{ "cells": [ { "cell_type": "markdown", "id": "0bbb320f-ee60-4d43-a07e-efa6ceb6527d", "metadata": {}, "source": [ "## Introduction to Data Science\n", "\n", "#### University of Redlands - DATA 101\n", "#### Prof: Joanna Bieri [joanna_bieri@redlands.edu](mailto:joanna_bieri@redlands.edu)\n", "#### [Class Website: data101.joannabieri.com](https://data101.joannabieri.com)\n", "\n", "---------------------------------------\n", "# Homework Day 4\n", "---------------------------------------\n", "\n", "GOALS:\n", "\n", "1. Answer all the questions from the lecture.\n", "2. Practice making graphs with different types of data\n", "\n", "----------------------------------------------------------\n", "\n", "For this homework you will load the loans data and practice making different kinds of plots!\n", "\n", "This homework has **7 questions** from the lecture and **2 Problems** and a **Challenge**.\n", "\n", "NOTE:\n", "* Questions tend to be short answer or things where you change very minor parts of some given code.\n", "* Problems tend to be more involved, like trying out our methods on a new column.\n", "* Challenge problems are optional! They are intended to challenge you to reach beyond the basics of the class. I hope you will try the challenge problems!" ] }, { "cell_type": "code", "execution_count": 1, "id": "85d851b3-4123-484c-a51a-561490abcc68", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:34.627239Z", "iopub.status.busy": "2026-09-02T21:58:34.626515Z", "iopub.status.idle": "2026-09-02T21:58:36.355078Z", "shell.execute_reply": "2026-09-02T21:58:36.354606Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "from plotly.subplots import make_subplots\n", "import plotly.io as pio\n", "pio.renderers.default = 'notebook_connected'\n" ] }, { "cell_type": "markdown", "id": "0349a1d2-1e7b-43fc-aab9-d9223d2bc80e", "metadata": {}, "source": [ "### Load the Data" ] }, { "cell_type": "code", "execution_count": 2, "id": "0d9997ae-4d12-48cc-8229-9ec76d59e9a5", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:36.356906Z", "iopub.status.busy": "2026-09-02T21:58:36.356733Z", "iopub.status.idle": "2026-09-02T21:58:37.079416Z", "shell.execute_reply": "2026-09-02T21:58:37.078809Z" } }, "outputs": [], "source": [ "file_location = 'https://joannabieri.com/introdatascience/data/loans_full_schema.csv'\n", "DF = pd.read_csv(file_location)" ] }, { "cell_type": "markdown", "id": "fd99158c-2f2e-4b89-9c80-599d27d40a04", "metadata": {}, "source": [ "### Check Observations and Variables\n", "\n", "**Q1** How many **observations** are there?\n", "\n", "**Q2** How many **variables** are there?" ] }, { "cell_type": "code", "execution_count": 3, "id": "81e4fe5c-38bc-4e94-a06b-36ac30a23d4c", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.081397Z", "iopub.status.busy": "2026-09-02T21:58:37.081211Z", "iopub.status.idle": "2026-09-02T21:58:37.083621Z", "shell.execute_reply": "2026-09-02T21:58:37.083118Z" } }, "outputs": [], "source": [ "# Enter your code here to find the data shape\n" ] }, { "cell_type": "markdown", "id": "8ba99b5c-e778-4a20-bf08-64c155f15aca", "metadata": {}, "source": [ "**(Double Click Here to answer the questions)**" ] }, { "cell_type": "markdown", "id": "5756a49a-1217-44a8-8dc0-98eb9475fb6c", "metadata": {}, "source": [ "### Reduce the number of variables" ] }, { "cell_type": "code", "execution_count": 4, "id": "b8ce2965-1253-4593-85c5-eb187fd2d028", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.085226Z", "iopub.status.busy": "2026-09-02T21:58:37.085059Z", "iopub.status.idle": "2026-09-02T21:58:37.098685Z", "shell.execute_reply": "2026-09-02T21:58:37.098241Z" } }, "outputs": [ { "data": { "text/html": [ "
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loan_amountinterest_ratetermgradestateannual_incomehomeownershipdebt_to_income
02800014.0760CNJ90000.0MORTGAGE18.01
1500012.6136CHI40000.0RENT5.04
2200017.0936DWI40000.0RENT21.15
3216006.7236APA30000.0RENT10.16
42300014.0736CCA35000.0RENT57.96
...........................
