165 lines
5.1 KiB
Plaintext
165 lines
5.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "74e4cf25-6649-4633-89ea-03ffc2e23caa",
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import Counter\n",
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"\n",
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"import pandas as pd\n",
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"import seaborn as sb\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# avoid burning my eyes @ night\n",
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"plt.style.use(\"dark_background\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f2b80545-2481-4ee8-8d43-ffd4a612a397",
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"metadata": {},
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"outputs": [],
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"source": [
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"FILE = \"data/survey_results_public.csv\"\n",
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"so_df = pd.read_csv(FILE)\n",
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"\n",
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"print(so_df.keys())\n",
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"so_df.describe()\n",
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"\n",
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"# print(so_df[:3])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e5070e38-8b93-4dc2-9ddb-9a06283ef8d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# get popularity of different programming languages\n",
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"\n",
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"#keys re: languages are:\n",
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"#LanguageHaveWorkedWith,LanguageWantToWorkWith,LanguageAdmired,LanguageDesired\n",
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"\n",
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"# draw horizontal bar plot\n",
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"# https://seaborn.pydata.org/examples/part_whole_bars.html\n",
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"\n",
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"# draw as strip chart\n",
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"# https://seaborn.pydata.org/generated/seaborn.stripplot.html#seaborn.stripplot\n",
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"\n",
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"def get_langs(dataset, key=\"LanguageHaveWorkedWith\"):\n",
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" lang_count = Counter()\n",
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" assert(key in dataset.keys())\n",
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" for response in dataset[key]:\n",
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" if type(response) == str:\n",
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" lang_count.update(response.split(';'))\n",
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" langs_by_popularity = dict(\n",
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" sorted(lang_count.items(), key=lambda item: item[1], reverse=True)\n",
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" )\n",
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" return langs_by_popularity\n",
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"\n",
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"def visualize_langs(langs, langs2, label1 = \"condition1\", label2 = \"condition2\"):\n",
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" DOT_COLOR1 = \"lightblue\"\n",
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" DOT_COLOR2 = \"red\"\n",
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" BG_COLOR = \"black\" \n",
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" df = pd.DataFrame(langs.items(), columns=['Languages', 'Count'])\n",
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" df2 = pd.DataFrame(langs2.items(), columns=['Languages', 'Count'])\n",
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" \n",
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" plt.figure(figsize=(10,15)) \n",
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" \n",
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" sb.stripplot(x='Count', y='Languages', data=df, \\\n",
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" size=5, color=DOT_COLOR1, label=\"have worked with\", jitter=True)\n",
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" sb.stripplot(x='Count', y='Languages', data=df2, \\\n",
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" size=5, color=DOT_COLOR2, label=\"want to work with\", jitter=True)\n",
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" \n",
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" # chatgpt draws my legend\n",
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" # Create custom legend handles to avoid duplicates\n",
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" # color = 'w' means do not draw line bissecting point\n",
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" blue_patch = plt.Line2D(\n",
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" [0], [0], marker='o', color=BG_COLOR, \\\n",
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" label=label1, markerfacecolor=DOT_COLOR1, markersize=10)\n",
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" red_patch = plt.Line2D(\n",
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" [0], [0], marker='o', color=BG_COLOR, \\\n",
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" label=label2, markerfacecolor=DOT_COLOR2, markersize=10)\n",
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" \n",
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" # Show the legend with custom handles\n",
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" plt.legend(handles=[blue_patch, red_patch], loc=\"center right\")\n",
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" \n",
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" plt.grid(axis='x', linestyle='--', alpha=0.75) \n",
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" plt.title(\"%s vs %s\" % (label1, label2))\n",
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" del df, df2\n",
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"\n",
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"l1 = get_langs( so_df )\n",
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"l2 = get_langs( so_df, \"LanguageWantToWorkWith\" )\n",
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"visualize_langs(l1,l2, label1=\"have worked with\", label2=\"want to work with\")\n",
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"\n",
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"l3 = get_langs( so_df, \"LanguageAdmired\")\n",
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"l4 = get_langs( so_df, \"LanguageWantToWorkWith\")\n",
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"visualize_langs(l3, l4, label1=\"admired\", label2=\"want to work with\")\n",
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"\n",
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"# determine extrinsic vs intrinsic motivation\n",
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"def get_difference(dict1, dict2):\n",
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" keys = dict1.keys()\n",
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" result = dict()\n",
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" for key in keys:\n",
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" result[key] = dict1[key] - dict2[key]\n",
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" return result\n",
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" \n",
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"motiv_diff = get_difference(l2, l1)\n",
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"print(motiv_diff)\n",
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"\n",
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"# determine level of hype\n",
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"hype = get_difference(l3, l4)\n",
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"print(hype)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e90cf119-c50d-468a-bc87-72dac41176ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"# print survey ans\n",
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"employment_status = Counter(so_df[\"MainBranch\"])\n",
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"print(employment_status)\n",
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"\n",
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"print(so_df[\"ConvertedCompYearly\"][:3])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f6b1a935-eeda-416f-8adf-5e854d3aa066",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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