Initial commit.

Exploring the popularity of programming languages

stackoverflow-survey.ipynb

@@ -0,0 +1,164 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74e4cf25-6649-4633-89ea-03ffc2e23caa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "\n",
+    "import pandas as pd\n",
+    "import seaborn as sb\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# avoid burning my eyes @ night\n",
+    "plt.style.use(\"dark_background\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2b80545-2481-4ee8-8d43-ffd4a612a397",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FILE = \"data/survey_results_public.csv\"\n",
+    "so_df = pd.read_csv(FILE)\n",
+    "\n",
+    "print(so_df.keys())\n",
+    "so_df.describe()\n",
+    "\n",
+    "# print(so_df[:3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5070e38-8b93-4dc2-9ddb-9a06283ef8d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get popularity of different programming languages\n",
+    "\n",
+    "#keys re: languages are:\n",
+    "#LanguageHaveWorkedWith,LanguageWantToWorkWith,LanguageAdmired,LanguageDesired\n",
+    "\n",
+    "# draw horizontal bar plot\n",
+    "# https://seaborn.pydata.org/examples/part_whole_bars.html\n",
+    "\n",
+    "# draw as strip chart\n",
+    "# https://seaborn.pydata.org/generated/seaborn.stripplot.html#seaborn.stripplot\n",
+    "\n",
+    "def get_langs(dataset, key=\"LanguageHaveWorkedWith\"):\n",
+    "    lang_count = Counter()\n",
+    "    assert(key in dataset.keys())\n",
+    "    for response in dataset[key]:\n",
+    "        if type(response) == str:\n",
+    "            lang_count.update(response.split(';'))\n",
+    "    langs_by_popularity = dict(\n",
+    "        sorted(lang_count.items(), key=lambda item: item[1], reverse=True)\n",
+    "    )\n",
+    "    return langs_by_popularity\n",
+    "\n",
+    "def visualize_langs(langs, langs2, label1 = \"condition1\", label2 = \"condition2\"):\n",
+    "    DOT_COLOR1 = \"lightblue\"\n",
+    "    DOT_COLOR2 = \"red\"\n",
+    "    BG_COLOR   = \"black\" \n",
+    "    df    = pd.DataFrame(langs.items(), columns=['Languages', 'Count'])\n",
+    "    df2   = pd.DataFrame(langs2.items(), columns=['Languages', 'Count'])\n",
+    "    \n",
+    "    plt.figure(figsize=(10,15)) \n",
+    "    \n",
+    "    sb.stripplot(x='Count', y='Languages', data=df, \\\n",
+    "                 size=5, color=DOT_COLOR1, label=\"have worked with\", jitter=True)\n",
+    "    sb.stripplot(x='Count', y='Languages', data=df2, \\\n",
+    "                 size=5, color=DOT_COLOR2, label=\"want to work with\", jitter=True)\n",
+    "    \n",
+    "    # chatgpt draws my legend\n",
+    "    # Create custom legend handles to avoid duplicates\n",
+    "    # color = 'w' means do not draw line bissecting point\n",
+    "    blue_patch = plt.Line2D(\n",
+    "        [0], [0], marker='o', color=BG_COLOR, \\\n",
+    "        label=label1, markerfacecolor=DOT_COLOR1, markersize=10)\n",
+    "    red_patch = plt.Line2D(\n",
+    "        [0], [0], marker='o', color=BG_COLOR, \\\n",
+    "        label=label2, markerfacecolor=DOT_COLOR2, markersize=10)\n",
+    "    \n",
+    "    # Show the legend with custom handles\n",
+    "    plt.legend(handles=[blue_patch, red_patch], loc=\"center right\")\n",
+    "    \n",
+    "    plt.grid(axis='x', linestyle='--', alpha=0.75) \n",
+    "    plt.title(\"%s vs %s\" % (label1, label2))\n",
+    "    del df, df2\n",
+    "\n",
+    "l1 = get_langs( so_df )\n",
+    "l2 = get_langs( so_df, \"LanguageWantToWorkWith\" )\n",
+    "visualize_langs(l1,l2, label1=\"have worked with\", label2=\"want to work with\")\n",
+    "\n",
+    "l3 = get_langs( so_df, \"LanguageAdmired\")\n",
+    "l4 = get_langs( so_df, \"LanguageWantToWorkWith\")\n",
+    "visualize_langs(l3, l4, label1=\"admired\", label2=\"want to work with\")\n",
+    "\n",
+    "# determine extrinsic vs intrinsic motivation\n",
+    "def get_difference(dict1, dict2):\n",
+    "    keys = dict1.keys()\n",
+    "    result = dict()\n",
+    "    for key in keys:\n",
+    "        result[key] = dict1[key] - dict2[key]\n",
+    "    return result\n",
+    "    \n",
+    "motiv_diff = get_difference(l2, l1)\n",
+    "print(motiv_diff)\n",
+    "\n",
+    "# determine level of hype\n",
+    "hype = get_difference(l3, l4)\n",
+    "print(hype)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e90cf119-c50d-468a-bc87-72dac41176ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print survey ans\n",
+    "employment_status = Counter(so_df[\"MainBranch\"])\n",
+    "print(employment_status)\n",
+    "\n",
+    "print(so_df[\"ConvertedCompYearly\"][:3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6b1a935-eeda-416f-8adf-5e854d3aa066",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}