dummy-data-examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy Data Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's often useful to create some dummy dataframes to experiment with pandas functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating random test-data with numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# | Draw sample(s) from the “standard normal” distribution\n",
    "a = np.random.randn(100, 4) # 100 samples, 4 columns\n",
    "\n",
    "# | Draw sample(s) from a uniform distribution \n",
    "# \"random float values\"\n",
    "b = np.random.uniform(low=0.0, high=1.0, size=(100,4))\n",
    "\n",
    "# random integer values\n",
    "c = np.random.randint(0, 1000, size=(100,4))\n",
    "\n",
    "# | Uniform sampling from given array/list\n",
    "d = np.random.choice(['C1','C2','C3'], size=(100,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.246948</td>\n",
       "      <td>0.088561</td>\n",
       "      <td>-0.469130</td>\n",
       "      <td>1.733602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.292812</td>\n",
       "      <td>-1.363095</td>\n",
       "      <td>0.935763</td>\n",
       "      <td>1.818374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.456810</td>\n",
       "      <td>-1.143127</td>\n",
       "      <td>-0.220597</td>\n",
       "      <td>-0.212127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.393341</td>\n",
       "      <td>0.717071</td>\n",
       "      <td>0.901208</td>\n",
       "      <td>-1.078046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.110282</td>\n",
       "      <td>1.980008</td>\n",
       "      <td>-0.565658</td>\n",
       "      <td>2.103793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.246948  0.088561 -0.469130  1.733602\n",
       "1 -0.292812 -1.363095  0.935763  1.818374\n",
       "2 -1.456810 -1.143127 -0.220597 -0.212127\n",
       "3  0.393341  0.717071  0.901208 -1.078046\n",
       "4  0.110282  1.980008 -0.565658  2.103793"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>D</th>\n",
       "      <th>CAT1</th>\n",
       "      <th>CAT2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>186</td>\n",
       "      <td>594</td>\n",
       "      <td>574</td>\n",
       "      <td>75</td>\n",
       "      <td>C3</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>786</td>\n",
       "      <td>37</td>\n",
       "      <td>701</td>\n",
       "      <td>306</td>\n",
       "      <td>C3</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207</td>\n",
       "      <td>308</td>\n",
       "      <td>857</td>\n",
       "      <td>298</td>\n",
       "      <td>C1</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>583</td>\n",
       "      <td>105</td>\n",
       "      <td>430</td>\n",
       "      <td>621</td>\n",
       "      <td>C1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>122</td>\n",
       "      <td>444</td>\n",
       "      <td>125</td>\n",
       "      <td>973</td>\n",
       "      <td>C2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C    D CAT1 CAT2\n",
       "0  186  594  574   75   C3   C2\n",
       "1  786   37  701  306   C3   C2\n",
       "2  207  308  857  298   C1   C2\n",
       "3  583  105  430  621   C1   C1\n",
       "4  122  444  125  973   C2   C2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randint(0, 1000, size=(100,4)), columns=list('ABCD'))\n",
    "# categorical column\n",
    "df['CAT1'] = np.random.choice(['C1','C2','C3'], size=len(df))\n",
    "df['CAT2'] = np.random.choice(['C1','C2','C3'], size=len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time-Series DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-03-31\n",
       "1   2017-06-30\n",
       "2   2017-09-30\n",
       "3   2017-12-31\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr_dates = pd.Series(pd.date_range('2017', periods=4, freq='Q'))\n",
    "sr_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-03-06 00:00:00+00:00</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-06 00:00:01+00:00</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-06 00:00:02+00:00</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-06 00:00:03+00:00</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-06 00:00:04+00:00</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                           0\n",
       "2012-03-06 00:00:00+00:00  6\n",
       "2012-03-06 00:00:01+00:00  9\n",
       "2012-03-06 00:00:02+00:00  1\n",
       "2012-03-06 00:00:03+00:00  8\n",
       "2012-03-06 00:00:04+00:00  0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times = pd.date_range('3/6/2012 00:00', periods=100, freq='S', tz='UTC')\n",
    "df = pd.DataFrame(np.random.randint(10, size=(100,1)), index=times)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MultiIndex DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Semester1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Semester2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Maths</th>\n",
