{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": [
"setup"
]
},
"source": [
"(c) 2016 - present. Enplus Advisors, Inc."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": [
"setup"
]
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"pd.set_option('display.float_format', '{:,.1f}'.format)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": [
"setup"
]
},
"outputs": [],
"source": [
"dat = pd.read_csv('data/weather-6m.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exercise"
]
},
"source": [
"**Exercise:**\n",
"\n",
"Calculate the average `air_temp` by `month`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"grp = dat.groupby('month')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"month\n",
"1 -10.0\n",
"2 -3.0\n",
"3 2.1\n",
"4 7.0\n",
"5 14.0\n",
"6 18.1\n",
"Name: air_temp, dtype: float64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grp['air_temp'].mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exercise"
]
},
"source": [
"**Exercise:**\n",
"\n",
"Compute summary statistics on `air_temp` and `dew_point` using \n",
"the `describe` method."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"8\" halign=\"left\">air_temp</th>\n",
" <th colspan=\"8\" halign=\"left\">dew_point</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>month</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>712.0</td>\n",
" <td>-10.0</td>\n",
" <td>6.2</td>\n",
" <td>-29.4</td>\n",
" <td>-13.3</td>\n",
" <td>-10.0</td>\n",
" <td>-5.6</td>\n",
" <td>2.8</td>\n",
" <td>712.0</td>\n",
" <td>-14.1</td>\n",
" <td>6.8</td>\n",
" <td>-32.8</td>\n",
" <td>-18.3</td>\n",
" <td>-13.9</td>\n",
" <td>-8.9</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>644.0</td>\n",
" <td>-3.0</td>\n",
" <td>6.8</td>\n",
" <td>-19.4</td>\n",
" <td>-7.2</td>\n",
" <td>-2.2</td>\n",
" <td>1.7</td>\n",
" <td>15.0</td>\n",
" <td>644.0</td>\n",
" <td>-7.3</td>\n",
" <td>7.3</td>\n",
" <td>-22.8</td>\n",
" <td>-12.2</td>\n",
" <td>-7.2</td>\n",
" <td>-2.2</td>\n",
" <td>8.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>713.0</td>\n",
" <td>2.1</td>\n",
" <td>6.7</td>\n",
" <td>-13.3</td>\n",
" <td>-1.7</td>\n",
" <td>2.2</td>\n",
" <td>5.6</td>\n",
" <td>22.8</td>\n",
" <td>713.0</td>\n",
" <td>-3.4</td>\n",
" <td>6.1</td>\n",
" <td>-17.2</td>\n",
" <td>-7.8</td>\n",
" <td>-2.8</td>\n",
" <td>0.6</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>691.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>-2.8</td>\n",
" <td>2.8</td>\n",
" <td>5.6</td>\n",
" <td>9.4</td>\n",
" <td>28.3</td>\n",
" <td>691.0</td>\n",
" <td>0.3</td>\n",
" <td>6.1</td>\n",
" <td>-13.3</td>\n",
" <td>-3.9</td>\n",
" <td>-1.1</td>\n",
" <td>3.9</td>\n",
" <td>16.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>713.0</td>\n",
" <td>14.0</td>\n",
" <td>5.1</td>\n",
" <td>1.1</td>\n",
" <td>10.6</td>\n",
" <td>13.9</td>\n",
" <td>17.2</td>\n",
" <td>28.3</td>\n",
" <td>713.0</td>\n",
" <td>6.2</td>\n",
" <td>4.6</td>\n",
" <td>-6.1</td>\n",
" <td>2.8</td>\n",
" <td>6.7</td>\n",
" <td>10.0</td>\n",
" <td>18.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>688.0</td>\n",
" <td>18.1</td>\n",
" <td>6.0</td>\n",
" <td>3.3</td>\n",
" <td>13.8</td>\n",
" <td>17.8</td>\n",
" <td>22.8</td>\n",
" <td>33.3</td>\n",
" <td>688.0</td>\n",
" <td>12.3</td>\n",
" <td>5.5</td>\n",
" <td>-3.3</td>\n",
" <td>8.9</td>\n",
" <td>11.7</td>\n",
" <td>17.2</td>\n",
" <td>23.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" air_temp dew_point \\\n",
" count mean std min 25% 50% 75% max count mean std \n",
