Questions/I_CompanySize_Technology.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from E_Salary_Cities import *\n",
    "from I_CompanySize_Technology import *"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-width:1; border-radius: 15px; border-style: solid; border-color: rgb(10, 10, 10); background-color: #316B83; text-align: center;font: 14pt 'Candara';font-weight:bold;\"><h1> State & Refine QS</h1></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "COMPANY_SIZE='Company size'\n",
    "TECHNOLOGY='Your main technology / programming language'\n",
    "BIG='1000+'\n",
    "MEDIUM='100-1000'\n",
    "df_2020,df_2019,df_2018=read()\n",
    "df_merged=merge([df_2020,df_2019],common_columns=[COMPANY_SIZE,TECHNOLOGY])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Company size</th>\n",
       "      <th>Your main technology / programming language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51-100</td>\n",
       "      <td>TypeScript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101-1000</td>\n",
       "      <td>Ruby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101-1000</td>\n",
       "      <td>Javascript / Typescript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51-100</td>\n",
       "      <td>Javascript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101-1000</td>\n",
       "      <td>C# .NET</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company size Your main technology / programming language\n",
       "0       51-100                                  TypeScript\n",
       "1     101-1000                                        Ruby\n",
       "2     101-1000                     Javascript / Typescript\n",
       "3       51-100                                  Javascript\n",
       "4     101-1000                                     C# .NET"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> #### _`QS: What is the most used technology in Software Engineering for a mid-sized level company? `_\n",
    "> #### _`QS: Can we say the same technology is the most popular for a big-sized company or not?  `_\n",
    ">|Set Expectation | Collect Info | Match Results| \n",
    ">|----|----|----|\n",
    ">| The Quetsion is Answerable & of interest & Novel | Did some reasearch online and poeple always ask what language do we need to learn  and Which coding languages are used by the world’s top companies?|_`Matches`_  because people may be interested in this question as the language can influence their job opportunities and career growth.Knowing these languages could potentially increase a programmer’s chances of being hired by these companies Such af FAANG companies |\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-width:1; border-radius: 15px; border-style: solid; border-color: rgb(10, 10, 10); background-color: #316B83; text-align: center;font: 14pt 'Candara';font-weight:bold;\"><h1> Explore Data</h1></div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***\n",
    "> ### _`Some Data Cleaning`_\n",
    "> ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before cleaning, Company sizes :  ['51-100' '101-1000' '11-50' '1000+' nan 'up to 10' '50-100' '10-50'\n",
      " '100-1000']\n",
      "After cleaning, Company sizes :  ['50-100' '100-1000' '10-50' '1000+' 'up to 10']\n"
     ]
    }
   ],
   "source": [
    "print(\"Before cleaning, Company sizes : \",df_merged[COMPANY_SIZE].unique())\n",
    "df_cleaned=clean_company_size(df=df_merged,company_size=COMPANY_SIZE)\n",
    "print(\"After cleaning, Company sizes : \",df_cleaned[COMPANY_SIZE].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_histogram(df=df_cleaned,column=COMPANY_SIZE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***\n",
    "> ### _`Interpretations:`_\n",
    "> - _`We can see that the big Sized Companies (1000+) have the highest % in the dataset`_\n",
    "> - _`Followed by Mid Sized Companies (100-1000) `_\n",
    "> - _`The number of people in each category of Bigsized and MidSized are quite close to each other `_\n",
    "> ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before cleaning : 274  unique technology \n",
      "After cleaning : 17  unique technology \n"
     ]
    }
   ],
   "source": [
    "print('Before cleaning : {}  unique technology '.format(df_cleaned[TECHNOLOGY].nunique()))\n",
    "df_cleaned=clean_technology(df=df_cleaned,column=TECHNOLOGY,filter=30)\n",
    "print('After cleaning : {}  unique technology '.format(df_cleaned[TECHNOLOGY].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_histogram(df=df_cleaned,column=TECHNOLOGY,title='The language Distribution among all companies', figsize=(15, 5))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***\n",
    "> ### _`Interpretations:`_\n",
    "> - _`We can see that generally python is the most used technology  among the rest of the technologies regardless of the company size`_\n",
    "> - _`Followd by Java which is still a very big peak`_\n",
    "> - _`This was done after filtering those technologies with frequency less than 30 in the dataset`_\n",
    "> ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=df_cleaned[df_cleaned[COMPANY_SIZE].isin([BIG,MEDIUM])]\n",
    "plot_histogram_grid(df, TECHNOLOGY, COMPANY_SIZE, figsize=(15, 5))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***\n",
