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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Objectives\n",
+    "\n",
+    "- Convert  object column to datetime datatype\n",
+    "- Set a column as a DateTimeIndex\n",
+    "- Check the datatype of the index for datetimeindex\n",
+    "- Explore the namespace provided by datetimeindex (i.e. .month, .weekday,...)\n",
+    "- Aggregate to another time resolution (i.e. resample)\n",
+    "\n",
+    "Content to cover\n",
+    "\n",
+    "- to_datetime\n",
+    "- set_index of a column\n",
+    "- resample\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Air quality data about $NO_2$ and particulate matter less than 2.5 micrometers is used, made available by [openaq](https://openaq.org) and using the [py-openaq](http://dhhagan.github.io/py-openaq/index.html) package:\n",
+    "\n",
+    "- The `air_quality_no2_long.csv\"` data set provides $NO_2$ values for the measurement stations _FR04014_, _BETR801_ and _London Westminster_ in respectively Paris, Antwerp and London. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>city</th>\n",
+       "      <th>country</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>location</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>value</th>\n",
+       "      <th>unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-21 00:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>µg/m³</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-20 23:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>21.8</td>\n",
+       "      <td>µg/m³</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-20 22:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>µg/m³</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-20 21:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>µg/m³</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-20 20:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>21.4</td>\n",
+       "      <td>µg/m³</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    city country                   datetime location parameter  value   unit\n",
+       "0  Paris      FR  2019-06-21 00:00:00+00:00  FR04014       no2   20.0  µg/m³\n",
+       "1  Paris      FR  2019-06-20 23:00:00+00:00  FR04014       no2   21.8  µg/m³\n",
+       "2  Paris      FR  2019-06-20 22:00:00+00:00  FR04014       no2   26.5  µg/m³\n",
+       "3  Paris      FR  2019-06-20 21:00:00+00:00  FR04014       no2   24.9  µg/m³\n",
+       "4  Paris      FR  2019-06-20 20:00:00+00:00  FR04014       no2   21.4  µg/m³"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality = pd.read_csv(\"../data/air_quality_no2_long.csv\")\n",
+    "air_quality = air_quality.rename(columns={\"date.utc\": \"datetime\"})\n",
+    "air_quality.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Paris', 'Antwerpen', 'London'], dtype=object)"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality.city.unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handle time series with ease"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using Pandas datetime properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> I want to work with the dates in the column 'date.utc' as datetime objects instead of plain text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      2019-06-21 00:00:00+00:00\n",
+       "1      2019-06-20 23:00:00+00:00\n",
+       "2      2019-06-20 22:00:00+00:00\n",
+       "3      2019-06-20 21:00:00+00:00\n",
+       "4      2019-06-20 20:00:00+00:00\n",
+       "                  ...           \n",
+       "2063   2019-05-07 06:00:00+00:00\n",
+       "2064   2019-05-07 04:00:00+00:00\n",
+       "2065   2019-05-07 03:00:00+00:00\n",
+       "2066   2019-05-07 02:00:00+00:00\n",
+       "2067   2019-05-07 01:00:00+00:00\n",
+       "Name: datetime, Length: 2068, dtype: datetime64[ns, UTC]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality[\"datetime\"] = pd.to_datetime(air_quality[\"datetime\"])\n",
+    "air_quality[\"datetime\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initially, the values in `datetime` are character strings and do not provide any datetime operations (e.g. extract the year, day of the week,...). By applying the `to_datetime` function, pandas interprets the strings and convert these to datetime (i.e. `datetime64[ns, UTC]`) objects. In pandas we call these datetime objects similar to `datetime.datetime` from the standard library a `Timestamp`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "__Note__: As many data sets do contain datetime information in one of the columns, Pandas input function like `read_csv` and `read_json` can do the transformation to dates when reading the data using the `parse_dates` parameter with a list of the columns to read as Timestamp:\n",