9995240007.3536ATX108000.0RENT22.28
99961000019.0336DPA121000.0MORTGAGE32.38
99973000023.8836ECT67000.0MORTGAGE45.26
9998240005.3236AWI80000.0MORTGAGE11.99
99991280010.9136BCT66000.0RENT20.82
\n", "

10000 rows × 8 columns

\n", "
" ], "text/plain": [ " loan_amount interest_rate term grade state annual_income \\\n", "0 28000 14.07 60 C NJ 90000.0 \n", "1 5000 12.61 36 C HI 40000.0 \n", "2 2000 17.09 36 D WI 40000.0 \n", "3 21600 6.72 36 A PA 30000.0 \n", "4 23000 14.07 36 C CA 35000.0 \n", "... ... ... ... ... ... ... \n", "9995 24000 7.35 36 A TX 108000.0 \n", "9996 10000 19.03 36 D PA 121000.0 \n", "9997 30000 23.88 36 E CT 67000.0 \n", "9998 24000 5.32 36 A WI 80000.0 \n", "9999 12800 10.91 36 B CT 66000.0 \n", "\n", " homeownership debt_to_income \n", "0 MORTGAGE 18.01 \n", "1 RENT 5.04 \n", "2 RENT 21.15 \n", "3 RENT 10.16 \n", "4 RENT 57.96 \n", "... ... ... \n", "9995 RENT 22.28 \n", "9996 MORTGAGE 32.38 \n", "9997 MORTGAGE 45.26 \n", "9998 MORTGAGE 11.99 \n", "9999 RENT 20.82 \n", "\n", "[10000 rows x 8 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "my_variables = ['loan_amount',\n", " 'interest_rate',\n", " 'term','grade',\n", " 'state',\n", " 'annual_income',\n", " 'homeownership',\n", " 'debt_to_income']\n", "\n", "DF = DF[my_variables]\n", "\n", "display(DF)" ] }, { "cell_type": "markdown", "id": "bd8255c0-9d90-402e-9017-64a382e2b5be", "metadata": {}, "source": [ "**Q3** Check out each of the variables (columns):\n", "\n", "1. What does each column tell you? What are the units?\n", "2. Is the data numerical? If so is it continuous or discrete?\n", "3. Is it categorical? If so is it ordinal or nominal?\n", "\n", " Here is a link to the full data description if you need to look up some of the column names.\n", "\n", "-----------------------------------------" ] }, { "cell_type": "markdown", "id": "d9f81c1b-0c24-4d84-b3c2-dc3d49c8df04", "metadata": {}, "source": [ "**(Double Click Here)**" ] }, { "cell_type": "markdown", "id": "08f9ffc9-e880-448b-bc5f-30d7c7f973d0", "metadata": {}, "source": [ "### Here is Example Code for a simple Histogram" ] }, { "cell_type": "code", "execution_count": 5, "id": "9fc91093-80fc-445e-815f-c5ab9945dc3e", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.100358Z", "iopub.status.busy": "2026-09-02T21:58:37.100193Z", "iopub.status.idle": "2026-09-02T21:58:37.255553Z", "shell.execute_reply": "2026-09-02T21:58:37.255113Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.histogram(DF,\n", " nbins=10,\n", " x='loan_amount',\n", " color_discrete_sequence=['lightseagreen'])\n", "\n", "fig.update_layout(bargap=0.1,\n", " title='Histogram of Loan Amounts.',\n", " title_x=0.5)\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "f5257051-a9c8-4b4f-8feb-45396ab10993", "metadata": {}, "source": [ "**Q4** You try changing the number of bins **nbins** what do you notice? Are there good choices? Bad choices?\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "05441dd1-937a-46d6-b597-2a6829d9d596", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.257163Z", "iopub.status.busy": "2026-09-02T21:58:37.256989Z", "iopub.status.idle": "2026-09-02T21:58:37.259102Z", "shell.execute_reply": "2026-09-02T21:58:37.258678Z" } }, "outputs": [], "source": [ "# Your code here (experiment here)\n" ] }, { "cell_type": "markdown", "id": "881549c1-542a-47c2-97b9-81917c7588a6", "metadata": {}, "source": [ "**(Double Click here to answer the questions)**" ] }, { "cell_type": "markdown", "id": "964f09f1-8d73-4b8e-8a51-17c024a55d60", "metadata": {}, "source": [ "**Problem 1** Create a histogram of your own! Try making a histogram of one of the other pieces of numerical data. Make it as fancy as you want. Include some categorical information. Do you learn anything from your graph? If so what?