Trending Technologies 2020.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Github_Data_2020_ready_for_text_analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
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       "      <th>fork</th>\n",
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       "      <th>...</th>\n",
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       "      <th>license.key</th>\n",
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       "      <th>license.url</th>\n",
       "      <th>license.node_id</th>\n",
       "      <th>license</th>\n",
       "      <th>lang</th>\n",
       "      <th>clean</th>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>311525798</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkzMTE1MjU3OTg=</td>\n",
       "      <td>Web-Dev-For-Beginners</td>\n",
       "      <td>microsoft/Web-Dev-For-Beginners</td>\n",
       "      <td>False</td>\n",
       "      <td>https://github.com/microsoft/Web-Dev-For-Begin...</td>\n",
       "      <td>24 Lessons, 12 Weeks, Get Started as a Web Dev...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/microsoft/Web-Dev...</td>\n",
       "      <td>...</td>\n",
       "      <td>Organization</td>\n",
       "      <td>False</td>\n",
       "      <td>mit</td>\n",
       "      <td>MIT License</td>\n",
       "      <td>MIT</td>\n",
       "      <td>https://api.github.com/licenses/mit</td>\n",
       "      <td>MDc6TGljZW5zZTEz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>24 lessons 12 weeks get started web developer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>264323169</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkyNjQzMjMxNjk=</td>\n",
       "      <td>graduation</td>\n",
       "      <td>education/graduation</td>\n",
       "      <td>False</td>\n",
       "      <td>https://github.com/education/graduation</td>\n",
       "      <td>$ git remote &lt;graduation&gt; yearbook</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/education/graduation</td>\n",
       "      <td>...</td>\n",
       "      <td>Organization</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>git remote graduation yearbook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>361198003</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkzNjExOTgwMDM=</td>\n",
       "      <td>rdp</td>\n",
       "      <td>c9ffin/rdp</td>\n",
       "      <td>False</td>\n",
       "      <td>https://github.com/c9ffin/rdp</td>\n",
       "      <td>You can get unlimited rdp for free.</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/c9ffin/rdp</td>\n",
       "      <td>...</td>\n",
       "      <td>User</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>get unlimited rdp free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>234170528</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkyMzQxNzA1Mjg=</td>\n",
       "      <td>React-Components-Insta-Clone</td>\n",
       "      <td>LambdaSchool/React-Components-Insta-Clone</td>\n",
       "      <td>False</td>\n",
       "      <td>https://github.com/LambdaSchool/React-Componen...</td>\n",
       "      <td>Module Project for Composing React Components ...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/LambdaSchool/Reac...</td>\n",
       "      <td>...</td>\n",
       "      <td>Organization</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>module project composing react components pass...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>243650536</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkyNDM2NTA1MzY=</td>\n",
       "      <td>User-Interface-II</td>\n",
       "      <td>LambdaSchool/User-Interface-II</td>\n",
       "      <td>False</td>\n",
       "      <td>https://github.com/LambdaSchool/User-Interface-II</td>\n",
       "      <td>Flexbox Challenge</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/LambdaSchool/User...</td>\n",
       "      <td>...</td>\n",
       "      <td>Organization</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>flexbox challenge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
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      "text/plain": [
       "   Unnamed: 0         id                           node_id  \\\n",
       "0           2  311525798  MDEwOlJlcG9zaXRvcnkzMTE1MjU3OTg=   \n",
       "1           3  264323169  MDEwOlJlcG9zaXRvcnkyNjQzMjMxNjk=   \n",
       "2           5  361198003  MDEwOlJlcG9zaXRvcnkzNjExOTgwMDM=   \n",
       "3           6  234170528  MDEwOlJlcG9zaXRvcnkyMzQxNzA1Mjg=   \n",
       "4           7  243650536  MDEwOlJlcG9zaXRvcnkyNDM2NTA1MzY=   \n",
       "\n",
       "                           name                                  full_name  \\\n",
       "0         Web-Dev-For-Beginners            microsoft/Web-Dev-For-Beginners   \n",
       "1                    graduation                       education/graduation   \n",
       "2                           rdp                                 c9ffin/rdp   \n",
       "3  React-Components-Insta-Clone  LambdaSchool/React-Components-Insta-Clone   \n",
       "4             User-Interface-II             LambdaSchool/User-Interface-II   \n",
       "\n",
       "   private                                           html_url  \\\n",
       "0    False  https://github.com/microsoft/Web-Dev-For-Begin...   \n",
       "1    False            https://github.com/education/graduation   \n",
       "2    False                      https://github.com/c9ffin/rdp   \n",
       "3    False  https://github.com/LambdaSchool/React-Componen...   \n",
       "4    False  https://github.com/LambdaSchool/User-Interface-II   \n",
       "\n",
       "                                         description   fork  \\\n",
       "0  24 Lessons, 12 Weeks, Get Started as a Web Dev...  False   \n",
       "1                $ git remote <graduation> yearbook   False   \n",
       "2                You can get unlimited rdp for free.  False   \n",
       "3  Module Project for Composing React Components ...  False   \n",
       "4                                  Flexbox Challenge  False   \n",
       "\n",
       "                                                 url  ...    owner.type  \\\n",
       "0  https://api.github.com/repos/microsoft/Web-Dev...  ...  Organization   \n",
       "1  https://api.github.com/repos/education/graduation  ...  Organization   \n",
       "2            https://api.github.com/repos/c9ffin/rdp  ...          User   \n",
       "3  https://api.github.com/repos/LambdaSchool/Reac...  ...  Organization   \n",
       "4  https://api.github.com/repos/LambdaSchool/User...  ...  Organization   \n",
       "\n",
       "  owner.site_admin license.key license.name license.spdx_id  \\\n",
       "0            False         mit  MIT License             MIT   \n",
       "1            False         NaN          NaN             NaN   \n",
