{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Class 5: Data Transformations, Grouped Data, and Data Visualization\n", "\n", "## Last class\n", "\n", "- read in a csv file to `pandas`\n", "- select rows and columns from a `pandas.DataFrame`\n", "- rename columns of a `pandas.DataFrame`\n", "\n", "## This class\n", "\n", "- Transformations: create a new column in a `pandas.DataFrame`\n", "- Data summaries: mean, median, and measures of variation\n", "- Data visualization: boxplots, histograms, and bar charts" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Midterm Information\n", "\n", "- Covers all the content we have covered up until this class.\n", "\n", "- A [past test](../../../practice/sample_midterm/GGR274_Midterm.ipynb) is available on the course website.\n", "\n", "- We have set up 2 x 2hr online midterm review sessions (Feb 6 & Feb 7) during which you can ask questions on the practice midterm. We strongly recommend you try the practice midterm before or during the review sessions.\n", "\n", "- The test was designed to be completed in 90 minutes.\n", "\n", "- The teaching team will post a link to the test by 09:00 AM, Feb. 11 on the course website.\n", "\n", "- You can submit anytime up until 11:00 AM on February 11.\n", "\n", "- The instructors will be available on a [zoom link](https://utoronto.zoom.us/j/89236564519) in case you have any questions from 09:00 AM - 11:00 AM. We will also be in the classroom, SS2118 for those who would like to use the space and in-person support.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Data Transformations\n", "\n", "- Time in the time use survey is measured in minutes, but it's not easy to interpret when the numbers get very large.\n", "\n", "> _I worked four thousand six hundred thirty two minutes last week!_\n", "\n", "- It would be easier if time was reported in hours.\n", "\n", "> _I worked over seventy seven hours last week!_\n", "\n", "- Converting time measured in minutes to time measured in hours is an example of a **data transformation**.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Time use survey" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/0j/ybsv4ncn5w50v40vdh5jjlww0000gn/T/ipykernel_15264/4239799222.py:1: DeprecationWarning: \n", "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", "but was not found to be installed on your system.\n", "If this would cause problems for you,\n", "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", " \n", " import pandas as pd\n" ] }, { "data": { "text/html": [ "
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CASEIDpumfidwght_persurvmnthwtbs_001agecxrygagegr10agehsdycageprgrdchh0014c...ree_02ree_03rlr_110lan_01lanhomelanhmultlanmtlanmtmulincg1hhincg1
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" ], "text/plain": [ " CASEID pumfid wght_per survmnth wtbs_001 agecxryg agegr10 \\\n", "0 10000 10000 616.6740 7 305.1159 96 5 \n", "1 10001 10001 8516.6140 7 0.0000 6 5 \n", "2 10002 10002 371.7520 1 362.7057 2 4 \n", "3 10003 10003 1019.3135 3 0.0000 96 6 \n", "4 10004 10004 1916.0708 9 11388.9706 96 2 \n", "\n", " agehsdyc ageprgrd chh0014c ... ree_02 ree_03 rlr_110 lan_01 \\\n", "0 62 96 0 ... 1 1 1 1 \n", "1 32 5 0 ... 5 6 3 1 \n", "2 9 10 3 ... 5 1 1 1 \n", "3 65 96 0 ... 3 2 2 1 \n", "4 25 96 0 ... 9 99 9 9 \n", "\n", " lanhome lanhmult lanmt lanmtmul incg1 hhincg1 \n", "0 1 1 1 1 1 1 \n", "1 5 2 5 2 5 8 \n", "2 1 1 1 1 3 8 \n", "3 1 1 1 1 2 2 \n", "4 99 9 99 9 2 4 \n", "\n", "[5 rows x 350 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "time_use_data = pd.read_csv(\"gss_tu2016_main_file.csv\")\n", "\n", "time_use_data.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Subset the data\n", "\n", "In this class we will explore the following columns. Let's create a subset of `time_use_data` with only these columns:\n", "\n", "_(columns from last week)_\n", " + `CASEID`: participant ID \n", " + `luc_rst`: large urban centre vs rural and small towns\n", " + `chh0014c`: number of kids 14 or under\n", " + `gtu_110`: feeling rushed\n", "\n", "_(plus...)_\n", " + `prv`: province of residence\n", " + `agegr10`: age group\n", " + `marstat`: marital status\n", " + `sex`: sex\n", " + `dur01`: duration spent sleeping\n", " + `dur08`: duration spent working \n", " " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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CASEIDprvluc_rstagegr10marstatsexchh0014cgtu_110dur01dur08
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" ], "text/plain": [ " CASEID prv luc_rst agegr10 marstat sex chh0014c gtu_110 dur01 \\\n", "0 10000 46 1 5 5 1 0 1 510 \n", "1 10001 59 1 5 1 1 0 3 420 \n", "2 10002 47 1 4 1 2 3 1 570 \n", "3 10003 35 1 6 5 2 0 2 510 \n", "4 10004 35 1 2 6 1 0 1 525 \n", "\n", " dur08 \n", "0 0 \n", "1 0 \n", "2 480 \n", "3 20 \n", "4 0 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "important_columns = [\n", " \"CASEID\", \n", " \"prv\", \"luc_rst\", # place of residence\n", " \"agegr10\", \"marstat\", \"sex\", # age, marital status, sex\n", " \"chh0014c\", # kids < 14\n", " \"gtu_110\", # feeling rushed\n", " \"dur01\", \"dur08\" # time sleeping and working\n", "]\n", "\n", "subset_time_use_data = time_use_data[important_columns]\n", "\n", "subset_time_use_data.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Rename columns so that they are meaningful\n", "\n", "- Use the `rename` function to rename columns." ] }, { "cell_type": "code", "execution_count": 3, "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", "
Participant IDprvUrban/RuralAge GroupMarital StatussexKids under 14Feeling RushedSleep durationWork duration
010000461551015100
110001591511034200
21000247141231570480
3100033516520251020
410004351261015250
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" ], "text/plain": [ " Participant ID prv Urban/Rural Age Group Marital Status sex \\\n", "0 10000 46 1 5 5 1 \n", "1 10001 59 1 5 1 1 \n", "2 10002 47 1 4 1 2 \n", "3 10003 35 1 6 5 2 \n", "4 10004 35 1 2 6 1 \n", "\n", " Kids under 14 Feeling Rushed Sleep duration Work duration \n", "0 0 1 510 0 \n", "1 0 3 420 0 \n", "2 3 1 570 480 \n", "3 0 2 510 20 \n", "4 0 1 525 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# dictionary of \"old name\":\"new name\"\n", "\n", "newnames = {\n", " \"CASEID\": \"Participant ID\",\n", " \"luc_rst\": \"Urban/Rural\",\n", " \"agegr10\": \"Age Group\",\n", " \"marstat\": \"Marital Status\",\n", " \"sex\": \"sex\",\n", " \"chh0014c\": \"Kids under 14\",\n", " \"gtu_110\": \"Feeling Rushed\",\n", " \"dur01\": \"Sleep duration\",\n", " \"dur08\": \"Work duration\"\n", " }\n", "\n", "subset_time_use_data_colnames = subset_time_use_data.rename(columns = newnames)\n", "\n", "subset_time_use_data_colnames.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Create a new column in a pandas DataFrame\n", "\n", "Create a new numeric column derived from another numeric column.\n", "\n", "The code below creates a new column `Sleep duration (hours)` by dividing `Sleep duration` by 60, since 1 hour = 60 minutes.\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Participant IDprvUrban/RuralAge GroupMarital StatussexKids under 14Feeling RushedSleep durationWork duration
010000461551015100
110001591511034200
21000247141231570480
3100033516520251020
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" ], "text/plain": [ " Participant ID prv Urban/Rural Age Group Marital Status sex \\\n", "0 10000 46 1 5 5 1 \n", "1 10001 59 1 5 1 1 \n", "2 10002 47 1 4 1 2 \n", "3 10003 35 1 6 5 2 \n", "4 10004 35 1 2 6 1 \n", "\n", " Kids under 14 Feeling Rushed Sleep duration Work duration \n", "0 0 1 510 0 \n", "1 0 3 420 0 \n", "2 3 1 570 480 \n", "3 0 2 510 20 \n", "4 0 1 525 0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subset_time_use_data_colnames # create a new column\n", "\n", "subset_time_use_data_colnames.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [ { "data": { "text/html": [ "
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Participant IDprvUrban/RuralAge GroupMarital StatussexKids under 14Feeling RushedSleep durationWork durationSleep duration (hours)
0100004615510151008.50
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210002471412315704809.50
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" ], "text/plain": [ " Participant ID prv Urban/Rural Age Group Marital Status sex \\\n", "0 10000 46 1 5 5 1 \n", "1 10001 59 1 5 1 1 \n", "2 10002 47 1 4 1 2 \n", "3 10003 35 1 6 5 2 \n", "4 10004 35 1 2 6 1 \n", "\n", " Kids under 14 Feeling Rushed Sleep duration Work duration \\\n", "0 0 1 510 0 \n", "1 0 3 420 0 \n", "2 3 1 570 480 \n", "3 0 2 510 20 \n", "4 0 1 525 0 \n", "\n", " Sleep duration (hours) \n", "0 8.50 \n", "1 7.00 \n", "2 9.50 \n", "3 8.50 \n", "4 8.75 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subset_time_use_data_colnames[\"Sleep duration (hours)\"] = subset_time_use_data_colnames[\"Sleep duration\"] / 60\n", "\n", "subset_time_use_data_colnames.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## What happened?\n", "\n", "To create a new column, use the `[]` brackets with the new column name at the left side of the assignment.\n", "\n", "The calculation of the values is done **element-wise**. This means all values in the given column are divided by the value 60 at once. You do not need to use a loop to iterate each of the rows!\n", "\n", "\n", "![][def]\n", "\n", "Reference is available [here](https://pandas.pydata.org/docs/getting_started/intro_tutorials/05_add_columns.html).\n", "\n", "[def]: images/min-to-hr.png" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Transform a column using if-then\n", "\n", "- The `Age Group` column has the following distribution." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Age Group\n", "5 3741\n", "6 2958\n", "4 2789\n", "3 2597\n", "2 2127\n", "7 1875\n", "1 1303\n", "Name: count, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subset_time_use_data_colnames[\"Age Group\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "It would be easier to interpret if the labels were used.\n", "\n", " Age group of respondent (groups of 10)\n", "\n", " VALUE LABEL\n", " 1 15 to 24 years\n", " 2 25 to 34 years\n", " 3 35 to 44 years\n", " 4 45 to 54 years\n", " 5 55 to 64 years\n", " 6 65 to 74 years\n", " 7 75 years and over\n", " 96 Valid skip\n", " 97 Don't know\n", " 98 Refusal\n", " 99 Not stated\n", "\n", " Data type: numeric\n", " Missing-data codes: 96-99\n", " Record/column: 1/35" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Age Group\n", "5 1\n", "6 1\n", "27 1\n", "54 1\n", "59 1\n", "... ...\n", "17341 1\n", "17357 1\n", "17374 1\n", "17378 1\n", "17385 1\n", "\n", "[1303 rows x 1 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "age_group_1 = subset_time_use_data_colnames[\"Age Group\"] == 1\n", "subset_time_use_data_colnames[age_group_1]\n", "subset_time_use_data_colnames.loc[age_group_1, [\"Age Group\"]]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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4100043512610152508.7525-34
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" ], "text/plain": [ " Participant ID prv Urban/Rural Age Group Marital Status sex \\\n", "0 10000 46 1 5 5 1 \n", "1 10001 59 1 5 1 1 \n", "2 10002 47 1 4 1 2 \n", "3 10003 35 1 6 5 2 \n", "4 10004 35 1 2 6 1 \n", "\n", " Kids under 14 Feeling Rushed Sleep duration Work duration \\\n", "0 0 1 510 0 \n", "1 0 3 420 0 \n", "2 3 1 570 480 \n", "3 0 2 510 20 \n", "4 0 1 525 0 \n", "\n", " Sleep duration (hours) Age group label \n", "0 8.50 55-64 \n", "1 7.00 55-64 \n", "2 9.50 45-54 \n", "3 8.50 65-74 \n", "4 8.75 25-34 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# make a copy of the dataframe\n", "subset_time_df = subset_time_use_data_colnames.copy()\n", "\n", "# create a new column called \"Age group label\"\n", "\n", "subset_time_df.loc[subset_time_df[\"Age Group\"] == 1, \"Age group label\"] = \"15-24\"\n", "\n", "subset_time_df.loc[subset_time_df[\"Age Group\"] == 2, \"Age group label\"] = \"25-34\"\n", "\n", "subset_time_df.loc[subset_time_df[\"Age Group\"] == 3, \"Age group label\"] = \"35-44\"\n", "\n", "subset_time_df.loc[subset_time_df[\"Age Group\"] == 4, \"Age group label\"] = \"45-54\"\n", "\n", "subset_time_df.loc[subset_time_df[\"Age Group\"] == 5, \"Age group label\"] = \"55-64\"\n", "\n", "subset_time_df.loc[subset_time_df[\"Age Group\"] == 6, \"Age group label\"] = \"65-74\"\n", "\n", "subset_time_df.loc[subset_time_df[\"Age Group\"] == 7, \"Age group label\"] = \"75+\"\n", "\n", "subset_time_df.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## What happened?\n", "\n", "![][def]\n", "\n", "[def]: images/age-group.png" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## To create a new column in a pandas DataFrame using if-then/if-then-else logic \n", "\n", "This line \n", "\n", "`subset_time_df.loc[subset_time_df[\"Age Group\"] == 1, \"Age group label\"] = \"15-24\" \n", "\n", "creates a new column in `subset_time_df` called `Age group label` according to the *if-then (conditional)* logic:\n", "\n", "**If** `subset_time_df[\"Age Group\"] == 1` **then** `Age group label` is assigned the value `15-24`.\n", " \n", "This line \n", "\n", "`subset_time_df.loc[subset_time_df[\"Age Group\"] == 2, 'Age group label'] = \"25-34\"`\n", "\n", "does the *else if* logic. Namely,\n", "\n", "**If** `subset_time_df[\"Age Group\"] == 2` **then** `Age group label` is assigned the value `25-34`.\n", " \n", "This line \n", "\n", "`subset_time_df.loc[subset_time_df[\"Age Group\"] == 3, \"Age group label\"] = \"35-44\"`\n", "\n", "does the *else if* logic. Namely,\n", "\n", "**If** `subset_time_df[\"Age Group\"] == 3` **then** `Age group label` is assigned the value `35-44`. \n", "\n", "etc ...\n", "\n", "See [idioms in the Pandas cookbook](https://pandas.pydata.org/docs/user_guide/cookbook.html#idioms) and [a reference on different ways to subset a pandas DataFrame](https://pandas.pydata.org/pandas-docs/version/1.0/getting_started/intro_tutorials/03_subset_data.html)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Merging pandas DataFrames\n", "\n", "- We want would like to look at the relationship between sleeping and working, on different aspects of time use at the provincial level. In addition we would like to add provincial level data of on income and housing.\n", "\n", "- The data is in `provincial_data.csv`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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COL0COL1COL2COL3COL4
