correlation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fa0d8fec-5bbf-4bfd-98aa-b7f2a5fa1f2f",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Correlation\n",
    "\n",
    "In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, \"correlation\" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.\n",
    "\n",
    "Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However, in general, the presence of a correlation is not sufficient to infer the presence of a causal relationship (i.e., correlation does not imply causation). \n",
    "\n",
    "In our analysis we have found that Java was the top language till April 2018, starting from July 2004. Then the top spot has been reserved by Python, till the end of our sample size. Seeing this we decided to run a comprehensive analysis of the correlation between python and Java. We have also included their correlation to other languages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0cf6c3cc-c7cb-4996-93bd-97e31e3a8aef",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "\n",
    "data = pd.read_csv('data/original_data.csv');\n",
    "data.to_csv('temp/data_ad4.csv', float_format='%.3f')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb773e2a-8241-4248-9609-97acd908b4d0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Python', ylabel='Java'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.regplot(x='Python', y='Java', data = data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db4492e-5a10-409c-878d-fd0c9c2abcf2",
   "metadata": {},
   "source": [
    "upwards = positve corealtion\n",
    "downwards = negative corealtion (this!)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8a7b680-2e8d-40c6-8923-987807e59dcb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data2 = data.drop(columns = ['Months','Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3e83232-5770-4521-8129-957e5a418da7",
   "metadata": {
    "tags": []
   },
   "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>Python</th>\n",
       "      <th>Java</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abap</th>\n",
       "      <td>0.210543</td>\n",
       "      <td>-0.275354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.677052</td>\n",
       "      <td>-0.711266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C/C++</th>\n",
       "      <td>-0.702704</td>\n",
       "      <td>0.681450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C#</th>\n",
       "      <td>0.085039</td>\n",
       "      <td>-0.169606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cobol</th>\n",
       "      <td>-0.514747</td>\n",
       "      <td>0.534975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dart</th>\n",
       "      <td>0.901937</td>\n",
       "      <td>-0.891201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delphi/Pascal</th>\n",
       "      <td>-0.803821</td>\n",
       "      <td>0.842192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Go</th>\n",
       "      <td>0.985374</td>\n",
       "      <td>-0.957954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Groovy</th>\n",
       "      <td>0.676539</td>\n",
       "      <td>-0.719915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haskell</th>\n",
       "      <td>0.028710</td>\n",
       "      <td>-0.049515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Java</th>\n",
       "      <td>-0.983492</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JavaScript</th>\n",
       "      <td>0.385331</td>\n",
       "      <td>-0.322988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>0.905960</td>\n",
       "      <td>-0.939636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kotlin</th>\n",
       "      <td>0.945595</td>\n",
       "      <td>-0.892701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lua</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.154700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matlab</th>\n",
       "      <td>-0.617304</td>\n",
       "      <td>0.529346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Objective-C</th>\n",
       "      <td>0.325187</td>\n",
       "      <td>-0.431106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perl</th>\n",
       "      <td>-0.783993</td>\n",
       "      <td>0.826646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHP</th>\n",
       "      <td>-0.943219</td>\n",
       "      <td>0.966258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.983492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>0.900140</td>\n",
       "      <td>-0.933396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ruby</th>\n",
       "      <td>-0.441982</td>\n",
       "      <td>0.411585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rust</th>\n",
       "      <td>0.925995</td>\n",
       "      <td>-0.916171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scala</th>\n",
       "      <td>0.733003</td>\n",
       "      <td>-0.777016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swift</th>\n",
       "      <td>0.695197</td>\n",
       "      <td>-0.745004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TypeScript</th>\n",
       "      <td>0.970837</td>\n",
       "      <td>-0.944515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VBA</th>\n",
       "      <td>-0.602485</td>\n",
       "      <td>0.588156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Visual Basic</th>\n",
       "      <td>-0.870000</td>\n",
       "      <td>0.898426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Python      Java\n",
       "Abap           0.210543 -0.275354\n",
       "Ada            0.677052 -0.711266\n",
       "C/C++         -0.702704  0.681450\n",
       "C#             0.085039 -0.169606\n",
       "Cobol         -0.514747  0.534975\n",
       "Dart           0.901937 -0.891201\n",
       "Delphi/Pascal -0.803821  0.842192\n",
       "Go             0.985374 -0.957954\n",
       "Groovy         0.676539 -0.719915\n",
       "Haskell        0.028710 -0.049515\n",
       "Java          -0.983492  1.000000\n",
       "JavaScript     0.385331 -0.322988\n",
       "Julia          0.905960 -0.939636\n",
       "Kotlin         0.945595 -0.892701\n",
       "Lua            0.069608 -0.154700\n",
       "Matlab        -0.617304  0.529346\n",
       "Objective-C    0.325187 -0.431106\n",
       "Perl          -0.783993  0.826646\n",
       "PHP           -0.943219  0.966258\n",
       "Python         1.000000 -0.983492\n",
       "R              0.900140 -0.933396\n",
       "Ruby          -0.441982  0.411585\n",
       "Rust           0.925995 -0.916171\n",
       "Scala          0.733003 -0.777016\n",
       "Swift          0.695197 -0.745004\n",
       "TypeScript     0.970837 -0.944515\n",
       "VBA           -0.602485  0.588156\n",
       "Visual Basic  -0.870000  0.898426"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corelation = data2.corr()\n",
    "\n",
    "# corelation['Java']\n",
    "\n",
    "corelation[['Python', 'Java']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df3539e8-fb0b-49b9-b993-015fcfd787dc",
   "metadata": {
    "tags": []
   },
   "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>Python</th>\n",
       "      <th>Java</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Months</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.6</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.7</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.9</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.8</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>30.7</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>30.1</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>30.1</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>30.1</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>29.6</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Python  Java\n",
       "Months              \n",
       "0          2.5  30.4\n",
       "1          2.6  30.0\n",
       "2          2.7  29.7\n",
       "3          2.9  29.1\n",
       "4          2.8  29.6\n",
       "...        ...   ...\n",
       "205       30.7  18.3\n",
       "206       30.1  18.0\n",
       "207       30.1  18.1\n",
       "208       30.1  17.6\n",
       "209       29.6  17.8\n",
       "\n",
       "[210 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 = data[['Months', 'Python', 'Java']]\n",
    "data3 = data3.set_index('Months')\n",
    "\n",
    "data3.head(210)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be11b3f9-f522-4f44-9cb5-2caaa1fa4997",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Months'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.lineplot(data = data3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}