"month \n",
"1 712.0 -10.0 6.2 -29.4 -13.3 -10.0 -5.6 2.8 712.0 -14.1 6.8 \n",
"2 644.0 -3.0 6.8 -19.4 -7.2 -2.2 1.7 15.0 644.0 -7.3 7.3 \n",
"3 713.0 2.1 6.7 -13.3 -1.7 2.2 5.6 22.8 713.0 -3.4 6.1 \n",
"4 691.0 7.0 6.0 -2.8 2.8 5.6 9.4 28.3 691.0 0.3 6.1 \n",
"5 713.0 14.0 5.1 1.1 10.6 13.9 17.2 28.3 713.0 6.2 4.6 \n",
"6 688.0 18.1 6.0 3.3 13.8 17.8 22.8 33.3 688.0 12.3 5.5 \n",
"\n",
" \n",
" min 25% 50% 75% max \n",
"month \n",
"1 -32.8 -18.3 -13.9 -8.9 1.0 \n",
"2 -22.8 -12.2 -7.2 -2.2 8.3 \n",
"3 -17.2 -7.8 -2.8 0.6 13.3 \n",
"4 -13.3 -3.9 -1.1 3.9 16.7 \n",
"5 -6.1 2.8 6.7 10.0 18.3 \n",
"6 -3.3 8.9 11.7 17.2 23.3 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grp[['air_temp', 'dew_point']].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exercise"
]
},
"source": [
"**Exercise:**\n",
"\n",
"For January and February and 0 - 11 hours, calculate the average and standard deviation of `air_temp` grouping by month and hour of the day. Name your result columns `air_temp_mean` and `air_temp_sd`.\n",
"\n",
"Your result `DataFrame` should have 24 rows, the number of months (2) times the number of hours (12). \n",
"\n",
"$2 * 12 = 24$\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"idx = dat.month.isin([1, 2]) & (dat.hour < 12)\n",
"grp2 = dat[idx].groupby(['month', 'hour'])\n",
"hourly_temp = grp2.agg(\n",
" air_temp_mean=('air_temp', 'mean'),\n",
" air_temp_sd=('air_temp', 'std')\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exercise"
]
},
"source": [
"**Exercise:**\n",
"\n",
"By month, calculate quantiles for `air_temp` using the quantiles defined in `breaks`. \n",
"\n",
"Hint: Use the `quantile` method defined on a `Series` (`pd.Series.quantile`).\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"tags": [
"exercise"
]
},
"outputs": [],
"source": [
"breaks = [0.01, 0.25, 0.5, 0.75, 0.99]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>air_temp</th>\n",
" <th>0.0</th>\n",
" <th>0.2</th>\n",
" <th>0.5</th>\n",
" <th>0.8</th>\n",
" <th>1.0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>month</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-25.0</td>\n",
" <td>-13.3</td>\n",
" <td>-10.0</td>\n",
" <td>-5.6</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-18.3</td>\n",
" <td>-7.2</td>\n",
" <td>-2.2</td>\n",
" <td>1.7</td>\n",
" <td>12.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-11.0</td>\n",
" <td>-1.7</td>\n",
" <td>2.2</td>\n",
" <td>5.6</td>\n",
" <td>19.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-2.2</td>\n",
" <td>2.8</td>\n",
" <td>5.6</td>\n",
" <td>9.4</td>\n",
" <td>23.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2.8</td>\n",
" <td>10.6</td>\n",
" <td>13.9</td>\n",
" <td>17.2</td>\n",
" <td>27.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6.1</td>\n",
" <td>13.8</td>\n",
" <td>17.8</td>\n",
" <td>22.8</td>\n",
" <td>32.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"air_temp 0.0 0.2 0.5 0.8 1.0\n",
"month \n",
"1 -25.0 -13.3 -10.0 -5.6 1.1\n",
"2 -18.3 -7.2 -2.2 1.7 12.8\n",
"3 -11.0 -1.7 2.2 5.6 19.3\n",
"4 -2.2 2.8 5.6 9.4 23.4\n",
"5 2.8 10.6 13.9 17.2 27.8\n",
"6 6.1 13.8 17.8 22.8 32.2"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grp3 = dat.groupby('month')\n",
"grp3.apply(lambda x: x.air_temp.quantile(breaks))"
]
}
],
"metadata": {
"celltoolbar": "Tags",
"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": "3.7.7"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": true,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}