diff --git a/lessons/pydata/eda-univariate-timeseries/info.yml b/lessons/pydata/eda-univariate-timeseries/info.yml index efd2e4114b..5f69919974 100644 --- a/lessons/pydata/eda-univariate-timeseries/info.yml +++ b/lessons/pydata/eda-univariate-timeseries/info.yml @@ -1,4 +1,4 @@ -title: EDA 3 - Analýza jedné proměnné a časových řad +title: Analýza jedné proměnné a časových řad style: ipynb attribution: Pro PyDataCZ napsal Jakub Urban, 2019. license: cc-by-sa-40 diff --git a/lessons/pydata/pandas_types/index.ipynb b/lessons/pydata/pandas_types/index.ipynb new file mode 100644 index 0000000000..556743a1f6 --- /dev/null +++ b/lessons/pydata/pandas_types/index.ipynb @@ -0,0 +1,7310 @@ +{ + "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, které jsme si pouze prohlíželi.\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": [ + "# Obligátní import\n", + "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 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)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "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 akceptovaných výjimek tohoto jinak uznávaného pravidla, zejména když se tabulka upravuje jen v úzkém rozsahu řádků kódů.\n", + " \n", + "`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.\n", + "\n", + "Mimochodem, kopii tabulky můžeš kdykoliv vytvořit metodou [`copy`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.copy.html) - to se hodí třeba při psaní funkcí, kde se vstupní tabulka z různých důvodů upravuje." + ] + }, + { + "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", + "# Objekt `planety` zůstal nezměněn." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolobezna_poloosaobezna_dobamesiceje_nezdrava_tycinka
jmeno
Merkur☿0.390.240False
Venuše♀0.720.620False
Země⊕1.001.001False
Mars♂1.521.882True
Jupiter♃5.2011.8679False
Saturn♄9.5429.4682False
Uran♅19.2284.0127False
Neptun♆30.06164.8014False
\n", + "
" + ], + "text/plain": [ + " symbol obezna_poloosa obezna_doba mesice je_nezdrava_tycinka\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 True\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" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety2 = planety.copy()\n", + "planety2[\"je_nezdrava_tycinka\"] = [False, False, False, True, False, False, False, False]\n", + "planety2\n", + "# Ani teď se původní `planety` nezmění" + ] + }, + { + "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": [ + "Pro hodnoty nového sloupce lze použít i jednu skalární hodnotu (v praxi se ale s touto potřebou nepotkáme tak často) - stejná hodnota se pak použije ve všech řádcích:" + ] + }, + { + "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
\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" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety[\"je_planeta\"] = True\n", + "planety" + ] + }, + { + "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 coby nový řádek vložíme pomocí indexeru `loc`, který jsme již dříve používali pro \"koukání\" do tabulky:" + ] + }, + { + "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♇39.48247.945True
\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": 7, + "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": [ + "**Úkol:** Zkus přidat Slunce nebo nějakou zcela smyšlenou planetu." + ] + }, + { + "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 statutu:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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
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" + ], + "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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety.loc[\"Pluto\", \"je_planeta\"] = False\n", + "planety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**⚠ Pozor:** Podobně jako u slovníku, ale možná poněkud neintuitivně, je možné zapsat hodnotu do řádku i sloupce, které neexistují!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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": 9, + "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 (počet řádků a sloupců) jako oblast, do které přiřazujeme:" + ] + }, + { + "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
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": 10, + "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`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html). 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 \"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):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety = planety.drop(\"Pluto\") # Přidej axis=\"rows\", chceš-li být explicitní\n", + "planety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol:** Zkus vytvořit tabulku bez Uranu a Neptunu (jedním příkazem)." + ] + }, + { + "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": 12, + "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
\n", + "
" + ], + "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": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety = planety.drop(\"je_planeta\", axis=\"columns\") \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` (tento argument lze, bohužel, použít i mnoha dalších operací):" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Jen na vlastní nebezpečí\n", + "\n", + "# 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", + "Následující kód (nemusíš mu rozumět) stáhne potřebný soubor a uloží ho v místním adresáři. Alternativně ho můžeš stáhnout manuálně z [https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/countries.csv](https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/countries.csv)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Soubor countries.csv už byl stažen, použijeme místní kopii.\n" + ] + } + ], + "source": [ + "# Nutné importy ze standardní knihovny\n", + "import os\n", + "from urllib.request import urlretrieve\n", + "\n", + "# Seznam souborů (viz níže)\n", + "zdroj = \"https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/countries.csv\"\n", + "jmeno = zdroj.rsplit(\"/\")[-1]\n", + "\n", + "if not os.path.exists(jmeno):\n", + " print(f\"Soubor {jmeno} ještě není stažen, jdeme na to...\")\n", + " urlretrieve(url=zdroj, filename=jmeno)\n", + " print(f\"Soubor {jmeno} úspěšně stažen.\")\n", + "else:\n", + " print(f\"Soubor {jmeno} už byl stažen, použijeme místní kopii.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A otevřeme ho pomocí již známé funkce `read_csv` (Poznámka: `pandas` umí otevřít soubor i přímo z internetu, ale raději použijeme místní kopii, aby ses mohl/a k práci vrátit i off-line)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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
\n", + "