+    "\n",
+    "    pd.read_csv(\"../data/air_quality_no2_long.csv\", parse_dates=[\"date.utc\"])\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Why are these `Timestamp` objects useful. Let's illustrate the added value with some example cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> What is the start and end date of the time series data set working with?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Timestamp('2019-05-07 01:00:00+0000', tz='UTC'),\n",
+       " Timestamp('2019-06-21 00:00:00+0000', tz='UTC'))"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality[\"datetime\"].min(), air_quality[\"datetime\"].max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using `Timestamp` for datetimes enable us to calculate with date information and make them comparable. Hence, we can use this to get the length of our time series:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timedelta('44 days 23:00:00')"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality[\"datetime\"].max() - air_quality[\"datetime\"].min()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Timedelta` object, similar to `datetime.timedelta` from the standard Python library and defining a time duration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__To user guide:__ The main time related concepts of Pandas are explained in :ref:`timeseries.overview`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> I want to add a new column to the dataframe containing only the month of the measurement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>city</th>\n",
+       "      <th>country</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>location</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>value</th>\n",
+       "      <th>unit</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-21 00:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>µg/m³</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-20 23:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>21.8</td>\n",
+       "      <td>µg/m³</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-20 22:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>µg/m³</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-20 21:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>µg/m³</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>2019-06-20 20:00:00+00:00</td>\n",
+       "      <td>FR04014</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>21.4</td>\n",
+       "      <td>µg/m³</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    city country                  datetime location parameter  value   unit  \\\n",
+       "0  Paris      FR 2019-06-21 00:00:00+00:00  FR04014       no2   20.0  µg/m³   \n",
+       "1  Paris      FR 2019-06-20 23:00:00+00:00  FR04014       no2   21.8  µg/m³   \n",
+       "2  Paris      FR 2019-06-20 22:00:00+00:00  FR04014       no2   26.5  µg/m³   \n",
+       "3  Paris      FR 2019-06-20 21:00:00+00:00  FR04014       no2   24.9  µg/m³   \n",
+       "4  Paris      FR 2019-06-20 20:00:00+00:00  FR04014       no2   21.4  µg/m³   \n",
+       "\n",
+       "   month  \n",
+       "0      6  \n",
+       "1      6  \n",
+       "2      6  \n",
+       "3      6  \n",
+       "4      6  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality[\"month\"] = air_quality[\"datetime\"].dt.month\n",
+    "air_quality.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By using `Timestamp` objects for dates, a lot of time-related properties are provided by Pandas. For example the `month`, but also `year`, `weekofyear`, `quarter`,... All of these properties are accessible by the `dt` accessor."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__To user guide:__ An overview of the existing properties is given in :ref:`timeseries.components`. More details about the `dt` accessor to return datetime like properties for the values of the Series are provided in :ref:`basics.dt_accessors`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> What is the average $NO_2$ concentration for each day of the week for each of the measurement locations?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime  location          \n",
+       "0         BETR801               27.875000\n",
+       "          FR04014               24.856250\n",