    "> ### _`Interpretations:`_\n",
    "> - _`Java is the most used technology in the midsized companies followed by python & javascript`_\n",
    "> - _`In BigSized companies it is python then Java. Still javascript is the 3rd most used technology in bigsized companies`_\n",
    "> ***"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">|Set Expectation | Collect Info | Match Results| \n",
    ">|----|----|----|\n",
    ">| Python to be the most used langauge in all company sizes |  Showed Several plots |_`DIDnot Match`_  looking back at the distribution of langauages used. Yes although python is the most used, still java is frequently used by a minor difference between them|\n"
   ]
  },
  {
   "attachments": {},
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    "<div style=\"border-width:1; border-radius: 15px; border-style: solid; border-color: rgb(10, 10, 10); background-color: #316B83; text-align: center;font: 14pt 'Candara';font-weight:bold;\"><h1> Build Models</h1></div>"
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   "source": [
    "> ***\n",
    "> ### _`Z_test for proportion`_\n",
    "> - _`We need to proof that \"Java\" which is the most used technology in Midsized companies , is the most used in Medium sized companies or not?`_\n",
    "> - _`So typically to perform such test, we will need to perform \"z test for propotion\" for EVERY  technology other than \"Java\" `_\n",
    "> \n",
    "> ${H}_{o} : \\% Java <= \\%technology $ \n",
    ">\n",
    "> _`where technology is all other technologies in Big sized companies other than Java `_ \n",
    ">\n",
    "> ${H}_{a} : \\% Java > \\%technology $\n",
    ">\n",
    "> #### _`Ztest for proportion `_\n",
    "> - _`Assumes that we have atleast 10 records to perform it`_\n",
    "> - _`Here it is right tailed test , so we must have z_values >0`_\n",
    "> - _`the restured Pvalue should be divide by 2`_\n",
    "> ***"
   ]
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  {
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       "   typescript   php  javascript  python    c#  scala    go  kotlin  swift  \\\n",
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    "\n",
    "df_big_companies= df_cleaned[df_cleaned[COMPANY_SIZE]==BIG]\n",
    "df_medium_companies = df_cleaned[df_cleaned[COMPANY_SIZE]==MEDIUM]\n",
    "# Get the technology with the highest count in medium sized companies\n",
    "max_technology = df_medium_companies[TECHNOLOGY].value_counts().idxmax()\n",
    "hypothesis_result,pvals=z_test_proportion_companies(max_technology,df_cleaned,COMPANY_SIZE,BIG,MEDIUM,TECHNOLOGY)\n",
    "hypothesis_result"
   ]
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   "execution_count": 11,
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    "# Get the technology with the highest count in big sized companies\n",
    "max_technology = df_big_companies[TECHNOLOGY].value_counts().idxmax()\n",
    "hypothesis_result,pvals=z_test_proportion_companies(max_technology,df_cleaned,COMPANY_SIZE,BIG,MEDIUM,TECHNOLOGY)\n",
    "hypothesis_result"
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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- https://mverbakel.github.io/2021-02-13/two-sample-proportions -->"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://mverbakel.github.io/2021-02-13/two-sample-proportions"
   ]
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    ">|Set Expectation | Collect Info | Match Results| \n",
    ">|----|----|----|\n",
    ">| Java to be the most used langauge in all Big sized companies |  Did z hypothesis testing for proprotion |_`DIDnot Match`_  looking back at the distribution of langauages , we found out that Python is the most used language in  Big sized companies |\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-width:1; border-radius: 15px; border-style: solid; border-color: rgb(10, 10, 10); background-color: #316B83; text-align: center;font: 14pt 'Candara';font-weight:bold;\"><h1> Interpret Results</h1></div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***\n",
    "> ### _`Interpretations:`_\n",
    "> - _`Java is the most used technology in the midsized companies exculding python`_\n",
    "> - _`It MAYBE the case that java < python in midsized companies  but we doesnot have evidence to suggest that over the population`_\n",
    "> ***"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-width:1; border-radius: 15px; border-style: solid; border-color: rgb(10, 10, 10); background-color: #316B83; text-align: center;font: 14pt 'Candara';font-weight:bold;\"><h1> Communicate Results</h1></div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***\n",
    "> ### _`Suggestions:`_\n",
    "> - _`It seems that Java & python are in a very competitive case. Although no certain case that shows that one language is more dominant than the other , it is still a good potential if you are a new learner and want to join the field to start with one of these 2 language.`_\n",
    "> -`So we are just narrowing down the space of searching to only 2 language & you might  be condifent that one of these is top in Big sized companies if you wish to join the big companies`\n",
    "> ***"
   ]
  }
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