P+EEGmYt3apbkqcUOt8mcDuphe3TieAT6q2I6ls7GBFsRSV3BSlCYQQEzFvyzFGSjkU88KuDrUPoWl7aNU+pvbCtuKO/5vrWSAbc40vCvPCznVds7ZK/v0e4FDPMXeaCawBIoEjtbZZURSropKbojSNO/CTlLJECNEPGH3H84nA/VUr6VOrWfIA5l2gAZYA+xu5ThLwgBBCI4Twwlzz+r6J8WVV7VG2DNDUOt+iqvN5A5NqveYS5iQFcF895y0EqrdksgP8pJR7gOeBboBLE2JTlA6nkpuiNE08oBVCnAD+G3PTZA0p5SngFWBfVdPl/1Q99TTw86rXLQN+2ch1vsTc9HcccxPi81X7yDXmHWC5EOIgEIZ5l/Dq850D0jDvQL2v1mv+APxFCPEtYKznvBuBXwshjmFuhv24qunzGPBnKWVeE2JTlA6ntrxRFEVROh1Vc1MURVE6HdUZrCg2RAgRC7x+x8PpUsp5lohHUayVapZUFEVROh3VLKkoiqJ0Oiq5KYqiKJ2OSm6KoihKp6OSm6IoitLp/H+QCG94H9R4egAAAABJRU5ErkJggg==\n", 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" ] @@ -1487,7 +991,9 @@ " countries[\"alcohol_adults\"],\n", " s=countries[\"population\"] / 1e6,\n", " color=countries[\"world_4region\"].map({\"europe\": \"blue\", \"asia\": \"yellow\", \"africa\": \"black\", \"americas\": \"red\"}),\n", - " edgecolor=\"black\"\n", + " edgecolor=\"black\",\n", + " marker=\"h\",\n", + " alpha=0.5\n", ");\n", "\n", "# Popisky os musíme doplnit ručně\n", @@ -3303,20 +2809,20 @@ "
\n", " \n", " \n", - "
\n", + "
\n", "