Dask/pandas-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.1 s, sys: 9.06 s, total: 31.2 s\n",
      "Wall time: 35 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_csv('sample_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.31 s, sys: 9.37 s, total: 16.7 s\n",
      "Wall time: 12.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_parquet('sample_data_parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alphabet</th>\n",
       "      <th>useless_letter</th>\n",
       "      <th>Z</th>\n",
       "      <th>Y</th>\n",
       "      <th>X</th>\n",
       "      <th>W</th>\n",
       "      <th>V</th>\n",
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       "      <th>D</th>\n",
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       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>ABCDEFGHIJKLMNOPQRSTUVWXYZ</td>\n",
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       "      <td>69</td>\n",
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       "      <td>64</td>\n",
       "      <td>83</td>\n",
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       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABCDEFGHIJKLMNOPQRSTUVWXYZ</td>\n",
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       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>60</td>\n",
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       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>94</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABCDEFGHIJKLMNOPQRSTUVWXYZ</td>\n",
       "      <td>N</td>\n",
       "      <td>40</td>\n",
       "      <td>67</td>\n",
       "      <td>36</td>\n",
       "      <td>54</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>60</td>\n",
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       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>89</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>8</td>\n",
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       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABCDEFGHIJKLMNOPQRSTUVWXYZ</td>\n",
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       "      <td>87</td>\n",
       "      <td>68</td>\n",
       "      <td>73</td>\n",
       "      <td>78</td>\n",
       "      <td>39</td>\n",
       "      <td>67</td>\n",
       "      <td>57</td>\n",
       "      <td>24</td>\n",
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       "      <td>9</td>\n",
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       "      <td>2</td>\n",
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       "      <td>18</td>\n",
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       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         alphabet useless_letter   Z   Y   X   W   V   U   T  \\\n",
       "index                                                                          \n",
       "0      ABCDEFGHIJKLMNOPQRSTUVWXYZ              U   1  66  69   4  70  64  83   \n",
       "1      ABCDEFGHIJKLMNOPQRSTUVWXYZ              L  25  26  36  34   0  75  60   \n",
       "2      ABCDEFGHIJKLMNOPQRSTUVWXYZ              N  40  67  36  54  46   5  57   \n",
       "3      ABCDEFGHIJKLMNOPQRSTUVWXYZ              C  91  98   8  36  17   3  29   \n",
       "4      ABCDEFGHIJKLMNOPQRSTUVWXYZ              J  87  68  73  78  39  67  57   \n",
       "\n",
       "        S  ...   J   I   H   G   F   E   D   C   B   A  \n",
       "index      ...                                          \n",
       "0      55  ...  36  28  33  57  23  86  48  30  91  84  \n",
       "1      73  ...  42  91   8  24  64  13  43  47  94  11  \n",
       "2      50  ...  82  76  24  60   3  55  64  28  26  89  \n",
       "3      90  ...  88  24   7  51  52  87   1   6  19  48  \n",
       "4      24  ...   9  14   0   2  51  18  95  71  28  13  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8999999</th>\n",
       "      <td>ABCDEFGHIJKLMNOPQRSTUVWXYZ</td>\n",
       "      <td>N</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>99</td>\n",
       "      <td>50</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>63</td>\n",
       "      <td>68</td>\n",
       "      <td>65</td>\n",
       "      <td>84</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>41</td>\n",
       "      <td>52</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9000000 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           alphabet useless_letter   Z   Y   X   W   V   U  \\\n",
       "index                                                                        \n",
       "0        ABCDEFGHIJKLMNOPQRSTUVWXYZ              U   1  66  69   4  70  64   \n",
       "1        ABCDEFGHIJKLMNOPQRSTUVWXYZ              L  25  26  36  34   0  75   \n",
       "2        ABCDEFGHIJKLMNOPQRSTUVWXYZ              N  40  67  36  54  46   5   \n",
       "3        ABCDEFGHIJKLMNOPQRSTUVWXYZ              C  91  98   8  36  17   3   \n",
       "4        ABCDEFGHIJKLMNOPQRSTUVWXYZ              J  87  68  73  78  39  67   \n",
       "...                             ...            ...  ..  ..  ..  ..  ..  ..   \n",
       "8999995  ABCDEFGHIJKLMNOPQRSTUVWXYZ              F  14   6  87  54  44  93   \n",
       "8999996  ABCDEFGHIJKLMNOPQRSTUVWXYZ              F   3  86  73   6  47  72   \n",
       "8999997  ABCDEFGHIJKLMNOPQRSTUVWXYZ              U   6  88  22  17   0  18   \n",
       "8999998  ABCDEFGHIJKLMNOPQRSTUVWXYZ              V  14  97   6   5   4  55   \n",
       "8999999  ABCDEFGHIJKLMNOPQRSTUVWXYZ              N   5  76  72  99  50  42   \n",
       "\n",
       "          T   S  ...   J   I   H   G   F   E   D   C   B   A  \n",
       "index            ...                                          \n",
       "0        83  55  ...  36  28  33  57  23  86  48  30  91  84  \n",
       "1        60  73  ...  42  91   8  24  64  13  43  47  94  11  \n",
       "2        57  50  ...  82  76  24  60   3  55  64  28  26  89  \n",
       "3        29  90  ...  88  24   7  51  52  87   1   6  19  48  \n",
       "4        57  24  ...   9  14   0   2  51  18  95  71  28  13  \n",
       "...      ..  ..  ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  \n",
       "8999995  39   5  ...  25  31  72  35  31  20  38  19  92  55  \n",
       "8999996  31  75  ...  55  60  72  60  85  58  34  79  40  22  \n",
       "8999997  47  16  ...  41  68  56  17  15   7  29  14  86   9  \n",
       "8999998  56  29  ...  71  86  38  92   4  20   0  58  96   2  \n",
       "8999999  68  43  ...  30  63  68  65  84  46  46  41  52  15  \n",
       "\n",
       "[9000000 rows x 28 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 36.7 s, sys: 45.7 s, total: 1min 22s\n",
      "Wall time: 1min 56s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['mean'] = df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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