{ "cells": [ { "cell_type": "markdown", "id": "dd53ccfc-98dc-4289-9176-34362b67d8e2", "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 11\n", "---------------------------------------\n", "\n", "GOALS:\n", "\n", "1. Understand Causal vs Experimental Studies\n", "2. Do a more free form data analysis\n", "3. Start doing your ethics reading\n", "\n", "----------------------------------------------------------\n", "\n", "This homework has **2 questions** and **10 exercises**." ] }, { "cell_type": "code", "execution_count": 1, "id": "d0153569-41e3-4696-9b34-83ba90a0b5cf", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:05.543688Z", "iopub.status.busy": "2026-10-07T17:29:05.543613Z", "iopub.status.idle": "2026-10-07T17:29:06.322970Z", "shell.execute_reply": "2026-10-07T17:29:06.322649Z" } }, "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", "\n", "# This stops a few warning messages from showing\n", "pd.options.mode.chained_assignment = None \n", "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)" ] }, { "cell_type": "code", "execution_count": 2, "id": "7048dfd1-2b03-4c4b-93e2-ff48f66af288", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:06.325203Z", "iopub.status.busy": "2026-10-07T17:29:06.325130Z", "iopub.status.idle": "2026-10-07T17:29:06.514004Z", "shell.execute_reply": "2026-10-07T17:29:06.513420Z" } }, "outputs": [ { "data": { "text/html": [ "
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The climate is changing and human activity is mainly responsibleThe climate is changing and human activity is partly responsible, together with other factorsThe climate is changing but human activity is not responsible at allThe climate is not changingDon't know
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" ], "text/plain": [ " The climate is changing and human activity is mainly responsible \\\n", "country \n", "GB 833 \n", "US 507 \n", "\n", " The climate is changing and human activity is partly responsible, together with other factors \\\n", "country \n", "GB 604 \n", "US 493 \n", "\n", " The climate is changing but human activity is not responsible at all \\\n", "country \n", "GB 49 \n", "US 120 \n", "\n", " The climate is not changing Don't know \n", "country \n", "GB 33 114 \n", "US 80 133 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file_name = 'https://joannabieri.com/introdatascience/data/yougov-climate.csv'\n", "DF = pd.read_csv(file_name,index_col=0)\n", "DF" ] }, { "cell_type": "markdown", "id": "ed9f27ed-eb76-470c-8f3d-cbc80e2b8ee9", "metadata": {}, "source": [ "In the lecture we analyzed the following questions for the first column in the data:\n", "\n", "### First question:\n", "\n", "What percent of **all respondents** think the climate is changing and human activity is mainly responsible? \n", "\n", "### Second question:\n", "\n", "What percent of **GB respondents** think the climate is changing and human activity is mainly responsible? \n", "\n", "### Third Question\n", "\n", "What percent of **US respondents** think the climate is changing and human activity is mainly responsible? \n", "\n", "**Q1** Repeat the analysis from the lecture notes for one of the other columns/questions.\n", "\n", "1. Percent total\n", "2. Percent from GB\n", "3. Percent from US\n", "\n", "Talk about the conditional probability in this case:\n", "\n", "eg. If a person is from ________ then there is a ________ probability that they believe _______. If a person answered _______ then they are more likely to be from ________. " ] }, { "cell_type": "markdown", "id": "b5874385-5dff-4fbb-ae93-aa8bd386478c", "metadata": {}, "source": [] }, { "cell_type": "markdown", "id": "249af7fa-4af0-4948-aa1c-6ac55a12b77d", "metadata": {}, "source": [ "## Berkeley admission data example\n", "\n", "- Study carried out by the Graduate Division of the University of California, Berkeley in the early 70’s to evaluate whether there was a gender bias in graduate admissions.