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": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Místo `set_index` vybereme index rovnou při načítání\n", + "countries = pd.read_csv(\"countries.csv\", index_col=\"name\")\n", + "\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": 16, + "metadata": { + "scrolled": true + }, + "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": 16, + "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": 17, + "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": 17, + "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 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 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." + ] + }, + { + "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 `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)." + ] + }, + { + "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": 18, + "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": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[\"year\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 123\n", + "2 12345\n", + "dtype: int64" + ] + }, + "execution_count": 19, + "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": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 123\n", + "2 12345\n", + "dtype: int16" + ] + }, + "execution_count": 20, + "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 (a dostaneš mnohem menší číslo, než jsi čekal/a):" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 123\n", + "2 57\n", + "dtype: int8" + ] + }, + "execution_count": 21, + "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": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 123\n", + "2 123456789012345678901234567890\n", + "dtype: object" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Toto vyhodí výjimku:\n", + "# pd.Series([0, 123, 123456789012345678901234567890], dtype=\"int64\")\n", + "\n", + "# Toto projde, 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 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." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol:** Zkus vytvořit `Series` s datovým typem `uint8`, obsahující (alespoň) jedno malé záporné číslo. Co se stane?" + ] + }, + { + "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": 23, + "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": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[\"bmi_men\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3.141593\n", + "dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Docela přesné pí\n", + "pd.Series([3.14159265])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3.140625\n", + "dtype: float16" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Ne už tak přesné pí\n", + "pd.Series([3.14159265], dtype=\"float16\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol**: Vytvoř pole typu `float64` jen ze samých celých čísel. Co se stane?" + ] + }, + { + "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": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "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": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[\"is_oecd\"].iloc[:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 True\n", + "1 False\n", + "2 False\n", + "dtype: bool" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Vytvoření nového sloupce\n", + "pd.Series([True, False, False])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Jde to ovšem i takto:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 True\n", + "1 False\n", + "2 False\n", + "dtype: bool" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([1, 0, 0], dtype=\"bool\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Úkol:** Co se stane, když vytvoříš `Series` typu `bool` z řetězců `\"True\"` a `\"False\"` (nezapomeň na uvozovky)?" + ] + }, + { + "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).\n", + "\n", + "*Poznámka: V době psaní těchto materiálů se připravuje `pandas` verze 1.0, která speciální typ pro řetězce zavádí.*" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "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": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[\"iso\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 pes\n", + "1 kočka\n", + "2 křeček\n", + "3 tarantule\n", + "4 hroznýš\n", + "dtype: object" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Domácí mazlíčci\n", + "pd.Series([\"pes\", \"kočka\", \"křeček\", \"tarantule\", \"hroznýš\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 dvě\n", + "2 3\n", + "dtype: object" + ] + }, + "execution_count": 31, + "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": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Eva [řízek, brambory, cola]\n", + "Evelína [smažák, hranolky]\n", + "Evženie [sodovka]\n", + "dtype: object" + ] + }, + "execution_count": 32, + "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": [ + "**Úkol:** Co za druh objektu (a jaký `dtype`) dostaneme, když se pokusíme získat jeden řádek z tabulky `planety`?" + ] + }, + { + "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": 33, + "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": 33, + "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`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.astype.html), která jako svůj argument akceptuje jméno dtype, na který chceme převést:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "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": 34, + "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": 35, + "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": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Očekávaná doba života ve dnech\n", + "countries[\"life_expectancy\"] * 365" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "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": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Hustota obyvatelstva\n", + "countries[\"population\"] / countries[\"area\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "řízek 129.9\n", + "smažák 109.9\n", + "dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Jak nám podražily 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": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "Afghanistan 26723 days 11:24:22.989293\n", + "Albania 23411 days 11:24:22.989293\n", + "Algeria 20921 days 11:24:22.989293\n", + "Andorra 9670 days 11:24:22.989293\n", + "Angola 15753 days 11:24:22.989293\n", + " ... \n", + "Venezuela 27092 days 11:24:22.989293\n", + "Vietnam 15460 days 11:24:22.989293\n", + "Yemen 26408 days 11:24:22.989293\n", + "Zambia 20136 days 11:24:22.989293\n", + "Zimbabwe 14390 days 11:24:22.989293\n", + "Name: un_accession, Length: 193, dtype: timedelta64[ns]" + ] + }, + "execution_count": 38, + "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": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 NaN\n", + "1 -inf\n", + "2 inf\n", + "dtype: float64" + ] + }, + "execution_count": 39, + "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": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 14\n", + "1 28\n", + "2 42\n", + "dtype: int8" + ] + }, + "execution_count": 40, + "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": 41, + "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": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 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" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Skoro nikde. A jak jsme na tom u nás?\n", + "countries.loc[\"Czechia\", \"alcohol_adults\"] > 15" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "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": 43, + "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": 44, + "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": 44, + "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 bratříčci, bude lepší, když při kombinaci s jinými operátory vždycky použiješ závorky." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "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": 45, + "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": 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