+       "          London Westminster    23.969697\n",
+       "1         BETR801               22.214286\n",
+       "          FR04014               30.999359\n",
+       "          London Westminster    24.885714\n",
+       "2         BETR801               21.125000\n",
+       "          FR04014               29.165753\n",
+       "          London Westminster    23.460432\n",
+       "3         BETR801               27.500000\n",
+       "          FR04014               28.600690\n",
+       "          London Westminster    24.780142\n",
+       "4         BETR801               28.400000\n",
+       "          FR04014               31.617986\n",
+       "          London Westminster    26.446809\n",
+       "5         BETR801               33.500000\n",
+       "          FR04014               25.266154\n",
+       "          London Westminster    24.977612\n",
+       "6         BETR801               21.896552\n",
+       "          FR04014               23.274306\n",
+       "          London Westminster    24.859155\n",
+       "Name: value, dtype: float64"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality.groupby([air_quality[\"datetime\"].dt.weekday, \"location\"])[\"value\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember the split-apply-combine pattern provided by `groupby` from [tutorial on statistics calculation](6_calculate_statistics.ipynb)? Here, we want to calculate a given statistic (e.g. mean $NO_2$) __for each weekday__ and __for each measurement location__. To group on weekdays, we use the datetime property `weekday` (with Monday=0 and Sunday=6) of Pandas `Timestamp`, which is also accessible by the `dt` accessor. The grouping on both locations and weekdays can be done to split the calculation of the mean on each of these combinations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-warning\">\n",
+    "    \n",
+    "__Note__: As we are working with a very short time series in these examples, the analysis does not provide a long-term representative result!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Plot the typical $NO_2$ pattern during the day of our time series of all statiosn together. In other words, what is the average value for each hour of the day?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9qmrsTnOrakHcgGcC75vw+HnAO7bg9VYbt+3laJxxxo0ubiF1GW0A9pnweG/g+yPKRZKWnIVUEP4d2D/JvkmWAc8GLhhxTpK0ZGw/6gTGVdVtSY4HPgdsB7y/qq7dgpc8zbiRrss444zbxuIWzE5lSdJoLaQuI0nSCFkQJEnAIiwIcx3+Isn7k2xKsm6AmH2SXJpkfZJrk5zQM27HJF9NcnWLe0Pfdbb47ZJ8LcmnB4i5McnaJGuSrB4gbpckH09yXfs7H90j5oC2nvHbT5O8ouf6Tmz/k3VJzkmyY8+4E1rMtTOta6r3OcluSS5Ocn2737Vn3DPb+u5IsnKA9Z3S/p/XJDkvyS49497YYtYkuSjJffrETXjulUkqye491/f6JDdNeB+f1Hd9SV7ePofXJnlLz/V9ZMK6bkyypmfciiSXj2/bSQ7sGfewJJe1z8WnktxrUsyUn+/ZtpcZ4mbcXmaIm3F7mSFu1u3lTuZyfOtCvdHtjP4mcH9gGXA18OCesYcAjwDWDbC+PYFHtOl7At/osz4gwD3a9A7AFcBBA6z3vwMfAj49QMyNwO5z+J+eBfxVm14G7DKH9+QHdCfCzLbsXsC3gbu1xx8Fnt8j7k+BdcDd6Q6U+Bdg/77vM/AW4KQ2fRLw5p5xD6I7OfLzwMoB1ncEsH2bfvMA67vXhOm/Ad7bdzumO6T7c8B3ptoOplnf64FXzvK/nyru8e09uGt7vEffPCc8/zbgdT3XdxFwVJt+EvD5nnH/DjyuTb8QeOOkmCk/37NtLzPEzbi9zBA34/YyQ9ys28vk22JrIcx5+Iuq+iLw40FWVlUbq+qqNv0zYD3dl9pscVVVP28Pd2i3Xnv3k+wNPBl43yC5zkX7xXQIcAZAVf2mqm4Z8GUOB75ZVd/pufz2wN2SbE/3Bd/nXJQHAZdX1S+r6jbgC8DTplpwmvf5GLrCR7t/ap+4qlpfVdOdKT9T3EUtT4DL6c656RP30wkPd2KKbWaG7fhU4H9OFTNL3IymiXsJcHJV3dqW2TTI+pIEeBZwTs+4AsZ/3e/MFNvMNHEHAF9s0xcDfzEpZrrP94zby3Rxs20vM8TNuL3MEDfr9jLZYisIewHfm/B4Az2+oLeGJMuBh9P92u+z/HatSbwJuLiqesUB/4fug33HgCkWcFGSK9MN/9HH/YHNwD+l66J6X5KdBlzvs5nigz1lglU3AW8FvgtsBH5SVRf1CF0HHJLkj5Lcne5X4j6zxEx076ra2HLYCOwxQOyWeiHw//ounOQfknwPeC7wup4xRwM3VdXVc8jv+Nbt8P6putKm8QDgsUmuSPKFJI8acJ2PBW6uqut7Lv8K4JT2f3kr8OqeceuAo9v0M5lhm5n0+e69vQz6vdAjbsbtZXLcoNvLYisImWLevB9Xm+QewLnAKyZV5WlV1e1VtYKu2h+Y5E97rOcpwKaqunIOaR5cVY+gG032ZUkO6RGzPV0z+z1V9XDgF3RN5F7SnWB4NPCxnsvvSvfra1/gPsBOSf5ytriqWk/XlL4YuJCuq/C2GYMWgCSvocvz7L4xVfWaqtqnxRw/2/KtQL6GnsVjkvcA+wEr6Ar023rGbQ/sChwE/A/go+1Xf1/H0vNHRPMS4MT2fzmR1qLt4YV0n4Ur6bpafjPVQnP5fM9H3Gzby1Rxg24vi60gDH34iyQ70L0JZ1fVJwaNb10wnweO7LH4wcDRSW6k6w47LMkHe67n++1+E3AeXffabDYAGya0Xj5OVyD6Ogq4qqpu7rn8E4BvV9Xmqvot8Angz/sEVtUZVfWIqjqErmug769LgJuT7AnQ7u/UxbG1JTkOeArw3GqdvAP6EJO6OKaxH12BvbptN3sDVyX549kCq+rm9sPlDuB0+m0z0G03n2hdo1+la83eaUf2VFpX4dOBj/RcF8BxdNsKdD8+euVZVddV1RFV9Ui6AvTNKfKZ6vM96/Yy1++F6eJm2156rK/X9rLYCsJQh79ov3rOANZX1dsHiBsbP1Igyd3ovgivmy2uql5dVXtX1XK6v+1fq2rWX9BJdkpyz/Fpup1Usx5NVVU/AL6X5IA263Dg67PFTTDoL73vAgcluXv73x5O1x86qyR7tPv70n2hDLLeC+i+VGj3nxwgdmBJjgReBRxdVb8cIG7/CQ+Ppt82s7aq9qiq5W272UC3A/IHPda354SHT6PHNtOcDxzWXuMBdAcj9B2t8wnAdVW1oefy0P3oe1ybPoyePwYmbDN3AV4LvHfS89N9vmfcXrbge2HKuNm2lxniBt5eZtzjvC3e6PqPv0FX7V8zQNw5dM3i39J9aF7UI+YxdF1S1wBr2u1JPeIeCnytxa1jiqMperzGofQ8yohuX8DV7XbtgP+XFcDqluv5wK494+4O/AjYecC/6w1tw10HfIB2pEqPuC/RFaurgcMHeZ+BPwIuofsiuQTYrWfc09r0rcDNwOd6xt1At69rfJuZ6mihqeLObf+Xa4BP0e04HGg7ZpqjzaZZ3weAtW19FwB79oxbBnyw5XoVcFjfPIEzgRcP+P49BriyvfdXAI/sGXcC3XfFN4CTaSM3zPb5nm17mSFuxu1lhrgZt5cZ4mbdXibfHLpCkgQsvi4jSdIcWRAkSYAFQZLUWBAkSYAFQZLUWBC0TUvy80mPn5/knUPO4ZltpMlLJ81fnuQ585Fbe+3eI/NKfVgQpCkk2W6AxV8EvLSqHj9p/nLgOXdeXFqYLAhatJLcL8klbXC2S9pZzCQ5M8kzJiz383Z/aLpx5T9Ed0LW5Nc7Nt3Y+euSvLnNex3diUHvTXLKpJCT6QZ5W5PkxDbvPkkuTDeW/lsmvPYR6cbmvyrJx9q4NJPX/8h019C4DHjZhPnLk3ypxV6V5M/b/A8kOWbCcmenG+hOmtogZ5F687bQbsDt/P7szDV0w1+8sz33KeC4Nv1C4Pw2fSbwjAmv8fN2fyjdAH77TrGe+7TXHqMbvO1fgae25z7P1OPbH8qEs8mB5wPfohuieUe6axPsQzfOzxeBndpyr2LqawFcw+/H7z+FNrY/3VnhO7bp/YHVbfpxE/7mnemuNbH9qN8zbwv3tv3cS4m0IPyqulFjga6fHhi/GtWj6cY1gm4YhjtduWsKX62qb08x/1F0F17Z3NZzNt21Is4fMN9Lquon7TW+DtwP2IXugib/1gYFXQZcNjEoyc50Fyf6woS/56g2vQPwziQr6ArkAwCq6gtJ3tXG7Hk6cG79flx96U4sCFpKxsdpuY3WXdoGBls2YZlfTBM7yPDNM7l1wvTtdJ/B0F0T49gZ4sL0Q7mfSDc2zsPo/q5fT3juA3Rj4T+brpUkTct9CFrMvkL3RQjdl+KX2/SNwCPb9DF0v7BncwXwuCS7tx3Ox9JdmW0mP6MbZ382lwMHJ/kv0F3DoI0S+jvVDZP+kySPabOeO+HpnYGN1Q1T/Ty6y5aOO5PuAjJU1bU9ctESZkHQYvY3wAuSXEP3RXlCm3863Zf7V4E/Y/pWwe9Ud2WsVwOX0o2qeVVVzTZM9jXAbW1H8InTLdS6oZ4PnNNyvRx44BSLvgB4V9up/KsJ898NHJfkcrruot/9PdVdi2I98E+z5Co52qm0mKW7Ytpaumsg/GTU+Whhs4UgLVJJxi+89A6LgfqwhSBJAmwhSJIaC4IkCbAgSJIaC4IkCbAgSJKa/w+sDp1EF/hIBwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "air_quality.groupby(air_quality[\"datetime\"].dt.hour)[\"value\"].mean().plot(kind='bar', rot=0)\n",
+    "plt.xlabel(\"Hour of the day\"); # xustom label for the x-axis using matplotlib\n",
+    "plt.ylabel(\"$NO_2\\ (µg/m^3)$\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to the previous case, we want to calculate a given statistic (e.g. mean $NO_2$) __for each hour of the day__ and we can use the split-apply-combine approach again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Datetime as index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the [notebook on reshaping](7_reshape_table_layout.ipynb), `pivot` was introduced to reshape the data table with each of the measurements locations as a separate column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>location</th>\n",
+       "      <th>BETR801</th>\n",
+       "      <th>FR04014</th>\n",
+       "      <th>London Westminster</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>2019-05-07 01:00:00+00:00</td>\n",
+       "      <td>50.5</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2019-05-07 02:00:00+00:00</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>27.7</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2019-05-07 03:00:00+00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50.4</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2019-05-07 04:00:00+00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2019-05-07 05:00:00+00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "location                   BETR801  FR04014  London Westminster\n",
+       "datetime                                                       \n",
+       "2019-05-07 01:00:00+00:00     50.5     25.0                23.0\n",
+       "2019-05-07 02:00:00+00:00     45.0     27.7                19.0\n",
+       "2019-05-07 03:00:00+00:00      NaN     50.4                19.0\n",
+       "2019-05-07 04:00:00+00:00      NaN     61.9                16.0\n",
+       "2019-05-07 05:00:00+00:00      NaN     72.4                 NaN"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_2 = air_quality.pivot(index=\"datetime\", columns=\"location\", values=\"value\")\n",
+    "no_2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "__Note__: By pivoting the data, the datetime information became the index of the table. In general, setting a column as an index can be achieved by the `set_index` function. \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Working with a datetime index (i.e. `DatetimeIndex`) provides powerfull functionalities. For example, we do not need the `dt` accessor to get the time series properties, but have these properties available on the index directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Int64Index([2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,\n",
+       "             ...\n",
+       "             2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019],\n",
+       "            dtype='int64', name='datetime', length=1033),\n",
+       " Int64Index([1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "             ...\n",