\n", "- The data come from six departments. For confidentiality we'll call them A-F. \n", "- We have information on whether the applicant was male or female and whether they were admitted or rejected. This is an old study so only two binary classifications were used. \n", "\n", "### Here is the data\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "18c756ff-0c11-409c-b377-7f271627f33b", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:06.516881Z", "iopub.status.busy": "2026-10-07T17:29:06.516821Z", "iopub.status.idle": "2026-10-07T17:29:06.697095Z", "shell.execute_reply": "2026-10-07T17:29:06.696387Z" } }, "outputs": [ { "data": { "text/html": [ "
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DepartmentMale YesMale NoFemale YesFemale No
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" ], "text/plain": [ " Department Male Yes Male No Female Yes Female No\n", "0 A 512 313 89 19\n", "1 B 353 207 17 8\n", "2 C 120 205 202 391\n", "3 D 138 279 131 244\n", "4 E 53 138 94 299\n", "5 F 22 351 24 317" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "file_name = 'https://joannabieri.com/introdatascience/data/berkley.csv'\n", "DF = pd.read_csv(file_name)\n", "display(DF)" ] }, { "cell_type": "code", "execution_count": 4, "id": "2946022d-d2f3-45fb-9426-fbde15d6427d", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:06.698551Z", "iopub.status.busy": "2026-10-07T17:29:06.698486Z", "iopub.status.idle": "2026-10-07T17:29:06.702584Z", "shell.execute_reply": "2026-10-07T17:29:06.702320Z" } }, "outputs": [ { "data": { "text/html": [ "
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DepartmentMFNumber
0AMale Yes512
1BMale Yes353
2CMale Yes120
3DMale Yes138
4EMale Yes53
5FMale Yes22
6AMale No313
7BMale No207
8CMale No205
9DMale No279
10EMale No138
11FMale No351
12AFemale Yes89
13BFemale Yes17
14CFemale Yes202
15DFemale Yes131
16EFemale Yes94
17FFemale Yes24
18AFemale No19
19BFemale No8
20CFemale No391
21DFemale No244
22EFemale No299
23FFemale No317
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" ], "text/plain": [ " Department MF Number\n", "0 A Male Yes 512\n", "1 B Male Yes 353\n", "2 C Male Yes 120\n", "3 D Male Yes 138\n", "4 E Male Yes 53\n", "5 F Male Yes 22\n", "6 A Male No 313\n", "7 B Male No 207\n", "8 C Male No 205\n", "9 D Male No 279\n", "10 E Male No 138\n", "11 F Male No 351\n", "12 A Female Yes 89\n", "13 B Female Yes 17\n", "14 C Female Yes 202\n", "15 D Female Yes 131\n", "16 E Female Yes 94\n", "17 F Female Yes 24\n", "18 A Female No 19\n", "19 B Female No 8\n", "20 C Female No 391\n", "21 D Female No 244\n", "22 E Female No 299\n", "23 F Female No 317" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF_melt = pd.melt(DF,id_vars=['Department'],var_name='MF',value_name='Number')\n", "DF_melt" ] }, { "cell_type": "code", "execution_count": 5, "id": "5d861ae5-ede6-4cfa-8a13-4b27e3de2b8e", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:06.703870Z", "iopub.status.busy": "2026-10-07T17:29:06.703820Z", "iopub.status.idle": "2026-10-07T17:29:06.705640Z", "shell.execute_reply": "2026-10-07T17:29:06.705486Z" } }, "outputs": [], "source": [ "DF_melt['gender'] = DF_melt['MF'].apply(lambda x: x.split(' ')[0]).copy()\n", "DF_melt['admitted'] = DF_melt['MF'].apply(lambda x: x.split(' ')[1]).copy()" ] }, { "cell_type": "code", "execution_count": 6, "id": "632666dc-2519-4a29-8f2e-d84bfb94ab1e", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:06.707346Z", "iopub.status.busy": "2026-10-07T17:29:06.707298Z", "iopub.status.idle": "2026-10-07T17:29:06.710002Z", "shell.execute_reply": "2026-10-07T17:29:06.709837Z" } }, "outputs": [ { "data": { "text/html": [ "
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DepartmentMFNumbergenderadmitted
0AMale Yes512MaleYes
1BMale Yes353MaleYes
2CMale Yes120MaleYes
3DMale Yes138MaleYes
4EMale Yes53MaleYes
5FMale Yes22MaleYes
6AMale No313MaleNo
7BMale No207MaleNo
8CMale No205MaleNo
9DMale No279MaleNo
10EMale No138MaleNo
11FMale No351MaleNo
12AFemale Yes89FemaleYes
13BFemale Yes17FemaleYes
14CFemale Yes202FemaleYes
15DFemale Yes131FemaleYes
16EFemale Yes94FemaleYes
17FFemale Yes24FemaleYes
18AFemale No19FemaleNo
19BFemale No8FemaleNo
20CFemale No391FemaleNo
21DFemale No244FemaleNo
22EFemale No299FemaleNo
23FFemale No317FemaleNo
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" ], "text/plain": [ " Department MF Number gender admitted\n", "0 A Male Yes 512 Male Yes\n", "1 B Male Yes 353 Male Yes\n", "2 C Male Yes 120 Male Yes\n", "3 D Male Yes 138 Male Yes\n", "4 E Male Yes 53 Male Yes\n", "5 F Male Yes 22 Male Yes\n", "6 A Male No 313 Male No\n", "7 B Male No 207 Male No\n", "8 C Male No 205 Male No\n", "9 D Male No 279 Male No\n", "10 E Male No 138 Male No\n", "11 F Male No 351 Male No\n", "12 A Female Yes 89 Female Yes\n", "13 B Female Yes 17 Female Yes\n", "14 C Female Yes 202 Female Yes\n", "15 D Female Yes 131 Female Yes\n", "16 E Female Yes 94 Female Yes\n", "17 F Female Yes 24 Female Yes\n", "18 A Female No 19 Female No\n", "19 B Female No 8 Female No\n", "20 C Female No 391 Female No\n", "21 D Female No 244 Female No\n", "22 E Female No 299 Female No\n", "23 F Female No 317 Female No" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF_melt" ] }, { "cell_type": "markdown", "id": "432131a7-8956-4a5f-adee-ca0c59c0ff64", "metadata": {}, "source": [ "### Gender distribution by department\n", "\n", "What can we say about the gender distribution if we look at the individual departments.\n", "\n", "Start with our original \"melted\" data frame:" ] }, { "cell_type": "markdown", "id": "022204a9-b9e4-4cc3-984f-d821c69b432b", "metadata": {}, "source": [ "#### Lets pivot!\n", "\n", "Pivot this data so that our departments become the column labels and our MF column becomes the index." ] }, { "cell_type": "code", "execution_count": 7, "id": "af17b1d9-39d9-4fa7-9f03-619e4cca6639", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:06.711796Z", "iopub.status.busy": "2026-10-07T17:29:06.711743Z", "iopub.status.idle": "2026-10-07T17:29:06.712852Z", "shell.execute_reply": "2026-10-07T17:29:06.712669Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "markdown", "id": "6f3a4052-70cb-45e1-9175-559660d97a6d", "metadata": {}, "source": [ "**Q2** Calculate the proportions for the other departments. We did department A in the lecture. You can use the code from the lecture or come up with your own way to do this. You can do this one by one or try using a FOR loop.\n", "\n", "Talk about what you see in these proportions. Which departments admit a large number of women? How do your number align with the visualization below?" ] }, { "cell_type": "code", "execution_count": 8, "id": "0e45185f-febe-4dc8-9d2a-392cb154218d", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:06.713751Z", "iopub.status.busy": "2026-10-07T17:29:06.713700Z", "iopub.status.idle": "2026-10-07T17:29:06.714884Z", "shell.execute_reply": "2026-10-07T17:29:06.714674Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "markdown", "id": "1443020f-a431-4c9a-b5c7-7b47229b4614", "metadata": {}, "source": [ "### Here is a plot of the proportions data" ] }, { "cell_type": "code", "execution_count": 9, "id": "fe801850-b1ab-4c10-b14a-5ddb8d1d6797", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:06.715479Z", "iopub.status.busy": "2026-10-07T17:29:06.715431Z", "iopub.status.idle": "2026-10-07T17:29:06.911473Z", "shell.execute_reply": "2026-10-07T17:29:06.911232Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.histogram(DF_melt,\n", " y='gender',\n", " x='Number',\n", " barnorm = \"percent\",\n", " color='admitted',\n", " facet_col='Department',\n", " facet_col_wrap=2,\n", " color_discrete_map = {'No':'#99d8c9','Yes':'#2ca25f'})\n", "\n", "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[1]))\n", "fig.update_xaxes(title_text='')\n", "\n", "fig.update_layout(title='Percent male and female applications',\n", " title_x=0.5,\n", " template=\"plotly_white\",\n", " xaxis_title=\"\",\n", " yaxis_title=\"Gender\",\n", " legend_title='Admitted',\n", " autosize=False,\n", " width=800,\n", " height=500)\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "a40fab13-19ce-4835-8a0c-145796c99d93", "metadata": {}, "source": [ "------------------------\n", "------------------------\n", "\n", "## Homework - Exercises\n", "\n", "** Homework content and data from Data Science in a Box - he-05-legos.Rmd\n", "\n", "This week we'll do some data gymnastics to refresh and review what we learned over the past few weeks using (**simulated**) data from Lego sales in 2018 for a sample of customers who bought Legos in the US. This is different than the data we used on the Exam!\n", "\n", "\n", "### Data" ] }, { "cell_type": "code", "execution_count": 10, "id": "c5775204-ebb3-4e1c-a300-d4985aec01d5", "metadata": { "execution": { "iopub.execute_input": "2026-10-07T17:29:06.912751Z", "iopub.status.busy": "2026-10-07T17:29:06.912664Z", "iopub.status.idle": "2026-10-07T17:29:07.205579Z", "shell.execute_reply": "2026-10-07T17:29:07.205315Z" } }, "outputs": [ { "data": { "text/html": [ "
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first_namelast_nameagephone_numberset_idnumberthemesubthemeyearnamepiecesus_priceimage_urlquantity
0KimberlyBeckstead24216-555-25492470176062DC Comics Super HeroesMighty Micros2018Robin vs. Bane77.09.99http://images.brickset.com/sets/images/76062-1...1
1NeelGarvin35819-555-31892562670595NinjagoRise of the Villains2018Ultra Stealth Raider1093.0119.99http://images.brickset.com/sets/images/70595-1...1
2NeelGarvin35819-555-31892466521031ArchitectureNaN2018Burj Khalifa333.039.99http://images.brickset.com/sets/images/21031-1...1
3ChelseaBouchard41NaN2469531048CreatorNaN2018Lakeside Lodge368.029.99http://images.brickset.com/sets/images/31048-1...1
4ChelseaBouchard41NaN2562670595NinjagoRise of the Villains2018Ultra Stealth Raider1093.0119.99http://images.brickset.com/sets/images/70595-1...1
.............................................
615TaliseNieukirk16801-555-23432490241556MixelsSeries 72018Tiketz62.04.99http://images.brickset.com/sets/images/41556-1...2
616SpencerMorgan28784-555-34552604141580MixelsSeries 92018Myke63.04.99NaN2
617SpencerMorgan28784-555-3455260605005051GearDigital Media2018Friends of Heartlake City Girlz 4 LifeNaN19.99NaN1
618AmeliaHageman40336-555-19502470276063DC Comics Super HeroesMighty Micros2018The Flash vs. Captain Cold88.09.99http://images.brickset.com/sets/images/76063-1...2
619AmeliaHageman40336-555-19502472010830DuploNaN2018Minnie's Café27.019.99http://images.brickset.com/sets/images/10830-1...4
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620 rows × 14 columns

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" ], "text/plain": [ " first_name last_name age phone_number set_id number \\\n", "0 Kimberly Beckstead 24 216-555-2549 24701 76062 \n", "1 Neel Garvin 35 819-555-3189 25626 70595 \n", "2 Neel Garvin 35 819-555-3189 24665 21031 \n", "3 Chelsea Bouchard 41 NaN 24695 31048 \n", "4 Chelsea Bouchard 41 NaN 25626 70595 \n", ".. ... ... ... ... ... ... \n", "615 Talise Nieukirk 16 801-555-2343 24902 41556 \n", "616 Spencer Morgan 28 784-555-3455 26041 41580 \n", "617 Spencer Morgan 28 784-555-3455 26060 5005051 \n", "618 Amelia Hageman 40 