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": 46, + "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": 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
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
\n", + "
" + ], + "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": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Prťavé země\n", + "countries[countries[\"population\"] < 100_000] # Podtržítko pomáhá oddělit tisíce vizuálně" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "...a samozřejmě kombinace:" + ] + }, + { + "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
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
\n", + "
" + ], + "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": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Chudší země EU\n", + "countries[countries[\"is_eu\"] & (countries[\"income_groups\"] != \"high_income\")]" + ] + }, + { + "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
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
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" + ], + "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": 49, + "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`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html), 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)." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "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": 50, + "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": 51, + "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": 51, + "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`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_index.html) seřadit řádky podle indexu. Jelikož `countries` už jsou srovnané, vyzkoušíme si to ještě jednou na planetách:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "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": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planety.sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pro řazení hodnot v `Series` se použije metoda [`sort_values`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html):" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "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": 53, + "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": 54, + "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": 54, + "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": 55, + "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": 55, + "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": "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": 56, + "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": 56, + "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": 57, + "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": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries.sort_index(axis=\"columns\")" + ] + }, + { + "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": 58, + "metadata": {}, + "outputs": [], + "source": [ + "planety.to_csv(\"planety.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "planety.to_excel(\"planety.xlsx\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Excel ani CSV nejsou formáty pro ukládání velikých dat zcela vhodné (jako alternativy se nabízí třeba [feather](https://github.com/wesm/feather) nebo [parquet](https://en.wikipedia.org/wiki/Apache_Parquet)), pro naše účely (malé soubory, čitelný textový formát) ale budou CSV postačovat." + ] + }, + { + "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`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_html.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": 60, + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "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/pandas_types/info.yml b/lessons/pydata/pandas_types/info.yml new file mode 100644 index 0000000000..1e138b59b4 --- /dev/null +++ b/lessons/pydata/pandas_types/info.yml @@ -0,0 +1,4 @@ +title: Datové typy v pandas +style: ipynb +attribution: Pro PyDataCZ napsal Jan Pipek, 2020. +license: cc-by-sa-40 \ No newline at end of file diff --git a/lessons/pydata/visualization_basics/index.ipynb b/lessons/pydata/visualization_basics/index.ipynb new file mode 100644 index 0000000000..b45bde16d6 --- /dev/null +++ b/lessons/pydata/visualization_basics/index.ipynb @@ -0,0 +1,4434 @@ +{ + "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 (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 Jakea 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 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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Příprava - zdroj dat\n", + "\n", + "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).\n", + "\n", + "Opět kód pro stažení...\n", + "\n", + "Případně můžeš manuálně stáhnout tyto soubory:\n", + "- https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/countries.csv\n", + "- https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/cze.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Soubor countries.csv už byl stažen, použijeme místní kopii.\n", + "Soubor cze.csv už byl stažen, použijeme místní kopii.\n", + "Všechny soubory jsou staženy.\n" + ] + } + ], + "source": [ + "# Nutné importy ze standardní knihovny\n", + "import os\n", + "from urllib.request import urlretrieve\n", + "\n", + "# Seznam souborů (viz níže)\n", + "zdroje = [\n", + " # Země\n", + " \"https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/countries.csv\",\n", + "\n", + " # Česká data\n", + " \"https://raw.githubusercontent.com/janpipek/data-pro-pyladies/master/data/cze.csv\"\n", + "]\n", + "\n", + "for zdroj in zdroje:\n", + " # Pouze poslední část cesty adresy datového zdroje je jeho jméno\n", + " jmeno = zdroj.rsplit(\"/\")[-1]\n", + " \n", + " if not os.path.exists(jmeno): \n", + " print(f\"Soubor {jmeno} ještě není stažen, jdeme na to...\")\n", + " urlretrieve(url=zdroj, filename=jmeno)\n", + " print(f\"Soubor {jmeno} úspěšně stažen.\")\n", + " else:\n", + " print(f\"Soubor {jmeno} už byl stažen, použijeme místní kopii.\")\n", + "print(\"Všechny soubory jsou staženy.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " year population alcohol_adults bmi_men bmi_women \\\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", + "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", + "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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "countries = pd.read_csv(\"countries.csv\").set_index(\"name\")\n", + "czech = pd.read_csv(\"cze.csv\")\n", + "czech.tail()" + ] + }, + { + "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()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.bar.