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543 lines
66 KiB
543 lines
66 KiB
2 years ago
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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"___\n",
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"\n",
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"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
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"___\n",
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"<center><em>Copyright Pierian Data</em></center>\n",
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"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Time Resampling\n",
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"\n",
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"Let's learn how to sample time series data! This will be useful later on in the course!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"%matplotlib inline"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import the data\n",
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"For this exercise we'll look at Starbucks stock data from 2015 to 2018 which includes daily closing prices and trading volumes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df = pd.read_csv('../Data/starbucks.csv', index_col='Date', parse_dates=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Note: the above code is a faster way of doing the following:\n",
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"<pre>df = pd.read_csv('../Data/starbucks.csv')\n",
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"df['Date'] = pd.to_datetime(df['Date'])\n",
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"df.set_index('Date',inplace=True)</pre>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Close</th>\n",
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" <th>Volume</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Date</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2015-01-02</th>\n",
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" <td>38.0061</td>\n",
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" <td>6906098</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2015-01-05</th>\n",
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" <td>37.2781</td>\n",
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" <td>11623796</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2015-01-06</th>\n",
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" <td>36.9748</td>\n",
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" <td>7664340</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2015-01-07</th>\n",
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" <td>37.8848</td>\n",
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" <td>9732554</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2015-01-08</th>\n",
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" <td>38.4961</td>\n",
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" <td>13170548</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Close Volume\n",
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"Date \n",
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"2015-01-02 38.0061 6906098\n",
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"2015-01-05 37.2781 11623796\n",
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"2015-01-06 36.9748 7664340\n",
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"2015-01-07 37.8848 9732554\n",
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"2015-01-08 38.4961 13170548"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## resample()\n",
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"\n",
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"A common operation with time series data is resampling based on the time series index. Let's see how to use the resample() method. [[reference](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"DatetimeIndex(['2015-01-02', '2015-01-05', '2015-01-06', '2015-01-07',\n",
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" '2015-01-08', '2015-01-09', '2015-01-12', '2015-01-13',\n",
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" '2015-01-14', '2015-01-15',\n",
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" ...\n",
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" '2018-12-17', '2018-12-18', '2018-12-19', '2018-12-20',\n",
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" '2018-12-21', '2018-12-24', '2018-12-26', '2018-12-27',\n",
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" '2018-12-28', '2018-12-31'],\n",
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" dtype='datetime64[ns]', name='Date', length=1006, freq=None)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Our index\n",
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"df.index"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"When calling `.resample()` you first need to pass in a **rule** parameter, then you need to call some sort of aggregation function.\n",
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"\n",
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"The **rule** parameter describes the frequency with which to apply the aggregation function (daily, monthly, yearly, etc.)<br>\n",
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"It is passed in using an \"offset alias\" - refer to the table below. [[reference](http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)]\n",
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"\n",
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"The aggregation function is needed because, due to resampling, we need some sort of mathematical rule to join the rows (mean, sum, count, etc.)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<table style=\"display: inline-block\">\n",
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" <caption style=\"text-align: center\"><strong>TIME SERIES OFFSET ALIASES</strong></caption>\n",
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"<tr><th>ALIAS</th><th>DESCRIPTION</th></tr>\n",
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"<tr><td>B</td><td>business day frequency</td></tr>\n",
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"<tr><td>C</td><td>custom business day frequency (experimental)</td></tr>\n",
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"<tr><td>D</td><td>calendar day frequency</td></tr>\n",
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"<tr><td>W</td><td>weekly frequency</td></tr>\n",
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"<tr><td>M</td><td>month end frequency</td></tr>\n",
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"<tr><td>SM</td><td>semi-month end frequency (15th and end of month)</td></tr>\n",
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"<tr><td>BM</td><td>business month end frequency</td></tr>\n",
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"<tr><td>CBM</td><td>custom business month end frequency</td></tr>\n",
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"<tr><td>MS</td><td>month start frequency</td></tr>\n",
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"<tr><td>SMS</td><td>semi-month start frequency (1st and 15th)</td></tr>\n",
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"<tr><td>BMS</td><td>business month start frequency</td></tr>\n",
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"<tr><td>CBMS</td><td>custom business month start frequency</td></tr>\n",
