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https://github.com/donnemartin/data-science-ipython-notebooks.git
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233b197660
Source: https://github.com/jakevdp/PythonDataScienceHandbook unmodified
1958 lines
662 KiB
Python
1958 lines
662 KiB
Python
{
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"cells": [
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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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"<!--BOOK_INFORMATION-->\n",
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"<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
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"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
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"\n",
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"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\n",
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"\n",
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"*No changes were made to the contents of this notebook from the original.*"
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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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"<!--NAVIGATION-->\n",
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"< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) >"
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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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"# Working with Time Series"
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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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"Pandas was developed in the context of financial modeling, so as you might expect, it contains a fairly extensive set of tools for working with dates, times, and time-indexed data.\n",
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"Date and time data comes in a few flavors, which we will discuss here:\n",
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"\n",
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"- *Time stamps* reference particular moments in time (e.g., July 4th, 2015 at 7:00am).\n",
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"- *Time intervals* and *periods* reference a length of time between a particular beginning and end point; for example, the year 2015. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24 hour-long periods comprising days).\n",
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"- *Time deltas* or *durations* reference an exact length of time (e.g., a duration of 22.56 seconds).\n",
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"\n",
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"In this section, we will introduce how to work with each of these types of date/time data in Pandas.\n",
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"This short section is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series.\n",
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"We will start with a brief discussion of tools for dealing with dates and times in Python, before moving more specifically to a discussion of the tools provided by Pandas.\n",
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"After listing some resources that go into more depth, we will review some short examples of working with time series data in Pandas."
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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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"## Dates and Times in Python\n",
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"\n",
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"The Python world has a number of available representations of dates, times, deltas, and timespans.\n",
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"While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other packages used in Python."
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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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"### Native Python dates and times: ``datetime`` and ``dateutil``\n",
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"\n",
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"Python's basic objects for working with dates and times reside in the built-in ``datetime`` module.\n",
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"Along with the third-party ``dateutil`` module, you can use it to quickly perform a host of useful functionalities on dates and times.\n",
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"For example, you can manually build a date using the ``datetime`` type:"
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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": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"datetime.datetime(2015, 7, 4, 0, 0)"
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]
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},
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"execution_count": 1,
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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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"from datetime import datetime\n",
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"datetime(year=2015, month=7, day=4)"
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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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"Or, using the ``dateutil`` module, you can parse dates from a variety of string formats:"
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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": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"datetime.datetime(2015, 7, 4, 0, 0)"
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]
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},
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"execution_count": 2,
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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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"from dateutil import parser\n",
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"date = parser.parse(\"4th of July, 2015\")\n",
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"date"
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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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"collapsed": false
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},
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"source": [
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"Once you have a ``datetime`` object, you can do things like printing the day of the week:"
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Saturday'"
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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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"date.strftime('%A')"
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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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"In the final line, we've used one of the standard string format codes for printing dates (``\"%A\"``), which you can read about in the [strftime section](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) of Python's [datetime documentation](https://docs.python.org/3/library/datetime.html).\n",
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"Documentation of other useful date utilities can be found in [dateutil's online documentation](http://labix.org/python-dateutil).\n",
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"A related package to be aware of is [``pytz``](http://pytz.sourceforge.net/), which contains tools for working with the most migrane-inducing piece of time series data: time zones.\n",
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"\n",
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"The power of ``datetime`` and ``dateutil`` lie in their flexibility and easy syntax: you can use these objects and their built-in methods to easily perform nearly any operation you might be interested in.\n",
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"Where they break down is when you wish to work with large arrays of dates and times:\n",
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"just as lists of Python numerical variables are suboptimal compared to NumPy-style typed numerical arrays, lists of Python datetime objects are suboptimal compared to typed arrays of encoded dates."