+       "             3, 3, 3, 3, 3, 3, 3, 3, 3, 4],\n",
+       "            dtype='int64', name='datetime', length=1033))"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_2.index.year, no_2.index.weekday"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some other advantages are the convenient subsetting of time period or the adapted time scale on plots. Let's apply this on our data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Create a plot of the $NO_2$ values in the different stations from the 20th of May till the end of 21st of May"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd89c58ec18>"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "no_2[\"2019-05-20\" : \"2019-05-21\"].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By providing a __string that parses to a datetime__, a specific subset of the data can be selected on a `DatetimeIndex`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__To user guide:__ More information on the `DatetimeIndex` and the slicing by using strings is provided in :ref:`timeseries.datetimeindex`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Resample a time series to another frequency"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Aggregate the current hourly time series values to the monthly maximum value in each of the stations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>location</th>\n",
+       "      <th>BETR801</th>\n",
+       "      <th>FR04014</th>\n",
+       "      <th>London Westminster</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>2019-05-31 00:00:00+00:00</td>\n",
+       "      <td>74.5</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>97.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2019-06-30 00:00:00+00:00</td>\n",
+       "      <td>52.5</td>\n",
+       "      <td>84.7</td>\n",
+       "      <td>52.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "location                   BETR801  FR04014  London Westminster\n",
+       "datetime                                                       \n",
+       "2019-05-31 00:00:00+00:00     74.5     97.0                97.0\n",
+       "2019-06-30 00:00:00+00:00     52.5     84.7                52.0"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monthly_max = no_2.resample(\"M\").max()\n",
+    "monthly_max"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A very powerful functionality on time series data with a datetime index, is the ability to `resample` time series to another frequency (e.g., converting secondly data into 5-minutely data). The method is similar to a groupby operation:\n",
+    "\n",
+    " - it provides a time-based grouping, by using a string (e.g. `M`, `5H`,...) that defines the target frequency \n",
+    " - it requires an aggregation function such as `mean`, `max`,..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__To user guide:__ An overview of the aliases used to define time series frequencies (alos called _offset aliases_) is given in :ref:`timeseries.offset_aliases`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When defined, the frequency of the time series is provided by the `freq` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<MonthEnd>"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monthly_max.index.freq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Make a plot of the daily median $NO_2$ value in each of the stations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd89c305e48>"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "no_2.resample(\"D\").mean().plot(style=\"-o\", figsize=(10, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__To user guide:__ More details on the power of time series `resampling` is provided in :ref:`timeseries.resampling`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## REMEMBER\n",
+    "\n",
+    "- Valid date strings can be converted to datetime objects using `to_datetime` function or as part of read functions.\n",
+    "- Datetime objects in Pandas supports calculations, logical operations and convenient date-related properties using the `dt` accessor.\n",
+    "- A `DatetimeIndex` contains these date-related properties and supports convenient slicing.\n",
+    "- `Resample` is a powerfull functionality to change the frequency of a time series."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__To user guide:__ More information on time series and date functionalities is given in :ref:`timeseries`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}