deveropers-in-lt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack Overflow Survey data can be downloaded from here:\n",
    "\n",
    "https://insights.stackoverflow.com/survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading to \"stackoverflow-survey-2018.zip\"\n",
      "\u001b[KDone. 19.10 MB in 5.32119s (3.59 MB/s)\n"
     ]
    }
   ],
   "source": [
    "!http -b -d 'https://drive.google.com/uc?export=download&id=1_9On2-nsBQIw3JiY43sWbrF8EjrqrR4U' -o stackoverflow-survey-2018.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  stackoverflow-survey-2018.zip\n",
      "  inflating: survey_results_public.csv  \n",
      "  inflating: Developer_Survey_Instrument_2018.pdf  \n",
      "   creating: __MACOSX/\n",
      "  inflating: __MACOSX/._Developer_Survey_Instrument_2018.pdf  \n",
      "  inflating: survey_results_schema.csv  \n",
      "  inflating: README_2018.txt         \n"
     ]
    }
   ],
   "source": [
    "!unzip stackoverflow-survey-2018.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey = pd.read_csv('survey_results_public.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "257"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey[survey.Country == 'Lithuania'].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JavaScript              136\n",
       "HTML                    133\n",
       "CSS                     126\n",
       "SQL                     125\n",
       "Java                     89\n",
       "C#                       81\n",
       "Bash/Shell               79\n",
       "PHP                      76\n",
       "Python                   57\n",
       "C++                      49\n",
       "C                        35\n",
       "TypeScript               30\n",
       "Ruby                     18\n",
       "Go                       14\n",
       "Swift                    13\n",
       "Scala                    10\n",
       "Kotlin                   10\n",
       "Lua                       9\n",
       "Objective-C               9\n",
       "Groovy                    8\n",
       "Assembly                  7\n",
       "VB.NET                    7\n",
       "Delphi/Object Pascal      7\n",
       "VBA                       7\n",
       "R                         6\n",
       "Perl                      5\n",
       "Matlab                    5\n",
       "CoffeeScript              4\n",
       "F#                        3\n",
       "Rust                      3\n",
       "Visual Basic 6            3\n",
       "Ocaml                     1\n",
       "Haskell                   1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey[survey.Country == 'Lithuania'] \\\n",
    "    .LanguageWorkedWith.str.split(';') \\\n",
    "    .apply(pd.Series, 1) \\\n",
    "    .unstack() \\\n",
    "    .reset_index(drop=True, level=0) \\\n",
    "    .dropna() \\\n",
    "    .value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JavaScript              89\n",
       "SQL                     72\n",
       "Python                  60\n",
       "HTML                    58\n",
       "C#                      58\n",
       "CSS                     55\n",
       "Bash/Shell              52\n",
       "Java                    51\n",
       "C++                     46\n",
       "TypeScript              38\n",
       "PHP                     35\n",
       "Go                      32\n",
       "Kotlin                  31\n",
       "C                       27\n",
       "Rust                    23\n",
       "Scala                   20\n",
       "Assembly                15\n",
       "Swift                   14\n",
       "F#                      12\n",
       "Haskell                 11\n",
       "R                       10\n",
       "Lua                      8\n",
       "Ruby                     8\n",
       "Objective-C              6\n",
       "Groovy                   5\n",
       "Clojure                  5\n",
       "Erlang                   4\n",
       "Delphi/Object Pascal     4\n",
       "CoffeeScript             4\n",
       "Hack                     3\n",
       "Perl                     3\n",
       "Ocaml                    3\n",
       "VBA                      3\n",
       "Matlab                   2\n",
       "Visual Basic 6           2\n",
       "VB.NET                   1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey[survey.Country == 'Lithuania'] \\\n",
    "    .LanguageDesireNextYear.str.split(';') \\\n",
    "    .apply(pd.Series, 1) \\\n",
    "    .unstack() \\\n",
    "    .reset_index(drop=True, level=0) \\\n",
    "    .dropna() \\\n",
    "    .value_counts()"
   ]
  }
 ],
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