       "2            False         NaN          NaN             NaN   \n",
       "3            False         NaN          NaN             NaN   \n",
       "4            False         NaN          NaN             NaN   \n",
       "\n",
       "                           license.url   license.node_id license lang  \\\n",
       "0  https://api.github.com/licenses/mit  MDc6TGljZW5zZTEz     NaN   en   \n",
       "1                                  NaN               NaN     NaN   en   \n",
       "2                                  NaN               NaN     NaN   en   \n",
       "3                                  NaN               NaN     NaN   en   \n",
       "4                                  NaN               NaN     NaN   en   \n",
       "\n",
       "                                               clean  \n",
       "0      24 lessons 12 weeks get started web developer  \n",
       "1                     git remote graduation yearbook  \n",
       "2                             get unlimited rdp free  \n",
       "3  module project composing react components pass...  \n",
       "4                                  flexbox challenge  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>watchers_count</th>\n",
       "      <th>size</th>\n",
       "      <th>forks_count</th>\n",
       "      <th>open_issues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>356.000000</td>\n",
       "      <td>3.560000e+02</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>765.786517</td>\n",
       "      <td>7.635213e+04</td>\n",
       "      <td>285.747191</td>\n",
       "      <td>32.766854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2283.421975</td>\n",
       "      <td>4.208247e+05</td>\n",
       "      <td>345.358451</td>\n",
       "      <td>69.142874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.127500e+02</td>\n",
       "      <td>143.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.502000e+03</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>484.750000</td>\n",
       "      <td>1.503700e+04</td>\n",
       "      <td>317.750000</td>\n",
       "      <td>32.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25031.000000</td>\n",
       "      <td>5.058376e+06</td>\n",
       "      <td>3553.000000</td>\n",
       "      <td>519.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       watchers_count          size  forks_count  open_issues\n",
       "count      356.000000  3.560000e+02   356.000000   356.000000\n",
       "mean       765.786517  7.635213e+04   285.747191    32.766854\n",
       "std       2283.421975  4.208247e+05   345.358451    69.142874\n",
       "min          0.000000  0.000000e+00    95.000000     0.000000\n",
       "25%          2.000000  2.127500e+02   143.750000     0.000000\n",
       "50%         27.000000  1.502000e+03   166.000000     5.000000\n",
       "75%        484.750000  1.503700e+04   317.750000    32.500000\n",
       "max      25031.000000  5.058376e+06  3553.000000   519.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['watchers_count', 'size', 'forks_count', 'open_issues']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top technologies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identifying the most popular phrases related to technologies by \n",
    "# focusing on most frequent bigrams\n",
    "\n",
    "import nltk         \n",
    "from nltk.collocations import *      #implements n-gram search tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_documents = df['clean'].apply(lambda x : x.split()).tolist()\n",
    "# here list_documents is a list of lists containing words in the column 'clean'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding bigrams document-wise and avoiding corpus wise by using from_documents\n",
    "\n",
    "bigram_measures = nltk.collocations.BigramAssocMeasures()\n",
    "bigram_finder = BigramCollocationFinder.from_documents(list_documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigram_finder.apply_freq_filter(3)  #considering bigrams with at least three occurences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigrams = bigram_finder.nbest(bigram_measures.raw_freq, 20)\n",
    "# we get top 20 bigrams according to raw_freq measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we find scores of these bigrams i.e. (here) the frequency corresponding to each\n",
    "\n",
    "scores = bigram_finder.score_ngrams(bigram_measures.raw_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ngram = list(bigram_finder.ngram_fd.items())   # bigram_finder.ngram_fd is a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ngram.sort(key = lambda item: item[-1], reverse = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('open', 'source'), 22),\n",
       " (('hacktoberfest', '2020'), 7),\n",
       " (('pfm', 'c'), 6),\n",
       " (('c', 'musicians'), 6),\n",
       " (('starter', 'project'), 5),\n",
       " (('let', 's'), 5),\n",
       " (('contributing', 'open'), 5),\n",
       " (('data', 'structures'), 4),\n",
       " (('second', 'task'), 4),\n",
       " (('example', 'repo'), 4)]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ngram[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequency = [(' '.join(k), v) for k, v in ngram]\n",
    "\n",
    "# this just joins the tuple containing bigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>open source</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hacktoberfest 2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pfm c</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c musicians</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>starter project</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0   1\n",
       "0         open source  22\n",
       "1  hacktoberfest 2020   7\n",
       "2               pfm c   6\n",
       "3         c musicians   6\n",
       "4     starter project   5"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>open source</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hacktoberfest 2020</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pfm c</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c musicians</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>starter project</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1\n",
       "0                     \n",
       "open source         22\n",
       "hacktoberfest 2020   7\n",
       "pfm c                6\n",
       "c musicians          6\n",
       "starter project      5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index([0], inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sort_values(by = [1], ascending = False).head(25).plot(kind = 'barh')\n",
    "plt.title('Trending Technologies')\n",
    "plt.ylabel('Technology')\n",
    "plt.xlabel('Popularity')\n",
    "plt.legend().set_visible(False)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}