modin.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "rocky-amount",
   "metadata": {},
   "outputs": [],
   "source": [
    "from distributed import Client\n",
    "client = Client(n_workers=6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "quality-minneapolis",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: The Dask Engine for Modin is experimental.\n"
     ]
    }
   ],
   "source": [
    "import modin.pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "elementary-approach",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "threatened-statement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 164 ms, sys: 47.2 ms, total: 211 ms\n",
      "Wall time: 875 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_csv(\"kiva_loans.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "turned-being",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.6 s, sys: 96 ms, total: 1.7 s\n",
      "Wall time: 1.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dff = pds.read_csv(\"kiva_loans.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "starting-change",
   "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>id</th>\n",
       "      <th>funded_amount</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>activity</th>\n",
       "      <th>sector</th>\n",
       "      <th>use</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>region</th>\n",
       "      <th>currency</th>\n",
       "      <th>partner_id</th>\n",
       "      <th>posted_time</th>\n",
       "      <th>disbursed_time</th>\n",
       "      <th>funded_time</th>\n",
       "      <th>term_in_months</th>\n",
       "      <th>lender_count</th>\n",
       "      <th>tags</th>\n",
       "      <th>borrower_genders</th>\n",
       "      <th>repayment_interval</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>379249</th>\n",
       "      <td>1025240</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Home Appliances</td>\n",
       "      <td>Personal Use</td>\n",
       "      <td>To buy a decent motorbike for commuting to work.</td>\n",
       "      <td>KH</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>Kandal province, Kandal Stoeng district</td>\n",
       "      <td>KHR</td>\n",
       "      <td>204.0</td>\n",
       "      <td>2016-02-19 03:18:40+00:00</td>\n",
       "      <td>2016-01-27 08:00:00+00:00</td>\n",
       "      <td>2016-03-11 02:48:40+00:00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17</td>\n",
       "      <td>#Biz Durable Asset, #Parent, #First Loan</td>\n",
       "      <td>male</td>\n",
       "      <td>monthly</td>\n",
       "      <td>2016-02-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  funded_amount  loan_amount         activity        sector  \\\n",
       "379249  1025240          500.0        500.0  Home Appliances  Personal Use   \n",
       "\n",
       "                                                     use country_code  \\\n",
       "379249  To buy a decent motorbike for commuting to work.           KH   \n",
       "\n",
       "         country                                   region currency  \\\n",
       "379249  Cambodia  Kandal province, Kandal Stoeng district      KHR   \n",
       "\n",
       "        partner_id                posted_time             disbursed_time  \\\n",
       "379249       204.0  2016-02-19 03:18:40+00:00  2016-01-27 08:00:00+00:00   \n",
       "\n",
       "                      funded_time  term_in_months  lender_count  \\\n",
       "379249  2016-03-11 02:48:40+00:00            14.0            17   \n",
       "\n",
       "                                            tags borrower_genders  \\\n",
       "379249  #Biz Durable Asset, #Parent, #First Loan             male   \n",
       "\n",
       "       repayment_interval        date  \n",
       "379249            monthly  2016-02-19  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "sophisticated-seven",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 49.9 ms, sys: 2.7 ms, total: 52.6 ms\n",
      "Wall time: 57.5 ms\n"
     ]
    },
    {
     "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>sector</th>\n",
       "      <th>term_in_months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>14.334500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arts</td>\n",
       "      <td>13.305473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clothing</td>\n",
       "      <td>12.090159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Construction</td>\n",
       "      <td>15.370613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Education</td>\n",
       "      <td>21.640731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>17.109639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food</td>\n",
       "      <td>11.613463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Health</td>\n",
       "      <td>19.173046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Housing</td>\n",
       "      <td>20.522902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>14.268847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Personal Use</td>\n",
       "      <td>13.423224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Retail</td>\n",
       "      <td>11.064268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Services</td>\n",
       "      <td>14.865485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>13.418160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Wholesale</td>\n",
       "      <td>14.716088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sector  term_in_months\n",
       "0      Agriculture       14.334500\n",
       "1             Arts       13.305473\n",
       "2         Clothing       12.090159\n",
       "3     Construction       15.370613\n",
       "4        Education       21.640731\n",
       "5    Entertainment       17.109639\n",
       "6             Food       11.613463\n",
       "7           Health       19.173046\n",
       "8          Housing       20.522902\n",
       "9    Manufacturing       14.268847\n",
       "10    Personal Use       13.423224\n",
       "11          Retail       11.064268\n",
       "12        Services       14.865485\n",
       "13  Transportation       13.418160\n",
       "14       Wholesale       14.716088"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# normal pandas\n",
    "dff.groupby(\"sector\")['term_in_months'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "broad-interaction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 91.1 ms, sys: 17.3 ms, total: 108 ms\n",
      "Wall time: 294 ms\n"
     ]
    },
    {
     "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>activity</th>\n",
       "      <th>term_in_months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adult Care</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>14.913740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Air Conditioning</td>\n",
       "      <td>15.694444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Animal Sales</td>\n",
       "      <td>13.701418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aquaculture</td>\n",
       "      <td>13.407407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Water Distribution</td>\n",
       "      <td>12.781915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Weaving</td>\n",
       "      <td>12.781155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Wedding Expenses</td>\n",
       "      <td>17.832099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Well digging</td>\n",
       "      <td>19.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Wholesale</td>\n",
       "      <td>13.633053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>163 rows x 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               activity  term_in_months\n",
       "0            Adult Care       22.500000\n",
       "1           Agriculture       14.913740\n",
       "2      Air Conditioning       15.694444\n",
       "3          Animal Sales       13.701418\n",
       "4           Aquaculture       13.407407\n",
       "..                  ...             ...\n",
       "158  Water Distribution       12.781915\n",
       "159             Weaving       12.781155\n",
       "160    Wedding Expenses       17.832099\n",
       "161        Well digging       19.714286\n",
       "162           Wholesale       13.633053\n",
       "\n",
       "[163 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Modin pandas\n",
    "df.groupby(\"activity\")['term_in_months'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "third-estimate",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: `Series.hist_series` defaulting to pandas implementation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "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": [
    "df[\"term_in_months\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "directed-supplement",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}