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11049.567272.02.310.6
21158.261163.02.311.1
31255.260764.02.312.0
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63559.974287.02.619.8
74661.768147.02.511.4
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94865.493835.02.615.1
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" ], "text/plain": [ " COL0 COL1 COL2 COL3 COL4\n", "0 1 60.2 70336.0 2.4 16.6\n", "1 10 49.5 67272.0 2.3 10.6\n", "2 11 58.2 61163.0 2.3 11.1\n", "3 12 55.2 60764.0 2.3 12.0\n", "4 13 54.6 59347.0 2.3 10.3\n", "5 24 59.5 59822.0 2.3 12.9\n", "6 35 59.9 74287.0 2.6 19.8\n", "7 46 61.7 68147.0 2.5 11.4\n", "8 47 63.5 75412.0 2.5 12.1\n", "9 48 65.4 93835.0 2.6 15.1\n", "10 59 59.6 69995.0 2.4 20.7\n", "11 60 68.5 84521.0 2.3 12.7\n", "12 61 66.2 117688.0 2.7 9.0\n", "13 62 53.6 97441.0 3.6 7.9" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prov_data = pd.read_csv(\"provincial_data.csv\")\n", "prov_data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "\n", "```\n", "COL0 - GEO UID\n", "COL1 - Labour - Total Sex / Employment rate\n", "COL2 - Income - Total Sex / Total - Income statistics in 2015 for private households by household size - 100% data / Median total income of households in 2015 ($)\n", "COL3 - Dwelling characteristics / Average household size\n", "\n", "COL4 - Housing - Total Sex / Total - Owner households in non-farm, non-reserve private dwellings - 25% sample data / % of owner households spending 30% or more of its income on shelter costs\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Let's give the columns meaningful names." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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", "
ProvEmployment RateIncomeHousehold SizePct house over 30
0160.270336.02.416.6
11049.567272.02.310.6
21158.261163.02.311.1
31255.260764.02.312.0
41354.659347.02.310.3
52459.559822.02.312.9
63559.974287.02.619.8
74661.768147.02.511.4
84763.575412.02.512.1
94865.493835.02.615.1
105959.669995.02.420.7
116068.584521.02.312.7
126166.2117688.02.79.0
136253.697441.03.67.9
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" ], "text/plain": [ " Prov Employment Rate Income Household Size Pct house over 30\n", "0 1 60.2 70336.0 2.4 16.6\n", "1 10 49.5 67272.0 2.3 10.6\n", "2 11 58.2 61163.0 2.3 11.1\n", "3 12 55.2 60764.0 2.3 12.0\n", "4 13 54.6 59347.0 2.3 10.3\n", "5 24 59.5 59822.0 2.3 12.9\n", "6 35 59.9 74287.0 2.6 19.8\n", "7 46 61.7 68147.0 2.5 11.4\n", "8 47 63.5 75412.0 2.5 12.1\n", "9 48 65.4 93835.0 2.6 15.1\n", "10 59 59.6 69995.0 2.4 20.7\n", "11 60 68.5 84521.0 2.3 12.7\n", "12 61 66.2 117688.0 2.7 9.0\n", "13 62 53.6 97441.0 3.6 7.9" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colnames = {\"COL0\": \"Prov\", \n", " \"COL1\": \"Employment Rate\", \n", " \"COL2\": \"Income\",\n", " \"COL3\": \"Household Size\",\n", " \"COL4\": \"Pct house over 30\"}\n", "\n", "prov_data.rename(columns=colnames, inplace=True)\n", "# inplace=True applies the column name change to the current DataFrame\n", "prov_data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Now, we are ready to use the `merge` function in `pandas`. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
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Participant IDprvUrban/RuralAge GroupMarital StatussexKids under 14Feeling RushedSleep durationWork durationSleep duration (hours)Age group labelProvEmployment RateIncomeHousehold SizePct house over 30
0100004615510151008.5055-644661.768147.02.511.4
1100015915110342007.0055-645959.669995.02.420.7
210002471412315704809.5045-544763.575412.02.512.1
31000335165202510208.5065-743559.974287.02.619.8
4100043512610152508.7525-343559.974287.02.619.8
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" ], "text/plain": [ " Participant ID prv Urban/Rural Age Group Marital Status sex \\\n", "0 10000 46 1 5 5 1 \n", "1 10001 59 1 5 1 1 \n", "2 10002 47 1 4 1 2 \n", "3 10003 35 1 6 5 2 \n", "4 10004 35 1 2 6 1 \n", "\n", " Kids under 14 Feeling Rushed Sleep duration Work duration \\\n", "0 0 1 510 0 \n", "1 0 3 420 0 \n", "2 3 1 570 480 \n", "3 0 2 510 20 \n", "4 0 1 525 0 \n", "\n", " Sleep duration (hours) Age group label Prov Employment Rate Income \\\n", "0 8.50 55-64 46 61.7 68147.0 \n", "1 7.00 55-64 59 59.6 69995.0 \n", "2 9.50 45-54 47 63.5 75412.0 \n", "3 8.50 65-74 35 59.9 74287.0 \n", "4 8.75 25-34 35 59.9 74287.0 \n", "\n", " Household Size Pct house over 30 \n", "0 2.5 11.4 \n", "1 2.4 20.7 \n", "2 2.5 12.1 \n", "3 2.6 19.8 \n", "4 2.6 19.8 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prov_data_df = subset_time_df.merge(\n", " prov_data, \n", " right_on=\"Prov\", # the right data frame is prov_data \n", " left_on=\"prv\" # the left data frame is subset_time_df\n", ") \n", "\n", "prov_data_df.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Is time spent resting different by province?\n", "\n", "### Grouped summaries of data \n", "\n", "- Many times we will want to calculate means, proportions, and other statistics by group membership.\n", "\n", "- This is so common that `pandas` has a built-in function called `groupby` that groups a data frame by another variable." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
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Participant IDprvUrban/RuralAge GroupMarital StatussexKids under 14Feeling RushedSleep durationWork durationSleep duration (hours)Age group labelProvEmployment RateIncomeHousehold SizePct house over 30
0100004615510151008.5055-644661.768147.02.511.4
1100015915110342007.0055-645959.669995.02.420.7
210002471412315704809.5045-544763.575412.02.512.1
31000335165202510208.5065-743559.974287.02.619.8
4100043512610152508.7525-343559.974287.02.619.8
\n", "
" ], "text/plain": [ " Participant ID prv Urban/Rural Age Group Marital Status sex \\\n", "0 10000 46 1 5 5 1 \n", "1 10001 59 1 5 1 1 \n", "2 10002 47 1 4 1 2 \n", "3 10003 35 1 6 5 2 \n", "4 10004 35 1 2 6 1 \n", "\n", " Kids under 14 Feeling Rushed Sleep duration Work duration \\\n", "0 0 1 510 0 \n", "1 0 3 420 0 \n", "2 3 1 570 480 \n", "3 0 2 510 20 \n", "4 0 1 525 0 \n", "\n", " Sleep duration (hours) Age group label Prov Employment Rate Income \\\n", "0 8.50 55-64 46 61.7 68147.0 \n", "1 7.00 55-64 59 59.6 69995.0 \n", "2 9.50 45-54 47 63.5 75412.0 \n", "3 8.50 65-74 35 59.9 74287.0 \n", "4 8.75 25-34 35 59.9 74287.0 \n", "\n", " Household Size Pct house over 30 \n", "0 2.5 11.4 \n", "1 2.4 20.7 \n", "2 2.5 12.1 \n", "3 2.6 19.8 \n", "4 2.6 19.8 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prov_data_df.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "pandas.core.groupby.generic.DataFrameGroupBy" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped_by_prov = prov_data_df.groupby(\"Prov\") #group the DataFrame by prov\n", "\n", "type(grouped_by_prov)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "Prov\n", "10 8.651739\n", "11 8.901090\n", "12 8.500917\n", "13 8.722338\n", "24 8.759245\n", "35 8.612381\n", "46 8.829857\n", "47 8.824515\n", "48 8.710498\n", "59 8.808136\n", "Name: Sleep duration (hours), dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rest_by_prov_mean = grouped_by_prov[\"Sleep duration (hours)\"].mean()\n", "\n", "rest_by_prov_mean" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "`rest_by_prov_mean` is a pandas Series with indexed by `Prov`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(rest_by_prov_mean)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## DataFrame Index" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "Index([10, 11, 12, 13, 24, 35, 46, 47, 48, 59], dtype='int64', name='Prov')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rest_by_prov_mean.index" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "From the \n", "\n", "```\n", " prv Province of residence\n", "\n", " VALUE LABEL\n", " 10 Newfoundland and Labroador\n", " 11 Prince Edward Island\n", " 12 Nova Scotia\n", " 13 New Brunswick\n", " 24 Quebec\n", " 35 Ontario\n", " 46 Manitoba\n", " 47 Saskatchewan\n", " 48 Alberta\n", " 59 British Columbia\n", " 96 Valid skip\n", " 97 Don't know\n", " 98 Refusal\n", " 99 Not stated\n", "\n", " Data type: numeric\n", " Missing-data codes: 96-99\n", " Record/columns: 1/57-58\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can reindex `rest_by_prov_mean`." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "Newfoundland and Labrador 8.651739\n", "Prince Edward Island 8.901090\n", "Nova Scotia 8.500917\n", "New Brunswick 8.722338\n", "Quebec 8.759245\n", "Ontario 8.612381\n", "Manitoba 8.829857\n", "Saskatchewan 8.824515\n", "Alberta 8.710498\n", "British Columbia 8.808136\n", "Name: Sleep duration (hours), dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index_new = [\n", " \"Newfoundland and Labrador\", \n", " \"Prince Edward Island\", \n", " \"Nova Scotia\", \n", " \"New Brunswick\",\n", " \"Quebec\",\n", " \"Ontario\", \n", " \"Manitoba\",\n", " \"Saskatchewan\", \n", " \"Alberta\",\n", " \"British Columbia\"\n", "]\n", "\n", "rest_by_prov_mean.index = index_new\n", "\n", "rest_by_prov_mean" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Let's also create a new column `Prov label` in `prov_data_df`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Participant IDprvUrban/RuralAge GroupMarital StatussexKids under 14Feeling RushedSleep durationWork durationSleep duration (hours)Age group labelProvEmployment RateIncomeHousehold SizePct house over 30Prov label
0100004615510151008.5055-644661.768147.02.511.4Manitoba
1100015915110342007.0055-645959.669995.02.420.7British Columbia
210002471412315704809.5045-544763.575412.02.512.1Saskatchewan
31000335165202510208.5065-743559.974287.02.619.8Ontario
4100043512610152508.7525-343559.974287.02.619.8Ontario
\n", "
" ], "text/plain": [ " Participant ID prv Urban/Rural Age Group Marital Status sex \\\n", "0 10000 46 1 5 5 1 \n", "1 10001 59 1 5 1 1 \n", "2 10002 47 1 4 1 2 \n", "3 10003 35 1 6 5 2 \n", "4 10004 35 1 2 6 1 \n", "\n", " Kids under 14 Feeling Rushed Sleep duration Work duration \\\n", "0 0 1 510 0 \n", "1 0 3 420 0 \n", "2 3 1 570 480 \n", "3 0 2 510 20 \n", "4 0 1 525 0 \n", "\n", " Sleep duration (hours) Age group label Prov Employment Rate Income \\\n", "0 8.50 55-64 46 61.7 68147.0 \n", "1 7.00 55-64 59 59.6 69995.0 \n", "2 9.50 45-54 47 63.5 75412.0 \n", "3 8.50 65-74 35 59.9 74287.0 \n", "4 8.75 25-34 35 59.9 74287.0 \n", "\n", " Household Size Pct house over 30 Prov label \n", "0 2.5 11.4 Manitoba \n", "1 2.4 20.7 British Columbia \n", "2 2.5 12.1 Saskatchewan \n", "3 2.6 19.8 Ontario \n", "4 2.6 19.8 Ontario " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prov_data_df.loc[prov_data_df[\"Prov\"] == 10, \"Prov label\"] = \"NL\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] == 11, \"Prov label\"] = \"Prince Edward Island\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] == 12, \"Prov label\"] = \"Nova Scotia\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] == 13, \"Prov label\"] = \"New Brunswick\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] == 24, \"Prov label\"] = \"Quebec\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] == 35, \"Prov label\"] = \"Ontario\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] == 46, \"Prov label\"] = \"Manitoba\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] == 47, \"Prov label\"] = \"Saskatchewan\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] == 48, \"Prov label\"] = \"Alberta\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] == 59, \"Prov label\"] = \"British Columbia\"\n", "\n", "prov_data_df.loc[prov_data_df[\"Prov\"] >= 96, \"Prov label\"] = None\n", "\n", "prov_data_df.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Summing across rows of a DataFrame\n", "\n", "How much time is spent by each respondent sleeping and working?\n", "\n", "The average time spent sleeping and working by province is:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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", "
Sleep durationWork duration
Prov label
Alberta522.629888199.491620
British Columbia528.488161165.233753
Manitoba529.791411175.156442
NL519.104339133.946281
New Brunswick523.340265148.428166
Nova Scotia510.055019158.398649
Ontario516.742869177.705566
Prince Edward Island534.065385134.126923
Quebec525.554724159.450461
Saskatchewan529.470905182.572198
\n", "
" ], "text/plain": [ " Sleep duration Work duration\n", "Prov label \n", "Alberta 522.629888 199.491620\n", "British Columbia 528.488161 165.233753\n", "Manitoba 529.791411 175.156442\n", "NL 519.104339 133.946281\n", "New Brunswick 523.340265 148.428166\n", "Nova Scotia 510.055019 158.398649\n", "Ontario 516.742869 177.705566\n", "Prince Edward Island 534.065385 134.126923\n", "Quebec 525.554724 159.450461\n", "Saskatchewan 529.470905 182.572198" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "restwork_by_prov_mean = prov_data_df.groupby(\"Prov label\")[[\"Sleep duration\", \"Work duration\"]].mean()\n", "\n", "restwork_by_prov_mean" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Two different ways to calulate row totals\n", "\n", "> There are more but we will focus on the two methods." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "We would like to calculate the total mean time spent sleeping and working." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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", "
Sleep durationWork durationTotal1
Prov label
Alberta522.629888199.491620722.121508
British Columbia528.488161165.233753693.721914
Manitoba529.791411175.156442704.947853
NL519.104339133.946281653.050620
New Brunswick523.340265148.428166671.768431
\n", "