336-555-1950 24702 76063 \n", "619 Amelia Hageman 40 336-555-1950 24720 10830 \n", "\n", " theme subtheme year \\\n", "0 DC Comics Super Heroes Mighty Micros 2018 \n", "1 Ninjago Rise of the Villains 2018 \n", "2 Architecture NaN 2018 \n", "3 Creator NaN 2018 \n", "4 Ninjago Rise of the Villains 2018 \n", ".. ... ... ... \n", "615 Mixels Series 7 2018 \n", "616 Mixels Series 9 2018 \n", "617 Gear Digital Media 2018 \n", "618 DC Comics Super Heroes Mighty Micros 2018 \n", "619 Duplo NaN 2018 \n", "\n", " name pieces us_price \\\n", "0 Robin vs. Bane 77.0 9.99 \n", "1 Ultra Stealth Raider 1093.0 119.99 \n", "2 Burj Khalifa 333.0 39.99 \n", "3 Lakeside Lodge 368.0 29.99 \n", "4 Ultra Stealth Raider 1093.0 119.99 \n", ".. ... ... ... \n", "615 Tiketz 62.0 4.99 \n", "616 Myke 63.0 4.99 \n", "617 Friends of Heartlake City Girlz 4 Life NaN 19.99 \n", "618 The Flash vs. Captain Cold 88.0 9.99 \n", "619 Minnie's Café 27.0 19.99 \n", "\n", " image_url quantity \n", "0 http://images.brickset.com/sets/images/76062-1... 1 \n", "1 http://images.brickset.com/sets/images/70595-1... 1 \n", "2 http://images.brickset.com/sets/images/21031-1... 1 \n", "3 http://images.brickset.com/sets/images/31048-1... 1 \n", "4 http://images.brickset.com/sets/images/70595-1... 1 \n", ".. ... ... \n", "615 http://images.brickset.com/sets/images/41556-1... 2 \n", "616 NaN 2 \n", "617 NaN 1 \n", "618 http://images.brickset.com/sets/images/76063-1... 2 \n", "619 http://images.brickset.com/sets/images/10830-1... 4 \n", "\n", "[620 rows x 14 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file_name = 'https://joannabieri.com/introdatascience/data/lego-sales.csv'\n", "DF = pd.read_csv(file_name)\n", "DF" ] }, { "cell_type": "markdown", "id": "b2474002-893e-4c33-8229-d9fb8a3f4b29", "metadata": {}, "source": [ "### Exercises\n", "\n", "* Answer the following questions using reproducible Python code.\n", "* For each question, state your answer in a sentence, e.g. \"In this sample, the first three common names of purchasers are ...\".\n", "* Note that the answers to all questions are within the context of this particular sample of sales, i.e. you shouldn't make inferences about the population of all Lego sales based on this sample.\n", "\n", "-------------\n", "\n", "1. Describe what you see in the data set (variables, observations, etc)\n", "\n", "2. What are the three most common first names of purchasers?\n", "\n", "3. What are the three most common themes of Lego sets purchased?\n", "\n", "4. Among the most common theme of Lego sets purchased, what is the most common subtheme?\n", "\n", "5. Create data frames for each of the ages in the following categories: \"18 and under\", \"19 - 25\", \"26 - 35\", \"36 - 50\", \"51 and over\". HINT - use masks and create separate data frames -OR- create a new column with these categorical labels (more advanced).\n", "\n", "6. Which age group has purchased the highest number of Lego sets.\n", "\n", "7. Which age group has spent the most money on Legos?\n", "\n", "8. Which Lego theme has made the most money for Lego? HINT: Simpler than #5, just use a groupby()\n", "\n", "9. Which area code has spent the most money on Legos? In the US the area code is the first 3 digits of a phone number. HINT: You will need to split the phone number and get just the first three. You decide what to do about reporting the NaNs.\n", "\n", "10. Come up with a question you want to answer using these data, and write it down. Then, create a data visualization that answers the question, and explain how your visualization answers the question." ] }, { "cell_type": "code", "execution_count": null, "id": "776800ff-22b6-48c3-8789-fd8a0a4f948d", "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": "3.13.15" } }, "nbformat": 4, "nbformat_minor": 5 }