\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "7f756baa-cd3c-404a-a68f-67b0d8f0bedf", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.260545Z", "iopub.status.busy": "2026-09-02T21:58:37.260389Z", "iopub.status.idle": "2026-09-02T21:58:37.262525Z", "shell.execute_reply": "2026-09-02T21:58:37.262060Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "markdown", "id": "896a46ff-c762-4cd9-b87c-f73602579937", "metadata": {}, "source": [ "### Here is example code for a histogram with a box plot included." ] }, { "cell_type": "code", "execution_count": 8, "id": "fb0a1b3b-7b5f-4dc4-82d5-8d747ff5fbff", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.263912Z", "iopub.status.busy": "2026-09-02T21:58:37.263760Z", "iopub.status.idle": "2026-09-02T21:58:37.339672Z", "shell.execute_reply": "2026-09-02T21:58:37.339298Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.histogram(DF,\n", " nbins=9,\n", " x='loan_amount',\n", " color='homeownership',\n", " opacity=0.5,\n", " color_discrete_map={'MORTGAGE': 'darkviolet', \n", " 'RENT': 'deeppink', \n", " 'OWN': 'darkturquoise'},\n", " marginal=\"box\"\n", " )\n", "\n", "fig.update_layout(bargap=0.1,\n", " title='Histogram of Loan Ammounts.',\n", " title_x=0.5,\n", " xaxis={'range':[-1000, 46000]},\n", " xaxis_title=\"Loan Amount ($)\",\n", " yaxis_title=\"Frequency\",\n", " legend_title=\"Homeownership\",\n", " autosize=False,\n", " width=800,\n", " height=600)\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "d709f10d-34ab-44aa-ba66-ef875ba6b832", "metadata": {}, "source": [ "**Q5** Change the above plot to marginal=\"violin\" and see what changes. Make some observations about the graph" ] }, { "cell_type": "markdown", "id": "34a45491-48b8-417c-aa2f-825f1412ddd0", "metadata": {}, "source": [ "**(Double Click Here)**" ] }, { "cell_type": "code", "execution_count": 9, "id": "17e27d50-8588-4b72-bcee-78865d30b812", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.341235Z", "iopub.status.busy": "2026-09-02T21:58:37.341066Z", "iopub.status.idle": "2026-09-02T21:58:37.345095Z", "shell.execute_reply": "2026-09-02T21:58:37.344772Z" } }, "outputs": [ { "data": { "text/plain": [ "homeownership\n", "MORTGAGE 4789\n", "RENT 3858\n", "OWN 1353\n", "Name: count, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "counts = DF['homeownership'].value_counts()\n", "display(counts)" ] }, { "cell_type": "markdown", "id": "730858ef-586a-49dd-b115-a3fcbe58e0dd", "metadata": {}, "source": [ "**Q6** Copy and paste the code above, but change it to get value counts for one of the other categorical columns" ] }, { "cell_type": "code", "execution_count": 10, "id": "4717523c-a4da-4283-945f-e71e6d3346ca", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.346551Z", "iopub.status.busy": "2026-09-02T21:58:37.346400Z", "iopub.status.idle": "2026-09-02T21:58:37.348206Z", "shell.execute_reply": "2026-09-02T21:58:37.347873Z" } }, "outputs": [], "source": [ "## Your code here\n" ] }, { "cell_type": "markdown", "id": "57e8fd36-63fa-450f-b77d-a6585206edd4", "metadata": {}, "source": [ "### Here is example code for a bar plot" ] }, { "cell_type": "code", "execution_count": 