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "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). Filtrování v `query` očekává řadu logických hodnot, tou je i samotný sloupec `\"is_eu\"`:" + ] + }, + { + "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 = countries.query(\"is_eu\") # nebo countries[countries[\"is_eu\"]]\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": 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.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": 7, + "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().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 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" + ] + }, + { + "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[\"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. Zvolíme genderově stereotypní barvy (ono je to někdy intuitivnější), ale ty si je samozřejmě můžeš upravit podle libosti." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.scatter.html) 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": 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", + "countries.plot.scatter(\n", + " x=\"alcohol_adults\",\n", + " y=\"life_expectancy\",\n", + ");" + ] + }, + { + "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 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 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 sebou\"). Hodí se, když máme velké množství symbolů v grafu a chceme jim dovolit, aby se překrývaly." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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=\"alcohol_adults\",\n", + " y=\"life_expectancy\",\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, rozdíly jsou řádové:" + ] + }, + { + "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": [ + "countries.plot.scatter(\n", + " x=\"area\",\n", + " y=\"population\",\n", + " figsize=(6,6)\n", + ");" + ] + }, + { + "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**. K tomu slouží argumenty `logx` a `logy` (podle příslušné osy)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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", + " figsize=(6, 6),\n", + " logx=True,\n", + " logy=True,\n", + ");" + ] + }, + { + "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`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.line.html). 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": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "czech.plot.line(x=\"year\", y=\"life_expectancy\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Samozřejmě můžeme opět vykreslit více sloupců." + ] + }, + { + "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(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": 16, + "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 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:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "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 spotřeby alkoholu. No pojďme to zkusit:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcohol_adultslife_expectancy
name
Afghanistan0.0358.69
Pakistan0.0567.96
Kuwait0.1079.96
Libya0.1075.47
Mauritania0.1170.57
.........
Marshall IslandsNaN65.00
MonacoNaNNaN
MontenegroNaN77.35
San MarinoNaNNaN
South SudanNaN60.72
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193 rows × 2 columns

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" + ], + "text/plain": [ + " 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]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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T3tLMebypzQPk65woVXy0rRW/emE9LrjvAzRHvfeSHCwY8AE820xMppvng4GXF4CB5xNCBu4iofCSArs5ZgVt892xpGcp5pxZo3DEuCGIO+x3f5d9MwqnPcwcXQtAzfxUKPDJrg4AwIGuGBprVamiL5kuoQQs454/tQHtPQlsO9Btuy9Fodi6vxuvrG3CztbMAvHTK3YDUGWnzxOaOlXisLmlC1+99wPsbu8t8ogKiwEfwBVKs2J88TyWoGUSSlceGXg+oQg0cDcJpSVqMPCEYFKCUmpaxg1dDgHcOoTz5ozBrf85A6GA+HROphR09CbSvnP2l5OEUqfJJAzxpIJESkFnXxKjatUKdiIJJSCbN1odUQOr0039QHccSUWdXH9kqffmB23dcbz1mcrAewcoacgW7Jz60+VHYn9XDOcvWoJt++1vkoMNAy6Ab2ruwq9fXKdrkkqW1QgNCSUPGrgmoQzUOhlUwMCjuo3QjoFzEorg5nj9o8tx5C2vuUoIbM1oXxIp6szAvU6TMjuk9cmAHafdJCaAtP0nUgraNBlFZ+D8JCYL4Jb1yrRzwinAMr97TVkQj3+003P7uudX70UiRbFgWgNimvT0eUFztA8VIRnHT6rHY1cdjd5ECufduwSf7Yu6rzwIMOAC+KUPfYh7396iPzrRDKoRxpIp/aJgj7r5ONfLQyoD744NTDbEbmptPQIN3OazNjFwwSTmC6v3AQA6BUkxPFhQZQzcGgizAdOtrePy8tUH5fQAzm5sI2vKQAgQ4wJtSmESivnSKvcQwPd1qAH8upMmor0ngX+u9OYdf2b5LkxuqNIbe/d+jvqWtkRjGF6t3kgPG1WDx68+GgTAV+9bgtWa3DWY4SmAE0K+Qwj5lBCyhhDyGCEkQgiZQAhZSgjZSAh5nBASct9S7mABmDEjhXqvkPeDv6/Gt/6m1j9hTCk/LpT8T2Iy5CNpg23ygKZrU0pdJRSegTv1KPU6LxDtSyCZsnOheNqEjtZujYHbuGOcJJR0Bk7Rqn0udRUhhAMS+jhmn0iJJZQyzU7a4xBcm7RJuLNnjcLkhiosfn+b6/e7/UA3lu9oxzmzR6EsxOZeBuaTXzZojsZQX2mkpU9qqMKT185DRSiAi+7/AB9tay3i6PIP1wBOCBkF4AYAcyilhwGQAVwA4P8A3EkpnQSgDcAV+Rwog3WSKJNU+qZoH3a1qZMc7HGa5qHekGEjzP+F5HbsikLx039+ih0ZuBPYJhkD74mnoFBAIvaOHzcGzuAWwNnWu2JJKC4SilcDP2PMaRq4h9PGyqQTKQUHuo0AHgnKYheKZT1DQrE/J5o6+iARYFhlCJccMw5r93ZiucCDzuOZFbtBCHDO7EaUB91Z/mBDSzSG+mpzXZFxQyvw5LXzUF8VxiUPfoh3Nw7exCavEkoAQBkhJACgHMBeAP8BgFU2/zOAc9w2klJozhN7/ISlWlLRu42QUtWiBvASSv408O4CXEhuE7hr9nRg8fvb8M3HlnveJmN9B7rjJvZdFQnaMsL9XTFdbnCqh9Kb8Pb9R/uSSOaYEMTQZiOhsLuFU7asSANv5QN4wBzAdQnFwsDLQ+7OpH2dfRhWGUZAlnDOrFGoigSw+H37yUxKKZ5dsRvzDhqKkTVlxk3icyah8AycobG2DI9fMw/jhpbjisXL8Mqn+4owuvzDNYBTSncDuB3ADqiBuwPAxwDaKaXsatwFQNh7ihByNSFkGSFk2dq9nZj5s1dyGjALDhQGU2Qp8e7HAr1BAAt8+Zju0R+XCyCheH36yETrZze1eFJBTzylT2DWlAUdJJS43ghY5EJhcGXg2va7YkkoCoUspZ+iNMNvzS6A64k8DutaNfh40mDgQ8qDiAQlkwtFl1AkawBnE9tOATymf4YV4QDOnzMGL67ei2abYl4rdrZj24EenDNbvfTKPOxjMIHVxhleLa7sV18Vxt+uPhqHNlbjv/66HP9YubvAI8w/vEgoQwCcDWACgEYAFQBOFywqvKoopfdRSudQSucAuXd0MVLgqYk9e9kuBdUvtriLC8WrA0AIjdF1FWASM9ekGRH4TbZ2x/UJzJqyoPBzZqx0ZI2a2GLn9gC8B5cuxsCdFBRPW4KepZc+Lvd1JYmYlJpEiqK1O4ba8iACspQmoRg2QvOlFQ5IIMTFhdLRh4ZqIw1+4dHjkFQoHv1QnMH5zPLdCAcknH7YCAD43EkozZqRYXiVfemA2vIQ/nrlUZgzbgi+/fhK/M3msxyo8CKhfAHAVkppC6U0AeBpAMcAqNUkFQAYDSD/5dZgXHQqA+cCuFcGnuZCSV9vc0sXpv3kZWxoys2KVAoaeDaglOpPEa3dcV1CqS4L6O/zMFwZ6oVkvanwf7sFF8aKDR947kYpNolp5/13k9J5Np1IKWjrTuj+8HBQNkkWCUWcyEMIQbllWSv2dfZhBBfAxw+rwEmT6/HXpTvSkpTiSQXPr9qDU6c2oErzmHuRaQYTWPZvfZVzbe3KcACLL5+LEybV46anV+PB97YWYngFgZerYweAowkh5UQVC08BsBbAmwBY5fRLAdg3tssDVNnE+NsbA0/XwEVxf2drD1IKxd6O3FJzs9HANzRF8f6m/Z6Xz6YSoxOuengZNrd0Y2ilGqBau+N6FmZNmRoorB81m8BkDDxukSp4Tbzb5abGvo9oHwvgooU8HYoOewlFhRuTly0B/EB3TK+DEglIiPHVCG0kFAAoCwVsg2tfIoWO3oQuoTBcesx4tERjeMmi4b69oQVtPQmce7ihXJaF1A+rv4nDga4Ybvr7qoyzQ/MNg4G7N0coC8m475IjcNq0EfjF82vxh9c3Doqyu1408KVQJyuXA1itrXMfgBsBfJcQsgnAUAAP5nGc6eOC2X3iiYlqk5iUUv1CEzFwdpGlcgyO2dgI59/5Di56YKn7ghq8MnCvuvGra9WiUKyMrCqhmAO4dZ+MCbHEFuskJh/QvT7ed8USWiKP/SnqtYqkbQBniTwuG+IdJXFNLmIMPBKUTan07GlDNPlaFpJsXSjMA85LKABw4qR6jBtajoctnY6eXbEbQytCpjK3zEbY3xLKql0d+NtHO3HuPe9jHVdGt9hgtU+8BHAACAdk3H3RbJw7exTueHUDbn1p/YAP4p6eTymlP6GUTqGUHkYpXUgpjVFKt1BK51JKD6aUnkcp9Z4n3R+g5uDrJdayIBZLKnpQEcU/Fnhz7S5TCB+42xidsgydMIxj4FFdQmEM3BLALQzcGij5zj6ZTGLaMvAMoWvgWSTyAGZHSSJJtQCuBo2yNBuhgoBEhDeF8qA9A2dNK0ZYArgkESw8ehyWbW/Dmt1qYkpHbwKvrmvCmTMbEeQ+IF0D72cXCrthR/sSOP/eJVjKNbMoJlqiMQQkgiHl3lNQArKE28+bia8dPRb3vr0FN//j0wHd8m7AZWIy8C4UwLsGDqgB3JBQ7Bl4rhOEhdAi85U2XR0JIigTtPYYEgqr52H9yFgSD9PArYGST+zJZBJTodTU/5Ih0yNu1QM4FX7fmWjgvYkk2noShoRicaEkFZpmIWQoC9lr4CyNfkRNOps8b84YlAVl/GWJail8cfVexJOK7j7htw/0/3nHbth3XTAb9VVhLHzow5Kw5TVHYxhWGYaUodVUkgh+cfZhuOaEg/CXD7bje0+tGrANqAduALdYB71q4ID6iMlbEK3orwDupvc64aL7P/DE4L2OkemFXkGIymxau1QXSllQRkhje9ab5f5oHBUhWQ/w1qeChElC8egDjyVBKYQBXB+jh6eLpFZ4io2dv5kYDR2cwcshzZ0xpBRqllAsiTxBG9mnPCTbyhssgFslFECVrr58+Cg8u3I32rrjeGbFbhw0rAIzR9eYlgsHJEguTpdswAL4mLpyPHXtMTh0ZDWufeRjPPHRzn7dT6ZojsZsLYRuIITgptOn4LunHoK/L9+FG/62wrGaZali4AZwmHthegrgep0Nox6HWANnDQUKr4EzvL/5AN7x0KzXKwNvjsbw4uq9nvcvETVRpbVHdaFURQI6U02TULpiqK8KIxhQF0hn4N4lFAbGwHPtlsQKWbELnR8LNTJ5HLfBa+B7NK2aTfKmBXBFgWzDwMtDsr2E0hFDRUjWHSVWXDJvHGJJBXe+tgFLt7biy7NHpck0hBCUBe33kS3YRyYRgrqKEB698igcN6ke3//7KvzxrU1F05Htkni8ghCCG06ZhB9/6VC8sHofrv7Lstzsw0XAgA3g1hooniQU7SeblGPbsYIVoXKq6eEFPTn6wL30eMzEhfJBBtolu1iZBl4ZCehs2Fp+YL/2KMsCXcLBRuhUC2RzS5c+MdWlMXARy84kXjD9m010mW4uWTDwPVq9acNGaK6FklRoWho9Q1ko4CihiNg3w5QR1ThqQh0e1mQUq3xi3kf/zr2wGzY7HSvCATxwyRycNbMRt730GX7x/Lqi6Mgt0b6sGTiPK48/CL8+dzre3tCCy/704YAqAz1gA7iaRs9PYno/gfgvSMTA2QWQq77cHU/mxE6slfBEyGSMMZtHxA+2HEBvPGUaqySpQapNc6FUhQN6ILPeLHUGro3XmjCT8OhCOeWOt/WbZk88haSiINdSKEz/ZsHRanH0Al4D79AYPZs4iwRkxJOKfv6xSUwRyoKSrcVvn0sAB4DLjhkPADhy/BCMqSsXLuPE8rMFu0Z4OSsUkPC7r87CZceMx0P/3orvPrGyoBJEUqtJU++QxJMJLpw7Fr/76ix8tK0NX3tgKTp6nKtmlgpKOoB//6lPcP87W7CnvTftxKcUWUgo6s8ujoGLAixj4Llq4Ao1F/vPFEEPDDyTpwTRBbaztQcX3PcBbnp6leV4VQZ+oDuOrr4EqiJBPZhab3r7u1QGzsZrfSowT2KKA5jo0bUvoWQ8QWVFm3YhsuBo0sC1n243ApElkJdQAOPmmEzZT2KWO/jA93X0pXnArTh1agNOndqAa0+caLuMk86eLdi1ZZ2PkCSCn5w5Fd9bMBnPrtyDKx9eVrBKiAe0HqxuSTyZ4OxZo/DHiw/H2j2duOD+D0wVNksVJRXA40kFt/xrLXa29uC9jfvxxLJduOWFdfjPe95P62cIZJGJqf2MxvgAnr6croH3w8x0LhOZXmphu1Yj5A4wJjgexig3NHWZJB+mgXf0JtDek0BlOKBrrvw240kF7T0J1FeF9cBlvamwGX5ZIrbBhe/lyMcJUXDNpBYK84DrGjh3E3PrSs8gCuB1nAsFMG5ASYXa3njLbIKrolA0R90ZeECWcP8lc3DKoQ22yzg5XbIFO8VEnwMhBNeffDBuPXc63tvYgovuX1qQBsOZJPFkggXTRuCBS+dg6/4unH/vEuztKO0WbUUL4JRSLLPU6n125W7c/+5WHH/bm/jag0Yyy/6uGFZYympSiw/cWyKPNonpooH3lwsFyE0H7w8N3BTABU8DvL7Z0mVknjINHAB2tfeiMmJIKPwuD3SrF9KwyrDuvrBOYjLZoqYsaMtAt3JtsPgbl1Nw9cLNW3UNPN3i6KUrPZBeWbAyHEA4oDJvxsD79CJpim0FxfKgjKRC0z6f1p44EimKEf2g55YFZXT25SbdWcHkIaenoQvmjsU9XzsCa/d24iuL3s97g2F2rvYnA2c44ZB6PPz1o9DcGcN5i5ZkVIq50ChaAP9wayu+smgJ1u4xMrvae8R37kSKYv3eqOmkpKBZpdID7hp4dz8G8FwYuBf1wJ2BG7+L9F+2vkyIyWrIGDigsuyqSEAoobAMwvoq1Y8rSyTNRsj+ro4EsLmlC6f97h385YPtpu+Tb/bLuytyrSbb1h1HWVBGVUTNUsxOAzdfJnyfTIOBcxKKnQZu49Nmn6GbhOIFU0ZU45Od7bj6Lx/r1sRckdI1cOflFkwbgT9fPhebW7rxzPL8Vv7LFwNnmDuhDo9edRS6Ykmcd+/72NRcmi3aihbAmRuhg2uxxTNjK6KxpN6MAVDJtCiVPqVQ/PrFdUIGwGf5MQhthLH+mcQEcrMSetm9202GmuQOweM7NdhVM9eUgRCCOi7DrcpGQlmlta2a1lgNQGXPdjZCVo52/b4o/vfZNbjkoQ/1R1S+Ea1sCuA5ulB61MJTufjArQHZFMAD5gqATpBJxCkAACAASURBVIk85Tap7k4e8Ezxwy9OwQ9On4J3NrTgC799G499uCNnNq4/pXmYNZ43cSgqw4Gc6wi5gZ2r+WDgDDNG1+Lxq+chpQDn3/sBPt1Tei3aiq6B83Ukoi7B7lOOrauZmJwLRft96/4u3Pv2Fry3Mb0gFHtkZuVRAedEHqfOMl6RS1MHLxdeymUSk4/vIhcKO0SZEFNXHUKAukojUFVGAvoFzEsoH29vw4jqCBpr1TT6kCylaeDsb5aKDwBXHT8By7a1YcGd7+DZFbuxhQvgfLzM1YXS1hPHkIqgrkubJRRv27FKIkNNDNwsoSRSiq2NsNym2bWeRt8PDDwgS7jmxIl4+dsn4LDGGvzg6dW48P4PcurU7kVC4dFQHe439m+HlmgMNWVBXcrKFyaPUFu0RQISLrzvA3y83blDUqFR9ADO67JubJUvpKPWAzfeY2yZBV9R7OMr3TE4J/LkzsC9Zh6K0B8M3E0DTyk8A7do4DwDjwTB4hK/zeU72vRmuoCqF9sxcD6AX3HcQXjxW8djUkMVvv34SlPvQsltFjMDtHbHMaQ8JLQ4eulKD6Rr4DwDD1smMVMKtbV/WuUWhqbOGCSCnJJSrBg/rAKPXnUUbj13Oj7d04kFv3sHi97enFXKuJ0LxQ4jaiL6TSlfaI725U0+sWLCsAo8+V/HoK4ihIUPLs2oWmi+UfwAnkxh0dub8d9PfKJLG6wWtRVr95oZuCiV3slCxRbvdnGh9KcGbue99gI+UO5s7cGsn7+CLS1dpmWcbjJLNh/Am58163+L9F/+8Zhn4BIBhnCBqjJsJPKwdZo71R6js8fW6ssFZQlJRUFvPIVO7UmHTbRWc1mGFWEZ44dV4Ilr5uHG06aY0s95picifZl8K209WgAPqNsXauCuDNyigXNPJuxcjXEauN0kJtuOlTQ0dRit1PoThBBcMHcsXvvuiTjxkHrc+uJ6nPPHf2csBeguFI8BvKEqgqYCSCj9kcTjFaNqy/DENfMwekgZLlv8Ed5Y31SwfTuh+AE8oeDWF9fj78t36R1sROd/OCBZGLjYB860ddFFrtsIHRh4MqXofun+KHCTaQDn9VZ+bBubo2jvSWB3u9nWZOdC6Uuk8M3HVuDet7dwY0m/uemTmBYNXCIEQVlCtTb5x2disnGxhrs8Aw9qEsrPn1+Lr//pIwBqBT/AKEcLABWaHixLBP910kR88pP5mD6qRtu3Mb5ca6G0aaVfnTRwN1g1cKGEwhqFKIqtjZBtx0oM9nW6e8BzQUN1BPcuPAJ/vPhw7OuI4ay7/43bXlrvOW2cfd/EY7RoqImgORrLa3Zmrmn02WB4dQSPXz0PkxuqcPXDH+Nfq7yXpsgXih/AuaDS1cc6p6QHpcNG1ZgmMWGtB069MHDNRhiztxH2mOpa5H4CZpqdxl/8fIDhJ3t52DHwvy/flZaIIBoLL6HwDJzFRiYXVJsCuPrex9vbEApImNZoFFViEsrejl5s1p4WWJcaPoBb9dSykKwzV9mFgXtFQitkpUooYosjkFkqPQC9lCyQroGnFHsXip7JqlgllD7HtmD9AUIIvjh9JF777gk4d/Yo/PGtzfji79/Fh1tbXdflnUpeMKI6gqRC9d6h/Q1KqcbA8/uZiTCkIoS/XnUUZo+txTcfW44nlxW3oFfRAzivB7LALcounDm61vS3YvGBs2uiV9fA7YNv1CETk/dt94cGnjEDl8UM3C61V3STSSkU972zJe11UQBnAU0mSGPggBHAK8PpmZjLd7RjxqgahALGaRSUJSRTFD3xFNp6EkikFF135gO4CCw+8DZCUV1tr66Kdu0zUycx0wtt6S4Ul8BkDVxDHWyEiZR9EwqdgadEDLwwbLK2PITfnDcTj1xxFBKKgvPvXYIfPbPaNLFvhVMijwgNmrSRr4nMzt4k4kmlYBq4FdWRIP789bk49uBh+N5Tq/BnS7ONQqLoAZxn4E5sdfroatPf1pZqTEpwlFCENkLzMrxvO9diVkDmDDzEMXB+bO0ZMPAX1+zF9gM9qAoHTK+LxsJuAEmFmli+ZGHgVZGAzppTCkUsmcLqXR04nJNPADVIxVOK/nje2h3X98F6atqB3TRkUwB3WMElnrAsTDMDN+cSeNgMrPF4iMBGaExiKraTmDL3+TH0JVJo70mkNXLIN46bNAwvf/sEXHncBDz24Q7Mv/MdvL5OrOvqEorHpyFmh8xXAM9nEo9XlIcCeODSOTh1agN+8s9P8ce3NhVlHEUP4CYG7hDsGqoiptl/tZxsuo3QyfXBN8y1rsfASzC5lpMFMg/gtgzcJoCLGPjjH+3EuKHlmDdxqOl10dMAY6StlsfdNAbOVyOkqqUznlJw+FhzAA8FJCRTiu4GaonGTJmYTmA3Da8auBtYSnddRUh/SsjGGsrGIBFg+qgaHFRfob9naOBeJjHTNfD+9IBnivJQAD8+Yyqevu5YVEeCuOLPy/DNx1akSW8ZSyianp8vJwpL4ilmAAfUFm1/vPhwnD1Lrcp4+8ufFby0btEDuFcGLkkEU0caLDw9lV79qWe5OdgIeQZk/bx5h0quGnhAIsKJQ+d1eA3cXkJhMUJUr2VXWy+mj6pJm0wTHQ9jpAe6zAGcXarDKsOQJYKKkDkTc7nmhz18nFnaCkgESYXqN8KWrhiXiekioWh7NUkojms4Q8zARRKK83ZYAJ/aWI3nvnmc6TjCAbON0GkSU1TN8Y31qktowrAK4TqFwKwxtXjum8fhu6cegpfX7MPpv39XdxABfLkFb99GfWUYhCBvThTWgzXf8wZeEJQl/Pb8Wbhw7hjc/eYm/Oy5tQUtrVv0AC7SwEWQCMGhI6v0v+1qoTgV8hHLKhYNnGPgmfbE7I4lcdSvXtN9ouGAlMUkJsfAuVWtDJwFOWtQppSqle2qI54eeVlAYzVNrNu/9JjxeOCSOZAlogeylEKxfEcbxtSVpV1EAVk9ZvY97I/GkEgpIERl8U5g4zVPYpoPYmdrD37w9Gr3AwPQ2p2ugfPfh9dEHuPJIH1BSSIIBSRjEtMhlZ7dnFnyVXO0D799ZQOOnzTM5OQpBkIBCTecMgn/7+LD0RKNYeWOdv09FpDc5goYArKEYZVhNGXYBcorSoWBM8gSwa++PB1XHDcBi9/fhpueXpW3VodWFD2A8wzVmuAwd0Kd/rtEgEN5Bm7pyGNIKCn9fStEjzdOGnimX8L+rhiaOmO6+yIUkDKexOTdGfzerQFcZ+CWMXb2JtGbSGFETcQmDT3dNgkYTLy2PKhtX123oTqCk6cMN41NoRQfb29Lk08AVcNPKlRPhtrfpRZqCkqSnoloB16qYLAeQjSWxLtalq1bOHHVwD0m8hjjsknQCUi6DzzhkEpvlVB+9a91iCUV/PzswzwHx3xj7nj1mlu92/CKK9Q7+2YYUZ2/ZJ7maB/CAcPiWgoghODHXzoUN5wyCU8s24Vv/W1Fv2Ryu8H1EyCETAbwOPfSQQBuBlAL4CoArO/XDymlL2Q6AD7AWQs/mSeziClgUGpu4sAuCsdMTMH+rRo4W78yHMio2w1g3IAYywtrxf4zARXclID0SUw16FChpxhQdcg1u9MTNhIpilDA+FzjXECTCFBXHkJ7T0Jo32Ov7WrrRVNnTMgaAzJBvFfRP4uWaAyEqE8W5UFvDFxyYOCZoK07jvKQjEhQ1m90QhuhGwMX2Bt58G3VUg4deVhgTykU72/ej2dX7sENp0wqqnxiRU15ELXlQTR19mFTcxckoko+XvVvhobqCHa15aeKX0tUbSBSKjc9BkIIvnvqIagIyfj1i6rP/u6LDtfnSfIBVwZOKf2MUjqLUjoLwBEAegA8o719J3svm+ANmAv5W4Muf8EQoqYH3/aVGfprJgbuQUIRRfC0AK5p4DVlwYwlFPY0waQglYFnpoHz46EOk5js3BVZ0gCVAYmCn/Xz4ZOV6irCwiDKwLa3bJumfwsYeFCWTJa0/V0xtUuNLOnV+OwgYrq5XKOtWhYmoJ5LsqXQFmv2YJf5a4zL/NOKSNCowZ1I2ZeTZa/3JlL432fXYExdGa47yb45Q7FQrvXV/N9n1+Cnmqab6ffQUB3OIwOPFc1C6AXXnDgRvzjnMLy2rhlX/PmjvDa5yFRCOQXAZkrp9v4agJPEwJ807KJmF6RqIxRo4HEHG6GH11gafVUkkPEkJu8FBjQNPMPHKLOzRhsjpQ6JPObt79Oq+zVUR4QMxZp9xwe04VVhxzR19h18tK0VZUEZU0ZUpS0TlAk6OZ/9/q4Y4im1yUEoIGHKiCrcft5M4fZ1DdylGqGxvHNUaetWC1nxY+O/j5U7VZ135pjatHV5uEooQclo6JCyr4XCtPF3N7Zgc0s3fnj6oXllZ9mCNZ7o6E2gJ6Y2l85GQmnvSeSlSbAawIs/gemEhUePwx3nzcSSzQew8MEPba/fXJFpAL8AwGPc398ghKwihDxECBHOwhBCriaELCOELBO97/QFS6YLWdue9jcFNc3ms9/Z3U5czMpdF++NpyARtXGrnQb+qxfW4YF3zYkyu9p6cP69S0yvhTht1CsUBZg5Ws1sZMG8L6GkSTFs2GkSSoc6wdNQHREyRmumKq8JD68O63c04YSd9tL6fVHMHFMjrN0RkCR09qYzcBbUXvr2CfjKEaPTB8bt03zjFi7qCa09Cf2GD2hp/knjeFfubEdjTcTVwucmoZQFZf3mnVKobU0Tdny7tYziGS43jmJBbf2WRF8ihYRCkVK8WwgZGjQrYXMeJjKZhFLq+M8jRuP/XXQ4Vu1qx0X3f5Bm1e0PeA7ghJAQgLMAPKm9dA+