" ], "text/plain": [ " Sleep duration Work duration Total1\n", "Prov label \n", "Alberta 522.629888 199.491620 722.121508\n", "British Columbia 528.488161 165.233753 693.721914\n", "Manitoba 529.791411 175.156442 704.947853\n", "NL 519.104339 133.946281 653.050620\n", "New Brunswick 523.340265 148.428166 671.768431" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "restwork_by_prov_mean[\"Total1\"] = restwork_by_prov_mean[\"Sleep duration\"] + restwork_by_prov_mean[\"Work duration\"]\n", "\n", "restwork_by_prov_mean.head()\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "slideshow": { "slide_type": "subslide" } }, "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", "
Sleep durationWork durationTotal1Total2
Prov label
Alberta522.629888199.491620722.121508722.121508
British Columbia528.488161165.233753693.721914693.721914
Manitoba529.791411175.156442704.947853704.947853
NL519.104339133.946281653.050620653.050620
New Brunswick523.340265148.428166671.768431671.768431
\n", "
" ], "text/plain": [ " Sleep duration Work duration Total1 Total2\n", "Prov label \n", "Alberta 522.629888 199.491620 722.121508 722.121508\n", "British Columbia 528.488161 165.233753 693.721914 693.721914\n", "Manitoba 529.791411 175.156442 704.947853 704.947853\n", "NL 519.104339 133.946281 653.050620 653.050620\n", "New Brunswick 523.340265 148.428166 671.768431 671.768431" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "restwork_by_prov_mean[\"Total2\"] = restwork_by_prov_mean[[\"Sleep duration\",\"Work duration\"]].sum(\n", " axis=1 # sum across all columns \n", ")\n", "\n", "restwork_by_prov_mean.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- In pandas `axis=1` refers to columns and `axis=0` refers to rows.\n", "- `DataFrame.sum(axis=1)`: sum across columns \n", "- `DataFrame.sum(axis=0)`: sum across rows." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Statistical Variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Categorical variable

\n", "
\n", "
\n", "\n", "
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\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Nominal variable

\n", "
\n", "
    \n", "
  • Categorical variables that don't have a natural ordering is called a nominal variable.
    • \n", "
  • For example, Canadian provinces are nominal.
    • \n", "
\n", "
\n", "
\n", "
\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "
\n", "

Ordinal variable

\n", "
\n", "
    \n", "
  • If a categorical variable has a natural ordering, it is called an ordinal variable.
    • \n", "
  • For example, if levels 96 (valid skip), 97 (don't know), 98 (refusal), 99 (not stated) are removed from gtu_110 then time use woudl be considered an ordinal variable since it's ordered from feeling rushed Everyday (1) to feeling rushed Never (6).
    • \n", "
  • Ordinal variables clearly order categories, but the \"distance\" between categories are unknown/meaningless.
    • \n", "
\n", "
\n", "
\n", "
\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "
\n", "

Quantitative variable

\n", "
\n", "
    \n", "
  • A quantitative variable consists of numerical measurements.
    • \n", "
\n", "
\n", "
\n", "
\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "
\n", "

Interval variable

\n", "
\n", "
    \n", "
  • An interval variable has numerical differences, but there is no relative scale and 0 holds no meaning.
    • \n", "
  • For example, absolute differences between 20℃ and 30℃, and between 10℃ and 20℃ are the same while relative differences between 15℃ and 30℃, and between 10℃ and 20℃ are not.
    • \n", "
  • This is because a 0 value is arbitrary. 0℃ does not mean \"no temperature\".
    • \n", "
\n", "
\n", "
\n", "
\n", "\n", "\n", "- An **interval variable** is one that does have numerical distances between any two levels of the measurement scale. For example, calendar years is an interval variable." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "
\n", "

Ratio variable

\n", "
\n", "
    \n", "
  • A ratio variable has both numerical differences and relative scales. A 0 holds semantic meaning.
    • \n", "
  • Time use in minutes and age in years are examples of ratio (statistical) variables.
    • \n", "
  • Time use of 0 minute means no time spent.
    • \n", "
\n", "
\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## What's the difference between a Statistical Variable and a Variable in python?\n", "\n", "- A variable in python is a location in computer memory to store a value.\n", "\n", "- A statistical variable is essentially a mathematical representation of data." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Examples of implementing statistical variables in python\n", "\n", "If 10 people are *randomly* selected, and asked how many minutes they slept last night, then this data could be represented by an **ratio variable**.\n", " + In python the data could be represented in `pandas.Series`, where the `Series` data type is float.\n", "\n", "If 10 people are *randomly* selected, and asked if they live in urban or rural areas, then this data could be represented by an **categorical variable**.\n", " + In python the data could be represented in `pandas.Series`, where the `Series` data type is Boolean (`True` if urban, and `False` if not urban).\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Statistical Distributions\n", "\n", "- What is a statistical distribution?\n", "\n", "- How can a distribution be summarized?\n", "\n", "- What questions can we answer using a distribution? " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## What is the distribution of kids under 14 in Ontario?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "1. Select rows in `prov_data_df` where `Prov label` is `Ontario`\n", "\n", "2. Select the column `Kids under 14`\n", "\n", "3. Compute the number of respondents who have 0 kids, 1 kid, etc. using `.value_counts()`" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "Kids under 14\n", "0 3918\n", "1 508\n", "2 430\n", "3 157\n", "Name: count, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ontkidsdist = prov_data_df.loc[\n", " prov_data_df[\"Prov label\"] == \"Ontario\",\n", " \"Kids under 14\"\n", "].value_counts()\n", "\n", "Ontkidsdist" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(Ontkidsdist)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### A bar plot of the distribution of kids under 14 in Ontario" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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AABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGCcWgWY9PR03XLLLWrXrp3Cw8N13333qbCw0K9m8ODBCggI8JumTp3qV1NUVKTExES1bt1a4eHhmjVrls6fP+9Xk5OTo/79+8tms6l79+7KyMio2xECAIBmp1YBJjc3V8nJydq1a5eysrJUUVGhhIQEnT592q9u8uTJOn78uDUtXLjQGrtw4YISExN17tw57dy5U6tWrVJGRobmzp1r1Rw+fFiJiYkaMmSI8vPzNWPGDE2aNElbtmy5wsMFAADNQVBtijdv3uw3n5GRofDwcOXl5WnQoEHW8tatW8vpdNa4ja1bt+qzzz7Ttm3bFBERoZtuuknPPPOMUlNTNW/ePAUHB2vFihXq1q2bXnzxRUlSr1699OGHH2rRokVyu921PUYAANDMXNE9MGVlZZKksLAwv+WrV69Whw4d1Lt3b6Wlpem7776zxjwej/r06aOIiAhrmdvtls/n0/79+62a+Ph4v2263W55PJ5L9lJeXi6fz+c3AQCA5qlWZ2C+r7KyUjNmzNBtt92m3r17W8sffPBBdenSRVFRUdq7d69SU1NVWFiot99+W5Lk9Xr9woska97r9V62xufz6cyZMwoJCbmon/T0dD399NN1PRwAAGCQOgeY5ORk7du3Tx9++KHf8ilTplh/9+nTR5GRkRo6dKgOHTqk66+/vu6d/oi0tDSlpKRY8z6fT9HR0Q22PwAA0HjqdAlp+vTp2rhxo95//3116tTpsrUDBgyQJH3xxReSJKfTqeLiYr+a6vnq+2YuVWO322s8+yJJNptNdrvdbwIAAM1TrQJMVVWVpk+frg0bNmj79u3q1q3bj66Tn58vSYqMjJQkuVwuFRQUqKSkxKrJysqS3W5XTEyMVZOdne23naysLLlcrtq0CwAAmqlaBZjk5GT96U9/0po1a9SuXTt5vV55vV6dOXNGknTo0CE988wzysvL05EjR/Tuu+8qKSlJgwYNUmxsrCQpISFBMTExGjdunD799FNt2bJFc+bMUXJysmw2myRp6tSp+vLLLzV79mwdOHBAy5Yt0/r16zVz5sx6PnwAAGCiWgWY5cuXq6ysTIMHD1ZkZKQ1rVu3TpIUHBysbdu2KSEhQT179tTjjz+u0aNH67333rO20aJFC23cuFEtWrSQy+XSb3/7WyUlJWn+/PlWTbdu3ZSZmamsrCz17dtXL774olauXMkj1AAAQJIUUFVVVdXYTTQEn88nh8OhsrKyRrkfpuuTmVd9n03BkQWJjd0CAMBgP/X7m99CAgAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADj1CrApKen65ZbblG7du0UHh6u++67T4WFhX41Z8+eVXJystq3b6+2bdtq9OjRKi4u9qspKipSYmKiWrdurfDwcM2aNUvnz5/3q8nJyVH//v1ls9nUvXt3ZWRk1O0IAQBAs1OrAJObm6vk5GTt2rVLWVlZqqioUEJCgk6fPm3VzJw5U++9957efPNN5ebm6tixYxo1apQ1fuHCBSUmJurcuXPauXOnVq1apYyMDM2dO9eqOXz4sBITEzVkyBDl5+drxowZmjRpkrZs2VIPhwwAAEwXUFVVVVXXlU+cOKHw8HDl5uZq0KBBKisrU8eOHbVmzRrdf//9kqQDBw6oV69e8ng8GjhwoDZt2qR77rlHx44dU0REhCRpxYoVSk1N1YkTJxQcHKzU1FRlZmZq37591r7GjBmj0tJSbd68+Sf15vP55HA4VFZWJrvdXtdDrLOuT2Ze9X02BUcWJDZ2CwAAg/3U7+8rugemrKxMkhQWFiZJysvLU0VFheLj462anj17qnPnzvJ4PJIkj8ejPn36WOFFktxut3w+n/bv32/VfH8b1TXV26hJeXm5fD6f3wQAAJqnOgeYyspKzZgxQ7fddpt69+4tSfJ6vQoODlZoaKhfbUREhLxer1Xz/fBSPV49drkan8+nM2fO1NhPenq6HA6HNUVHR9f10AAAQBNX5wCTnJysffv2ae3atfXZT52lpaWprKzMmo4ePdrYLQEAgAYSVJeVpk+fro0bN2rHjh3q1KmTtdzpdOrcuXMqLS31OwtTXFwsp9Np1ezZs8dve9VPKX2/5odPLhUXF8tutyskJKTGnmw2m2w2W10OBwAAGKZWZ2Cqqqo0ffp0bdiwQdu3b1e3bt38xuPi4tSyZUtlZ2dbywoLC1VUVCSXyyVJcrlcKigoUElJiVWTlZUlu92umJgYq+b726iuqd4GAAC4ttXqDExycrLWrFmjv/zlL2rXrp11z4rD4VBISIgcDocmTpyolJQUhYWFyW6369FHH5XL5dLAgQMlSQkJCYqJidG4ceO0cOFCeb1ezZkzR8nJydYZlKlTp2rp0qWaPXu2JkyYoO3bt2v9+vXKzLw2n+wBAAD+anUGZvny5SorK9PgwYMVGRlpTevWrbNqFi1apHvuuUejR4/WoEGD5HQ69fbbb1vjLVq00MaNG9WiRQu5XC799re/VVJSkubPn2/VdOvWTZmZmcrKylLfvn314osvauXKlXK73fVwyAAAwHRX9B6Ypoz3wDQO3gMDALgSV+U9MAAAAI2BAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjFPrALNjxw6NGDFCUVFRCggI0DvvvOM3/tBDDykgIMBvGjZsmF/Nt99+q7Fjx8putys0NFQTJ07UqVOn/Gr27t2rO+64Q61atVJ0dLQWLlxY+6MDAADNUq0DzOnTp9W3b1+9/PLLl6wZNmyYjh8/bk1//vOf/cbHjh2r/fv3KysrSxs3btSOHTs0ZcoUa9zn8ykhIUFdunRRXl6eXnjhBc2bN09//OMfa9suAABohoJqu8Lw4cM1fPjwy9bYbDY5nc4axz7//HNt3rxZH3/8sW6++WZJ0n//93/r7rvv1u9//3tFRUVp9erVOnfunF577TUFBwfrxhtvVH5+vv7whz/4BR0AAHBtapB7YHJychQeHq4ePXpo2rRp+uabb6wxj8ej0NBQK7xIUnx8vAIDA7V7926rZtCgQQoODrZq3G63CgsL9c9//rPGfZaXl8vn8/lNAACgear3ADNs2DC98cYbys7O1n/9138pNzdXw4cP14ULFyRJXq9X4eHhfusEBQUpLCxMXq/XqomIiPCrqZ6vrvmh9PR0ORwOa4qOjq7vQwMAAE1ErS8h/ZgxY8ZYf/fp00exsbG6/vrrlZOTo6FDh9b37ixpaWlKSUmx5n0+HyEGAIBmqsEfo77uuuvUoUMHffHFF5Ikp9OpkpISv5rz58/r22+/te6bcTqdKi4u9qupnr/UvTU2m012u91vAgAAzVODB5ivvvpK33zzjSIjIyVJLpdLpaWlysvLs2q2b9+uyspKDRgwwKrZsWOHKioqrJqsrCz16NFDP/vZzxq6ZQAA0MTVOsCcOnVK+fn5ys/PlyQdPnxY+fn5Kioq0qlTpzRr1izt2rVLR44cUXZ2tkaOHKnu3bvL7XZLknr16qVhw4Zp8uTJ2rNnjz766CNNnz5dY8aMUVRUlCTpwQcfVHBwsCZOnKj9+/dr3bp1Wrx4sd8lIgAAcO2qdYD55JNP1K9fP/Xr10+SlJKSon79+mnu3Llq0aKF9u7dq3vvvVc33HCDJk6cqLi4OH3wwQey2WzWNlavXq2ePXtq6NChuvvuu3X77bf7vePF4XBo69atOnz4sOLi4vT4449r7ty5PEINAAAkSQFVVVVVjd1EQ/D5fHI4HCorK2uU+2G6Ppl51ffZFBxZkNjYLQAADPZTv7/5LSQAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAME6tA8yOHTs0YsQIRUVFKSAgQO+8847feFVVlebOnavIyEiFhIQoPj5eBw8e9Kv59ttvNXbsWNntdoWGhmrixIk6deqUX83evXt1xx13qFWrVoqOjtbChQtrf3QAAKBZqnWAOX36tPr27auXX365xvGFCxdqyZIlWrFihXbv3q02bdrI7Xbr7NmzVs3YsWO1f/9+ZWVlaePGjdqxY4emTJlijft8PiUkJKhLly7Ky8vTCy+8oHnz5umPf/xjHQ4RAAA0NwFVVVVVdV45IEAbNmzQfffdJ+lfZ1+ioqL0+OOP64knnpAklZWVKSIiQhkZGRozZow+//xzxcTE6OOPP9bNN98sSdq8ebPuvvtuffXVV4qKitLy5cv1u9/9Tl6vV8HBwZKkJ598Uu+8844OHDjwk3rz+XxyOBwqKyuT3W6v6yHWWdcnM6/6PpuCIwsSG7sFAIDBfur3d73eA3P48GF5vV7Fx8dbyxwOhwYMGCCPxyNJ8ng8Cg0NtcKLJMXHxyswMFC7d++2agYNGmSFF0lyu90qLCzUP//5zxr3XV5eLp/P5zcBAIDmqV4DjNfrlSRFRET4LY+IiLDGvF6vwsPD/caDgoIUFhbmV1PTNr6/jx9KT0+Xw+Gwpujo6Cs/IAAA0CQ1m6eQ0tLSVFZWZk1Hjx5t7JYAAEADqdcA43Q6JUnFxcV+y4uLi60xp9OpkpISv/Hz58/r22+/9aupaRvf38cP2Ww22e12vwkAADRP9RpgunXrJqfTqezsbGuZz+fT7t275XK5JEkul0ulpaXKy8uzarZv367KykoNGDDAqtmxY4cqKiqsmqysLPXo0UM/+9nP6rNlAABgoFoHmFOnTik/P1/5+fmS/nXjbn5+voqKihQQEKAZM2bo2Wef1bvvvquCggIlJSUpKirKelKpV69eGjZsmCZPnqw9e/boo48+0vTp0zVmzBhFRUVJkh588EEFBwdr4sSJ2r9/v9atW6fFixcrJSWl3g4cAACYK6i2K3zyyScaMmSINV8dKsaPH6+MjAzNnj1bp0+f1pQpU1RaWqrbb79dmzdvVqtWrax1Vq9erenTp2vo0KEKDAzU6NGjtWTJEmvc4XBo69atSk5OVlxcnDp06KC5c+f6vSsGAABcu67oPTBNGe+BaRy8BwYAcCUa5T0wAAAAVwMBBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYp94DzLx58xQQEOA39ezZ0xo/e/askpOT1b59e7Vt21ajR49WcXGx3zaKioqUmJio1q1bKzw8XLNmzdL58+fru1UAAGCooIbY6I033qht27b9306C/m83M2fOVGZmpt588005HA5Nnz5do0aN0kcffSRJunDhghITE+V0OrVz504dP35cSUlJatmypZ5//vmGaBcAABimQQJMUFCQnE7nRcvLysr06quvas2aNbrzzjslSa+//rp69eqlXbt2aeDAgdq6das+++wzbdu2TREREbrpppv0zDPPKDU1VfPmzVNwcHBDtAwAAAzSIAHm4MGDioqKUqtWreRyuZSenq7OnTsrLy9PFRUVio+Pt2p79uypzp07y+PxaODAgfJ4POrTp48iIiKsGrfbrWnTpmn//v3q169fjfssLy9XeXm5Ne/z+Rri0IAadX0ys7FbaBRHFiQ2dgsArlH1fg/MgAEDlJGRoc2bN2v58uU6fPiw7rjjDp08eVJer1fBwcEKDQ31WyciIkJer1eS5PV6/cJL9Xj12KWkp6fL4XBYU3R0dP0eGAAAaDLq/QzM8OHDrb9jY2M1YMAAdenSRevXr1dISEh9786SlpamlJQUa97n8xFiAABophr8MerQ0FDdcMMN+uKLL+R0OnXu3DmVlpb61RQXF1v3zDidzoueSqqer+m+mmo2m012u91vAgAAzVODB5hTp07p0KFDioyMVFxcnFq2bKns7GxrvLCwUEVFRXK5XJIkl8ulgoIClZSUWDVZWVmy2+2KiYlp6HYBAIAB6v0S0hNPPKERI0aoS5cuOnbsmJ566im1aNFCv/nNb+RwODRx4kSlpKQoLCxMdrtdjz76qFwulwYOHChJSkhIUExMjMaNG6eFCxfK6/Vqzpw5Sk5Ols1mq+92AQCAgeo9wHz11Vf6zW9+o2+++UYdO3bU7bffrl27dqljx46SpEWLFikwMFCjR49WeXm53G63li1bZq3fokULbdy4UdOmTZPL5VKbNm00fvx4zZ8/v75bBQAAhqr3ALN27drLjrdq1Uovv/yyXn755UvWdOnSRX/961/ruzUAqBc8Ng80Pn4LCQAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAME5QYzcAAEBT1vXJzMZuoVEcWZDY2C1cFmdgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxmnSAefnll9W1a1e1atVKAwYM0J49exq7JQAA0AQ02QCzbt06paSk6KmnntLf/vY39e3bV263WyUlJY3dGgAAaGRNNsD84Q9/0OTJk/Xwww8rJiZGK1asUOvWrfXaa681dmsAAKCRNckfczx37pzy8vKUlpZmLQsMDFR8fLw8Hk+N65SXl6u8vNyaLysrkyT5fL6GbfYSKsu/a5T9NrbG+vdubHze1xY+72sLn3fj7LeqquqydU0ywHz99de6cOGCIiIi/JZHRETowIEDNa6Tnp6up59++qLl0dHRDdIjauZ4qbE7wNXE531t4fO+tjT2533y5Ek5HI5LjjfJAFMXaWlpSklJseYrKyv17bffqn379goICGjEzq4un8+n6OhoHT16VHa7vbHbQQPj87628HlfW67Vz7uqqkonT55UVFTUZeuaZIDp0KGDWrRooeLiYr/lxcXFcjqdNa5js9lks9n8loWGhjZUi02e3W6/pv4Df63j87628HlfW67Fz/tyZ16qNcmbeIODgxUXF6fs7GxrWWVlpbKzs+VyuRqxMwAA0BQ0yTMwkpSSkqLx48fr5ptv1q233qqXXnpJp0+f1sMPP9zYrQEAgEbWZAPMAw88oBMnTmju3Lnyer266aabtHnz5otu7IU/m82mp5566qLLaWie+LyvLXze1xY+78sLqPqx55QAAACamCZ5DwwAAMDlEGAAAIBxCDAAAMA4BBgAAGAcAgwAADBOk32MGj/N119/rddee00ej0der1eS5HQ69Ytf/EIPPfSQOnbs2MgdAgBQ/zgDY7CPP/5YN9xwg5YsWSKHw6FBgwZp0KBBcjgcWrJkiXr27KlPPvmksdvEVXL06FFNmDChsdtAPTpz5ow+/PBDffbZZxeNnT17Vm+88UYjdIWG8vnnn+v111+3frT4wIEDmjZtmiZMmKDt27c3cndND++BMdjAgQPVt29frVix4qIfrKyqqtLUqVO1d+9eeTyeRuoQV9Onn36q/v3768KFC43dCurBP/7xDyUkJKioqEgBAQG6/fbbtXbtWkVGRkr612/DRUVF8Xk3E5s3b9bIkSPVtm1bfffdd9qwYYOSkpLUt29fVVZWKjc3V1u3btWdd97Z2K02GQQYg4WEhOjvf/+7evbsWeP4gQMH1K9fP505c+Yqd4aG8O677152/Msvv9Tjjz/OF1oz8atf/UoVFRXKyMhQaWmpZsyYoc8++0w5OTnq3LkzAaaZ+cUvfqE777xTzz77rNauXatHHnlE06ZN03PPPSdJSktLU15enrZu3drInTYdBBiDdevWTU8//bSSkpJqHH/jjTc0d+5cHTly5Oo2hgYRGBiogIAAXe6/sgEBAXyhNRMRERHatm2b+vTpI+lfZ1UfeeQR/fWvf9X777+vNm3aEGCaEYfDoby8PHXv3l2VlZWy2Wzas2eP+vXrJ0nat2+f4uPjrXsdwU28RnviiSc0ZcoU5eXlaejQodbvRBUXFys7O1uvvPKKfv/73zdyl6gvkZGRWrZsmUaOHFnjeH5+vuLi4q5yV2goZ86cUVDQ//1PdEBAgJYvX67p06frl7/8pdasWdOI3aEhVN8KEBgYqFatWsnhcFhj7dq1U1lZWWO11iQRYAyWnJysDh06aNGiRVq2bJn1/8RatGihuLg4ZWRk6Ne//nUjd4n6EhcXp7y8vEsGmB87OwOzVN+E36tXL7/lS5culSTde++9jdEWGkjXrl118OBBXX/99ZIkj8ejzp07W+NFRUXW/U/4FwKM4R544AE98MADqqio0Ndffy1J6tChg1q2bNnInaG+zZo1S6dPn77kePfu3fX+++9fxY7QkH71q1/pz3/+s8aNG3fR2NKlS1VZWakVK1Y0QmdoCNOmTfO7HNi7d2+/8U2bNnED7w9wDwwAADAO74EBAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAlzV48GDNmDHjsjVdu3bVSy+9dFX6qY2AgAC98847jd0GgAZAgAGuYQ899JDuu+8+v2VvvfWWWrVqpRdffFGS9Pbbb+uZZ55phO6aruPHj+vBBx/UDTfcoMDAwB8NeGvXrlVAQMBF/9YA6o4AA8CycuVKjR07VsuXL9fjjz8uSQoLC1O7du0aubPGce7cuRqXl5eXq2PHjpozZ4769u172W0cOXJETzzxhO64446GaBG4ZhFgAEiSFi5cqEcffVRr167Vww8/bC3/4SWkkpISjRgxQiEhIerWrZtWr17tt52qqirNmzdPnTt3ls1mU1RUlB577LFL7rems0AzZszQ4MGD/Xp47LHHNHv2bIWFhcnpdGrevHl+6xw8eFCDBg1Sq1atFBMTo6ysrIv2dfToUf36179WaGiowsLCNHLkSL/fCqvu5bnnnlNUVJR69OhRY89du3bV4sWLlZSU5Pe69x+6cOGCxo4dq6efflrXXXfdJesA1B5v4gWg1NRULVu2TBs3btTQoUMvW/vQQw/p2LFjev/999WyZUs99thjKikpscb/93//V4sWLdLatWt14403yuv16tNPP73iHletWqWUlBTt3r1bHo9HDz30kG677Tbdddddqqys1KhRoxQREaHdu3errKzsoss6FRUVcrvdcrlc+uCDDxQUFKRnn31Ww4YN0969exUcHCxJys7Olt1urzEA1db8+fMVHh6uiRMn6oMPPrji7QH4PwQY4Bq3adMm/eUvf1F2dvaPvqr8H//4hzZt2qQ9e/bolltukSS9+uqrfr/XU1RUJKfTqfj4eLVs2VKdO3fWrbfeesV9xsbG6qmnnpIk/fznP9fSpUuVnZ2tu+66S9u2bdOBAwe0ZcsWRUVFSZKef/55DR8+3Fp/3bp1qqys1MqVK60fzXv99dcVGhqqnJwcJSQkSJLatGmjlStXWoGmrj788EO9+uqrys/Pv6LtAKgZl5CAa1xsbKy6du2qp556SqdOnbps7eeff66goCC/X73u2bOnQkNDrfl///d/15kzZ3Tddddp8uTJ2rBhg86fP18vfX5fZGSkdebn888/V3R0tBVeJMnlcvnVf/rpp/riiy/Url07tW3bVm3btlVYWJjOnj2rQ4cOWXV9+vS54vBy8uRJjRs3Tq+88oo6dOhwRdsCUDPOwADXuH/7t3/TW2+9pSFDhmjYsGHatGnTFd20Gx0drcLCQm3btk1ZWVl65JFH9MILLyg3N7fGHxkNDAy86Fe0KyoqLqr74boBAQGqrKz8yX2dOnVKcXFxF92zI0kdO3a0/m7Tps1P3ualHDp0SEeOHNGIESOsZdW9BgUFqbCw0PrVYQB1wxkYAOrSpYtyc3Pl9Xo1bNgwnTx5ssa6nj176vz588rLy7OWFRYWqrS01K8uJCREI0aM0JIlS5STkyOPx6OCgoIat9mxY0cdP37cb1ltL7v06tVLR48e9dvOrl27/Gr69++vgwcPKjw8XN27d/ebLncjbl307NlTBQUFys/Pt6Z7771XQ4YMUX5+vqKjo+t1f8C1iAADQNK/zpzk5OSopKREbrdbPp/vopoePXpo2LBh+o//+A/t3r1beXl5mjRpkkJCQqyajIwMvfrqq9q3b5++/PJL/elPf1JISIi6dOlS437vvPNOffLJJ3rjjTd08OBBPfXUU9q3b1+teo+Pj9cNN9yg8ePH69NPP9UHH3yg3/3ud341Y8eOVYcOHTRy5Eh98MEHOnz4sHJycvTYY4/pq6++qtX+JFnB5NSpUzpx4oTy8/P12WefSZJatWql3r17+02hoaFq166devfufcWXqAAQYAB8T6dOnZSTk6Ovv/76kiHm9ddfV1RUlH75y19q1KhRmjJlisLDw63x0NBQvfLKK7rtttsUGxurbdu26b333lP79u1r3Kfb7db/+3//T7Nnz9Ytt9yikydPKikpqVZ9BwYGasOGDTpz5oxuvfVWTZo0Sc8995xfTevWrbVjxw517txZo0aNUq9evTRx4kSdPXtWdru9VvuTpH79+qlfv37Ky8vTmjVr1K9fP91999213g6Augmo+uHFZwAAgCaOMzAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMM7/B+rBlL3qiVpwAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Ontkidsdist.plot.bar();" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "If we want to plot proportions instead of counts then we can transform `Ontkidsdist` by dividing by the total number of observations." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "Kids under 14\n", "0 0.781568\n", "1 0.101337\n", "2 0.085777\n", "3 0.031319\n", "Name: count, dtype: float64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ontkidsdist_prop = Ontkidsdist / Ontkidsdist.sum()\n", "Ontkidsdist_prop" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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qqqr6Paanp0dXXHGF1qxZo9NPP/24BgYAACNLXDHS3d2txsZGud3uQ09gtcrtdqu+vr7f4+666y5NnTpVV1999TGdp6urS+FwuNcGAABGprhipKOjQz09PXI4HL32OxwOBQKBPo956aWXtHnzZm3atOmYz+Pz+WS322Ob0+mMZ0wAADCMnNC/ptm3b5+uvPJKbdq0Sampqcd8XElJiUKhUGxrbW09gVMCAACTxsSzODU1VQkJCQoGg732B4NBpaWlHbb+3Xff1d69e7Vs2bLYvkgk8uWJx4zRnj17dMYZZxx2nM1mk81mi2c0AAAwTMV1ZSQxMVFZWVny+/2xfZFIRH6/X3l5eYetnzVrlt544w01NzfHtksuuUQXXHCBmpubefsFAADEd2VEkrxer4qKipSdna2cnByVl5ers7NTHo9HklRYWKj09HT5fD4lJSVp7ty5vY5PSUmRpMP2AwCA0SnuGCkoKFB7e7tKS0sVCASUmZmp2tra2E2tLS0tslr5YFcAAHBsLNFoNGp6iKMJh8Oy2+0KhUJKTk4e8vO7Vm8b8nOeDPauX2p6BADAMHasv7+5hAEAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFEDipGKigq5XC4lJSUpNzdXDQ0N/a7dunWrsrOzlZKSogkTJigzM1O//e1vBzwwAAAYWeKOkZqaGnm9XpWVlampqUkZGRnKz89XW1tbn+snT56s2267TfX19Xr99dfl8Xjk8Xj0zDPPHPfwAABg+LNEo9FoPAfk5uZq4cKF2rhxoyQpEonI6XTqhhtu0OrVq4/pOc4++2wtXbpUa9euPab14XBYdrtdoVBIycnJ8Yw7KFyrtw35OU8Ge9cvNT0CAGAYO9bf33FdGenu7lZjY6PcbvehJ7Ba5Xa7VV9ff9Tjo9Go/H6/9uzZo/POO6/fdV1dXQqHw702AAAwMsUVIx0dHerp6ZHD4ei13+FwKBAI9HtcKBTSxIkTlZiYqKVLl+pf//VfddFFF/W73ufzyW63xzan0xnPmAAAYBgZkr+mmTRpkpqbm/XnP/9Z69atk9frVV1dXb/rS0pKFAqFYltra+tQjAkAAAwYE8/i1NRUJSQkKBgM9tofDAaVlpbW73FWq1UzZ86UJGVmZmrXrl3y+Xw6//zz+1xvs9lks9niGQ0AAAxTcV0ZSUxMVFZWlvx+f2xfJBKR3+9XXl7eMT9PJBJRV1dXPKcGAAAjVFxXRiTJ6/WqqKhI2dnZysnJUXl5uTo7O+XxeCRJhYWFSk9Pl8/nk/Tl/R/Z2dk644wz1NXVpe3bt+u3v/2tHnroocH9SQAAwLAUd4wUFBSovb1dpaWlCgQCyszMVG1tbeym1paWFlmthy64dHZ26tprr9UHH3ygcePGadasWXr88cdVUFAweD8FAAAYtuL+nBET+JwRM/icEQDA8TghnzMCAAAw2IgRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABg1IBipKKiQi6XS0lJScrNzVVDQ0O/azdt2qRzzz1Xp5xyik455RS53e4jrgcAAKNL3DFSU1Mjr9ersrIyNTU1KSMjQ/n5+Wpra+tzfV1dnS677DI9//zzqq+vl9Pp1MUXX6wPP/zwuIcHAADDnyUajUbjOSA3N1cLFy7Uxo0bJUmRSEROp1M33HCDVq9efdTje3p6dMopp2jjxo0qLCw8pnOGw2HZ7XaFQiElJyfHM+6gcK3eNuTnPBnsXb/U9AgAgGHsWH9/x3VlpLu7W42NjXK73YeewGqV2+1WfX39MT3H/v37dfDgQU2ePLnfNV1dXQqHw702AAAwMsUVIx0dHerp6ZHD4ei13+FwKBAIHNNz3HrrrZo+fXqvoPkqn88nu90e25xOZzxjAgCAYWRI/5pm/fr1qq6u1pNPPqmkpKR+15WUlCgUCsW21tbWIZwSAAAMpTHxLE5NTVVCQoKCwWCv/cFgUGlpaUc89r777tP69eu1Y8cOzZ8//4hrbTabbDZbPKMBAIBhKq4rI4mJicrKypLf74/ti0Qi8vv9ysvL6/e4X/ziF1q7dq1qa2uVnZ098GkBAMCIE9eVEUnyer0qKipSdna2cnJyVF5ers7OTnk8HklSYWGh0tPT5fP5JEn/8i//otLSUm3ZskUulyt2b8nEiRM1ceLEQfxRAADAcBR3jBQUFKi9vV2lpaUKBALKzMxUbW1t7KbWlpYWWa2HLrg89NBD6u7u1j/+4z/2ep6ysjLdeeedxzc9AAAY9uL+nBET+JwRM/icEQDA8TghnzMCAAAw2IgRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABg1IBipKKiQi6XS0lJScrNzVVDQ0O/a//617/qH/7hH+RyuWSxWFReXj7QWQEAwAgUd4zU1NTI6/WqrKxMTU1NysjIUH5+vtra2vpcv3//fp1++ulav3690tLSjntgAAAwssQdIxs2bFBxcbE8Ho/mzJmjyspKjR8/XlVVVX2uX7hwoe6991794Ac/kM1mO+6BAQDAyDImnsXd3d1qbGxUSUlJbJ/VapXb7VZ9ff2gDdXV1aWurq7YP4fD4UF7buBoXKu3mR7BiL3rl5oeAcAoFdeVkY6ODvX09MjhcPTa73A4FAgEBm0on88nu90e25xO56A9NwAAOLmclH9NU1JSolAoFNtaW1tNjwQAAE6QuN6mSU1NVUJCgoLBYK/9wWBwUG9Otdls3F8CAMAoEdeVkcTERGVlZcnv98f2RSIR+f1+5eXlDfpwAABg5Ivryogkeb1eFRUVKTs7Wzk5OSovL1dnZ6c8Ho8kqbCwUOnp6fL5fJK+vOn1zTffjP3rDz/8UM3NzZo4caJmzpw5iD8KAAAYjuKOkYKCArW3t6u0tFSBQECZmZmqra2N3dTa0tIiq/XQBZePPvpICxYsiP3zfffdp/vuu0+LFy9WXV3d8f8EAABgWIs7RiTp+uuv1/XXX9/nY18NDJfLpWg0OpDTAMAJx59yA+adlH9NAwAARg9iBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEaNMT0AAABDxbV6m+kRjNi7fqnpEY6IKyMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMCoAcVIRUWFXC6XkpKSlJubq4aGhiOu/8Mf/qBZs2YpKSlJ8+bN0/bt2wc0LAAAGHnijpGamhp5vV6VlZWpqalJGRkZys/PV1tbW5/rX375ZV122WW6+uqr9Ze//EUrVqzQihUrtHPnzuMeHgAADH9xx8iGDRtUXFwsj8ejOXPmqLKyUuPHj1dVVVWf63/1q19pyZIl+ulPf6rZs2dr7dq1Ovvss7Vx48bjHh4AAAx/cX1RXnd3txobG1VSUhLbZ7Va5Xa7VV9f3+cx9fX18nq9vfbl5+frqaee6vc8XV1d6urqiv1zKBSSJIXD4XjGHTSRrv1GzmuaqX+/TeP1Hl14vUcXXm8z541Go0dcF1eMdHR0qKenRw6Ho9d+h8Oh3bt393lMIBDoc30gEOj3PD6fT2vWrDlsv9PpjGdcHCd7uekJMJR4vUcXXu/RxfTrvW/fPtnt9n4fjytGhkpJSUmvqymRSESffPKJTj31VFksFoOTDa1wOCyn06nW1lYlJyebHgcnGK/36MLrPbqM1tc7Go1q3759mj59+hHXxRUjqampSkhIUDAY7LU/GAwqLS2tz2PS0tLiWi9JNptNNput176UlJR4Rh1RkpOTR9V/eEc7Xu/Rhdd7dBmNr/eRroj8TVw3sCYmJiorK0t+vz+2LxKJyO/3Ky8vr89j8vLyeq2XpOeee67f9QAAYHSJ+20ar9eroqIiZWdnKycnR+Xl5ers7JTH45EkFRYWKj09XT6fT5J00003afHixbr//vu1dOlSVVdX69VXX9Wvf/3rwf1JAADAsBR3jBQUFKi9vV2lpaUKBALKzMxUbW1t7CbVlpYWWa2HLrgsWrRIW7Zs0e23366f//zn+sY3vqGnnnpKc+fOHbyfYoSy2WwqKys77C0rjEy83qMLr/fowut9ZJbo0f7eBgAA4ATiu2kAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGHVSfhz8aNXR0aGqqirV19fHvrsnLS1NixYt0lVXXaUpU6YYnhAAgMHHlZGTxJ///GedeeaZeuCBB2S323XeeefpvPPOk91u1wMPPKBZs2bp1VdfNT0mhkhra6t++MMfmh4Dg+jAgQN66aWX9Oabbx722Oeff67HHnvMwFQ4UXbt2qVHHnkk9iWyu3fv1qpVq/TDH/5Q//Vf/2V4upMPnzNykvjmN7+pjIwMVVZWHvZlgNFoVD/60Y/0+uuvq76+3tCEGEqvvfaazj77bPX09JgeBYPgrbfe0sUXX6yWlhZZLBZ961vfUnV1taZNmybpy+/rmj59Oq/3CFFbW6vly5dr4sSJ2r9/v5588kkVFhYqIyNDkUhEL7zwgp599lldeOGFpkc9aRAjJ4lx48bpL3/5i2bNmtXn47t379aCBQt04MCBIZ4MJ8Kf/vSnIz7+3nvv6Sc/+Qm/nEaI7373uzp48KAeffRRffrpp7r55pv15ptvqq6uTjNmzCBGRphFixbpwgsv1N13363q6mpde+21WrVqldatWyfpy2+mb2xs1LPPPmt40pMHMXKS+PrXv641a9aosLCwz8cfe+wxlZaWau/evUM7GE4Iq9Uqi8WiI/3Xz2Kx8MtphHA4HNqxY4fmzZsn6curnddee622b9+u559/XhMmTCBGRhC73a7GxkbNnDlTkUhENptNDQ0NWrBggSRp586dcrvdsXsDwQ2sJ41bbrlF11xzjRobG/Xtb3879l0/wWBQfr9fmzZt0n333Wd4SgyWadOm6cEHH9Ty5cv7fLy5uVlZWVlDPBVOlAMHDmjMmEP/c2uxWPTQQw/p+uuv1+LFi7VlyxaD0+FE+Nvb7VarVUlJSbLb7bHHJk2apFAoZGq0kxIxcpK47rrrlJqaql/+8pd68MEHY/8PKSEhQVlZWXr00Uf1/e9/3/CUGCxZWVlqbGzsN0aOdtUEw8vfbkCfPXt2r/0bN26UJF1yySUmxsIJ4nK59Pbbb+uMM86QJNXX12vGjBmxx1taWmL3C+FLxMhJpKCgQAUFBTp48KA6OjokSampqRo7dqzhyTDYfvrTn6qzs7Pfx2fOnKnnn39+CCfCifTd735Xv//973XllVce9tjGjRsViURUWVlpYDKcCKtWrer1lttXv6X+6aef5ubVr+CeEQAAYBSfMwIAAIwiRgAAgFHECAAAMIoYAQAARhEjwChy/vnn6+abbz7iGpfLpfLy8iGZJx4Wi0VPPfWU6TEAnADECDBCXHXVVVqxYkWvfU888YSSkpJ0//33S5K2bt2qtWvXGpju5PXxxx/r8ssv15lnnimr1XrUWKuurpbFYjns32sAA0eMACPUww8/rCuuuEIPPfSQfvKTn0iSJk+erEmTJhmezIzu7u4+93d1dWnKlCm6/fbblZGRccTn2Lt3r2655Rade+65J2JEYNQiRoAR6Be/+IVuuOEGVVdXy+PxxPZ/9W2atrY2LVu2TOPGjdPXv/51/e53v+v1PNFoVHfeeadmzJghm82m6dOn68Ybb+z3vH1dnbn55pt1/vnn95rhxhtv1M9+9jNNnjxZaWlpuvPOO3sd8/bbb+u8885TUlKS5syZo+eee+6wc7W2tur73/++UlJSNHnyZC1fvrzXdzf9bZZ169Zp+vTpOuuss/qc2eVy6Ve/+pUKCwt7fWT3V/X09OiKK67QmjVrdPrpp/e7DkD8+ARWYIS59dZb9eCDD+o///M/9e1vf/uIa6+66ip99NFHev755zV27FjdeOONamtriz3+7//+7/rlL3+p6upq/d3f/Z0CgYBee+21457xN7/5jbxer1555RXV19frqquu0jnnnKOLLrpIkUhEl156qRwOh1555RWFQqHD3jo5ePCg8vPzlZeXpxdffFFjxozR3XffrSVLluj1119XYmKiJMnv9ys5ObnPmInXXXfdpalTp+rqq6/Wiy++eNzPB+AQYgQYQZ5++mn98Y9/lN/vP+rHTb/11lt6+umn1dDQoIULF0qSNm/e3Ov7U1paWpSWlia3262xY8dqxowZysnJOe4558+fr7KyMknSN77xDW3cuFF+v18XXXSRduzYod27d+uZZ57R9OnTJUn33HOP/v7v/z52fE1NjSKRiB5++OHYF5I98sgjSklJUV1dnS6++GJJ0oQJE/Twww/H4mSgXnrpJW3evFnNzc3H9TwA+sbbNMAIMn/+fLlcLpWVlemzzz474tpdu3ZpzJgxvb4deNasWUpJSYn98/e+9z0dOHBAp59+uoqLi/Xkk0/qiy++GJQ5/3/Tpk2LXZHZtWuXnE5nLEQkKS8vr9f61157Te+8844mTZqkiRMnauLEiZo8ebI+//xzvfvuu7F18+bNO+4Q2bdvn6688kpt2rRJqampx/VcAPrGlRFgBElPT9cTTzyhCy64QEuWLNHTTz99XDesOp1O7dmzRzt27NBzzz2na6+9Vvfee69eeOGFPr/A0Wq1HvZtwwcPHjxs3VePtVgsikQixzzXZ599pqysrMPucZGkKVOmxP71hAkTjvk5+/Puu+9q7969WrZsWWzf32YdM2aM9uzZE/t2VgADw5URYIQ57bTT9MILLygQCGjJkiXat29fn+tmzZqlL774Qo2NjbF9e/bs0aefftpr3bhx47Rs2TI98MADqqurU319vd54440+n3PKlCn6+OOPe+2L962N2bNnq7W1tdfz/M///E+vNWeffbbefvttTZ06VTNnzuy1Hekm1IGYNWuW3njjDTU3N8e2Sy65RBdccIGam5vldDoH9XzAaESMACOQ0+lUXV2d2tralJ+fr3A4fNias846S0uWLNE///M/65VXXlFjY6NWrlypcePGxdY8+uij2rx5s3bu3Kn33ntPjz/+uMaNG6fTTjutz/NeeOGFevXVV/XYY4/p7bffVllZmXbu3BnX7G63W2eeeaaKior02muv6cUXX9Rtt93Wa80VV1yh1NRULV++XC+++KL+93//V3V1dbrxxhv1wQcfxHU+SbHI+Oyzz9Te3q7m5ma9+eabkqSkpCTNnTu315aSkqJJkyZp7ty5x/02EABiBBixvva1r6murk4dHR39Bskjjzyi6dOna/Hixbr00kt1zTXXaOrUqbHHU1JStGnTJp1zzjmaP3++duzYof/4j//Qqaee2uc58/Pzdccdd+hnP/uZFi5cqH379qmwsDCuua1Wq5588kkdOHBAOTk5WrlypdatW9drzfjx4/Xf//3fmjFjhi699FLNnj1bV199tT7//HMlJyfHdT5JWrBggRYsWKDGxkZt2bJFCxYs0He+8524nwfAwFiiX32DFwAAYAhxZQQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYNT/A9+u252IbsJ6AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Ontkidsdist_prop.plot.bar();" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Distribution of counts by province" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "Prov label Kids under 14\n", "Alberta 0 1077\n", " 1 152\n", " 2 145\n", " 3 58\n", "British Columbia 0 1672\n", " 1 145\n", " 2 132\n", " 3 36\n", "Manitoba 0 763\n", " 1 93\n", " 2 88\n", " 3 34\n", "NL 0 766\n", " 1 103\n", " 2 84\n", " 3 15\n", "New Brunswick 0 875\n", " 2 79\n", " 1 77\n", " 3 27\n", "Nova Scotia 0 858\n", " 2 86\n", " 1 77\n", " 3 15\n", "Ontario 0 3918\n", " 1 508\n", " 2 430\n", " 3 157\n", "Prince Edward Island 0 432\n", " 1 43\n", " 2 30\n", " 3 15\n", "Quebec 0 2755\n", " 2 305\n", " 1 300\n", " 3 112\n", "Saskatchewan 0 716\n", " 1 89\n", " 2 79\n", " 3 44\n", "Name: count, dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prov_data_df.groupby([\"Prov label\"])[\"Kids under 14\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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48OGIjIxEyZIl8f333yM8PLyAVpmIiIhsQb6vE1NY8ToxRKQ3vE4MkUbXiSEiIiJ6mtjEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0iU2MURERKRLbGKIiIhIl9jEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0iW7pz0AIiKiwqbMe9Fmp5+dEqHxSOhxuCeGiIiIdIlNDBEREekSmxgiIiLSJTYxREREpEtsYoiIiEiX2MQQERGRLrGJISIiIl1iE0NERES6xCaGiIiIdIlNDBEREekSmxgiIiLSJTYxREREpEtsYoiIiEiX2MQQERGRLrGJISIiIl1iE0NERES6xCaGiIiIdElVEzN58mTUrVsXbm5u8PX1RYcOHXDs2DGTmCZNmsBgMJg8BgwYYBKTkJCAiIgIODs7w9fXFyNHjkRGRoZJzObNm1GrVi04ODggJCQECxYssGwNiYiIyCapamJiYmIwaNAg7NixAxs2bEB6ejpatWqFO3fumMS98cYbuHTpkvKYOnWqMi8zMxMRERFIS0vD9u3bsXDhQixYsADjxo1TYs6cOYOIiAg0bdoUcXFxGDZsGPr164d169blc3WJiIjIVtipCV67dq3J8wULFsDX1xd79+5Fo0aNlOnOzs7w9/c3u4z169cjPj4ef/75J/z8/FCzZk1MnDgRo0ePxvjx42Fvb485c+YgODgYX3zxBQCgUqVK2Lp1K2bMmIHw8HC160hEREQ2KF/HxCQnJwMAvL29TaYvXrwYxYoVQ9WqVTFmzBjcvXtXmRcbG4tq1arBz89PmRYeHo6UlBQcPnxYiWnRooXJMsPDwxEbG5uf4RIREZENUbUn5mFZWVkYNmwYGjZsiKpVqyrTu3btitKlSyMwMBAHDx7E6NGjcezYMfz6668AgMTERJMGBoDyPDEx8bExKSkpuHfvHpycnHKMJzU1FampqcrzlJQUS1eNiIiIdMDiJmbQoEH4559/sHXrVpPp/fv3V/6/WrVqCAgIQPPmzXHq1CmUK1fO8pE+weTJkzFhwgSrLZ+IiIgKF4t+Tho8eDBWr16NTZs2oWTJko+NrV+/PgDg5MmTAAB/f38kJSWZxGQ/zz6OJrcYd3d3s3thAGDMmDFITk5WHufPn1e/YkRERKQbqpoYEcHgwYPx22+/4a+//kJwcPATc+Li4gAAAQEBAICwsDAcOnQIly9fVmI2bNgAd3d3VK5cWYnZuHGjyXI2bNiAsLCwXOs4ODjA3d3d5EFERES2S1UTM2jQIPz4449YsmQJ3NzckJiYiMTERNy7dw8AcOrUKUycOBF79+7F2bNnsXLlSvTo0QONGjVC9erVAQCtWrVC5cqV0b17dxw4cADr1q3Dhx9+iEGDBsHBwQEAMGDAAJw+fRqjRo3C0aNHMWvWLCxbtgzDhw8v4NUnIiIivVLVxMyePRvJyclo0qQJAgIClMfSpUsBAPb29vjzzz/RqlUrVKxYEe+88w46deqEVatWKcsoUqQIVq9ejSJFiiAsLAyvv/46evTogY8//liJCQ4ORnR0NDZs2IAaNWrgiy++wPfff8/Tq4mIiEih6sBeEXns/FKlSiEmJuaJyyldujT++OOPx8Y0adIE+/fvVzM8IiIi+g/hvZOIiIhIl9jEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0iU2MURERKRLbGKIiIhIl9jEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0iU2MURERKRLbGKIiIhIl9jEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0iU2MURERKRLbGKIiIhIl9jEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0iU2MURERKRLbGKIiIhIl9jEEBERkS6pamImT56MunXrws3NDb6+vujQoQOOHTtmEnP//n0MGjQIPj4+cHV1RadOnZCUlGQSk5CQgIiICDg7O8PX1xcjR45ERkaGSczmzZtRq1YtODg4ICQkBAsWLLBsDYmIiMgmqWpiYmJiMGjQIOzYsQMbNmxAeno6WrVqhTt37igxw4cPx6pVq/Dzzz8jJiYGFy9exEsvvaTMz8zMREREBNLS0rB9+3YsXLgQCxYswLhx45SYM2fOICIiAk2bNkVcXByGDRuGfv36Yd26dQWwykRERGQLDCIiliZfuXIFvr6+iImJQaNGjZCcnIzixYtjyZIlePnllwEAR48eRaVKlRAbG4sGDRpgzZo1ePHFF3Hx4kX4+fkBAObMmYPRo0fjypUrsLe3x+jRoxEdHY1//vlHqdWlSxfcvHkTa9euzdPYUlJS4OHhgeTkZLi7u1u6ikREminzXrTZ6WenRGg8EuJr8fSo+fc7X8fEJCcnAwC8vb0BAHv37kV6ejpatGihxFSsWBFBQUGIjY0FAMTGxqJatWpKAwMA4eHhSElJweHDh5WYh5eRHZO9DCIiIiI7SxOzsrIwbNgwNGzYEFWrVgUAJCYmwt7eHp6eniaxfn5+SExMVGIebmCy52fPe1xMSkoK7t27BycnpxzjSU1NRWpqqvI8JSXF0lUjIiIiHbB4T8ygQYPwzz//ICoqqiDHY7HJkyfDw8NDeZQqVeppD4mIiIisyKImZvDgwVi9ejU2bdqEkiVLKtP9/f2RlpaGmzdvmsQnJSXB399fiXn0bKXs50+KcXd3N7sXBgDGjBmD5ORk5XH+/HlLVo2IiIh0QlUTIyIYPHgwfvvtN/z1118IDg42mV+7dm0ULVoUGzduVKYdO3YMCQkJCAsLAwCEhYXh0KFDuHz5shKzYcMGuLu7o3LlykrMw8vIjslehjkODg5wd3c3eRAREZHtUnVMzKBBg7BkyRKsWLECbm5uyjEsHh4ecHJygoeHB/r27YsRI0bA29sb7u7uGDJkCMLCwtCgQQMAQKtWrVC5cmV0794dU6dORWJiIj788EMMGjQIDg4OAIABAwbgq6++wqhRo9CnTx/89ddfWLZsGaKjzR8tTkRERP89qvbEzJ49G8nJyWjSpAkCAgKUx9KlS5WYGTNm4MUXX0SnTp3QqFEj+Pv749dff1XmFylSBKtXr0aRIkUQFhaG119/HT169MDHH3+sxAQHByM6OhobNmxAjRo18MUXX+D7779HeHh4AawyERER2YJ8XSemMON1YohIb3htksKDr8XTo9l1YoiIiIieFjYxREREpEtsYoiIiEiX2MQQERGRLrGJISIiIl1iE0NERES6xCaGiIiIdIlNDBEREekSmxgiIiLSJTYxREREpEtsYoiIiEiX2MQQERGRLrGJISIiIl1iE0NERES6xCaGiIiIdIlNDBEREekSmxgiIiLSJTYxREREpEtsYoiIiEiX2MQQERGRLrGJISIiIl1iE0NERES6xCaGiIiIdMnuaQ+A1CnzXrTZ6WenRGg8EiIioqeLe2KIiIhIl9jEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0iU2MURERKRLbGKIiIhIl9jEEBERkS6pbmK2bNmCtm3bIjAwEAaDAb///rvJ/F69esFgMJg8XnjhBZOY69evo1u3bnB3d4enpyf69u2L27dvm8QcPHgQzz//PBwdHVGqVClMnTpV/doRERGRzVLdxNy5cwc1atTA119/nWvMCy+8gEuXLimPn376yWR+t27dcPjwYWzYsAGrV6/Gli1b0L9/f2V+SkoKWrVqhdKlS2Pv3r2YNm0axo8fj2+//VbtcImIiMhGqb4BZOvWrdG6devHxjg4OMDf39/svCNHjmDt2rXYvXs36tSpAwD43//+hzZt2uDzzz9HYGAgFi9ejLS0NMybNw/29vaoUqUK4uLiMH36dJNmh4iIiP67rHJMzObNm+Hr64sKFSpg4MCBuHbtmjIvNjYWnp6eSgMDAC1atIDRaMTOnTuVmEaNGsHe3l6JCQ8Px7Fjx3Djxg1rDJmIiIh0RvWemCd54YUX8NJLLyE4OBinTp3C+++/j9atWyM2NhZFihRBYmIifH19TQdhZwdvb28kJiYCABITExEcHGwS4+fnp8zz8vLKUTc1NRWpqanK85SUlIJeNSIiIipECryJ6dKli/L/1apVQ/Xq1VGuXDls3rwZzZs3L+hyismTJ2PChAlWWz4REREVLlY/xbps2bIoVqwYTp48CQDw9/fH5cuXTWIyMjJw/fp15Tgaf39/JCUlmcRkP8/tWJsxY8YgOTlZeZw/f76gV4WIiIgKEas3MRcuXMC1a9cQEBAAAAgLC8PNmzexd+9eJeavv/5CVlYW6tevr8Rs2bIF6enpSsyGDRtQoUIFsz8lAQ8OJnZ3dzd5EBERke1S3cTcvn0bcXFxiIuLAwCcOXMGcXFxSEhIwO3btzFy5Ejs2LEDZ8+excaNG9G+fXuEhIQgPDwcAFCpUiW88MILeOONN7Br1y5s27YNgwcPRpcuXRAYGAgA6Nq1K+zt7dG3b18cPnwYS5cuRWRkJEaMGFFwa05ERES6prqJ2bNnD0JDQxEaGgoAGDFiBEJDQzFu3DgUKVIEBw8eRLt27fDMM8+gb9++qF27Nv7++284ODgoy1i8eDEqVqyI5s2bo02bNnjuuedMrgHj4eGB9evX48yZM6hduzbeeecdjBs3jqdXExERkUL1gb1NmjSBiOQ6f926dU9chre3N5YsWfLYmOrVq+Pvv/9WOzwiIiL6j+C9k4iIiEiX2MQQERGRLrGJISIiIl1iE0NERES6xCaGiIiIdIlNDBEREekSmxgiIiLSJTYxREREpEtsYoiIiEiX2MQQERGRLrGJISIiIl1iE0NERES6xCaGiIiIdIlNDBEREekSmxgiIiLSJTYxREREpEtsYoiIiEiX2MQQERGRLrGJISIiIl1iE0NERES6xCaGiIiIdIlNDBEREekSmxgiIiLSJTYxREREpEtsYoiIiEiX2MQQERGRLrGJISIiIl1iE0NERES6xCaGiIiIdIlNDBEREekSmxgiIiLSJTYxREREpEt2T3sAWinzXrTZ6WenRGg8EiIiIioI3BNDREREuqS6idmyZQvatm2LwMBAGAwG/P777ybzRQTjxo1DQEAAnJyc0KJFC5w4ccIk5vr16+jWrRvc3d3h6emJvn374vbt2yYxBw8exPPPPw9HR0eUKlUKU6dOVb92REREZLNUNzF37txBjRo18PXXX5udP3XqVMycORNz5szBzp074eLigvDwcNy/f1+J6datGw4fPowNGzZg9erV2LJlC/r376/MT0lJQatWrVC6dGns3bsX06ZNw/jx4/Htt99asIpERERki1QfE9O6dWu0bt3a7DwRwZdffokPP/wQ7du3BwD88MMP8PPzw++//44uXbrgyJEjWLt2LXbv3o06deoAAP73v/+hTZs2+PzzzxEYGIjFixcjLS0N8+bNg729PapUqYK4uDhMnz7dpNkhIiKi/64CPSbmzJkzSExMRIsWLZRpHh4eqF+/PmJjYwEAsbGx8PT0VBoYAGjRogWMRiN27typxDRq1Aj29vZKTHh4OI4dO4YbN24U5JCJiIhIpwr07KTExEQAgJ+fn8l0Pz8/ZV5iYiJ8fX1NB2FnB29vb5OY4ODgHMvInufl5ZWjdmpqKlJTU5XnKSkp+VwbIiIiKsxs5uykyZMnw8PDQ3mUKlXqaQ+JiIiIrKhAmxh/f38AQFJSksn0pKQkZZ6/vz8uX75sMj8jIwPXr183iTG3jIdrPGrMmDFITk5WHufPn8//ChEREVGhVaBNTHBwMPz9/bFx40ZlWkpKCnbu3ImwsDAAQFhYGG7evIm9e/cqMX/99ReysrJQv359JWbLli1IT09XYjZs2IAKFSqY/SkJABwcHODu7m7yICIiItuluom5ffs24uLiEBcXB+DBwbxxcXFISEiAwWDAsGHD8Mknn2DlypU4dOgQevTogcDAQHTo0AEAUKlSJbzwwgt44403sGvXLmzbtg2DBw9Gly5dEBgYCADo2rUr7O3t0bdvXxw+fBhLly5FZGQkRowYUWArTkRERPqm+sDePXv2oGnTpsrz7MaiZ8+eWLBgAUaNGoU7d+6gf//+uHnzJp577jmsXbsWjo6OSs7ixYsxePBgNG/eHEajEZ06dcLMmTOV+R4eHli/fj0GDRqE2rVro1ixYhg3bhxPryYiIiKF6iamSZMmEJFc5xsMBnz88cf4+OOPc43x9vbGkiVLHlunevXq+Pvvv9UOj4iIiP4jbObsJCIiIvpvYRNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0qUCvXcSEelLmfeic513dkqEhiMhIlKPe2KIiIhIl7gnhoh0L7c9StybRGTbuCeGiIiIdIlNDBEREekSmxgiIiLSJTYxREREpEtsYoiIiEiX2MQQERGRLrGJISIiIl3idWJIc7ZyTQ9bWQ8iIr3inhgiIiLSJTYxREREpEtsYoiIiEiXeEwMERGRDfov3KWee2KIiIhIl9jEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0iU2MURERKRLbGKIiIhIl9jEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpUoE3MePHj4fBYDB5VKxYUZl///59DBo0CD4+PnB1dUWnTp2QlJRksoyEhARERETA2dkZvr6+GDlyJDIyMgp6qERERKRjdtZYaJUqVfDnn3/+XxG7/yszfPhwREdH4+eff4aHhwcGDx6Ml156Cdu2bQMAZGZmIiIiAv7+/ti+fTsuXbqEHj16oGjRovj000+tMVwiIiLSIas0MXZ2dvD3988xPTk5GXPnzsWSJUvQrFkzAMD8+fNRqVIl7NixAw0aNMD69esRHx+PP//8E35+fqhZsyYmTpyI0aNHY/z48bC3t7fGkImIiEhnrHJMzIkTJxAYGIiyZcuiW7duSEhIAADs3bsX6enpaNGihRJbsWJFBAUFITY2FgAQGxuLatWqwc/PT4kJDw9HSkoKDh8+bI3hEhERkQ4V+J6Y+vXrY8GCBahQoQIuXbqECRMm4Pnnn8c///yDxMRE2Nvbw9PT0yTHz88PiYmJAIDExESTBiZ7fva83KSmpiI1NVV5npKSUkBrRERUOJV5L9rs9LNTIjQeCdHTUeBNTOvWrZX/r169OurXr4/SpUtj2bJlcHJyKuhyismTJ2PChAlWWz4REREVLlY/xdrT0xPPPPMMTp48CX9/f6SlpeHmzZsmMUlJScoxNP7+/jnOVsp+bu44m2xjxoxBcnKy8jh//nzBrggREREVKlZvYm7fvo1Tp04hICAAtWvXRtGiRbFx40Zl/rFjx5CQkICwsDAAQFhYGA4dOoTLly8rMRs2bIC7uzsqV66cax0HBwe4u7ubPIiIiMh2FfjPSe+++y7atm2L0qVL4+LFi/joo49QpEgRvPbaa/Dw8EDfvn0xYsQIeHt7w93dHUOGDEFYWBgaNGgAAGjVqhUqV66M7t27Y+rUqUhMTMSHH36IQYMGwcHBoaCHS0RERDpV4E3MhQsX8Nprr+HatWsoXrw4nnvuOezYsQPFixcHAMyYMQNGoxGdOnVCamoqwsPDMWvWLCW/SJEiWL16NQYOHIiwsDC4uLigZ8+e+Pjjjwt6qERERKRjBd7EREVFPXa+o6Mjvv76a3z99de5xpQuXRp//PFHQQ+NiIiIbIhVLnZHRESFT26nZAMFd1o2T/smLfEGkERERKRLbGKIiIhIl9jEEBERkS6xiSEiIiJdYhNDREREusQmhoiIiHSJTQwRERHpEpsYIiIi0iU2MURERKRLbGKIiIhIl3jbASIionzi7RaeDjYxRERETwEbn/zjz0lERESkS2xiiIiISJfYxBAREZEusYkhIiIiXWITQ0RERLrEJoaIiIh0iU0MERER6RKbGCIiItIlNjFERESkS2xiiIiISJfYxBAREZEu8d5JRESUK97fhwozNjG5yO2DC/DDS0REVBiwiSlAhfEbS2EcExERUUFgE0MmuAeKiIj0ggf2EhERkS5xTwyRRvjTHhHlB7chObGJIV3gh5eIiB7FJoZskq00PYVxPQrjmNTisV9E5qn9fD/t7QGbGCKyqqe9kSsotrIeRLaETcxTxI0iERGR5Qr12Ulff/01ypQpA0dHR9SvXx+7du162kMiIiKiQqLQ7olZunQpRowYgTlz5qB+/fr48ssvER4ejmPHjsHX1/dpD48ewj1KRJQf3Ib8txTk611om5jp06fjjTfeQO/evQEAc+bMQXR0NObNm4f33nvvKY+ObA0P9Cxc+I8aPQ7fH5StUDYxaWlp2Lt3L8aMGaNMMxqNaNGiBWJjY5/iyIgKN27cCw++FoUHv6TYrkLZxFy9ehWZmZnw8/Mzme7n54ejR4+azUlNTUVqaqryPDk5GQCQkpICAMhKvWs2L3v+o3KLtySnoOK1qMH11r4G11v7Glxv7WtwvbWvodf1zv6viOS6DIUUQv/++68AkO3bt5tMHzlypNSrV89szkcffSQA+OCDDz744IMPG3icP3/+if1CodwTU6xYMRQpUgRJSUkm05OSkuDv7282Z8yYMRgxYoTyPCsrC9evX4ePjw8MBoMyPSUlBaVKlcL58+fh7u7+xLGojbeVGoVxTFrUKIxj0qJGYRyTFjUK45i0qFEYx6RFjcI4Ji1qFMYxPS5HRHDr1i0EBgY+cRmFsomxt7dH7dq1sXHjRnTo0AHAg6Zk48aNGDx4sNkcBwcHODg4mEzz9PTMtYa7u3ue/9CWxNtKjcI4Ji1qFMYxaVGjMI5JixqFcUxa1CiMY9KiRmEckxY1CuOYcsvx8PDIU26hbGIAYMSIEejZsyfq1KmDevXq4csvv8SdO3eUs5WIiIjov63QNjGvvvoqrly5gnHjxiExMRE1a9bE2rVrcxzsS0RERP9NhbaJAYDBgwfn+vORpRwcHPDRRx/l+OmpoOJtpUZhHJMWNQrjmLSoURjHpEWNwjgmLWoUxjFpUaMwjkmLGoVxTJbmPMogkpdzmIiIiIgKl0J97yQiIiKi3LCJISIiIl1iE0NERES6xCaGiIiIdKlQn52UXzdv3sRvv/2Gv//+G+fOncPdu3dRvHhxhIaGIjw8HM8++6xJ/JEjRxAVFZVrfKdOnXIcRa22htp4LdajMI7JnNTU1MfGqK2RnzElJCSY5FSpUiXX2NTUVOzcuTNHjeDg4Meur5oaauIL6+ttybgAID09HYmJiUq8t7f3Y/+uauMB9a+FJTWyPel9nt8aeV2+mhxLXjtrb6csfT8B1vvsPfz3VLtNsIUxWZrzODZ5dtLFixcxbtw4LF68GIGBgahXrx4CAwPh5OSE69ev459//sHevXtRunRpfPTRRyhfvjxGjRqFrVu3omHDhmbj//77b6SkpGDUqFEYNmwYrl27pqrG888/ryr+1Vdftfp6dO7cGZMmTSpUYxo2bJjyhl6zZo2ykTt//jyysrLg4uKC0NBQtGrVCr1790ZgYCD27dunqkbjxo3x4Ycfqh7T2bNnMXv2bERFReHChQsmNyezt7fH888/j/79+6NTp04wGo3Ytm0bIiMjsWrVKqSnp8PDw0OpkZqairJly6J///4YMGAA3NzcAEB1DTXxiYmJhe49aMln6dVXX8WtW7fw448/IioqCrt27UJaWhpEBAaDASVLlkSrVq3Qv39/1K1bFwBUx1vyWlhSQ8373NL1ULN8tTlq3x+vvvqq6s+r2u2UJdtaa3/2jMYHP3qo3SbYwpgs+duqov72jIWfr6+vjBw5Ug4fPpxrzN27d2XJkiXSoEED8fLykq+//lpu3Ljx2OVu375dXn31VZk0aZLqGq6urqrip02bZvX1cHFxKXRjmjRpkvz6669Svnx58ff3lz59+sicOXNk5cqVsmHDBlm6dKmMHTtWmjRpIg4ODvLmm29KUFCQqhqWjGnIkCHi7u4ur7zyivzwww9y9OhRSUlJkfT0dElKSpKNGzfK+PHjpWLFilKlShV5/vnnpUSJEjJy5EjZsmWL3L1712TZp06dkgULFkh4eLj4+/vL+vXrVdfo3Lmzqnhvb+9C+XqrrfHiiy+Kt7e31K1bVz7++GNZu3atHDx4UE6cOCE7d+6UuXPnSq9evcTT01PCw8PlvffeUxV//Phx1a/F0KFDVddQ+z6fMGGCqhpfffWVquVfvnxZ9ZiKFSum+j1VpkwZq26nLNnWWvuzt2vXLmnbtq2qbUL79u11P6Zdu3ap/tvu2rXrse+LR9lkE3P16lVV8ZcuXVIVn5aWprrG8ePHVcVfvXrV6uuhNl6LMaWlpUmDBg1k9erVkpmZ+djYCxcuyOjRo2XatGmqaty5c0f1mN577708r/uaNWvkzTfflLS0tDzFHz58WP7880/VNTp27KgqfsGCBXmKzabV6622RseOHeWff/55Ytz9+/dl9uzZUq9ePVXxc+fOVf1aNGzYUHUNte/z0NBQVTXKli2ravnTp09XPaaJEyc+cTwPu3r1ap4/F9nUvqcs2dZa+7P3yy+/yJw5c1RtE7p06aL7Mf3yyy+q/7a//PJLnmKz2eTPSURERGT7bPrA3rS0NPz++++IjY1FYmIiAMDf3x/PPvss2rdvD3t7+zwvKykpCd988w3GjRuXY96FCxfg6ekJV1dXk+np6emIjY1Fo0aNlGnXrl3DwYMHUaNGDXh7e+Pq1auYO3cuUlNT8corr6BSpUpPZT3UrIMl63H16lXMmzfP7Dr06tULxYsXz/M65ObevXvYu3cvvL29UblyZZN59+/fx7Jly9CjRw9l2pEjR7Bjxw6EhYWhYsWKOHr0KCIjI5GamorXX38dzZo1y7VWcnKyyXrk9Y6rWhERZGVloUiRIjnmWfp+UvseyU1u70FLPhvZUlNTASBfBwiS5QpyGwUUzHYqP+8nPX++sxX2dQDyth55WYhNOnHihJQtW1YcHR2lcePG0rlzZ+ncubM0btxYHB0dJSQkRE6cOJHn5cXFxYnRaDSZdvHiRalbt64YjUYpUqSIdO/eXW7duqXMT0xMNMnZuXOneHh4iMFgEC8vL9mzZ48EBwdL+fLlpVy5cuLk5CR79+7VdD3UroMl67Fr1y7x8vKSEiVKSM+ePWXUqFEyatQo6dmzp5QsWVK8vb1l9+7deV4HEZGEhATp3bu38vzYsWNSunRpMRgMYjQapVGjRnLx4sVc12PNmjVib28v3t7e4ujoKGvWrJHixYtLixYtpFmzZlKkSBHZuHFjjrrfffedVKpUSYxGo8mjUqVK8v3336taB3PvKRGR6Oho6du3r4wcOVKOHDliMu/69evStGlT5Xl6erp88MEH0qhRIxk3bpyIiEydOlWcnZ3F3t5eevToIampqUq8Je8nS94jatfbks/G+vXrpXXr1uLp6am8Dp6entK6dWvZsGGD2boTJ06Ur7/+Wq5cuWIyLzk52eT9lBfx8fESHBxsMu27776THj16yLx580REJCoqSipWrCjBwcHK6/Oow4cPy8CBA6VmzZri7+8v/v7+UrNmTRk4cOBjj+sw5+TJkybvD5EHr9+iRYskOjra5L0gInL79m2ZMGFCjuWsX79exo0bp3wGYmJi5IUXXpCmTZsq65atoLdRIvnfTlnyfhJR//lW81m1ZL3Vfr4tWQe1YxJRv96WrEde2WwT06JFC2nfvr0kJyfnmJecnCzt27eXVq1aKdMOHDjw2MfSpUtzvJA9evSQ+vXry+7du2XDhg1Su3ZtqVOnjly/fl1EHnywDAaDyZj69esnKSkpMm3aNClZsqT069dPmd+7d2/p0KGDpuuhdh0sWY/69etL//79JSsrK8c6ZGVlSf/+/aVBgwY55j3Oox+sDh06SEREhFy5ckVOnDghEREREhwcLOfOnVPW4+H4sLAw+eCDD0RE5KeffhIvLy95//33lfnvvfeetGzZ0qRm9ofuvffek02bNkl8fLzEx8fLpk2bZMyYMeLi4qLq+Jy4uLgcf9vFixdLkSJFJCIiQp577jlxdHSUH3/8UZn/6Hp8+OGH4ufnJyNGjJDKlSvLgAEDpFSpUvLjjz/KwoULpUSJEvLZZ58p8WrfTyLq3yOWfJbUvqcWLFggdnZ20qVLF5k/f7788ccf8scff8j8+fPltddek6JFi8oPP/ygxK9bt07s7e2lSpUqEhQUJD4+PvLXX3/l+nfNi0ffgzNmzBAXFxd56aWXJCAgQD755BPx8fGRTz75RCZMmCDu7u7yzTffmCzjjz/+EHt7e2nQoIF89NFHMmvWLJk1a5Z89NFH8uyzz4qDg4OsXbvW4jHt2rVLPD09xd3dXZycnCQkJMTkuBpz671o0SKxs7OTWrVqiaurq8yfP188PT2lX79+0qdPH7G3t5eff/5ZibfkPWXt7ZQl21q1n2+1n9W8eHSboPbzXdDbKHNjsmS91a6HGjbbxDg5OcmhQ4dynX/w4EFxcnJSnmd/gzcYDDke2dMffWECAwNl586dyvP79+9L27ZtpWbNmnLt2rUcL6aXl5fEx8eLyIMDGo1Go0n+3r17pUSJEpquh9p1sGQ9HB0dc3TrDzty5Ig4OjqaTFuxYsVjHzNmzDAZl6+vrxw8eFB5npWVJQMGDJCgoCA5depUjvVwd3dXvh1mZmaKnZ2d7Nu3T5l/6NAh8fPzMxlTUFCQLF26NNf1iIqKklKlSinPO3bs+NhHs2bNcvxta9asKZGRkcrzpUuXiouLi/IN6tH1KFu2rKxatUpEHnwjNhqNEhUVZZJftWpV5bna95OI+veIJZ8lte+p8uXLy1dffZXrenz99dcSEhKiPA8LC1Oa1KysLPnss8/E1dVV1qxZIyLmN7zDhw9/7OP11183yalYsaIsXrxYRET27dsndnZ2Jt98v//+e6ldu7ZJjerVq8vYsWNzXY+PPvpIqlWrpjyPjIx87GPUqFEmY2rRooX07t1bMjMzJSUlRQYOHCg+Pj7Ke93cej/8Hvzzzz/FyclJpk+frsz//PPPpWHDhspzS95T1t5OWbKtVfv5VvtZFVG/TVD7+Va7DpaMyZL1VrseatjsMTGenp44e/Ysqlatanb+2bNn4enpqTz39vbG1KlT0bx5c7Pxhw8fRtu2bU2mJScnw8vLS3nu4OCAX3/9Fa+88gqaNm2KH3/80SQ+LS0NTk5OAICiRYvC2dkZxYoVU+YXK1YM165d03Q91K6DJevh7++PXbt2oWLFimbHtGvXLvj5+ZlM69ChAwwGg8n1BB5lMBiU/7937x7s7OxM5s2ePRuDBw9G48aNsWTJklzzjUYjHB0dTX4zdnNzQ3Jyskn85cuXUa1atVzHU61aNVy9elV5vmrVKrRs2TLHumXLzMzMMe3EiRMmr0/nzp1RvHhxtGvXDunp6ejYsaNJ/MWLF1GjRg0AQEhICOzt7ZXnAFC3bl2cO3dOea72/QSof49Y8llS+55KSEhAixYtzC4fAJo3b4533nnHpOaiRYsAPHjdR40ahZIlS+Lll19GVFRUjmu3AEBkZCRq1qwJd3d3szVu375t8vzcuXN47rnnAAChoaEoUqQIGjRooMxv3Lgx3n33XZOc48ePo1u3brmux2uvvYbPPvtMeT5s2DAEBATkeoxJWlqayfO9e/fi66+/htFohJubG2bNmoWgoCA0b94c69atQ1BQUI5lPPwebN68OTIyMkxey4iICEyePFl5bsl7ytrbKUu2tWo/32o/q4D6bYLaz7fadbBkTJast9r1UMWi1kcHxo4dK15eXjJ9+nQ5cOCAJCYmSmJiohw4cECmT58u3t7e8tFHHynxrVq1euzpguZ2/VerVk2WL1+eIzY9PV06dOggQUFBOb6pPXycxerVq03Oy9+xY4eULFlS0/VQuw6WrMdXX30lDg4O8vbbb8uKFStkx44dsmPHDlmxYoW8/fbb4uTkJF9//bVJjcDAQPn9999zXY/9+/ebjKtu3bomPx88bNCgQcpxE9mqV6+ufAsXebDnJT09XXm+ZcuWHMc7PP/889KjRw+TuGwZGRnSo0cPadSokTKtWrVqj/0N+tF1EBEJCAiQ2NjYHLGbN28WV1dX+eCDD0xy/Pz8TPZAPfvss3LhwgXl+ZEjR8Td3V15rvb9lL0eat4jlnyW1L6natWqJSNHjsy1xqhRo6RWrVrK8+LFi8uePXtyxP3000/i7Owss2fPzvFaPPPMM7Jo0aJcazz6+vn4+Cjf/kVESpYsKWfPnlWenzhxQlxdXU2WUbFiRfniiy9yrfHFF19IhQoVlOdlypR57DftR8fk5eUlBw4cyBE3bdo08fT0lF9//TXHent6esrRo0eV566urnLq1Cnl+enTp8XZ2Vl5bsl7ytrbKUu2tWo/32o/q9nroWaboPbzrXYdLBmTJeutdj3UsNkmRkRkypQpEhAQoOyazN5NGRAQkOP3t19//fWxG6zr16/nuM7GqFGjcvzWmy09PV3atWtn8mKOHz9efvrpp1xrvP/++/LSSy9puh5q18HS9YiKipL69euLnZ2dstvYzs5O6tevb3aj3LZt28fuZn90I/fpp59K69atc40fOHCgSfzs2bNl9erVucaPGTNG+vbtazLtwIED4u/vLz4+PtKxY0cZMGCADBgwQDp27Cg+Pj4SEBBgslu9V69e8tZbb+VaIz4+XsqUKWMyrX379rkeALpp0yZxcXExeT2aNm362Ou/LFu2LMdPGGreTyLq3yOWfJbUvqey/xbVqlWT4cOHy5QpU2TKlCkyfPhwqV69uri6ukpMTIwS37Jly1yPBViyZIkULVo0x/u8a9euMmzYsFzH9Oh7sGHDhia7yB+1atWqHLvMly1bJnZ2dtK2bVuJjIyUqKgoiYqKksjISGnXrp3Y29ub/OPdqVMnGTVqVJ7H9Pzzz8vs2bPNxn722Wfi4OCQY73r1Klj8gUiOTnZ5Hi2DRs2yDPPPGOSo/Y9Ze3tlCXbKLWfb7WfVRH12wS1n2+162DJmCxZb0u2U3n1n7hOzJkzZ0xONXvSPWvyKiMjA3fv3s11d3NGRgb+/fdflC5dOk/Lu3v3LooUKZLraaLWWI+CXgfg8euRnp6u7M4sVqwYihYtanYZf//9N+7cuYMXXnjB7Pw7d+5gz549aNy4cZ7HVRCyL/u+Y8cOk9ciLCwMXbt2Nfk7pqamIjMzE87OznlefkxMDLZv344xY8aYnb9p0yb88MMPmD9/PoAHP0cULVo01/fCkiVLYGdnh86dO+eYl9f3kzXeI2qZe09lX8rc3GsxYMAAlClTRon97bffsGXLFsyYMcPs8pcsWYLvvvsOmzZtUqYlJiYiNTU1z+u1bds2uLi4oGbNmmbnz5o1C1lZWRg8eLDJ9O3bt2PmzJk5Tk8OCwvD0KFDERYWpsTGx8fj7t27qFOnjtka6enpuHjxojLm77//HjExMcpPaY/67LPPMGfOHJw5c0aZ9ttvv8HHxyfXU+anTJmCO3fuYOLEiTnm6X1bq+bzrfazCqjfJljy+VazDpaMyZL1zs926kn+E00MERER2R6Vd1oiIiIiKhzYxBAREZEusYkhIiIiXWITQ0RERLr0n25iEhISzF5wrKDibaWGFmPasmVLjovLFTS1NbQYU2FUGN+DluZQ4VAY31N8P9kIi07MthEGg0GeeeYZ+eWXX6wSbys1tBqTt7e3fP7553muERMTIzdv3lQ1JjU1LBlT7969c73oXkHEa1GjML4HLcmZMGGCbNmyJc/LVxuvVY2FCxfKyZMnC028JTmF8T1lyZhs4fNdGMdkaY6IjV/s7kk2b94s8+bNk86dO1sl3lZqaDGms2fPyl9//fXYq7A+Sm2TobaGJWNq3LixlC5dWmrUqGGVeC1qbNq0SfXrrTZHixplypQRJycnefHFF60Sr1UNg8Eg9vb2Mnjw4EIRb0mOrWzXbOHzXRjHZGmOyH+8iSF9s6TJ0Mrhw4etGq9VDb27e/euREdHWy1eqxqnT5/OcWuOpxlvaY6tsIXPd2EckyU5bGKICkhSUpJMmjTJavEFWWPJkiW55rz77rtmp6vN0aIGEf23/Seu2Lt8+XIsW7YMCQkJOe7yum/fvnzH20oNa48pMzMTM2bMyDX++vXrZmuoobZGQY7pwIEDqFWrVp4PFlQbX5A1PD098dNPP6F169Ym04cPH46oqChcunQpx7LU5mhRI9vdu3fNvn7Vq1cvkHgtamRmZuL333/HkSNHAABVqlRBu3btUKRIkacSn5ec0NBQk7vJP0729kBtjrXjHzV58mT4+fmhT58+JtPnzZuHK1euYPTo0fmK16rGiRMnsGnTJly+fBlZWVkm88aNG5cj3pIcLWo80dPuoqwtMjJSXF1dZfDgwWJvby9vvvmmtGjRQjw8POT999/Pd7yt1NBiTGPHjpWAgAD5/PPPxdHRUSZOnCh9+/YVHx8fiYyMNFsjIyNDpk2bJnXr1hU/Pz/x8vIyeeS3hiVjyk1cXFyOG58VZHxB1li9erV4eHjI33//rUwbPHiwBAYGypEjR8wuS22OFjUuX74sERERyk0HH33kN16rGidOnJBnnnlGnJ2dJTQ0VEJDQ8XZ2VkqVKhg9gBaa8fnNWf8+PHK47333hN3d3dp0KCBDB8+XIYPHy5hYWHi7u4u7733nrJctTnWjn9U6dKlZdu2bTmm79ixI8cNWy2J16LGt99+K0WKFBE/Pz+pUaOG1KxZU3mEhoaaHZPaHC1q5IXNNzEVKlRQdlE/fEv5sWPHyqBBg/Idbys1tBhT2bJllTtHu7q6KhvCyMhIee2118zWUNtkqK1hyZhyo6cmRkRk8eLF4uXlJXv27JGBAwdKYGCgHDt27LHLU5tj7Rpdu3aVhg0byu7du8XFxUXWr18vixYtkgoVKpi9S7naeK1qtG7dWl544QW5du2aMu3q1avywgsvSJs2bTSPtySnb9++8uGHH+aYPm7cOOndu7fZGmpzrB0vIuLg4CCnT5/OMf3UqVPi4OCQ73gtagQFBcmUKVPM1s6N2hwtauSFzTcxTk5OcvbsWRERKV68uMTFxYmIyPHjx8Xb2zvf8bZSQ4sxOTs7y7lz50RExN/fX/bu3SsiDz6I7u7uZmuobTLU1rBkTLnRWxMjIvL111+Lg4ODlCxZUk6cOJGnZarNsWYNf39/2blzp4iIuLm5Kc3OihUrpGHDhvmO16qGs7OzHDx4MMf0uLg4cXFx0Tzekhx3d3c5fvx4junHjx/P9bOkNsfa8SIiISEhsmjRohzTf/jhBwkODs53vBY13NzclC+VeaU2R4saeWGn/gcoffH398f169dRunRpBAUFYceOHahRowbOnDkDMXM4kNp4W6mhxZhKliyJS5cuISgoCOXKlcP69etRq1Yt7N69Gw4ODmZrJCYmolq1agAAV1dX5eJzL774IsaOHZvvGmriR4wYYXaM2a5cuZKveGvXyC22ePHiqFWrFmbNmqVMmz59ukU5WtR42J07d+Dr6wsA8PLywpUrV/DMM8+gWrVqZo93UBuvVQ0HBwfcunUrx/Tbt2/D3t5e83hLcpycnLBt2zaUL1/eZPq2bdvg6OhotobaHGvHA8Abb7yBYcOGIT09Hc2aNQMAbNy4EaNGjcI777yT73gtarzyyitYv349BgwYYLa+OWpztKiRFzbfxDRr1gwrV65EaGgoevfujeHDh2P58uXYs2cPXnrppXzH20oNLcbUsWNHbNy4EfXr18eQIUPw+uuvY+7cuUhISMDw4cPN1lDblKitoSZ+//79Zsf4sEaNGlkcb+0aucWGhIQgJSVFmf/wQZFqc7So8bAKFSrg2LFjKFOmDGrUqIFvvvkGZcqUwZw5cxAQEJDveK1qvPjii+jfvz/mzp2LevXqAQB27tyJAQMGoF27dprHW5IzbNgwDBw4EPv27TOJnzdvntkvHJbkWDseAEaOHIlr167hrbfeUg7KdnR0xOjRozFmzJh8x2tRIyQkBGPHjsWOHTtQrVo1FC1a1GT+22+/ne8cLWrkSYHu1ymEMjMzJT09XXn+008/yZAhQ2TmzJmSmpqa73hbqaHFmB61fft2+eKLL2TlypW5xowePVo5RTgqKkrs7OwkJCRE7O3tZfTo0QVSIz/x9HQtWrRI5s+fLyIie/bskWLFionRaBRHR0eJiorKd7xWNW7cuCHt2rVTLiJnb28vRqNROnToYPaq1NaOtzRn6dKl8uyzzyoH3j/77LOydOlSs7GW5lg7PtutW7dk165dcujQIbl//36Bx1uzRpkyZXJ95PYTl9ocLWrkhc2fYp2QkIBSpUrl+BYnIjh//jyCgoLyFa9VDXogNjYWsbGxKF++PNq2bfu0h6NbycnJyMzMhLe3t8n069evw87ODu7u7vnO0aLGo+7evYujR48iKCgIxYoVe2ysJfHWrnHixAkcPXoUAFCpUiWEhIQ81XhLc2zByZMncerUKTRq1AhOTk4Qkceeuq02Xqsats7mm5giRYrg0qVLym/U2a5duwZfX98c189QG2/NGgcPHkTVqlVhNBpx8ODBx65n9erVrR5vyZgedezYMfzvf/9TrjtRqVIlDBkyBBUqVHjsstRQWyOv8R9//HGe6mdf60BtvFY1AKB169Zo27Yt3nrrLZPpc+bMwcqVK/HHH3/kWIbaHC1qUOGTlpZm9hogj/typjbHmvHXrl1D586dsWnTJhgMBpw4cQJly5ZFnz594OXlhS+++CJf8VrV+K+w+SbGaDQiKSkJxYsXN5l+7tw5VK5cGXfu3MlXvDVrGI1GJCYmwtfXF0ajEQaDwewBswaDAZmZmVaPt2RMD/vll1/QpUsX1KlTB2FhYQCAHTt2YPfu3YiKikKnTp1yLAdQ15SoraEmPjQ01Oz4stf32LFjuH//vrLeauO1qgEA3t7e2LZtGypVqmQy/ejRo2jYsCGuXbuWY3lqc6xVY8SIEZg4cSJcXFyeeGBz9sHGauIB2EQNLcb0sBMnTqBPnz7Yvn27yfTsvQXmvgCqzbF2PAD06NEDly9fxvfff49KlSrhwIEDKFu2LNatW4cRI0bg8OHD+YrXqsaFCxewcuVKsxdcfPS1szRHixpPYrMH9mZ/AA0GA8aOHQtnZ2dlXmZmJnbu3ImaNWtaHK9FjTNnziiNzpkzZ564ztaOtzQn26hRozBmzJgcew8++ugjjBo1ymwTk1uTUbVqVbNNidoaauJzOwA1Li4O7733Hv755x+88cYbFsdrVQMAUlNTkZGRkWN6eno67t27Z3aZanOsVWP//v1IT09X/v9J1MY/jRr79u3L9WeBhw+atma8pTnZevXqBTs7O6xevRoBAQF5+plDbY614wFg/fr1WLduHUqWLGkyvXz58jh37ly+47WosXHjRrRr1w5ly5bF0aNHUbVqVZw9exYiglq1apkdk9ocLWrkiUVH0uhAkyZNpEmTJmIwGOTZZ59Vnjdp0kRatWol/fv3N7l+gNp4rWrYEicnJ7PX/Dh+/Lg4OTmZzSlbtqyMHTs2x/Rx48ZJ2bJl813DkjFlO336tHTr1k3s7Oykc+fOT3zt1MZbs0aTJk3M3oH4rbfekueee65AcrSoQYWHs7NzrldiLqgca8eLPLgeVfbn5uGLeO7evdvs9a/UxmtRo27dujJu3DiT+Fu3bkm7du1k1qxZZsekNkeLGnlhs01Mtl69eklKSorV4rWqISJy9OhRGTRokDRr1kyaNWsmgwYNkqNHjz61eLU5rVu3lnnz5uWYPm/ePGnVqpXZHLVNhtoalozpypUryq0WmjVrJrt27TIbZ2m8FjW2bt0qjo6O8vzzzyuXaH/++efF0dFRtmzZUiA5WtTo3bu32c/S7du3zV6RVW28FjXS0tKkSJEicujQIbP1tY63NKdOnTomt4uwRo6140UebBOyr/Lr6uoqp0+flszMTHnllVekU6dO+Y7XosbDFwb19PSUf/75R0QeXKiwdOnSZsekNkeLGnlh001MYfywW1JDRGT58uViZ2eX4x4gdnZ2snz5cs3j85qzYsUK5TF79mwpXry4DBo0SBYtWiSLFi2SQYMGia+vr8yePdtsjbw0GWprWDqm27dvy/jx48Xd3V1q1aol69ate8wrpj5eqxrZ9u/fL127dpXKlStL7dq1pXfv3k/c06M2x9o1jEajJCUl5Zh+5coVKVKkSL7jtaoRHBysXPE6L6wdb0nOxo0bJSwsTDZt2iRXr16V5ORkk0dB5Fg7XkTk0KFD4uvrKy+88ILY29vLyy+/LJUqVRI/Pz+z95lSG69FDT8/P4mPjxcRkUqVKsmKFStE5PFXaFabo0WNvLDpJkakcH7YLamh9mcVa8fnNcdgMOTp8fCl8dU2GWprWDImkQcfQGdnZxk9erTExcXJgQMHzD4sjdeqhi1ITk6WmzdvisFgkJMnT5r8w3T9+nVZuHChBAQEWByvVY1s33//vbRp08bkPkWPY+14S3Ie/tw8/DD3WbI0x9rx2W7evCmffPKJvPLKK9K6dWv54IMP5OLFiwUWb+0a7du3l2+//VZERN555x0JCQmRTz75RGrVqiXNmzcvkBwtauSFzZ+dNHfuXPz6669YtGhRjmtPFES8VjWcnZ1x8ODBHNdoOHHiBGrUqIG7d+9qGm9pTl4YjcY8xeV2doG1PDyuR8/Kyn7+6JlcauKtXSMlJUW5zkpKSspj1/XhODU5WtQAoJwZlxuDwYAJEybggw8+sCheqxrZQkNDcfLkSaSnp6N06dJwcXExmf/o7QqsHW9JTkxMTC5r/kDjxo1zTFObY+14W3H69Gncvn0b1atXx507d/DOO+9g+/btKF++PKZPn47SpUvnO0eLGnlhs2cnZfvqq69w8uRJBAYG5umDqDZeqxpNmjTB33//naNh2Lp1K55//nnN4y3NyYtHr+VQWKg9G0ttvLVreHl5Kdcn8vT0NPsP7qNNktocLWoAwKZNmyAiaNasGX755ReTLwP29vYoXbo0AgMDlWlq47Wqka1Dhw5mp+fG2vGW5FjSEKjNsXZ8ths3bmDu3LnKZR0qV66M3r175/qlU228tWuULVtW+X8XFxfMmTPnieusNkeLGnlh83tiJkyY8Nj5H330Ub7irVlj5cqVyrSLFy9i3Lhx6Ny5Mxo0aADgwanGP//8MyZMmIABAwZYPd6SMT0qJiYGn3/+uckHceTIkflqevJbQ4sxFQYxMTFo2LAh7OzssHnz5sfuNcje+KvN0aLGw86dO4dSpUrlee+d2nitatiSu3fvmr0GiLmLX1qaY834LVu2oG3btvDw8ECdOnUAAHv37sXNmzexatWqHPc7UxuvRY1x48ahadOmCAsLy/VGl49Sm6NFjTyx6Eco0oS1j/Ww5NgQS48nEXlwT5nsU38jIyMlMjJSOnfuLEWLFpXFixfn+nfYvHmzvPjii1KuXDkpV66ctG3bNtezW9TWyGv8uXPn8vai/X87d+5UFX/hwgVNatiiGzduyOeffy59+/aVvn37yvTp03O9t48l8VrV0LvLly9LREREjuNPsh8FkWPteBGRqlWryhtvvCEZGRnKtIyMDOnfv79UrVo13/Fa1GjRooW4uLiIg4ODPPfcc/LBBx/Ihg0b5O7du2bHY0mOFjXy4j/RxNy4cUO+++47ee+995SD1Pbu3ZvrRl1tvFY19K5ixYoyffr0HNO/+OILqVixotkctU2J2hp5jff19ZX+/fs/9tTlmzdvyrfffitVqlQRV1dXVfGRkZGa1Mj2/PPPy9ixY+XPP/+Ue/fu5Zr/MLU5WtTIvk5GiRIlpGPHjtKxY0cpWbKk+Pj4yN69e/Mdr1WNjIwMmTZtmtStW1f8/PyUmxVmP7SOtySna9eu0rBhQ9m9e7e4uLjI+vXrZdGiRVKhQgVZvXq12Rpqc6wdLyLi6Oho9jIRR48eFUdHx3zHa1UjPT1dtm7dKp9++qmEh4eLm5ub2NvbS8OGDc3GW5KjRY0nsfkm5sCBA1K8eHEJCQkROzs75SJBH3zwgXTv3j3f8VrVsAX29vZmr/ly4sQJcXBwMJujtilRWyOv8VevXpXhw4eLh4eH+Pn5SZs2baRfv34yePBg6datm4SGhoq9vb00aNBAoqOjVcdrVSPbxIkTpWXLlsq3ooYNG8oHH3wg69evlzt37ph9LdTmaFHjueeek169epncTT09PV169uwpzz//fL7jtaoxduxYCQgIkM8//1wcHR1l4sSJ0rdvX/Hx8TFpPrWKtyTH399f2Tvo5uYmx44dE5EHZxvm9g+U2hxrx4uIPPvss/Lbb7/lmP7bb79J/fr18x2vVY1sx44dkzlz5sjLL78sdnZ24uPj89h4S3K0qJEbm29imjdvLiNHjhQR0ysdbtu2zezFddTGa1VDRGTXrl3y2WefyTvvvKNclyX78TTi1eaUK1dO5syZk2P67NmzJSQkxOzy1TYlamuojb979678/PPPMnToUOnQoYOEh4dLt27d5PPPPzd77R+18VrVyJaeni7bt2+XyZMnS3h4uBQtWjTXhtLSHGvWcHR0NHtF1sOHD5u9GKLaeK1qlC1bVtkz8PAFwSIjI+W1117TPN6SHDc3Nzlz5oyIiAQFBcnWrVtF5MFVpHNbb7U51op/+HIEUVFREhQUJNOmTZO///5b/v77b5k2bZqUKVNGoqKiLIrXqka2b775Rl577TUJDAwUHx8f6dChg3z55ZcSFxcnWVlZZl8LtTla1MgLm29i3N3dlQ/fww3D2bNnzW4U1cZrVWPSpEliMBikYsWK0rhxY5NbFjRt2lTzeEtyZs2aJfb29jJgwAD54Ycf5IcffpA333xTHBwczDYSIuqbDLU1LBmTLTl27Jh888030qVLFwkICBBvb2/p0KFDgeZYs4avr6/ZC/ytXbtWfH198x2vVQ1nZ2flmCh/f3/lZ6dTp06Ju7u75vGW5NSpU0fWrl0rIiJt27aV7t27y4ULF2TUqFG5XmtKbY614rOP41NzPKGaeK1qPFzL19dXPvvsM7l165bZv31+c7SokRc238QUL15c9u3bJyKmDcP69eulZMmS+Y7Xqoavr6/Mnz8/T+usRbylOb/++qs0bNhQvL29xdvbWxo2bCi///57rvGWNBlqa6iNtwUPfxvq2LFjnr4Nqc3RosaQIUOkZMmSEhUVJQkJCZKQkCA//fSTlCxZUoYOHZrveK1qPPPMM7Jjxw4REWnYsKFMnjxZRESioqKkePHimsdbkrNo0SJle7Bnzx4pVqyYGI1GcXR0NLu3wJIca8WfPXs2zw9L4rWqke23336T4cOHS2hoqDg6OkpYWJiMGTNG1q1bl+tPuWpztKiRFzbfxPTt21c6dOggaWlpyj0nzp07J6GhoWY3KGrjtarh7++v6uaQ1o63NMcS/8Umw9oMBoMUL15cRo8enecNiNocLWqkpqbK22+/Lfb29soZJw4ODjJs2DC5f/9+vuO1qjF69GiZNGmSiDxoEuzs7CQkJETs7e1l9OjRmsdbmvOwO3fuyN69e+XKlStPjLU0x9rxtuDmzZuyatUq6dGjR55+yrUkR4saubH5JubmzZvSokUL8fT0lCJFikipUqWkaNGi0qhRI7l9+3a+47Wq8dlnn+Xa4DyNeEtygoOD5erVqzmm37hxQ4KDg1XVLqgaWoypMLp+/bqsWLFChg8fLrVq1RInJyeTb0UFkaNFjWx37tyRgwcPysGDB/PULKmN16pGttjYWPniiy9k5cqVhSLe0hy9+uGHH+TZZ5+VgIAAZU/HjBkzcv3ypDZeixpXr16VX375RYYMGSLVqlUTo9GoHIeSG7U5WtR4Epu/2F22rVu34uDBg7h9+zZq1aqFFi1aFGi8tWtkZWUhIiICx48fR+XKlVG0aFGT+b/++qum8ZbkGI1GJCYmwtfX12R6UlISgoKCkJqamqNG2bJlsXv3bvj4+JhMv3nzJmrVqoXTp0/nq4YlY7JFJ0+exCeffILFixcjKysrT7dzUJtjjRrJycnIzMzMcdXS69evw87OTrlNgaXxWtXYsmULnn32WdjZmV5EPSMjA9u3bzd78TNrxuc1Z8SIETnycjN9+nQAUJ1j7fhHzZ49G+PGjcOwYcMwadIk/PPPPyhbtiwWLFiAhQsXYtOmTfmK16JGtWrVcOTIEXh5eaFRo0Zo0qQJGjdu/NgLDqrN0aJGXtj8bQeyPffcc3juueesFm/tGm+//TY2bdqEpk2bwsfH57FXNdUiXk3Ow1f5XbduHTw8PJTnmZmZ2LhxI8qUKWM29+zZs2b/sUtNTcW///5rcY38jMkWXLt2DTExMdi8eTM2b96M+Ph4eHp6om3btrleql1tjhY1unTpgrZt2+Ktt94ymb5s2TKsXLkSf/zxR77itarRtGlT5dYLD0tOTkbTpk1zfAasHZ/XnP379+fIM+fhbYPaHGvHP+p///sfvvvuO3To0AFTpkxRptepUwfvvvtuvuO1qDFgwAA0btwYVatWNVvfHLU5WtTIE4v23+jMn3/+KREREVK2bFkpW7asREREyIYNGwosXosarq6uuV6c6WnEq8l5+Cj6R4+st7e3l2eeeUZWrVplkpN9F2uDwSA//PCDyZ2tf/31Vxk0aJA888wzFtewZEy2xGg0iq+vr3Tq1ElmzpwpBw8eLPAcLWp4eXlJfHx8julHjhwRb2/vfMdrVcNgMMjly5dzTD927Ji4ublpHm9pji1wdHRUfq55+MSL48eP53ohOjXxWtUQeXB81tGjR02uWfQkanO0qPE4Nr8nZtasWRg6dChefvllDB06FMCD+/u0adMGM2bMwKBBg/IVr1UNb29vlCtXLs/rbe14NTnZN3QMDg7G7t27UaxYsSfmZN98zmAwoGfPnibzihYtijJlyuCLL76wuIYlY7IlBw8eRJUqVayao0WN1NRUZGRk5Jienp6Oe/fu5Tve2jVeeuklAA/e57169YKDg4MyLzMzEwcPHsSzzz6rWbylOeakpKTgr7/+QsWKFVGxYsUnxluSY4344OBgxMXF5bij8tq1a1GpUqV8x2tR4969exg8eDAWLlwIADh+/DjKli2LIUOGoESJEnjvvffynaNFjTzJdxtUyJUoUUL+97//5Zj+1VdfSWBgYL7jtaoxb9486dy5c54PELR2vKU5apUpU+Y/dSaBVu7evWvyup09e1ZmzJihXFOjIHK0qNGkSRMZPHhwjulvvfWWPPfcc/mOt3aNXr16Sa9evcRgMMirr76qPO/Vq5f0799fPv30U5P3v7XjLc0REXnllVeU7drdu3elfPnyUrRoUbGzs5Ply5eb/duqzbF2vIjId999JyVKlJCoqChxcXGRn376ST755BPl//Mbr0WNt99+W2rXri1///23uLi4KHtufv/9d6lZs6bZManN0aJGXth8E+Pi4mL2iq/Hjx8XFxeXfMdrVaNmzZri5uYmrq6uUrVqVQkNDTV5aB2f15zIyEjlHjjZ9z7K7WEptTW0GFNh17JlS5k9e7aIPDgTy9fXV0qWLCmOjo4ya9asAsnRosbWrVvF0dFRnn/+eRk/fryMHz9enn/+eXF0dDR7k1C18VrVGD9+fK5nJj6NeEty/Pz8JC4uTkREFi9eLCEhIXLnzh2ZNWtWrv9Aqc2xdny2H3/8UUJCQpSfmEuUKCHff/99gcVbu0ZQUJDExsaKiOnPTydOnMj1p0C1OVrUyAubPzupa9euCA0NxciRI02mf/7559izZw+ioqLyFa9VjQkTJjx2PT/66CNN4/OaExwcjD179sDHxwfBwcG5xhoMBuVMo5kzZ6J///5wdHTEzJkzH1vj7bffVl3DkjHZmmLFiiEmJgZVqlTB999/j//973/Yv38/fvnlF4wbNw5HjhzJd44WNQAgLi4O06ZNQ1xcHJycnFC9enWMGTMG5cuXN7vuauO1qqF3Tk5OOH78OEqVKoUePXogMDAQU6ZMQUJCAipXrozbt2/nO8fa8Y+6e/cubt++nePg5oKKt1YNZ2dn5QwmNzc3HDhwAGXLlsWBAwfQqFEjJCcn5ztHixp5YZPHxDz8D1/lypUxadIkbN68GWFhYQAeHH+ybds2vPPOOxbFa1XjYeaaiMexdnxec86cOWP2/x9nxowZ6NatGxwdHTFjxoxc4wwGA95++23VNSwZk625e/cu3NzcAADr16/HSy+9BKPRiAYNGuDcuXMFkqNFDQCoWbMmFi9enOd1VxuvRY2kpCS8++672LhxIy5fvoxHv1s+evaQteMtySlVqhRiY2Ph7e2NtWvXKl/Gbty4AUdHR7PrrTbH2vGPcnZ2hrOz8xPjLI23Vo06deogOjoaQ4YMAfB/Z2J9//33yr85+c3RokZe2GQT8+g/fF5eXoiPj0d8fLwyzdPTE/PmzcOHH36oOl6rGv9VbDKsLyQkBL///js6duyIdevWYfjw4QCAy5cvm72OiSU51qyRkZGBzMxMk4NOk5KSMGfOHNy5cwft2rUzuXyB2nitamTr1asXEhISMHbsWAQEBDzxEgfWjrckZ9iwYejWrRtcXV1RunRpNGnSBMCD681Uq1atQHKsHQ88OIj2cev66N5ZtfFa1Pj000/RunVrxMfHIyMjA5GRkYiPj8f27dsRExNjdhlqc7SokRc2/3OSrTAajY99Ez/6rcja8ZbkZGZmYsGCBco3u+wzhLL99ddfuS4rr9TW0GJMhdHy5cvRtWtXZGZmonnz5li/fj0AYPLkydiyZQvWrFmT7xxr1ujduzfs7e3xzTffAABu3bqFKlWq4P79+wgICEB8fDxWrFiBNm3aWBSvVY1sbm5u+Pvvv1GzZs0nvnZaxFuas2fPHpw/fx4tW7aEq6srACA6Ohqenp5o2LBhgeRYOz4yMtLkeXp6Ovbv34+1a9di5MiROc6gURuvVY1Tp05hypQpOHDggHJB1dGjR+favFmSo0WNJ2EToxMrVqwweZ79Jl64cCEmTJiAvn37ahpvSc7gwYOxYMECREREmP1mZ+6nI7VNhtoalozJViQmJuLSpUuoUaMGjEYjAGDXrl1wd3fP9fRTtTnWqvHMM8/gq6++QqtWrQAAX3/9NT799FPEx8fDw8MDo0ePxq5du5QrmaqN16pGtsqVK2Px4sUIDQ01/2JpHG9pji37+uuvsWfPHsyfP98q8VrVsDU22cRocZlqrS+FnZslS5Zg6dKlORqKpxX/uJxixYrhhx9+MPtNNDdqmwy1NSwZEz19Li4u+Oeff5QDs1966SWULFlSOfYsPj4eTZo0weXLly2K16pGtvXr1+OLL77AN998k6crRVs7Xk1OXrdtD2/X1OZYOz4vTp8+jZo1ayIlJcUq8QVdIysrCydPnjT75c/cbSYsydGixpPY5DExeb3stKXxWtXIiwYNGqB///6FJv5xOfb29ggJCVG1rKioKCxbtizPTYbaGpaMyRbcuXMHU6ZMyXUPl7nf8dXmWLOGo6OjyYXjduzYgWnTpinPHR0dTc48URuvVY1sr776Ku7evYty5crB2dk5x33Irl+/rmm8mpy8bNse/fKhNsfa8XmxfPnyHPfDKsj4gqyxY8cOdO3aFefOnctxQLbBYDB7eIDaHC1q5IVNNjHmdtcWZLxWNZ7k3r17mDlzJkqUKFEo4p+U88477yAyMhJfffVVnjcgapsMtTUsGZMt6NevH2JiYtC9e/c8H+ipNseaNWrWrIlFixZh8uTJ+Pvvv5GUlIRmzZop80+dOoXAwECL47Wqke3LL7984t9Gy3g1OYVx+5mf7W1oaKjJ+05EkJiYiCtXrmDWrFn5jteixoABA5QzgfL62VObo0WNvLDJn5OeRESwdu1azJ07F8uXLy/weGvU8PLyyvEmvnXrFpydnfHjjz+iXbt2msZbktOxY0ds2rQJ3t7eqFKlSp7ulP3FF1/g9OnTeW4y1NawZEy2wNPTE9HR0bkebFkQOdasERMTg9atWyMgIACXLl3Ca6+9hrlz5yrz33rrLdy5c0e5vLnaeK1qUOHz6PWvjEYjihcvjiZNmpg9jkttvBY1XFxccODAAVVfANXmaFEjL2xyT0xuzpw5g3nz5mHBggW4cuUKWrRoUaDx1qwxY8YMk3/Es9/E9evXh5eXl+bxluR4enqiY8eOZpeVm61bt2LTpk1Ys2ZNnpoMtTUsGZMt8PLyUr2rW22ONWs0btwYe/fuxfr16+Hv749XXnnFZH7NmjVRr149i+O1qpHt33//xS+//ILjx48DACpUqICXXnop172g1o63NMcWFJZrbOUnvn79+jh58qSqZkFtjhY18sLm98SkpqZi+fLlmDt3LrZu3YrMzEx8/vnn6Nu3r9lrVaiN16rGf1Xv3r0fO/+/fFR+fvz4449YsWIFFi5cmOcLbanN0aKGLZg1axZGjBiBtLQ05fOfkpICe3t7TJ8+HW+99Zam8Zbm2IqHmzd7e3tUqFABnTt3zvXLnNp4a9U4ePCg8v+nTp3Chx9+iJEjR6JatWo5vvxVr17dohwtaqhm0c0KdGDPnj0ycOBA8fT0lDp16khkZKQkJiaKnZ2dHD58ON/xWtQ4cOBAnh9axFuaQ4WPte6VpXUNvVu9erUUKVJE3nnnHbl48aIy/eLFizJ8+HCxs7OT6OhozeItzbEVX3/9tTg4OIjBYBAPDw/x8PAQg8Egzs7OsmTJEhERycrKkn379lkUb80aBoNBjEajcm+lRx/Z84xGozIWtTla1FDLZn9Oql+/PoYMGYIdO3agQoUKBR6vRY2aNWvCYDDkOIr7UdlHdVs73pIxPWr58uVYtmwZEhISkJaWZjJv3759j11mXqmtocWYCpsOHTpYPUeLGno3bdo0vPfee/jkk09MpgcEBGD69OlwdnbG1KlTlbPzrB1vaY4tiI6Oxttvv41hw4bhnXfeQUBAAADg0qVLmDZtGnr27IlSpUph1qxZqFixIi5evKgqPjQ01Ko1hg0bhqFDh6paZ7VXRLfkCurWvuq6zf6cFB4ejtjYWLRt2xbdu3dHeHg4DAYDihYtigMHDqBy5cr5iteiRm73ijGndOnSVo+3ZEwPmzlzJj744AP06tUL3377LXr37o1Tp05h9+7dGDRoECZNmmR2OWqaDLU1LB0TUUFwd3fH7t27c/1Sc+zYMdStW1e5Doi149XmPPxTwZOY+3khLznWjs/WpEkTPPfcczmat2wffvghvvjiC/j7+2Pz5s3o2bOnqvjs2x5Yu8Z/jkX7b3QiISFBJkyYIGXKlBE/Pz95++23xc7OTuLj4wskXqsatqJChQrK7s+Hb8M+duxYGTRokNmcyMhIcXV1lcGDB4u9vb28+eab0qJFC/Hw8JD3338/3zUsGRNRQXF2dlbec+acOnVKnJ2dNYtXm/PozwGPe2RTm2Pt+Gxubm5y9OjRXNf76NGjYjAY5Ny5cxbFa1VDROTTTz+VuXPn5oifO3euTJkyxeyy1OZoUSMvbLqJedj69evltddeE0dHRylfvryMGTNG9u7dW2DxWtQ4efKkDB48WJo3by7NmzeXIUOGyMmTJ59avNocJycnOXv2rIiIFC9eXOLi4kRE5Pjx4+Lt7W02R22TobaGJWOyBU/awBdEjhY19K5u3boyffr0XOd/8cUXUrduXc3i1eacPXtWefz2229Srlw5mTNnjnJc3Jw5c6R8+fLy22+/Kflqc6wdn62wNYiW1hARKV26tGzbti3H9B07dkiZMmXMLkttjhY18uI/08Rku379usycOVNq1qyZp42i2nhr1Vi7dq3Y29tLvXr1ZPjw4TJ8+HCpV6+eODg4yPr16zWPtyQnODhYOVitdu3aMmfOHBERWbdunXh5eZmtobbJUFvDkjHZgt9//93k8fPPP8v7778vJUqUkO+//75AcrSokZGRIdOmTZO6deuKn5+feHl5mTzyG2/tGgsWLBAnJyf5+uuvJT09XZmenp4uX331lTg5Ocn8+fM1i7c0R+RB82PugN/o6GipVatWjumW5FgzvrA1iJbWEBFxcHCQ06dP55h+6tQpcXBwMLsstTla1MiL/1wT87An7VnJb3xB1qhZs6aMHj06x/TRo0fnemaINeMtyenbt6+MHz9eRETZGLZo0UI8PT2lT58+ZmuobTLU1rBkTLZs8eLF0q5dO6vmFGSNsWPHSkBAgHz++efi6OgoEydOlL59+4qPj49ERkbmO16LGu+8844YDAZxd3eX0NBQqVmzpri7u4vRaJRhw4ZpHm9pjqOjo9mfxePj48XR0bFAcqwZXxgbREsbypCQEFm0aFGO6T/88IMEBwfnmG5JjhY18sImm5hHfx98kp07d6qKv3DhgiY1Hubg4CDHjx/PEXfs2LFcu2RrxluSk5mZafJB/Omnn2TIkCEyc+ZMSU1NNVtDbZOhtoYlY7Jlp06dEhcXF6vmFGSNsmXLyurVq0Xkwc+N2T9lRkZGymuvvZbveK1qxMbGyttvvy2tW7eW1q1by9ChQyU2NjaXv4b14y3JCQ0Nle7du5t8blJTU6V79+65fhFSm2Pt+MLYIFpS47PPPhMfHx+ZN2+e8tPa3LlzxcfHRz799NMCydGiRl7YZBPj6+sr/fv3l127duUac/PmTfn222+lSpUq4urqqio+MjJSkxoPK1mypCxbtixHztKlS6VUqVI5pls73tIctdhkaOfu3bsydOhQeeaZZ6yWU9A1nJ2dlS8U/v7+yp7MU6dOibu7e77jtaphC3bu3Cm+vr5SvHhx5Ri54sWLi6+vb65f4tTmWDteJGfz9vbbb6tqEJ8Ur0WNrKwsGTVqlDg6OirHkzk7O8uECRMkKyurQHK0qJEXNnmdmPj4eEyaNAktW7aEo6MjateujcDAQDg6OuLGjRuIj4/H4cOHUatWLUydOhX169dXFd+mTRt069bN6jUe9sYbb6B///44ffo0nn32WQDAtm3b8Nlnn5m97by149XkJCQk5Ol1CwoKyjHNaDTCaDQqz7t06YIuXbrkiFNbIz9jsgVPuu9VQeRoUaNkyZK4dOkSgoKCUK5cOaxfvx61atXC7t274eDgkO94a9ZISEhQ9f7atWtXrrctKIj4f//9F5mZmarG9O+//yq3IahXrx5Onz6NxYsX4+jRowAe3Am7a9eucHFxMZuvNsfa8QDQoEEDNGjQIM9/A7XxWtQwGAz47LPPMHbsWBw5cgROTk4oX758ru9xS3K0qJEnFrU+OnH37l35+eefZejQodKhQwcJDw+Xbt26yeeffy6HDh3Kd7xWNUQedLDTp0+XEiVKKFc6LFGihHz55Ze5dsnWjFeT8/DZJQ9fpfHhaY8e0Hzu3Lk8PSytYcmYbMmCBQtMHj/88IOsWbNGrl+/XmA5WtQYPXq0TJo0SUREoqKixM7OTkJCQsTe3j7X47XUxFuzhrX35mqxhzl7j3FaWpqULVtW1aUi1OZYM97WDkHo3bu3pKSk5Ii5ffu29O7d22y+2hwtauSFzV7szpbdunULAODm5lYo4p+UY2dnh5IlS6JXr15o27Yt7OzM7wCsUaOG8v9FihRR/j/7LfroN/SHrwqstoYlY7IVGRkZ+PTTT9GnTx+ULFnSKjla1DBnx44d2L59O8qXL4+2bdsWeHxB1rh27RomTZqEefPmPXHv7NixY5W9udaKb9OmjeoxPbzHuESJEvjzzz9RqVKlPP0dLcmxVryfnx86dOiAfv36oW7dumZjkpOTsWzZMkRGRuLcuXPo2rVrnuP79++PSZMmWb3G22+/DeDB9vPSpUvw9fU1ib969Sr8/f2RkZGRY1lqc7SokRdsYgq5e/fuYcOGDWjatGmOBiElJQWbN29GeHi4sjvO2vGW5CQmJmLhwoWYP38+bt68iddffx19+/Z97IZFbZOhtoYlY7Ilbm5uOHToEMqUKWO1HC1q3L9/H46Ojnlevtp4LWrcu3cP0dHR2Lp1K86dO4d79+6hWLFiCA0NRXh4OKpWrappvKU5n376KY4fP47vv/8+189rfnOsFW/thtKSBtGSGikpKRAReHl54cSJEyhevLiyjpmZmVi1ahXee+89XLx4UZmuNkeLGmqwiSnkIiMjsXLlSmzcuNHs/BYtWqBjx44YNGiQJvGW5mTbunUr5s+fj59//hmVK1dG37590bdvX5PjXoD8NRl5rWFpvC1o3749XnrpJfTs2dNqOVrUcHd3R8eOHfH666+jefPmT3zN1MZrVcMWdOzYERs3boSrqyuqVauW45iTX3/9Nd851o4vjA2imnij0Wiyx/pRBoMBEyZMwAcffGBxjhY11GATU8jVq1cPY8eOzXW39erVq/Hxxx9j165dmsRbmvOopKQkvPbaa4iJicGVK1fg7e2da6ylTYaaGpbE69mcOXMwYcIEdOvWDbVr186xcW/Xrl2+c7So8dtvv2HJkiWIjo6Gh4cHXn31Vbz++uuoU6eO2fVWG69VDVvQu3fvx86fP39+vnOsHa93MTExEBE0a9YMv/zyi8k2zN7eHqVLl0ZgYGC+crSooYpFR9KQZjw9PR97QNi5c+fE09NTs3hLc7Jt27ZN+vbtK+7u7lK3bl2ZPXu2ZGZm5rqshyUmJkrTpk3FaDTKtWvXco1TWyM/Y9Kr7IOZzT0ed0sANTla1MiWkpIi8+bNk5YtW0qRIkWkfPnyMmHChAKL16oGUUE4e/as6m2Y2hwtauQFm5hCztXVVfbs2ZPr/D179oirq6tm8ZbkXLx4UaZMmSIVKlQQX19fGT58+GPPxHpUXpoMtTXyOyYqvA4fPqzqNiFq47WqQZRfd+7ckSNHjij3jcp+FGSOFjUexyavE2NLqlSpgj///BO1a9c2O3/9+vWoUqWKZvGW5AQFBaFEiRLo2bMn2rVrh6JFiyIrKwsHDx40yatevbry/5cuXcIPP/yA+fPn48aNG+jWrRu2bdtm9jdmS2pYMiYqvO7fv4+VK1diyZIlWLt2Lfz8/DBy5MgCi9eqht4tX74cy5YtQ0JCAtLS0kzm7du3r0ByrB1vC65cuYLevXtjzZo1Zudnn9WZnxwtauSJRa0Paeabb74RFxcXWbVqVY55K1euFBcXF/nmm280i7ck59GfBB6+NktuPxXY2dlJ6dKlZdy4cbJnz54cHfujnbvaGpaMyVakpKTInj175NatWyLy4H5d3bt3l5dffll+/PHHAsnRoobIg5uQ9ujRQ9zd3cXb21v69+8vMTExua672nitatiCyMhIcXV1lcGDB4u9vb28+eab0qJFC/Hw8JD333+/QHKsHW8runbtKg0bNpTdu3eLi4uLrF+/XhYtWiQVKlRQbomR3xwtauQFmxgd6NatmxgMBqlUqZJ06NBBOnToIBUrVhSj0ShdunTRPF5tTvY9Mp70eJjaJkNtDUvGZAtiYmLEzc1NDAaDeHt7y7p168TNzU0qVqwoVapUEaPRKN9++22+crSokc3JyUleeeUV+f333yUtLe2J6682XqsatqBChQqyZMkSEXnwk/OpU6dE5MENMQcNGlQgOdaOtxX+/v7KxfLc3Nzk2LFjIiKyYsUKadiwYYHkaFEjL9jE6MTSpUulffv2UrlyZalUqZK0b99eli5d+tTiLc3Jq/9qk2Ftzz//vPTp00cuXLggH3/8sXh6esqYMWOU+RMnTpQaNWrkK0eLGtnMXf3zcdTGa1XDFjg5OSmfyeLFi0tcXJyIiBw/fly8vb0LJMfa8bbCzc1Nzpw5IyIiQUFBsnXrVhEROX36tDg5ORVIjhY18oJNDFmV2kttP3r3bmvUyO8dxfXMw8NDjhw5IiIP7uZrNBqVDbuIyIkTJ3IcyK02R4saD8vIyJDly5fLxIkTZeLEifLLL79IRkZGrn8DtfFa1dC74OBg2bdvn4iI1K5dW+bMmSMiIuvWrRMvL68CybF2vK2oU6eOrF27VkRE2rZtK927d5cLFy7IqFGjpGzZsgWSo0WNvGATQ1Zlyb1Y1DYlxYoV0/SO4npmMBgkKSlJef7wLnaRB6exP3oskNocLWpkO3HihJQvX16cnZ0lNDRUQkNDxdnZWSpUqCAnT57Md7xWNWxB3759Zfz48SIi8tVXX4mTk5O0aNFCPD09pU+fPgWSY+14W7Fo0SKZP3++iDw4W7RYsWJiNBrF0dFRoqKiCiRHixp5wYvdkVVZci8Wtfcx6datG65cuWLVy3nbiiJFiiAxMVG57Le7uzsOHDiA4OBgAA8u+BcYGGhyloDaHC1qZGvTpg1EBIsXL1YuoHXt2jW8/vrrMBqNiI6Ozle8VjVsQVZWFrKyspTL+0dFRSn3jHrzzTdhb2+f7xxrx9uqu3fv4ujRowgKCkKxYsWskqNFDXPYxJAm1Fw629Kb0GlxyXC9MxqNqFq1qrJRP3jwICpWrKhszDMyMnD48GGTZkFtjhY1srm4uGDHjh2oVq2ayfQDBw6gYcOGuH37dr7itapBVJB++uknvPbaa2bnjRw5EtOmTct3jhY18oJNDBVa/8Umw9omTJiQp7iPPvrI4hwtamTz9vbG6tWr8eyzz5pM37ZtG9q2bYvr16/nK16rGragUaNGaNKkCRo3boyGDRvm6SaYanOsHW8rPD098dNPP6F169Ym04cPH46oqChcunQp3zla1MgTi36EIiIqBLp37y5VqlSRHTt2SFZWlmRlZUlsbKxUrVpVevbsme94rWrYgokTJ0rLli3FxcVFHBwcpGHDhvLBBx/I+vXr5c6dOwWSY+14W7F69Wrx8PCQv//+W5k2ePBgCQwMVA6gz2+OFjXygk2MTty+fVs+/PBDCQsLk3LlyklwcLDJQ+t4S3OICtKNGzekXbt2YjAYxN7eXuzt7cVoNEqHDh3k5s2b+Y7XqoYtSU9Pl+3bt8vkyZMlPDxcihYtKg4ODgWaY+14W7B48WLx8vKSPXv2yMCBAyUwMFC5LktB5WhR40l42wGd6NevH2JiYtC9e3cEBAQ89rbmWsRbmkNUkDw9PbFixQqcOHECR48eBQBUqlQJISEhBRKvVQ1bcvr0aRw6dAgHDhzAwYMH4ebmhkaNGhVojrXjbUHXrl1x8+ZNNGzYEMWLF0dMTMwT34Nqc7So8SQ8JkYnPD09ER0djYYNGxaKeEtziMg2de3aFTExMUhNTUWjRo3QuHFjNGnSBNWrV8/1C47aHGvH69mIESPMTv/5559Rq1YtlCtXTpk2ffp0i3K0qKEW98TohJeXl3K6ZmGItzSHqCB8/PHHeYobN26cRfFa1bAlUVFRKFasGPr164dmzZrhueeeg7Ozc4HmWDtez/bv3292ekhICFJSUpT5DzdvanO0qKEW98ToxI8//ogVK1Zg4cKFefoQWjve0hwqHO7fv6/6TA21OdasERoamus8g8GAY8eO4f79+8op2WrjtaphS27cuIG///4bmzdvRkxMDI4cOYKaNWuiSZMmaNKkCVq1apXvHGvHk/6wiSnEQkNDTbrTkydPQkRQpkwZFC1a1CR23759Vo+3ZExUODk6OqJevXrK7vVnn30WTk5OBZqjRY1HxcXF4b333sNff/2FPn36YM6cOQUar1UNW3Dy5El88sknWLx4MbKysvLUvKnNsXa8rUhJScFff/2FihUromLFilbJ0aKGOfw5qRDr0KFDoYq3NIcKnz///BNbtmzB5s2bMWPGDGRkZKBOnTpK89CyZct852hRI9uZM2cwduxYLF26FC+99BIOHz6M8uXL57r+auO1qqFn165dQ0xMDDZv3ozNmzcjPj4enp6eaNu2LRo3blwgOdaOtxWdO3dGo0aNMHjwYNy7dw916tTB2bNnISKIiopCp06d8p2jRY08sfi8JiKyCdmnn/bs2VPs7OzM3qMovznWqnHlyhUZPHiw2NvbS7NmzR57PyxL4rWqYQuMRqP4+vpKp06dZObMmXLw4MECz7F2vK3w8/NTbqC6ePFiCQkJkTt37sisWbOkZs2aBZKjRY28YBOjEwkJCXL+/Hnl+c6dO2Xo0KHyzTffPJV4S3Oo8Dh27Jh888038tprr0lAQIB4e3tLhw4d5MsvvyywHGvVuH37towfP17c3d2lVq1asm7duseuq9p4rWrYkn/++cfqOdaOtxWOjo6SkJAgIg8uvjh69GgRETl37py4uLgUSI4WNfKCTYxOPPfcc/LDDz+IiMilS5fEzc1NwsLCpFixYjJhwgTN4y3NocIhMDBQvLy8pGPHjhIZGSlxcXGSlZVVoDnWrOHn5yfOzs4yevRoiYuLkwMHDph9WBqvVQ0iayhfvrwsXbpUbt++LcWLF5eNGzeKiEhcXJz4+PgUSI4WNfKCx8ToxD///IN69eoBAJYtW4Zq1aph27ZtWL9+PQYMGJDjtE1rx1uaQ4VD8eLFcfToUSQmJiIxMRFJSUm4d+/eY88yU5tjzRqXL18GAEydOhXTpk2DPHR+gsFggIjAYDAoB26qjdeqht49eqD/4+R2csCTcqwdb4uGDRuGbt26wdXVFaVLl0aTJk0AAFu2bMlxY1JLc7SokRdsYnQiPT0dDg4OAB4c/NiuXTsAQMWKFc3eNMva8ZbmUOEQFxeHmzdvYsuWLYiJicH777+P+Ph41KxZE02bNsWkSZPynWPNGmfOnFG1vmrjtaqhdw8f6H///n3MmjULlStXRlhYGABgx44dOHz4MN566y2Lc6wdb4veeust1K9fHwkJCWjZsiWMRiMAoGzZsvjkk08KJEeLGnli0f4b0ly9evVk9OjRsmXLFnF0dFQOjoqNjZUSJUpoHm9pDhU+V69eleXLl0v37t3zfNCt2hwtatDT1bdvX/nwww9zTB83bpz07t27QHKsHU/6w+vE6MTmzZvRsWNHpKSkoGfPnpg3bx4A4P3338fRo0fx66+/ahpvaQ4VDr/++qvJaafe3t547rnn0KRJEzRu3Bg1atTId461aiQkJCAoKCjP67pr1y7lZ8+8+Pfff5GZmWn1GiVKlMhzvB54eHhgz549OU4jP3HiBOrUqYPk5OR851g73pZcuHABK1euREJCAtLS0kzm5XZ5f7U5WtR4oqfdRVHeZWRkyPXr102mnTlzRpKSkp5KvKU59PQVL15cOnXqJP/73//yfNqp2hxr1fD19ZX+/fs/9tTlmzdvyrfffitVqlQRV1dXVfGRkZGa1LA1fn5+Mn/+/BzT58+fL76+vgWSY+14W/Hnn3+Ks7OzVK1aVezs7KRmzZri6ekpHh4e0rRp0wLJ0aJGXvCYGB0pUqQIvLy8TKaVKVPmqcVbmkNPX/ZBqNbMsVaN+Ph4TJo0CS1btoSjoyNq166NwMBAODo64saNG4iPj8fhw4dRq1YtTJ06FfXr11cV36ZNG3Tr1s3qNWzNsGHDMHDgQOzbt0/ZK7Vz507MmzcPY8eOLZAca8fbijFjxuDdd9/FhAkT4Obmhl9++QW+vr7o1q0bXnjhhQLJ0aJGXvDnpEKsVq1a2LhxI7y8vJ54xP2+ffusHm/JmKjwOnXqFObPn49Tp04hMjISvr6+WLNmDYKCglClSpUCybFmjXv37iE6Ohpbt27FuXPncO/ePRQrVgyhoaEIDw9H1apVTZarNl6rGrZk2bJliIyMxJEjRwAAlSpVwtChQ9G5c+cCy7F2vC1wc3NDXFwcypUrBy8vL2zduhVVqlTBgQMH0L59e5w9ezbfOVrUyAvuiSnE2rdvr5z90759+yeeNmjteEtzqPCJiYlB69at0bBhQ2zZsgWTJk2Cr68vDhw4gLlz52L58uX5zrF2DScnJ7z88st4+eWX87TOauO1qmFLOnfurLo5UJtj7Xhb4OLiohxvEhAQgFOnTilfAK5evVogOVrUyBOLfoQiIl1r0KCBfPHFFyIi4urqKqdOnRKRB1ddzu3MMrU5WtSgp2/nzp2SkZGR6/z79+/L0qVL85Vj7Xhb0759e/n2229FROSdd96RkJAQ+eSTT6RWrVrSvHnzAsnRokZesInRieDgYLl69WqO6Tdu3JDg4GDN4y3NocLBxcVFTp8+LSKmzcKZM2fEwcGhQHK0qEFPn9FoNDmQ383NTXndREQSExNznB6vNsfa8bbm1KlTytWhb9++LW+++aZUq1ZNXnrpJTl79myB5GhRIy/4c5JOnD171uyVPlNTU3HhwgXN4y3NocLB09MTly5dQnBwsMn0/fv353rqr9ocLWrQ0yePHFb56PO8xDwpx9rxtqZs2bLK/7u4uGDOnDkFnqNFjbxgE1PIrVy5Uvn/devWwcPDQ3memZmJjRs3mmzwrR1vaQ4VLl26dMHo0aPx888/w2AwICsrC9u2bcO7776LHj16FEiOFjVIHyw5dk5tjrXj9er+/ftYunQp7t69i5YtWyIkJKTAc7SokSuL9t+QZgwGgxgMBjEajcr/Zz/s7e3lmWeekVWrVmkWb2kOFS6pqanSr18/sbOzE4PBIEWLFhWj0Sivv/56rscSqM3RogY9fQaDweSnm4d/BhQx/9ON2hxrx9uK4cOHy+DBg5XnqampUrNmTSlatKh4eHiIi4uLbN++PV85WtRQg02MTpQpU0auXLlSaOItzaHC5dy5cxIdHS1Lly6V48ePWyVHixr09BgMBtm0aZNyl24XFxeJjo5Wnm/cuNFsE6Mmx9rxtqJKlSqyYsUK5fm8efPEy8tLzp49K1lZWdKrVy9p06ZNvnK0qKEGrxNDREQWMxqNyl26H5Xb3bvV5lg73la4u7tj3759yk8zr732Gtzc3PDtt98CeHCD1TZt2uDixYsW52hRQw0eE1OIzZw5E/3794ejoyNmzpz52Ni3337b6vGWjIkKl48//jhPcePGjbM4R4saVHgUxjuE/xfvKA48aN4ebtx27NhhcmViT09P3LhxI185WtRQg3tiCrHg4GDs2bMHPj4+KFOmTK4HohkMBpw+fdrq8ZaMiQqX0NDQXOcZDAYcO3YM9+/fN/mGqjZHixpElFNYWBheeeUVjBgxAocPH0b16tVx8uRJ5USLmJgY9OzZ0+TKuGpztKihikU/QhGRTdm/f7+Eh4dL0aJF5c0337RKjhY1iP7Lfv31V7G3t5dmzZqJn5+fvPjiiybzR40aJa+88kq+crSooYZRfdtDWktPT0e5cuWUe3887XhLc6jwOXPmDF5//XXUrVsXHh4eOHz48BOv3aA2R4saRAR07NgRf/zxB6pXr47hw4dj6dKlJvOdnZ3x1ltv5StHixqqWNT6kOYCAwMlPj6+0MRbmkOFw5UrV2Tw4MHKt6Ndu3YVeI4WNYjov417YnRi0KBB+Oyzz5CRkVEo4i3Noafrzp07mDBhAsqVK4ft27dj1apV2LhxI+rWrVtgOVrUICJTCQkJquL//fdf1Tm7du2yeo1///1XVTwP7NWJjh07YuPGjXB1dUW1atXg4uJiMv/XX3/VNN7SHHq6/P39cevWLQwZMgSvvfZargdmV69e3eIcLWpQ4ZSRkYHNmzfj1KlT6Nq1K9zc3HDx4kW4u7vD1dW1QHKsHa9Xfn5+6NChA/r165dr85+cnIxly5YhMjIS/fv3x6RJk1TlnDt3Dl27drVqjf79+6s6s5VNjE707t37sfPnz5+vabylOfR0GY3/t/P10etoPO6aHmpytKhBhc+5c+fwwgsvICEhAampqTh+/DjKli2LoUOHIjU11ewxTWpzrB2vZ9euXcOkSZMwb948ODo6onbt2ggMDISjoyNu3LiB+Ph4HD58GLVq1cLYsWPRpk0b1Tn169e3eo02bdqoWm82MUT/IefOnctTXOnSpS3O0aIGFT4dOnSAm5sb5s6dCx8fHxw4cABly5bF5s2b8cYbb+DEiRP5zrF2vC24d+8eoqOjsXXrVpw7dw737t1DsWLFEBoaivDwcFStWjXfOVrUyCs2MTqVlpaGtLS0PO8OtXa8pTlEZBt8fHywfft2VKhQAW5ubkrDcPbsWVSuXBl3797Nd46140l/eGCvDsyfPx9DhgzB4sWLAQBjxoyBm5sbPDw80LJlS1y7dk3TeEtz6On6rx74R9rIysoy+3PfhQsX4ObmViA51o4nHdL+hChS45NPPhEnJydp0aKFeHt7y4ABA8Tf31+mTJkiU6dOlZIlS8qAAQM0i7c0h54+X19f6d+//2NPW75586Z8++23UqVKFYmMjFSd4+rqavUakZGReVth0lTnzp3ljTfeEJEHd40+ffq03Lp1S5o1aya9evUqkBxrx5P+sIkp5EJCQmTJkiUiIrJ7924xGo2yfPlyZf4ff/whQUFBmsVbmkNP39WrV2X48OHi4eEhfn5+0qZNG+nXr58MHjxYunXrJqGhoWJvby8NGjSQ6Ohoi3K0qEGF0/nz56Vy5cpSqVIlsbOzkwYNGoiPj49UqFBBkpKSCiTH2vGkPzwmppBzcHDAyZMnUapUKeX5wYMHUaFCBQAPdq0HBwcjLS1Nk3hLc6jw+K8d+EfaycjIwNKlS3HgwAHcvn0btWrVQrdu3eDk5FRgOdaOJ31hE1PIGY1GJCYmwtfXFwBMDk4DgKSkJAQGBpqc3mrNeEtziIiICprd0x4APVl8fDwSExMBACKCo0eP4vbt2wCAq1evah5vaQ4R2a7JkyfDz88Pffr0MZk+b948XLlyBaNHj853jrXjSYee3i9ZlBcGg0GMRqMYDIYcj+zpRqNRs3hLc4jItpUuXVq2bduWY/qOHTukTJkyBZJj7XjSH+6JKeTOnDlTqOItzSEi25aYmIiAgIAc04sXL45Lly4VSI6140l/2MQUcmqvUGrteEtziMi2lSpVCtu2bUNwcLDJ9G3btiEwMLBAcqwdT/rDJqYQS0hIQFBQUJ7jd+3ahXr16lkt/t9//0VmZqaqMf37778oUaJEnuOJSJ/eeOMNDBs2DOnp6WjWrBkAYOPGjRg1ahTeeeedAsmxdjzp0NP+PYtyZ+0LjfHCZERUULKysmTUqFHi6OgoRqNRjEajODs7y4QJEwosx9rxpD88xboQs/YdRgvjHUmJSN9u376NI0eOwMnJCeXLl4eDg0OB51g7nvSDTYwOWPtCY7wwGRER6RGbGCIiyrc7d+5gypQp2LhxIy5fvoysrCyT+adPn853jrXjSX94YC8REeVbv379EBMTg+7duyMgIAAGg6HAc6wdT/rDPTFERJRvnp6eiI6ORsOGDa2WY+140h/j0x4AERHpn5eXF7y9va2aY+140h82MURElG8TJ07EuHHjcPfuXavlWDue9Ic/JxERUb6Fhobi1KlTEBGUKVMGRYsWNZm/b9++fOdYO570hwf2EhFRvnXo0MHqOdaOJ/3hnhgiIiLSJR4TQ0RERLrEn5OIiMgi3t7eOH78OIoVKwYvL6/HXofl+vXrFuVYO570jU0MERFZZMaMGXBzcwMAfPnll1bJsXY86RuPiSEionzJyMjAkiVLEB4eDj8/P6vkWDue9IlNDBER5ZuzszOOHDmC0qVLWy3H2vGkPzywl4iI8q1evXrYv3+/VXOsHU/6w2NiiIgo39566y288847uHDhAmrXrg0XFxeT+dWrV893jrXjSX/4cxIREeWb0Zhzx77BYICIwGAwIDMzM9851o4n/eGeGCIiyrczZ85YPcfa8aQ/bGKIiChfUlJScPz4caSlpaFevXooXrx4gedYO570iU0MERFZLC4uDm3atEFSUhJEBG5ubli2bBnCw8MLLMfa8aRfPCaGiIgsFh4ejtu3b+Pzzz+Ho6MjJk6ciEOHDuHEiRMFlmPteNIvNjFERGSxYsWKYf369ahVqxYA4ObNm/D29sbNmzfh7u5eIDnWjif94nViiIjIYtevX0fJkiWV556ennBxccG1a9cKLMfa8aRfPCaGiIjyJT4+HomJicpzEcGRI0dw69YtZdqj12RRm2PteNIn/pxEREQWMxqNyrVXHpXbNVnU5lg7nvSLe2KIiMhivD4MPU3cE0NERES6xAN7iYjIIgkJCari//33X9U5u3btsmr8v//+qyqeChc2MUREZJG6devizTffxO7du3ONSU5OxnfffYeqVavil19+UZ3TvHlzq8b/8ssveVtZKpR4TAwREVkkPj4ekyZNQsuWLeHo6IjatWsjMDAQjo6OuHHjBuLj43H48GHUqlULU6dORZs2bdCtWzdVOfXr17dqfJs2bZ72n5HygcfEEBFRvty7dw/R0dHYunUrzp07h3v37qFYsWIIDQ1FeHg4qlatmu8ca8eTPrGJISIiIl3iMTFERESkS2xiiIiISJfYxBAREZEusYkhIiIiXWITQ0RERLrEJoboP8hgMOD333/Pc/z48eNRs2bNfNU8e/YsDAYD4uLi8rWcMmXK4Msvv3xsjNr100JBrT8R/R82MUQa6dWrFwwGAwwGA+zt7RESEoKPP/4YGRkZT3tohUaTJk0wbNgwk2mRkZFwcHBAVFQUAGD37t3o37//Uxhd4XX48GF06tQJZcqUgcFgeGKTN2XKFBgMhhx/ayK9YRNDpKEXXngBly5dwokTJ/DOO+9g/PjxmDZtmtnYtLQ0jUdX+Hz00Ud4//33sWLFCnTp0gUAULx4cTg7Oz/lkT0dub0n7t69i7Jly2LKlCnw9/d/7DJ2796Nb775BtWrV7fGEIk0xSaGSEMODg7w9/dH6dKlMXDgQLRo0QIrV64E8GBPTYcOHTBp0iQEBgaiQoUKAIBDhw6hWbNmcHJygo+PD/r374/bt28DANavXw9HR0fcvHnTpM7QoUPRrFmzPI9r9OjReOaZZ+Ds7IyyZcti7NixSE9PzxH3zTffoFSpUnB2dkbnzp2RnJxsMv/7779HpUqV4OjoiIoVK2LWrFlq/jwKEcGQIUMwc+ZMbNiwAS+88IIy79Gfk06cOIFGjRrB0dERlStXxoYNG0yWlZaWhsGDByMgIACOjo4oXbo0Jk+enGttc3uDOnTogF69epmM4dNPP0WfPn3g5uaGoKAgfPvttyY5u3btQmhoKBwdHVGnTh3s378/R61//vkHrVu3hqurK/z8/NC9e3dcvXrVZCyDBw/GsGHDUKxYMYSHh5sdc926dTFt2jR06dIFDg4Oua7b7du30a1bN3z33Xfw8vLKNY5IL9jEED1FTk5OJt+uN27ciGPHjmHDhg1YvXo17ty5g/DwcHh5eWH37t34+eef8eeff2Lw4MEAgObNm8PT09PkJnaZmZlYunQpunXrludxuLm5YcGCBYiPj0dkZCS+++47zJgxwyTm5MmTWLZsGVatWoW1a9di//79eOutt5T5ixcvxrhx4zBp0iQcOXIEn376KcaOHYuFCxeq+ptkZGTg9ddfx/LlyxETE4Nnn30219isrCy89NJLsLe3x86dOzFnzhyMHj3aJGbmzJlYuXIlli1bhmPHjmHx4sUoU6aMqjGZ88UXXyjNyVtvvYWBAwfi2LFjAB40Cy+++CIqV66MvXv3Yvz48Xj33XdN8m/evIlmzZohNDQUe/bswdq1a5GUlITOnTubxC1cuBD29vbYtm0b5syZk68xDxo0CBEREWjRokW+lkNUaAgRaaJnz57Svn17ERHJysqSDRs2iIODg7z77rvKfD8/P0lNTVVyvv32W/Hy8pLbt28r06Kjo8VoNEpiYqKIiAwdOlSaNWumzF+3bp04ODjIjRs3ch0LAPntt99ynT9t2jSpXbu28vyjjz6SIkWKyIULF5Rpa9asEaPRKJcuXRIRkXLlysmSJUtMljNx4kQJCwsTEZEzZ84IANm/f3+udRs3biz29vZib28vR44cMRtTunRpmTFjhrKudnZ28u+//5qM6+H1GzJkiDRr1kyysrJyrfvoGIYOHWoyrX379tKzZ0+TMbz++uvK86ysLPH19ZXZs2eLiMg333wjPj4+cu/ePSVm9uzZJus/ceJEadWqlUmd8+fPCwA5duyYMpbQ0NA8jfvhsWX/fR72008/SdWqVZUxmVtPIr3hXayJNLR69Wq4uroiPT0dWVlZ6Nq1K8aPH6/Mr1atGuzt7ZXnR44cQY0aNeDi4qJMa9iwIbKysnDs2DH4+fmhW7duaNCgAS5evIjAwEAsXrwYERER8PT0zPO4li5dipkzZ+LUqVO4ffs2MjIy4O7ubhITFBSEEiVKKM/DwsKUcbi5ueHUqVPo27cv3njjDSUmIyMDHh4eKv5CwHPPPYe4uDiMHTsWP/30E+zsct9MHTlyBKVKlUJgYKDJuB7Wq1cvtGzZEhUqVMALL7yAF198Ea1atVI1JnMePqbEYDDA398fly9fVsZVvXp1ODo65jquAwcOYNOmTXB1dc2x7FOnTuGZZ54BANSuXTvfYz1//jyG/r/27i+kqT+M4/h7pwWRFZGEJUFWKxJcudG8zPyDhFEXsTZqlBjzIvp/1cXoIkKiEC9GdwUaERkRuygNCR1RLiGq1UaIZZZRgy52VS2EVRfR6GzZz9WvPwc+Lziwc3jY99nhsD083++XHTzIjRs3TDmJWJ2mk0T+oLq6OuLxOE+ePCGTyXDu3DlTgfLt6+nyeDysWLGCnp4eMpkMkUikqKmkO3fuEAgEaG5u5tq1azx48IBQKFTUwuKva3TOnDlDPB7PHclkkuHh4aI+j9PpZGBggGg0it/v/+XdW263m/HxcY4fP04mk8Hn8+H1eqeMNwyDT3n/i/u99UEzZ840ndtsNj5+/DjtvN6+fcvmzZtN9+vrs7F+/fpc3M88E/nu3bvHmzdvcLvd2O127HY7N2/eJBwOY7fbyWazvzyGyN+gTozIH1RSUoLD4Zh2fGVlJd3d3bx79y73YzY0NIRhGLmFvwCBQIALFy6wZMkSDMNg06ZN0x4jFouxdOlSQqFQ7tqLFy8K4iYmJnLdHoDh4eFcHmVlZZSXl/Ps2bOiCqipVFdXMzAwQGNjIz6fj0uXLhUUDfDl/rx8+ZJUKsXixYtzeeWbN28efr8fv9+P1+tl48aNpNNpFixYUBC7cOFCUqlU7jybzZJMJqmrq5t2/pWVlZw/f54PHz7kOh/5ebndbq5cuUJFRcUPu03/h4aGBhKJhOlaa2srq1ev5siRI8yYMeO3ji/yu6gTI/IPCwQCzJo1i5aWFpLJJNFolP3797Nz507KyspMcffv36e9vR2v1/vDHSr5Vq5cycTEBD09PYyNjREOh4lEIgVxX/N4+PAht27d4sCBA/h8vtyW3mPHjnHixAnC4TCjo6MkEgm6urro7Oz8qc++du1aBgcHuX37Nj6f77vdkMbGRlatWmXK69tiDKCzs5OLFy8yMjLC6Ogoly9fZtGiRVNOt9XX19Pb20tvby8jIyPs2bOnYPfXf9mxYwc2m422tjYeP35MX18fHR0dppi9e/eSTqfZvn07d+/eZWxsjP7+flpbW4vujExOTuY6OZOTk7x69Yp4PM7Tp0+BLwu3q6qqTEdJSQmlpaVUVVUVNZbIv0RFjMg/bPbs2fT395NOp/F4PHi9XhoaGjh9+rQpzuFwUFNTw6NHj4ruhGzZsoXDhw+zb98+qquricViHD16tCDO4XCwdetWmpubaWpqYs2aNaYt1MFgkLNnz9LV1YXT6aS2tpbu7m6WLVs25dgVFRWmNUH5nE4ng4ODxGIxtm3bVjDFZRgGkUiETCZDTU0NwWCQ9vZ2U8zcuXM5deoU69atw+Px8Pz5c/r6+jCM73/97d69m5aWFnbt2kVtbS3Lly8vqgsDMGfOHK5evUoikcDlchEKhTh58qQppry8nKGhIbLZLE1NTTidTg4dOsT8+fOnzG0qr1+/xuVy4XK5SKVSdHR04HK5CAaDRb2PiNXYPuVP/oqI/AHv37+ntLSU69evs2HDhr+djohYkDoxIvJXRKNR6uvrVcCIyE9TJ0ZEREQsSZ0YERERsSQVMSIiImJJKmJERETEklTEiIiIiCWpiBERERFLUhEjIiIilqQiRkRERCxJRYyIiIhYkooYERERsSQVMSIiImJJnwHOU9hM/IGkTgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# an exmaple of a bad plot\n", "prov_data_df.groupby([\"Prov label\"])[\"Kids under 14\"].value_counts().plot.bar();" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ----- WON'T BE TESTED -----\n", "# the code below is beyond what will be tested and is provided for the purpose \n", "# of demonstrating examples of visualizations that achieve different goals\n", "\n", "# You can \"unstack\" the index an create a better view to achieve different goals.