{ "cells": [ { "cell_type": "markdown", "id": "8c10e39e", "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", "# Day 4 - In Class Challenge - CDC Health Data - SOLUTIONS\n", "---------------------------------------\n", "\n", "These are one set of answers, not the only set. Groups will pick different plot types and that is fine as long as the plot actually answers the question. The written answers below are what I would hope to hear when a group presents.\n", "\n", "Numbers quoted below come from the 60 person sample. If you swap in a different sample they will change." ] }, { "cell_type": "code", "execution_count": 1, "id": "210f4c65", "metadata": {}, "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'" ] }, { "cell_type": "code", "execution_count": 2, "id": "4f03fe0b", "metadata": {}, "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 = pd.read_csv(file_location)\n", "display(DF)" ] }, { "cell_type": "markdown", "id": "4aff515d", "metadata": {}, "source": [ "---------------------------------------\n", "## Warm Up\n", "---------------------------------------\n", "\n", "**Q1** Observations and variables." ] }, { "cell_type": "code", "execution_count": 3, "id": "f97d68a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(60, 9)\n", "Observations (rows): 60\n", "Variables (columns): 9\n" ] } ], "source": [ "print(DF.shape)\n", "print('Observations (rows):', len(DF))\n", "print('Variables (columns):', len(DF.columns))" ] }, { "cell_type": "markdown", "id": "0b6cf530", "metadata": {}, "source": [ "60 observations (people) and 9 variables.\n", "\n", "**Q2** Numerical vs categorical.\n", "\n", "Numerical:\n", "* `height` (inches), `weight` (pounds), `wtdesire` (pounds), `age` (years). Height, weight and age are really continuous quantities, but they were recorded as whole numbers so they *look* discrete in the data. Either answer is fine as long as the group can say why.\n", "\n", "Categorical:\n", "* `genhlth` is **ordinal**: poor, fair, good, very good, excellent has a natural order.\n", "* `gender` is **nominal**.\n", "* `exerany`, `hlthplan`, `smoke100` are the tricky ones. They are stored as 0 and 1 but they are yes/no questions, so they are **categorical (nominal)**. The 1 does not mean \"one of something\". If you average them you get a proportion, which is useful, but you should not treat them like weight.\n", "\n", "A quick way to see this is that `describe()` happily computes a mean of `smoke100` (about 0.52) which just means 52% of the sample has smoked 100 cigarettes." ] }, { "cell_type": "code", "execution_count": 4, "id": "ce12f067", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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exeranyhlthplansmoke100heightweightwtdesireage
count60.00000060.00000060.00000060.00000060.00000060.00000060.000000
mean0.7500000.8333330.51666767.183333173.300000155.08333343.766667
std0.4366670.3758230.5039394.15174749.03485429.89659714.813378
min0.0000000.0000000.00000060.000000104.000000104.00000019.000000
25%0.7500001.0000000.00000063.750000143.750000135.00000032.750000
50%1.0000001.0000001.00000067.000000165.000000150.00000043.000000
75%1.0000001.0000001.00000071.000000192.750000176.25000052.000000
max1.0000001.0000001.00000075.000000400.000000225.00000077.000000
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" ], "text/plain": [ " exerany hlthplan smoke100 height weight wtdesire \\\n", "count 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 \n", "mean 0.750000 0.833333 0.516667 67.183333 173.300000 155.083333 \n", "std 0.436667 0.375823 0.503939 4.151747 49.034854 29.896597 \n", "min 0.000000 0.000000 0.000000 60.000000 104.000000 104.000000 \n", "25% 0.750000 1.000000 0.000000 63.750000 143.750000 135.000000 \n", "50% 1.000000 1.000000 1.000000 67.000000 165.000000 150.000000 \n", "75% 1.000000 1.000000 1.000000 71.000000 192.750000 176.250000 \n", "max 1.000000 1.000000 1.000000 75.000000 400.000000 225.000000 \n", "\n", " age \n", "count 60.000000 \n", "mean 43.766667 \n", "std 14.813378 \n", "min 19.000000 \n", "25% 32.750000 \n", "50% 43.000000 \n", "75% 52.000000 \n", "max 77.000000 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF.describe()" ] }, { "cell_type": "markdown", "id": "18ad4eb2", "metadata": {}, "source": [ "**Q3** Counts of `genhlth`, in order." ] }, { "cell_type": "code", "execution_count": 5, "id": "f9e3c510", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "genhlth\n", "very good 18\n", "excellent 17\n", "good 17\n", "fair 7\n", "poor 1\n", "Name: count, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "counts = DF['genhlth'].value_counts()\n", "display(counts)\n", "\n", "health_order = ['poor', 'fair', 'good', 'very good', 'excellent']\n", "\n", "fig = px.bar(DF,\n", " x='genhlth',\n", " category_orders={'genhlth': health_order},\n", " color_discrete_sequence=['lightseagreen'])\n", "fig.update_traces(dict(marker_line_width=0))\n", "fig.update_layout(title='How people rate their own health',\n", " title_x=0.5,\n", " xaxis_title='Self rated general health',\n", " yaxis_title='Number of people')\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "9ee244f7", "metadata": {}, "source": [ "Very good 18, excellent 17, good 17, fair 7, poor 1. Once the bars are in order the shape is lopsided toward the good end. Almost everyone says they are at least \"good\", only 8 of 60 say fair or poor. People tend to rate their own health well (or people in poor health are less likely to answer a phone survey, which is worth mentioning)." ] }, { "cell_type": "markdown", "id": "53ff66f6", "metadata": {}, "source": [ "---------------------------------------\n", "## Challenge 1 - How much weight do people want to lose?\n", "---------------------------------------\n", "\n", "**Q4** Scatter plot of weight vs desired weight." ] }, { "cell_type": "code", "execution_count": 6, "id": "22f3a928", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(DF,\n", " x='weight',\n", " y='wtdesire',\n", " color='gender',\n", " hover_data='age',\n", " title='Desired weight vs current weight')\n", "fig.update_traces(marker=dict(size=10))\n", "fig.update_layout(title_x=0.5,\n", " xaxis_title='Current weight (pounds)',\n", " yaxis_title='Desired weight (pounds)')\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "80fcfeaf", "metadata": {}, "source": [ "**Q5** A point on the line `wtdesire = weight` is a person who is happy with their weight. A point *above* the line wants to gain weight. A point *below* the line wants to lose weight. Almost everyone is below the line, most people want to lose weight.\n", "\n", "**Q6** Find the people who want to gain weight with a filter." ] }, { "cell_type": "code", "execution_count": 7, "id": "2de7e8c8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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genhlthexeranyhlthplansmoke100heightweightwtdesireagegender
2excellent1116913715047m
11good0016512013019f
58excellent1107317519028m
\n", "
" ], "text/plain": [ " genhlth exerany hlthplan smoke100 height weight wtdesire age \\\n", "2 excellent 1 1 1 69 137 150 47 \n", "11 good 0 0 1 65 120 130 19 \n", "58 excellent 1 1 0 73 175 190 28 \n", "\n", " gender \n", "2 m \n", "11 f \n", "58 m " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Number who want to gain weight: 3\n", "gender\n", "m 2\n", "f 1\n", "Name: count, dtype: int64\n" ] } ], "source": [ "gainers = DF[DF['wtdesire'] > DF['weight']]\n", "display(gainers)\n", "print('Number who want to gain weight:', len(gainers))\n", "print(gainers['gender'].value_counts())" ] }, { "cell_type": "markdown", "id": "1fb9b708", "metadata": {}, "source": [ "Only 3 people out of 60 want to gain weight, 2 men and 1 woman. All three only want to add 10 to 15 pounds, and none of them is heavy to start with.\n", "\n", "**Q7** The outlier and the mean." ] }, { "cell_type": "code", "execution_count": 8, "id": "6359c572", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
genhlthexeranyhlthplansmoke100heightweightwtdesireagegender
26good0006740020037f
\n", "
" ], "text/plain": [ " genhlth exerany hlthplan smoke100 height weight wtdesire age gender\n", "26 good 0 0 0 67 400 200 37 f" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Mean weight, everyone: 173.3\n", "Mean weight, without outlier: 167.2\n", "Median weight, everyone: 165.0\n" ] } ], "source": [ "# The point way off by itself\n", "display(DF[DF['weight'] > 300])\n", "\n", "mean_all = DF['weight'].mean()\n", "mean_no_outlier = DF[DF['weight'] < 300]['weight'].mean()\n", "median_all = DF['weight'].median()\n", "\n", "print(f'Mean weight, everyone: {mean_all:.1f}')\n", "print(f'Mean weight, without outlier: {mean_no_outlier:.1f}')\n", "print(f'Median weight, everyone: {median_all:.1f}')" ] }, { "cell_type": "markdown", "id": "2be8c0db", "metadata": {}, "source": [ "The outlier is a 37 year old woman, 67 inches tall, 400 pounds, who wants to weigh 200. One person out of 60 pulls the mean from about 167 up to about 173 pounds. The median (165) barely notices her. For a \"typical\" person the median is the better description here, because the mean is being dragged around by a single observation.\n", "\n", "**Q8** New column and compare by gender." ] }, { "cell_type": "code", "execution_count": 9, "id": "bffeb33a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "DF['want_to_lose'] = DF['weight'] - DF['wtdesire']\n", "\n", "fig = px.box(DF,\n", " x='want_to_lose',\n", " y='gender',\n", " color='gender',\n", " title='Pounds people want to lose, by gender')\n", "fig.update_layout(title_x=0.5,\n", " xaxis_title='Current weight minus desired weight (pounds)',\n", " yaxis_title='Gender')\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 10, "id": "2eef8da2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Women\n" ] }, { "data": { "text/plain": [ "count 31.000000\n", "mean 26.354839\n", "std 42.640003\n", "min -10.000000\n", "25% 0.000000\n", "50% 20.000000\n", "75% 27.500000\n", "max 200.000000\n", "Name: want_to_lose, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Men\n" ] }, { "data": { "text/plain": [ "count 29.000000\n", "mean 9.517241\n", "std 16.101510\n", "min -15.000000\n", "25% 0.000000\n", "50% 5.000000\n", "75% 15.000000\n", "max 70.000000\n", "Name: want_to_lose, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Women')\n", "display(DF[DF['gender'] == 'f']['want_to_lose'].describe())\n", "print('Men')\n", "display(DF[DF['gender'] == 'm']['want_to_lose'].describe())" ] }, { "cell_type": "markdown", "id": "8d7b89ca", "metadata": {}, "source": [ "Women in this sample want to lose more: median 20 pounds vs 5 pounds for men, mean about 26 vs about 10. The women's mean is pulled up by the 400 pound outlier (she alone wants to lose 200), so the medians are the fairer comparison. Even so, the difference between the groups (about 15 pounds at the median) is smaller than the spread inside each group (the women's box runs from 0 to about 28), so there is a lot of overlap." ] }, { "cell_type": "markdown", "id": "4a65304e", "metadata": {}, "source": [ "---------------------------------------\n", "## Challenge 2 - Does how people feel match the numbers?\n", "---------------------------------------\n", "\n", "**Q9** BMI column." ] }, { "cell_type": "code", "execution_count": 11, "id": "a32445a4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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genderheightweightbmigenhlth
0m7526533.119111very good
1m7215020.341435excellent
2m6913720.229154excellent
3f6615925.660468good
4f6314525.682792very good
5f6412521.453857very good
6m7217523.731674excellent
7f6010420.308889very good
8f6514023.294675good
9m7018826.972245very good
10m6717026.622856very good
11f6512019.966864good
12m7329038.256709good
13m6617027.435721fair
14f6512019.966864good
15f6414024.028320very good
16f6316429.048123fair
17m6917025.101869good
18f6217031.090010good
19m7319826.120098good
20f6213023.774714fair
21f6630048.415978good
22m7419224.648649very good
23m7217523.731674good
24f6819529.646410good
25m7015522.237755excellent
26f6740062.642014good
27m6814522.044766excellent
28f6214526.517950very good
29f6316529.225246good
30m6814021.284602excellent
31m7322029.022331very good
32m7120027.891291excellent
33f6412020.595703excellent
34f6518029.950296very good
35f6413523.170166fair
36f6917025.101869excellent
37f6315126.745528excellent
38f6718028.188906good
39m7119527.194009very good
40f6312021.254724fair
41m6817526.605753good
42f6211521.031478excellent
43f6616025.821855very good
44m7220027.121914excellent
45m7015422.094286good
46f6221038.405307very good
47f6213524.689126very good
48f6614823.885216very good
49m7223031.190201excellent
50f6019237.493333fair
51m7420025.675676excellent
52m7221028.478009poor
53m7019928.550408excellent
54m6516026.622485excellent
55f6113224.938457good
56m7216522.375579very good
57f6216029.261186fair
58m7317523.085945excellent
59m6916524.363579very good
\n", "
" ], "text/plain": [ " gender height weight bmi genhlth\n", "0 m 75 265 33.119111 very good\n", "1 m 72 150 20.341435 excellent\n", "2 m 69 137 20.229154 excellent\n", "3 f 66 159 25.660468 good\n", "4 f 63 145 25.682792 very good\n", "5 f 64 125 21.453857 very good\n", "6 m 72 175 23.731674 excellent\n", "7 f 60 104 20.308889 very good\n", "8 f 65 140 23.294675 good\n", "9 m 70 188 26.972245 very good\n", "10 m 67 170 26.622856 very good\n", "11 f 65 120 19.966864 good\n", "12 m 73 290 38.256709 good\n", "13 m 66 170 27.435721 fair\n", "14 f 65 120 19.966864 good\n", "15 f 64 140 24.028320 very good\n", "16 f 63 164 29.048123 fair\n", "17 m 69 170 25.101869 good\n", "18 f 62 170 31.090010 good\n", "19 m 73 198 26.120098 good\n", "20 f 62 130 23.774714 fair\n", "21 f 66 300 48.415978 good\n", "22 m 74 192 24.648649 very good\n", "23 m 72 175 23.731674 good\n", "24 f 68 195 29.646410 good\n", "25 m 70 155 22.237755 excellent\n", "26 f 67 400 62.642014 good\n", "27 m 68 145 22.044766 excellent\n", "28 f 62 145 26.517950 very good\n", "29 f 63 165 29.225246 good\n", "30 m 68 140 21.284602 excellent\n", "31 m 73 220 29.022331 very good\n", "32 m 71 200 27.891291 excellent\n", "33 f 64 120 20.595703 excellent\n", "34 f 65 180 29.950296 very good\n", "35 f 64 135 23.170166 fair\n", "36 f 69 170 25.101869 excellent\n", "37 f 63 151 26.745528 excellent\n", "38 f 67 180 28.188906 good\n", "39 m 71 195 27.194009 very good\n", "40 f 63 120 21.254724 fair\n", "41 m 68 175 26.605753 good\n", "42 f 62 115 21.031478 excellent\n", "43 f 66 160 25.821855 very good\n", "44 m 72 200 27.121914 excellent\n", "45 m 70 154 22.094286 good\n", "46 f 62 210 38.405307 very good\n", "47 f 62 135 24.689126 very good\n", "48 f 66 148 23.885216 very good\n", "49 m 72 230 31.190201 excellent\n", "50 f 60 192 37.493333 fair\n", "51 m 74 200 25.675676 excellent\n", "52 m 72 210 28.478009 poor\n", "53 m 70 199 28.550408 excellent\n", "54 m 65 160 26.622485 excellent\n", "55 f 61 132 24.938457 good\n", "56 m 72 165 22.375579 very good\n", "57 f 62 160 29.261186 fair\n", "58 m 73 175 23.085945 excellent\n", "59 m 69 165 24.363579 very good" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "count 60.000000\n", "mean 26.890102\n", "std 6.936902\n", "min 19.966864\n", "25% 23.149111\n", "50% 25.679234\n", "75% 28.496109\n", "max 62.642014\n", "Name: bmi, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF['bmi'] = 703 * DF['weight'] / DF['height']**2\n", "display(DF[['gender', 'height', 'weight', 'bmi', 'genhlth']])\n", "DF['bmi'].describe()" ] }, { "cell_type": "markdown", "id": "b36d1ac6", "metadata": {}, "source": [ "**Q10** BMI by self rated health, in order." ] }, { "cell_type": "code", "execution_count": 12, "id": "ad2ed826", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "genhlth\n", "poor 28.478009\n", "fair 27.435721\n", "good 26.120098\n", "very good 25.752323\n", "excellent 23.731674\n", "Name: bmi, dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig = px.box(DF,\n", " x='genhlth',\n", " y='bmi',\n", " color='genhlth',\n", " category_orders={'genhlth': health_order},\n", " title='BMI by self rated health')\n", "fig.update_layout(title_x=0.5,\n", " xaxis_title='Self rated general health',\n", " yaxis_title='BMI',\n", " showlegend=False)\n", "fig.show()\n", "\n", "# Check the medians with a table\n", "DF.groupby('genhlth')['bmi'].median().reindex(health_order)" ] }, { "cell_type": "markdown", "id": "8e7f3d29", "metadata": {}, "source": [ "Yes, mostly. The median BMI goes down steadily as the rating goes up: poor about 28.5, fair about 27.4, good about 26.1, very good about 25.8, excellent about 23.7. Where it does not line up: the \"poor\" group is a single person so that box is not really a box, and \"very good\" contains the 400 pound outlier with a BMI over 60, which shows a high BMI does not stop someone rating their own health well. The boxes overlap a lot, so BMI does not *determine* how people feel, it just trends with it.\n", "\n", "**Q11** BMI by exercise." ] }, { "cell_type": "code", "execution_count": 13, "id": "1d669645", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# First try - the legend says 0 and 1\n", "fig = px.histogram(DF, x='bmi', color='exerany', nbins=15, opacity=0.7)\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 14, "id": "a40f1045", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "exercise\n", "no 28.478009\n", "yes 25.660468\n", "Name: bmi, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF['exercise'] = DF['exerany'].replace({0: 'no', 1: 'yes'})\n", "\n", "fig = px.histogram(DF,\n", " x='bmi',\n", " color='exercise',\n", " nbins=15,\n", " opacity=0.7,\n", " barmode='overlay',\n", " title='BMI for people who did and did not exercise last month')\n", "fig.update_layout(title_x=0.5,\n", " xaxis_title='BMI',\n", " yaxis_title='Number of people',\n", " legend_title='Exercised last month')\n", "fig.show()\n", "\n", "DF.groupby('exercise')['bmi'].median()" ] }, { "cell_type": "markdown", "id": "46ae49bc", "metadata": {}, "source": [ "The annoying part is that the legend reads 0 and 1 and the reader has to go look up which is which. After the `replace` the legend says yes and no.\n", "\n", "The exercisers sit a little lower, median BMI about 25.7 vs about 28.5 for the non exercisers, but there are only 15 non exercisers so their histogram is very bumpy. The two groups overlap a lot. `barmode='overlay'` is optional, the default stacked histogram also works but is harder to compare." ] }, { "cell_type": "markdown", "id": "41aec548", "metadata": {}, "source": [ "---------------------------------------\n", "## Challenge 3 - Smoking and health, and being a skeptic\n", "---------------------------------------\n", "\n", "**Q12** Bar plot of health rating colored by smoking." ] }, { "cell_type": "code", "execution_count": 15, "id": "defb061b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "DF['smoker'] = DF['smoke100'].replace({0: 'no', 1: 'yes'})\n", "\n", "fig = px.bar(DF,\n", " x='genhlth',\n", " color='smoker',\n", " category_orders={'genhlth': health_order},\n", " barmode='group',\n", " title='Self rated health for smokers and non smokers')\n", "fig.update_traces(dict(marker_line_width=0))\n", "fig.update_layout(title_x=0.5,\n", " xaxis_title='Self rated general health',\n", " yaxis_title='Number of people',\n", " legend_title='Smoked 100+ cigarettes')\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "fa8ab1c3", "metadata": {}, "source": [ "It is hard to say from the bars. The only \"poor\" is a smoker and the smokers have one fewer \"excellent\", so a group might say smokers rate their health a little worse. The stacked version (the default) is even harder to read.\n", "\n", "**Q13** Compare within each group." ] }, { "cell_type": "code", "execution_count": 16, "id": "8f5032e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Smokers\n" ] }, { "data": { "text/plain": [ "genhlth\n", "very good 9\n", "good 9\n", "excellent 8\n", "fair 4\n", "poor 1\n", "Name: count, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Non smokers\n" ] }, { "data": { "text/plain": [ "genhlth\n", "excellent 9\n", "very good 9\n", "good 8\n", "fair 3\n", "Name: count, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Smokers')\n", "display(DF[DF['smoker'] == 'yes']['genhlth'].value_counts())\n", "print('Non smokers')\n", "display(DF[DF['smoker'] == 'no']['genhlth'].value_counts())" ] }, { "cell_type": "code", "execution_count": 17, "id": "ec789677", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yes0.030.130.290.290.26
\n", "
" ], "text/plain": [ "genhlth poor fair good very good excellent\n", "smoker \n", "no NaN 0.10 0.28 0.31 0.31\n", "yes 0.03 0.13 0.29 0.29 0.26" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Optional - as a fraction of each group, which is the fair comparison\n", "DF.groupby('smoker')['genhlth'].value_counts(normalize=True).unstack().reindex(columns=health_order).round(2)" ] }, { "cell_type": "markdown", "id": "4f75cd9f", "metadata": {}, "source": [ "There are 31 smokers and 29 non smokers, so the groups are close in size here and the bar plot was not lying much. As fractions: about 26% of smokers say excellent vs 31% of non smokers, and 13% vs 10% say fair. The one \"poor\" is a smoker. So smokers rate their health *slightly* worse, but the difference is one or two people in each category. A group that said \"no real difference\" is right, and a group that said \"smokers a little worse\" is also right, as long as they say how small it is.