{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Pandas DataFrame" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "d = { \"SNAME\": [\"Alice\",\"Bob\",\"Charlie\"], \"AGE\": [24, 32, 26], \"MAJOR\": [\"CSC\",\"MATH\",\"CSC\"]}\n", "df = pd.DataFrame(d)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " SNAME AGE MAJOR\n", "0 Alice 24 CSC\n", "1 Bob 32 MATH\n", "2 Charlie 26 CSC\n", "(3, 3)\n" ] } ], "source": [ "print(df)\n", "print(df.shape) # I ran this cell after renaming the index" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['SNAME', 'AGE', 'MAJOR'], dtype='object')\n", "RangeIndex(start=0, stop=3, step=1)\n" ] } ], "source": [ "print(df.columns)\n", "print(df.index)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Alice\n", "1 Bob\n", "2 Charlie\n", "Name: SNAME, dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"SNAME\"] # project out columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SNAMEMAJOR
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" ], "text/plain": [ " SNAME MAJOR\n", "0 Alice CSC\n", "1 Bob MATH\n", "2 Charlie CSC" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[[\"SNAME\",\"MAJOR\"]]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SNAMEAGEMAJOR
0Alice24CSC
1Bob32MATH
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" ], "text/plain": [ " SNAME AGE MAJOR\n", "0 Alice 24 CSC\n", "1 Bob 32 MATH" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0:2]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SNAMEAGEMAJOR
s1Alice24CSC
s2Bob32MATH
s3Charlie26CSC
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" ], "text/plain": [ " SNAME AGE MAJOR\n", "s1 Alice 24 CSC\n", "s2 Bob 32 MATH\n", "s3 Charlie 26 CSC" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index = [\"s1\",\"s2\",\"s3\"]\n", "df" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SNAMEAGEMAJOR
s1Alice24CSC
s3Charlie26CSC
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" ], "text/plain": [ " SNAME AGE MAJOR\n", "s1 Alice 24 CSC\n", "s3 Charlie 26 CSC" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# selection of rows\n", "df[df[\"AGE\"] < 30]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SNAMEAGEMAJOR
s1Alice24CSC
\n", "
" ], "text/plain": [ " SNAME AGE MAJOR\n", "s1 Alice 24 CSC" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"s1\":\"s1\"]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SNAMEAGEMAJOR
s1Alice24CSC
s2Bob32MATH
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" ], "text/plain": [ " SNAME AGE MAJOR\n", "s1 Alice 24 CSC\n", "s2 Bob 32 MATH" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[\"s1\":\"s2\"]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SNAMEAGE
s1Alice24
s2Bob32
\n", "
" ], "text/plain": [ " SNAME AGE\n", "s1 Alice 24\n", "s2 Bob 32" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[\"s1\":\"s2\",[\"SNAME\",\"AGE\"]]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGEMAJOR
s232MATH
\n", "
" ], "text/plain": [ " AGE MAJOR\n", "s2 32 MATH" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[1:2,[1,2]]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "32" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.at[\"s2\",\"AGE\"]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " sname age major\n", "s1 Alice 24 CSC\n", "s2 Bob 32 MATH\n", "s3 Charlie 26 CSC\n" ] } ], "source": [ "df.columns = [\"sname\",\"age\",\"major\"]\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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snameagemajor
s1Alice24CSC
s3Charlie26CSC
\n", "
" ], "text/plain": [ " sname age major\n", "s1 Alice 24 CSC\n", "s3 Charlie 26 CSC" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.query('age < 30')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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snameagemajor
s1Alice24CSC
\n", "
" ], "text/plain": [ " sname age major\n", "s1 Alice 24 CSC" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.query('age < 30 and sname.str.startswith(\"A\")') # corresponds to WHERE clause in SQL " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PLOTLY" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(70, 11)\n", "Index(['Department', 'F2014', 'F2015', 'F2016', 'F2017', 'F2018', 'F2019',\n", " 'F2020', 'F2021', 'F2022', 'F2023'],\n", " dtype='object')\n", "RangeIndex(start=0, stop=70, step=1)\n" ] } ], "source": [ "# produce a x-y plot for computer science; x=semester, y=enrollment\n", "import plotly.express as px\n", "\n", "enroll = pd.read_csv(\"enrollment_by_department.csv\")\n", "print(enroll.shape)\n", "print(enroll.columns)\n", "print(enroll.index)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Department F2014 F2015 F2016 F2017 F2018 \\\n", "0 Accountancy (AC) 285 274 276 289 252 \n", "1 Accountancy (ACCT) 1099 1002 889 769 814 \n", "2 Africana Studies (AAS) 54 69 82 82 89 \n", "3 Anthropology (ANTH) 185 185 173 176 188 \n", "4 Applied Linguistics And Esl (ALSL) 226 257 243 236 237 \n", ".. ... ... ... ... ... ... \n", "65 Social Work (SW) 410 400 357 338 350 \n", "66 Sociology (SOC) 620 652 697 658 619 \n", "67 Urban Studies Institute (URB) 0 0 0 0 0 \n", "68 Women'S Gender & Sexuality Stu (WGSS) 47 54 41 46 50 \n", "69 World Languages And Cultures (WLC) 0 53 110 142 156 \n", "\n", " F2019 F2020 F2021 F2022 F2023 \n", "0 218 236 216 172 106 \n", "1 781 744 677 657 546 \n", "2 78 81 80 80 52 \n", "3 182 181 166 160 124 \n", "4 243 224 199 163 121 \n", ".. ... ... ... ... ... \n", "65 376 396 426 410 368 \n", "66 567 544 501 496 357 \n", "67 5 7 7 11 12 \n", "68 49 47 54 50 35 \n", "69 144 128 114 100 63 \n", "\n", "[70 rows x 11 columns]\n" ] } ], "source": [ "print(enroll)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 12\n", "F2014 1237\n", "F2015 1383\n", "F2016 1581\n", "F2017 1733\n", "F2018 1972\n", "F2019 2162\n", "F2020 2340\n", "F2021 2635\n", "F2022 3236\n", "F2023 2975\n" ] } ], "source": [ "csc = enroll[enroll[\"Department\"] == \"Computer Science (CSC)\"].T[1:]\n", "print(csc)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " index 12\n", "0 F2014 1237\n", "1 F2015 1383\n", "2 F2016 1581\n", "3 F2017 1733\n", "4 F2018 1972\n", "5 F2019 2162\n", "6 F2020 2340\n", "7 F2021 2635\n", "8 F2022 3236\n", "9 F2023 2975\n" ] } ], "source": [ "csc.reset_index(inplace=True)\n", "print(csc)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " semester enrollment\n", "0 F2014 1237\n", "1 F2015 1383\n", "2 F2016 1581\n", "3 F2017 1733\n", "4 F2018 1972\n", "5 F2019 2162\n", "6 F2020 2340\n", "7 F2021 2635\n", "8 F2022 3236\n", "9 F2023 2975\n" ] } ], "source": [ "csc.columns = [\"semester\",\"enrollment\"]\n", "print(csc)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "semester=%{x}
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"scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, 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['department','semester','enrollment']\n", "#print(csc)\n", "\n", "chem = enroll[enroll[\"Department\"] == \"Chemistry (CHEM)\"].T[1:]\n", "chem.reset_index(inplace=True)\n", "chem[\"department\"] = \"CHEM\"\n", "chem = chem.iloc[:,[2,0,1]]\n", "chem.columns = ['department','semester','enrollment']\n", "#print(chem)\n", "\n", "csc_chem = pd.concat([csc,chem])\n", "\n", "print(csc_chem)\n", "\n", "fig = px.line(csc_chem, x=\"semester\", y=\"enrollment\", color=\"department\", title='Enrollments: CSC v CHEM')\n", "fig.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def plot_two_departments(df,dept1,dept2):\n", " d1 = df[df[\"Department\"] == dept1].T[1:]\n", " d1.reset_index(inplace=True)\n", " d1[\"department\"] = dept1\n", " d1 = d1.iloc[:,[2,0,1]]\n", " d1.columns = ['department','semester','enrollment']\n", "\n", " d2 = df[df[\"Department\"] == dept2].T[1:]\n", " d2.reset_index(inplace=True)\n", " d2[\"department\"] = dept2\n", " d2 = d2.iloc[:,[2,0,1]]\n", " d2.columns = ['department','semester','enrollment']\n", "\n", " combined = pd.concat([d1,d2])\n", "\n", " print(combined)\n", "\n", " fig = px.line(combined, x=\"semester\", y=\"enrollment\", color=\"department\", \n", " title='Enrollments: '+dept1+' v ' + dept2)\n", " fig.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " department semester enrollment\n", "0 Computer Science (CSC) F2014 1237\n", "1 Computer Science (CSC) F2015 1383\n", "2 Computer Science (CSC) F2016 1581\n", "3 Computer Science (CSC) F2017 1733\n", "4 Computer Science (CSC) F2018 1972\n", "5 Computer Science (CSC) F2019 2162\n", "6 Computer Science (CSC) F2020 2340\n", "7 Computer Science (CSC) F2021 2635\n", "8 Computer Science (CSC) F2022 3236\n", "9 Computer Science (CSC) F2023 2975\n", "0 Chemistry (CHEM) F2014 703\n", "1 Chemistry (CHEM) F2015 709\n", "2 Chemistry (CHEM) F2016 754\n", "3 Chemistry (CHEM) F2017 694\n", "4 Chemistry (CHEM) F2018 669\n", "5 Chemistry (CHEM) F2019 625\n", "6 Chemistry (CHEM) F2020 581\n", "7 Chemistry (CHEM) F2021 505\n", "8 Chemistry (CHEM) F2022 439\n", "9 Chemistry (CHEM) F2023 358\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "department=Computer Science (CSC)
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