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"<tr><td>Q</td><td>quarter end frequency</td></tr>\n",
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"<tr><td></td><td><font color=white>intentionally left blank</font></td></tr></table>\n",
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"\n",
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"<table style=\"display: inline-block; margin-left: 40px\">\n",
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"<caption style=\"text-align: center\"></caption>\n",
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"<tr><th>ALIAS</th><th>DESCRIPTION</th></tr>\n",
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"<tr><td>BQ</td><td>business quarter endfrequency</td></tr>\n",
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"<tr><td>QS</td><td>quarter start frequency</td></tr>\n",
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"<tr><td>BQS</td><td>business quarter start frequency</td></tr>\n",
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"<tr><td>A</td><td>year end frequency</td></tr>\n",
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"<tr><td>BA</td><td>business year end frequency</td></tr>\n",
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"<tr><td>AS</td><td>year start frequency</td></tr>\n",
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"<tr><td>BAS</td><td>business year start frequency</td></tr>\n",
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"<tr><td>BH</td><td>business hour frequency</td></tr>\n",
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"<tr><td>H</td><td>hourly frequency</td></tr>\n",
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"<tr><td>T, min</td><td>minutely frequency</td></tr>\n",
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"<tr><td>S</td><td>secondly frequency</td></tr>\n",
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"<tr><td>L, ms</td><td>milliseconds</td></tr>\n",
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"<tr><td>U, us</td><td>microseconds</td></tr>\n",
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"<tr><td>N</td><td>nanoseconds</td></tr></table>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Close</th>\n",
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" <th>Volume</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Date</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2015-12-31</th>\n",
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" <td>50.078100</td>\n",
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" <td>8.649190e+06</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2016-12-31</th>\n",
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" <td>53.891732</td>\n",
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" <td>9.300633e+06</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2017-12-31</th>\n",
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" <td>55.457310</td>\n",
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" <td>9.296078e+06</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2018-12-31</th>\n",
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" <td>56.870005</td>\n",
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" <td>1.122883e+07</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Close Volume\n",
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"Date \n",
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"2015-12-31 50.078100 8.649190e+06\n",
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"2016-12-31 53.891732 9.300633e+06\n",
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"2017-12-31 55.457310 9.296078e+06\n",
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"2018-12-31 56.870005 1.122883e+07"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Yearly Means\n",
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"df.resample(rule='A').mean()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Resampling rule 'A' takes all of the data points in a given year, applies the aggregation function (in this case we calculate the mean), and reports the result as the last day of that year."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Custom Resampling Functions\n",
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"\n",
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"We're not limited to pandas built-in summary functions (min/max/mean etc.). We can define our own function:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
|
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"def first_day(entry):\n",
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" \"\"\"\n",
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" Returns the first instance of the period, regardless of sampling rate.\n",
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|
" \"\"\"\n",
|
||
|
|
" if len(entry): # handles the case of missing data\n",
|
||
|
|
" return entry[0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"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></th>\n",
|
||
|
|
" <th>Close</th>\n",
|
||
|
|
" <th>Volume</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Date</th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2015-12-31</th>\n",
|
||
|
|
" <td>38.0061</td>\n",
|
||
|
|
" <td>6906098</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2016-12-31</th>\n",
|
||
|
|
" <td>55.0780</td>\n",
|
||
|
|
" <td>13521544</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2017-12-31</th>\n",
|
||
|
|
" <td>53.1100</td>\n",
|
||
|
|
" <td>7809307</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2018-12-31</th>\n",
|
||
|
|
" <td>56.3243</td>\n",
|
||
|
|
" <td>7215978</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Close Volume\n",
|
||
|
|
"Date \n",
|
||
|
|
"2015-12-31 38.0061 6906098\n",
|
||
|
|
"2016-12-31 55.0780 13521544\n",
|
||
|
|
"2017-12-31 53.1100 7809307\n",
|
||
|
|
"2018-12-31 56.3243 7215978"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.resample(rule='A').apply(first_day)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Plotting"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df['Close'].resample('A').mean().plot.bar(title='Yearly Mean Closing Price for Starbucks');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Pandas treats each sample as its own trace, and by default assigns different colors to each one. If you want, you can pass a <strong>color</strong> argument to assign your own color collection, or to set a uniform color. For example, <tt>color='#1f77b4'</tt> sets a uniform \"steel blue\" color.\n",
|
||
|
|
"\n",
|
||
|
|
"Also, the above code can be broken into two lines for improved readability."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"title = 'Yearly Mean Closing Price for Starbucks'\n",
|
||
|
|
"df['Close'].resample('A').mean().plot.bar(title=title,color=['#1f77b4']);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1152x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"title = 'Monthly Max Closing Price for Starbucks'\n",
|
||
|
|
"df['Close'].resample('M').max().plot.bar(figsize=(16,6), title=title,color='#1f77b4');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"That is it! Up next we'll learn about time shifts!"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"anaconda-cloud": {},
|
||
|
|
"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": "3.7.6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|