Python3/Great Python modules for data processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numerizer in /home/emmett/.local/lib/python3.8/site-packages (0.2.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install numerizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numerizer import numerize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3\n",
      "105\n",
      "20 point 1\n",
      "6.6\n",
      "1/4\n",
      "1000000000000000\n"
     ]
    }
   ],
   "source": [
    "print(numerize('one two three'))\n",
    "print(numerize('One Hundred and five'))\n",
    "print(numerize('twenty point one'))\n",
    "print(numerize('six and three fifths'))\n",
    "print(numerize('one quarter'))\n",
    "print(numerize('one million billion'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: faker in /home/emmett/.local/lib/python3.8/site-packages (7.0.1)\r\n",
      "Requirement already satisfied: text-unidecode==1.3 in /home/emmett/.local/lib/python3.8/site-packages (from faker) (1.3)\r\n",
      "Requirement already satisfied: python-dateutil>=2.4 in /usr/lib/python3/dist-packages (from faker) (2.8.1)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install faker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from faker import Faker\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake = Faker()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"Name\" : [fake.name() for x in range(1, 200)],\n",
    "                   \"Website\" : [fake.url() for x in range(1, 200)]\n",
    "                  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Name</th>\n",
       "      <th>Website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rickey Watkins</td>\n",
       "      <td>https://duran-moore.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Douglas Anderson</td>\n",
       "      <td>http://www.garcia.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jesse Nelson</td>\n",
       "      <td>http://www.aguilar.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kayla Long</td>\n",
       "      <td>https://cox-santana.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jeffrey Johnson</td>\n",
       "      <td>https://www.briggs.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alyssa White</td>\n",
       "      <td>http://munoz.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>John Hall</td>\n",
       "      <td>http://garcia-knight.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Denise Flores</td>\n",
       "      <td>http://www.dillon-matthews.net/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cheryl Joseph</td>\n",
       "      <td>https://daugherty.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Patricia Floyd</td>\n",
       "      <td>https://odonnell.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Angela Short</td>\n",
       "      <td>http://chandler-howell.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Thomas Johnson</td>\n",
       "      <td>http://hall-vasquez.net/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bradley Bush</td>\n",
       "      <td>http://www.fleming.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jeffrey Larson</td>\n",
       "      <td>http://butler.net/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Monique Parker</td>\n",
       "      <td>http://scott-david.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Richard Cardenas</td>\n",
       "      <td>https://cooper-reese.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Samuel Thomas</td>\n",
       "      <td>https://www.mcdonald.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Erik Taylor</td>\n",
       "      <td>http://pierce.biz/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Alexander Boone</td>\n",
       "      <td>https://hernandez-mendez.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hunter Harris</td>\n",
       "      <td>http://www.moody.com/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name                          Website\n",
       "0     Rickey Watkins         https://duran-moore.com/\n",
       "1   Douglas Anderson           http://www.garcia.com/\n",
       "2       Jesse Nelson          http://www.aguilar.com/\n",
       "3         Kayla Long         https://cox-santana.com/\n",
       "4    Jeffrey Johnson          https://www.briggs.com/\n",
       "5       Alyssa White                http://munoz.com/\n",
       "6          John Hall        http://garcia-knight.com/\n",
       "7      Denise Flores  http://www.dillon-matthews.net/\n",
       "8      Cheryl Joseph           https://daugherty.com/\n",
       "9     Patricia Floyd            https://odonnell.com/\n",
       "10      Angela Short      http://chandler-howell.com/\n",
       "11    Thomas Johnson         http://hall-vasquez.net/\n",
       "12      Bradley Bush          http://www.fleming.com/\n",
       "13    Jeffrey Larson               http://butler.net/\n",
       "14    Monique Parker          http://scott-david.com/\n",
       "15  Richard Cardenas        https://cooper-reese.com/\n",
       "16     Samuel Thomas        https://www.mcdonald.com/\n",
       "17       Erik Taylor               http://pierce.biz/\n",
       "18   Alexander Boone    https://hernandez-mendez.com/\n",
       "19     Hunter Harris            http://www.moody.com/"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: missingno in /home/emmett/.local/lib/python3.8/site-packages (0.4.2)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from missingno) (1.20.2)\n",
      "Requirement already satisfied: seaborn in /home/emmett/.local/lib/python3.8/site-packages (from missingno) (0.11.1)\n",
      "Requirement already satisfied: scipy in /home/emmett/.local/lib/python3.8/site-packages (from missingno) (1.6.2)\n",
      "Requirement already satisfied: matplotlib in /home/emmett/.local/lib/python3.8/site-packages (from missingno) (3.4.1)\n",
      "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.8/dist-packages (from seaborn->missingno) (1.2.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/emmett/.local/lib/python3.8/site-packages (from matplotlib->missingno) (8.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/emmett/.local/lib/python3.8/site-packages (from matplotlib->missingno) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/lib/python3/dist-packages (from matplotlib->missingno) (2.8.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /usr/lib/python3/dist-packages (from matplotlib->missingno) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/emmett/.local/lib/python3.8/site-packages (from matplotlib->missingno) (1.3.1)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas>=0.23->seaborn->missingno) (2020.1)\n",
      "Requirement already satisfied: six in /usr/lib/python3/dist-packages (from cycler>=0.10->matplotlib->missingno) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as mi\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"A\" : [5, np.NaN, 10, np.NaN], \n",
    "                  \"B\" : [\"eight\", \"seven\", \"two\", \"three\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.matrix(df, figsize = (10,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: emot in /home/emmett/.local/lib/python3.8/site-packages (2.1)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install emot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from emot.emo_unicode import UNICODE_EMO, EMOTICONS\n",
    "def convert_emoticons(text):\n",
    "    for emot in EMOTICONS:\n",
    "        text = re.sub(u'('+emot+')', \"_\".join(EMOTICONS[emot].replace(\",\",\"\").split()), text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hi Happy_face_smiley, how are you? Tongue_sticking_out_cheeky_playful_or_blowing_a_raspberry'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_emoticons(\"Hi :3, how are you? :P\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pillow in /home/emmett/.local/lib/python3.8/site-packages (8.2.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import PIL as pil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = pil.Image.open(\"logo.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "im.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=120x120 at 0x7F181CA46CD0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.rotate(80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting arrow\n",
      "  Downloading arrow-1.0.3-py3-none-any.whl (54 kB)\n",
      "\u001b[K     |████████████████████████████████| 54 kB 808 kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.0 in /usr/lib/python3/dist-packages (from arrow) (2.8.1)\n",
      "Installing collected packages: arrow\n",
      "Successfully installed arrow-1.0.3\n"
     ]
    }
   ],
   "source": [
    "!pip install arrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "utc = arrow.utcnow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "local = utc.to('US/Eastern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'19 seconds ago'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local.humanize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'il y a 34 secondes'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local.humanize(locale='fr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 4
}