11, "id": "2c11c9db-2ef2-4b45-a1ad-88e3517cdca5", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.349641Z", "iopub.status.busy": "2026-09-02T21:58:37.349490Z", "iopub.status.idle": "2026-09-02T21:58:37.407572Z", "shell.execute_reply": "2026-09-02T21:58:37.407138Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.bar(DF,\n", " x='homeownership',\n", " color_discrete_sequence=['lightseagreen'])\n", "fig.update_traces(dict(marker_line_width=0))\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "5d2deee1-2cad-4d0a-ba6c-8ff5b5583f43", "metadata": {}, "source": [ "**Q7** Can you figure out how to add x labels, y labels, and a title to this graph?" ] }, { "cell_type": "code", "execution_count": 12, "id": "9aec5a83-f4c4-484c-aa65-949cb569a2ae", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.409068Z", "iopub.status.busy": "2026-09-02T21:58:37.408911Z", "iopub.status.idle": "2026-09-02T21:58:37.411024Z", "shell.execute_reply": "2026-09-02T21:58:37.410604Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "markdown", "id": "c2d4e5b8-f92a-4700-8a3f-a845aa1e8ec1", "metadata": {}, "source": [ "**Problem 2** Try to make your own bar plot of one of the other categorical columns. Add some categorical fill or facets. See how fancy you can make your graph. Make sure it is also still really informative." ] }, { "cell_type": "code", "execution_count": 13, "id": "457ca6f2-ec41-4ad0-b1f7-07d9e4d0696a", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.412437Z", "iopub.status.busy": "2026-09-02T21:58:37.412289Z", "iopub.status.idle": "2026-09-02T21:58:37.414250Z", "shell.execute_reply": "2026-09-02T21:58:37.413914Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "markdown", "id": "6ef7d349-9b27-40d2-b877-4adff1b5aa81", "metadata": {}, "source": [ "### Challenge:\n", "\n", "Here is a data set that contains demographic data from the Behavioral Risk Factors Surveillance System from the CDC. It is a small subset of 60 observations.\n", "\n", "Here is a link to the variable information:\n", "\n", "https://www.openintro.org/data/index.php?data=cdc.samp\n", "\n", "Your goal is to look at the columns and then make a graph from the data using what we learned in today's class." ] }, { "cell_type": "code", "execution_count": 14, "id": "fbf20c0a-427d-49d6-8590-96699c4f89d0", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T21:58:37.415712Z", "iopub.status.busy": "2026-09-02T21:58:37.415565Z", "iopub.status.idle": "2026-09-02T21:58:37.655052Z", "shell.execute_reply": "2026-09-02T21:58:37.653936Z" } }, "outputs": [ { "data": { "text/html": [ "
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genhlthexeranyhlthplansmoke100heightweightwtdesireagegender
0very good1117526522545m
1excellent1107215015024m
2excellent1116913715047m
3good1116615912526f
4very good1106314512533f
5very good1116412512028f
6excellent1117217517534m
7very good1116010410442f
8good1116514014050f
9very good1117018817057m
10very good1106717015557m
11good0016512013019f
12good1107329022042m
13fair1016617016043m
14good1106512012060f
15very good0106414011034f
16fair1116316414448f
17good1116917017037m
18good0106217015074f
19good1107319819052m
20fair1106213013061f
21good0116630015068f
22very good1117419218524m
23good1007217517524m
24good0116819517059f
25excellent1107015515549m
26good0006740020037f
27excellent0106814514047m
28very good1116214512056f
29good0116316514074f
30excellent1116814014028m
31very good1107322020024m
32excellent1117120018568m
33excellent1016412011033f
34very good1116518014043f
35fair0016413513549f
36excellent1106917015037f
37excellent1106315113551f
38good1116718014052f
39very good1117119518050m
40fair1006312012047f
41good1106817517538m
42excellent0106211511543f
43very good1116616014035f
44excellent1117220018850m
45good1017015415431m
46very good0106221018077f
47very good0106213513569f
48very good1106614814023f
