{ "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": [ "0 10559\n", "1 9441\n", "Name: smoke100, 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": [ "0 0.52795\n", "1 0.47205\n", "Name: smoke100, 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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\n", 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" ] }, "metadata": { "image/png": { "height": 320, "width": 617 } }, "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": 320, "width": 617 } }, "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": 324, "width": 590 } }, "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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" ], "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
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" ], "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
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" ], "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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8guH69tprr3IosbHvZ0tYv+/1A5aWrF+3du3avP766xk2bFib99Ya/fr1a1Xd+rfirVy5coPzTz/9dPn48ssvzy233NKqede/9XH90La13n333UbjWvv36sMGtEnrvquNfU8A8FEglAKArUBNTU2+/vWvZ8SIERk1alTWrVuXP//5z/nJT36Sf/zHf2xUu/6qiZ49e7b6Nro+ffo0OUdzJk2alN/85jflQKpz58655pprmt1jpyXrr45pyQf3UHrnnXea3YeqOet/ttZeN/nw309b25S+1+/5g3MUbWO3kDalqb2flixZUj6eOXPmJvWybNmyzR7T0t5Y6+vVq9eHvtaH/a42dY8sANjauX0PALYin/zkJxvdkjVlypQNalavXl0+/jD/uF2/9r333tto/Q9/+MNyINUwZurUqa2+XnPXbskHV0W1ps8PKur7aWub0vcH6yrRd1urq6vb7DnW/3O7pW1KSAsAvE8oBQBbmdGjR5eP58+fn1dffbXR+fVvR6utrW31vOvXbmx1xx133JGrrrpqg/f/9//+341uM9uUa3+YutbeetfcmC31/WwJm9L3B+sq0XdbW/8z3HHHHSmVSh/61wUXXLBZ101av9rKfk8AsOmEUgCwlfngbUMLFy5s9Hr9J4KtXLkyb731VqvmfeWVV5qc44PefPPNfOMb3yi/PvHEE8t7Pa1YsSJf+cpXsm7dulZds8EHg7XmrP80ts6dO29SKLX+Z3vttddafetTa7+fLWX9azb1VLqmrN/zB+dor9a/XfPNN98s7LpdunRpdNtka5/gt6lP+gMAhFIAsNV5++23G73u0qVLo9d77713oyfZtfZJcevX7bvvvs3WfeMb3yiHATvttFOuuOKK3HDDDeVbxWbMmJEf//jHrbpmg9Y+SW/9un322SfV1R/+R5X1P1tdXV2eeeaZjY6pr69v9OS1pr6f9XvZEnv8rH/N9Z/E15L1f0932GGHDBgwoM37KtqnP/3p8vGMGTMKvfb+++9fPr7//vtbNebBBx/cQt1s3Af/fth7CoD2RigFAFuZhx56qHxcVVWVHXfcsdH5rl27Zr/99iu/bnhCXkteeOGFRqHLQQcd1GTdlVdemTvuuCPJ+//gve6669KrV6/ss88+ueiii8p1P/rRj1odNCXvhwtz585tsaa+vr7Rk9bGjBnT6vnXt9tuu2X77bcvv27N9/PHP/4xb7zxRovXXv9paO++++4m9daS9X9P7rrrrlbdPnbDDTeUjzf1+9raHHnkkeXj3/72t1mxYkVh1z766KPLx//3//7fRpuuN+Xee++tyJMaG6z/ZzLZMn8uAWBLEkoBwFZkxowZueuuu8qvP/nJTzb5JLavfe1r5ePf/OY3eeKJJ1qc99xzzy0fDxgwoNE/vhu8+OKL+fu///vy67//+7/P2LFjy6+/+93v5uCDD06SrF27Nl/+8pdb/aj6+vr6nH/++S3WXHvttY3+gf/Vr361VXN/UFVVVaOxl1122Qa3QK5v3bp1+d73vld+/clPfjIjRozYoK5fv37l48WLF2f58uWb1F9zTjrppNTU1CR5P1z40Y9+1GL973//+zzyyCPl11//+tfbtJ9KOf7448tB7NKlS/MP//APhV37y1/+cnmz/dra2pxxxhnN1i5fvrzF80Xo3bt3o4cDVDIgA4BNIZQCgK3AqlWrcvXVV+fYY49t9OSwf/zHf2yy/q//+q8zaNCgJO+HKuPHj29yH6JSqZQf/OAHue2228rvnX322enYsWOjuoaQqeHJZ3vvvXejlVHJ+yun/s//+T/lDaFfeOGFRiHWxlxzzTW57LLLmjw3ffr0Rv/AP+aYY7L33nu3eu4POv3009O9e/ck74cLn/vc57J48eIN6tasWZNvfvObjVaRnXfeeU3OOWjQoPKeTaVSqdnPsqm22267nHrqqeXX/9//9//l6quvbrL2scceaxRM7rvvvk0Gje3RNttsk4svvrj8+he/+EXOPPPMrFq1qsVx77zzTn7+85/nxBNP3ORr9+nTp9Hv/4033piTTz55g1Dzv//7v3PYYYflmWeeSZ8+fTb5epurQ4cOjf6eTJ48udAnDwLA5uq48RIAYHM9+eSTTYYG69aty5IlS/Lcc89t8I/u008/PZ///OebnK979+656qqrctxxx6W+vj6vvvpq9t5775x66qkZO3ZsunfvnpdeeinXXHNN/uu//qs8bsyYMTnzzDM3mO+iiy4q72PUuXPnXH/99Y1WYDTYaaedMnny5HzlK19JklxxxRX53Oc+l+OOO67Fz/9Xf/VXueWWW3LGGWfkzjvvzJe//OXstNNOWbp0aaZOnZrrrrsua9euTZL06NEjkydPbnG+jdlxxx3z05/+tBzyzJw5M3vuuWe+/e1vZ9SoUenUqVOeffbZXHHFFXn66afL40455ZR84QtfaHbek08+OT//+c+TJN///vfzq1/9KsOHD2/0XV100UXZa6+9NqnvH//4x7nrrrvy4osvplQq5Rvf+EZ+97vf5aSTTspOO+2Ut99+O9OmTcu///u/Z82aNUmSmpqaXHvttZu0/9bW6uSTT84jjzxSDv5+/vOf5ze/+U2+9KUv5YADDsj222+f+vr6LF26NM8880weeeSR3HPPPVmzZk2jPak2xXnnnZd77723vFfUTTfdlFtuuSUjRoxI7969M2/evLz44otJkiFDhuSss87KWWedlSTlfdeKdPLJJ5dvpW24/XavvfZqdGvfGWeckUMPPbTw3gBgo0oAwBbx1a9+tZTkQ//q3r176dJLLy3V19dv9Bo33HBDqWPHjq2a98ADDyy98847G8wxY8aMRnP85Cc/2eh1TzjhhHL9DjvsUHrzzTc3qDn44IPLNddcc03pe9/73kZ7rKmpKd17770tXvuD87bk4osvbvX3/oUvfKH03nvvtTjf0qVLS7vvvnuL89x3330b+/pa9Prrr5d22223Vv9Z2dj3NWfOnEZj2tL6f8bPP//8Vo0ZPHhwq76r+vr60gUXXFCqqqr6UH9/Pv3pTzc53zXXXFOuOfjgg1vscfny5aXPfvazLV5nzz33LL3wwgulyy+/vPze//pf/6vZOdcfO2fOnI1+T/fdd1+5fvDgwc3Wvffee6XPfOYzLfa6sb8nAFApH53/pAYA7VB1dXV69uyZXXbZJccff3wuv/zyzJs3L2eccUaqqqo2Ov6UU07JrFmzctRRRzW7UqZv376ZNGlS7rvvvvKtdw3q6uryla98pbxKaezYsa26Je+Xv/xlBg4cmCR544038o1vfGOjYy666KJcc801zT4hbvTo0XnsscdyyCGHbHSu1jrnnHPy4IMP5oADDmi2ZqeddsrVV1+d3/72txtd6dK7d+/8+c9/zs9+9rMceuih2WGHHdp8dcyOO+6YmTNn5rzzzkvPnj2brOnYsWNOOOGEPPXUU236fW1Nqqqqcv7552fmzJkZP358ttlmmxZrP/GJT+TCCy/MzTffvNnX7tGjR+644478/ve/z/jx4zNw4MB06tQp/fv3z2c+85lcfvnl+dOf/pShQ4eWn1SZpCK38nXq1Cn33HNPrr766hxzzDEZOHBgeW8yANjaVZVKnh0LAB8Fb775Zh544IEsWLAgK1euTN++fbPHHnvkgAMOqMitXWPHjs0DDzyQ5P39pBr2QFq3bl0eeOCBvPjii1m6dGl22GGHjBo1KrvvvvsW7ef111/P9OnTs2jRoqxZsyZ9+/bNvvvum3333XeLXndzrFmzJg899FBefPHFLFmyJD169MiOO+6YsWPHZtttt610e4Wqq6vL9OnT89prr2XJkiWprq5O7969s8suu2Sfffap2N5On/vc53LnnXcmSS6//PJ8+9vfrkgfANAeCaUAgC2iuVAKPioWLlyYIUOGZPXq1Une3wB9n332qXBXANB+uH0PAADW05r/ZrtmzZr87d/+bTmQ2meffQRSAPAhCaUAAGA948aNyw9/+MM8++yzG5wrlUqZPn16DjnkkNx9993l9//pn/6pyBYB4COhY6UbAACArcnChQtzwQUX5IILLkjv3r2z2267pUePHlmxYkVeeOGFLF26tFH9N77xjRx//PEV