{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f190d0c2-9b60-4af2-a17d-d94547de8c7f",
   "metadata": {},
   "source": [
    "# Selecting rows and columns in pandas DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1916d87-2731-45b2-ba28-a211bc29a4df",
   "metadata": {},
   "source": [
    "Often, it makes sense to select certain columns or rows for our analysis. Lets have a look on how we can do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bff2f03f-5392-46ba-b77e-faa510428675",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462f6a8d-9544-4586-b5c3-4dcd4ecc11fe",
   "metadata": {},
   "source": [
    "Therefore, we create a dictionary with rando values and turn it into a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f0b71329-5775-496f-9bd2-8d69355d7292",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
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       "      <td>2</td>\n",
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      "text/plain": [
       "    A   B   C\n",
       "0   0   2   2\n",
       "1   1   3   3\n",
       "2  22   2  44\n",
       "3  21   2   2\n",
       "4  12  12  52\n",
       "5  23  22  52"
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   "source": [
    "data = {\n",
    "    'A': [0, 1, 22, 21, 12, 23],\n",
    "    'B': [2, 3, 2,  2,  12, 22],\n",
    "    'C': [2, 3, 44,  2,  52, 52],\n",
    "}\n",
    "\n",
    "table = pd.DataFrame(data)\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bec258d-8dd5-4229-944b-c200aeb7c310",
   "metadata": {},
   "source": [
    "## Selecting columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb49cd6a-c159-4491-a4c5-24432858dbc8",
   "metadata": {},
   "source": [
    "Now we can select one or more columns by putting them as 'strings' into [square brackets]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ff85932-0cff-4d6b-97e5-764b84127b4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "    B   C\n",
       "0   2   2\n",
       "1   3   3\n",
       "2   2  44\n",
       "3   2   2\n",
       "4  12  52\n",
       "5  22  52"
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   "source": [
    "selected_columns = table[['B', 'C']]\n",
    "selected_columns"
   ]
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  {
   "cell_type": "markdown",
   "id": "6eea6d6a-e2f9-4395-9866-dfded6507b8e",
   "metadata": {},
   "source": [
    "## Selecting rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e2eb8e-f2a0-4e5e-ad09-3e6ba28ffc7b",
   "metadata": {},
   "source": [
    "Now we are selecting rows which have in column 'A' a value higher than 20:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "11550485-3305-42d6-b752-5332e4f77976",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "    A   B   C\n",
       "2  22   2  44\n",
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    "selected_rows = table['A'] > 20\n",
    "table[selected_rows]"
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  {
   "cell_type": "markdown",
   "id": "ba05a667-7c5f-402b-b709-9ceca87d9ec5",
   "metadata": {},
   "source": [
    "We can also shorten these two lines of code into one line. See for example here if we want to get the rows which have in column 'A' a value lower than 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a6328a6-affc-40a2-a711-55941b728bf0",
   "metadata": {
    "tags": []
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   "outputs": [
    {
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       "    A   B   C\n",
       "0   0   2   2\n",
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   "source": [
    "table[table['A'] < 20]"
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