       "      <th>Science</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Science</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alisa</th>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>67</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby</th>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>45</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cathrine</th>\n",
       "      <td>45</td>\n",
       "      <td>67</td>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack</th>\n",
       "      <td>67</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Semester1         Semester2        \n",
       "             Maths Science     Maths Science\n",
       "Alisa           12      45        67      56\n",
       "Bobby           78      89        45      67\n",
       "Cathrine        45      67        89      90\n",
       "Jack            67      44        56      55"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header = pd.MultiIndex.from_product([['Semester1','Semester2'],['Maths','Science']])\n",
    "data = ([[12,45,67,56],[78,89,45,67],[45,67,89,90],[67,44,56,55]])\n",
    " \n",
    "df = pd.DataFrame(data, index=['Alisa','Bobby','Cathrine','Jack'], columns=header)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Heart Disease</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Yes</th>\n",
       "      <th colspan=\"2\" halign=\"left\">No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>High Blood Pressure</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Single</th>\n",
       "      <td>95</td>\n",
       "      <td>47</td>\n",
       "      <td>87</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Single</th>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>85</td>\n",
       "      <td>51</td>\n",
       "      <td>87</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Heart Disease         Yes      No    \n",
       "High Blood Pressure   Yes  No Yes  No\n",
       "Sex    Marital Status                \n",
       "Female Single          95  47  87  80\n",
       "       Married         25  24  32  11\n",
       "Male   Single          64   8  24  83\n",
       "       Married         85  51  87  41"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colidx = pd.MultiIndex.from_product([('Yes', 'No'), ('Yes', 'No')],\n",
    "                                    names=['Heart Disease', 'High Blood Pressure'])\n",
    "rowidx = pd.MultiIndex.from_product([('Female', 'Male'), ('Single', 'Married')], \n",
    "                                    names=['Sex', 'Marital Status'])\n",
    "\n",
    "df = pd.DataFrame(np.random.randint(100, size=(len(rowidx), len(colidx))), index=rowidx, columns=colidx)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">A</th>\n",
       "      <th colspan=\"2\" halign=\"left\">B</th>\n",
       "      <th colspan=\"2\" halign=\"left\">C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>O</th>\n",
       "      <th>I</th>\n",
       "      <th>O</th>\n",
       "      <th>I</th>\n",
       "      <th>O</th>\n",
       "      <th>I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.153210</td>\n",
       "      <td>1.874311</td>\n",
       "      <td>0.698654</td>\n",
       "      <td>0.856726</td>\n",
       "      <td>-1.640441</td>\n",
       "      <td>-1.386755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.998808</td>\n",
       "      <td>-0.132932</td>\n",
       "      <td>-0.227019</td>\n",
       "      <td>0.005667</td>\n",
       "      <td>0.287349</td>\n",
       "      <td>-0.198939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.279573</td>\n",
       "      <td>2.360184</td>\n",
       "      <td>-0.589590</td>\n",
       "      <td>0.849242</td>\n",
       "      <td>-0.936124</td>\n",
       "      <td>-1.089161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.236291</td>\n",
       "      <td>0.673723</td>\n",
       "      <td>0.235151</td>\n",
       "      <td>-0.635083</td>\n",
       "      <td>-0.049784</td>\n",
       "      <td>0.913887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.559644</td>\n",
       "      <td>1.028673</td>\n",
       "      <td>-0.175795</td>\n",
       "      <td>0.220123</td>\n",
       "      <td>0.247714</td>\n",
       "      <td>-1.221123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A                   B                   C          \n",
       "          O         I         O         I         O         I\n",
       "0 -0.153210  1.874311  0.698654  0.856726 -1.640441 -1.386755\n",
       "1 -0.998808 -0.132932 -0.227019  0.005667  0.287349 -0.198939\n",
       "2 -0.279573  2.360184 -0.589590  0.849242 -0.936124 -1.089161\n",
       "3 -1.236291  0.673723  0.235151 -0.635083 -0.049784  0.913887\n",
       "4  1.559644  1.028673 -0.175795  0.220123  0.247714 -1.221123"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_idx = pd.MultiIndex.from_product([['A', 'B', 'C'],  ['O', 'I']])\n",
    "# col_idx = pd.MultiIndex.from_tuples([(x, y) for x in ['A', 'B', 'C'] for y in ['O', 'I']])\n",
    " \n",
    "df = pd.DataFrame(np.random.randn(10, len(col_idx)), columns=col_idx)\n",
    "df.head()\n",
    "\n"
   ]
  }
 ],
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