6hYA2i+hFAAArGf9BwO8/fbbefTRR5us69SpU84555xceOGFRbUGAB8pQikAAFjP//t//y+33XZb7r333jz55JOZO3duli9fng4dOuRjH/tYdt111xx66KH5m7/5m+y0006VbhcA2i1P3wMAAACgcDY6BwAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwHSvdQNEWLFhQ6RYAAFrUp0+fJMnixYsr3AkAQMsGDBiwyWOtlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAArXsdINAADwF7Nnz85NN92U2traVFdXZ8yYMRk2bFil2wIAaHNCKQCArcBDDz2USy65JDNmzNjg3AEHHJCzzjorBx10UAU6AwDYMqpKpVKp0k0UacGCBZVuAQCgkV//+tc555xzUl9fn27duuX4449P//79M2fOnEydOjV1dXWprq7OpEmTctJJJ1W6XQCAsgEDBmzyWKEUAEAFPfTQQznllFNSX1+fCRMm5PTTT8+QIUOSJIsXL86KFSty2WWXZfLkyamurs6NN95oxRQAsNXYnFDKRucAABV0ySWXlAOp8847L927d290vnv37jnvvPMyYcKE1NfX59JLL61QpwAAbUsoBQBQIbNnz86MGTPSrVu3nH766S3WTpgwId26dcsjjzyS2bNnF9QhAMCWI5QCAKiQ6dOnJ0mOO+64DVZIfVCPHj1y7LHHNhoHANCeCaUAACqktrY2SbLDDju0qr6hrmEcAEB7JpQCAKiQHj16JEneeOONVtU31DWMAwBoz4RSAAAVMmbMmCTJ1KlTs2LFihZra2trc9dddzUaBwDQngmlAAAqZNiwYTnggANSV1eXyy67rMXayZMnp66uLqNGjcqwYcMK6hAAYMsRSgEAVNBZZ52V6urqTJ48Of/yL/+ywX5RtbW1+Zd/+ZdMnjw51dXVOfPMMyvUKQBA26oqlUqlSjdRpAULFlS6BQCARn7961/nnHPOSX19fbp165YvfvGL6d+/f+bMmZO77rordXV1qa6uzqRJk3LSSSdVul0AgLIBAwZs8lihFADAVuChhx7KpZdemkceeWSDc6NGjcqZZ56Zgw46qAKdAQA0Tyj1IQilAICt2ezZs/P444+ntrY21dXVGTNmjD2kAICtllDqQxBKAQBbuz59+iRJFi9eXOFOAABatjmhlI3OAQAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwnVsq4lKpVIefvjh3HfffZkzZ05WrVqV3r17Z8SIERk3blwGDBjQ7Nj6+vrce++9ue+++zJ//vzU19enX79+GTNmTI499th07NhmbQIAAACwFagqlUqlzZ1k7dq1+dnPfpY///nPSZIOHTqkpqYmdXV1SZJOnTrlrLPOyv7779/k2EmTJuXxxx9PknTs2DHV1dVZvXp1kmSXXXbJ+eefn5qams1tM0myYMGCNpkHAGBL6dOnT5Jk8eLFFe4EAKBlLS1C2pg2CaWuu+66TJ06NR06dMhXvvKVHH744enUqVOWLFmSa6+9No8++mg6deqUn/zkJ+nXr1+jsddff31uv/32bLPNNvnmN7+Zz3zmM6mqqsqsWbPyi1/8IitWrMiYMWNyxhlnbG6bSYRSAMDWTygFALQXmxNKbfaeUsuWLcsf/vCHJMnnPve5HHvssenUqVOS5GMf+1jOOuus9O/fP6tXr87NN9/caOw777yTu+++O0nypS99KWPHjk11dXWqqqoycuTI/N3f/V2S5OGHH85rr722ua0CAAAAsJXY7FDq6aefztq1a5Mkxx133AbnO3TokGOOOSZJ8uijj2bVqlXlczNmzMiaNWvStWvXHH744RuM/eQnP5n+/funVCpl+vTpm9sqAAAAAFuJzQ6l3nrrrSRJt27d0qtXryZrGpZyrVmzJs8//3z5/WeeeSZJsvvuu5dXV33QPvvsk+T98AsAAACAj4bNDqWqqqqSvP8Eveasf27u3Lnl43nz5iVJdtxxx2bHDho0KEkyf/78tMH2VwAAAABsBTpu7gR9+/ZNkrz77rtZsmRJPvaxj21Q0xA+Jcnbb79dPn7nnXeSJL179252/oZzq1atyqpVq9KlS5cW+zn33HObfP/iiy9O8peNQwEAtlYdO77/I5qfWwCAj7LNXim1xx57pEOHDkmS22+/fYPzq1evLm9mnqTRnlINx83dupcknTt3bnIsAAAAAO3XZq+U2nbbbXPEEUdk2rRpmTZtWrp06ZIjjzwyPXv2zOuvv57rr78+b731Vjp06JB169aVb/fbUhpWRDXHo5UBgK1dwwopP7cAAFu7hn3EN8Vmh1JJ8uUvfzlvvPFGHn/88fzud7/L7373u0bnTzzxxNx9991Zvnx5unXrVn6/pqYmdXV1Wb16dbNzv/fee43qAQAAAGj/2iSU6tSpU84999z813/9V6ZPn5758+cneX8D86OOOip77rlnbr311iRJv379yuN69+6durq6RvtMfVDDuZqamo3uJwUAAABA+9AmoVSSVFdXZ8yYMRkzZswG51555ZWsW7cuSbLbbruV3x80aFDmzZvX6Il8H9SwSfrAgQPbqlUAAAAAKmyzNzpvjYcffjhJMmTIkAwaNKj8/p577pkkef7555u9he/JJ59MkowYMWILdwkAAABAUbZ4KPXqq69m2rRpSZLx48c3OvfpT38622yzTerq6nLvvfduMPaxxx7LggULUlVVldGjR2/pVgEAAAAoSJuEUk8//XTuuOOOLFq0KPX19UmSlStX5j//8z9z4YUXZs2aNRk1alQOPPDARuO23XbbHHPMMUmS66+/Pg8++GB5/KxZs3L55ZcnSUaPHp3Bgwe3RasAAAAAbAWqSqVSaXMnuf/++zNlypQkSYcOHVJTU5OVK1emYerRo0fnO9/5Tjp23HALq7Vr12bSpEl5/PHHkyTbbLNNqqury0/d22WXXfKDH/ygzTY5X7BgQZvMAwCwpfTp0ydJsnjx4gp3AgDQsgEDBmzy2DYJpRYtWpRp06blueeey+LFi/Puu++mV69eGTp0aA499NB84hOfaHF8fX197rnnntx///2ZN29e6uvr079//4wePTrHHXdck2HWphJKAQBbO6EUANBeVDyUak+EUgDA1k4oBQC0F5sTShXy9D0AAAAAWJ9QCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCdax0AwAA/MXs2bNz0003pba2NtXV1RkzZkyGDRtW6bYAANqcUAoAYCvw0EMP5ZJLLsmMGTM2OHfAAQfkrLPOykEHHVSBzgAAtoyqUqlUqnQTRVqwYEGlWwAAaOTXv/51zjnnnNTX16dbt245/vjj079//8yZMydTp05NXV1dqqurM2nSpJx00kmVbhcAoGzAgAGbPFYoBQBQQQ899FBOOeWU1NfXZ8KECTn99NMzZMiQJMnixYuzYsWKXHbZZZk8eXKqq6tz4403WjEFAGw1NieUstE5AEAFXXLJJeVA6rzzzkv37t0bne/evXvOO++8TJgwIfX19bn00ksr1CkAQNsSSgEAVMjs2bMzY8aMdOvWLaeffnqLtRMmTEi3bt3yyCOPZPbs2QV1CACw5QilAAAqZPr06UmS4447boMVUh/Uo0ePHHvssY3GAQC0Z0IpAIAKqa2tTZLssMMOrapvqGsYBwDQngmlAAAqpEePHkmSN954o1X1DXUN4wAA2jOhFABAhYwZMyZJMnXq1KxYsaLF2tra2tx1112NxgEAtGdCKQCAChk2bFgOOOCA1NXV5bLLLmuxdvLkyamrq8uoUaMybNiwgjoEANhyOla6AQCA/8nOOuusnHLKKZk8eXKS5KijjsoLL7yQ2traVFdXZ999980f/vCHTJ48OdXV1TnzzDMr3DEAQNuoKpVKpUo3UaQFCxZUugUAgEZ+/etfZ+LEiWnpx7Kqqqr85Cc/yUknnVRgZwAALRswYMAmj3X7HgAAAACFs1IKAKCCHnrooZxyyimpr6/PhAkTcvTRR2f27Nnl2/f222+/TJs2rXz73o033piDDjqo0m0DACTZvJVS9pQCAKigSy65pBxInXfeeUmSI444IkmyePHiJMm+++6b5P3Nzi+99FKhFADwkeD2PQCACpk9e3ZmzJiRbt265fTTT2+xdsKECenWrVseeeSRzJ49u6AOAQC2HKEUAECFTJ8+PUly3HHHpXv37i3W9ujRI8cee2yjcQAA7ZlQCgCgQmpra5MkO+ywQ6vqG+oaxgEAtGdCKQCACunRo0eS5I033mhVfUNdwzgAgPZMKAUAUCFjxoxJkkydOjUrVqxosba2tjZ33XVXo3EAAO2ZUAoAoEKGDRuWAw44IHV1dbnssstarJ08eXLq6uoyatSoDBs2rKAOAQC2nI6VbgAA4H+ys846K6ecckomT56cJBk+fHj++Mc/ZtmyZenSpUuOPfbYPP/885k8eXKqq6tz5plnVrhjAIC2UVUqlUqVbqJICxYsqHQLAACN/PrXv87ZZ5+90bqf/vSnOemkkwroCACgdQYMGLDJY92+BwBQYY8++mib1gEAtAdu3wMAqKApU6bklltuSZKMHDky5557bubOnZva2tpUV1dn+PDhufjiizNz5szcfPPNGTp0aE477bQKdw0AsPncvgcAUEHDhw9PbW1tRo4cmdtvvz1J0qdPnyTJ4sWLy3Xjxo3LzJkz07Nnzzz33HMV6RUA4IPcvgcA0A5NmzYttbW1SZIrr7yyxdorrrgiSbJ8+fJMmzZti/cGALClCaUAACrkt7/9bZJkyJAh6devX4u1/fv3z+DBgxuNAwBoz4RSAAAVsnz58iR/uV1vYxrqGsYBALRnQikAgArp2bNnksZ7R7Wkoa5hHABAeyaUAgCokOOPPz5J8uqrr2bRokUt1i5cuDCvvfZao3EAAO2ZUAoAoEKOPvro9OjRI0nyrW99K0ly9dVXZ//998/w4cNzxBFH5Oqrr06SnHrqqUneXyV19NFHV6ZhAIA2VFUqlUqVbqJICxYsqHQLAABlU6ZMyY9//ONW13/ve9/LaaedtgU7AgBovQEDBmzyWKEUAECFjRgxIkuXLi2/rqqqSseOHbN27dqs/6Paxz72sTz55JOVaBEAoEmbE0q5fQ8AoIImTpzYKJBKklKplDVr1uSD/+1wyZIlmThxYpHtAQBsMVZKAQBU0E477ZR169alX79+mTlzZqZNm5Y77rgjy5YtS5cuXXL88cfn6KOPzsiRI7No0aJ06NAhr7/+eqXbBgBIsnkrpTq2YR8AAHwIV199ddatW5ckufnmm5O8v/n5l7/85STJ4sWLy7U33XRTxo4dm3Xr1uXqq6/O3/7t3xbfMABAG3L7HgBAhdx0001Jku7du2eXXXZpsXbo0KHp1q1bo3EAAO2ZUAoAoEJWrFiR5P1QqjV69OjRaBwAQHsmlAIAqJCGMKq1IVNtbW2jcQAA7ZlQCgCgQk466aQk74dSL7/8cou1L774Yurq6hqNAwBoz9rs6Xv19fV54IEHMn369Lz66qtZuXJlOnfunAEDBmT//ffPMcccky5dujQ5du3atZk6dWqmT59efqrMwIEDc8ghh+Swww5LVVVVW7SYxNP3AICtywefvpckffr0SdJ4o3NP3wMAtkab8/S9Ngml3nvvvVx88cV5+umny+917do17777bhqm79u3b37wgx9khx12aDR25cqVufDCC/PKK68kSTp37px169Zl7dq1SZL99tsvEydOTIcOHTa3zSRCKQBg6zJx4sTceOONSZJ+/fqlY8eOmTdvXvn8oEGDsnbt2ixatChJcsopp2TSpEkV6RUA4IMqHkrdeOONue2221JVVZWTTz45Rx55ZLp27Zq1a9fm0Ucfza9+9avU1dVlzz33zPnnn99o7M9+9rM88sgj6d69e77zne9kv/32S6lUyoMPPpirrroqa9asyRe+8IWcfPLJm9tmEqEUALD1+fznP5/HHntso3X7779/fv/73xfQEQBA62xOKNUme0pNnz49STJ27NiMHz8+Xbt2TZJ07Ngxo0ePzle/+tUkyTPPPNNoI885c+bkkUceSZKcdtppGTlyZKqqqlJdXZ2xY8fmS1/6UpJk6tSpWbZsWVu0CgCw1WlNIPVh6gAA2oM2CaUaAqOdd965yfMf//jHy8erV68uHzeEWQ37Tn3Q4Ycfnq5du2b16tV59NFH26JVAICtyq677tro9bHHHpsRI0Zk5513zh577JFjjz22xXoAgPaqY1tM0rdv3yxYsCBz5sxp8nzDflG9evXKdtttV37/mWeeSZLsvffeTY7r1KlThg8fnlmzZuXpp5/OkUce2RbtAgBsNd59993y8fz585M0vdH5wIEDN6gHAGjP2mSl1GGHHZYkuf/++3Pbbbdl5cqVSd5/qt5//dd/5brrrktVVVW+8pWvlMeUSqXyD1477rhjs3MPGjQoyV9+SAMA+Kg46KCDyscXXXRRi7Xr78u5/jgAgPaqTTY6r6+vzzXXXJM//OEP5ffWf/re0KFD88UvfjEjR44sn1+5cmW+9rWvJUnOOeecJm/fS5K77ror1157bbp3755///d/32gv5557bpPvX3zxxUka3z4IAFBJnTt3Lh+/99575eOOHd9fzN7wNOKN1QMAVEqnTp02eWybrJSqrq7O1772tfz1X/91OnTokOT90Kkh71q1alWWL1/eaMyqVavKxy19gIZz69cDAAAA0L61yZ5S77zzTv7t3/4tL730Ug4++OB89rOfzQ477JB33nknM2bMyK233prLL788CxcuzCmnnNIWl2xWw4qo5qy/NwMAwNZi/Z9RmtpTqqV6AIBKGTBgwCaPbZOVUpMnT85LL72UQw89NN/5zncyePDg1NTUpF+/fhk/fny+9a1vJUl+//vfZ+7cuUmSmpqa8viWbqlrOLd+PQDAR8H6Tyi+5pprWqy98sormxwHANBebXYoNW/evDz55JNJkuOOO67Jms985jPp0aNHSqVSZs6cmSTp0qVLeV+Et99+u9n5ly5dmiTp3bv35rYKALBVeeihh8rH3//+91us/eEPf9jkOACA9qpNQqkG22+/fbN1DefefPPNJElVVVX5yXoNq6damr/hMcgAAB8lXbp0KR8PHDiw0Yqo5P0VUuv/HLR+PQBAe7bZoVR19V+maM2+B+v/ILXnnnsmSZ566qkmx6xevTrPP/98kmTEiBGb2yoAwFbnpZdeavT6hz/8YTp37pzOnTtn4MCBjVZINVUPANBebXYoNXjw4PLxPffc02TNY489lmXLliVJhg4dWn5/9OjRSZL58+eXb+tb3z333JOVK1emU6dO+dSnPrW5rQIAbJXmz5+/0RVQXbp0yfz58wvqCABgy9vsUGqHHXbIPvvskySZOnVqbrzxxnIAtWrVqtx///2ZMmVKkqRv377Zf//9y2N33nnnjBo1KkkyZcqUzJo1K0lSX1+fBx54IDfccEOS9/eq6tWr1+a2CgCw1XrppZcyf/78DTYx//jHP5758+dbIQUAfORUlUql0uZO8vbbb+fCCy9s9F/vunTpknfffbf8ulevXvnHf/zH7Lzzzo3Grly5MhdeeGFeeeWVJEnnzp1TX1+fNWvWJEn222+/TJw4MR06dNjcNpMkCxYsaJN5AAC2lD59+iRpeWsEAICtwYABAzZ5bJuEUsn7+z/98Y9/zKOPPpq5c+dm5cqV6dy5c/r165f99tsvxxxzTHr27Nnk2LVr1+bOO+/Mww8/nEWLFqW6ujqDBg3KIYccksMOOyxVVVVt0WISoRQAsPUTSgEA7cVWEUq1F0IpAPifa903x1W6hY+kDlfdXukWAIAK2ZxQarP3lAIAAACAD8tKKQCArUzDii4rkACArZ2VUgAAAAC0K0IpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcB3bYpITTjih1bUXXHBB9thjjw3eX7t2baZOnZrp06dn0aJF6dChQwYOHJhDDjkkhx12WKqqqtqiVQAAAAC2Am0SSvXq1avF8++++25Wr16djh07Zscdd9zg/MqVK3PhhRfmlVdeSZJ07tw5q1evzosvvpgXX3wxjz32WCZOnJgOHTq0RbsAAAAAVFibhFJXXXVVi+cnTpyY1157Lfvtt1969Oixwfkrrrgir7zySrp3757vfOc72W+//VIqlfLggw/mqquuyqxZs3LzzTfn5JNPbot2AQAAAKiwLb6n1KuvvprXXnstSTJ27NgNzs+ZMyePPPJIkuS0007LyJEjU1VVlerq6owdOzZf+tKXkiRTp07NsmXLtnS7AAAAABRgi4dS999/f5L3b/Hbd999Nzg/ffr0JMmAAQOy//77b3D+8MMPT9euXbN69eo8+uijW7RXAAAAAIqxRUOpdevW5eGHH06SjB49usk9oZ555pkkyd57793kHJ06dcrw4cOTJE8//fQW6hQAAACAIm3RUOrxxx8v33LX1K17pVIp8+fPT5ImN0BvMGjQoCQp1wIAAADQvrXJRufNabh1b/DgwRkyZMgG599999289957SZLevXs3O892222XJHn77bc3es1zzz23yfcvvvjiJEmfPn02OgcA8OG8fdF3s3rmI5Vu4yNn3TfHVbqFj4xOI0el9/d/Wuk2AID1bLGVUitWrMisWbOSJAcffHCTNatWrSofd+rUqdm5Gs6tXw8AbD0EUmzt/BkFgK3PFlspNX369KxduzYdOnTIQQcdtKUus4GGFVHNWbx4cUGdAMD/PB2uur3SLXwkNKzs9nNL22hYceb7BIC2N2DAgE0eu8VWSj3wwANJkk984hPp1atXkzU1NTXl49WrVzc7V8O59esBAAAAaL+2SCg1b968vPzyy0mav3UvSbp06ZLOnTsnaXm/qKVLlyZped8pAAAAANqPLRJKNWxw3r179+y///7N1lVVVZWfrDd37txm6+bNm5ckGThwYNs1CQAAAEDFtHkoVV9fn4ceeihJMnr06HTs2PK2VXvuuWeS5Kmnnmry/OrVq/P8888nSUaMGNGGnQIAAABQKW0eSj355JPlW/FaunWvwejRo5Mk8+fPz8yZMzc4f88992TlypXp1KlTPvWpT7VtswAAAABURJuHUg0bnA8aNCi77rrrRut33nnnjBo1KkkyZcqUzJo1K8n7K64eeOCB3HDDDUmS4447rtkN0wEAAABoX1q+t+5DWrlyZf785z8nad0qqQannnpq3njjjbzyyiv513/913Tu3Dn19fVZs2ZNkmS//fbLCSec0JatAgAAAFBBbRpKPfLII1m9enWqqqpy0EEHtXpc165dc9FFF+XOO+/Mww8/nEWLFqVjx44ZMmRIDjnkkBx22GGpqqpqy1YBAAAAqKA2DaUOO+ywHHbYYZvWSMeOGT9+fMaPH9+WLQEAAACwFWrzPaUAAAAAYGOEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGqSqVSqdJNFGnBggWVbgEAPnLu+M07lW4BNupzJ25b6RYA4CNnwIABmzzWSikAAAAACtex0g0AAO3fsX/86yRJh6tur3AnHw19+vRJkixevLjCnXw0rPvmuPcPTvTnEwC2JlZKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhetY6QYAgI+Odd8cV+kWPhLeqHQDAAAFsFIKAICPvhH7V7oDAOADqkqlUqnSTRRpwYIFlW4BAKBFDSvOOlx1e4U7AQBo2YABAzZ5rJVSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4Tq29YQLFizItGnT8sQTT2Tp0qWprq7Odtttl6FDh2bs2LHZY489Nhizdu3aTJ06NdOnT8+iRYvSoUOHDBw4MIccckgOO+ywVFVVtXWbAAAAAFRQm4ZSd911V66//vqsXbs2SVJTU5O1a9dm/vz5mT9/fqqrqzcIpVauXJkLL7wwr7zySpKkc+fOWb16dV588cW8+OKLeeyxxzJx4sR06NChLVsFAAAAoILaLJT6z//8z1x77bWpqqrK5z//+Rx11FHp06dPkuSdd97Jk08+WQ6r1nfFFVfklVdeSffu3fOd73wn++23X0qlUh588MFcddVVmTVrVm6++eacfPLJbdUqAAAAABXWJqHUm2++mf/zf/5PkuSb3/xmDj/88Ebnt91223zmM5/ZYNycOXPyyCOPJElOO+20jBw5MklSVVWVsWPHZuXKlbn22mszderUHHvssenVq1dbtAsAAABAhbXJRud33XVX3nvvvQwdOnSDQKol06dPT5IMGDAg+++//wbnDz/88HTt2jWrV6/Oo48+2hatAgAAALAVaJNQ6uGHH06SjB49+kONe+aZZ5Ike++9d5PnO3XqlOHDhydJnn766c3oEAAAAICtyWbfvrdo0aIsW7YsSTJkyJC88MIL+d3vfpcXXngh7733Xvr27ZuRI0dm3LhxjW6/K5VKmT9/fpJkxx13bHb+QYMGZdasWeVaAAAAANq/NgmlGjz77LO59dZbU19fny5duqSqqioLFizIggULMn369Hz/+98vB1Dvvvtu3nvvvSRJ7969m51/u+22S5K8/fbbrern3HPPbfL9iy++OEnKm68DAGyt3vj//6+fWwCAj7LNDqXq6urKx7fccksGDBiQ0047LUOHDk19fX2eeOKJTJkyJW+//XZ++tOf5qc//Wk6dOiQVatWlcd16tSp2fkbzq1fDwAAAED7ttmhVKlUKh9XVVVl4sSJGTBgQJKkuro6++67b/7u7/4u//qv/5oFCxbk0UcfzYEHHri5l21Ww4qo5ixevHiLXRsAoC35uQUA2No1ZECbYrM3Oq+pqSkff+ITn2iymf322y/9+/dP8pcNy9cft3r16mbnbzi3fj0AAAAA7dtmh1Lr7wfVUjrWcG7JkiVJki5duqRz585JWt4vaunSpRtcBwAAAID2bbNDqUGDBqWqqupDj6uqqsqgQYOSJHPnzm22bt68eUmSgQMHblqDAAAAAGx1NjuU6ty5c3bbbbckyYIFC5qtazjXt2/f8nt77rlnkuSpp55qcszq1avz/PPPJ0lGjBixua0CAAAAsJXY7FAqSQ4++OAkyX//9383GUzNmjUrCxcuTPL+/lINRo8enSSZP39+Zs6cucG4e+65JytXrkynTp3yqU99qi1aBQAAAGAr0Cah1CGHHJJBgwalvr4+P/nJT/LSSy8lSerr6/Pf//3fufzyy5MkQ4cOzb777lset/POO2fUqFFJkilTpmTWrFnlcQ888EBuuOGGJMlxxx2XXr16tUWrAAAAAGwFqkqlUqktJnrjjTdywQUXNNrIvL6+Pu+9916S9/ee+v73v5/tttuu0biVK1fmwgsvzCuvvJLk/dsB6+vrs2bNmiTvr6yaOHFiOnTo0BZttniLIQDA1mDdN8clSTpcdXuFOwEAaFlLD73bmDYLpZL3A6bbb789f/rTn/Lmm2+mqqoqAwYMyKhRo3L00UenpqamyXFr167NnXfemYcffjiLFi1KdXV1Bg0alEMOOSSHHXbYJm2k3hyhFACwtRNKAQDtxVYTSrUHQikAYGsnlAIA2ovNCaXaZE8pAAAAAPgwhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhqkqlUqnSTRRpwYIFlW4BAKiQdd8cV+kWPpI6XHV7pVsAACpkwIABmzzWSikAAAAACmelFADAVqZPnz5JksWLF1e4EwCAllkpBQAAAEC7IpQCAAAAoHBCKQCArcjVV1+d/fffP8OHD88RRxyRq6++utItAQBsEfaUAgDYCkycODG/+c1vsm7dug3OdejQISeeeGImTZpUgc4AAJq3OXtKCaUAACrs85//fB577LHy6x49eqRnz55ZtmxZVqxYUX5///33z+9///tKtAgA0CQbnQMAtFMTJ04sB1L9+vXLgw8+mMWLF+eVV17J7Nmz8+CDD6Zfv35JksceeywTJ06sZLsAAG3GSikAgAraaaedsm7duvTr1y8zZ85MkvTp0ydJsnjx4nLdyJEjs2jRonTo0CGvv/56RXoFAPggK6UAANqhq6++uryH1M0339xi7U033ZQkWbdunc3PAYCPBKEUAECFNARN3bt3zy677NJi7dChQ9OtW7dG4wAA2jOhFABAhTRsYt69e/dW1ffo0aPROACA9kwoBQBQIQ1hVGtDptra2kbjAADaM6EUAECFnHTSSUneD6VefvnlFmtffPHF1NXVNRoHANCeefoeAEAFefoeANCebc7T9zq2YR8AAHxIJ554Ym688cYsWrQoI0eOzIABA/LEE0+kVCqlqqoq++yzTxYsWJBFixaV6wEAPgqslAIAqLDPf/7zeeyxxzZat//+++f3v/99AR0BALTO5qyUsqcUAECFPfnkk21aBwDQHgilAAAqaP/998/q1avLr0866aSMGDEiO++8c/bYY49Gm5qvXr06+++/fyXaBABoc27fAwCooIEDB5aP58+fn6Tpjc6bqgMAqDS37wEAtEPrr4L61a9+1WLtL3/5yybHAQC0V1ZKAQBUyE477ZR169Ylabz6qamVUslfVkt16NAhr7/+ekFdAgA0z0opAIB2qL6+vtBxAABbE6EUAECFVFdv2o9imzoOAGBr4icaAIAKOfDAA8vHd999d4u1d9xxR5PjAADaK3tKAQBUkKfvAQDt2ebsKdWxDfsAAOBD6t+/fxYuXJjk/eDp1FNPzRtvvJFly5alS5cu2XHHHXPFFVc0qgcA+CiwUgoAoMJ23nnnrF69eqN1nTp1ypw5cwroCACgdTx9DwCgHfv85z/fqrrx48dv2UYAAAoklAIAqKApU6bklltuSZKMHDkyn/rUp9KhQ4dUV1enQ4cO+dSnPpWRI0cmSW6++eZMmTKlku0CALQZt+8BAFTQ8OHDU1tbm5EjR+b2229P0vRG5+PGjcvMmTPTs2fPPPfccxXpFQDgg9y+BwDQDk2bNi21tbVJkiuvvLLF2obNzpcvX55p06Zt8d4AALY0oRQAQIX89re/TZIMGTIk/fr1a7G2f//+GTx4cKNxAADtmVAKAKBCli9fnuQvt+ttTENdwzgAgPZMKAUAUCE9e/ZM0njvqJY01DWMAwBoz4RSAAAVcvzxxydJXn311SxatKjF2oULF+a1115rNA4AoD0TSgEAVMjRRx+dHj16JEm+9a1vtVh76qmnJnl/ldTRRx+9xXsDANjSOla6AQCA/8nOOOOM/PjHP87MmTMzbty4nHTSSXn44YezbNmydOnSJYccckhuuummzJw5M0ly+umnV7hjAIC2UVUqlUqVbqJICxYsqHQLAACNnHXWWbnllls2WnfCCSfkZz/7WQEdAQC0zoABAzZ5rNv3AAAAACic2/cAACpoypQp5VVSI0eOzCmnnJKHHnqofPveYYcdlhtvvDEzZ87MzTffnKFDh+a0006rcNcAAJvP7XsAABU0fPjw1NbWZuTIkbn99tuTJH369EmSLF68uFw3bty4zJw5Mz179sxzzz1XkV4BAD7I7XsAAO3QtGnTUltbmyS58sorW6y94oorkiTLly/PtGnTtnhvAABbmlAKAKBCfvvb3yZJhgwZkn79+rVY279//wwePLjROACA9kwoBQBQIcuXL0/yl9v1NqahrmEcAEB7JpQCAKiQnj17Jmm8d1RLGuoaxgEAtGdCKQCACjn++OOTJK+++moWLVrUYu3ChQvz2muvNRoHANCetcnT9+6///5MmTKlxZrOnTvnP/7jP5o8V19fn3vvvTf33Xdf5s+fn/r6+vTr1y9jxozJsccem44dO25ui2WevgcAbE08fQ8AaM825+l7bZf2JOnQoUO6d+/e5Lmampom31+7dm0mTZqUxx9//P2GOnZMdXV1Xn311bz66qt55JFHcv755zc7HgCgPTvjjDPy4x//ODNnzsy4ceNyzjnnZN68eamtrU11dXWGDRuWf/u3f8vMmTOTJKeffnqFOwYAaBttGkoNGzYsF1xwwYcac9NNN+Xxxx/PNttsk29+85v5zGc+k6qqqsyaNSu/+MUv8vLLL+fKK6/MGWec0ZatAgBsFU477bS88MILueWWWzJz5syceOKJzdaecMIJOe200wrsDgBgy6nonlLvvPNO7r777iTJl770pYwdOzbV1dWpqqrKyJEj83d/93dJkocffri8hwIAwEfNpz/96TatAwBoD9p0pdSHNWPGjKxZsyZdu3bN4YcfvsH5T37yk+nfv38WLlyY6dOnZ/DgwRXoEgBgy3nooYdyzjnnJEkmTJiQ3XffPf/5n/+ZZcuWpUuXLjnuuOPy3HPPZfLkyZk4cWIGDhyYgw46qMJdAwBsvoqGUs8880ySZPfdd0+nTp2arNlnn32ycOHCPP3000W2BgBQiEsuuST19fWZMGFCzjvvvCTJN77xjSR/2eh8/PjxSZLJkyfn0ksvFUoBAB8JbRpKzZ07N3//93+fN954Ix06dEjfvn0zYsSIHHvssdl+++03qJ83b16SZMcdd2x2zkGDBiVJ5s+fn1KplKqqqrZsGQCgYmbPnp0ZM2akW7duG93AfMKECbnmmmvyyCOPZPbs2Rk2bFhBXQIAbBltGkrV1tZmxYoV6datW959993MnTs3c+fOzR//+Md8+9vfzpgxYxrVv/POO0mS3r17Nztnw7lVq1Zl1apV6dKlS4s9nHvuuU2+f/HFFyf5yyOWAQAq7aabbkqSHH/88RkyZEj5/Y4d3/8Rbf2fW/r06ZMvfvGL+Y//+I88/vjjGT16dKG9AgC0tTYJpXr37p0TTjghn/70p9O/f/907Ngxa9asyVNPPZXrr78+8+bNyy9+8Ytst9122WOPPcrjVq1alSTN3rqXJJ07d25Uv7FQCgCgvaitrU2S9O/fv1X1DXUN4wAA2rM2CaX22Wef7LPPPo3e22abbbLffvtl+PDh+Yd/+IcsWrQoN954Yy666KK2uGSzGlZENadhbwYAgEqrrn7/Qchz5sxp9DNKwwqpD/7cMmfOnPI4P9MAAFuDAQMGbPLY6jbso0ldu3bNF77whSTJiy++mOXLl5fP1dTUJElWr17d7Pj33ntvg3oAgI+Chq0Npk6dmhUrVrRYW1tbm7vuuqvROACA9myLh1JJMnTo0CRJqVTKm2++WX6/Yb+ot99+u9mxDedqamrcugcAfKQMGzYsBxxwQOrq6nLZZZe1WDt58uTU1dVl1KhRNjkHAD4S2nSj8w9r0KBBmTdvXubOndtsTcMT+gYOHFhUWwAAhTnrrLNyyimnZPLkyUmSo446Ki+88EJqa2tTXV2dfffdN3/4wx8yefLkVFdX58wzz6xwxwAAbaOQUOrFF18sH2+//fbl4z333DMzZszI888/n9WrVze54fmTTz6ZJBkxYsSWbxQAoGAHHXRQ/u3f/i0TJ07M5MmTy+HUB1VVVWXSpEk56KCDCu4QAGDL2Ozb90qlUovnV65cmdtuuy1Jsuuuu6Znz57lc5/+9KezzTbbpK6uLvfee+8GYx977LEsWLAgVVVVHnsMAAAA8BGy2Sul3nrrrVx66aU57LDDsvfee5efFrN27do8/fTT+Y//+I8sXLgwVVVVOeWUUxqN3XbbbXPMMcfk9ttvz/XXX5+uXbtmzJgxqa6uzqxZs3L55ZcnSUaPHp3BgwdvbqsAAFudhx56KOecc05KpVImTJiQo48+OrNnzy7fvrfffvtl2rRpmTx5ciZOnJiBAwdaLQUAfCRUlTa21Gkj3nzzzUyYMKH8eptttklNTU1WrlyZdevWJUk6d+6cb37zm/nMZz6zwfi1a9dm0qRJefzxx8vjq6ury0/d22WXXfKDH/ygzTY5X7BgQZvMAwDQFo4//vjMmDEjEyZMyHnnnZck5f/It3jx4nLdv/zLv2Ty5MkZNWpUbr311or0CgDwQQMGDNjksZsdSq1evTr33HNPnn/++bz22mtZvnx53n333XTu3Dn9+/fPXnvtlSOPPDJ9+/Ztdo76+vrcc889uf/++zNv3rzU19enf//+GT16dI477rh07Nh2W18JpQCArcXs2bNz6KGHplu3bpk1a1a6d++epOlQqra2NiNHjixve+AJfADA1mBzQqnNTns6deqUY445Jsccc8wmz1FdXZ0jjjgiRxxxxOa2AwDQbkyfPj1Jctxxx5UDqeb06NEjxx57bG655ZZMnz5dKAUAtHubvdE5AACbpra2Nkmyww47tKq+oa5hHABAeyaUAgCokB49eiRJ3njjjVbVN9Q1jAMAaM+EUgAAFTJmzJgkydSpU7NixYoWa2tra3PXXXc1GgcA0J4JpQAAKmTYsGE54IADUldXl8suu6zF2smTJ6euri6jRo2ynxQA8JHQdo+1AwDgQzvrrLNyyimnZPLkyUmS4cOH549//GOWLVuWLl265Nhjj83zzz+fyZMnp7q6OmeeeWaFOwYAaBtVpVKpVOkmirRgwYJKtwAA0Mivf/3rnH322Rut++lPf5qTTjqpgI4AAFpnwIABmzzW7XsAABX26KOPtmkdAEB74PY9AIAKmjJlSm655ZYkyciRI3Puuedm7ty5qa2tTXV1dYYPH56LL744M2fOzM0335yhQ4fmtNNOq3DXAACbz+17AAAVNHz48NTW1mbkyJG5/fbbkyR9+vRJkixevLhcN27cuMycOTM9e/bMc889V5FeAQA+yO17AADt0LRp01JbW5skufLKK1usveKKK5Iky5cvz7Rp07Z4bwAAW5pQCgCgQn77298mSYYMGZJ+/fq1WNu/f/8MHjy40TgAgPZMKAUAUCHLly9P8pfb9Tamoa5hHABAeyaUAgCokJ49eyZpvHdUSxrqGsYBALRnQikAgAo5/vjjkySvvvpqFi1a1GLtwoUL89prrzUaBwDQngmlAAAq5Oijj06PHj2SJN/61rdarD311FOTvL9K6uijj97ivQEAbGlCKQCACjrjjDOSJDNnzsy4ceOycOHCRucXLlyYcePGZebMmUmS008/vfAeAQC2hKpSqVSqdBNFWrBgQaVbAABo5Kyzzsott9xSfv3xj38822+/faNb9pLkhBNOyM9+9rNKtAgA0KQBAwZs8lihFADAVmDKlCm57LLLmnyyXs+ePXP66afntNNOq0BnAADNE0p9CEIpAGBrNm3atNxxxx1ZtmxZunTpkuOPP94eUgDAVkso9SEIpQCArV2fPn2SJIsXL65wJwAALducUMpG5wAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUrqpUKpUq3QQAAAAA/7NYKQUAAABA4YRSAABbmXPPPTfnnntupdsAANiihFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFK6qVCqVKt0EAAAAAP+zWCkFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAJDkO9/5Tk444YQ888wz7fqab775Zk444YSccMIJbTYnAMCW0LHSDQAAsPWYOnVq6urqMnbs2Gy//faVbgcA+AgTSgEAVMgOO+yQbbbZJp07d650K2V33XVX3nrrrey5555CKQBgixJKAQBUyA9+8INKtwAAUDH2lAIAAACgcFZKAQB8wIoVK/Lb3/42f/rTn/L222+nR48e2XfffXPiiSemd+/eTY558803c+edd+aJJ57IkiVLUl1dnf79+2fUqFE5+uijU1NTs8GY73znO3nrrbdy/vnnZ88999zg/Lx583LLLbfkmWeeybvvvpu+ffvmwAMPzPjx43Pbbbfl1ltvzcEHH5zvfOc7zX6W119/Pb/73e/yzDPPZOXKlenbt2/GjBmT8ePHp2PHv/woePPNN+fWW28tv/7hD3/YaJ499tgjF1xwwca+OgCAVhNKAQCsZ8mSJZkyZUreeuutdO7cOVVVVXn77bdz77335qmnnsrFF1+c7t27Nxrz6KOP5uc//3nWrFmTJOncuXPWrFmTOXPmZM6cOZk+fXq+//3vZ9ttt211H08++WQuvvji8pxdunTJm2++mVtvvTVPPPFEkyHWBz3xxBOZNGlSVq9ena5du2bt2rVZsGBBbr755rzyyis555xzyrU1NTXp1atXli9fnlKplG7dujUKrT74mQEANpdQCgBgPddcc0369u2bM888M7vttlvWrVuXxx9/PL/4xS/y1ltv5bbbbsuXv/zlcv1LL72USy65JEnyxS9+MUceeWS222671NfX56WXXso111yTl19+Ob/4xS/yve99r1U9LF++PJdeemnWrFmTXXfdNd/+9rez0047Ze3atZkxY0auvPLKLFiwYKPzXHLJJRk5cmS+9KUvZfvtt8+qVasybdq0/PrXv85jjz2WWbNmZb/99kuSjBs3LuPGjSuv3jr77LNbFXwBAGwqe0oBAKynY8eO+ad/+qfstttuSZIOHTpk//33zxe/+MUkyYwZMxrVX3fddVm3bl2+/vWv56STTsp2222XJKmurs5uu+2W733ve+ndu3eeeOKJvPzyy63qYdq0aamtrU2vXr3yve99LzvttFO5tzFjxuTb3/526urqNjrPLrvskrPOOqv8FL2ampqMHz8+++67b5OfBQCgSEIpAID1HH744enRo8cG73/yk59M8v7eUatWrUqSLFq0KLNnz063bt1y6KGHNjlf9+7d84lPfCLJ+7fktcajjz6aJDnssMPSrVu3Dc4feOCB2WGHHTY6z/jx41NVVbXB+5/61KeSJHPnzm1VPwAAW4Lb9wAA1rPLLrs0+X7DCqgkWblyZWpqavLCCy8kSVatWpVvf/vbzc7ZEGItWbJko9dfs2ZN5s2blyQZPnx4s3XDhg3LG2+80eJcG/ssrVltBQCwpQilAADW06VLlybf79SpU/l47dq1SZK33347SbJu3bosW7Zso3O/9957G62pq6tLqVRKkmaf9Jc0Dsma09xn2WabbZK83zcAQKUIpQAANlFDeDR48OBMmjSpwt0AALQv9pQCANhEvXr1StK62/Jaq1u3buV9oBpWYjWlpXMAAO2BUAoAYBM1PKFvxYoVefHFF9tkzm222SaDBg1Kkjz//PPN1rV0bnM0BGINq8AAALYUoRQAwCYaOHBghg4dmiS5/vrry3tNNWX16tVZs2ZNq+ZteDrePffck5UrV25wfsaMGRvd5HxTNexD1dR1AQDaklAKAGAz/M3f/E222WabPPfcc/nRj36U559/PvX19UmS+vr6vP7667n11lszYcKEVt9yd8wxx6Rbt25ZtmxZ/vmf/zlz585N8v7G5A8//HAuv/zydOvWbYt8nh133DFJMn369KxevXqLXAMAILHROQDAZtl1111z9tln59JLL81zzz2XH/zgB9lmm21SU1OTlStXNnrCXcOtcRvTs2fPnHnmmZk0aVJeeOGFfPe7303Xrl2zZs2arFmzJsOGDcvuu++e2267rfwkvbZy6KGHZvr06ZkxY0Yee+yx9OrVK9XV1Rk6dGjOOuusNr0WAPA/m5VSAACbad99982ll16aL37xi9l5553TsWPH1NXVpWvXrhk2bFjGjx+ff/3Xf03fvn1bPecnPvGJ/Mu//EsOOOCA9OjRI2vWrMn222+fE044IT/4wQ/Kq5i6du3app9lr732ytlnn5099tgjnTp1ytKlS/PWW2/lnXfeadPrAABUlexiCQDQ7vzgBz/I888/n9NOOy1jx46tdDsAAB+alVIAAO3MCy+8kOeffz5VVVXZa6+9Kt0OAMAmsacUAMBW6I9//GOWL1+eAw88MNtvv32qq6uzatWqPProo7nuuuuSJKNGjUqfPn0q3CkAwKYRSgEAbIUWL16c3/3ud7nppptSXV2drl27pq6uLg07LwwZMiRf//rXK9wlAMCmE0oBAGyFDjzwwKxevTrPPvtslixZkhUrVqRr164ZNGhQPv3pT+fII49Mp06dKt0mAMAms9E5AAAAAIWz0TkAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhfv/AYboNGJ9HhyMAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "image/png": { "height": 324, "width": 594 } }, "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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" ] }, "metadata": { "image/png": { "height": 340, "width": 612 } }, "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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Ky8trsD8vL4/eLECIbNq0ScXFxV7bi4uLVVBQEMaMAGsoKCjwee1t375dhYWFYcwIsB5GMFgbRTIgAIF8W8436kDwGYahnJycRq8vt9ut7Oxsrj0gyFwul9577z2/ce+++y5DUIAgasrqllx7QOgwgsHaKJIBfjgcDg0aNMhv3CWXXCKHwxGGjADrKCgoUFFRkdf2oqIivlEHgszlcgX0jXlZWRk36gCAiMIIBlAkA/xwuVzavXu337hdu3ZxswAEEd+oA+aw2+1KTEz0G5eYmMgKe0AQsbolYC5GMECiSAYAAIA67Ha7hg0b5jfuiiuu4EYdCLK0tDSlpKR4bU9JSWF1SyBEGMEAiSIZ4Bff6gHm4NoDzOFyufT111/7jVu/fj29OIEgs9lsysrKanQVy6ioKK9tAE4PIxhQiyIZEAC+1QPMwbUHmCOQm3Bu1IHQSE5OVkZGRoP9GRkZSk5ODn9CAGAhFMmAAPCtHmAOrj0g/OjFCZjv6quvbrBv5MiRJmQCWAN/+1CLIhkQoOTkZA0dOrTB/qFDh/KtHhBCXHtA+NGLEzDXZ5991mDf559/bkImgHXwtw8SRTKgSRpbzYQVToDQq6mpCWgfgOCgFydgnpKSEuXl5TXYv3LlSpWWloY/IcAi+NsHiSIZELCSkhJ9+eWXDfZ/+eWXfGABQqikpETr169vsH/dunVce0AIMS8SEH6GYSgnJ8frF7PZ2dl8QQuEEH/7QJEMCICvDyxut5sPLECIGIahuXPnem2fO3cu1x4QQhMmTFB8fLxnOz4+XpmZmSZmBES2goICFRUVeW0vKipSYWFhGDMCrIe/fdZGkQwIAB9YAHMUFBSouLjYa/v27dtVUFAQxowAa0lMTNT48eM925mZmUpMTDQxIyByuVwu5eTk+I3Lzs6Wy+UKQ0aANfG3z9pYlgHwoykfWFJSUljtBAgSl8uld99912/cu+++q379+nHtASEyYsQIVVVVSZKGDx9ucjZAZDtx4kRQYgCcHv72WRd3FEAAAhnOxZAvILhcLpfKysr8xpWVlcnlclEkA0LEbrdr3LhxZqcBRDyXy6WKigq/cRUVFfzdA0KMv33WxXBLwA+73a5LL73Ub9xll13GhxUgiOx2e0Bd2xMTE7n2AACtnsvlCviLWYZbAkBoUCQD/HC5XPr666/9xq1fv54PLEAQ2e123XrrrX7jbrvtNopkAIBWz+FwqG3btn7j2rZtK4fDEYaMAMB6KJIBAFqsAQMGqHfv3l7be/furbS0tDBmBABAaNjtdt15551+46ZMmcKXQwAQIhTJAD/sdrsmTZrkNy4rK4sPLECQ2Ww23XHHHV7b77jjDtlstvAlBABACA0cOFDdu3f32t69e3cNGDAgfAkBgMVQJAMCkJaWppSUFK/tKSkpSk1NDWNGgHUkJydr6NChDfYPGzZMycnJJmQEAEBo2Gw2ZWZmem3PzMzkyyEACCGKZEAAbDabsrKyGv1QEhUV5bUNQHBMnDix3jVms9l04403mpgRAADBZxiGVq5c6bV95cqVrKgOACFEkQwIUHJysgYPHtxg/+DBg+nNAoTYiRMn6t0UGIahEydOmJgRAADBV1BQoKKiIq/tRUVFKiwsDGNGAGAtFMmAJti9e3dA+wAE16uvvhrQPgAAWiuXy6WcnBy/cdnZ2ayoDgAhQpEMCNDGjRu1b9++Bvt//PFH5efnm5ARYA0bN25UaWlpg/2lpaVcewAAAACChiIZEAC32605c+Z4bX/nnXfkdrvDlxBgEVx7AACrYEV1ADAfRTIgALm5uSovL/faXl5erkWLFoUxI8AauPYAAFaSmpqqtm3bem1v27at+vfvH8aMAMBaKJIBfjidTi1btsxv3NKlS+V0OsOQEWANXHsAAKspLCz0++XQ5s2bw5gRAFgLRTIAAAAAMBkT9wOA+SiSAX44HA5dd911fuPGjh0rh8MRhowAa+DaAwAAABBOFMmAAGRmZvqdH2L8+PFhzAiwBq49AIBVMHE/AJiPIhkQgKioKN15551e2++8805FRXE5AcHGtQcAsJK0tDT17t3ba3ufPn2UmpoaxowAwFq4swAClJ6e3miPlrZt2yo9Pd2EjABrSE9PV3JycoP9ycnJXHsAAEsxDMPsFABLcLlcWrx4sRYvXswcgBZDkQwIUElJSaOrDZWXl6u0tNSEjADreOCBBwLaBwBAa1ZQUKDi4mKv7cXFxSosLAxjRoA1ffrpp1qwYIEWLFigzz77zOx0EEYROZg9Ly9Pr776qs+YuLg4vffee422ud1urVixQitXrlRpaancbrfOPvtsDRs2TNdddx1zAFiQYRg+VxvKzs7WI488IpvNFsasAOvo2rWrUlNTPTcGaWlp6tq1q8lZAQAQPE1Z3TIlJYV7EiBEysrKtHDhQs/2ggULNHToUCUmJpqXFMImot9Zo6OjlZCQ0Gibt5XQXC6XZsyYoW+//VbSqQk0o6KitGvXLu3atUtfffWVnnrqKVZSs5iCggIVFRV5bS8qKlJhYaHS0tLCmBVgXRSkAQAAEArz589XTU2NZ7umpkYffPCBpkyZYmJWCJeILpKdf/75evrpp5t0THZ2tr799lvFxMTonnvu0ZVXXimbzab8/Hy98sorKi4u1ptvvqmHH344NEmjxeFbPcB8P/74Y73hJZs2bdKPP/5IbzIAQMSoXd3y5Zdf9hnH6pZA6JSUlGjt2rUN9q9Zs0ajRo1qdJ5cRBbmJKvj6NGjWr58uSTp5ptvVkZGhqKiomSz2XTxxRfr/vvvlyStXbtWu3fvNjNVhFlVVVVQYgA0T2ND6P0NqwcAoLXxt7pl7969Wd0SCBHDMDR37lyv7XPmzGHxDAugSFbHunXrVFNTo/j4eF199dUN2i+55BJ17dpVhmFozZo1JmQIANazcePGRhfHKC0tVX5+vgkZAQAQOkePHm1WG4DTs2nTJr8LZxQUFIQxI5iBIlkdtXNOXXjhhYqNjW00pnbOqe+++y5secF8MTExQYkB0DRut1tz5szx2v7OO+/I7XaHLyEAAELo22+/1aFDh7y2Hzp0SJs2bQpfQoBFuFwuvfvuu37j5s6dK5fLFYaMYJaILpLt2bNHjzzyiG6++WbddtttevTRRzVnzhwdOHCg0fiSkhJJUvfu3b2es1u3bpJO9WCgq6U12O12XXrppX7jLrvsMuaHAIIsNzdX5eXlXtvLy8u1aNGiMGYEAEBouFwuzZo1y2/cm2++yU06EGQul0tlZWV+48rKyrj+IlxE39GXlZXpxIkTatu2rSorK7Vnzx7t2bNHn332me677z4NGzasXnxt9+UOHTp4PWdtm9PplNPpVJs2bXzmMG3atEb3T58+XZLUqVOnQF8OTFJTU6Mvv/zSb9zatWt1xx130KMMCJKKigrPPJG+LFu2TDfddJPi4+PDkBUAAKFx+PDhgOfBjY2NVceOHcOQFWANFRUVAcd27NiRz50RLCKLZB06dNDEiRN16aWXqmvXrrLb7aqpqdHmzZv1/vvvq6SkRK+88oo6duyoiy66yHOc0+mUJK9DLSUpLi6uXry/Ihlav5qaGh07dsxv3LFjx1RTU0ORDAiiQHrs0qsXABAJmjIigdELQHDFxMSoXbt2OnLkiM+4du3acb8X4SLy3TUtLc0zd1itmJgYpaen64ILLtAf/vAH7du3T//zP/+j5557LqS51PYY8+bgwYMhfX6cPpfLJYfD4XPIlyQ5HA4dP368Sd9CAPDt4osv1tdff+0zZtCgQaqoqODaAwC0ai6XS7GxsaqurvYZFxsbq4qKCr9xAJpm6NChWrJkic+YYcOGBdSBAuZKSkpq9rERPSdZY+Lj4/WLX/xCkrRt2zYdP37c0+ZwOCTJ5x+cul2ga+MR+aKjo4MSAyBwLpdLu3fv9hu3a9cu5oYAAESEQO4vuAcBgs/lcumbb77xG/f111/zuTPCWa5IJknnnXeepFNDdOpO4l8735ivLpa1bQ6Hg6GWFmKz2YISAwAAAHgTFeX/9iyQGABNF+icgIhsvMPWUbty5Z49e7zG1K6AmZycHJac0DJQJAPCz263a9KkSX7jsrKymJsFABAR6s5/fDoxAIDmsWSRbNu2bZ7HZ511ludxSkqKJGnr1q1eh1wWFhZKkvr37x/CDNHS1NTUBCUGQNOkpaWpe/fuXtu7d++u1NTUMGYEAEBo8OUQYC5fC/g1JQatW8QVyfytclZRUaGFCxdKkvr06aMzzjjD03bppZcqJiZG5eXlWrFiRYNjN2zYoL1798pms2no0KFBzRstl8vlUmVlpd+4yspKxqcDQWYYhs8FTg4ePMjqlgCAiNGvXz+/MbVf7AMIHrvdrqysLL9xkydPpkgd4SKuSPbTTz/piSee0IoVK+rdWLlcLm3atElPPvmkfvzxR9lsNt100031jm3fvr3GjBkjSXr//fe1evVqud1uSVJ+fr5ee+01SadWvejRo0eYXhEAWFdubq7PInVlZaUWLVoUxowAAAidWbNm+Y156623wpAJYD1paWk+i9ApKSmMYLCAiCyBbtu2zTOkMiYmRg6HQxUVFTp58qSkU+P477nnnka/qcnKytKePXv07bffaubMmXrjjTcUFRXlmaCvd+/euueee8L3YmA6h8OhsWPHavHixT7jrr/+elYbAoLI6XRq2bJlfuOWLl2q0aNHc/0BAFq1EydOBLy63i233KKEhIQwZAVYh81m0/Dhw1VUVNRo+/Dhw5mH2gIirkjWvn173Xnnndq6dat2796t48ePq6KiQnFxceratav69euna6+9Vp07d270eLvdrmnTpunzzz9XXl6eSkpK5Ha71bNnTw0dOlRjx46le6UFZWZm6uOPP/Y671hsbKzGjx8f5qwAAAg9l8ul5cuXS5LGjBnD5yAgRJoybQdTfADBZxiGVq5c6bV95cqVGjhwIIWyCBdxn3JiY2M1ZswYz7DJ5oiKitI111yja665JoiZoTUzDMPnh5GamhrmRQKCzOFw6LrrrvPbi3Ps2LH0IgNCaMWKFVqwYIGkU9cln4+A0EhISFBMTIzfxaBiYmLoRQaEQEFBgddeZJJUVFSkwsJCpaWlhTErhFvEzUkGhMKbb77pswhmGEZAc0gAaJrMzEy1bdvWa3vbtm3pxQmEUFlZWb15/3Jzc1VWVmZiRkBki4+PD0oMgKZxuVzKycnxG5ednU1PzghHkQzwoynzQ5w4cSIMGQHWERUVpTvvvNNr+5133qmoKP6UAaGycOFCVVRUeLYrKiqUm5trYkZAZIuLiwtKDACgebizAPxgfgjAXOnp6UpOTm6wPzk5Wenp6SZkBFhDSUmJ8vLyGuzPy8tTaWlp+BMCIpzdbldWVpbfuMmTJzM3IBBkdrtdkyZN8huXlZXF9RfhKJIBfiQkJAT8rR7zQwChcfvttwe0D0BwGIahnJycRqcacLvdys7OZi5OIARSU1P9TjPQv3//MGYEWAfXHySKZIBfdrtd99xzj9+4qVOn8q0CECKrVq1qsG/16tUmZAJYQ6CTFwMIrsLCQpWXl3ttLy8v1+bNm8OYEWAdXH+QKJIBARk4cKDatWvntb1du3YaMGBA+BICLKSkpERr165tsH/NmjUM+QJCgMmLAXNw7QHm4fpDLYpkQAAMw1B1dbXX9urqaoadACFgGIbmzp3rtX3OnDlcewAAAACCgiIZEIDc3FxVVlZ6ba+srNSiRYvCmBFgDZs2bVJxcbHX9uLiYhUUFIQxIyDy2e12DR482G/c4MGDmWYACCImDgfMw/WHWhTJAD+cTqeWLl3qN27JkiVyOp1hyAiwBpfLpffee89v3Lvvvku3dyCIXC5XQHP+rV69mmsPCLK0tDT17t3ba3ufPn2UmpoaxowA6/B3/fXu3ZvrzwIokgEAWiSXy6WysjK/cWVlZdyoA0Hkcrl04sQJv3EnTpzg2gNC4OjRo17bjhw5Er5EAAvyN8UOIh9FMsAPu92uNm3a+I1r06YNXW+BILLb7UpMTPQbl5iYyLUHBJHdbldCQoLfuISEBK49IMjy8/N16NAhr+2HDh3St99+G8aMAOsoKCjQnj17vLbv2bOHaT4sgCIZAKBFstvtPru81+rTpw836kAQ2e12XXHFFX7jrrzySq49IIhcLpfeeustv3GzZs2iFycQZC6XS++++67fOKb5iHwUyQA/XC6XysvL/caVl5fzhgkEkdPp1MaNG/3GbdiwgfkAgSByuVz65ptv/MZ9/fXX/N0DgujEiROqqqryG1dVVRXQkGgAgXO5XDp27JjfuGPHjvG3L8JRJAP8aMrNNzfqQPA05QaAmwUAAAA0l8vlkmEYfuMMw6BIFuEokgEAWqSmDONiyBcQPHa7XZMmTfIbl5WVxbUHBFFCQoLi4uL8xsXFxQU0byAAoOkokgF+OByOkMQC8I1rDzBPWlqaUlJSvLanpKQoNTU1jBkBkc9ut+uee+7xGzd16lQK1ECQORwOtW3b1m9c27Zt+dwZ4SiSAX40pTstXW+B4GFlWcA8NptNWVlZstlsDdqioqK8tgE4PQMGDPD5N81utystLS2MGQHWYLfbNWXKFL9xd911F587IxxFMgAAADSQnJysjIyMBvszMjKUnJwc/oQACygoKPD5pavL5VJhYWEYMwKsY8CAAerevbvX9u7du1OktgCKZIAfDodDUVH+L5WoqCi63gJB5HK5AloMw+l00osTCJEJEyYoPj7esx0fH6/MzEwTMwIil8vl0nvvvec37t133+XvHhACNptNQ4cO9do+bNgwelFbAEUywI8TJ07I7Xb7jXO73aywBwRZoKsMAQiNxMREjR8/3rOdmZmpxMREEzMCIpfL5dKxY8f8xh07dowiGRACbrdbixcv9tq+aNGigO4L0boxmBYA0CIxHyDQMowYMUJVVVWSpOHDh5ucDRC5nE5nwF8OOZ1ORjAAQZabm6vy8nKv7eXl5Vq0aJEmTJgQvqQQdhTJAD+aMjEjkzgCACKN3W7XuHHjzE4DiHh85gTM43Q6tWzZMr9xS5cu1ejRoylSRzCGWwJ+2O32gMae22w2PrAAQeRwOAK6pux2Ox9UAACtHkUyADAfRTLAD4fDoeuuu85v3NixY7lRB4LI5XIFPB8gwy0BAK1dIIvVNCcWgH/c86EWRTIgAEweDpgj0F6cAAC0dg6HI+C/e9ykA8GXmZmpuLg4r+1xcXH1FrNBZKJIBvjhdDq1fPlyv3HLli3jWz0giBwOh0aPHu03bsyYMdwsACHkcrm0ePFiLV68mF6bQAg5HA5dfPHFfuMGDRrE3z0gRHx1fKBThDVQJAP8cLlcAfck4+YBCK4DBw74jdm/f38YMgGs69NPP9WCBQu0YMECffbZZ2anA0Qsl8ul3bt3+43btWsXnzmBEFi4cKGqq6u9tldXVys3NzeMGcEMpz3j46uvvirpVLff+++/v8H+5vj5uQAz1U7c769QxsT9QHCdOHFC33zzjd+4b775RrfeeqsSEhLCkBVgLWVlZVq4cKFne8GCBRo6dKgSExPNSwqIYFVVVUGJAdA0TqdTS5Ys8Ru3ePFiRjFEuNO+o1+1apXncd3CVt39zUGRDC2F3W5Xu3btdPToUZ9x7dq1o0gGAIgo8+fPV01NjWe7pqZGH3zwgaZMmWJiVkDkqqioCEoMgKZpSu9MenJGNoZbAn7Y7XZdccUVfuOuuOIKimRAENX24vSHXpxAaJSUlGjt2rUN9q9Zs0alpaUmZAREthMnTgR08+1yuXTixIkwZARYB0Uy1Drtu4qcnJwm7QdaG5fLpXXr1vmNW7duncaNG8fNOhBEUVFROnnypN8YAMFlGIbmzp3rtX3OnDn6z//8T1aXBQBEhKbcw3G/F9m4swAC4GsCx6bEAAicw+HQdddd5zdu7NixzAsBBNmmTZtUXFzstb24uFgFBQVhzAiIfAkJCYqLi/MbFxcXxzycQJAlJCTovPPO8xvXt29frr8IR5EMCABFMsAc119/vd+YsWPHhiETwDpcLpfeffddv3Fz585lyAkQRHa7XSNHjvQbN3LkSHqyAEHmcrn8zkEtSUeOHOFvX4SjSAb44XK55HQ6/cY5nU7eMIEge/vtt/3GzJ49OwyZANbhcrlUVlbmN66srIy/e0AQuVwu5eXl+Y3Ly8vj2gNCgIUzIFEkA/xyOp0yDMNvnGEYARXTAATmxIkT+vrrr/3GrV+/ngmMgSAL9O8egOBxOp0B36TzmRMILpfLpcrKSr9xlZWVFKkjXMj76brdbuXn52vLli06cOCAKisr5Xa7fR5js9n0pz/9KdSpAQFhEkcAgJXY7XbFx8ervLzcZ1x8fDx/9wAAAZkyZYrZKQSF2+3WAw88YHYafjHSovlC+slm69ateuWVV3TgwIFQPg0QUk2ZEJzJw4HgcTgciouLU1VVld84rj0guGJiYoISAyBwfDELAOYL2bvrnj179Pzzz9ebzNzhcKht27YsF45WJZAJHOvGdurUKXTJAGiAIV9A8AW6wh6A4GnKEEqn08kXRAAQAiErkn344YeeAtnYsWM1evRonXXWWaF6OiBkmvqBBUBwOJ1Ov73IJKmqqkpOp5PluIEgsdvtmjRpkl5++WWfcVlZWfRmAYKInmSIZK1l+F9+fr5mzpzZaNuvfvUrpaenhzkjhFvIJu7fsmWLJGnMmDG67bbbKJCh1WK4JWCOpkyKygSqQHClpaUpJSXFa3tKSopSU1PDmBEQ+RISEtSuXTu/ce3bt+eLISBE0tPTlZyc3GB/cnIyBTKLCFmRrHZliCFDhoTqKYCwoEgGmCMhISGgb8rtdjs3C0CQ2Ww2ZWVlNTpFhq82AM134sQJHTt2zG/c0aNHWdUZCKHGJuZvDZP1IzhCViQ788wzJdEVGK0fXd8B8wRS/KJABoRGcnKykpKSGuxPSkpq9Ft2AKeHHtRAy9C1a1cNHDiwwT5YQ8iKZLVd9Hft2hWqpwDCwm63B9RDzOFwUCQDgozJwwHzlJSUqLS0tMH+0tLSRvcDOD18MQu0HPfcc4/ZKcAkISuSXX/99YqNjdXixYuZzBytXiCr57HCHhBcdrtdv/zlL/3G3XDDDdwsAEFmGIZycnK8tmdnZ/N3Dwiypq6oDiB0mEbHukJWJEtKStL999+v/fv369lnn1VJSUmongoIqaausAcgeD799NOgxABomoKCAhUVFXltLyoqUmFhYRgzAiIfPckAwHwhfXe9/PLLddZZZ2n69Ol69NFHdc455yg5OVmxsbE+j7PZbLr//vtDmRoQsKZMjHrixAnmRwKC5OjRo9q2bZvfuO+//15Hjx5V+/btQ58UYAEul0vZ2dl+4/7xj38oJSWFm3UgSJryZStfzAJAaIT0U82+ffs0b948HT9+XJL0ww8/6IcffgjoWIpkaClY3RIAYDXV1dVBiQEQuJMnT4YkFgAQuJAVyQ4fPqynnnqK8fJo9ej6Dpijffv26tChg44cOeIzrmPHjvQiA4KspqYmKDEAAtelS5eQxAIAAheyO/qPPvrIUyAbPHiwRo0apZ49e6pt27ay2Wyhelog6Jra9Z3hlkBwOJ1OT09kX44dOyan00lPTiBIXC5XQH/7nE6nXC4XXxABQcIXswBgvpC9uxYUFEiShg4dqocffjhUTwOEHB9YAABWYrfblZCQoGPHjvmMS0hI4O8eEER2u13t27f3OxKnffv2XHsAECIhHW4pSSNGjAjVUwBhQZEMMIfD4VCvXr20fft2n3HnnnsuvciAILLb7briiiu0ZMkSn3FXXnklf/fQ6kyZMsXsFE7b0aNHNXXqVLPTCMjs2bPNTgEAmiQqVCc+44wzJElt27YN1VMAYcFKQ4A5nE6n3wKZJG3bto1rDwgil8ulr7/+2m/c+vXr5XK5wpARAABAeISsSHb++edLkkpLS0P1FEBYNOUGgJsFIHhOnDgRklgA/rG6JQAAsKKQ9ZEfM2aM1q1bp2XLlunyyy9XVFTI6nFASDHcEgAAIDK0luF/s2fP1po1a+rtu+KKK3TnnXealBEAWENIe5LdcsstKi4u1ksvvaSysrJQPRUAIAI1ZZ4x5iQDgis2NjYoMQCa58Ybb1RMTEy9fTfccINJ2QCAdYSs28uHH34oSerTp482bNigBx54QP3791dycrLi4uL8Hs8fAbQUCQkJIYkFAKAlstvt6tmzpw4cOOAzrlevXvSgBkIkMTFRv/jFLzR//vx6+wAAoRWyTzYffPBBve3q6mpt3LhRGzduDOh4imRoKfbt29ek2J49e4YuGcBCmrpoBkVqIDicTmdAn9c2bNig22+/nZ6cQIhcffXV9YpkAIDQY6IwwI+TJ0+GJBYAAADwhp6aABB+IXvnnTlzZqhODYTVmWeeGZJYAL6xaAZgDofDodGjR2vp0qU+48aMGUMvMgAAEFFCdlfRuXPnUJ0aCKumDvkCEBwUyQDzGIbhN8btdochEwAAgPBhuCXgBzfqgDkcDkeDlb0aExMTQ28WIIicTqeWL1/uN2758uV8OQQAACJK2O7o9+7dq8LCQu3evVtlZWWSTq3Q0qNHD6WmpiopKSlcqQAAAMALp9MZUE8ywzDkdDopUgMAgIgR8iLZvn379Pbbb6uwsNBnXGpqqu666y6dffbZIcnD6XTqt7/9rQ4dOiRJeuCBB5SRkdForMvl0tKlS7VmzRrt27dP0dHRSk5O1vDhwzVy5EjZbLaQ5IiWiZ5kgDlcLpdqamr8xtXU1MjlcnH9AUHSlKIXBTIAABBJQjrc8rvvvtPvf/97vwUySSosLNTvf/97bdmyJSS5ZGdnewpkvlRUVOiPf/yj5s2bp927d0uSqqurtW3bNr355puaPn06KxhazIkTJ0ISC8A3rj3AHHa7XdHR0X7joqOjKU4DAICIErJPNocPH9aLL76oqqoqSad6io0cOVJ9+vRR+/btJUlHjx7V9u3b9fnnn6uwsFBVVVWaMWOG/uu//ksdOnQIWi47duzQxx9/rPPOO0/btm3zGfvGG29ox44dSkhI0IMPPqj09HQZhqHVq1dr1qxZys/P1/z58zV58uSg5YeW7cCBA02K7datWwizAazj2LFjTYrt1KlTCLMBrOPEiRMBfSF48uRJnThxwvO5DgAAoLULWU+yRYsWqbKyUjabTffee6+eeOIJXXbZZerUqZPsdrvsdrs6deqkyy67TE888YSmTp0q6VRPrtzc3KDl4Xa7NWvWLEnS3Xff7TN2586d+uqrrySdGo558cUXy2azKSoqShkZGbr55pslSUuXLm3SzRtat44dO4YkFoBvbdu2DUksAN+YZgAAAFhVyIpk3377rSTpyiuv1IgRI/zGjxw5UldddZUkKT8/P2h5fPzxxyouLta1116rXr16+Yxds2aNJCkpKUmDBg1q0H711VcrPj5e1dXVWr9+fdByRMtWWVkZklgAvjVl1TxW2AOCx+FwBDzckjnJAABAJAlZkax2/q9hw4YFfExt7OHDh4OSw+HDh5WTk6N27dopKyvLb3xRUZGkU0NDGxMbG6sLLrhA0qn51mANFMkAAFbicrkCHm7pcrnCkBEAAEB4hKxI1qZNG0lSYmJiwMfUxtYee7pmz56tyspK3XrrrYqPj/cZaxiGSktLJUndu3f3Glc731RtLCJf3759QxILwLemzDHGfGRA8Njt9oCGMLdt25bhlgAAIKKE7JNNcnKyjh8/rv379/sd5lhr//79khSUic83bNigr7/+WikpKbryyiv9xldWVnoWGfC1aEDtnFNHjhwJKI9p06Y1un/69OmSuLFrDWJjYwOO7dixo84444wQZgNYh9vtDjg2Pj6e91MgSGpqahQXF6fy8nKfcXFxcTrzzDMVExMTpswAa+PvHGAerj/rCFlPstrC1D//+c+Aj/nkk08kyTM3WXM5nU7Nnj1b0dHRuuuuuwI+ppavokhtG/PfWMfRo0dDEgvAN+YkA8wTFxcXlBgAAIDWJGQ9yUaMGKH169dr06ZNev311zVlyhSvxafq6mrNnj1bW7ZsUXp6ujIyMk7ruefPn6+DBw9q/PjxQemVdjpqe4x5c/DgwTBl0nJNmTLF7BSC5le/+pXZKfg1e/Zss1MAAlJRUdGkWN5PgeC5+OKLtWTJEp8xgwYNYrVvIIz4OweYh+uvdUlKSmr2saddJNuyZYvXtrFjx6qsrEwrV65Ufn6+hgwZoj59+qhdu3aSpGPHjmn79u366quvdOzYMfXp00fXXXedtmzZoosuuqhZ+ezatUvLli3TmWeeqRtuuCHg4+quzlRdXe01rraN1ZwAILTat28fklgAvrlcLs+K37588cUXGjduHPOSAQCAiHHan2r+/Oc/BxR37Ngxffzxxz5jtm/frueee042m03Z2dnNyuedd96R2+3W5MmTJXkfglNTUyOn0ymbzaa4uDi1adNGcXFxqqqq8jnfWO3Km77mLQMAnL6mfGN38OBBnX322SHMBrAOl8ulsrIyv3FlZWVyuVwUyQAAQMRokZ9qDMNo9rG1N1UzZ870GTdr1izNmjVLnTt31iuvvCKbzaZu3bqpuLhYe/bs8XpcSUmJpFMLEyA4WsPwP7fbrfvuu8/rUvd2u12vv/66oqJCNs0fYDnerrfTjQXgm91uV1xcnN8hz3FxcRTIAABARDntTzb3339/MPJoEVJSUlRcXKzNmzc32l5dXa2tW7dKkvr37x/O1GCyqKgo3XfffV6Lr/fddx8FMiDIGG4JmOfkyZNBiQEAAGhNTrtIdrqT7AfbK6+84rN94sSJkqQHHnigQe5Dhw7VokWLVFpaqo0bN+riiy+u1/7555+roqJCsbGxGjx4cFDzRsuXnp6ujh07eobc1jrzzDOVnp5uUlZA5Nq7d2+TYvv27RvCbADrcDqdqqqq8htXVVUlp9OphISEMGQFAAAQenR9qaNXr14aMmSIJOnVV19Vfn6+pFND7VatWqV58+ZJOrUgQe3iA7CWRx99tMG+Rx55xIRMAAAIDbvdHlDv6KioKIZbAgCAiMInm5+59957tX//fu3YsUMvvPCC4uLi5Ha7VVNTI+lUb6La3miwnq5du6pfv3767rvv6u0DEHxnnHFGSGIB+OZwODRw4EBt3LjRZ1x6ejqrfQMAgIhCT7KfiY+P13PPPaebbrpJPXr0kHTqG9XzzjtPU6dO1bRp0xQdHW1yljDTAw88YHYKgCUUFxeHJBaAby6XS//617/8xm3ZsoVFMwAAQESxXE+y+fPn+42x2+2aMGGCJkyYEPqE0OrwrTkQHrGxsSGJBeCb0+n0u7KlJFVUVDAnGQAAiCj0JAMAtEhNWUWYFYeB4GFOMgAAYFUUyQAALVJThnEx5AsIHofD0WCF78YMGjSI3tUAACCiUCQDALRIe/fuDUksAN9cLpd27NjhN664uJgCNQAAiCgUyQAALRJzkgHmCXROMgAAgEhCkQwA0CI1Za4j5kUCgsflcsnpdPqNczqd9CQDAAARhSIZAKBFKisrC0ksAN+cTqcMw/AbZxhGQMU0AACA1oIiGQAAADzoxQkAAKyKIhkAoEWKjo4OSSwA3yiSAQAAq6JIBgBokeLj40MSC8A/m80WlBgAAIDWhCIZAKBFasqE4EweDgRXoHOSAQAARBKKZACAFunkyZMhiQXg29GjR0MSCwAA0NJRJAMAtEgUyQBzNGXFSla3BAAAkYQiGQAAADwSEhJCEgsAANDSUSQDALRIP/30U0hiAQAAAKAxrNsNABY1ZcoUs1MImrfffltvv/222Wn4NXv2bLNTAPyy2wP/eNiUWAAAgJaOnmQAAADwYLglAACwKopkAAAA8CgpKQlJLAAAQEtHH3kAsKjWMPTP7Xbr7rvv9hnz1ltvKSqK73wAAAAAnB7uKgAALVZUVJR+9atfeW3/1a9+RYEMCLJu3bqFJBYAAKCl484CANCipaenKy4ursF+h8Oh9PR0EzICIpvT6QxJLAAAQEtHkQwA0OL96U9/arDvySefNCETIPK5XK6QxAIAALR0FMkAAC1e165dde655zbYByD4KJIBAACrokgGAGgVfve735mdAgAAAIAIRpEMANAqOBwOs1MALKEp1xrXJQAAiCQUyQAAAOBht9tDEgsAANDSUSQDAACAR2lpaUhiAQAAWjqKZAAAAAAAALA8imQAAADw6NKlS0hiAQAAWjqKZAAAAAAAALA8imQAAADwOHr0aEhiAQAAWjqKZAAAAPBwuVwhiQUAAGjpKJIBAADAw+FwhCQWAACgpaNIBgAAAA+KZAAAwKrsZicAAABgNVOmTDE7haB45JFHzE4hILNnzzY7BQAA0ArQkwwAAAAAAACWR5EMAAAAAAAAlsdwSwAAgDBrLcP/7rnnHp08ebLevujoaM2aNcukjAAAAEKHnmQAAABo1EMPPRTQPgAAgEhAkQwAAACNSk1N1RlnnNFgHwAAQCSiSAYAAACvnn76abNTAAAACAuKZAAAAPCqffv2ZqcAAAAQFhTJAAAAAAAAYHkUyQAAAAAAAGB5FMkAAAAAAABgeRTJAAAAAAAAYHkUyQAAAAAAAGB5FMkAAAAAAABgeRTJAAAAAAAAYHkUyQAAAAAAAGB5FMkAAAAAAABgeRTJAAAAAAAAYHkUyQAAAAAAAGB5FMkAAAAAAABgeRTJAAAAAAAAYHkUyQAAAAAAAGB5FMkAAAAAAABgeXazEwAAAAAAAIGZMmWK2SlYDj/z0Js9e7bZKUiiJxkAAAAAAABAkQwAAAAAAABguCUAAAAAAK3QnMdSzE4BaLY7ZhSZnUIDFMkAAAAANBlz9IQXP+/waCnzIgEwB8MtAQAAAAAAYHkR2ZOsuLhY33zzjYqLi7Vv3z4dP35cNTU1SkxMVO/evZWRkaHBgwd7Pd7lcmnp0qVas2aN9u3bp+joaCUnJ2v48OEaOXKkbDZbGF8NAAAAAAAAQi0ii2Sff/65PvvsM8+2w+GQzWbTkSNHtGHDBm3YsEGXXnqpfv3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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 342, "width": 612 } }, "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": 324, "width": 628 } }, "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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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 324, "width": 628 } }, "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": [ "***\n", "www.featureranking.com" ] } ], "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }