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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Homework 5\n",
"\n",
"## Logistics\n",
"\n",
"**Due date**: The homework is due 23:59 on Monday, February 10.\n",
"\n",
"You will submit your work on [MarkUs](https://markus.teach.cs.toronto.edu/markus/main/login_remote_auth).\n",
"To submit your work:\n",
"\n",
"1. Download this file (`Homework_5.ipynb`) from JupyterHub. (See [our JupyterHub Guide](../../../guides/jupyterhub_guide.ipynb) for detailed instructions.)\n",
"2. Submit this file to MarkUs under the **hw5** assignment. (See [our MarkUs Guide](../../../guides/markus_guide.ipynb) for detailed instructions.)\n",
"All homeworks will take place in a Jupyter notebook (like this one). When you are done, you will download this notebook and submit it to MarkUs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"In this homework we explore: \n",
"- row, column selection\n",
"- create new columns\n",
"- grouping\n",
"- summary statistics\n",
"- visualizing distributions\n",
"\n",
"**Question:** Explore sleeping, exercising, and socializing among Canadians."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 1\n",
"\n",
"a) Use the `pandas` method `read_csv` to read the file `gss_tu2016_filtered.csv` into a DataFrame. Store this `DataFrame` in a variable called `time_use_df`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/0j/ybsv4ncn5w50v40vdh5jjlww0000gn/T/ipykernel_85471/2651432375.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"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"time_use_df = pd.read_csv(\"gss_tu2016_filtered.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"b) Create a subset of `time_use_df` with only the following columns: `dur41`, `dur47`, `sleepdur`, `agegr10`, `prv`. To do this follow these steps:\n",
"\n",
"- Create a list called `analysis_columns` with the column names.\n",
"- Use `analysis_columns` to select these columns from `time_use_df` and store this `DataFrame` in a variable called `time_use_subset_df`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
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" dur41 dur47 sleepdur agegr10 prv\n",
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"[17390 rows x 5 columns]"
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"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"analysis_columns = [\"dur41\", \"dur47\", \"sleepdur\", \"agegr10\", \"prv\"]\n",
"\n",
"time_use_subset_df = time_use_df[analysis_columns]\n",
"\n",
"time_use_subset_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"c) In the next steps you will rename the columns of `time_use_subset_df` according to the following table:\n",
"\n",
"Old name | New name\n",
"---------|------------\n",
"`dur41` |`Socializing time`\n",
"`dur47` |`Exercising time`\n",
"`sleepdur`| `Sleep time`\n",
"`agegr10`|`Age group`\n",
"`prv` |`Province` \n",
"\n",
"Step 1: Create a dictionary called `new_col_names` with each *Old name* as a key and each *New name* as the corresponding value."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'dur41': 'Socializing time',\n",
" 'dur47': 'Exercising time',\n",
" 'sleepdur': 'Sleep time',\n",
" 'agegr10': 'Age group',\n",
" 'prv': 'Province'}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_col_names = {\n",
" \"dur41\": \"Socializing time\", \n",
" \"dur47\": \"Exercising time\", \n",
" \"sleepdur\": \"Sleep time\", \n",
" \"agegr10\": \"Age group\",\n",
" \"prv\": \"Province\"\n",
"}\n",
"\n",
"new_col_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 2: Use `new_col_names` to rename the columns of `time_use_subset_df` and store the DataFrame with renamed columns in a variable called `time_use_subset_renamed_df`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"time_use_subset_renamed_df = time_use_subset_df.rename(columns=new_col_names)\n",
"\n",
"time_use_subset_renamed_df\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 2\n",
"\n",
"Create columns in `time_use_subset_renamed_df` that converts time use from minutes to hours. Since 60 minutes is equal to 1 hour we can divide the time use columns by 60 to compute the time in hours.\n",
"\n",
"To do this create new columns in `time_use_subset_renamed_df` called \n",
"\n",
" + `Socializing time (hour)`, \n",
" + `Exercising time (hour)`, and \n",
" + `Sleep time (hour)` \n",
" \n",
"These columns are (respectively) `Socializing time`, `Exercising time`, and `Sleep time` in hours."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"time_use_subset_renamed_df[\"Socializing time (hour)\"] = time_use_subset_renamed_df[\"Socializing time\"] / 60\n",
"\n",
"time_use_subset_renamed_df[\"Exercising time (hour)\"] = time_use_subset_renamed_df[\"Exercising time\"] / 60\n",
"\n",
"time_use_subset_renamed_df[\"Sleep time (hour)\"] = time_use_subset_renamed_df[\"Sleep time\"] / 60\n",
"\n",
"time_use_subset_renamed_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 3\n",
"\n",
"Some respondents in the time use survey spent no time exercising, socializing, and sleeping. In this section we will create a `DataFrame` that only has respondents who spent time sleeping, exercising, and socializing. In other words respondents that spent no time on these activities will be excluded.\n",
"\n",
"a) Create a boolean `Series` called `well_balanced` that is `True` if time spent exercising **and** time spent sleeping **and** time spent socializing are all greater than 0, and `False` otherwise."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"well_balanced = (\n",
" (time_use_subset_renamed_df[\"Sleep time (hour)\"] > 0) & \n",
" (time_use_subset_renamed_df[\"Exercising time (hour)\"] > 0) & \n",
" (time_use_subset_renamed_df[\"Socializing time (hour)\"] > 0)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"b) Use `well_balanced` to filter (i.e. select) the rows of `time_use_subset_renamed_df` where respondents had non-zero times of sleeping, exercising, and socializing. Store this filtered DataFrame in `well_balanced_df`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
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\n",
"
\n",
"
\n",
"
17325
\n",
"
25
\n",
"
15
\n",
"
640
\n",
"
6
\n",
"
47
\n",
"
0.416667
\n",
"
0.250000
\n",
"
10.666667
\n",
"
\n",
"
\n",
"
17336
\n",
"
105
\n",
"
100
\n",
"
525
\n",
"
6
\n",
"
59
\n",
"
1.750000
\n",
"
1.666667
\n",
"
8.750000
\n",
"
\n",
"
\n",
"
17351
\n",
"
40
\n",
"
90
\n",
"
540
\n",
"
5
\n",
"
46
\n",
"
0.666667
\n",
"
1.500000
\n",
"
9.000000
\n",
"
\n",
"
\n",
"
17366
\n",
"
120
\n",
"
90
\n",
"
490
\n",
"
6
\n",
"
59
\n",
"
2.000000
\n",
"
1.500000
\n",
"
8.166667
\n",
"
\n",
"
\n",
"
17387
\n",
"
125
\n",
"
77
\n",
"
510
\n",
"
7
\n",
"
24
\n",
"
2.083333
\n",
"
1.283333
\n",
"
8.500000
\n",
"
\n",
" \n",
"
\n",
"
741 rows × 8 columns
\n",
"
"
],
"text/plain": [
" Socializing time Exercising time Sleep time Age group Province \\\n",
"3 395 60 510 6 35 \n",
"7 180 60 440 5 59 \n",
"23 80 230 330 6 46 \n",
"48 455 15 270 6 35 \n",
"54 130 185 670 1 12 \n",
"... ... ... ... ... ... \n",
"17325 25 15 640 6 47 \n",
"17336 105 100 525 6 59 \n",
"17351 40 90 540 5 46 \n",
"17366 120 90 490 6 59 \n",
"17387 125 77 510 7 24 \n",
"\n",
" Socializing time (hour) Exercising time (hour) Sleep time (hour) \n",
"3 6.583333 1.000000 8.500000 \n",
"7 3.000000 1.000000 7.333333 \n",
"23 1.333333 3.833333 5.500000 \n",
"48 7.583333 0.250000 4.500000 \n",
"54 2.166667 3.083333 11.166667 \n",
"... ... ... ... \n",
"17325 0.416667 0.250000 10.666667 \n",
"17336 1.750000 1.666667 8.750000 \n",
"17351 0.666667 1.500000 9.000000 \n",
"17366 2.000000 1.500000 8.166667 \n",
"17387 2.083333 1.283333 8.500000 \n",
"\n",
"[741 rows x 8 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"well_balanced_df = time_use_subset_renamed_df[well_balanced]\n",
"\n",
"well_balanced_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"c) The number of rows in a `pandas` `DataFrame` can be computed by `len()`. For example, `len(well_balanced_df)` is the number of rows in `well_balanced_df`. Compute the number of respondents who were *removed* from `time_use_subset_renamed_df` when it was filtered using `well_balanced` and store this number in a variable called `diff`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16649"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diff = len(time_use_subset_renamed_df) - len(well_balanced_df)\n",
"\n",
"diff"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"d) Use `diff` to compute the percentage of respondents removed from `time_use_subset_renamed_df`. Round the percentage to two decimal places, and store the result value in a variable called `pct_lost`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"95.74"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pct_lost = round(diff / len(time_use_subset_renamed_df) * 100, 2)\n",
"\n",
"pct_lost"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 4\n",
"\n",
"In this section you will explore the distributions of time spent socializing, exercising, and sleeping by age group and province.\n",
"\n",
"a) Compute the mean hours spent sleeping, socializing, and exercising by age group using `.groupby` on `well_balanced_df`. Store this DataFrame in a variable called `group_means`."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"group_means = well_balanced_df.groupby(\"Age group\")[[\n",
" \"Socializing time (hour)\", \n",
" \"Exercising time (hour)\",\n",
" \"Sleep time (hour)\"\n",
"]].mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"b) Create a new column in `group_means` called `Total time (hour)` that is the sum of the time (in hours) spent sleeping, exercising, and socializing."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"group_means[\"Total time (hour)\"] = group_means.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"c) Create a new index for `group_means` using the labels of Age group found in the [code book](gss_tu2016_codebook.txt) (`gss_tu2016_codebook.txt`) and store the values in a _list_ called `index_new`. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"index_new = [\n",
" \"15-24\",\n",
" \"25-34\",\n",
" \"35-44\",\n",
" \"45-54\",\n",
" \"55-64\",\n",
" \"65-74\",\n",
" \"75+\"\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"d) Change the index of `group_means` to correspond to `index_new`. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
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Socializing time (hour)
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Exercising time (hour)
\n",
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Sleep time (hour)
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Total time (hour)
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15-24
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" \n",
"
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"
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],
"text/plain": [
" Socializing time (hour) Exercising time (hour) Sleep time (hour) \\\n",
"15-24 2.707031 1.372917 8.918750 \n",
"25-34 2.127004 1.321730 8.060338 \n",
"35-44 1.802305 1.254433 8.079787 \n",
"45-54 1.754045 1.121359 8.249191 \n",
"55-64 2.236025 1.150311 8.266046 \n",
"65-74 2.073184 1.221688 8.342949 \n",
"75+ 2.068452 1.016270 8.640873 \n",
"\n",
" Total time (hour) \n",
"15-24 12.998698 \n",
"25-34 11.509072 \n",
"35-44 11.136525 \n",
"45-54 11.124595 \n",
"55-64 11.652381 \n",
"65-74 11.637821 \n",
"75+ 11.725595 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"group_means.index = index_new\n",
"\n",
"\n",
"# Display group_means to check that the index has been updated.\n",
"# On the left-hand side you should see the Age group labels, from \"15-24\" to \"75+\".\n",
"group_means"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"e) Sort `group_means` in descending order of `Total time (hour)`. Store this sorted `DataFrame` in a variable called `group_means_sorted`\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Socializing time (hour)
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"
Exercising time (hour)
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"
Sleep time (hour)
\n",
"
Total time (hour)
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"
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" \n",
" \n",
"
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"
15-24
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8.918750
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12.998698
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75+
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],
"text/plain": [
" Socializing time (hour) Exercising time (hour) Sleep time (hour) \\\n",
"15-24 2.707031 1.372917 8.918750 \n",
"75+ 2.068452 1.016270 8.640873 \n",
"55-64 2.236025 1.150311 8.266046 \n",
"65-74 2.073184 1.221688 8.342949 \n",
"25-34 2.127004 1.321730 8.060338 \n",
"35-44 1.802305 1.254433 8.079787 \n",
"45-54 1.754045 1.121359 8.249191 \n",
"\n",
" Total time (hour) \n",
"15-24 12.998698 \n",
"75+ 11.725595 \n",
"55-64 11.652381 \n",
"65-74 11.637821 \n",
"25-34 11.509072 \n",
"35-44 11.136525 \n",
"45-54 11.124595 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"group_means_sorted = group_means.sort_values(by=\"Total time (hour)\", ascending=False)\n",
"\n",
"group_means_sorted\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"f) First, create a copy of `well_balanced_df` (using the `DataFrame` `.copy()` method), and store it in a variable called `time_spent_with_total_df`. Create a column `Total time (hour)` in `time_spent_with_total_df` by adding the three columns `Socializing time (hour)`, `Exercising time (hour)`, and `Sleep time (hour)`."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"time_spent_with_total_df = well_balanced_df.copy()\n",
"time_spent_with_total_df[\"Total time (hour)\"] = well_balanced_df[\n",
" [\"Exercising time (hour)\", \"Socializing time (hour)\", \"Sleep time (hour)\"]\n",
"].sum(axis=1)\n",
"\n",
"# # or\n",
"# well_balanced_df[\"Total time (hour)\"] = (\n",
"# well_balanced_df[\"Exercising time (hour)\"] +\n",
"# well_balanced_df[\"Socializing time (hour)\"] +\n",
"# well_balanced_df[\"Sleep time (hour)\"]\n",
"# )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"g) Complete the code below to create three side-by-side boxplots from `time_spent_with_total_df` using `layout=(2, 2)` and `figsize=(20, 20)` of time spent (in hours) socializing, exercising, sleeping, and their sum for each age group. Store these boxplots in a variable called `time_boxplots`."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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4MEOGDMn3vve99sAq+dfVHQ899NCbrp/867klu+22W6699toO+5wzZ07uuOOOHHbYYe1t2223XZ5//vn8/ve/b29buHBhbr755k63fd9997U/syZJ/vrXv+aWW27JwQcfnKqqqlRVVeUjH/lIbrrppsyZM2eZ9Z9++un2/z7ssMPyxBNPZNq0ae1tL7/8cq6++uqVOs42l112WYfXl1xySZIs8/yXT3ziE1m0aFE++9nP5sUXX8zHP/7xN932zTffnJdeeimnnHJKGhoalvlTU1OTm266Ka+++mqqqqpy0EEHpampKU888UT7NubNm9fhWUxJUl9fn6qqqpx33nntz+FpUxRFnn322eXWtCr7WdE2Vvbzu9122yVJZsyY0d7vpZdeyrXXXtvptp944okOn58XXngh3/ve97Lbbrtliy22WKn6AACgK7jyBgAAOnHbbbe1X0Hz1FNP5YYbbsjcuXPzn//5n+2/SD7iiCOy//7750tf+lIWLFiQ973vfbnjjjtyyy235Iwzzmj/RfMFF1yQBx54ID/96U+z/vrrZ9ddd82Xv/zlnHPOOWloaOgQYPTu3TvTp0/Pcccdlw996EO57bbbcuutt2bcuHHtV5N05oILLsidd96Z4cOH5+STT06PHj1y1VVX5dVXX82FF17Y3m+33XZLVVVVvva1r+X5559Pr1692h9435kJEyaktrY2++yzTz71qU9l0aJFufTSSzNkyJAOgc6KfP3rX8+hhx6avfbaKyeccEIWL16cSy65JBtssEHGjx/f3u+YY47JF7/4xRx11FE5/fTT8/LLL+eKK67Ie9/73g4hTZshQ4bkkEMOyemnn55evXrl8ssvT5Kcd9557X2++tWv5u67786HPvShfOYzn8nOO++cf/zjH/ntb3+bu+66K//4xz+SJJ/5zGdy6aWX5pOf/GTuv//+DBgwIN///vfTt2/flTrGNvPnz8+RRx6ZUaNG5b777st1112XY489Nu973/s69Hv/+9+fIUOG5MYbb8xOO+2UD3zgA2+67euvvz4bb7xx9t57706XH3nkkfnWt76VW2+9NfX19Rk/fnzuuOOO7LPPPvnc5z7XHjQOGTIkDzzwQPt62223XS644IKMHTs2CxYsSF1dXdZff/3Mnz8/N998c0466aSMGTNmuXWt7H5WZGU/vwcffHC23nrrnHDCCfnCF76QqqqqfPe7382mm26av/zlL8ts973vfW9OOOGEzJ49O5tvvnm++93v5sknn8yUKVNWqi4AAOgyBQAA0G7KlClFkg5/evfuXey2227FFVdcUbS2tnbo/89//rM488wzi4EDBxbrrLNOsf322xdf//rX2/vdf//9RY8ePYrTTjutw3pLly4thg0bVgwcOLBYtGhRURRFcdxxxxXrrrtu8eijjxYHH3xw0bdv32LzzTcvzj333KKlpaXD+kmKc889t0Pbb3/72+KQQw4p1ltvvaJv377F/vvvX9x7773LHOO3vvWtYtttty2qqqqKJMXdd9+9wvfkBz/4QbHjjjsWvXr1KoYMGVL87//+b/GRj3yk2HHHHdv7zJ8/v0hSfP3rX+90G3fddVexzz77FH369Cn69etXHHHEEcXDDz+8TL877rijGDJkSNGzZ89ihx12KK677rri3HPPLd44dUlSnHLKKcV1111XbL/99kWvXr2K97///Z0ey5NPPlmccsopxVZbbVWss846xRZbbFEceOCBxdVXX92h32OPPVYceeSRRd++fYtNNtmk+PznP19Mnz59pd6jthoffvjhoqGhoVh//fWL/v37F6eeemqxePHiTte58MILiyTFhAkTVrjttmPo0aNH8YlPfGK5fV5++eWib9++xVFHHdXe9tOf/rR4//vfX/Ts2bPYbrvtim9/+9vF6NGji969ey+z/k033VQMHz68WHfddYt111232HHHHYtTTjmleOSRR960vpXdT9u4dWZlP7/3339/8aEPfajo2bNnsfXWWxeTJ09u/3c7f/789n7bbLNNcfjhhxe33357seuuuxa9evUqdtxxx+LGG2980+MBAICuVlEUb7guHgAA6BLHH398pk2bttJXtHSl3XbbLZtuumnuvPPOLtl/RUVFTjnllGVuWfdu8s1vfjNnnnlmFixYkK233vod229dXV3+7//+r9NnBr0b97M8gwYNypAhQ9Lc3Nwl+wcAgLfCM28AAIDleu2117J06dIObT//+c/z4IMPZr/99uuaorqBoijyne98JyNHjnxbg5vFixd3eD137tz85Cc/WeNj907tBwAA1haeeQMAACzX448/noMOOigf//jHM3DgwPzxj3/MlVdemS222CL/8R//0dXlveu89NJL+d///d/cfffdeeihh3LLLbe8rfvbdtttc/zxx2fbbbfNY489liuuuCI9e/bM2Wef/a7cDwAArC2ENwAAwHL1798/u+++e7797W/n6aefzrrrrpvDDz88X/3qV7Pxxht3dXnvOk8//XSOPfbYbLjhhhk3blyOPPLIt3V/o0aNytSpU/P3v/89vXr1yl577ZUJEyZk++23f1fuBwAA1haeeQMAAAAAAFAinnkDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8A3qLjjz8+gwYNWq11Bw0alOOPP7799c9//vNUVFTk5z//+VvaThmMHz8+FRUVXV3GMn7961+nZ8+eeeyxx9rbBg0alJqami6sqnN77rlnzj777K4uAwAA1kr77bdf9ttvv1Va55prrklFRUUWLFjwttS0uioqKjJ+/PiuLmMZhx12WD7zmc+0v257/37zm990YVXLmj59etZbb708/fTTXV0KsBYR3gDdykMPPZSGhoZss8026d27d7bccst8+MMfziWXXNLVpXVLL7/8csaPH7/KYVNX+tKXvpR/+7d/yzbbbNPVpbypL37xi7nsssvy97//vatLAQCA1db2C/nl/fnlL3/Z1SV2Cz/5yU9KGdAszy9+8Yvccccd+eIXv9jVpbypUaNGZfDgwZk4cWJXlwKsRSqKoii6ugiANeHee+/N/vvvn6233jrHHXdctthii/z1r3/NL3/5yzz66KOZN2/e27Lf1157La2trenVq9cqrzto0KDst99+ueaaa5Ikra2tWbJkSXr27JnKypXP11999dVUVlZmnXXWWeUa3opnnnkmm266ac4999xlJglLly7N0qVL07t373e0phV54IEH8v73vz/33ntv9tprr/b2QYMGZciQIWlubu7C6pbV2tqaLbfcMp/5zGdy/vnnd3U5AACwWq655pp86lOfyvnnn5/q6upllo8aNSqbbLJJF1S2YkuWLEmS9OzZc6XXaWlpyWuvvZZevXq943ciOPXUU3PZZZels1/1vfLKK+nRo0d69Ojxjta0InV1dVm8eHFuv/329ra2z8rs2bPzwQ9+sAurW9YVV1yRMWPG5O9//3vWX3/9ri4HWAuU5//YAG/Rf//3f2eDDTbI7Nmzs+GGG3ZY9tRTT71t+12TgUllZeVqhR2rExy93co2MUiSKVOmZOutt86ee+7Z1aWs0Msvv5y+ffumsrIyDQ0N+d73vpfzzjuvlLehAwCAlXXooYd2+S/kX3rppay77ror1XdVQps2VVVVqaqqWuX13m5lOqku+dcc/dZbb82VV17Z1aWs0CuvvNJ+cuVHPvKRnHbaabnxxhvz6U9/uqtLA9YCbpsGdBuPPvpodtlll2WCmyTZbLPNOrxeunRpvvKVr2S77bZLr169MmjQoIwbNy6vvvrqMuvedtttGTlyZNZff/3069cvw4YNyw033NC+vLNn3lx00UXZe++9s/HGG6dPnz7ZfffdM23atDc9hjc+82ZFtxd4/b2X3/jMm7b1fvGLX+Sss87KpptumnXXXTdHHXXUMvfobW1tzfjx4zNw4MD07ds3+++/fx5++OE3fY7OggULsummmyZJe7Dw+vsod/bMm4qKipx66qm58cYbs/POO6dPnz7Za6+98tBDDyVJrrrqqgwePDi9e/fOfvvt1+l9on/1q19l1KhR2WCDDdK3b9+MHDkyv/jFL970vU2SpqamHHDAAcsNQWbNmpU99tgjvXv3zrbbbpvvfe97y/T585//nI9+9KPZaKON0rdv3+y555659dZbO/RZ3n2uO3um0X777ZchQ4bk/vvvz7777pu+fftm3Lhx7cs//OEP57HHHssDDzywUscIAADvVueee24qKyvz05/+tEP7SSedlJ49e+bBBx9sb1uZeUHbnOThhx/Osccem/79+2f48OHty6+77rrsscce6du3b/r375999903d9xxR/vyzp55c8kll2SXXXZpX+eDH/xgh/lhZ3OBtmdsrsx84/e//31GjhyZPn365D3veU8uuOCCTJky5U2fo3P88cfnsssuS5IO88Y2b3zmTdt786c//Skf//jHs8EGG2TTTTfNf/3Xf6Uoivz1r39NbW1t+vXrly222CKTJk1aZp+vvvpqzj333AwePDi9evXKVlttlbPPPrvTefUb3XrrrVm6dGkOOuigTpe/+uqrbzqXTZLLL788u+yyS3r16pWBAwfmlFNOyXPPPdehz/Lmtm8c37b52g9+8IOcc8452XLLLdO3b9+88MILSf71e4Vdd901t9xyy5seH8CaUK5TogHegm222Sb33Xdf5syZkyFDhqyw74knnphrr702DQ0NGT16dH71q19l4sSJ+cMf/pCbb765vd8111yTT3/609lll10yduzYbLjhhvnd736X6dOn59hjj13u9r/5zW/myCOPzL//+79nyZIl+cEPfpCPfvSjaW5uzuGHH77Sx7Tvvvvm+9//foe2xx57LOecc84ygVRnTjvttPTv3z/nnntuFixYkG984xs59dRT88Mf/rC9z9ixY3PhhRfmiCOOyCGHHJIHH3wwhxxySF555ZUVbnvTTTfNFVdckc997nM56qijUl9fnyTZddddV7jezJkz87//+7855ZRTkiQTJ05MTU1Nzj777Fx++eU5+eSTs2jRolx44YX59Kc/nZ/97Gft6/7sZz/LoYcemt133719YjdlypQccMABmTlzZvbYY4/l7vfxxx/PX/7yl3zgAx/odPm8efPS0NCQE044Iccdd1y++93v5vjjj8/uu++eXXbZJUny5JNPZu+9987LL7+c008/PRtvvHGuvfbaHHnkkZk2bVqOOuqoFR778jz77LM59NBDc8wxx+TjH/94Nt988/Zlu+++e5J/3Q/6/e9//2ptHwAAyuD555/PM88806GtoqIiG2+8cZLknHPOyY9//OOccMIJeeihh7L++uvn9ttvz7e+9a185Stfyfve974kqz4v+OhHP5rtt98+EyZMaL+l2HnnnZfx48dn7733zvnnn5+ePXvmV7/6VX72s5/l4IMP7rT+b33rWzn99NPT0NCQz3/+83nllVfy+9//Pr/61a9WOD9MVm6+8fjjj2f//fdPRUVFxo4dm3XXXTff/va3V+pOC5/97GfzxBNP5M4771xmDrkiH/vYx7LTTjvlq1/9am699dZccMEF2WijjXLVVVflgAMOyNe+9rVcf/31GTNmTIYNG5Z99903yb9OAjzyyCMza9asnHTSSdlpp53y0EMP5eKLL86f/vSnNDU1rXC/9957bzbeeOPlPot0Zeay48ePz3nnnZeDDjoon/vc5/LII4/kiiuuyOzZs/OLX/xite+S8ZWvfCU9e/bMmDFj8uqrr3a4Amv33Xd/02MDWGMKgG7ijjvuKKqqqoqqqqpir732Ks4+++zi9ttvL5YsWdKh3wMPPFAkKU488cQO7WPGjCmSFD/72c+KoiiK5557rlh//fWLD33oQ8XixYs79G1tbW3/7+OOO67YZpttOix/+eWXO7xesmRJMWTIkOKAAw7o0L7NNtsUxx13XPvru+++u0hS3H333Z0e4+LFi4vdd9+9GDhwYLFw4cLlbmfKlClFkuKggw7qUOuZZ55ZVFVVFc8991xRFEXx97//vejRo0dRV1fXYT/jx48vknTYZmeefvrpIklx7rnnLrPs3HPPLd74NZOk6NWrVzF//vz2tquuuqpIUmyxxRbFCy+80N4+duzYIkl739bW1mL77bcvDjnkkA7H9PLLLxfV1dXFhz/84RXWetdddxVJih//+MfLLNtmm22KJMWMGTPa25566qmiV69exejRo9vbzjjjjCJJMXPmzPa2f/7zn0V1dXUxaNCgoqWlpSiK//f+v/44i6Lz8R05cmSRpLjyyiuXW3vPnj2Lz33ucys8PgAAKKu2n487+9OrV68OfR966KGiZ8+exYknnlgsWrSo2HLLLYsPfvCDxWuvvVYUxarNC9rmJP/2b//WYR9z584tKisri6OOOqr9Z/g2r9/myJEji5EjR7a/rq2tLXbZZZeVOtbXzwVWdr5x2mmnFRUVFcXvfve79rZnn3222GijjTqdX7zRKaecsswcrM0b521t781JJ53U3rZ06dLiPe95T1FRUVF89atfbW9ftGhR0adPnw7zw+9///tFZWVlh7lRURTFlVdeWSQpfvGLX6yw1uHDhxe77777Mu0rO5d96qmnip49exYHH3xwhzG89NJLiyTFd7/73fa2N86X27xxfNvma9tuu+0yc/o2EyZMKJIUTz755AqPD2BNcNs0oNv48Ic/nPvuuy9HHnlkHnzwwVx44YU55JBDsuWWW+Z///d/2/v95Cc/SZKcddZZHdYfPXp0krTfAuvOO+/MP//5z/znf/7nMvcHfrNnj/Tp06f9vxctWpTnn38+I0aMyG9/+9vVP8AkJ598ch566KHcdNNN2WKLLd60/0knndSh1hEjRqSlpSWPPfZYkuSnP/1pli5dmpNPPrnDeqeddtpbqnNFDjzwwA63mfvQhz6UJPnIRz7S4aGPbe1//vOfkyQPPPBA5s6dm2OPPTbPPvtsnnnmmTzzzDN56aWXcuCBB2bGjBlpbW1d7n6fffbZJEn//v07Xb7zzjtnxIgR7a833XTT7LDDDu37T/712dljjz063GphvfXWy0knnZQFCxbk4YcfXtm3oYNevXrlU5/61HKX9+/ff5kzFAEA4N3msssuy5133tnhz2233dahz5AhQ3Leeefl29/+dg455JA888wzufbaa9ufp7k684L/+I//6PC6qakpra2t+fKXv5zKyo6/GlvRXG/DDTfM3/72t8yePXuVj31l5hvTp0/PXnvtld122629baONNsq///u/r/L+VtaJJ57Y/t9VVVX54Ac/mKIocsIJJ7S3b7jhhsvUeuONN2annXbKjjvu2D4GzzzzTA444IAkyd13373C/T777LPLnZslbz6Xveuuu7JkyZKcccYZHcbwM5/5TPr167fMra1XxXHHHddhTv96bTWbnwHvBLdNA7qVYcOGpbGxMUuWLMmDDz6Ym2++ORdffHEaGhrywAMPZOedd85jjz2WysrKDB48uMO6W2yxRTbccMP2HwYfffTRJHnTW7B1prm5ORdccEEeeOCBDvf7fSsPnL/qqqsyZcqUXHXVVdlzzz1Xap2tt966w+u2HzQXLVqUJO3H+sb3YqONNlrhD9JvxRtr2mCDDZIkW221VaftbbXOnTs3yb9+kF6e559//k3rLv7/2yS8WV3Jv96vtv0n/3q/2kKl19tpp53al6/O52XLLbdc4cNQi6J4S58dAAAogz322CMf/OAH37TfF77whfzgBz/Ir3/960yYMCE777xz+7LVmRdUV1d3WP7oo4+msrKyw3ZXxhe/+MXcdddd2WOPPTJ48OAcfPDBOfbYY7PPPvu86borO9/Ya6+9lun3xvnamtTZ/Kx3797ZZJNNlmlvOyEu+dc4/OEPf2h/DuobPfXUU2+67+XNzTqra3lz2R122KFDv549e2bbbbdtX7463vh5eb22ms3PgHeC8Abolnr27Jlhw4Zl2LBhee9735tPfepTufHGG3Puuee293m7ftiaOXNmjjzyyOy77765/PLLM2DAgKyzzjqZMmVKhwdZropf//rX+fznP58TTzwxJ5100kqvV1VV1Wn7in5Ifrstr6Y3q7Xt7Lmvf/3rHc5Ee7311ltvufttu4/26ydHq7L/VbG8z1ZLS0un7cs7q6vNc889t8zkCQAAuqs///nP7SHNQw891GHZ6swL3uzn7ZW100475ZFHHklzc3OmT5+em266KZdffnm+/OUv57zzzlvhumWcmyWd17Uytba2tmbo0KGZPHlyp33feHLeG2288cbLnZutbA0ra0Xzs872s6LPS1vN5mfAO0F4A3R7bWd2LVy4MEmyzTbbpLW1NXPnzm2/YiL518Pon3vuufYHJm633XZJkjlz5qzSmU433XRTevfundtvv73DgyWnTJmyWvU//fTTaWhoyG677ZbLLrtstbaxPG3HOm/evA5nFz377LMr/EG6zTt5tlHbePTr1y8HHXTQKq+/4447Jknmz5+/2jVss802eeSRR5Zp/+Mf/9i+PPl/Z4U999xzHfqtztlfjz/+eJYsWdL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LRo8enZqamjQ1NWXYsGHp06dPhg0blqamptTU1GTMmDFpaWnp6lIBAAAAeBPCGwDoBmbOnJkFCxZk3Lhxqazs+PVeWVmZsWPHZv78+Zk5c2YXVQgAAADAyhLeAEA3sHDhwiTJkCFDOl3e1t7WDwAAAIDyEt4AQDcwYMCAJMmcOXM6Xd7W3tYPAAAAgPIS3gBANzBixIgMGjQoEyZMSGtra4dlra2tmThxYqqrqzNixIguqhAAAACAlSW8AYBuoKqqKpMmTUpzc3Pq6uoye/bsLF68OLNnz05dXV2am5tz0UUXpaqqqqtLBQAAAOBN9OjqAgCANaO+vj7Tpk3L6NGjM2rUqPb26urqTJs2LfX19V1YHQAAAAArS3gDAN1IfX19amtrc+utt+aee+7JyJEjc/jhh7viBgAAAOBdRHgDAN1MVVVVhg8fnqIoMnz4cMENAAAAwLuMZ94AQDfT0tKSWbNmZcaMGZk1a1ZaWlq6uiQAAAAAVoHwBgC6kcbGxgwePDi1tbWZPHlyamtrM3jw4DQ2NnZ1aQAAAACsJOENAHQTjY2NaWhoyNChQzN9+vRMnTo106dPz9ChQ9PQ0CDAAQAAAHiXEN4AQDfQ0tKS0aNHp6amJk1NTRk2bFj69OmTYcOGpampKTU1NRkzZoxbqAEAAAC8CwhvAKAbmDlzZhYsWJBx48alsrLj13tlZWXGjh2b+fPnZ+bMmV1UIQAAAAArS3gDAN3AwoULkyRDhgzpdHlbe1s/AAAAAMpLeAMA3cCAAQOSJHPmzOl0eVt7Wz8AAAAAykt4AwDdwIgRIzJo0KBMmDAhra2tHZa1trZm4sSJqa6uzogRI7qoQtq0tLRk1qxZmTFjRmbNmuU5RAAAAMAyhDcA0A1UVVVl0qRJaW5uTl1dXWbPnp3Fixdn9uzZqaurS3Nzcy666KJUVVV1dalrtcbGxgwePDi1tbWZPHlyamtrM3jw4DQ2NnZ1aQAAAECJ9OjqAgCANaO+vj7Tpk3L6NGjM2rUqPb26urqTJs2LfX19V1YHY2NjWloaEhNTU2uvPLKLFq0KP37989ll12WhoYGYwQAAAC0c+UNAHQj9fX1mTdvXm655ZacddZZueWWWzJ37lyhQBdraWnJ6NGjU1NTk6ampgwbNix9+vTJsGHD0tTUlJqamowZM8Yt1AAAAIAkrrwBgG6nqqoqw4cPT1EUGT58uFullcDMmTOzYMGCTJ06NZWVHc+dqayszNixY7P33ntn5syZ2W+//bqmSAAAAKA0XHkDAPA2W7hwYZJkyJAhnS5va2/rBwAAAKzdXHmzCl5++eX88Y9/XKV1nnp2Ue596NH06rteNtu4/yqtu+OOO6Zv376rtA4AUD4DBgxIksyZMyd77rnnMsvnzJnToR8AAACwdhPerII//vGP2X333Vdr3QtXY537778/H/jAB1ZrfwBAeYwYMSKDBg3KhAkT0tTU1GFZa2trJk6cmOrq6owYMaJrCgQAAABKRXizCnbcccfcf//9q7TOA39emC81z8t/1wzObtuu2tm0O+644yr1BwDKqaqqKpMmTUpDQ0Pq6upyyimnZPHixZk9e3Yuu+yyNDc3Z9q0aZ5PBAAAACQR3qySvn37rvKVMIt7/yW9fpPssPPQfGDnrd+mygCAsquvr8+0adMyevTojBo1qr29uro606ZNS319fRdWR5uWlpbMmjUrM2bMSEVFRQ4//HChGgAAAO844Q0AwDukvr4+tbW1ufXWW3PPPfdk5MiRwoESaWxszOjRo7NgwYIkyeTJkzNo0KBMmjRJuAYAAMA7qrKrCwAA1qzXXzkwa9astLS0dHVJUHqNjY1paGjI0KFDM3369EydOjXTp0/P0KFD09DQkMbGxq4uEQAAgLWI8AYAupHGxsYMHjw4tbW1mTx5cmprazN48GC/eC4J41NOLS0tGT16dGpqatLU1JRhw4alT58+GTZsWJqamlJTU5MxY8YIQgEAAHjHCG8AoJtw5UC5GZ/ymjlzZhYsWJBx48alsrLjj8eVlZUZO3Zs5s+fn5kzZ3ZRhQAAAKxthDcA0A24cqDcjE+5LVy4MEkyZMiQTpe3tbf1AwAAgLeb8AYAugFXDpSb8Sm3AQMGJEnmzJnT6fK29rZ+AAAA8HYT3gBAN+DKgXIzPuU2YsSIDBo0KBMmTEhra2uHZa2trZk4cWKqq6szYsSILqoQAACAtY3wBgC6AVcOlJvxKbeqqqpMmjQpzc3Nqaury+zZs7N48eLMnj07dXV1aW5uzkUXXZSqqqquLhUAAIC1hPAGALoBVw6Um/Epv/r6+kybNi0PPfRQRo0alX/7t3/LqFGjMmfOnEybNi319fVdXSIAAABrEeENAHQDrhwoN+Pz7lBfX5958+bllltuyVlnnZVbbrklc+fOFdwAAADwjuvR1QUAAGtG25UDo0ePzqhRo9rbq6urXTlQAsbn3aGqqirDhw9PURQZPny4QA0AAIAuIbwBgG6kvr4+hx56aE499dT89re/zQc+8IFceuml6dOnT1eXRv41PjU1Nfn617+ee++9N3vvvXe+8IUvpGfPnl1dGgAAAFAibpsGAN3I2WefnX79+uW73/1uHnjggXz3u99Nv379cvbZZ3d1aSRpbGzMDjvskHPOOSc/+clPcs4552SHHXZIY2NjV5cGAAAAlIjwBiillpaWzJo1KzNmzMisWbPS0tLS1SVB6Z199tn5+te/no033jgXX3xxpkyZkosvvjgbb7xxvv71rwtwulhjY2MaGhoydOjQTJ8+PVOnTs306dMzdOjQNDQ0CHAAAACAdsIboHQaGxszePDg1NbWZvLkyamtrc3gwYP9YhNWYMmSJbn44ouz+eab529/+1s++clPpn///vnkJz+Zv/3tb9l8881z8cUXZ8mSJV1d6lqppaUlo0ePTk1NTZqamjJs2LD06dMnw4YNS1NTU2pqajJmzBhBNQAAAJBEeAOUjDPTYfVcfvnlWbp0aS644IL06NHxkXY9evTI+eefn6VLl+byyy/vogrXbjNnzsyCBQsybty4VFZ2/PGrsrIyY8eOzfz58zNz5swuqhAAAAAoE+ENUBrOTIfV9+ijjyZJampqOl3e1t7Wj3fWwoULkyRDhgzpdHlbe1s/AAAAYO0mvAFKw5npsPq22267JElzc3Ony9va2/rxzhowYECSZM6cOZ0ub2tv6wcAAACs3YQ3QGm8/sz0lpaWzJo1KzNmzMisWbPS0tLizHRYgZNPPjk9evTIOeeck6VLl3ZYtnTp0nz5y19Ojx49cvLJJ3dRhWu3ESNGZNCgQZkwYUJaW1s7LGttbc3EiRNTXV2dESNGdFGFtOns+wcAAADeacIboDTazji/9NJLM3jw4NTW1mby5Mmpra3N4MGDc+mll3boB/w/PXv2zJlnnpknn3wy73nPe3LttdfmH//4R6699tq85z3vyZNPPpkzzzwzPXv27OpS10pVVVWZNGlSmpubU1dXl9mzZ2fx4sWZPXt26urq0tzcnIsuuihVVVVdXeparbGxsdPvH89bAwAA4J3W4827ALwzRowYkc022yxjx45NTU1NrrzyyixatCj9+/fPpZdemnHjxmWzzTZzZjosx4UXXpgkufjii3PWWWe1t/fo0SNf+MIX2pfTNerr6zNt2rSMHj06o0aNam+vrq7OtGnTUl9f34XV0djYmIaGhmW+fy677LI0NDQYIwAAAN5RrrwBSqUoiq4uAd7VLrzwwrz00ku54IILcthhh+WCCy7ISy+9JLgpifr6+sybNy+33HJLzjrrrNxyyy2ZO3euUKCLtbS0ZPTo0ampqUlTU1OGDRuWPn36ZNiwYWlqakpNTU3GjBnjFmoAAAC8Y4Q3QGnMnDkzTz/9dCZOnJg5c+Zk1KhR+bd/+7eMGjUq//d//5cJEybkqaeeysyZM7u6VCi1nj175nOf+1xOOumkfO5zn3OrNHgTM2fOzIIFCzJu3LhUVnb88biysjJjx47N/Pnzff8AAADwjhHeAKWxcOHCJMmpp57a6Znpp556aod+AO82nqlSTm3fK0OGDOl0eVu77x8AAADeKcIboDQGDBiQJJkzZ06qqqoyfPjw7Lvvvhk+fHiqqqoyZ86cDv0A3k3anqkydOjQTJ8+PVOnTs306dMzdOjQNDQ0CHC60Ou/fzrj+wcAAIB3mvAGKI0RI0Zk0KBBmTBhQlpbWzssa21tzcSJE1NdXZ0RI0Z0UYUAq8czVcrN9w8AAABlI7wBSqOqqiqTJk1Kc3Nz6urqMnv27CxevDizZ89OXV1dmpubc9FFF6WqqqqrSwVYJZ6pUm6+fwAAACibHl1dAHSVlpaWzJo1KzNmzEhFRUUOP/xwv5Qpgfr6+kybNi2jR4/O/8fevUfXWdf54n8naSkNWChgoXJrJCMVW0VquE1Ci5ex2DCJMTOjHscbc1RAXUoK2spSmNF2hLbokiq6nGHUo9WfMaZjqp3REdvEotaOeOjxFCm2XqCCYi9AI9Akvz88iYSGS0rT/SR5vdZi1T6XPJ/dj3tnP/u9v9/v/PnzB7ZXVVWltbU1TU1NJawO4MBYU6X4/P4BAACgSIy8YVyyYHTx9fX1Dfr746exARhNrKkyOjQ1NWXr1q1ZvXp1rrjiiqxevTp33nmn4AYAAIBDTnjDuGPB6GLr788LX/jCQf154QtfqD/AqPXYNVUeffTRgZGfXV1defTRR62pUiAVFRWpra3NBRdckNraWqNyAQAAKAnTpjGuPH7B6F27dqWzs3NgwejGxsYsXLgwDQ0NPqwpAf0Bxqr+NVWam5tz1FFHpbu7O0myYsWKTJ48OX/84x/T2trqtQ0AAABIYuQN44wFo4tNf4Cx7vFTQiZJWVnZkNsBAACA8Ut4w7hiwehi05/Ro6enZ9C0Tz09PaUuCQqtf2ThxRdfnN27dw9aU2XXrl25+OKLs3DhQs+lAvD6BgAAQBEIbxhXLBhdbPozOrS1taW6ujoNDQ1ZsWJFGhoaUl1dbT0ieBKPHVk4ceLEQWuqTJw40cjCgvD6BgAAQFEIbxhXHrtgdG9v76B9vb29FowuMf0pvra2tjQ3N2f27NlZu3ZtVq1albVr12b27Nlpbm72ASc8ASMLi6//9W3WrFm57rrr8s53vjPXXXddZs2a5fUNAACAQ054w7jSv2B0R0dHGhsbs3HjxnR3d2fjxo1pbGxMR0dHli1bZsHoEtGfYuuf9qm+vj7t7e2pqanJ5MmTU1NTk/b29tTX15v2CZ6AkYXF1v/6NmfOnNx+++256qqrcuONN+aqq67K7bffnjlz5nh9AwAA4JCaUOoC4FBrampKa2trWlpaMn/+/IHtVVVVaW1tTVNTUwmrQ3+Kq3/ap1WrVqW8fHD2X15enkWLFuX8889PZ2dn5s2bV5oioaAeO7Kwvb190D4jC0uv//Vt+/btufjii/PpT386O3fuzNSpU7Ny5cp84xvfGDjO6xsAAACHgpE3jEtNTU3ZunXroAWj77zzTsFAQehPMZn2CQ6ckYXFdvfddydJLrrooiFHFl500UWDjgMAAICRZuQN41ZFRUVqa2vT19eX2tpaH5gVjP4Uz2OnfTr33HP322/aJ3hyRhYW1+9+97skf+rRUCMLGxsb861vfWvgOAAAABhpRt4A8LQ8dtqn3t7eQftM+1QsPT096erqyvr169PV1WWdjgIxsrCYnv3sZydJ2trahnx965/qrv84AAAAGGnCGwCeFtM+jQ5tbW2prq5OQ0NDVqxYkYaGhlRXV6etra3UpfH/9I8svOCCC4wsLIgTTzwxSbJ27dohX9/Wrl076DgAAAAYaaZNA+BpM+1TsbW1taW5uTn19fW56aabBi243tzcrEfwBPpHFh533HG5/fbb93t9mzNnTu6//34jCwEAADhkhDcADEtTU1MaGhqyZs2arFu3LnPnzs2CBQuMHiixnp6etLS0pL6+Pu3t7dm1a1c6OzsHFlxvbGzMwoUL09DQoFfwOP0jC5ubm7NgwYJceuml+fWvf52TTz45nZ2dWbNmTVpbWz13AAAAOGSENwAMW/+0T319faZ9KojOzs5s3749q1atGnLB9UWLFuX8889PZ2dn5s2bV5oiocAeO7Kwo6NjYLuRhQAAAJSC8AYAxoAdO3YkSWbNmjXk/v7t/ccB+zOyEAAAgKIQ3gDAGDB9+vQkyebNm3Puuefut3/z5s2DjgOGZmQhAAAARVD+1IcAAEXXv+D6kiVL0tvbO2hfb29vli5dmqqqKguuAwAAAIwCRt4AwBjw2AXXGxsbc/nll6e7uzsbN27MypUr09HRYcF1xqW9e/dmy5Ytwzrnvvt3ZsPtd2VS5ZGZduzUYZ07c+bMVFZWDuscAAAAeDzhDQCMEY9dcH3+/PkD2y24Xiw9PT3p6urK+vXrU1ZWZk2VEbZly5bMmTPngM697gDO2bRpU84666wDuh4AAAD0E94AwBhiwfVia2trS0tLS7Zv354kWbFiRWbMmJHly5cL10bIzJkzs2nTpmGdc9svduQDHVvzkfrqnPnc4a0TNXPmzGEdDwAAAEMR3gBAwR3ItE+HTT4iE6aemMMmH5Gf/vSnwzrXtE8jo62tLc3Nzamvr89NN92UnTt3ZurUqVm5cmWam5uNjhohlZWVwx4J0334rzLpx8npZ8zOWWecMkKVAQAAwBMT3gBAwZn2afTr6elJS0tL6uvr097enl27dqWzszM1NTVpb29PY2NjFi5cmIaGBqOkAAAAAOENY4cFiYGxyrRPo19nZ2e2b9+eVatWpby8fNC+8vLyLFq0KOeff346Ozszb9680hQJAAAAFIbwhjHDN9OBscq0T6Pfjh07kiSzZs0acn//9v7jAAAAgPFNeMOY4ZvpABTV9Ol/+h2zefPmnHvuufvt37x586DjAAAAgPFNeMOY4ZvpY0tPT0+6urqyfv36lJWVZcGCBdaBAEaturq6zJgxI0uWLEl7e/ugfb29vVm6dGmqqqpSV1dXmgIBAIAR4zMO4ECUP/UhAIdWW1tbqqur09DQkBUrVqShoSHV1dVpa2srdWkAB6SioiLLly9PR0dHGhsbs3HjxnR3d2fjxo1pbGxMR0dHli1b5gYOAADGGJ9xAAdKeAMUSltbW5qbmzN79uysXbs2q1atytq1azN79uw0Nzd7cwOMWk1NTWltbc3tt9+e+fPn53Wve13mz5+fzZs3p7W1NU1NTaUuEQAAOIh8xgE8E8IboDB6enrS0tKS+vr6tLe3p6amJpMnT05NTU3a29tTX1+fhQsXpqenp9SljnuPHfLd1dWlJ/A0NTU1ZevWrVm9enWuuOKKrF69OnfeeafgBgAAxhifcQDPlPAGKIzOzs5s3749ixcvTnn54Jen8vLyLFq0KNu2bUtnZ2eJKiQx5BueqYqKitTW1uaCCy5IbW2tqdIAAGAM8hkH8EwJb4DC2LFjR5Jk1qxZQ+7v395/HIeeId8AAIwUo7uBseSxn3EM9frmMw7gqQhvgMKYPn16kmTz5s1D7u/f3n8ch5Yh3wAAjBSju4Gxpv+zixtvvHHI17cbb7xx0HEAjye8AQqjrq4uM2bMyJIlS9Lb2ztoX29vb5YuXZqqqqrU1dWVqMLxzZBvAABGgtHdwFhUV1eXadOmZdGiRZk1a9ag17dZs2Zl8eLFmTZtms84gCckvAEKo6KiIsuXL09HR0caGxuzcePGdHd3Z+PGjWlsbExHR0eWLVtmfYgSMa0dAAAHm9HdwFjW19dX6hKAUWxCqQsAeKympqa0trampaUl8+fPH9heVVWV1tbWNDU1lbC68e2x09qde+65++03rR3j1d69e7Nly5ZhnXPf/Tuz4fa7MqnyyEw7duqwzp05c2YqKyuHdQ4AFFX/6O5Vq1Y94eju888/P52dnZk3b15pigQ4AJ2dnfnd736XpUuX5tOf/vR+n3EsWbIkixcv9voGPCHhDVA4TU1Nqa+vz/XXX58NGzbk/PPPz5VXXpnDDjus1KWNa4+d1u5rX/vawGKLZWVlueiii0xrx7i1ZcuWzJkz54DOve4Aztm0aVPOOuusA7oeABSN0d3AWNX/uvXOd74zV155ZdasWZN169Zl7ty5WbBgQfbu3ZvFixd7fQOekPAGKJy2tra0tLRk+/btSZJvfvOb+exnP5vly5cbeVNC/dPaNTc356ijjkp3d3eSZMWKFZk8eXL++Mc/prW11bR2jDszZ87Mpk2bhnXObb/YkQ90bM1H6qtz5nOHN1pt5syZwzoeAIrM6G5grHr861ttbW36+vpSW1ubiooKr2/AUxLeAIXSv1hpfX19brrppuzcuTNTp07NypUr09zcbOq0Ahhqzt6ysjJz+TJuVVZWDnskTPfhv8qkHyennzE7Z51xyghVBgDF99jR3e3t7YP29fb2Gt0NjFpe34BnqvypDwE4NCxWWmz9/bn44ouze/furF69OldccUVWr16dXbt25eKLL9YfAACGpX90d0dHRxobG7Nx48Z0d3dn48aNaWxsTEdHR5YtW2Z0NzDqeH0Dnikjb4DCsFhpsekPAAAjoampKa2trWlpadlvQW8j74HRzOsb8EwIb4DCsFhpsfX/u99111153eteN7Am0YoVKzJjxox8+MMfHnQcAAA8XU1NTWloaNhvQW/fSAdGO69vwIES3gCFYbHSYuv/d//7v//7Idck+vu///tBxwEAwHBUVFTst6A3wFjg9Q04ENa8AQrjsYv59fb2DtpnMb/SO//88zNhwoRMmzYtbW1tg9Ykamtry7Rp0zJhwoScf/75pS4VAAAA4Gnp6elJV1dX1q9fn66uLmv5UhjCG6AwLOZXbBs2bMi+ffty7733pqmpaVB/mpqacu+992bfvn3ZsGFDqUsFAAAAeEptbW2prq5OQ0NDVqxYkYaGhlRXV6etra3UpYFp04BisZhfcfWvZfO//tf/ytVXX71ff/7X//pfecMb3mDNGwAAAKDw2tra0tzcPOTU8M3NzT6HouQO+siba665JmVlZYP+mzlz5sG+DDCGNTU1ZevWrVm9enWuuOKKrF69OnfeeadfmCXWv5bNaaedNmR/nvvc5w46DgAAAKCIenp60tLSkvr6+rS3tw+aGr69vT319fVZuHChKdQoqRGZNu0FL3hBduzYMfBfV1fXSFwGgEPosWsSlZWVpba2NhdccEFqa2tTVlZmTSIAAAAYgjVViqezszPbt2/P4sWLU14++CPy8vLyLFq0KNu2bUtnZ2eJKoQRCm8mTJiQE044YeC/4447biQuA4xR5hstJmsSAQAAwPD4jKOY+qd8nzVr1pD7+7ebGp5SGpE1b+6888485znPyeGHH57zzjsvS5cuzSmnnDLksQ8//HAefvjhgb/v2bMnSfLoo4/m0UcfHYnyDqn+JL2np2dMPJ6xRn+K5+tf/3pe+9rX5lWvelVWrlyZPXv2ZMqUKfnkJz+Z5ubmfPnLX86rX/3qUpc5bl188cX58pe/nPe97337rXnz5S9/ORdffLHnUkF4fSs2/Sm2sdafsfAYOLTG+j3avn37Bv4cC49nrNGfYtOfYtOf4vEZR3E9+9nPTpLcdtttOeecc/Z7/tx2220Dx3k+ldZYe20bzmMo6+vr6zuYF//Wt76VBx98MKeffnp27NiRa6+9NnfffXc2b96cZz3rWfsdf8011+Taa6/db/uXvvSlVFZWHszSSuLXDybLbp+QhbP35eQjS10Nj6c/xdLT05NLL700p556ahYtWjRo2Gpvb2+WLl2aX/3qV/nkJz9pdEeJ9fT05Gc/+9nAYn5nnHGGnhSM17di059iG2v92bt3b17/+tdn9+7dmTJlSqnLYRQY6/doAHAo+Iyj2PSHUhnO/dlBH3lz0UUXDfzvF77whTnnnHNy6qmn5v/7//6/XHLJJfsdv2jRolxxxRUDf9+zZ09OPvnk/NVf/dWYuLm8dctvktt/lhe+8IU5b+ZJpS6Hx9GfYlm3bl3uu+++fO1rX8s555yTnTt35tZbb815552XqVOn5rjjjssFF1yQKVOmZO7cuaUud9yrra0d1B+KxetbselPsY21/vSPmoCna6zfoz3+PSbFoj/Fpj/Fpj/F4jOO4tu3b19e+9rX5l/+5V9y6aWX5oEHHsiznvWsfOpTn8qPf/zjgRlGKK2x9to2nPuzEZk27bGOPvroPO95z8vWrVuH3D9p0qRMmjRpv+0TJ07MxIkTR7q8EdefzFZUVIyJxzPW6E+x/O53v0uSnHnmmZk4cWImTPjTS9SECRMyceLEnHnmmQPH6VfpPb4/FIvXt2LTn2Iba/0ZC4+BQ2us36N5D1Ns+lNs+lNs+lMsPuMovr/927/NhAkT0tLSkvr6+oHtVVVVaW1tTVNTUwmrI/nTCKkf/OAHWb9+fcrKyrJgwYJRPxJqOM/38qc+5Jl58MEHc9ddd2X69OkjfSlglOt/ndi8efOQ+/u3ez0BAAAAisxnHKNDU1NTtm7dmtWrV+eKK67I6tWrc+eddwpuCqCtrS3V1dVpaGjIihUr0tDQkOrq6rS1tZW6tEPmoI+8WbhwYS6++OKceuqpueeee/KhD30oFRUVed3rXnewLwWMInv37s2WLVue9Jgjjjgiz3nOc/K+970vy5cvz+937s6G2+/KpMojc9zUo/L+978/J554Yo444oj893//95P+rJkzZ5qTfRieTn8e7777dw70Z9qxwxu2qj8AAACMZXV1dZkxY0aWLFmS9vb2Qfv611SpqqpKXV1daQpkQEVFRWpra9PX15fa2tpRP7JjLGhra0tzc3Pq6+tz0003Day5vHLlyjQ3N4+bkVEHPbz5zW9+k9e97nW5//778+xnPzu1tbX5wQ9+kGc/+9kH+1LAKLJly5bMmTPnaR17zz33pKamZuDv1z1u/9lnn/2UP2PTpk0566yzhlPiuDac/jze4/vzdOgPAAAAY1lFRUWWL1+e5ubmNDY25vLLL093d3c2btyYlStXpqOjI62trYICeJyenp6Bqeza29uza9eudHZ2pqamJu3t7WlsbMzChQvT0NAw5p8/Bz28+fKXv3ywfyQwBsycOTObNm16Wsd+97vfzQ033JB77rlnYNuJJ56Y97znPXnpS1/6tK/H0zec/vS77Rc78oGOrflIfXXOfO7whnnrDwAAAGNdU1NTWltb09LSkvnz5w9st6YKPLHOzs5s3749q1atSnn54FVfysvLs2jRopx//vnp7OzMvHnzSlPkIXLQwxuAoVRWVj7tkRZnnXVW3vve9+aTX2jNP7Ztygeb5uSyv28e82l6KQ2nP/26D/9VJv04Of2M2TnrjFNGqDIAAACeSk9PT7q6usbUot5jRVNTUxoaGrJmzZqsW7cuc+fO1R94Ejt27EiSzJo1a8j9/dv7jxvLyp/6EIBDr6KiImedfV6OOGNuzjr7PG9qAAAAYAgW9S6+/jVVLrjgAmuqwFOYPv1Ps7ts3rx5yP392/uPG8uENwAAAEDJPXbkQFdXV3p6ekpdEhRe/6Les2fPztq1a7Nq1aqsXbs2s2fPTnNzswAHnga/f4qlrq4uM2bMyJIlS9Lb2ztoX29vb5YuXZqqqqrU1dWVqMJDR3gDAAAAlJSRAzB8j1/Uu6amJpMnTx5Y1Lu+vj4LFy70QTQ8Cb9/iqeioiLLly9PR0dHGhsbs3HjxnR3d2fjxo1pbGxMR0dHli1bNi5GsAlvAAAAgJIxcgAOTP+i3osXL37CRb23bduWzs7OElUIxeb3T3E1NTWltbU1t99+e+bPn5/Xve51mT9/fjZv3pzW1tY0NTWVusRDQngDAAAAlISRA3DgLOoNB87vn+JramrK1q1bs3r16lxxxRVZvXp17rzzznET3CTCGwAAAKBEjByAA2dRbzhwfv+MDhUVFamtrc0FF1yQ2tracTFV2mMJbwAAAICSMHIADpxFveHA+f3DaCC8AQAAAErCyAE4cBb1hgPn9w+jgfAGAAAAKAkjB+CZsag3HBi/fxgNhDcAAABASRg5AM+cRb1h+Pz+YTSYUOoCAAAAgPGrf+RAS0tL5s+fP7C9qqrKyAF4mvoX9e7r6xuXi3rDgfD7h6IT3gAAAAAl1dTUlIaGhqxZsybr1q3L3Llzs2DBAh9AAzCi/P6hyIQ3AAAAQMkZOQBAKfj9Q1FZ8wYAAAAAAKBAhDcAAAAAAAAFIrwBAABgXHjkkUfyqU99Kp/5zGfyqU99Ko888kipS4JRo6enJ11dXVm/fn26urrS09NT6pIAYEwT3gAAADDmXXXVVTniiCNy9dVX55vf/GauvvrqHHHEEbnqqqtKXRoUXltbW6qrq9PQ0JAVK1akoaEh1dXVaWtrK3VpADBmCW8AAAAY06666qpcf/31OfbYY3PDDTfk5ptvzg033JBjjz02119/vQAHnkRbW1uam5sze/bsrF27NqtWrcratWsze/bsNDc3C3AAYIQIbwAAABizHnnkkdxwww05/vjj85vf/CZvfOMbM3Xq1LzxjW/Mb37zmxx//PG54YYbTKEGQ+jp6UlLS0vq6+vT3t6empqaTJ48OTU1NWlvb099fX0WLlxoCjUAGAHCGwAAAMasT37yk9m3b18+/OEPZ8KECYP2TZgwIf/4j/+Yffv25ZOf/GSJKoTi6uzszPbt27N48eKUlw/+CKm8vDyLFi3Ktm3b0tnZWaIKAWDsmvDUhwAAAMDodNdddyVJ6uvrh9zfv73/OODPduzYkSSZNWvWkPv7t/cfBzAadXd358orr8x///d/56yzzsqNN96YyZMnl7osGN/hzbbfP5SHHt43ste4v3vgz6Pu3j2i1zpi0oRUHXfEiF4DAABgNDnttNOSJB0dHfmHf/iH/fZ3dHQMOg74s+nTpydJNm/enHPPPXe//Zs3bx50HMBo09jYmNWrVw/8/bbbbsu//uu/pqGhIe3t7aUrDDKOw5ttv38oFy773iG73tVrtibZOuLXuWXhPAEOAADA/3PZZZflyiuvzNVXX503v/nNg/bt27cvH/zgBzNhwoRcdtllpSkQCqyuri4zZszIkiVL9vsQs7e3N0uXLk1VVVXq6upKUyCMEj09Penq6sr69etTVlaWBQsWpKKiotRljXv9wc1hhx2WSy+9NDNnzsyWLVvyqU99KqtXr05jY6MAh5Iat+FN/4ibj/3dmameduSIXee++3fmlh/9NBee/aJMO3bqiF1n630P5j1fuW3ERxIBAACMJocddlje+9735vrrr89JJ52U973vfTn66KPzuc99Lh/96Edz77335sorr8xhhx1W6lKhcCoqKrJ8+fI0NzensbExl19+ebq7u7Nx48asXLkyHR0daW1t9SE0PIm2tra0tLRk+/btSZIVK1ZkxowZWb58eZqamkpb3DjW3d09ENw88MADefDBB9PZ2Zm//du/zXXXXZdnPetZWb16dbq7u02hRsmM2/CmX/W0IzPrxKNG7Of/YXJPHtiWnHnSlBxzzMhdBwAAgKFdd911SZIbbrghV1xxxcD2CRMm5MorrxzYD+yvqakpra2tueKKKzJ//vyB7TNmzEhra6sPn+FJtLW1pbm5OfX19bnpppuyc+fOTJ06NStXrkxzc7PnUAldeeWVSZIrrrhivy9wHHbYYXnPe96T6667LldeeWVuvPHGUpQIKS91AQAAADDSrrvuujz00EP58Ic/nFe96lX58Ic/nIceekhwA09TWVlZqUuAUaWnpyctLS2pr69Pe3t7ampqMnny5NTU1KS9vT319fVZuHBhenp6Sl3quHTnnXcmyZDr4SXJJZdcMug4KAXhDQAAAONC/5z2b3vb23LppZeaKg2ehv6RA7Nnz87atWuzatWqrF27NrNnz05zc3Pa2tpKXSIUUmdnZ7Zv357FixenvHzwR7Dl5eVZtGhRtm3bls7OzhJVOL79xV/8RZLks5/97JD7/+Vf/mXQcVAKwhsAAACg5B67oHdXV5dvoxeAkQNw4Hbs2JEkmTVr1pCvb7NmzRp0HIfW9ddfn+RPaxB1d3cP6k93d3c+9rGPDToOSmHcr3kDAAAAlJYFvYupf+TAqlWrnnDkwPnnn5/Ozs7MmzevNEVCQU2fPj1JcuONN+bTn/70fq9vb3vb2wYdx6E1efLkNDQ0ZPXq1amsrBzYvmLFioH/3dDQkMmTJ5eiPEhi5A0AAABQQqblKq7HjhwYipED8MTq6uoybdq0LFq0KLNmzRr0+jZr1qwsXrw406ZNS11dXalLHbfe+MY3PqP9MNKENwAAAEBJmJar2PpHBGzevHnI/f3bjRyAofX19ZW6BJ5A/++fiy++OA8++GDe+ta35swzz8xb3/rWPPjgg7n44ov9/qHkhDcAAABASVjQu9jq6uoyY8aMLFmyJL29vYP29fb2ZunSpamqqjJyAIbQ2dmZ3/3ud1m6dGk2b96c+fPn53Wve13mz5+f//N//k+WLFmS++67z+tbiTz2988RRxyR66+/Ptdcc02uv/76HHHEEX7/UAjCGwAAAKAkTMtVbBUVFVm+fHk6OjrS2NiYjRs3pru7Oxs3bkxjY2M6OjqybNmyVFRUlLpUKJz+1613vvOd2bp1a1avXp0rrrgiq1evzp133pl3vvOdg47j0PL7h9FAeAMAAACUhGm5iq+pqSmtra25/fbbB40c2Lx5c1pbW9PU1FTqEqGQHvv6VlFRkdra2lxwwQWpra1NRUWF17cS8/uH0UB4AwAAAJSEablGh6ampiFHDghu4Il5fSs2/WE0EN4AAAAAJWFartFjqJEDwBPz+lZs+sNoMKHUBQAAAADjV/+0XC0tLZk/f/7A9qqqKtNyAaOa17di0x+KTngDAAAAlFRTU1MaGhqyZs2arFu3LnPnzs2CBQt84xkY9by+FZv+UGTCGwAAAKDk+qfl6uvrMy0XAIeM3z8UlTVvAAAAAABGQFtbW6qrq9PQ0JAVK1akoaEh1dXVaWtrK3VpQMEJbwAAAAAADrK2trY0Nzd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"text/plain": [
"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"time_boxplots = time_spent_with_total_df.boxplot(\n",
" column=[\"Socializing time (hour)\", \"Exercising time (hour)\", \"Sleep time (hour)\", \"Total time (hour)\"], \n",
" by=\"Age group\", \n",
" figsize=(20, 20),\n",
" layout=(2, 2)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 5\n",
"\n",
"The tick marks on the horizontal axes of `time_boxplots` are not informative unless the viewer knows which age group each value represents. Fix labels of boxplot by recoding `Age group` using the labels in the code book (see `gss_tu2016_codebook.txt`).\n",
"\n",
"a) First, create a copy of `time_spent_with_total_df` (using the `DataFrame` `.copy()` method), and store it in a variable called `time_spent_age_label_df`. For that new `DataFrame`, recode `Age group` by adding a column called `Age group label` with the Age group labels found in the code book."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
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17366
\n",
"
120
\n",
"
90
\n",
"
490
\n",
"
6
\n",
"
59
\n",
"
2.000000
\n",
"
1.500000
\n",
"
8.166667
\n",
"
11.666667
\n",
"
65-74
\n",
"
\n",
"
\n",
"
17387
\n",
"
125
\n",
"
77
\n",
"
510
\n",
"
7
\n",
"
24
\n",
"
2.083333
\n",
"
1.283333
\n",
"
8.500000
\n",
"
11.866667
\n",
"
75+
\n",
"
\n",
" \n",
"
\n",
"
741 rows × 10 columns
\n",
"
"
],
"text/plain": [
" Socializing time Exercising time Sleep time Age group Province \\\n",
"3 395 60 510 6 35 \n",
"7 180 60 440 5 59 \n",
"23 80 230 330 6 46 \n",
"48 455 15 270 6 35 \n",
"54 130 185 670 1 12 \n",
"... ... ... ... ... ... \n",
"17325 25 15 640 6 47 \n",
"17336 105 100 525 6 59 \n",
"17351 40 90 540 5 46 \n",
"17366 120 90 490 6 59 \n",
"17387 125 77 510 7 24 \n",
"\n",
" Socializing time (hour) Exercising time (hour) Sleep time (hour) \\\n",
"3 6.583333 1.000000 8.500000 \n",
"7 3.000000 1.000000 7.333333 \n",
"23 1.333333 3.833333 5.500000 \n",
"48 7.583333 0.250000 4.500000 \n",
"54 2.166667 3.083333 11.166667 \n",
"... ... ... ... \n",
"17325 0.416667 0.250000 10.666667 \n",
"17336 1.750000 1.666667 8.750000 \n",
"17351 0.666667 1.500000 9.000000 \n",
"17366 2.000000 1.500000 8.166667 \n",
"17387 2.083333 1.283333 8.500000 \n",
"\n",
" Total time (hour) Age group label \n",
"3 16.083333 65-74 \n",
"7 11.333333 55-64 \n",
"23 10.666667 65-74 \n",
"48 12.333333 65-74 \n",
"54 16.416667 15-24 \n",
"... ... ... \n",
"17325 11.333333 65-74 \n",
"17336 12.166667 65-74 \n",
"17351 11.166667 55-64 \n",
"17366 11.666667 65-74 \n",
"17387 11.866667 75+ \n",
"\n",
"[741 rows x 10 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"time_spent_age_label_df = time_spent_with_total_df.copy()\n",
"\n",
"time_spent_age_label_df.loc[time_spent_age_label_df[\"Age group\"] == 1, \"Age group label\"] = \"15-24\"\n",
"\n",
"time_spent_age_label_df.loc[time_spent_age_label_df[\"Age group\"] == 2, \"Age group label\"] = \"25-34\"\n",
"\n",
"time_spent_age_label_df.loc[time_spent_age_label_df[\"Age group\"] == 3, \"Age group label\"] = \"35-44\"\n",
"\n",
"time_spent_age_label_df.loc[time_spent_age_label_df[\"Age group\"] == 4, \"Age group label\"] = \"45-54\"\n",
"\n",
"time_spent_age_label_df.loc[time_spent_age_label_df[\"Age group\"] == 5, \"Age group label\"] = \"55-64\"\n",
"\n",
"time_spent_age_label_df.loc[time_spent_age_label_df[\"Age group\"] == 6, \"Age group label\"] = \"65-74\"\n",
"\n",
"time_spent_age_label_df.loc[time_spent_age_label_df[\"Age group\"] == 7, \"Age group label\"] = \"75+\"\n",
"\n",
"time_spent_age_label_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"b) Create the same boxplots as in Task 4 g) using `layout = (2, 2)` and `figsize = (20, 20)`, but use `Age group label` to create the boxplot, so that the ticks on horizontal axes of the boxplot are informative. Store this boxplot in a variable called `time_boxplots_age_label`.\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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zn3P++efny1/+ch577LEcf/zxeeKJJ7Luuus22Ud9fX3+9re/NV4dkCR//etfk2SpDy/faKON0rlz5zz99NNLLHvqqadSUVGRLbbYIsnS//jdnIb633uFR4Pm2pa1jaXVtuGGGzZeddO1a9fMmzdviX7vvYLpvRquXHivv/71r+ncuXPjrdvWWWed1NXVfWAwstVWW2XGjBkpiqLJe9Rc3cvy/lDumWeeSX19fZOxq6yszDHHHJNrr702F154YSZOnJivfvWrzQYn7/XWW2/ljjvuyOc+97kmt3drcOqpp+amm27K/vvvn4033jgdO3ZcrrHbZpttUhRFevbs2eRztzxWZD9LsyKf365duyZJ5s2bl/XWW6+x39I+I88888wSY/pB309LM2HChAwbNixjxoxpbHv77beb/cwm734W3hukvfnmm5kzZ04OPfTQJO++78m77+HyBHcAALCqeOYNAACtxqJFi3LPPfekffv2jbdFO/TQQ1NXV5fLLrusSd9LLrkkbdq0aXzmx6JFi3Lssceme/fu+dGPfpRrr702L730Uk4//fRm9/Xe7RVFkcsuuyzt2rXLgQce2Gz/ysrKHHzwwbnjjjua3J7rpZdeys0335x+/fo13oKpIShZ2h+c36t79+7p3bt3rr/++sbAKnn36o4nnnjiA9dP3n1uyS677JLrrruuyT5nzJiRe+65p/EP2cm7f8x+/fXX8+c//7mxbc6cObn99tub3fbDDz/c5NZt//jHP3LHHXfk4IMPTmVlZSorK/OZz3wmt912W2bMmLHE+q+88krjfx966KF54YUXMmHChMa2+fPnL/V2a0tz+eWXN3l96aWXJskSz3/54he/mLlz5+ZrX/ta3nzzzXzhC1/4wG3ffvvteeutt3LSSSelpqZmia8hQ4bktttuyzvvvJPKysocdNBBmThxYl544YXGbTzzzDNNnsWUJNXV1amsrMx5553X5FZgybufv9dee22pNa3Ifpa1jeX9/DYEHlOmTGns99Zbb+W6665rdtsvvPBCk8/PG2+8keuvvz677LLLCt0yraHO978/l1566VKv+rnmmmuyaNGixtdXXnllFi9e3PhZGDRoULp06ZJRo0Y16dfgvZ9PAABYlVx5AwDAx9Zdd93VeAXNyy+/nJtvvjkzZ87Mf/3XfzX+Ifnwww/P/vvvn29/+9uZPXt2PvnJT+aee+7JHXfckdNOO63xD80XXHBBHnvssfz617/OOuusk5133jnf+c53cs4556SmpqZJgNGxY8dMnjw5w4YNy5577pm77rord955Z84+++zGq0mac8EFF+Tee+9Nv379cuKJJ6Zt27a5+uqr88477+Siiy5q7LfLLruksrIyF154YV5//fV06NCh8YH3zRk1alSqqqqy77775stf/nLmzp2byy67LL17924S6CzLD37wgxxyyCHZe++9c9xxx2XBggW59NJLs+6662bkyJGN/Y4++uh861vfypFHHplTTz018+fPz5VXXplPfOITzT5fp3fv3hk0aFBOPfXUdOjQIVdccUWS5Lzzzmvs8/3vfz/3339/9txzz3z1q1/NjjvumH/961/505/+lPvuuy//+te/kiRf/epXc9lll+VLX/pSHnnkkXTr1i033HBDOnfuvFzH2GDWrFk54ogjMnjw4Dz88MO58cYbc8wxx+STn/xkk3677rprevfunVtvvTU77LBDPvWpT33gtm+66aZssMEG2WeffZpdfsQRR+THP/5x7rzzzlRXV2fkyJG55557su++++brX/96Y9DYu3fvPPbYY43rbbPNNrngggsyYsSIzJ49O0OHDs0666yTWbNm5fbbb88JJ5yQM888c6l1Le9+lmV5P78HH3xwttxyyxx33HH55je/mcrKyvzsZz/LRhttlL///e9LbPcTn/hEjjvuuEyfPj2bbLJJfvazn+Wll17KuHHjlquu9xoyZEhuuOGGrLvuutlxxx3z8MMP57777ssGG2zQbP+FCxfmwAMPzFFHHZWnn346V1xxRfr165cjjjgiybvPtLnyyivzxS9+MZ/61Kdy9NFHNx7HnXfemX333XeJYBgAAFaJAgAAPmbGjRtXJGny1bFjx2KXXXYprrzyyqK+vr5J/3//+9/F6aefXnTv3r1o165dse222xY/+MEPGvs98sgjRdu2bYtTTjmlyXqLFy8u+vbtW3Tv3r2YO3duURRFMWzYsGKttdYqnn322eLggw8uOnfuXGyyySbFueeeW9TV1TVZP0lx7rnnNmn705/+VAwaNKhYe+21i86dOxf7779/8dBDDy1xjD/+8Y+LrbfeuqisrCySFPfff/8y35Of//znxfbbb1906NCh6N27d/G///u/xWc+85li++23b+wza9asIknxgx/8oNlt3HfffcW+++5bdOrUqejSpUtx+OGHF08++eQS/e65556id+/eRfv27YvtttuuuPHGG4tzzz23eP/0Iklx0kknFTfeeGOx7bbbFh06dCh23XXXZo/lpZdeKk466aRiiy22KNq1a1dsuummxYEHHlhcc801Tfo999xzxRFHHFF07ty52HDDDYtvfOMbxeTJk5frPWqo8cknnyxqamqKddZZp+jatWtx8sknFwsWLGh2nYsuuqhIUowaNWqZ2244hrZt2xZf/OIXl9pn/vz5RefOnYsjjzyyse3Xv/51seuuuxbt27cvttlmm+InP/lJMXz48KJjx45LrH/bbbcV/fr1K9Zaa61irbXWKrbffvvipJNOKp5++ukPrG9599Mwbs1Z3s/vI488Uuy5555F+/btiy233LIYO3Zs4/ftrFmzGvtttdVWxWGHHVbcfffdxc4771x06NCh2H777Ytbb731A4+nodb3fo/NnTu3+PKXv1xsuOGGxdprr10MGjSoeOqpp4qtttqqGDZsWGO/hloefPDB4oQTTii6du1arL322sV//Md/FK+99toS+7n//vuLQYMGFeuuu27RsWPHYptttimOPfbY4o9//GNjn+a+BwAAYGW1KYr3XVMOAAAs1bHHHpsJEyYs9xUtLWmXXXbJRhttlHvvvbdF9t+mTZucdNJJH+srE370ox/l9NNPz+zZs7Plllt+ZPsdOnRo/u///q/ZZwZ9HPezND169Ejv3r0zadKkFtk/AACUlWfeAADAx9yiRYuyePHiJm0PPPBAHn/88QwcOLBlimoFiqLIT3/60wwYMGC1BjcLFixo8nrmzJn51a9+tcrH7qPaDwAA8OF55g0AAHzMPf/88znooIPyhS98Id27d89TTz2Vq666Kptuumn+8z//s6XL+9h566238r//+7+5//7788QTT+SOO+5Yrfvbeuutc+yxx2brrbfOc889lyuvvDLt27fPWWed9bHcDwAA8OEJbwAA4GOua9eu2W233fKTn/wkr7zyStZaa60cdthh+f73v7/UB7WzdK+88kqOOeaYrLfeejn77LMbH16/ugwePDjjx4/Piy++mA4dOmTvvffOqFGjsu22234s9wMAAHx4nnkDAAAAAABQIp55AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAD6kY489Nj169FipdXv06JFjjz228fUDDzyQNm3a5IEHHvhQ2ymDkSNHpk2bNi1dxhL+8Ic/pH379nnuueca23r06JEhQ4a0YFXN22uvvXLWWWe1dBkAALBGGjhwYAYOHLhC61x77bVp06ZNZs+evVpqWllt2rTJyJEjW7qMJRx66KH56le/2vi64f374x//2IJVLWny5MlZe+2188orr7R0KcAaRHgDtCpPPPFEampqstVWW6Vjx47ZbLPN8ulPfzqXXnppS5fWKs2fPz8jR45c4bCpJX3729/O5z//+Wy11VYtXcoH+ta3vpXLL788L774YkuXAgAAK63hD/JL+/rd737X0iW2Cr/61a9KGdAszW9/+9vcc889+da3vtXSpXygwYMHp1evXhk9enRLlwKsQdoURVG0dBEAq8JDDz2U/fffP1tuuWWGDRuWTTfdNP/4xz/yu9/9Ls8++2yeeeaZ1bLfRYsWpb6+Ph06dFjhdXv06JGBAwfm2muvTZLU19dn4cKFad++fSoqlj9ff+edd1JRUZF27dqtcA0fxquvvpqNNtoo55577hKThMWLF2fx4sXp2LHjR1rTsjz22GPZdddd89BDD2XvvfdubO/Ro0d69+6dSZMmtWB1S6qvr89mm22Wr371qzn//PNbuhwAAFgp1157bb785S/n/PPPT8+ePZdYPnjw4Gy44YYtUNmyLVy4MEnSvn375V6nrq4uixYtSocOHT7yOxGcfPLJufzyy9Pcn/refvvttG3bNm3btv1Ia1qWoUOHZsGCBbn77rsb2xo+K9OnT8/uu+/egtUt6corr8yZZ56ZF198Meuss05LlwOsAcrzf2yAD+l73/te1l133UyfPj3rrbdek2Uvv/zyatvvqgxMKioqVirsWJngaHUr28QgScaNG5ctt9wye+21V0uXskzz589P586dU1FRkZqamlx//fU577zzSnkbOgAAWF6HHHJIi/9B/q233spaa621XH1XJLRpUFlZmcrKyhVeb3Ur00l1ybtz9DvvvDNXXXVVS5eyTG+//XbjyZWf+cxncsopp+TWW2/NV77ylZYuDVgDuG0a0Go8++yz2WmnnZYIbpJk4403bvJ68eLF+e53v5ttttkmHTp0SI8ePXL22WfnnXfeWWLdu+66KwMGDMg666yTLl26pG/fvrn55psblzf3zJuLL744++yzTzbYYIN06tQpu+22WyZMmPCBx/D+Z94s6/YC77338vufedOw3m9/+9ucccYZ2WijjbLWWmvlyCOPXOIevfX19Rk5cmS6d++ezp07Z//998+TTz75gc/RmT17djbaaKMkaQwW3nsf5eaeedOmTZucfPLJufXWW7PjjjumU6dO2XvvvfPEE08kSa6++ur06tUrHTt2zMCBA5u9T/Tvf//7DB48OOuuu246d+6cAQMG5Le//e0HvrdJMnHixBxwwAFLDUGmTZuWPfbYIx07dszWW2+d66+/fok+f/vb3/LZz34266+/fjp37py99tord955Z5M+S7vPdXPPNBo4cGB69+6dRx55JPvtt186d+6cs88+u3H5pz/96Tz33HN57LHHlusYAQDg4+rcc89NRUVFfv3rXzdpP+GEE9K+ffs8/vjjjW3LMy9omJM8+eSTOeaYY9K1a9f069evcfmNN96YPfbYI507d07Xrl2z33775Z577mlc3twzby699NLstNNOjevsvvvuTeaHzc0FGp6xuTzzjT//+c8ZMGBAOnXqlM033zwXXHBBxo0b94HP0Tn22GNz+eWXJ0mTeWOD9z/zpuG9+etf/5ovfOELWXfddbPRRhvlv//7v1MURf7xj3+kqqoqXbp0yaabbpoxY8Yssc933nkn5557bnr16pUOHTpkiy22yFlnndXsvPr97rzzzixevDgHHXRQs8vfeeedD5zLJskVV1yRnXbaKR06dEj37t1z0kknZd68eU36LG1u+/7xbZiv/fznP88555yTzTbbLJ07d84bb7yR5N2/K+y888654447PvD4AFaFcp0SDfAhbLXVVnn44YczY8aM9O7de5l9jz/++Fx33XWpqanJ8OHD8/vf/z6jR4/OX/7yl9x+++2N/a699tp85StfyU477ZQRI0ZkvfXWy6OPPprJkyfnmGOOWer2f/SjH+WII47If/zHf2ThwoX5+c9/ns9+9rOZNGlSDjvssOU+pv322y833HBDk7bnnnsu55xzzhKBVHNOOeWUdO3aNeeee25mz56dH/7whzn55JNzyy23NPYZMWJELrroohx++OEZNGhQHn/88QwaNChvv/32Mre90UYb5corr8zXv/71HHnkkamurk6S7Lzzzstcb+rUqfnf//3fnHTSSUmS0aNHZ8iQITnrrLNyxRVX5MQTT8zcuXNz0UUX5Stf+Up+85vfNK77m9/8Joccckh22223xonduHHjcsABB2Tq1KnZY489lrrf559/Pn//+9/zqU99qtnlzzzzTGpqanLcccdl2LBh+dnPfpZjjz02u+22W3baaackyUsvvZR99tkn8+fPz6mnnpoNNtgg1113XY444ohMmDAhRx555DKPfWlee+21HHLIITn66KPzhS98IZtssknjst122y3Ju/eD3nXXXVdq+wAAUAavv/56Xn311SZtbdq0yQYbbJAkOeecc/LLX/4yxx13XJ544omss846ufvuu/PjH/843/3ud/PJT34yyYrPCz772c9m2223zahRoxpvKXbeeedl5MiR2WeffXL++eenffv2+f3vf5/f/OY3Ofjgg5ut/8c//nFOPfXU1NTU5Bvf+Ebefvvt/PnPf87vf//7Zc4Pk+Wbbzz//PPZf//906ZNm4wYMSJrrbVWfvKTnyzXnRa+9rWv5YUXXsi99967xBxyWT73uc9lhx12yPe///3ceeedueCCC7L++uvn6quvzgEHHJALL7wwN910U84888z07ds3++23X5J3TwI84ogjMm3atJxwwgnZYYcd8sQTT+SSSy7JX//610ycOHGZ+33ooYeywQYbLPVZpMszlx05cmTOO++8HHTQQfn617+ep59+OldeeWWmT5+e3/72tyt9l4zvfve7ad++fc4888y88847Ta7A2m233T7w2ABWmQKglbjnnnuKysrKorKysth7772Ls846q7j77ruLhQsXNun32GOPFUmK448/vkn7mWeeWSQpfvOb3xRFURTz5s0r1llnnWLPPfcsFixY0KRvfX19438PGzas2GqrrZosnz9/fpPXCxcuLHr37l0ccMABTdq32mqrYtiwYY2v77///iJJcf/99zd7jAsWLCh22223onv37sWcOXOWup1x48YVSYqDDjqoSa2nn356UVlZWcybN68oiqJ48cUXi7Zt2xZDhw5tsp+RI0cWSZpsszmvvPJKkaQ499xzl1h27rnnFu//MZOk6NChQzFr1qzGtquvvrpIUmy66abFG2+80dg+YsSIIklj3/r6+mLbbbctBg0a1OSY5s+fX/Ts2bP49Kc/vcxa77vvviJJ8ctf/nKJZVtttVWRpJgyZUpj28svv1x06NChGD58eGPbaaedViQppk6d2tj273//u+jZs2fRo0ePoq6uriiK//f+v/c4i6L58R0wYECRpLjqqquWWnv79u2Lr3/968s8PgAAKKuG34+b++rQoUOTvk888UTRvn374vjjjy/mzp1bbLbZZsXuu+9eLFq0qCiKFZsXNMxJPv/5zzfZx8yZM4uKioriyCOPbPwdvsF7tzlgwIBiwIABja+rqqqKnXbaabmO9b1zgeWdb5xyyilFmzZtikcffbSx7bXXXivWX3/9ZucX73fSSSctMQdr8P55W8N7c8IJJzS2LV68uNh8882LNm3aFN///vcb2+fOnVt06tSpyfzwhhtuKCoqKprMjYqiKK666qoiSfHb3/52mbX269ev2G233ZZoX9657Msvv1y0b9++OPjgg5uM4WWXXVYkKX72s581tr1/vtzg/ePbMF/beuutl5jTNxg1alSRpHjppZeWeXwAq4LbpgGtxqc//ek8/PDDOeKII/L444/noosuyqBBg7LZZpvlf//3fxv7/epXv0qSnHHGGU3WHz58eJI03gLr3nvvzb///e/813/91xL3B/6gZ4906tSp8b/nzp2b119/Pf3798+f/vSnlT/AJCeeeGKeeOKJ3Hbbbdl0000/sP8JJ5zQpNb+/funrq4uzz33XJLk17/+dRYvXpwTTzyxyXqnnHLKh6pzWQ488MAmt5nbc889kySf+cxnmjz0saH9b3/7W5Lksccey8yZM3PMMcfktddey6uvvppXX301b731Vg488MBMmTIl9fX1S93va6+9liTp2rVrs8t33HHH9O/fv/H1RhttlO22265x/8m7n5099tijya0W1l577ZxwwgmZPXt2nnzyyeV9G5ro0KFDvvzlLy91edeuXZc4QxEAAD5uLr/88tx7771Nvu66664mfXr37p3zzjsvP/nJTzJo0KC8+uqrue666xqfp7ky84L//M//bPJ64sSJqa+vz3e+851UVDT909iy5nrrrbde/vnPf2b69OkrfOzLM9+YPHly9t577+yyyy6Nbeuvv37+4z/+Y4X3t7yOP/74xv+urKzM7rvvnqIoctxxxzW2r7feekvUeuutt2aHHXbI9ttv3zgGr776ag444IAkyf3337/M/b722mtLnZslHzyXve+++7Jw4cKcdtppTcbwq1/9arp06bLEra1XxLBhw5rM6d+roWbzM+Cj4LZpQKvSt2/f1NbWZuHChXn88cdz++2355JLLklNTU0ee+yx7LjjjnnuuedSUVGRXr16NVl30003zXrrrdf4y+Czzz6bJB94C7bmTJo0KRdccEEee+yxJvf7/TAPnL/66qszbty4XH311dlrr72Wa50tt9yyyeuGXzTnzp2bJI3H+v73Yv3111/mL9IfxvtrWnfddZMkW2yxRbPtDbXOnDkzybu/SC/N66+//oF1F///bRI+qK7k3ferYf/Ju+9XQ6j0XjvssEPj8pX5vGy22WbLfBhqURQf6rMDAABlsMcee2T33Xf/wH7f/OY38/Of/zx/+MMfMmrUqOy4446Ny1ZmXtCzZ88my5999tlUVFQ02e7y+Na3vpX77rsve+yxR3r16pWDDz44xxxzTPbdd98PXHd55xt77733Ev3eP19blZqbn3Xs2DEbbrjhEu0NJ8Ql747DX/7yl8bnoL7fyy+//IH7XtrcrLm6ljaX3W677Zr0a9++fbbeeuvG5Svj/Z+X92qo2fwM+CgIb4BWqX379unbt2/69u2bT3ziE/nyl7+cW2+9Neeee25jn9X1y9bUqVNzxBFHZL/99ssVV1yRbt26pV27dhk3blyTB1muiD/84Q/5xje+keOPPz4nnHDCcq9XWVnZbPuyfkle3ZZW0wfV2nD23A9+8IMmZ6K919prr73U/TbcR/u9k6MV2f+KWNpnq66urtn2pZ3V1WDevHlLTJ4AAKC1+tvf/tYY0jzxxBNNlq3MvOCDft9eXjvssEOefvrpTJo0KZMnT85tt92WK664It/5zndy3nnnLXPdMs7NkubrWp5a6+vr06dPn4wdO7bZvu8/Oe/9Nthgg6XOzZa3huW1rPlZc/tZ1ueloWbzM+CjILwBWr2GM7vmzJmTJNlqq61SX1+fmTNnNl4xkbz7MPp58+Y1PjBxm222SZLMmDFjhc50uu2229KxY8fcfffdTR4sOW7cuJWq/5VXXklNTU122WWXXH755Su1jaVpONZnnnmmydlFr7322jJ/kW7wUZ5t1DAeXbp0yUEHHbTC62+//fZJklmzZq10DVtttVWefvrpJdqfeuqpxuXJ/zsrbN68eU36rczZX88//3wWLlzY5LMKAACtVX19fY499th06dIlp512WkaNGpWamppUV1cn+fDzgoZt1NfX58knn1xqALQ0a621Vj73uc/lc5/7XBYuXJjq6up873vfy4gRI5a43faK2mqrrfLMM88s0d5cW3M+6vnZ448/ngMPPHCl9rv99tvntttuW+n9N8y9nn766Wy99daN7QsXLsysWbOafDa6du26xNwseXd+9t51l8esWbOy4YYbLvWKI4BVyTNvgFbj/vvvb/YsnIZn3DRcTn3ooYcmSX74wx826ddwxtBhhx2WJDn44IOzzjrrZPTo0Xn77beb9F3W2T6VlZVp06ZNk6ssZs+enYkTJ67YAeXdM4GOPvroLFy4MLfddtsyb621Mg488MC0bds2V155ZZP2yy67bLnW79y5c5IlQ4rVYbfddss222yTiy++OG+++eYSy1955ZVlrr/ZZptliy22yB//+MeVruHQQw/NH/7whzz88MONbW+99Vauueaa9OjRo/G2Cw0TyilTpjT2q6uryzXXXLPC+3zkkUeSJPvss89K1w0AAB8XY8eOzUMPPZRrrrkm3/3ud7PPPvvk61//euMzRj7svCBJhg4dmoqKipx//vlLPB9nWXO99942LHn3jg877rhjiqLIokWLlufwlmnQoEF5+OGH89hjjzW2/etf/8pNN920XOuvtdZaST6a+dlRRx2V559/Pj/+8Y+XWLZgwYK89dZby1x/7733zty5c5s8R2dFHHTQQWnfvn3+53/+p8mY/fSnP83rr7/eOK9P3p2f/e53v8vChQsb2yZNmpR//OMfK7zfRx55pNlb2wGsDq68AVqNU045JfPnz8+RRx6Z7bffPgsXLsxDDz2UW265JT169Gh8IPwnP/nJDBs2LNdcc03mzZuXAQMG5A9/+EOuu+66DB06NPvvv3+Sd8/kuuSSS3L88cenb9++OeaYY9K1a9c8/vjjmT9/fq677rpm6zjssMMyduzYDB48OMccc0xefvnlXH755enVq1f+/Oc/r9AxXXXVVfnNb36T//zP/1zigY+bbLJJPv3pT6/EO9V0G9/4xjcyZsyYHHHEERk8eHAef/zx3HXXXdlwww0/8AyqTp06Zccdd8wtt9yST3ziE1l//fXTu3fvlXruywepqKjIT37ykxxyyCHZaaed8uUvfzmbbbZZnn/++dx///3p0qVLfvnLXy5zG1VVVbn99ttX+hky//Vf/5Xx48fnkEMOyamnnpr1118/1113XWbNmpXbbrut8UGZO+20U/baa6+MGDEi//rXv7L++uvn5z//eRYvXrzC+7z33nuz5ZZbZtddd13hdQEAoEzuuuuuxqvW32ufffbJ1ltvnb/85S/57//+7xx77LE5/PDDkyTXXnttdtlll5x44on5xS9+sUrmBb169cq3v/3tfPe7303//v1TXV2dDh06ZPr06enevXtGjx7d7HoHH3xwNt100+y7777ZZJNN8pe//CWXXXZZDjvssKyzzjof+v0566yzcuONN+bTn/50TjnllKy11lr5yU9+ki233DL/+te/PnAOs9tuuyVJTj311AwaNCiVlZU5+uijP3RdzfniF7+YX/ziF41z1X333Td1dXV56qmn8otf/CJ33333Mp9vdNhhh6Vt27a57777VujW4A022mijjBgxIuedd14GDx6cI444Ik8//XSuuOKK9O3bN1/4whca+x5//PGZMGFCBg8enKOOOirPPvtsbrzxxsaT7pbXyy+/nD//+c856aSTVrhegJUhvAFajYsvvji33nprfvWrX+Waa67JwoULs+WWW+bEE0/MOeeck/XWW6+x709+8pNsvfXWufbaa3P77bdn0003zYgRI5o8EydJjjvuuGy88cb5/ve/n+9+97tp165dtt9++5x++ulLreOAAw7IT3/603z/+9/Paaedlp49e+bCCy/M7NmzVzi8aThr7KqrrspVV13VZNmAAQM+dHiTJBdeeGE6d+6cH//4x7nvvvuy995755577km/fv2W67L/n/zkJznllFNy+umnZ+HChTn33HNXS3iTJAMHDszDDz+c7373u7nsssvy5ptvZtNNN82ee+6Zr33tax+4/le+8pVcdtll+e1vf5t+/fqt8P432WSTPPTQQ/nWt76VSy+9NG+//XZ23nnn/PKXv2xyZleS3HTTTfna176W73//+1lvvfVy3HHHZf/991+hMauvr89tt92W4447zgMxAQD42PvOd77TbPu4ceOy1VZbZdiwYdlwww2b3CVh2223zejRo/ONb3wjv/jFL3LUUUd96HlBkpx//vnp2bNnLr300nz7299O586ds/POO+eLX/ziUtf52te+lptuuiljx47Nm2++mc033zynnnpqzjnnnBV6H5Zmiy22yP33359TTz01o0aNykYbbZSTTjopa621Vk499dQPnJ9VV1fnlFNOyc9//vPceOONKYpitYU3FRUVmThxYi655JJcf/31uf3229O5c+dsvfXW+cY3vpFPfOITy1x/k002yaGHHppf/OIXKxXeJMnIkSOz0UYb5bLLLsvpp5+e9ddfPyeccEJGjRqVdu3aNfYbNGhQxowZk7Fjx+a0007L7rvvnkmTJmX48OErtL/a2tp06NAhRx111ErVC7Ci2hQt/WQ0AEpn3rx56dq1ay644IJ8+9vfbulyVqkDDzww3bt3zw033NDSpXygiRMn5phjjsmzzz6bbt26tXQ5AABACzjttNNy9dVX580330xlZWVLl7PKTJ06NQMHDsxTTz2VbbfdtqXL+UC77rprBg4cmEsuuaSlSwHWEJ55A7CGW7BgwRJtDWe6DRw48KMt5iMwatSo3HLLLXnuuedaupQPdOGFF+bkk08W3AAAwBri/fOz1157LTfccEP69evXqoKbJOnfv38OPvjgXHTRRS1dygeaPHlyZs6cmREjRrR0KcAaxJU3AGu4a6+9Ntdee20OPfTQrL322pk2bVrGjx+fgw8+OHfffXdLlwcAALDG2GWXXTJw4MDssMMOeemll/LTn/40L7zwQn79619nv/32a+nyAPgIeeYNwBpu5513Ttu2bXPRRRfljTfeyCabbJJvfOMbueCCC1q6NAAAgDXKoYcemgkTJuSaa65JmzZt8qlPfSo//elPBTcAayBX3gAAAAAAAJSIZ94AAAAAAACUiPAGAAAAAACgREr3zJv6+vq88MILWWedddKmTZuWLgcAAD7WiqLIv//973Tv3j0VFc7dYsWZowEAwKqxIvOz0oU3L7zwQrbYYouWLgMAAFqVf/zjH9l8881bugw+hszRAABg1Vqe+Vnpwpt11lknybvFd+nSpYWr+fD+9a9/5eGHH87ee++d9ddfv6XL4X2MT7kZn3IzPuVmfMrN+JRbaxufN954I1tssUXj79mwoszR+CgZn3IzPuVmfMrN+JSb8Smv1jY2KzI/K11403AZfpcuXVrFxGDx4sXp3Llzqzme1sb4lJvxKTfjU27Gp9yMT7m11vFxuytWljkaHyXjU27Gp9yMT7kZn3IzPuXVWsdmeeZnbnoNAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsIbAAAAAACAEhHeAAAAAAAAlIjwBgAAAAAAoESENwAAAAAAACUivAEAAAAAACgR4Q0AAAAAAECJCG8AAAAAAABKRHgDAAAAAABQIsI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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"time_boxplots_age_label = time_spent_age_label_df.boxplot(\n",
" column = [\"Socializing time (hour)\", \"Exercising time (hour)\", \"Sleep time (hour)\", \"Total time (hour)\"], \n",
" by = \"Age group label\", \n",
" figsize = (20, 20),\n",
" layout = (2, 2)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 6 (Written Discussion)\n",
"\n",
"a) Which age group spends the most time sleeping, exercising, and socializing in total? Does your ranking change if you use mean or median as a summary measure of time? Briefly explain why or why not your ranking changes, and which ranking is the best representation.\n",
"\n",
"b) Which age group shows the most variability in time spent socializing? Provide a brief explanation of why this group shows the most variability.\n",
"\n",
"c) State one limitations of basing this data analysis on only respondents that spent more than zero time sleeping, exercising, and socializing. Briefly explain why it's a limitation to your findings in Tasks 4 and 5."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> *Sample solutions*\n",
">\n",
"> a) \n",
"> - Yes, the rankings change for the total if the mean or median is used.\n",
"> - If the median is used then the rankings are: 15-24, 25-34, 75+ ,...\n",
"> - If the mean is used then the rankings are: 15-25, 75+, 55-64, ...\n",
"> - There are outliers in sleep and socializing that we can see on the boxplots that are pushing the mean higher, but the median is not influenced by these observations.\n",
"> - The median would be a more suitable choice since it's not influenced by outliers.\n",
">\n",
"> b) The length of the boxplot is longest for 15-24 age group.\n",
">\n",
"> c) 96% of the data is excluded so results might be different if these observations are included."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Marking Rubric\n",
"\n",
"\n",
"Section | 0 | 1 | 2 | 3\n",
"------------|---|---|---|---\n",
"Computational questions including visualizations (for each part) |auto test fails | auto test passes | NA | NA \n",
"Qualitative questions (for each part) | No answer | The question is answered but no explanation is given | The question is answered but the explanation is irrelevant or not supported | The question is answered and the explanation is supported"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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"
},
"vscode": {
"interpreter": {
"hash": "8b8edaa195e148f815789564e9a10f57d8b792ac9e1a5daafce5fbae42bebd0e"
}
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},
"nbformat": 4,
"nbformat_minor": 2
}