{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some define **Statistics** as the field that focuses on turning information into knowledge. The first step in that process is to summarize and describe the raw information - the data. In this lab, you will gain insight into public health by generating simple graphical and numerical summaries of a data set collected by the Centers for Disease Control and Prevention (CDC)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting started" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Behavioral Risk Factor Surveillance System (BRFSS) is an annual telephone survey of 350,000 people in the United States. As its name implies, the BRFSS is designed to identify risk factors in the adult population and report emerging health trends. For example, respondents are asked about their diet and weekly physical activity, their HIV/AIDS status, possible tobacco use, and even their level of healthcare coverage. The BRFSS's [website](http://www.cdc.gov/brfss) contains a complete description of the survey, including the research questions that motivate the study and many interesting results derived from the data.\n", "\n", "We will focus on a random sample of 20,000 people from the BRFSS survey conducted in 2000. While there are over 200 variables in this data set, we will work with a small subset.\n", "\n", "We begin by importing the dataset of 20,000 observations from the Cloud." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20000, 9)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import io\n", "import requests\n", "\n", "df_url = 'https://raw.githubusercontent.com/akmand/datasets/master/openintro/brfss_2000.csv'\n", "url_content = requests.get(df_url, verify=False).content\n", "cdc = pd.read_csv(io.StringIO(url_content.decode('utf-8')))\n", "cdc.shape" ] }, { "cell_type": "code", "execution_count": 2, "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", " \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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
exeranyhlthplansmoke100heightweightwtdesireagegendergenhlth
67431107117017023mvery good
193601016412011745fvery good
81041117019217064mgood
85351116416514067fexcellent
82751106913014069mvery good
35110106312812837fvery good
15211106817613537fgood
9760116415012543ffair
144841116818518578mgood
35911107116517534mfair
\n", "
" ], "text/plain": [ " exerany hlthplan smoke100 height weight wtdesire age gender \\\n", "6743 1 1 0 71 170 170 23 m \n", "19360 1 0 1 64 120 117 45 f \n", "8104 1 1 1 70 192 170 64 m \n", "8535 1 1 1 64 165 140 67 f \n", "8275 1 1 0 69 130 140 69 m \n", "3511 0 1 0 63 128 128 37 f \n", "1521 1 1 0 68 176 135 37 f \n", "976 0 1 1 64 150 125 43 f \n", "14484 1 1 1 68 185 185 78 m \n", "3591 1 1 0 71 165 175 34 m \n", "\n", " genhlth \n", "6743 very good \n", "19360 very good \n", "8104 good \n", "8535 excellent \n", "8275 very good \n", "3511 very good \n", "1521 good \n", "976 fair \n", "14484 good \n", "3591 fair " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.sample(10, random_state=999)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data set `cdc` that shows up is a data matrix, with each row representing a case and each column representing a variable. These kind of data format are called data frame, which is a term that will be used throughout the labs.\n", "\n", "To view the names of the variables, use `columns.values`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['exerany', 'hlthplan', 'smoke100', 'height', 'weight', 'wtdesire',\n", " 'age', 'gender', 'genhlth'], dtype=object)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.columns.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This returns the names `genhlth`, `exerany`, `hlthplan`, `smoke100`, `height`, `weight`, `wtdesire`, `age`, and `gender`. Each one of these variables corresponds to a question that was asked in the survey. For example, for `genhlth`, respondents were asked to evaluate their general health, responding either excellent, very good, good, fair or poor. The `exerany` variable indicates whether the respondent exercised in the past month (1) or did not (0). Likewise, `hlthplan` indicates whether the respondent had some form of health coverage (1) or did not (0). The `smoke100` variable indicates whether the respondent had smoked at least 100 cigarettes in their lifetime. The other variables record the respondent's `height` in inches, `weight` in pounds as well as their desired weight, `wtdesire`, `age` in years, and `gender`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Exercise 1

\n", "How many cases are there in this data set? How many variables? For each variable, identify its data type (e.g. categorical, discrete).\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can have a look at the first few entries (rows) of our data with the command" ] }, { "cell_type": "code", "execution_count": 4, "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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
exeranyhlthplansmoke100heightweightwtdesireagegendergenhlth
00107017517577mgood
10116412511533fgood
21116010510549fgood
31106613212442fgood
40106115013055fvery good
\n", "
" ], "text/plain": [ " exerany hlthplan smoke100 height weight wtdesire age gender \\\n", "0 0 1 0 70 175 175 77 m \n", "1 0 1 1 64 125 115 33 f \n", "2 1 1 1 60 105 105 49 f \n", "3 1 1 0 66 132 124 42 f \n", "4 0 1 0 61 150 130 55 f \n", "\n", " genhlth \n", "0 good \n", "1 good \n", "2 good \n", "3 good \n", "4 very good " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and similarly we can look at the last few by typing." ] }, { "cell_type": "code", "execution_count": 5, "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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
exeranyhlthplansmoke100heightweightwtdesireagegendergenhlth
199951106621514023fgood
199960107320018535mexcellent
199970106521615057fpoor
199981106716516581fgood
199991116917016583mgood
\n", "
" ], "text/plain": [ " exerany hlthplan smoke100 height weight wtdesire age gender \\\n", "19995 1 1 0 66 215 140 23 f \n", "19996 0 1 0 73 200 185 35 m \n", "19997 0 1 0 65 216 150 57 f \n", "19998 1 1 0 67 165 165 81 f \n", "19999 1 1 1 69 170 165 83 m \n", "\n", " genhlth \n", "19995 good \n", "19996 excellent \n", "19997 poor \n", "19998 good \n", "19999 good " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You could also look at all of the data frame at once by typing its name into the console, but that might be unwise here. We know `cdc` has 20,000 rows, so viewing the entire data set would mean flooding your screen. It's better to take small peeks at the data with `head`, `tail` or the subsetting techniques that you'll learn in a moment." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summaries and tables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The BRFSS questionnaire is a massive trove of information. A good first step in any analysis is to distill all of that information into a few summary statistics and graphics. As a simple example, the function `describe` returns a numerical summary: count, mean, standard deviation, minimum, first quartile, median, third quartile, and maximum. For `weight` this is" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 20000.00000\n", "mean 169.68295\n", "std 40.08097\n", "min 68.00000\n", "25% 140.00000\n", "50% 165.00000\n", "75% 190.00000\n", "max 500.00000\n", "Name: weight, dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc['weight'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you wanted to compute the interquartile range for the respondents’ weight, you would look at the output from the summary command above and then enter" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "50" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "190 - 140" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python also has built-in functions to compute summary statistics one by one." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count: 20000\n", "mean: 169.68295\n", "std: 40.08096996712\n", "var: 1606.4841535051753\n", "min: 68\n", "25%: 140.0\n", "50%: 165.0\n", "75%: 190.0\n", "max: 500\n" ] } ], "source": [ "print('count:', cdc['weight'].count())\n", "print('mean: ', cdc['weight'].mean())\n", "print('std: ', cdc['weight'].std())\n", "print('var: ', cdc['weight'].var())\n", "print('min: ', cdc['weight'].min())\n", "print('25%: ', cdc['weight'].quantile(0.25))\n", "print('50%: ', cdc['weight'].median()) # Remember that the median is also the second quartile \n", "print('75%: ', cdc['weight'].quantile(0.75))\n", "print('max: ', cdc['weight'].max())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While it makes sense to describe a quantitative variable like `weight` in terms of these statistics, what about categorical data? We would instead consider the sample frequency or relative frequency distribution. The function `value_counts` does this for you by counting the number of times each kind of response was given. For example, to see the number of people who have smoked 100 cigarettes in their lifetime, type" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "smoke100\n", "0 10559\n", "1 9441\n", "Name: count, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc['smoke100'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or instead look at the relative frequency distribution by typing" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "smoke100\n", "0 0.52795\n", "1 0.47205\n", "Name: proportion, dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc['smoke100'].value_counts(normalize = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how Python automatically shows the relative frequency distributions by setting the parameter `normalize` as `True`.\n", "\n", "Now let's import `matplotlib` library to create plots. When running Python using the command line, the graphs are typically shown in a separate window. In a Jupyter Notebook, you can simply output the graphs within the notebook itself by running the `%matplotlib` inline magic command." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can change the format to svg for better quality figures. You can also try the retina format and see which one looks better on your computer's screen." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also change the default style of plots. Let's go for our favourite style, `ggplot` from R." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "plt.style.use('ggplot')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also make the size of plots and font sizes bigger." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (10,5)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['font.size'] = 12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can make a bar plot of the entries in the table by putting the table inside the barplot command." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 477, "width": 857 } }, "output_type": "display_data" } ], "source": [ "cdc['smoke100'].value_counts().plot(kind = 'bar', color = 'turquoise', title = 'Bar plot of smoke100')\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice what we’ve done here! We created the bar plot using `kind = bar`. You could also break this into two steps by typing the following:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 477, "width": 857 } }, "output_type": "display_data" } ], "source": [ "smoke = cdc['smoke100'].value_counts()\n", "smoke.plot(kind = 'bar', color = 'turquoise', title = 'Bar plot of smoke100')\n", "plt.show(); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we've made a new object, called `smoke` (the contents of which we can see by typing `smoke` into the console) and then used it in as the input for `plot`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Exercise 2

\n", "Create a numerical summary for height and age, and compute the interquartile range for each. Compute the relative frequency distribution for gender and exerany. How many males are in the sample? What proportion of the sample reports being in excellent health?\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `value_counts()` with `groupby` command can be used to tabulate any number of variables that you provide. For example, to examine which participants have smoked across each gender, we could use the following." ] }, { "cell_type": "code", "execution_count": 18, "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", "
smoke10001
gender
f60124419
m45475022
\n", "
" ], "text/plain": [ "smoke100 0 1\n", "gender \n", "f 6012 4419\n", "m 4547 5022" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.groupby('gender')['smoke100'].value_counts().unstack() \n", "# By doing unstack we are transforming the last level of the index to the columns. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we see column labels of 0 and 1. Recall that 1 indicates a respondent has smoked at least 100 cigarettes. The rows refer to gender. To create a mosaic plot of this table, we would enter the following command." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 458, "width": 815 } }, "output_type": "display_data" } ], "source": [ "from statsmodels.graphics.mosaicplot import mosaic\n", "\n", "gender_colors = lambda key: {'color': 'lightcoral' if 'f' in key else 'lightblue'}\n", "mosaic(cdc, ['gender', 'smoke100'], title='Mosaic plot of smoke100 and gender', \n", " properties = gender_colors, gap = 0.02)\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Exercise 3

\n", "What does the mosaic plot reveal about smoking habits and gender?\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interlude: How Python thinks about data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataFrames are like a type of spreadsheet. Each row is a different observation (a different respondent) and each column is a different variable (the first is `genhlth`, the second `exerany` and so on). We can see the size of the DataFrame by typing" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20000, 9)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which will return the number of rows and columns. Now, if we want to access a subset of the full DataFrame, we can use row-and-column notation. For example, to see the sixth variable of the 567th respondent, use the format" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "190" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.iloc[566, 5] # This is the equivalent of cdc[567,6] in R." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which gives us the weight of the 567th person (or observation). Remember that, in Python indexing starts at 0, so the first element of a list or DataFrame is selected by the 0-th index.\n", "\n", "To see the weights for the first 10 respondents we can type" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 175\n", "1 115\n", "2 105\n", "3 124\n", "4 130\n", "5 114\n", "6 185\n", "7 160\n", "8 130\n", "9 170\n", "Name: wtdesire, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.iloc[0:10, 5] # Keep in mind that the ending index is excluded in Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, if we want all of the data for the first 10 respondents, type" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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exeranyhlthplansmoke100heightweightwtdesireagegendergenhlth
00107017517577mgood
10116412511533fgood
21116010510549fgood
31106613212442fgood
40106115013055fvery good
51106411411455fvery good
61107119418531mvery good
70106717016045mvery good
80116515013027fgood
91107018017044mgood
\n", "
" ], "text/plain": [ " exerany hlthplan smoke100 height weight wtdesire age gender \\\n", "0 0 1 0 70 175 175 77 m \n", "1 0 1 1 64 125 115 33 f \n", "2 1 1 1 60 105 105 49 f \n", "3 1 1 0 66 132 124 42 f \n", "4 0 1 0 61 150 130 55 f \n", "5 1 1 0 64 114 114 55 f \n", "6 1 1 0 71 194 185 31 m \n", "7 0 1 0 67 170 160 45 m \n", "8 0 1 1 65 150 130 27 f \n", "9 1 1 0 70 180 170 44 m \n", "\n", " genhlth \n", "0 good \n", "1 good \n", "2 good \n", "3 good \n", "4 very good \n", "5 very good \n", "6 very good \n", "7 very good \n", "8 good \n", "9 good " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.iloc[0:10,]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By leaving out an index or a range (we didn't type anything between the comma and the square bracket), we get all the columns. As a rule, we omit the column number to see all columns in a DataFrame. To access all the observations, just leave a colon inside of the bracket. Try the following to see the weights for all 20,000 respondents fly by on your screen" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 175\n", "1 115\n", "2 105\n", "3 124\n", "4 130\n", " ... \n", "19995 140\n", "19996 185\n", "19997 150\n", "19998 165\n", "19999 165\n", "Name: wtdesire, Length: 20000, dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc.iloc[:, 5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall that the sixth column represents respondents' weight, so the command above reported all of the weights in the data set. An alternative method to access the weight data is by referring to the name. Previously, we typed `cdc` to see all the variables contained in the cdc data set. We can use any of the variable names to select items in our data set." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 175\n", "1 125\n", "2 105\n", "3 132\n", "4 150\n", " ... \n", "19995 215\n", "19996 200\n", "19997 216\n", "19998 165\n", "19999 170\n", "Name: weight, Length: 20000, dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc['weight']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tells Python to look in DataFrame `cdc` for the column called `weight`. Since that's a single vector, we can subset it by just adding another single index inside square brackets. We see the weight for the 567th respondent by typing" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "160" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc['weight'][566]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, for just the first 10 respondents" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 175\n", "1 125\n", "2 105\n", "3 132\n", "4 150\n", "5 114\n", "6 194\n", "7 170\n", "8 150\n", "9 180\n", "Name: weight, dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc['weight'][0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The command above returns the same result as the `cdc.iloc[0:10, 5]` command." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A little more on subsetting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's often useful to extract all individuals (cases) in a data set that have specific characteristics. We accomplish this through conditioning commands. First, consider expressions like" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 True\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "19995 False\n", "19996 True\n", "19997 False\n", "19998 False\n", "19999 True\n", "Name: gender, Length: 20000, dtype: bool" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc['gender'] == 'm'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 True\n", "1 True\n", "2 True\n", "3 True\n", "4 True\n", " ... \n", "19995 False\n", "19996 True\n", "19997 True\n", "19998 True\n", "19999 True\n", "Name: age, Length: 20000, dtype: bool" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc['age'] > 30" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These commands produce a series of `TRUE` and `FALSE` values. There is one value for each respondent, where `TRUE` indicates that the person was male (via the first command) or older than 30 (second command).\n", "\n", "Suppose we want to extract just the data for the men in the sample, or just for those over 30. For example, the command" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "mdata = cdc[cdc['gender'] == 'm']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "will create a new data set called `mdata` that contains only the men from the `cdc` data set. In addition to finding it in your workspace alongside its dimensions, you can take a peek at the first several rows as usual" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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exeranyhlthplansmoke100heightweightwtdesireagegendergenhlth
00107017517577mgood
61107119418531mvery good
70106717016045mvery good
91107018017044mgood
101116918617546mexcellent
\n", "
" ], "text/plain": [ " exerany hlthplan smoke100 height weight wtdesire age gender \\\n", "0 0 1 0 70 175 175 77 m \n", "6 1 1 0 71 194 185 31 m \n", "7 0 1 0 67 170 160 45 m \n", "9 1 1 0 70 180 170 44 m \n", "10 1 1 1 69 186 175 46 m \n", "\n", " genhlth \n", "0 good \n", "6 very good \n", "7 very good \n", "9 good \n", "10 excellent " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mdata.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can carve up the data based on values of one or more variables. As an aside, we can use several of these conditions together with `&` and `|`. The `&` is read \"and\" so that" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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exeranyhlthplansmoke100heightweightwtdesireagegendergenhlth
00107017517577mgood
61107119418531mvery good
70106717016045mvery good
91107018017044mgood
101116918617546mexcellent
\n", "
" ], "text/plain": [ " exerany hlthplan smoke100 height weight wtdesire age gender \\\n", "0 0 1 0 70 175 175 77 m \n", "6 1 1 0 71 194 185 31 m \n", "7 0 1 0 67 170 160 45 m \n", "9 1 1 0 70 180 170 44 m \n", "10 1 1 1 69 186 175 46 m \n", "\n", " genhlth \n", "0 good \n", "6 very good \n", "7 very good \n", "9 good \n", "10 excellent " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_and_over30 = cdc[(cdc['gender'] == 'm') & (cdc['age'] > 30)]\n", "m_and_over30.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "will give you the data for men over the age of 30. The `|` character is read \"or\" so that" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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exeranyhlthplansmoke100heightweightwtdesireagegendergenhlth
00107017517577mgood
10116412511533fgood
21116010510549fgood
31106613212442fgood
40106115013055fvery good
\n", "
" ], "text/plain": [ " exerany hlthplan smoke100 height weight wtdesire age gender \\\n", "0 0 1 0 70 175 175 77 m \n", "1 0 1 1 64 125 115 33 f \n", "2 1 1 1 60 105 105 49 f \n", "3 1 1 0 66 132 124 42 f \n", "4 0 1 0 61 150 130 55 f \n", "\n", " genhlth \n", "0 good \n", "1 good \n", "2 good \n", "3 good \n", "4 very good " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_or_over30 = cdc[(cdc['gender'] == 'm') | (cdc['age'] > 30)]\n", "m_or_over30.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "will take people who are men or over the age of 30 (why that's an interesting group is hard to say, but right now the mechanics of this are the important thing). In principle, you may use as many \"and\" and \"or\" clauses as you like when forming a subset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Exercise 4

\n", "Create a new object called under23_and_smoke that contains all observations of respondents under the age of 23 that have smoked 100 cigarettes in their lifetime. Write the command you used to create the new object as the answer to this exercise.
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quantitative data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With our subsetting tools in hand, we'll now return to the task of the day: making basic summaries of the BRFSS questionnaire. We've already looked at categorical data such as `smoke100` and `gender` so now let's turn our attention to quantitative data. Two common ways to visualize quantitative data are with box plots and histograms. We can construct a box plot for a single variable with the following command." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 458, "width": 825 } }, "output_type": "display_data" } ], "source": [ "cdc['height'].plot(kind = 'box', title = 'Boxplot of height')\n", "plt.show(); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can compare the locations of the components of the box by examining the summary statistics." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 20000.000000\n", "mean 67.182900\n", "std 4.125954\n", "min 48.000000\n", "25% 64.000000\n", "50% 67.000000\n", "75% 70.000000\n", "max 93.000000\n", "Name: height, dtype: float64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdc['height'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Confirm that the median and upper and lower quartiles reported in the numerical summary match those in the graph. The purpose of a boxplot is to provide a thumbnail sketch of a variable for the purpose of comparing across several categories. So we can, for example, compare the heights of men and women with" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 473, "width": 850 } }, "output_type": "display_data" } ], "source": [ "cdc.boxplot(column = 'height', by = 'gender')\n", "plt.title('Boxplot of height by gender')\n", "plt.suptitle('')\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next let's consider a new variable that doesn't show up directly in this data set: Body Mass Index ([BMI](http://en.wikipedia.org/wiki/Body_mass_index)). BMI is a weight to height ratio and can be calculated as:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**BMI** = $\\displaystyle\\frac{mass_{kg}}{height^2_{m}} = \\frac{mass_{lb}}{height^2_{in}}\\times703$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "703 is the approximate conversion factor to change units from metric (meters and kilograms) to imperial (inches and pounds) units.\n", "\n", "The following two lines first make a new object called `bmi` and then creates box plots of these values using `seaborn` library, defining groups by the variable `genhlth`." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "bmi = (cdc['weight'] / (cdc['height'])**2) * 703" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 483, "width": 851 } }, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "\n", "sns.boxplot(x = cdc['genhlth'], y = bmi).set(\n", " xlabel='genhlth', ylabel='bmi', title = 'Boxplot of BMI by genhlth')\n", "plt.show(); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the first line above is just some arithmetic, but it's applied to all 20,000 numbers in the `cdc` data set. That is, for each of the 20,000 participants, we take their weight, divide by their height-squared and then multiply by 703. The result is 20,000 BMI values, one for each respondent." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Exercise 5

\n", "What does this box plot show? Pick another categorical variable from the data set and see how it relates to BMI. List the variable you chose, why you might think it would have a relationship to BMI, and indicate what the figure seems to suggest.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's make some histograms. We can look at the histogram for the age of our respondents with the command" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 458, "width": 872 } }, "output_type": "display_data" } ], "source": [ "cdc['age'].plot(kind = 'hist', color = 'springgreen', edgecolor = 'black', \n", " linewidth = 1.2, title = 'Histogram of age')\n", "plt.show(); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Histograms are generally a very good way to see the shape of a single distribution, but that shape can change depending on how the data is split between the different bins. You can control the number of bins by adding an argument to the command. In the next two lines, we first make a default histogram of `bmi` and then one with the bin size of 50." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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BMWnSpDj77LOTtlZ8/fXX46WXXqp0VckTTzxR5l1xF1xwQbzwwguV3rNXr17xxhtvxPDhw2Pu3LmVvrOtpkaMGBGvvfZaNGnSpEbXXXTRRXHxxRfX6JpmzZrFz372szj++OPju9/9buL8E088Ef/1X/8Vbdu2rVG9iINbMXbo0CFmzJhR6XulTj755Hj00Ufj85//fOJcUVFRXHXVVVFYWBgdO3aMt956Kzp16lRhjVNOOSUeffTRuPTSSxPniouLY8KECUf0+/f111+Pl19+Oelcjx49YvLkyZX2e9xxx8Vzzz0X11xzTbz00kuJ8xs2bIj//M//jAceeOBItXzEPPTQQ/G3v/2t3M+Ki4tjz549sWbNmliyZEmZ99qlp6fHD37wgxr9bKrKFVdcEa1bt0684+zNN9+MdevWRefOncudn/retNT3qgEAUH+EaAAAcBRZtGhRLFq0qL7bqJbUMOqSSy6pp06qZ9++ffHII48kncvJyYnHH3+80jDrkA4dOsT48eNj+PDhSdtQPvDAA5WGaOPGjUsat2zZMv7whz9U656tWrWK8ePHxxlnnFEmHKitFi1axOOPP17jAC3i4LaHtTV27NgYP358fPjhhxFx8PfjpZdeqvW7oR5++OFKA7RDLr744jjllFOSnqtD22o+8sgjlQZSh1xyySVx8sknJ71Db+rUqUc0RPv1r39d5twf/vCHavWbmZkZ48ePj5NOOinpHXZ/+MMf4u67747s7Ow67fVIe++99+K9996r0TW9evWKz33uc3HbbbfFgAED6rSfJk2axA033BC/+93vIuJgkPfEE0/Ev/7rv5aZO3369KTg/ZRTTokhQ4bUaT8AANReetVTAAAAai411Cmp4P1mDcWLL74YeXl5SefuuuuuaNeuXbVrnHPOOXH55ZcnnZs6dWosW7as3PkfffRRmf/4f+utt1YrCDnktNNOK3PPw3HjjTdGt27d6qxedaWlpZX5Ot5+++1a1Ro4cGDSyrCqXHTRRWXOnXrqqUkr1KqSugLv/fffr/a1NbVu3br4+9//Xub+5513XrVrtGnTJv7t3/4t6dyuXbvimWeeqZMeG7q8vLzYtm1b7Nix44jUTw1/H3/88XLnjR8/vtLrAACoX0I0AADgiEjdumzChAlRWFhYT91Ubdq0aUnjjIyMuPHGG2tc55Zbbilzbvr06eXOfeutt8qcGzNmTI3vWZtrPolaNdW1a9ek8Zw5c2pV5+qrr67R/H79+tV5jQ0bNhyx7/cZM2aUCaVrE76MGTMmGjVqlHSuou/VY822bdviySefTATfpVfk1YUzzzwz+vbtmxgvXrw4Zs6cmTRnz5498eyzzybGtf2ZAwDAkSNEAwCAo8h//Md/RElJyWH9k7ry4UgZNWpU0vjdd9+NCy+8MObOnfuJ3L+mUlc9DRgwIDp06FDjOqNGjYqMjIxKax8ya9aspHHLli3j1FNPrfE9zz777BpfU56srKz4zGc+Uye1Ig5u6fmb3/wmvvKVr8TgwYOja9eu0apVq8jIyIi0tLQy/9x6661J19c22Kjpdnjt27c/IjV27txZoxrVVd730+jRo2tcJycnJwYPHlxl7YZu/Pjxlf7M27dvX2zcuDGmTJkSd999d/Ts2TPp+r/+9a9x1llnxfr16+u0r9R3m6W+++zZZ5+N3bt3J8YXXnhhrX7mAABw5AjRAACAI+Lqq6+OE088MencxIkTY9CgQXHaaafFj370o5g4cWLs2bOnnjpMtnTp0qRxbcOkpk2bximnnJJ0bsmSJeXOXbFiRdK4f//+tXqvWPv27evkP7537969Vu9CS/XPf/4zRo8eHd27d49vf/vb8dhjj8WcOXNi7dq1sXPnziguLq5WnW3bttXq/h07dqzR/BYtWpQ5V9Nfz/JqlA5I6lLq9+oJJ5wQrVu3rlWt1O/zZcuWVfv352jRpEmTOP7442P48OHxox/9KD766KP43ve+lzTn448/jssuu6xOVw/eeOONSYH6M888E/v27UuMU/+HhtTQDQCA+idEAwAAjoisrKz4y1/+Em3atCnz2QcffBD/+Z//GaNHj45WrVrFaaedFmPHjo0XXnihXkK1AwcOxN69e5POdenSpdb1Uq+tKAzavn170ri81UzVVZN3t1WktkFMaf/5n/8ZZ5xxRkycOPGw34NX2xCqZcuWNZpfXnBZFzWOVBiV+v1Ul9+rJSUlR+w9YQ1Fo0aN4v7774+vfvWrSefnzJkTDz30UJ3dp0OHDnHhhRcmxjt27Ijnn38+Ig6GdqW3zszJyYnLLruszu4NAEDdEKIBAABHzKBBg2LOnDnxuc99rsI5RUVF8cEHH8T//M//xFVXXRXHHXdcjBkzJubNm/eJ9VleyJWdnV3req1atUoab926tdx5qSFaTYOb0g6n30OaN29+WNf/13/9V/zoRz8qNzxKS0uL448/PgYPHhyjR4+OSy65JK6++uqkf1K3Fqyt2qzmOxI1jpTU79e6/F6NqPj79Vhz7733lnkn3AMPPHDY4W9pqe+qO7Sl42OPPZZ0ny9+8YtlegEAoP5l1ncDAADAsa1nz57x+uuvx6xZs+Lxxx+Pl19+OdauXVvh/L1798aECRPiz3/+c3zzm9+M+++/Pxo3bvwJdvzJSf26Dhw4UOtah3NtXVi0aFH85Cc/KXP+kksuiVtuuSXOOeecyMnJqbTG+PHjy6wOgiOlQ4cOMWTIkKQVYStXrowFCxZE//796+Qel156aeTk5EReXl5EREyaNClWrVoVTzzxRNI8WzkCADRMVqIBAACfiCFDhsRvf/vbWLNmTXz88ccxfvz4uOWWW+Lkk08ud9VPSUlJPPjgg3HDDTcc8Xc0lbfl5M6dO2tdL3U7vLZt25Y7L3X7xMO55+FcWxd+8YtfREFBQdK58ePHx8svvxxXXHFFlQFaRNlfN8qX+v1al9+rERV/vx6LygvL5syZU2f1GzVqFF/84hcT4+Li4vjqV78aq1evTpwbOHBgrd/BCADAkSVEAwAAPnEnnHBC3HzzzfHoo4/GokWLYv369fHoo4/G2WefXWbuiy++GBMmTDii/TRq1CiaNWuWdG7dunW1rpd6bXkhXUTZ95gtX768VvcrLCxM+o/yn7Ti4uL429/+lnTuC1/4Qo1X1+Tm5tZhV8eu1O+nuvxeTUtLK3eLx2NVec/mli1b6vQeqVs6Tpo0qdLPAQBoOIRoAABAvevQoUPccsstMX369Hj22WfLvBto3LhxR7yH3r17J43/+c9/1qpOfn5+LFq0KOlcnz59yp07aNCgpPGyZctqtapo0aJFkZ+fX+Pr6sratWvLBGBf+tKXalynLlcAHctSv1eXL19e61V8qb/mvXr1ivT0T89/KtizZ0+Zc3X5TrSIiNNOOy0GDhxY7mdZWVm1elYAAPhkfHr+ZgwAABwVrrnmmvjmN7+ZdG727Nlltgo8pLz/4F+b/wg+dOjQpPG8efNi8+bNNa4zadKkKCwsrLT2IZ/97GeTxiUlJfHyyy/X+J4vvvhija+pS+X9Op1wwgk1qrFv376YOXNmXbV0TEv9fiopKYmJEyfWuM62bdvKhGgVfa8eq1auXFnm3PHHH1/n96lotdnnP//5aN++fZ3fDwCAuiFEAwAAGpxRo0YljYuKiiIvL6/cuanbMEYcDGRq6txzz00aFxYWxh//+Mca1/nf//3fMufOOeeccueeffbZ0aJFi6Rzv//972t0vwMHDsRjjz1Wo2vqWlFRUZlzqUFiVf70pz/V+3vdjhZnn312mfcIjh8/vsZ1/vSnP8X+/fuTzqU+B8eyAwcOxLRp08qcP/HEE+v8Xl/60pciKyurzPmabnkKAMAnS4gGAAA0OKn/YT8iyoRNh5T3/qb169fX+J5XXHFFmXeU/fznP4+tW7dWu8bMmTPjhRdeSDp33nnnVfgf5Vu0aBFjxoxJOjd9+vR48sknq33P+++/v9bvUqsr5a2kqcl2mFu3bo2f/OQnddjRsa1Tp05x8cUXJ53729/+Vm4gVJHt27fHvffem3QuOzs7rr322jrp8WjwyCOPxLZt25LOtW3btswK0brQvn37mDx5crz22mtJ/3z+85+v83sBAFB3hGgAAMAR8dRTT5UbhlXH008/nTTu0KFDhSFaVlZW9OjRI+ncjBkzanzPJk2axK233pp0Ljc3N77yla9Ua1XV5s2b4+abby6zleR3vvOdSq/79re/XWaFyte+9rWYPn16lfd85pln4sc//nGV8460Hj16RNu2bZPO/frXv67Wr1t+fn586UtfqlXw+Wk2duzYMue+8pWvxMaNG6u8tqioKL761a/Gpk2bks7fcsst0bJlyzrrsSGbOnVq/OAHPyhzfsyYMZGRkXFE7jls2LC48MILk/7JzMw8IvcCAKBuCNEAAIAj4rbbbotu3brFv//7v8eHH35YrWsKCgrixz/+cTzzzDNJ56taHTNkyJCk8Z///Od46aWXatZwRNx5553RvXv3pHN//etf4+qrr670/WgffvhhjBw5MpYuXZp0/sILL4xLL7200nuecsop8X//7/9NOrdnz54YPXp0/Pu//3uZoCMiYsWKFfH1r389brjhhigqKopGjRpF165dq/ryjpj09PQyK2ref//9+PKXv1zp1prLli2Lz33uc/H3v/89IkKgUAPnn39+XHHFFUnnli9fHiNHjowPPvigwutyc3Pj2muvLbNislOnTvHv//7vR6DThmXRokXx7W9/O0aNGlXme7Ndu3ZWRAIAkMS/oQAAAEfM5s2b495774177703TjjhhDj77LPjM5/5TPTq1SvatGkTzZs3j3379sWaNWvi3XffjaeffjpWr16dVKNNmzZx1113VXqfMWPGJK1eKygoiCuuuCI6duwYffv2jezs7EhPT/5/CMeNG1dmG8JWrVrFH//4xzjvvPOS3vP117/+NaZNmxZf+MIX4vzzz4/OnTtHQUFBrFy5Ml566aV48cU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ypJ577jl16tTJNPxbadm5c6c6duyoJ554QrGxsU7v98cff6hhw4YaMmSIpk2bpp07d9pNIG6Vl5en33//XXfccYfat2+vXbt2lbT5ki588/lf//qXBgwYoBUrVhT69zt79qymTZumK664Qnv37rWr8fjjj6tPnz5aunSp8vLyHNbIzs7W559/rvbt22vPnj2l0n5XnDhxQkOHDlV0dLQWLVqkrKysQrc1DEN//PGHHnzwQbVt21arV68ux5ZWHPn5+Tp06JBpXfPmzUtc19p7zNq7rDDz5s1TamqqbTk6OlpNmjQpcXs8YfHixablAQMGOL2vtWeqYRhasWKFU/suX77cbl1SUpLT7zvW/Z3paVvRnDp1SgMHDtQ999xT5HtDVlaWvvzyS7Vt29bh41aZzZw5Ux06dNDMmTOLHDZt7969GjNmjEaOHOn2ELUnT57UzTffrG7duunbb78ttjfr0aNH9c477+iyyy7TJ5984tYxS9PcuXPVvn17/fe//y3yfWPLli0aN26cBg0apJMnT5ZjC82WLFmiNm3aaOrUqXbzShV09OhRPfzww+rXr59SUlLKrX0LFixQq1at9Pzzz2v//v1O7ZOWlqZly5Zp4sSJevbZZ8u4heWjsp0Xubm5mjRpktq2bauZM2fq1KlTRW5/8OBBvfHGG2rRooU+/vjjcmpl5WbtnS5Jffr0cbmOo302btzoUg1fX1+7obIXLVrkclsAwFMI0QAAqII2bdqkwYMHuxTEVARz587VoEGD3ApEVqxYoZ49e2rHjh0u7Tdv3jyNHDlSCQkJTu9z4MAB9evXTwcPHnS1mYXatWuX+vbt6/QFoIISEhKKvfhQlL1796pHjx5au3at2zUuGj9+vN59991iQ7yL4uLiNGTIENvFHsMwdNddd+ntt992usbJkycVFRWl06dPu91uV+3evVtXXXWVy5OrS9L+/ft19dVX6/vvvy+DllVsy5YtMw3lKEn9+vUrcd0777zTNDTXd99959T8JzNmzDAtjxs3rsRt8YSsrCy787dv375O7+/r66v+/fub1jkT8uTn52vlypUOf+fM/sePH7cLnSpbiJaYmKg+ffq4FIxnZGTommuu0e+//16GLSs/7733nsaNG6ezZ886vc9PP/2k0aNHu3ysHTt2qEePHvrf//7n9HvERRkZGZowYYIeffRRl49bWubNm6ebb75Z6enpTu8TExOjqKioIr/kU1Z+/PFHXXvttXav20XZsGGDhgwZovPnz5dhyy6YP3++brzxRo88NhVJZTsv0tLSNGzYME2dOtXl50lqaqoeeOABPfrooy7f10uNoy+zXHnllS7X6dq1q1O1i2P9vOfOZ2gA8JSKNekJAAAosZycHN19993KyMgwrQ8PD9dNN92kbt26qWnTpqpRo4bOnTun9PR0xcXFaefOnfrtt9+0efPmQv8prVu3rkaNGiXpQk8ia8+Hfv36FTvvU1BQkMP1q1ev1s0332zXc6lWrVq69dZbNWTIEDVu3Fi5ubmKi4vTTz/9pPnz55v++T527JgGDhyo7du3q0GDBkW2Q7rwD+Ctt96q3Nxc0/qQkBCNHj1agwcPVuPGjZWXl6fDhw9r0aJFmjNnjrKzs5WUlKRbbrmlVOZky8rK0siRI23f3Pb399dNN92kIUOGKDw8XIGBgTp+/Lg2b96suLi4ImtVq1ZNvXv3VteuXdWuXTs1btxYwcHB8vPzU3p6ug4cOKCNGzfqxx9/NF0Uy8jI0K233qqtW7e6PXfX+++/b+sB5OXlpeuuu07XX3+9WrZsqRo1aujEiRNaunSpPv/8c1PAER8fr8mTJ+u///2vXn31VX3zzTe2Gtdee62tRs2aNXXy5EktXbpUn332manGiRMn9Pjjj+uLL75wq+2uOHXqlKKioux6Bvj4+Oj666/XiBEj1Lx5c1WvXl3Hjh3TsmXL9N1335m+mZ+Tk6PbbrtNoaGhGjx4sN0xgoKCbOfaRT/++KNpuWvXroqIiHDYRlfmuygvJ0+e1EMPPWRa5+3trXvvvbfEtZs0aaLo6Gj9+uuvki5cZJs/f36RF+mPHDli6m0VEhKiESNGlLgtnrBlyxbl5OSY1nXq1MmlGtHR0frll19sy870RPvzzz9NPfkKWr58uR5//PEi97cGbSEhIW5d4LNq27at7fyJj4/Xn3/+afq99dyyqlevnlPHOX/+vG688Ubt27dPkhQYGKhbb71V0dHRCg8PV0BAgI4fP67ly5dr+vTpppApJydH48aN09atW+Xn5+fK3XNKwfu4ePFi07HbtGlT7GtEs2bNnDrOokWL9Pbbb9uWr7jiCo0aNUpdu3ZVnTp1lJGRoR07duirr77Spk2bTPvOnz9fs2bN0pgxY5w61t69e9WnTx+dOXPGtL5OnTq65ZZb1LNnT4WHhys4OFhnzpzR3r17tXDhQv3yyy+mzxbvvfeemjRposcee8yp45aWuLg4vfPOO7a2hIWF6bbbbtPgwYPVqFEjZWZmav/+/frf//5nd3F59+7dGj58uDZu3Fhuc9du3bpVn3zyia03eKtWrWyPc7169XTu3Dnt27dP3333ne2196I//vhDb7zxhp555pkya9/p06d177332oUwl19+uUaNGqXOnTurcePGCgwMVFZWltLS0nTo0CHt2LFDK1euLLUe+FbdunWz+wzurMzMTC1ZssSlfSrbeWEYhm666SaH7zFXXXWVRo8erXbt2ik0NFQJCQnatGmTZs2apQMHDpi2fe+99xQUFKRXXnnF4XGGDx9uCldXr15t6p0XERHhMBySpHbt2rlz11xy8uRJTZ48WevWrVNsbKxOnz4tPz8/2zxw3bp1U79+/TR06FCFhIS4dQzrY1a7dm0FBwe7XCc4OFi1atUyPcfc+SJhx44dTcuJiYmKjY0tlREJAKDMeWgYSQAAKqzKPifa/Pnz7bZ/8sknnZ6j4uTJk8Ybb7xhjB8/vsjtXJ0joigpKSlG06ZN7eoNHz7cOHHiRKH7bdmyxeH8M0OGDCl2vpXc3FyHc8Vdc801xsmTJwvdb//+/Ub37t1t21vn/HJnTrSCPwMGDHBq7pKCFi5caERFRRk//PCDkZGR4dQ+mZmZxpNPPmk3d9o///lPp/Z3dJ5crBUREWH8/vvvhe67e/duo2HDhqZ9fXx8jAULFhi+vr6GdGE+p6Jq7Nq1y6hfv76phre3t3H06FGn2l8S1113nd19b9WqlfHnn38Wuk9CQoJx/fXX2+3XsGFD49SpU04d153Xj9JSkjnRUlNTjU8//dTuby7JmDx5conacHFONMMwjNmzZ9u9DhTl5ZdfNm1///33m35fmeZEe//99+3OhbNnz7pUY/v27XaP7759+4rc59VXXzVtX61aNdvtwMBAIzs7u8j977zzTtP+I0aMKHJ7d+aUKcm8osUdv+Dr5zXXXFPk3EpxcXEO52v64Ycf3G6Ps4qam8sVjt73Lz4GQUFBxrffflvovvn5+XbPF0lGu3btnDr22bNnjQ4dOpj2rV69uvHaa68V+zzbtm2bcfnll5v29fX1LfI1uzRYH/eCz5cRI0YU+dq/ZMkSh3Navfbaa8Uet7TmRLvYXl9fX+P99983zp8/X+i+X375pd38paGhocX+bUrivffes2vvBx984PR8e7GxscYzzzxjPP3008VuZ31s3P28W5Tc3Fxj+PDhdsf697//Xeg+lfG8ePvtt+3uY40aNYp8/cjJyTGeffZZu/18fHyMNWvWOHVcd+YwLk3uzpVXs2ZN41//+leR/w8VplatWqZanTp1crv91udZ7dq1Xa5x+PBhu/v3/fffu90mAChPhGgAAFg4uvjQpk0bY9SoUSX66dq1q13dsgjRJkyYYNp28ODBpfTImJXmRYUnn3zSrtbw4cONnJycYvc9ceKE0aJFC7v9582bV+R+//3vf+32GTJkiFPHTE1NNTp16uTwn92ShGgDBgwwsrKyit3fytkLRo68++67pjYEBAQ4NWm7o/NEktGgQQPjyJEjxe6/cOFCu339/PwM6UKwdOzYsWJrLFiwwK7GG2+84dT9dteSJUvsjtmsWTOn2pubm2vccMMNdvtPmjTJqWO78/pRWhy9DnXt2rXQ17uRI0caV199tdGmTRu7C6vShZBn8uTJLj13iwvRsrKyjJCQENMxigpVra8bGzduNP2+MoVo//jHP0zHDA8Pd6tOgwYNTHU++uijIrePiooyXcx88MEHTfvHxMQUuX/jxo1N20+bNq3I7StaiHbxZ9SoUUUGDBft2LHD9jp38WfYsGFut8dZZRmiSRcu2lvPn8Lcfvvtdvtv2rSp2P0mT55s2sff399Yvny5021PTk62CzGvu+46p/d3h/Vxv/hzzTXXOPXFpj/++MMICgqyu9+JiYlF7ldaIZp0IZiaO3euU/f36aefttu/LC+QDxs2zHSsf/zjH2VynPIK0caNG2d3nNGjRxf5PlnZzovk5GQjMDDQdLxq1aoV+15x0VtvvWX3GDkbDFXWEO3iT926dY2lS5c6fbzc3Fy7GldffbXb7Y+OjrZ7bXDmfc8qICDAVOeZZ55xu00AUJ4I0QAAsCgsHCiLn7II0a699lrTtu+//34pPTJmpXVR4ezZs3YXq8PCwpzunWMYhrFmzRq7HlXFXVy1hpo1a9Z0Kgi56K+//nIYDrgbotWoUcOIj493+vilJT8/3+jYsaOpLV988UWx+xV2nvz8889OH9tRL0JJxi+//OJ0Deu3qIcPH+70vu6wXrSTig8KCkpOTjbq1q1r99w7c+ZMsfu68/pRWhy9DrnzExkZaTzwwAPG33//XSptKBiiGYZh3H///abfv/rqqw5rrVmzxrRdmzZt7LapTCGa9eJWr1693KozZswYU50bb7yx0G2zsrIMf39/27Y9evQwYmJiTPsX1cNj165ddn/PPXv2FNm+ihiiNWrUyEhNTXW6xm233WZ3/pfkixDOKOsQ7a233nK6xp49e1zePzU11QgODjbt895777nc/nXr1plqeHl5FfucKwlHIVpISEixIVhBb775pl2N4r4sUpoh2oMPPuh0W1NTU02vCa7u76r27dubjvXTTz+VyXHKI0R75pln7I4xcOBA49y5c4XuUxnPizfeeMPufj733HMu1bC+3zn796iIIVqTJk2Mvn37Gtdee60xbNgwo0ePHna9x6x/G2c/+yUlJdntf8MNN7jdfkejMDjzpTuryMhIU43bb7/d7TYBQHn6f7NvAwCAKuHi3BUXGRV80u358+fr9OnTpnWTJ09WnTp1nK7Rt29f3XDDDaZ1q1evtpsL4KI9e/bYzZFz3333qVGjRk4fs0uXLnbHLIk77rhD4eHhpVbPWV5eXnb3Y8OGDW7V6tixo6677jqntx82bJjduk6dOumaa65xusbw4cNNy1u2bHF6X1cdO3bMbq6S4cOHa+DAgU7XqF27tt0cMenp6frhhx9KpY0V3enTp5WSkmI3d0tpGTdunGn5yy+/dLjdjBkzityvsomPjzctu/JaVtCgQYNMyytXrrSbp/Ki3377zTQvYXR0tHr16qXAwEDbOuucZwVZf9ekSRO1bt3anWZ71KRJk1SrVi2ntx85cqRpOT093TanWmUUFhamBx980OntW7dubTcf2+bNm4vcZ/r06UpLS7MtN23a1G5+RWf06tVLPXr0sC0bhmE3t2tZe+SRR1yad/SRRx5R/fr1TevKY+5PSfLz89NTTz3l9Pa1atVSVFSUaV1xf9uSqGyfdwvzySefaMqUKaZ1HTt21Pz581WtWrVC96uM58V///tf03JoaKhLzzFJpjkYL5o+fXqJ2lVegoOD9Y9//ENz585VSkqKjhw5ojVr1mjBggVatGiRNm7cqOTkZP3++++6++677ebLNAxD9913n1auXFnssQq+P1/k7+/vdtsdzQF97tw5l+s0btzYtGz9/AIAFRUhGgAAVYz1n5NZs2bZTbpekaxZs8a07OPjozvuuMPlOuPHj7db99tvvzncdt26dXbrxo4d6/Ix3dmnPGq5qmnTpqZldy96jRo1yqXtHU3cXtIaJ06cKLPn+9q1a+0u0rkTvowdO9buwlhhz9WqJiUlRbNnz7YF36dOnSrV+t27dzddoN+7d6/Wr19v2iYzM1Nz5syxLbv7mlORJCcnm5aDg4PdqhMdHW1aTklJKfT1wBqCRUdHq3r16urTp49t3ebNm5WamurU/tYAr7K45ZZbXNq+U6dOduuOHj1aWs0pd9dee63Di6tFsT4Gxd1/65cXbr31Vvn4+Lh0zIusIY/1M0hZu/POO13a3s/PT7fddptp3d69e5WYmFiazXKoV69edp8pi+Pq37YkrG0r7EsTFdn8+fPtgq/w8HAtXry42NfxynZeJCQk2H1h4LbbbnM52OnUqZOuvPJK07rK8Blq4sSJOnHihD7//HONHDlSISEhDrfz9vZWt27dNGPGDK1bt07NmjUz/T43N1f33HOPcnJyijyeo8/CRYWyxalevbrdutzcXJfrWJ/X1s8vAFBREaIBAOCE559/XsaFYZDd/rH2fCgr1guRf/zxh4YOHapt27aVy/FdZe311KFDBzVo0MDlOoMGDbK7eFBYj6pNmzaZlmvWrOnwwmZxCl4sLgk/Pz9dccUVpVJLko4cOaIPPvhA48aNU9euXdW0aVPVqlVLPj4+8vLysvu57777TPu7G2wU/CazMxx9G780ahT8ZnZpcvR8soYOzggLC1PXrl2LrV3RzZgxo8jXvKysLJ08eVKrVq3SSy+9pObNm5v2//nnn9WzZ08dP368VNt19913m5ZnzpxpWp4zZ44yMjJsy0OHDnXrNaciOXv2rGnZ1VDjoqZNm6pVq1amdYX1JluxYoXtdmBgoHr16iXJ/B6Ul5fn8BvzeXl5Wr16tWmdO+eSp9WvX9/uAqcz+1iVVc/M8nDVVVe5vI/1MSjq/ufm5toF4dbXT1c0adLEtLx79263a7mqefPmdq+DznAUMFs/x5SFsv7blpT1cZk3b55Gjx6tgwcPltkxS9O6det02223mXrUhYaGasmSJcX2Jq6M54WjzzmDBw92q9aQIUNMy4cPHy71zxKl7YorrjD11HZGt27dFBMTo3r16pnWx8XF6ZNPPilyX19fX7t1xQVvRXHU68zaU84Z1s8nmZmZbrcJAMoTIRoAAFXMqFGj1KJFC9O6FStWqHPnzurSpYueffZZrVixosL807J//37TsrthUkBAgNq0aWNaV9gQWbGxsabl9u3by8vLy+Vj1q1bt1QuvkdERJRoiJWL/vrrL0VHRysiIkITJ07UzJkztXnzZh09elRpaWmFDstmlZKS4tbxGzZs6NL2NWrUsFvn6uPpqEbBgKQ0WZ+rl112WaHfJC6O9Xl+4MABp/8+lYW/v7/q16+v/v3769lnn9WePXv02GOPmbY5ePCgrr/++lLtPXjHHXeYAvUffvhBWVlZtmXrFxqsoVtlZL24VZJvm1svTDsK0ZKTk01Dp/br1892TGsY5mj/P/74w+7iemXsiebqa5504UsbVmX1mlUeSuMxKOr+79271y4kHj16tMMvhDjzYx160jqcdFnq3Llzqe23Z8+ekjXGCWX9ty2p++67z+49+Pvvv1fLli3Vu3dvvfrqq1q3bp1bQ86Vtd27d+v66683Dbnn7++vn3/+2e6zrCOV8bywfoaS3P/M72i/yjwsblGaN2+ujz76yG59ccO6Ouo55miIR2cV/BxV1DGKY92nJG0CgPJEiAYAQBXj5+en//3vf6pdu7bd77Zu3apXXnlF0dHRqlWrlrp06aJJkyZp3rx5HgnVcnJy7C4CWL8N6wrrvoWFQdbhxVyZn8TKlbnbCuNuEFPQK6+8om7dumnFihUlnhfE3Yteji4OF8VRcFkaNcoqjLI+n0rzuWoYRqXujeKMatWq6e2339Y999xjWr9582Z9+OGHpXacBg0aaOjQobblM2fOaO7cuZIuhHYFh30KCwvT9ddfX2rH9hTrRamSfNvcGoKtX7/e7iJXTEyM6TwruE+XLl0UFhZmW3YUolnXtW3b1q0L9p7mzrCZ3t72/4JX5gC9NB6Dou5/UlKSy/Vd4e6XRtzh7nPc0X7l0e7S+NuW5TxloaGh+u677+y+hGQYhtavX69nnnlGffr0UXBwsHr27KnJkydryZIlJXp9LA3Hjx/X0KFDTcPYeXt769tvv1Xv3r2dqlEZzwtrTS8vL5eHC73I0eev8jyXy9uoUaPs5pL8+++/dfLkyUL3cTRXp6MgzFnWfb28vFyaD/Qia6jtbs95AChvhGgAAFRBnTt31ubNm3X11VcXuk1eXp62bt2qqVOn6sYbb1S9evU0duxYbd++vdza6egfXnfn8pHs/2EsbJx9a4jmanBTUEnae1FQUFCJ9n/11Vf17LPPOrwQ6eXlpfr166tr166Kjo7Wtddeq1GjRpl+SjIEkPVYFaFGWbE+X0vzuSpdOvNCTJkyxa6n1HvvvVeqF1utc9VdHNJx5syZpuPcfvvtJeq1VVFYh4gqyYWyqKgo04Xw7Oxsu/lmHM2HdpGXl5dpfp19+/bpyJEjTu9fmVTk16vyUtaPQVn3FCvPOWPdfc/w8fGx63VdHoFBZXh+DxkyROvXr1f37t0L3SYnJ0cbN27Um2++qWHDhql+/fqaMGGC3agE5eHMmTMaNmyYDh8+bFo/bdo0jRw50uk6lfG8sD5ng4KCHH6pwBmX4meo6667zrRsGIZ+//33Qrf38/Oze5wSEhLcPr5134vDxLvK+uVJV4e4BABPsR8kFwAAVAnNmzfX0qVLtWnTJn355ZdasGBBkRO8nz17VrNmzdK3336rBx98UG+//bZbw3RUBqXZa8PT32jevXu3XnjhBbv11157rcaPH6++ffuaeoU4MmPGDLveQUBZadCggXr06GEKZuLi4rRz5061b9++VI5x3XXXKSwszHahMSYmRvHx8frqq69M21WFoRwlme6rVLJ5iEJCQnTllVfqjz/+sK1bvny5ae6agiFYvXr11LFjR1ON6OhozZkzx7T9xWDz7NmzdnPjVMahHFE+HL3H9uvXr0Q9yFG1dOnSRZs2bdLy5cv19ddfa/HixUXO7ZqamqpPPvlE06dP1/PPP6+nn37a7TDHFefOndOIESP0999/m9Y//fTTmjBhgku1OC8uPR06dLBbl5iYWOQ+4eHhpi9HxsfHu318a/AbERHhVh3rvMXF/Y8CABUFIRoAAFVcjx491KNHD3300Uc6dOiQ1qxZo7Vr12rdunXau3evXe8PwzA0bdo0HT16VD/++GOZXlhwNOSk9Z8rV1gvHIeGhjrczjp8YkmOWZJ9S8Obb76p3Nxc07oZM2a4FA5U9WEES4v1+Vqaz1Wp8OdrVdS+fXu73k2bN28utRCtWrVquv322/XBBx9IujBc3D333GO6CNSxY0e352OpaCIiIkzzwRw7dqxE9QYNGmQXol0UHx+vgwcP2pajoqLseqw4mhftYoj222+/mS4A+/r6asCAASVqL6ouR6+Ljz32WKUchtXd94y8vDy7YZYdfX661EVHR9tee3bt2qU1a9Zo3bp1Wrt2reLi4uy2P3/+vJ599lklJiZq6tSpZdo2wzB05513atWqVab1d911l6ZMmeJyvcp4Xlifs5mZmcrPz3fr/4xL8TOUo+HjixvWs0WLFqYQLTU1VWlpaS73ik1LS7N7zCMjI12qcZH184m7YRwAlDeGcwQA4BJy2WWX6e6779b06dO1e/duHT9+XNOnT1efPn3stp0/f75mzZpVpu2pVq2a3TAeJbn4a923sItM1n9EDx065Nbxzp8/b/fNzPKUn5+vX375xbTutttuc7l3TVnPrVFVWJ9PpflcdXduicrK0blZVM8Bd1iHdIyJiSny95VZ8+bNTctF9Tp2hjUE27p1q62nmzNDMV522WWmNq1YscJ227p/t27dSmVYXFRN7lw4rqhOnDhRavsRohWtbdu2uv/++/X1118rNjZWsbGxmjp1ql2vWUn64IMP7L7UUdoeffRR/fDDD6Z1Q4cO1fTp092qVxnPC+tz1jAMHT9+3K1ajj5/VfVzwtEwzcXNJ9alSxe7dZs3b3b52AW/VHNRp06dXK4j2f/trJ9fAKCiIkQDAOAS1qBBA40fP16//fab5syZYzc30Mcff1zmbWjZsqVp+a+//nKrTnZ2tnbv3m1a16pVK4fbdu7c2bR84MABt74hvnv3bmVnZ7u8X2k5evSo3UWTMWPGuFzHnX+oL0XW5+qhQ4fc7sVnfcwjIyPLZTipiiIzM9NuXWnOiSZduHjk6IKpdGGuEHfOlYrKOszTsWPHHD7Gzurdu7f8/f1ty/n5+bYQ0tn5zAquT0hIsH0bvqrMh4byERkZaTfvjrufEzxt69atpbbf5ZdfXrLGXGKaNWumhx9+2DYXsFVZft5966239P7775vWde3aVf/73//k6+ve4FCV8bywfoaS3G+zo8+thX3mryocBYf16tUrcp+rrrrKbt3atWtdPrajfRzVLs7hw4ft/m8q7HMaAFQ0l85/6gAAoEg33XSTHnzwQdO633//3W6owIscXfB35yJ4r169TMvbt28vdox/R2JiYuwmQrfWvsj6j59hGFqwYIHLx5w/f77L+5QmR4/TZZdd5lKNrKwsrV+/vrSaVKVZn0+GYZh62DgrJSXF7gJQYc/VqsrR0Fr169cv9eMU1tvsmmuuqVJzx3Tt2tW0bBiGdu7c6XY9f39/9e7d27Ru+fLlMgzD1KMvMjKy0KGYHA3pmJSUpG3btpnWl/V8aKX1XlWZWR+DynT/a9WqZff8dud1tyK42CPKVdZetNKFobrhOi8vLz388MMaOXKkab07wYIzZs2apcmTJ5vWtWjRQgsXLlRQUJDbdSvjeeHoc471SxXOWrZsmWk5IiJCjRo1cqtWZeHodaC4oRD79u1rNw/0kiVLXD720qVLTcv+/v7q27evy3Ws8wFKF3qjA0BlQIgGAABsrBcz8/LybEN4WVmHYZQcDzVSnH79+pmWz58/r6+//trlOv/973/t1hX2D16fPn1Uo0YN07pPP/3UpePl5ORo5syZLu1T2vLy8uzWWYPE4nzzzTcen9etsujTp4/d3E8zZsxwuc4333yjc+fOmdZZz4OqLCcnR2vWrLFb36JFi1I/1pgxY+Tn52e33tUhTyu6zp07210os4ZVrnIUgv3999+m8H7w4MGF7j9o0CDT+bJ8+XKtWLHCFOAEBQWpZ8+eJWpncUrrvaoysz4Gle3+DxkyxLS8Z88ety4EVwTffPONS9vn5uZq9uzZpnWtW7cutgcKimb9vOvOl7eKc3EuyIKveXXr1tWSJUtK5e9X2c6LevXqqXXr1qZ1s2fPtvs8VJzt27fbDS9Y1T9DnThxwu5vGxwcrO7duxe5X0BAgK655hrTuvXr12vXrl1OH3vHjh3asGGDad21115r6q3uLOvnkvr166tZs2Yu1wEATyBEAwAANo7+kbWGTRc5mr/JnbkNRowYYTe3w+uvv67k5GSna6xfv17z5s0zrRs4cGChF+Vr1KihsWPHmtb99ttvdheqivL222+7PZdaaXHUk8aVoXGSk5P1wgsvlGKLqrZGjRpp+PDhpnW//PKLw0CoMKmpqZoyZYppXXBwsG6++eZSaWNl8PnnnyslJcW0LjQ01K2hgYpTt25drVy5UosXLzb9WC8qVXb+/v7q37+/aZ0rz0tHrCHaoUOH7ObvKWooxrCwMNPQuWvWrLG7CNi3b1+7YYRLW2m9V1Vm1segst3/CRMm2M398/jjjysjI8NDLXLfu+++69LcVVOnTtXJkydN6+65557SbtYlx/p5t7DPuu7asmWLbrzxRtNoDkFBQVq4cKEiIyNL5RiV8bz4xz/+YVpOSkrSG2+84VKNxx9/vNi6Vc0TTzyhs2fPmtZdffXVTg0HOn78eLt1r776qtPHfv31152q6Qzr5xJrEAwAFRkhGgAAVcx3333n8rc6L/r+++9Nyw0aNCj0woKfn5/dtwfdGQ7H399f9913n2ldUlKSxo0b51SvqsTERN199912w1M98sgjRe43ceJEux4q9957r1OTy//www967rnnit2urDVr1kyhoaGmde+//75Tj1t2drbGjBlT6S6metqkSZPs1o0bN87uIqcjeXl5uueee5SQkGBaP378eNWsWbPU2liRrV692uEFsLFjx9rN71JaevfuraFDh5p+3J2HpiIbNmyYaXn16tUlqnfFFVeodu3apnWfffaZ7ba3t7eioqKKrFEwZMvIyNC3335r+n1ZD+UoOZ4np6yGbquorI/BunXrKtWQjg0aNNCECRNM63bs2KHRo0e7Pfdfdna2XShcHlJSUjR+/HiHPcmttmzZYvdFF39//0KHqb2UZGVl6ccff3TqcbTKz8/XnDlzTOsczdflrtjYWA0fPlzp6em2db6+vpozZ06pDl1XGc+Lf/zjH3bDWL722mtOffaWLoTQ1qEFu3TpUqF7oqWlpbk9H6IkvfDCC3Y9WL28vPT00087tf+wYcPUqVMn07pZs2Y5NYz9zz//rFmzZpnWde7cWUOHDnXq2AWdP39e69ats2sbAFQWhGgAAFQx999/v8LDw/Wf//zH4djzjuTm5uq5557TDz/8YFpfXO8Y65wc3377rX766SfXGqwL37C0juv/888/a9SoUUUOsfP3338rKipK+/fvN60fOnSorrvuuiKP2aZNG7t/QDMzMxUdHa3//Oc/dkGHdOHCyD//+U+NHj1aeXl5qlatmpo2bVrc3Ssz3t7edj1qtmzZojvvvLPI4boOHDigq6++2tYrpCoGCmVl8ODBGjFihGndoUOHFBUVVeRFkqSkJN188812PSYbNWqk//znP2XQ0opl9+7dmjhxogYNGmT33KxTpw49IkvBiBEjTMMnHjlyRLt373a7nre3twYOHGhal5OTY7t95ZVXKiQkpMga1p5qBfd39PuyEBERYTff3ksvvVSiOeMqG+t7dVxcnJ577jm7v0dF9sILL6hdu3amdQsXLlT37t21cuVKp+ts375dzz77rCIiInT//feXdjOLdPH8/Pnnn3XLLbcUOly2dGHOpyFDhtj1Knruueeq1HyO7jp37pxuuukmRUZG6vXXX9eBAwec2u/s2bO69957tXHjRtP60uoNnpKSoqFDh9p9seazzz4rk8Cgsp0XISEheuWVV0zrsrOzde2119oFmwXl5ubqxRdf1GOPPWZa7+Pjow8++KBM2lpakpOT1aVLFw0fPlzz589Xdna2U/vFxsbqxhtv1Isvvmj3uzvvvFNdunRxqo6Xl5fee+89u/W33nprkXM7z507V7feeqvdeke1nLFhwwZTuOvv70+IBqBS4YoJAABVUGJioqZMmaIpU6bosssuU58+fXTFFVcoMjJStWvXVlBQkLKysnTkyBH98ccf+v7773X48GFTjdq1a9tNhm41duxYU++13NxcjRgxQg0bNlTbtm0VHBwsb2/zd3Y+/vhjuwtAtWrV0tdff62BAweavlX8888/a82aNbrttts0ePBgNW7cWLm5uYqLi9NPP/2k+fPnm4bKkS7MuTBz5ky7uasceeaZZ7Rq1SpTj42cnBxNmTJFr732mtq3b6/GjRsrLy9Phw8f1p49e0z7v/TSS1q8eLGOHDliW+fMcUvTU089pVmzZik/P9+2bvbs2Vq7dq3Gjx+vnj17qm7dusrIyFBsbKwWLVqkefPm2S6eBgQEaOLEiS4Pp3Mpmz59un7//XdTL77du3erW7duuuGGG3TDDTeoefPmql69uo4fP65ly5Zp9uzZdkOUent766uvvrLrTVhZfPjhh/rll18K/f25c+d05swZ7dmzR6dOnXK4TWBgoH788Ue7Hk9wXbNmzdSnTx/TN/rnzp2rZ555xu2a0dHRmjt3bqG/K07fvn1VvXp1h72j69SpY/ft+LIyduxYvfPOO7bl2NhYtW/fXq1bt1bz5s0VGBhoeu2uV6+ePvroo3JpW3m4+eab9cgjj5j+Dq+88oqmTp2qjh07KiwszO7LFA899JAGDBhQzi0tXM2aNbVgwQJ1797dNBzirl27FBUVpU6dOmno0KHq3r276tWrp5o1ayo9PV2pqak6ePCgtmzZovXr15u+dFNWvV8Lc8cdd+jHH39UZmam5s6dq9WrV2vMmDGKjo5Ww4YNlZWVpf3792vOnDkO57a68sorHfbkvZTFxcXpqaee0lNPPaX27durZ8+e6tKli5o1a6batWsrICBAmZmZiouL04YNG/T999/bvR9ddtlldiMiuGvbtm3at2+faV1oaKgWLlyohQsXulWzqNejynheTJo0SUuWLDH1KEtLS9Mtt9yiXr16afTo0WrXrp1CQkKUmJio33//Xd98843dF+akC5/je/fuXabtLS0Xh5OuUaOGBgwYoC5duqhDhw6qU6eOatWqpby8PCUnJ2vnzp2KiYnRkiVLHPa07Nmzp8vvTwMGDNC//vUv/d///Z9tXVZWlkaOHKlBgwbpjjvuUGRkpAzD0IEDB/TVV185DGEfe+wxu6GjnfXjjz+alq+//nqHwy0DQIVlAAAAk5UrVxqSTD/PP/98ievOmDHDru6MGTPc2q8otWrVstve1Z+AgABj8eLFxbYtLy/PGDRokEu1Y2NjC603b948w9/f3+12N2nSxNixY0ex7S4oIyPD6N+/v8vHevDBBw3DMIy+ffua1j/wwAPFHvOuu+4y7dO/f3+X2mz1+uuvu/V4+fj4GHPnznX5OWYYjs+Tov62FbmGO/bs2WNERES4/VytXr268cMPP7h0THdeP0qLo+dISX/Cw8ONNWvWlKgN27dvL7P7HBYWZjrWXXfd5dR+1udFREREmbXRavr06aZjd+7cuUT19u7dW+jfb8WKFU7VGDhwoMP9b7nlFpfb4+5r58mTJ40GDRo4/dws7G9WWq/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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 458, "width": 872 } }, "output_type": "display_data" } ], "source": [ "bmi.plot(kind = 'hist', color = 'slateblue', edgecolor = 'black', \n", " linewidth = 1.2, title = 'Histogram of BMI')\n", "plt.show(); \n", "\n", "bmi.plot(kind = 'hist', color = 'gold', edgecolor = 'black', \n", " linewidth = 1.2, title = 'Histogram of BMI (with the bin size of 50)', bins = 50)\n", "plt.show(); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do these two histograms compare?\n", "\n", "At this point, we've done a good first pass at analyzing the information in the BRFSS questionnaire. We've found an interesting association between smoking habit and gender, and we can say something about the relationship between people's assessment of their general health and their own BMI. We've also picked up essential computing tools – summary statistics, subsetting, and plots – that will serve us well throughout this course." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## On Your Own\n", "\n", "
    \n", "
  1. Make a scatterplot of weight versus desired weight. Describe the relationship between these two variables.

    2. \n", "
  2. Let's consider a new variable: the difference between desired weight (wtdesire) and current weight (weight). Create this new variable by subtracting the two columns in the DataFrame and assigning them to a new object called wdiff.

    3. \n", "
  3. What type of data is wdiff? If an observation wdiff is 0, what does this mean about the person's weight and desired weight. What if wdiff is positive or negative?

    4. \n", "
  4. Describe the distribution of wdiff in terms of its center, shape, and spread, including any plots you use. What does this tell us about how people feel about their current weight?\n", "

    5. \n", "
  5. Using numerical summaries and a side-by-side box plot, determine if men tend to view their weight differently than women.

    6. \n", "
  6. Now it's time to get creative. Find the mean and standard deviation of weight and determine what proportion of the weights are within one standard deviation of the mean.
    7. \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "This lab was adapted by David Akman and Imran Ture from OpenIntro by Andrew Bray and Mine Çetinkaya-Rundel.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 4 }