AiQBmAdgL4A7RepTS+yilcyilc0TvOzFwkRuBnUfWWiiKhYELxyJ4zSpzd8eTqAgFhHWtGV5b15TW4X3xv7elLZctA9c732gDFt292Q3LepPZ19mLYZWq71kUhK3NBPhjrK80GLhQA+denDFaHHyC2iQmoE6ItmguFC/9PXX5xiUT0yvae+Km3AG11K1xvCt2tGHWWPcg6uRCAYAwp4EnFPumxkwDZ7Wyh3FFsUoJZSEZ3fEUehMpJFMKFJqNhJIfL3hfQn0yKGUJhcfp00fivkvmYFNzF75675J+T27KhIGfDmA5pbQJACilTZTSFKVUAXA/gLnZDMCp6a9IC9UDOMxyQ9KigYtdKOn7SNfAUygLyQjIxJaB98RSadsSacZZMXBqMDj2dNDem37nZuNOZ+BGb0XRXFqaBq6YGbioAzkD/5qdbszLB2PrytHWk0BvImXrzBBtnx+3U+Bw2yIrJWuMzQjgzVGtkuIYd/ueMS6HScykgpSidomym8RkN+amzr6C1LLOFhWahNITTyGZollLKED/Z2Oyej2FrESYK06ePByLL5+LPe29OP/eJf06uZtJAL8QnHxCCBnJvfdlAGuyGYDTJB9P2gwGrv6klJrkD0WXUJwYuLuNsCeRQkU4AFmSbDXw7ngybUui0zsUkIWNhJ2gUEMrZcdkTeJRj139PZ2Bx/SLx4sGzkszvK4oCpxermE+UI+pKweg1rsOeWDgbPteNXAnJFIKolohK4ZgwNDAmc95ticGrkkodpOYAQmxREp3LdndrFhgTyq0pBkkk1B6EykkFPXGlOn3kLcA3lVaHnCvmDdxKB658ii0dcdx3qIlaWWhs4WnAE4IKQdwKoCnuZdvI4SsJoSsAnAygO9kMwArQ2XFgQCzP1dn4NrfCjXLH2mTmA6ZmObX0l0o5SFZyyhMD76UqoWa0vR0wfkd1i7sTEB5CUXbvVVC4WO2dYz7Onr1VGZRwLUWLeLXr68KOxZ48uIO4aWSsVoA39vR64mBsw/RrR64FzAPeJ1pEtPoFrRyZzsCEsFho2qE6/Nwn8RUJRTmCHKrRgiUdgAqC8noiqkFoxgD99qNh6G6LIBIUNL7ffYXjF6YpT2JKcLssUPwt6vnIZ5Ui4jx5bGzhacATintoZQOpZR2cK8tpJROp5TOoJSeRSnNqjiulYFXhvmSo9xALQwclloo1kxMcdal4DXL37wGLrIRxrRHZSs5FyWDhDLQwH/87GosfHCpqRypokso5qAr6gUKqOy6jSuMJAo4fMVBAKZJveFVvISSPkYvj9F8AB8zxGDgXjRwkdbspIE7kUKjEiHHwCVJr4y4Ykc7Dh1Z7ckFok+gO7pQFP2JzW4SMzBAAnh5SNYn3JIpBUoWk5iEEDRUR9AU7d9JzBata1QpP8E4YWpjNZ64dh4CkoQL7vtAd0Jli+JnYibMkz6sezgh4u7kevimVsudKqk4+sAFECXyOGngPTY2RdH5Hc5AA3/kgx14d+N+VULRNXD1vc5ee9bM32QYO2moEQdwWSJpj7QJxSyhsH2Kgj//kl32Iq+Bj6w1WFLQRhfmIaxG6LqWGCwAWSWUREq9Aa/a1e5JPgGMG5eTD7wvmdI99Z4YeIGbEWSCspCsP6kkFNXtlc2TUEN1/3fmYYlh/OT0QMPE+ko8ee081JQFcfH9H2CpxRCRCYoewPuS5gmuSq0EKoGYiZknMY3tJBWKvoRisGwXyyALNGkaeDyFirBsq4GzYldWCUV0fmftQrF0vrFKKHw9c/4mwzq8j6xhGrh528Or0utT8DZCnhWKMzHdr2J+Ao8vkRoMuK9rnaj2uk8R2kQBXFa/j43NUXTHU5jl0cZnPfes0CUUnYG7B/BSnoTjM2ZVBp65hALkJ51+3b4o6vPQfq7QGFNXjievnYeRtWW49E8f4i2u9WEmKPqnkEiZ03QruRrWorKijPmlMXCu/oYd+JAbsjg9GFQNPICgjQZuN0kqZuByFho4IFtuLu091gBu/M3fZPgsTCA9+DVUR9IYOGONVeEAykKy8XkIDsibhGIswwdwO2cGD5HW7LSaU2xnWZY8U2MulBX6BKa3AlJuNsJIQJVQEm4MnFu/lCWUirAhK2XrQgGM7vT9VWJ1ze4OvLq2CV89cky/bK/YaKiO4PGrj8bE+kpc9fAyvLQmcxW6KAH8rLvfM/3NJ+SwinUKNbNaycLO0rvSmy1ydho4C9ysPrRVQumOp1CutfdKCTTwbptEoVw1cAaFpmvgXhm4XlvaZhKTXVA8WNCp1xihow+cl1A8TGJWlwV1u2FGPnDB3EemYJOYrDgXwHzgFCt2tKG2PIjxQ8s9bUuUYMQjrB0jm0C3u1lJEtE/w/rK0p2E40seJBQFKZrd99BQHUEsqfRbFuJvXv4MteVBXHXCQf2yvVLA0MowHr3qaEwfVYPrH12BZ1bsymj9ogTwVbvMRZb4IFTBnTzmTEzGwFVQWFLpKTUl8YgnLCnCmsuFeXCtJLs3nkJ5KKD2dhRp4FrLNWvgt9XAk0pGDCSlGGzH8IHbB3D+KYE1hWadeMQMXCyhME3WWQP3IKFwN0hZInoTB7v0ctH2cylgxdDKFbJiCGr9OlfsaMfsMbWek4SYfGCn+7N9RDV5zclxw4J7KTNwvmpkMkVVCSWLr6Q/Gzss3XIAb29owX+dONG1rvxAQ01ZEH+54igcNaEO333iE/x16XbP6xZdQgHMAZy/+wsvMJ2Bm9dTJRRnuYJSIwGFBXI+tMaTCuIpBRUhGQFJEk5i2jPwdIRkCZSqMsf/vbQeF973geP42HbZhB/bRUdvwnRRsQAetEy0sgvN0GzTA3hHr7k+BWPgXz9ugrZPBxeKh8nFkBa82HgZA87WhZJtV/o2SxIPG8OBrjg2tXRhlocEHuu47HbHrK9d2vfiJBexm3MpuyjKeA1coSZSkQmM1mq5OVEopbjt5c/QUB3GpceMz2lbpYqKcAAPXXYk/mPycPzomTW4X9DTVoSSCOB8nOSz00wauIUFWeuBJxWzA8WO9TK2xKQUnkkzBl8eDkCWiKlSH4NeawVWBp5+grObRDyp4J63NmPJlgOmTiciKJQaGrh2gJ29CdRy7ciYBl5TFjJp4ElLKVPrNceCBl+fIpmiOPqgOiyYNkI9Lo8+cDswBs5ulOyGnEkmplNDB69os6TRA0AwIGF3ey8o9ZbAkzYGmwcp1hezyxMDJwjKxLW9XDFhrdueSClZfQ96Mk+OTpQ31jfj4+1tuOGUSSVZ/Ku/EAnKWLTwCHxpxkjc8sI63PnqBten95II4Dz4L0hYzIrTUKw2Qqc6KNoqOlvSGTi3jZ6EegEaiTwiF4o4UUh0frObRCyp6NLQe1ozAjuYNXD1tfaeOKpNAVwd55DyoJmBU+qoH+uMKGpcUHFLnRK2NdHlarIR2lzPbOwscDPW7iUTU1wLxXU1IVp7EiYPuHUMbhUIeeiSlk0EZ8dqMHD7QUsSwbDKcFaujkKBn8QE1PM3mwDOnDa5SCiKQvGblz/D+KHlOH/O4Ji8dEJQlnDXBbNx3hGj8fvXN+KWf61zDOJFa2nxk398Knydr7EhspPZauAWCcVuElOXUJgGzi3IgnN5SEZAdrERWl4XPc6zya14UsGssbX496YDeOuzZnxx+si0ZRn47icKVfXHjt4EDmkwSrdG+xIgRNXOPtsXRU9cdc4kU84MXNQtPGnp4eikgXt5jGaTw4zFse14YeDsMzS7jxyWd3KhdMfTJimZDj+xviIjBswXUBNBl1B0Bm5/swpIpKT1b8AsoQBqsp0HE1EawgEZdRWhnNLpn1u1B+v3RfH7C2Z5kuEGA2SJ4P/+cwYqwgE88N5W9DhVbC3guEzY0Sou6FIW4lLpBUxM0if4YHGhmG2E4ouN6kFVJKGw9VkmplgDZwzcfRLTYOApPZC9vaElfUELglwiT1c8CYWa3RSdfUlUhgIIyhKaozH88c3N+rGYnSLmQbFHWj69OZE0169mxyW6YL1kSLIbCLtRsu3kIxPTCXYaOODdPmiMyzjnRLBKKEGHu44skZLWv4F0CSWeVDLOxGQYXhXOOp0+kVLw21c34NCR1ThzRmNW2xiokCSCn5w5FdefPBGPLt1hv1wBx+QJZgkF3O9mBq5QmiYfuBWPp5TTwHUbofE+z8BlLYDzgfqBd7fg2RW79W3xEJ3evAbObhRNnTEc6HKe1OEZOCtkVWORUKoiAZ3VLtveCiC9FrWVRVeXBRAOSKZ0+oSimNahNuuqrzkOG4DBcstCAdOxeAvgGgO3lYG8uXkSKQXRWDJdA9cDeGZ1uFnwspNQGClgT2dOTyrjh1VgWqN7/ZViwhrAY0kla8ln6shqvL6+Gf/z5Cd6JUGvePyjndh+oAffXzC5pCWnfIEQgu8tmIKfnTXNdpnS6QqqIRwQByDdCMDNJ/HBNelFQoGadGHaDz+JyTTwcEAPREnFYKgPL9muPzl4SaXnNXDerrixuQtDHVKpeQ2ceWhrOTYZ7UugKhLUA8Wa3Z1QFLU/pmjewBgjSUvmSaQUT/o0YF9OlUdQn8SUtDGwAO7FRgjTOoD5xpjw2OJOb2Zs1cC179xrBqZ1XHZgEkrUg4TyxDXz+i2xJV+wtr6LJRVUhLMLFT8/5zDUV4fx0Htb8fKaffjWFybh0mPGu97Qe+Mp3PX6Rhw5fghOmlyf1b4HCy49Zjwus3mvpBh4SJYg22i4xkXNHmcNF0pQJh5thFRn4Iw1iRh4RUjWx8GzfL70arqEYq+BxzQGPqauDACwqdm5lKSJgfc6MHBtua5YElv2d0PhCmEB4oBrTeZRdXOBhJK1C4XZCFlNG00D9yCiinzg/GpeG0S3dWuFrMrNOvfoIWUYWRPB5Ib0VnBOcJVQgt4nMYHcmlQUAuy7Y8hFQqkMB/CD0w/FS98+AYePG4Jf/msdTv/9u3h3o7OUuPj9bWiOxvD906aU/OdVTJRWAA9Iphrg5mJWmoTCMXAmSzDPdp+LjZB3oThp4GWaCwUwp6rzWZVp1QhdNHBKgVG1ZSgPya4B3NDAjQBucqHEEloANz6s1bvbkbT4dUVjGl4dMdkIEykFQe6phx1X9hKKui0W1JgV0gsD1+uBm47B+N1rg2hZIjhpcr1eDZFh4dHj8M73T864jobhQhEjYpFQvJXOLV3IEjE9CWc7icljYn0lFl9+JB64ZA7iSQULH/wQ1/xlGXYK5sI6ehNY9PZmnDy5HkeOr8ttx4McJRfA7SxkRBup/hI3iRmQCVJanW6nQMFr4Eyf5gMxY/AVIUNf5r3gCZ6BW7YtdqGYNXBZIjh4eCU2t3RhU3MUz6/aI/SayyIJhQvgXX1JVULhjvWTnR2uNkIAaKiySijUNOlmMPC0VU2M3j6VnjFw9XNO6QHcu43QrpiVtf2eHTM7eHglFl8+N80qSAjJysnADtt+EtMsoQwGtwSvg2drI7SCEIIvTG3AK985Ad9bMBnvbNiPL/z2bfz21Q0mC/B972xGR28C/7Ngcs77HOwoqTMtJEsWDVfEwI0JJRZsQrKk2wjtWn0BanCSCMEl88bhpEPq9dcYevREHjEDN3XXyciFomgOEYKD6yuxqbkLL3/ahG88ukJoVeSrEbY7TWJyAXX17g7HRJ7PfnkaAFVC6Y6ndMeEtV+l7gPPUkJh29IDOHtKymQS00YD9yqh9Df4GvQiWCWUbLIWSw28jBJPKv16TJGgjOtPPhhv/M+JWDBtBO56fSO+8Nu38cLqvWiO9uGh97bhzJmNJT/ZWwooSgDnH894BAPEkoWHtN/1S4ma2V1KoehNJPUTz6l5w8/PPgzHHDwMgFlC6Y4lEZCISYtn+6CUmjVwy7ZFlsOIiYGrgWDi8Ers7ejTMzJFF4aVgQdlYppYijIGzq376Z4OxJMpm3kDw/eu16fQrF3JlKWLup6JmTYss0XRJq+c3UBYUDNusl4kFHUZu5ZqTg2w8wmvGjgrs+Cl9nmpoywPDNyKkTVluOvC2Xj86qNRFQngur8uxxd//y4SKQX/feoh/b6/wYiinGnWWW4GJwbOAgafVMFLKCwT02qBMoHyTgdjOwysmQMhRJdQWK0QK1O2XswidhiSjUlMqnm0Dx5eCQDY2KTqcN3W0QAAIABJREFU4KLJIVkiIIRp4HHUlIX0cBlLqPVaeAY+tq4cfQkFn+2Lmhi4iEWzVlTNWpnPeEoxBVdnG6EXBi6WULwwcMPrz+/T+N2qgReK57Kh22ngspYerzPwAa6BA+oTXyggfprrbxx10FA8/83j8IuzpyGpUFwybzzGD6vI3w4HEYpiI7SbpQ8FZNtEFF0f1WuhGOw5yEko5VoasLArPbe+pMsUxvs9Wjs1fowsAFkDtLUaoahsbNjCwCVC9AD+2b6oOg7BZyERdVnmQqkpM76maExl7lWRgP6UMH10DXa09mDbgR4cOrLatB0rGrT05qZonzC4OmrgHlLcDRshk1DMrzvB+t2o+zF+L76EYo9IQDY08EEgodzy5cOwbFsbfvys2qs837JQQJawcN54XHzUuKzLJ3weUZQALpIbAKMEKYMwlV5nzoYGrmZNqvXAWTcRoYRCadpEmUlC4W4AskUDtwYP6/YVwTHxLhSWJTmurhxBmWB3e6/tjYwQtW40k1DMHnA1SPAMfPzQclRFAoj2Jc02QhED5yrEMV+1SAPP1oUypq4cVx0/ASdNHg4gOxeK3Y2i+BKKvX87EpIRjbr7wAcKpoyoNpUtLpSV7/OYsJMLinKm2QXwsCzZdiS3fq8qA1d/D8gSUoqiSihhh0lMcAlBSL8oWUd6wNByWc/JhIVhr93baSq+zpJDvv2FScbxCDTwgCxhgvZ4aHeySoSAEAJK1W481glMAKgKGxq4RAima93V3Tq6V4YDqAwH0NTZp/fDNKfS6x9QGkw3V+HI1WV+9KWputaeiQuFjd2uGqH1JloopuYmoQDGfIe6/OAIQjwZyNYH7iO/cL2qCCGTCSEruX+dhJBvE0LqCCGvEkI2aj89FZgYUR1J81AzhAKS7QSW1aHAT2KGZIIUVTVsFoCFmZgCDdxqI2SToLqNUAtyIvb3ncc/0X9n222sLTOOh3OhMA0cMHRwu4tClVAMHzgfwNnkp9WFwgokBUxPMOLtD68Oo7kzptsizQzcPpEnGxamcE9JbtBvrjY3bpFjpxBwm8QEjHoogLenjYEA/rwYLDelwQbXAE4p/YxSOotSOgvAEQB6ADwD4CYAr1NKJwF4XfvbFbPG1Noy8Nlja231T6v0wSQUQtSTS9HqgVsrqZmOBTQt28+cyJPSy76maeAeW6PxDpuArEpCcc5GCAAH12sB3I6BS8TQwO0YuMUHzp4Y7G6APJgXXNSE16hG6HycXmO5zsBtnEfmbTpr4MWCYV21B1/Dx0vW6UAAf16UwNfgQ4BMz7RTAGymlG4HcDaAP2uv/xnAOV42EAxIph6YDMcePBT/PX+yrQPBriu9RIheeKonntRrGYs1cEFNFT6VPp5EedhchClhI6HYwWqRDMmSpoEbgWmixsDtgqRE1ACeSFFEY0mbAG5m4CGt67vd58djRI3aLTwuYuAOmZjZQJ9o9tTUWP3plk2qv1cgH4pezMpJA+ckFC9PGwMB/I3IZ+CliUwD+AUAHtN+b6CU7gUA7edw0QqEkKsJIcsIIcsAdYaen/BjJwa7GM1tuwSP8Zau9MyxkVQU9CUU3aJo50JhmyTEsOox9MZTKLc04RW5UEQ+drYZvqMQoOrgjIGzQ9MlFFsNXA1cRiGr9G481ZGg7kKh1BivOZHHRUJJCTRw7XPrL8bF16txg1smZrHgJXYxBi6RwTMRxxc58zXw0oTnAE4ICQE4C8CTmeyAUnofpXQOpXQOoFn+uKDJThIiYF+i68Bg4BQpqkoiskSMUrAOEgqo+aagyhTG292xpF51zXChqEGOD+CVDpXZRAw8nlJAeQZeX6lLPyIwBt7arVbVq7Gk0QNAJcfA1S4+WvU/D+y1oSqCeErRy3uKGLgbu/XKfjPxgYsTeTztJq/QJRQHDYXduAeLfAJYJZQS+CJ8pCGTs+10AMsppU3a302EkJEAoP1s9rKRgExMF0LIEvBMBawEVy97hVIgpVXSkyWip4Y7SiigpqBGYDziU62WSrlFA2cuFF4DF5XWZLsLB83HEw5KiCUU8M0WIkEZo4eU2bJLVQNXW6kB6Wn0FVq9cnYDSCkUQU1CcbMRAkZnnt3tvdo6IhuhcNWMYUw0Z1mNsAQCh5705aCCMwlloBey4sEfyyBwRg5KZPK1XAhDPgGAfwK4VPv9UgD/8LIRq53MGsDd9E9eA09pBaL4AG6X5QmYNXDAzMDjKQVJhRoB3EFCcWbg5v2HZAmxlHkSEwAmDa9ylFAkQtDWI5ZQqiLq33y9FhYg7Uqx8mDJPLva1ADO9HOA78jjwsA9xindhZKRD9x5ORYsC2cjdN8Rk1AGi/4NmOctfA28NOEpkYcQUg7gVADXcC/fCuAJQsgVAHYAOM/LtqwB2yo5uLsojAmllFY+VSZGAHdKpaewVDjUNPBNzV2ojgS09c2ZmKJEHqcAbg1U4YCsMnDF/HRx6THjsbEpKtwG84G3cRIKK2oV7UvqHmtd5kkpCEhB/Zj47YigM/C2zBk4SzDyioyqEQpkGdExBGUJfYnCJfV4shHqDHzwUFVfQil9eArglNIeAEMtrx2A6krJbIeWyMACutVlAoiTRfj3U1oDA0mz6gFGQ1ZhPXBK0zTw19c34953tuC/TpoIwJBg+OAImLvBWLt2q9tmYyYIBSR9PKEA08DN/SpPPKQeJx4i7jTCJjFZanZNWcgI4LEkDo6k32SYhMIft91Fxzzju9rVWsxCDdzWo07Sygg4wSh34H0Sk9+66Cmi0GxQ5FiyIhIYhAzcn8QseRScLqRJKGwSU/vbLguPwaSBay3E+JNLFFwZrAxcIkZ3nP3ahB7r5ci3VAOAeMoopOTUXooQ81NFOCAhljDbCN0gaan0DNYO6kxCkTmZx2hQYT4+ESJBGbXlQV1CEQVXu5Fmeh1nxsBV8IFSxMpZkCxUSDEaOjhp4IMvgJvnU4o4EB+2KEIAt0oMmWrgxsXEGDif0KJnYrr4wAG1zyQr+t+td6RnDNysgSeSxgZFFyl/cfPHxBi4tdmCE5gLBVCLQlllpyqNgQc5Bm5NPGLbscOI6ggXwNMHZreu8aSUqQslEwbOH0P6coVm4F5uvINRQjGRKT+ClySKzsCtk35uDoQ0Bi6ZGbhTJqa6AWPZ5755HB65Yi4AoEvvSG+WJ5hXmm/m4HRBE5gdF7oGTr0HPUky9lFr6esIQNfrzS6U9BZxTtfc8OqIY3C1dchkeB2z+0kmLhQTAxeMo9CP854kFMbAB5ELRe1gpB6PL6GUJooewBm7ZH5rpwJW6mvGRZ6iho2QQbcRWtZj2jC/ybqKkD6eHosNkV2IwnKyLudymEurDtto4E5gSUZAunwCGBJKgJN5jD6a5u3YoUHTwQFxcLVbVa8KaT98EwwXivdTzaSBixi4nD5fkk/o55zDMoNRQgEgzC/wUTooeDlZq4TCAnhMcxW4Pbaxi1ahFEmNgfNs0a4jjzE5Jx6X1cViLSfLp9LbyTP6MckWDVwvJ+vtIpA5CUUYwPVkIybzKPrn6lVCYU4UQBxc3QK4V+gs30sxKwHT5W9CPz1zKpqiMby0Zl9GY+g3eGHggyiRB9BIQqI0/Pg+0lHws80aLPiKfYClkJHDdij4RB7jdTsboVEl1bxVtjumgRsSCisnm56J6VQXhRCzVZI5UrKdxBQzcIsLJWUwcK8SCvOCA+JJTHsN3PzTDXecPxOThld6k1AEJX75Y7js2Am48bQpBZ9QMxj45yuRBzCemAeRtD+oUHAGbtXSZIvbwzyJac/AQVkij9G/EuB6MVouNl1CsWySBY2emNGRHjDLE4B7AOf3Zp3EZE2NvYIl8gDOEopJAxdIKE43jOEcAxcFV3sNPLMAdcaMRpwxo9HTskbGo/P+DJZbmGD5ebURAsbx+Ay8NFHw+6qdlma08nKZxLS4UGTJYAdlQdmWnRkM3Lo99Sdj4GU25WT5oO1cmJCYGHhYY+A0AwbOa+CiScw0Bq5QnUXzNwqn3fESSrnAFumUyJMvGIHS+RiKlRXoSQMfZFSVEQM/gJcmCnq2EaQ/illPC7dyqFYXiiwZTSB4+cSOLaUzcBV9CVVHZsHXqoFn0s7LjoF7jTu5MHCzhGK/wxF8AA+my05OiTxAfrgvX2nSaRyFlim87E2XUAYbA2culEF2XIMFJUMX2EXrlkpvqoWiUMjEYPWRoGxbfN8uw5D/m+nfgFEHQlTMit86Y4uPLd2hv2Zm4DJSCkUipXieyZcll0nMiEWnVxQugBvLOQXwYZVGn02nyWKvr/cHjAlq4zUnH3ihSKHoycCKwcrADQmlyAPxIUTBz7aNTV2mv1kAZczRdKcXMnCDpamJPGIGboXTBBRDBbe+JKkyRkqzNyZsGDi7ppdsOaAdDxDivO0smCdS1HPAMdkIuYbGDFUiH7jsPAFohVug6a9Enkwg2qZYAy90NBGTAh6DlYHrEsogO67BgoIH8DNnNqK2PIjJDVUAOElE+0k8M3CqJfIYE6EsgBOSzpZsJRXud2slw4BEkHBpqSaanLSm0jsdjwheJRR+opWx8ZRJP87+oiuKBq795G+2okMolh7rrR744Ap07BzzNfDSRGE1cEJw6MhqrLx5PkYNKdNeU9+jAgbuqoFTMwNnATjMFZNKH4P939YaJwFJEifycFCoZdIN6TZCp+MRQSJEnwuodZBQeAbOfle4YbrtT9RZiB+D0+v5vJ5da6GwRJ78DcEEXrazQ8TSyWmwQNRr1UfpoOA2QgYWXPiADHgoJ8tdTCyRhwWvMu0iUvtQmgOuXacZswaezsCTgp6YfIBRKEV33Lwvayq94/EIIEtEH6eIgRvt0zQGzskzpklMlwj+4Y++oPvcvSKfTEy3EbrIQHKBk2X0IXjoiTnYJvuYNOdLKKWJogVwK38yJBRuCQcNHJQipShqOVldA1cPJxyUEUumTOt56fXIT2ICqjTDUvztXCiUQm9NxsB35eEZuFdJgxAjcIkCuD4+AQM3+8Cd9+O0bbsLVk/kcd50VhBNQItuGIXOlcmkK/1gS+TRU+kH12ENGhTtec96XYokFKdEHtWFAlNrMV5CsWfg9mOxMnC+9jUvofAXskKpKYATQlBfGdb92+EsJRR27NUOQZZ3obBAl28NvBAuFLdkJL2Zc/6GYoL1KVGEoCwhIJFBq4EPtieLwYKiMXB2OljZjaihLX8NswtaUVQGLpN0CUUYwPX9icdCYWRh8vtilja79HlrAAeAhfPG4cyZavZhKKtJTDWVvioScLxweK86W8yrD9zLGBxfz4cLRftpcgwJdqM3c86kNVAO4CfOnRAJyoPORugn8pQ2ChrA+VPAmhDC4g7/6O5YThaadCAbAZwx6JBWwpWHUY1QxOoJQGmaC0UiRpCwd6EALdE+0/jCARkN1cbNhN+eF8haOVlRFiYPXgNvrFUnhb91yqSM9yeC3fWaz8ksUTlZJx94slAB3KNgdMqhwzFn3JA8j6aw8FPpSxuFZeAO+raI3RiTWunr6ZmYhAhdKOkauHi//LCs3XxkSSyhmLZLKVq6YsL32FiMsXvVwFVd3ymJBzCXvC0PBbDt1i+Zls3lorMNWgWQUNySkXjtv5BwK2fz+wtmF2YgBYRfzKq0UTwJRddQ1B+KID4KNXAuqYLVA5e8SCgOFx9jctZJTIkQve4J3xOTd0kogklMHtm4UCRCcMT4IUKPeXXECOqGBi4+uFxIk70P3Pzk1J8gpucr7TUHCaVQDJz1G3VqZj1Y4fvASxtF1MCJ6ad4GfsXKaVIpVhHHvU1JqGEgzI6exPm9WxS6XmUWWqCSJIRrO194BTNpklM8/tefeASMZinRIAbT5siXI5n4AYTFY8tPxp41pt0hfdJTOfj7m9MbqjCD784BefMHlWQ/ZUSDBeKH8BLEZ4ejAghtYSQpwgh6wkh6wgh8wghPyWE7CaErNT+fdF1O4I/nOpMOGViAlxHHu35jjUkFvrAmY3QYXwiFwpzddjZCEWTmDy8ZmK6VWFkbJNngW5MNJ+TmPm4nkVuDyED16Wj/h+DCIQQXH3CRAyvirgvPMgQ9F0oJQ2vDPz3AF6ilH6FEBICUA5gAYA7KaW3Z7Nja4AQxSCWr2Hyhms/RT0xDQYu0MB1Bm4/JuskpuzBhWL1gVufKMw+cPt9q5+HvVe9q08td2ti4IIuPOZt2u/PFTbr5jPTUOT2KAUG/nlGoMDt63xkBterkRBSDeAEAA8CAKU0Tiltz3XHXs4HsQ+caeBU78bO1wMHNA1cc6H84OnVeGbFLtt64DysEgrhXSg2PvBESsGB7rjtNr1q4CKrJA+WUMTfENwYeD584EE9jT0fNsJ0F4poL+yGXSgN/PMMPZXeZ+AlCS906iAALQD+RAhZQQh5gBBSob33DULIKkLIQ4QQoX+KEHI1IWQZIWSZwjEma0af2IVif0Eb9cCNTEzDhSIjnlLQE0/i8Y92YOv+Hq4jj/2JmJaJybtQbBh4a3fcxICz18CdJRQR3NwYuVxzdmPIq89ZZ+DO4zB6gfoBPN/wu9KXNrxcjQEAhwO4h1I6G0A3gJsA3ANgIoBZAPYCuEO0MqX0PkrpHErpHFky2KhuQhFYx/TBiSx/3EWu2wgtPnCVgaewZncnFArMHF3jaCNkSPeBE6QUqv8ToanTXv9mY9G35xBR+bdEi4lcNAGXQJYPDTyYx0dqtkm3rkIBF+nIR/+B3bDzUT7YR+7wEsB3AdhFKV2q/f0UgMMppU2U0hSlVAFwP4C5rlsSyASix2ZjcQcbITUSeVgVwdoytXY2sxGu2qUqPTNG1zqm0jOkuVA0DdypiXFTZ5/te4AqcxjJi04SChH+nrYcdwQs0PO6OI+cfOA2q+az67rE3531caQPRPIllIIhKPmTmKUM10lMSuk+QshOQshkSulnAE4BsJYQMpJSuldb7MsA1mS057TzwT6RxzSJyU10MQZ+ypTheOKaeRg7tByAEcA/2dWBxpoI6qvCaGbZko4SSrqNUKHU0VPe7BLACSEIByT0JRRHScOsgae/LwpVhBD8+tzpOGpCnes2M4XdumwSMx+XsyB+C1HoVPrPMwJ+Ik9Jw6sL5ZsA/qo5ULYAuBzAXYSQWVCvt20Arslkx7oP3EFCCQg6rZsyMTUbYUCWMJcLYmGNSS/f3oYZo2u1Fdh+7WEN4KoLhbowcGcJBVBtjWoA92YjdGbq5r8vnDvWfps5sCa7sYYC+WNiTpZSHoVOpf88w3Ch+Ay8FOEpgFNKVwKYY3l5YaY7408Ba00k0UUrynxjgV9RqNrpXRCkWD3u3e29uOgoNcB50cAjIasLRZVQrEk8/Eibon0oD6l9L2NJRbj9cFAG+pKuiTyi3/V9ZhGr8jKJmUcJhc+ydUKxUuk/jwj6DR1KGsUrJ2v5TXQpCgO4tiJrdSYq38nX456pMXC7hg48rBq4LBEoijsDr68KO94YQh4mgtzK6Brv2e/HitwmMcWv6xJKPiYxuacrJ/gMvHDwy8mWNooWwK3BRXTRVgom59hae9p71e2IAjjn/Jg+ukbdvoeGDtYkFTW9ndqm0QOqBl5fGXa8MbAbinNATZ+c5OGlKXPaFnPSwJ1dKPnMgvSqgfuJPPlHII83bB+5o0i9qQTVCAUR3FodUF1PXfGpj3cBsGHgWvLMhGEVekU/Ly4UK5iN0C6NHgAOdMcxvDrMTbjaSzqZNHSwQyYJNPmoX8Fucsk8BE/22YmKePFg3vpsZCUfmUF3ofgRvCRRtHrgaRq4YHk+i5HBGgRF/RHZBT5DY9/89jOVIFICCcV6s6mvDDvKHmxS1Tmg9k8jBh75CODskdrpqSRb6N+tS2C+cO5YbG7pwje52uc+8gPGwP2emKWJIpqDCPe/dzZFCMEfLz5c/1vUgjCsB/Ba/TWnhg52kCW1mJVbsKqvChuJSYL3wx4eQ90KOBV6EtMOBgPvf/prTGK6d7755TnTHXt6+ugfBP1ysiWNImrg6k+v1jEevGVQFhhUR9aUQSLA0QcZy+mbz+A8JEwDdxF866vCjtv1ooHzR+9RKndFPqxfRhegfEgo6k9fGikd+LVQShvFb+igwematbYW45mXSJub2liNFTfPFzK0TE5DLy4UwMzARQh56CvI38BEy2UT0/LJwPkGF/2FTJ/GfOQf7Gl2sDVrHiwomYYOdhNXK/73VAQDZpbNu0XsTixr8DbKyWY2CSjygVtRXxnRNUKxD9x9EtOtgBNDppOw/Q32SO12U8sGonKyPoqL4yYNwy/OnoapI6uLPRQfAhR4EpP3Opt/2rGuIRUhx216nVzx0tAhbdsEri4UwDsD93rzECfyZB7U8utCyUeQ1TRwP36XDCJBGQvnjS/2MHzYoGga+LmHjwYAnHhIPYDMJYJIMLNHOy8NHayQ9FR6Y3QHD69MW25oZUgPzqJJUuamcWTgLgWcvLyXvqznRT2DBfB8uFCcyir48OEjHUXzgc8aU4ttt34JE+u1gJjhRcuyNDO1N9kFtZE16e2yWABnweqt/zkJL9xwvGmZuooQgrLkzMADmWng/YWBJqEY4/UjuA8fXlD0Ntt604YML9rKcAD7u+LeGbjDe+9+/2RUCyY81YYOQFxrz1ZdFkQoIJnYcn1lGIAz22UTQU5lRPIRsvIx78R8wUl/EtOHj6KjaIk8+mtZWsdYmr1Xe5OTD3xMXblwHUKguVDUdUOB9AhcX8UCuP0kJlvPUf7w6oP3thiAPCXyaJ93Ii+ZmOpPP3778OENxWfgrLYzF8GX/++prunUTELxmuKbTSYma6nGfOBMPuCfFvQA7rAdQwP35gMXvp9FVMuHBs5uRnlh4LoG7odwHz68oOhl2iUB66qrCGGYJk3YgQXwlMeLPbtMRjUTk7lQQoKkoeFVZglFtB9DA3can7cBZhKUs0nkOWtmo+P7LLEjLzbCvLSJ8OFj8KIEAnh21jEWwLv6kh7XcG9qbIVECBRFDVYhWRKuazBwez+7p0lMl7EUyht914Wzse3WL9m+b0xi5mE8fiamDx8ZoaABXBS/suVcTAPvinkL4NlVIzTKyQZFRVfAa+DafgTLhAOZ1UJxQrE5amNtGQBg8oh0O2WuYMfmSyg+fHhD0TXwbOt1jB9aASC9k7wdctLAk4ppApPFl1DAaOVmPElky8Cdg1a2Me17CybjhEn12a0swGGjavDMdcdg+qga94UzBH8u/OrL07GhKdrv+/DhYzCh6AE8W6vb5cdOQF1FCOfMGuVp+dFDyvC3q48WJuLYgRCCFJNQBA6Uhy49EiNrykyviQJt2EMAz2RMmeD6kw/OeZ9WzB47pN+3CZhthKwVng8fPuxRdBthtkFNloiezekF5aEAjj5oaIb7UBm1KqE4q02efOD9IKEMZmSbE+DDx+cVBZ7ETI9gpVxnWHeh2DBwfujsOET6bURr6ODkWc+HjfD/t3fvMXaUdRjHv89uu0GgApWC1VCqpVIxpgVWpGAaQETRKKBySbDWS1JCUECNhmiiGMMfxisGJYIgFbmoRZCoIZCKgCGoRUoplEiCFZHSloiBqEAvP/+Yme7s2bPnzHb37Lyz+3yS5pydc9nfTk6fffc377zTNLunEfpKaWaVVApwSftLWi3pcUkbJS2VNFvSXZKeyG/36O9q1T4PZnTZLJRg+45dw6YQtgvTTtMIj1uQrehWvsDECA05iNlLQyfST4PfVmYToGp8Xg7cERGLgMXARuASYE1ELATW5F+PvYDER+C7Al4ZbQTe5n676BmY0cfypfO7jMC7HMTsXm7zeRqh2Zh0DXBJrwaWAdcARMQrEfFv4DRgVf60VcDpe1RAuvlNf9/QNMJ2J/GUqcMslCqWHzu/2hMT3l/jVRxnKFpOZtZZlRH4G4FtwI8lPSTpR5L2AQ6OiM0A+e1B7V4saaWktZLWbt/+ysjHE06koeVk24/Ay8a7jt6X339Ex8d7sVphagYPPYCLT17IN89cXHcpZo1QJcBnAEcBV0bEkcB/GEO7JCKuiojBiBgcGBh5cYaEOyj09WVnYrbOQmnX7ihGjb3O2ZR/4Y2XJC4++U27T44ys86qBPjTwNMR8cf869Vkgb5F0lyA/HbrHhWQcIIXZ2K+vGOUEXip9B8uP5oLTzqMBXP26UktU3/8bWZj1TXAI+JZ4B+SDs83vRN4DLgdWJFvWwH8ao8KSDe/6c+nEba2UD590kIG+vt4y+uGzkY8ZPbefPaUw3tyJfiyhH/fmdkkq3oiz6eBGyQNAE8CHycL/59L+iTwFHBmtzeZyBN5JoMkopiFUmqhHH/Ygfz1slNrrMzMrGKAR8Q6YLDNQ+8cbwEJ5/fuaX8vbe8+C6Xn3EMxsxa1n0bT65bDeBTtnZe272TmjDTqTKMKM0tBbRc1boLiakEvb9/FQH+9c5N9dqKZtZrUxawO2HvkNMKUFf350c7EnGiXn7OEp5//X8fnJPwHi5lNskkN8P3aXPk9ZeXrbQ6MckGHiXRah6Vxp8F5PGY2RrX3wFNWHu1Oxgi8iql8Io+ZjU0aqQTM3W+vuksYobz4VCoBbmZWqP2KPAC3nL+UebN7cwbjeJTnqHe7oEOvuYNiZq2SCPCjD51ddwlt9SU4AvdBTDMrpJFKiSqf5l/3iTw+iGlmrRzgHQybheIRuJklJo1USlTfsGmEdffAPQQ3s+Ec4B2k2ANv3OmsZtYzqaRSkso98NpnoXgAbmYtHOAdpDgP3D1wMyukkUqJUoIHMc3MCk6lDvqTOohpZjacA7yDviTXQjEzy6SRSokaNgul9ivyeAxuZsM5wDsYthZKKiNwD8HNLJdGKiWqPOiuewTu8beZtXKAd5DiLBSvB25mhUqpJGmTpEckrZO0Nt92qaR/5tvWSXpvb0udfEnNQvEQ3MxajGU52RMj4rmWbd+JiG9OZEEp6UtxBO4BuJnl0kilRPXle6e/T8POyjQYsewyAAAHwklEQVQzS0HVAA/gTkkPSlpZ2v4pSeslXSvpgHYvlLRS0lpJa7dt2zbugidTMQKfOQkXNO4m3EMxsxZVA/z4iDgKOBW4QNIy4EpgAbAE2Ax8q90LI+KqiBiMiME5c+ZMRM2Tphh1193/Lqv/V4mZpaJSMkXEM/ntVuBW4JiI2BIROyNiF3A1cEzvyqxH0TUZmNFfbyF4GqGZjdQ1wCXtI2lWcR84BdggaW7paWcAG3pTYn2KFspAAi2UgnwU08xyVWahHAzcmgfHDODGiLhD0vWSlpANDjcB5/WsyprsDvAEZqC4BW5mrboGeEQ8CSxus315TypKSEoBbmbWysnUQTGNsO6r8ZiZteNk6iClEbg7KGbWqv5kSliS0wh9DNPMcukkU4KGphHWv5t8Io+Ztao/mRI2NI0wnd3k1QjNrJBOMiUopR64mVkrJ1MHRQ88pVko7oGbWSGdZEqQkuqB112BmaWm/mRK2O5ZKAkEeMEDcDMrpJNMCUrxIKaZWcHJ1EFKBzHDp/KYWYv6kylhu+eBJzQC90FMMyukk0wJSmkWig9imlmr+pMpYUqohVLweuBmVkgnmRK090A/i147i0WvnVV3Ke6Am9kIVS7oMG3N7O/jjouX1V3GMB5/m1nBI3Azs4byCLwhzn37PB7f/ALnn7Cg7lLMLBEO8IaYtddMvnvOkXWXYWYJcQvFzKyhKo3AJW0CXgR2AjsiYlDSbOBnwHyyq9KfFRHP96ZMMzNrNZYR+IkRsSQiBvOvLwHWRMRCYE3+tZmZTZLxtFBOA1bl91cBp4+/HDMzq6pqgAdwp6QHJa3Mtx0cEZsB8tuDelGgmZm1V3UWyvER8Yykg4C7JD1e9Rvkgb8SYN68eXtQopmZtVNpBB4Rz+S3W4FbgWOALZLmAuS3W0d57VURMRgRg3PmzJmYqs3MrHuAS9pH0qziPnAKsAG4HViRP20F8KteFWlmZiMpuqxTKumNZKNuyFouN0bEZZJeA/wcmAc8BZwZEf/q8l7bgL+Pu+qp60DgubqLSJz3UXfeR901bR8dGhEjWhhdA9wmj6S1pWma1ob3UXfeR91NlX3kMzHNzBrKAW5m1lAO8LRcVXcBDeB91J33UXdTYh+5B25m1lAegZuZNZQD3MysoRzgiZC0SdIjktZJWlt3PSmQdK2krZI2lLbNlnSXpCfy2wPqrLFuo+yjSyX9M/8srZP03jprrJOkQyTdLWmjpEclXZRvnxKfIwd4WlqX7J3urgPe07LNyxgPdx0j9xHAd/LP0pKI+O0k15SSHcDnIuLNwLHABZKOYIp8jhzglqyIuBdoPbvXyxiXjLKPLBcRmyPiL/n9F4GNwOuZIp8jB3g62i3ZayN5GeNqPiVpfd5iaWR7YKJJmg8cCfyRKfI5coCn4/iIOAo4lezPvGV1F2SNdSWwAFgCbAa+VW859ZO0L3ALcHFEvFB3PRPFAZ6IUZbstZEqLWM8nUXElojYGRG7gKuZ5p8lSTPJwvuGiPhlvnlKfI4c4AnosGSvjeRljLsogil3BtP4syRJwDXAxoj4dumhKfE58pmYCRhtyd4aS0qCpJuAE8iW/twCfAW4jTEuYzyVjbKPTiBrnwSwCTiv6PdON5LeAdwHPALsyjd/kawP3vjPkQPczKyh3EIxM2soB7iZWUM5wM3MGsoBbmbWUA5wM7OGcoCbmTWUA9ySky+te+AevO46SR8ew/Pnl5dh3VNVvq+kj0m6Ir9/er4intm4OMDNJt/pgAPcxs0BbrWSdFu+AuOj7VZhlPTRfFW9hyVdn287VNKafPsaSfNKL1km6X5JTxajYmW+IWlDftGMsyvWNl/SfZL+kv87rvR+V0h6TNJvKK1kV/7rQdKgpN+3vOdxwAeAb+QXW1gg6cL8vdZLunlMO9CmtRl1F2DT3ici4l+SXgX8WdItxQOS3gJ8iWylxuckzc4fugL4SUSskvQJ4HsMrec8F3gHsIhsvYvVwAfJTi1fTHbK+Z8l3Vuhtq3AuyLiJUkLgZuAQbL1RQ4H3gocDDwGXFvlh42I+yXdDvw6IlbnP+clwBsi4mVJ+1d5HzPwCNzqd6Gkh4EHgEOAhaXHTgJWR8RzAKW1KpYCN+b3rycL7MJtEbErIh4jC1fyx2/KV+jbAtwDvK1CbTOBqyU9AvyCobbHstL7PQP8rvqP29Z64AZJHyG7goxZJQ5wq42kE4CTgaURsRh4CNir/BSyBZm6KT/n5ZbXl2/H6jNkC0QtJht5D4zyPct2MPT/aq9RntPqfcD3gaOBByX5L2OrxAFuddoPeD4i/itpEdk1C8vWAGdJeg1kF6LNt98PnJPfPxf4Q5fvcy9wtqR+SXPIRtB/qljf5nxd7eVAf+n9zsnfby5wYuk1m8iCGOBDo7zvi0CxfHAfcEhE3A18Adgf2LdCbWYOcKvVHcAMSeuBr5G1UXaLiEeBy4B78jZLsZ7zhcDH89ctBy7q8n1uJWtTPEzW7vhCRDxbob4fACskPQC8CfhP6f2eIFui9Eqylkzhq8Dlku4Ddo7yvjcDn5f0EFnL6Kd5m+YhsosR/7tCbWZeTtbMrKk8AjczaygfLLFpT9K7ga+3bP5bRJxRRz1mVbmFYmbWUG6hmJk1lAPczKyhHOBmZg3lADcza6j/A4IkZHlB5iiNAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "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\"]]" + ] + }, + { + "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": [ + "**Úkol:** Zkus si nakreslit spojnicový graf časového vývoje některých dalších ukazatelů pro Česko. Co se stane, když zkusíš do jednoho obrázku dostat třeba \"life_expectancy_female\" a \"calories_per_day\"?" + ] + }, + { + "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": 19, + "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[\"alcohol_adults\"],\n", + " countries[\"life_expectancy\"],\n", + " s=countries[\"population\"] / 1e6,\n", + " color=countries[\"world_4region\"].map({\"europe\": \"blue\", \"asia\": \"yellow\", \"africa\": \"black\", \"americas\": \"red\"}),\n", + " edgecolor=\"black\",\n", + " marker=\"h\",\n", + " alpha=0.5\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": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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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=\"alcohol_adults\",\n", + " y=\"life_expectancy\", # Rozumí názvům sloupců :-)\n", + " size=\"population\", # Velikost podle sloupce (nepříliš vhodná)\n", + " hue=\"world_4region\", # Umí přiřadit barvičky podle nějaké \n", + " edgecolor=\"black\", # Toto se předá matplotlibu (viz předchozí případ)\n", + " marker=\"h\" # Toto se předá matplotlibu (viz předchozí případ)\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": 21, + "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}

world_4region=asia
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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=\"alcohol_adults\",\n", + " y=\"life_expectancy\",\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": 22, + "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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