\n", "\n", "The habit to take away: whenever you color a bar plot by a group, check whether the groups are the same size before you compare the heights.\n", "\n", "**Q14** Being a skeptic. Any two of these (or others) are good:\n", "\n", "* **Tiny sample.** 60 people, and the difference is one or two people per category. If we picked a different 60 people the answer could easily flip.\n", "* **Self reported.** Both smoking and health are what people *said* on the phone, not measured. People tend to say they weigh a little less and feel a little better than they really do.\n", "* **Other differences between the groups.** Smokers in this sample might be older, exercise less, or have different jobs. Any of those could explain a health difference without smoking causing it. (We will call these confounding variables later in the semester.)\n", "* **Who answers the phone.** People in poor health are less likely to be surveyed at all, which is one reason \"poor\" has only one person.\n", "* **Direction.** Even if there is a link, this data cannot say smoking caused the lower rating. That takes a very different kind of study." ] }, { "cell_type": "markdown", "id": "d9a6dfba", "metadata": {}, "source": [ "---------------------------------------\n", "## Challenge (optional) - Age\n", "---------------------------------------\n", "\n", "All three questions have a real signal. Here is one plot for each." ] }, { "cell_type": "code", "execution_count": 18, "id": "eb7fc9ea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "genhlth\n", "poor 64.0\n", "fair 48.0\n", "good 50.0\n", "very good 41.0\n", "excellent 37.0\n", "Name: age, dtype: float64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig = px.box(DF,\n", " x='age',\n", " y='genhlth',\n", " color='genhlth',\n", " category_orders={'genhlth': health_order},\n", " title='Age by self rated health')\n", "fig.update_layout(title_x=0.5, xaxis_title='Age (years)', yaxis_title='Self rated health', showlegend=False)\n", "fig.show()\n", "\n", "DF.groupby('genhlth')['age'].median().reindex(health_order)" ] }, { "cell_type": "markdown", "id": "ec375dd5", "metadata": {}, "source": [ "Older people rate their health lower. Median age is about 37 for excellent, 41 very good, 50 good, 48 fair, and the one poor is 64." ] }, { "cell_type": "code", "execution_count": 19, "id": "2367854a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(DF,\n", " x='age',\n", " y='want_to_lose',\n", " color='gender',\n", " title='Pounds people want to lose vs age')\n", "fig.update_layout(title_x=0.5, xaxis_title='Age (years)', yaxis_title='Pounds to lose')\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "c6b7b2e2", "metadata": {}, "source": [ "No clear trend with age once you ignore the 400 pound outlier. The gender difference from Q8 is a lot easier to see than any age pattern." ] }, { "cell_type": "code", "execution_count": 20, "id": "56f1c5bb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "health_plan\n", "no 32.5\n", "yes 47.0\n", "Name: age, dtype: float64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF['health_plan'] = DF['hlthplan'].replace({0: 'no', 1: 'yes'})\n", "\n", "fig = px.box(DF,\n", " x='age',\n", " y='health_plan',\n", " color='health_plan',\n", " title='Age of people with and without a health plan')\n", "fig.update_layout(title_x=0.5, xaxis_title='Age (years)', yaxis_title='Has a health plan', showlegend=False)\n", "fig.show()\n", "\n", "DF.groupby('health_plan')['age'].median()" ] }, { "cell_type": "markdown", "id": "08aaa014", "metadata": {}, "source": [ "The 10 people without a health plan are younger, median age about 32 vs about 47 for people with one. Only 10 people though!\n", "\n", "---------------------------------------\n", "## FREE FORM\n", "---------------------------------------\n", "\n", "No solution here, this is whatever the group came up with. When they present, listen for: a real title and labeled axes with units, one sentence saying what the plot shows, and one honest reason they might be wrong (sample size and self reporting are the easy ones)." ] } ], "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 }