\n", "prov_kids14_dist_df = prov_data_df.groupby([\"Prov label\"])[\"Kids under 14\"].value_counts()\n", "prov_kids14_dist_unstacked_df = prov_kids14_dist_df.unstack(level=1)\n", "\n", "# using \"df.plot.barh()\" to plot horizontal bars to compari proportions\n", "prov_kids14_totals = prov_kids14_dist_unstacked_df.sum(axis=1)\n", "prov_kids14_dist_unstacked_df[\"None\"] = prov_kids14_dist_unstacked_df[0] / prov_kids14_totals\n", "prov_kids14_dist_unstacked_df[\"One\"] = prov_kids14_dist_unstacked_df[1] / prov_kids14_totals\n", "prov_kids14_dist_unstacked_df[\"Two\"] = prov_kids14_dist_unstacked_df[2] / prov_kids14_totals\n", "prov_kids14_dist_unstacked_df[\"Three or more\"] = prov_kids14_dist_unstacked_df[3] / prov_kids14_totals\n", "\n", "## comparing within provinces\n", "prov_kids14_dist_unstacked_df[[\"None\", \"One\", \"Two\", \"Three or more\"]].plot.barh()\n", "## comparing between provinces\n", "prov_kids14_dist_unstacked_df[[\"None\", \"One\", \"Two\", \"Three or more\"]].plot.barh(stacked=True)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ----- WON'T BE TESTED -----\n", "# the code below is beyond what will be tested and is provided for the purpose \n", "# of demonstrating examples of visualizations that achieve different goals\n", "\n", "# using \"matplotlib\" to create \"subplots\" - will briefly cover in Week 10\n", "# ustack provinces instead of responses\n", "prov_kids14_dist_unstacked_prov_df = prov_kids14_dist_df.unstack(level=0)\n", "prov_kids14_dist_with_ch14_df = prov_kids14_dist_unstacked_prov_df.loc[[1, 2, 3]] # only compare the distribution with 1 or more children\n", "prov_kids14_dist_with_ch14_df = prov_kids14_dist_with_ch14_df / prov_kids14_dist_with_ch14_df.sum(axis=0) # get proportions\n", "# prov_kids14_dist_with_ch14_df.loc[1].sort_values() # check the order by # participants with 1 child < 14\n", "\n", "import matplotlib.pyplot as plt\n", "fig, axes = plt.subplots(nrows=3, ncols=4, figsize = (12,12))\n", "# row 1, column 1\n", "ax = axes[0][0]\n", "prov_kids14_dist_with_ch14_df[\"Quebec\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"Quebec\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "# row 1, column 2\n", "ax = axes[0][1]\n", "prov_kids14_dist_with_ch14_df[\"Saskatchewan\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"Saskatchewan\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "# row 1, column 3\n", "ax = axes[0][2]\n", "prov_kids14_dist_with_ch14_df[\"New Brunswick\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"New Brunswick\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "# row 1, column 4\n", "ax = axes[0][3]\n", "prov_kids14_dist_with_ch14_df[\"Alberta\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"Alberta\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "# row 2, column 1\n", "ax = axes[1][0]\n", "prov_kids14_dist_with_ch14_df[\"Manitoba\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"Manitoba\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "# row 2, column 2\n", "ax = axes[1][1]\n", "prov_kids14_dist_with_ch14_df[\"Nova Scotia\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"Nova Scotia\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "# row 2, column 3\n", "ax = axes[1][2]\n", "prov_kids14_dist_with_ch14_df[\"British Columbia\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"British Columbia\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "# row 2, column 4\n", "ax = axes[1][3]\n", "prov_kids14_dist_with_ch14_df[\"Ontario\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"Ontario\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "# row 3, column 1\n", "ax = axes[2][0]\n", "prov_kids14_dist_with_ch14_df[\"Prince Edward Island\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"Prince Edward Island\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "# row 3, column 2\n", "ax = axes[2][1]\n", "prov_kids14_dist_with_ch14_df[\"NL\"].plot(ax=ax, kind=\"bar\", rot=0)\n", "ax.set_title(\"NL\")\n", "ax.set_xticklabels([\"1\", \"2\", \"3+\"])\n", "ax.set_xlabel(\"\")\n", "ax.set_ylim([0, 0.6])\n", "\n", "fig.delaxes(axes[2][2])\n", "fig.delaxes(axes[2][3])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Summarizing the distribution of a quantitative variable" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "`dur01` is time spent sleeping, resting, etc. \n", "\n", "```\n", "dur01 Duration - Sleeping, resting, relaxing, sick in bed\n", "\n", " VALUE LABEL\n", " 0 No time spent doing this activity\n", " 9996 Valid skip\n", " 9997 Don't know\n", " 9998 Refusal\n", " 9999 Not stated\n", "\n", " Data type: numeric\n", " Missing-data codes: 9996-9999\n", " Record/columns: 1/65-68\n", "```" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "dtype('float64')" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Sleepduration = prov_data_df[\"Sleep duration (hours)\"]\n", "\n", "print(type(Sleepduration))\n", "\n", "Sleepduration.dtypes" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "count 17390.000000\n", "mean 8.706552\n", "std 2.217733\n", "min 0.000000\n", "25% 7.500000\n", "50% 8.500000\n", "75% 9.750000\n", "max 24.000000\n", "Name: Sleep duration (hours), dtype: float64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Sleepduration.describe()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The distributions of quantitative variables are often described as:\n", "\n", "- a measure of **centre** such as mean, median, and mode\n", "\n", "- a measure of **spread** such as standard deviation and inter-quartile range\n", "\n", "- a measure of **range** - the largest value minus the smallest value (or max - min)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Quantiles\n", "\n", "- The median value is the 50% quantile. 50% of the values fall below this value. The median is also called the second quartile.\n", "\n", "- The 25% quantile is the value where 25% of the values fall below. This is often the first quartile (Q1) \n", "\n", "- The 75% quantile is the value where 75% of the values fall below. This is often the third quartile (Q3)\n", "\n", "- There are 17390 values. If we sort sleep duration values from largest to smallest then find the value in the middle (17390 / 2 = 8695) then that value is the median." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Variation\n", "\n", "- One of the most important concepts in statistical reasoning.\n", "\n", "- Standard deviation is average deviation from the mean. **Large** values mean large variation and **small** values mean small variation. \n", "\n", "- Small samples often have large variation, so estimating a statistic from a small sample is usually less reliable." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Question\n", "\n", "A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50% of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50%, sometimes lower.\n", "\n", "\n", "For a period of 1 year, each hospital recorded the days on which more than 60% of the babies born were boys. Which hospital do you think recorded more such days?\n", "\n", "- The larger hospital\n", "- The smaller hospital\n", "- About the same (that is, within 5% of each other) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Histograms\n", "\n", "Histograms display the frequency distribution of a quantitative variable." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Sleepduration_hist = Sleepduration.plot.hist(\n", " bins=10, \n", " edgecolor=\"black\", \n", " color=\"grey\", \n", " figsize = (8, 6)\n", ");\n", "\n", "Sleepduration_hist.set_xlabel(\"Sleep duration (hours)\");" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "0 (7.2, 9.6]\n", "1 (4.8, 7.2]\n", "2 (7.2, 9.6]\n", "3 (7.2, 9.6]\n", "4 (7.2, 9.6]\n", " ... \n", "17385 (7.2, 9.6]\n", "17386 (9.6, 12.0]\n", "17387 (7.2, 9.6]\n", "17388 (12.0, 14.4]\n", "17389 (7.2, 9.6]\n", "Name: Sleep duration (hours), Length: 17390, dtype: category\n", "Categories (10, interval[float64, right]): [(-0.024, 2.4] < (2.4, 4.8] < (4.8, 7.2] < (7.2, 9.6] ... (14.4, 16.8] < (16.8, 19.2] < (19.2, 21.6] < (21.6, 24.0]]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.cut(Sleepduration, bins=10)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Boxplots \n", "\n", "- Another way to visualize the distribution of a quantitative variable\n", "\n", "- A box plot is a method for graphically depicting groups of numerical data through their quartiles. \n", "- The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). \n", "- The whiskers extend from the edges of box to show the range of the data. \n", "- By default, they extend no more than 1.5 * IQR (IQR = Q3 - Q1) from the edges of the box, ending at the farthest data point within that interval. Outliers are plotted as separate dots." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Sleepduration_boxplot = Sleepduration.plot.box(figsize = (10,8));\n", "\n", "Sleepduration_boxplot;" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Anatomy of a Boxplot\n", "\n", "![](images/boxplot_anatomy.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Two pandas methods to create a boxplot are:\n", "\n", "1. `pandas.DataFrame.plot.box`: plot a Series or columns of a DataFrame\n", " \n", "2. `pandas.DataFrame.boxplot`: plot the columns of a DataFrame with easy to use syntax for boxplots by a group.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Boxplots are helpful for comparing the distributions between groups." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Sleephours_boxplot = prov_data_df.boxplot(\n", " column=\"Sleep duration (hours)\", \n", " by=\"Prov label\", \n", " figsize=(10,8),\n", " vert=False,\n", " # rot = 45, \n", " grid=False\n", ");\n", "\n", "Sleephours_boxplot;" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Compare the distribution of `Sleep duration` and `Work duration` by specifying the `column` parameter of `DataFrame.boxplot` as a list." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Sleepwork_boxplot = prov_data_df.boxplot(\n", " column = [\"Sleep duration\", \"Work duration\"], \n", " by = \"Prov label\", \n", " figsize = (10,8), \n", " # rot = 90, \n", " vert=False,\n", " grid = False, \n", " layout = (1,2)\n", ");\n", "\n", "Sleepwork_boxplot;" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "To further customize the look of your plots, see the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.boxplot.html)." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Sleep durationWork durationTotal1Total2
Prov label
NL519.104339133.946281653.050620653.050620
Prince Edward Island534.065385134.126923668.192308668.192308
New Brunswick523.340265148.428166671.768431671.768431
Nova Scotia510.055019158.398649668.453668668.453668
Quebec525.554724159.450461685.005184685.005184
British Columbia528.488161165.233753693.721914693.721914
Manitoba529.791411175.156442704.947853704.947853
Ontario516.742869177.705566694.448434694.448434
Saskatchewan529.470905182.572198712.043103712.043103
Alberta522.629888199.491620722.121508722.121508
\n", "
" ], "text/plain": [ " Sleep duration Work duration Total1 Total2\n", "Prov label \n", "NL 519.104339 133.946281 653.050620 653.050620\n", "Prince Edward Island 534.065385 134.126923 668.192308 668.192308\n", "New Brunswick 523.340265 148.428166 671.768431 671.768431\n", "Nova Scotia 510.055019 158.398649 668.453668 668.453668\n", "Quebec 525.554724 159.450461 685.005184 685.005184\n", "British Columbia 528.488161 165.233753 693.721914 693.721914\n", "Manitoba 529.791411 175.156442 704.947853 704.947853\n", "Ontario 516.742869 177.705566 694.448434 694.448434\n", "Saskatchewan 529.470905 182.572198 712.043103 712.043103\n", "Alberta 522.629888 199.491620 722.121508 722.121508" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "restwork_by_prov_mean.sort_values(\"Work duration\")\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prov_data_df[\"Province ordered\"] = pd.Categorical(\n", " prov_data_df[\"Prov label\"], \n", " categories=restwork_by_prov_mean.sort_values(\"Work duration\").index,\n", " ordered = True\n", ")\n", "Sleepwork_boxplot = prov_data_df.boxplot(\n", " column = [\"Sleep duration\", \"Work duration\"], \n", " by = \"Province ordered\", \n", " figsize = (12,8), \n", " rot = 0, \n", " grid = False, \n", " layout = (1,2),\n", " vert = False\n", ");\n", "print(type(Sleepwork_boxplot[0]));\n", "Sleepwork_boxplot[0].set_ylabel(None);\n", "Sleepwork_boxplot;\n" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "ggr274", "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": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 }