49excellent1007223022525m
50fair1116019214748f
51excellent1117420020051m
52poor0117221019064m
53excellent1117019918035m
54excellent0106516014030m
55good1106113210855f
56very good1107216516019m
57fair0006216014032f
58excellent1107317519028m
59very good1106916516540m
\n", "
" ], "text/plain": [ " genhlth exerany hlthplan smoke100 height weight wtdesire age \\\n", "0 very good 1 1 1 75 265 225 45 \n", "1 excellent 1 1 0 72 150 150 24 \n", "2 excellent 1 1 1 69 137 150 47 \n", "3 good 1 1 1 66 159 125 26 \n", "4 very good 1 1 0 63 145 125 33 \n", "5 very good 1 1 1 64 125 120 28 \n", "6 excellent 1 1 1 72 175 175 34 \n", "7 very good 1 1 1 60 104 104 42 \n", "8 good 1 1 1 65 140 140 50 \n", "9 very good 1 1 1 70 188 170 57 \n", "10 very good 1 1 0 67 170 155 57 \n", "11 good 0 0 1 65 120 130 19 \n", "12 good 1 1 0 73 290 220 42 \n", "13 fair 1 0 1 66 170 160 43 \n", "14 good 1 1 0 65 120 120 60 \n", "15 very good 0 1 0 64 140 110 34 \n", "16 fair 1 1 1 63 164 144 48 \n", "17 good 1 1 1 69 170 170 37 \n", "18 good 0 1 0 62 170 150 74 \n", "19 good 1 1 0 73 198 190 52 \n", "20 fair 1 1 0 62 130 130 61 \n", "21 good 0 1 1 66 300 150 68 \n", "22 very good 1 1 1 74 192 185 24 \n", "23 good 1 0 0 72 175 175 24 \n", "24 good 0 1 1 68 195 170 59 \n", "25 excellent 1 1 0 70 155 155 49 \n", "26 good 0 0 0 67 400 200 37 \n", "27 excellent 0 1 0 68 145 140 47 \n", "28 very good 1 1 1 62 145 120 56 \n", "29 good 0 1 1 63 165 140 74 \n", "30 excellent 1 1 1 68 140 140 28 \n", "31 very good 1 1 0 73 220 200 24 \n", "32 excellent 1 1 1 71 200 185 68 \n", "33 excellent 1 0 1 64 120 110 33 \n", "34 very good 1 1 1 65 180 140 43 \n", "35 fair 0 0 1 64 135 135 49 \n", "36 excellent 1 1 0 69 170 150 37 \n", "37 excellent 1 1 0 63 151 135 51 \n", "38 good 1 1 1 67 180 140 52 \n", "39 very good 1 1 1 71 195 180 50 \n", "40 fair 1 0 0 63 120 120 47 \n", "41 good 1 1 0 68 175 175 38 \n", "42 excellent 0 1 0 62 115 115 43 \n", "43 very good 1 1 1 66 160 140 35 \n", "44 excellent 1 1 1 72 200 188 50 \n", "45 good 1 0 1 70 154 154 31 \n", "46 very good 0 1 0 62 210 180 77 \n", "47 very good 0 1 0 62 135 135 69 \n", "48 very good 1 1 0 66 148 140 23 \n", "49 excellent 1 0 0 72 230 225 25 \n", "50 fair 1 1 1 60 192 147 48 \n", "51 excellent 1 1 1 74 200 200 51 \n", "52 poor 0 1 1 72 210 190 64 \n", "53 excellent 1 1 1 70 199 180 35 \n", "54 excellent 0 1 0 65 160 140 30 \n", "55 good 1 1 0 61 132 108 55 \n", "56 very good 1 1 0 72 165 160 19 \n", "57 fair 0 0 0 62 160 140 32 \n", "58 excellent 1 1 0 73 175 190 28 \n", "59 very good 1 1 0 69 165 165 40 \n", "\n", " gender \n", "0 m \n", "1 m \n", "2 m \n", "3 f \n", "4 f \n", "5 f \n", "6 m \n", "7 f \n", "8 f \n", "9 m \n", "10 m \n", "11 f \n", "12 m \n", "13 m \n", "14 f \n", "15 f \n", "16 f \n", "17 m \n", "18 f \n", "19 m \n", "20 f \n", "21 f \n", "22 m \n", "23 m \n", "24 f \n", "25 m \n", "26 f \n", "27 m \n", "28 f \n", "29 f \n", "30 m \n", "31 m \n", "32 m \n", "33 f \n", "34 f \n", "35 f \n", "36 f \n", "37 f \n", "38 f \n", "39 m \n", "40 f \n", "41 m \n", "42 f \n", "43 f \n", "44 m \n", "45 m \n", "46 f \n", "47 f \n", "48 f \n", "49 m \n", "50 f \n", "51 m \n", "52 m \n", "53 m \n", "54 m \n", "55 f \n", "56 m \n", "57 f \n", "58 m \n", "59 m " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "file_location = 'https://joannabieri.com/introdatascience/data/cdc.samp.csv'\n", "DF_new = pd.read_csv(file_location)\n", "display(DF_new)" ] }, { "cell_type": "code", "execution_count": null, "id": "2db2d740-6da7-4e4a-becb-97a0da56be17", "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }