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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Objectives\n",
+    "\n",
+    "- Add a new column to a DataFrame with a chosen name\n",
+    "- Understand that operations work element-wise (no loops required)\n",
+    "- Rename an existing column\n",
+    "\n",
+    "Content to cover\n",
+    "\n",
+    "- df[“...”] = f(other column)\n",
+    "- df[“...”] = f(other columns), eg. df[“...”]  + df[“...”]\n",
+    "- df.rename\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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": [
+    "For this tutorial, air quality data about $NO_2$ is used, made available by [openaq](https://openaq.org) and using the [py-openaq](http://dhhagan.github.io/py-openaq/index.html) package. The `air_quality_no2.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": 30,
+   "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>station_antwerp</th>\n",
+       "      <th>station_paris</th>\n",
+       "      <th>station_london</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",
+       "      <th>2019-04-09 03:00:00</th>\n",
+       "      <td>22.5</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>67.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 04:00:00</th>\n",
+       "      <td>53.5</td>\n",
+       "      <td>27.4</td>\n",
+       "      <td>67.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 05:00:00</th>\n",
+       "      <td>54.5</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 06:00:00</th>\n",
+       "      <td>34.5</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 07:00:00</th>\n",
+       "      <td>46.5</td>\n",
+       "      <td>59.5</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     station_antwerp  station_paris  station_london\n",
+       "datetime                                                           \n",
+       "2019-04-09 03:00:00             22.5           24.4            67.0\n",
+       "2019-04-09 04:00:00             53.5           27.4            67.0\n",
+       "2019-04-09 05:00:00             54.5           34.2            41.0\n",
+       "2019-04-09 06:00:00             34.5           48.5            41.0\n",
+       "2019-04-09 07:00:00             46.5           59.5            41.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality = pd.read_csv(\"../data/air_quality_no2.csv\", \n",
+    "                          index_col=0, parse_dates=True)\n",
+    "air_quality.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add new columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](../schemas/05_newcolumn_1.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> I want to express the $NO_2$ concentration of the station in London in mg/m$^3$ (conversion factor 1.882)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(_Assume temperature of 25 degrees Celsius and pressure of 1013 hPa. The molecular weight of $NO_2$ is 46.01 g/mol, resulting in 1 ppm $NO_2$ being equivalent to 1.882 mg/m$^3$_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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>station_antwerp</th>\n",
+       "      <th>station_paris</th>\n",
+       "      <th>station_london</th>\n",
+       "      <th>london_mg_per_cubic</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 03:00:00</th>\n",
+       "      <td>22.5</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>126.094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 04:00:00</th>\n",
+       "      <td>53.5</td>\n",
+       "      <td>27.4</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>126.094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 05:00:00</th>\n",
+       "      <td>54.5</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 06:00:00</th>\n",
+       "      <td>34.5</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 07:00:00</th>\n",
+       "      <td>46.5</td>\n",
+       "      <td>59.5</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     station_antwerp  station_paris  station_london  \\\n",
+       "datetime                                                              \n",
+       "2019-04-09 03:00:00             22.5           24.4            67.0   \n",
+       "2019-04-09 04:00:00             53.5           27.4            67.0   \n",
+       "2019-04-09 05:00:00             54.5           34.2            41.0   \n",
+       "2019-04-09 06:00:00             34.5           48.5            41.0   \n",
+       "2019-04-09 07:00:00             46.5           59.5            41.0   \n",
+       "\n",
+       "                     london_mg_per_cubic  \n",
+       "datetime                                  \n",
+       "2019-04-09 03:00:00              126.094  \n",
+       "2019-04-09 04:00:00              126.094  \n",
+       "2019-04-09 05:00:00               77.162  \n",
+       "2019-04-09 06:00:00               77.162  \n",
+       "2019-04-09 07:00:00               77.162  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality[\"london_mg_per_cubic\"] = air_quality[\"station_london\"] * 1.882\n",
+    "air_quality.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a new column, use the `[]` brackets with the new column name at the left side of the assignment. When familiar to Python dictionaries, the syntax is similar to adding new key/value combinations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "__Note__: The calculation of the values is done __element_wise__. This means all values in the given column are multiplied by the value 1.882 at once. You do not need to use a loop to iterate each of the rows!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](../schemas/05_newcolumn_2.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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>station_antwerp</th>\n",
+       "      <th>station_paris</th>\n",
+       "      <th>station_london</th>\n",
+       "      <th>london_mg_per_cubic</th>\n",
+       "      <th>ratio_paris_antwerp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 03:00:00</th>\n",
+       "      <td>22.5</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>126.094</td>\n",
+       "      <td>1.084444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 04:00:00</th>\n",
+       "      <td>53.5</td>\n",
+       "      <td>27.4</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>126.094</td>\n",
+       "      <td>0.512150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 05:00:00</th>\n",
+       "      <td>54.5</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "      <td>0.627523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 06:00:00</th>\n",
+       "      <td>34.5</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "      <td>1.405797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 07:00:00</th>\n",
+       "      <td>46.5</td>\n",
+       "      <td>59.5</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "      <td>1.279570</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     station_antwerp  station_paris  station_london  \\\n",
+       "datetime                                                              \n",
+       "2019-04-09 03:00:00             22.5           24.4            67.0   \n",
+       "2019-04-09 04:00:00             53.5           27.4            67.0   \n",
+       "2019-04-09 05:00:00             54.5           34.2            41.0   \n",
+       "2019-04-09 06:00:00             34.5           48.5            41.0   \n",
+       "2019-04-09 07:00:00             46.5           59.5            41.0   \n",
+       "\n",
+       "                     london_mg_per_cubic  ratio_paris_antwerp  \n",
+       "datetime                                                       \n",
+       "2019-04-09 03:00:00              126.094             1.084444  \n",
+       "2019-04-09 04:00:00              126.094             0.512150  \n",
+       "2019-04-09 05:00:00               77.162             0.627523  \n",
+       "2019-04-09 06:00:00               77.162             1.405797  \n",
+       "2019-04-09 07:00:00               77.162             1.279570  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality[\"ratio_paris_antwerp\"] = air_quality[\"station_paris\"] / air_quality[\"station_antwerp\"]\n",
+    "air_quality.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The calculation is again element-wise, so the `/` is applied _for the values in each row_. Also other mathematical operators (+, -, *, /) or logical operators (<, >, =,...) work element wise. The latter was already used in the [subset data tutorial](./3_subset_data.ipynb) to filter rows of a table using a conditional expression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> I want to rename the data columns to the corresponding station identifiers used by openAQ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air_quality_renamed = air_quality.rename(columns = {\"station_antwerp\": \"BETR801\", \n",
+    "                                                    \"station_paris\": \"FR04014\",\n",
+    "                                                    \"station_london\": \"London Westminster\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "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>BETR801</th>\n",
+       "      <th>FR04014</th>\n",
+       "      <th>London Westminster</th>\n",
+       "      <th>london_mg_per_cubic</th>\n",
+       "      <th>ratio_paris_antwerp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 03:00:00</th>\n",
+       "      <td>22.5</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>126.094</td>\n",
+       "      <td>1.084444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 04:00:00</th>\n",
+       "      <td>53.5</td>\n",
+       "      <td>27.4</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>126.094</td>\n",
+       "      <td>0.512150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 05:00:00</th>\n",
+       "      <td>54.5</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "      <td>0.627523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 06:00:00</th>\n",
+       "      <td>34.5</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "      <td>1.405797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 07:00:00</th>\n",
+       "      <td>46.5</td>\n",
+       "      <td>59.5</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "      <td>1.279570</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     BETR801  FR04014  London Westminster  \\\n",
+       "datetime                                                    \n",
+       "2019-04-09 03:00:00     22.5     24.4                67.0   \n",
+       "2019-04-09 04:00:00     53.5     27.4                67.0   \n",
+       "2019-04-09 05:00:00     54.5     34.2                41.0   \n",
+       "2019-04-09 06:00:00     34.5     48.5                41.0   \n",
+       "2019-04-09 07:00:00     46.5     59.5                41.0   \n",
+       "\n",
+       "                     london_mg_per_cubic  ratio_paris_antwerp  \n",
+       "datetime                                                       \n",
+       "2019-04-09 03:00:00              126.094             1.084444  \n",
+       "2019-04-09 04:00:00              126.094             0.512150  \n",
+       "2019-04-09 05:00:00               77.162             0.627523  \n",
+       "2019-04-09 06:00:00               77.162             1.405797  \n",
+       "2019-04-09 07:00:00               77.162             1.279570  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality_renamed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `rename` function can be used for both row labels as column names. Provide a dictionary with the keys the current names and the values the new names to update the corresponding names."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The mapping should not be restricted to fixed names only, but can be a mapping function as well. For example, converting the column names to lower case letters can be done using a function as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "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>betr801</th>\n",
+       "      <th>fr04014</th>\n",
+       "      <th>london westminster</th>\n",
+       "      <th>london_mg_per_cubic</th>\n",
+       "      <th>ratio_paris_antwerp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 03:00:00</th>\n",
+       "      <td>22.5</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>126.094</td>\n",
+       "      <td>1.084444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 04:00:00</th>\n",
+       "      <td>53.5</td>\n",
+       "      <td>27.4</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>126.094</td>\n",
+       "      <td>0.512150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 05:00:00</th>\n",
+       "      <td>54.5</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "      <td>0.627523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 06:00:00</th>\n",
+       "      <td>34.5</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "      <td>1.405797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-09 07:00:00</th>\n",
+       "      <td>46.5</td>\n",
+       "      <td>59.5</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>77.162</td>\n",
+       "      <td>1.279570</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     betr801  fr04014  london westminster  \\\n",
+       "datetime                                                    \n",
+       "2019-04-09 03:00:00     22.5     24.4                67.0   \n",
+       "2019-04-09 04:00:00     53.5     27.4                67.0   \n",
+       "2019-04-09 05:00:00     54.5     34.2                41.0   \n",
+       "2019-04-09 06:00:00     34.5     48.5                41.0   \n",
+       "2019-04-09 07:00:00     46.5     59.5                41.0   \n",
+       "\n",
+       "                     london_mg_per_cubic  ratio_paris_antwerp  \n",
+       "datetime                                                       \n",
+       "2019-04-09 03:00:00              126.094             1.084444  \n",
+       "2019-04-09 04:00:00              126.094             0.512150  \n",
+       "2019-04-09 05:00:00               77.162             0.627523  \n",
+       "2019-04-09 06:00:00               77.162             1.405797  \n",
+       "2019-04-09 07:00:00               77.162             1.279570  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "air_quality_renamed = air_quality_renamed.rename(columns = str.lower)\n",
+    "air_quality_renamed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__To user guide:__ Further details about column or row label renaming is provided in :ref:`basics.rename`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## REMEMBER\n",
+    "\n",
+    "- Create a new column by assigning the output to the DataFrame with a new column name in between the `[]`.\n",
+    "- Operations are element-wise, no need to loop over rows.\n",
+    "- Use `rename` with a diwtionary or function to rename row labels or column names."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__To user guide:__ Further details about column addition and deletion is provided in :ref:`TODO` ([label](https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#column-selection-addition-deletion) to add in sphinx)"
+   ]
+  }
+ ],
+ "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
+}