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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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"### Typed arrays of times: NumPy's ``datetime64``\n",
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"\n",
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"The weaknesses of Python's datetime format inspired the NumPy team to add a set of native time series data type to NumPy.\n",
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"The ``datetime64`` dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented very compactly.\n",
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"The ``datetime64`` requires a very specific input format:"
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(datetime.date(2015, 7, 4), dtype='datetime64[D]')"
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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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"import numpy as np\n",
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"date = np.array('2015-07-04', dtype=np.datetime64)\n",
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"date"
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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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"Once we have this date formatted, however, we can quickly do vectorized operations on it:"
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
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" '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
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" '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'], dtype='datetime64[D]')"
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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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"date + np.arange(12)"
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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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"Because of the uniform type in NumPy ``datetime64`` arrays, this type of operation can be accomplished much more quickly than if we were working directly with Python's ``datetime`` objects, especially as arrays get large\n",
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"(we introduced this type of vectorization in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)).\n",
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"\n",
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"One detail of the ``datetime64`` and ``timedelta64`` objects is that they are built on a *fundamental time unit*.\n",
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"Because the ``datetime64`` object is limited to 64-bit precision, the range of encodable times is $2^{64}$ times this fundamental unit.\n",
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"In other words, ``datetime64`` imposes a trade-off between *time resolution* and *maximum time span*.\n",
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"\n",
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"For example, if you want a time resolution of one nanosecond, you only have enough information to encode a range of $2^{64}$ nanoseconds, or just under 600 years.\n",
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"NumPy will infer the desired unit from the input; for example, here is a day-based datetime:"
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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": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"numpy.datetime64('2015-07-04')"
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]
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},
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"execution_count": 6,
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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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"np.datetime64('2015-07-04')"
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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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"Here is a minute-based datetime:"
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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": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"numpy.datetime64('2015-07-04T12:00')"
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]
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},
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"execution_count": 7,
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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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"np.datetime64('2015-07-04 12:00')"
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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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"Notice that the time zone is automatically set to the local time on the computer executing the code.\n",
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"You can force any desired fundamental unit using one of many format codes; for example, here we'll force a nanosecond-based time:"
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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": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"numpy.datetime64('2015-07-04T12:59:59.500000000')"
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]
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},
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"execution_count": 8,
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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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"np.datetime64('2015-07-04 12:59:59.50', 'ns')"
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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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"The following table, drawn from the [NumPy datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html), lists the available format codes along with the relative and absolute timespans that they can encode:"
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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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"|Code | Meaning | Time span (relative) | Time span (absolute) |\n",
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"|--------|-------------|----------------------|------------------------|\n",
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"| ``Y`` | Year\t | ± 9.2e18 years | [9.2e18 BC, 9.2e18 AD] |\n",
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"| ``M`` | Month | ± 7.6e17 years | [7.6e17 BC, 7.6e17 AD] |\n",
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"| ``W`` | Week\t | ± 1.7e17 years | [1.7e17 BC, 1.7e17 AD] |\n",
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"| ``D`` | Day | ± 2.5e16 years | [2.5e16 BC, 2.5e16 AD] |\n",
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"| ``h`` | Hour | ± 1.0e15 years | [1.0e15 BC, 1.0e15 AD] |\n",
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"| ``m`` | Minute | ± 1.7e13 years | [1.7e13 BC, 1.7e13 AD] |\n",
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"| ``s`` | Second | ± 2.9e12 years | [ 2.9e9 BC, 2.9e9 AD] |\n",
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"| ``ms`` | Millisecond | ± 2.9e9 years | [ 2.9e6 BC, 2.9e6 AD] |\n",
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"| ``us`` | Microsecond | ± 2.9e6 years | [290301 BC, 294241 AD] |\n",
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"| ``ns`` | Nanosecond | ± 292 years | [ 1678 AD, 2262 AD] |\n",
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"| ``ps`` | Picosecond | ± 106 days | [ 1969 AD, 1970 AD] |\n",
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"| ``fs`` | Femtosecond | ± 2.6 hours | [ 1969 AD, 1970 AD] |\n",
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"| ``as`` | Attosecond | ± 9.2 seconds | [ 1969 AD, 1970 AD] |"
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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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"For the types of data we see in the real world, a useful default is ``datetime64[ns]``, as it can encode a useful range of modern dates with a suitably fine precision.\n",
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"\n",
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"Finally, we will note that while the ``datetime64`` data type addresses some of the deficiencies of the built-in Python ``datetime`` type, it lacks many of the convenient methods and functions provided by ``datetime`` and especially ``dateutil``.\n",
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"More information can be found in [NumPy's datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html)."
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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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"### Dates and times in pandas: best of both worlds\n",
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"\n",
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"Pandas builds upon all the tools just discussed to provide a ``Timestamp`` object, which combines the ease-of-use of ``datetime`` and ``dateutil`` with the efficient storage and vectorized interface of ``numpy.datetime64``.\n",
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"From a group of these ``Timestamp`` objects, Pandas can construct a ``DatetimeIndex`` that can be used to index data in a ``Series`` or ``DataFrame``; we'll see many examples of this below.\n",
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"\n",
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"For example, we can use Pandas tools to repeat the demonstration from above.\n",
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"We can parse a flexibly formatted string date, and use format codes to output the day of the week:"
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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": 9,
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||
"metadata": {
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||
"collapsed": false
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||
},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Timestamp('2015-07-04 00:00:00')"
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]
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},
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"execution_count": 9,
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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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"import pandas as pd\n",
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"date = pd.to_datetime(\"4th of July, 2015\")\n",
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"date"
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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": 10,
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||
"metadata": {
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||
"collapsed": false
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||
},
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||
"outputs": [
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||
{
|
||
"data": {
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||
"text/plain": [
|
||
"'Saturday'"
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]
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},
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||
"execution_count": 10,
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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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"date.strftime('%A')"
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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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"Additionally, we can do NumPy-style vectorized operations directly on this same object:"
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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": 11,
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||
"metadata": {
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||
"collapsed": false
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||
},
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||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"DatetimeIndex(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
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" '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
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" '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n",
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" dtype='datetime64[ns]', freq=None)"
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||
]
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||
},
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||
"execution_count": 11,
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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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"date + pd.to_timedelta(np.arange(12), 'D')"
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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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"In the next section, we will take a closer look at manipulating time series data with the tools provided by Pandas."
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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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"## Pandas Time Series: Indexing by Time\n",
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"\n",
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"Where the Pandas time series tools really become useful is when you begin to *index data by timestamps*.\n",
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||
"For example, we can construct a ``Series`` object that has time indexed data:"
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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": 12,
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"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"2014-07-04 0\n",
|
||
"2014-08-04 1\n",
|
||
"2015-07-04 2\n",
|
||
"2015-08-04 3\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"index = pd.DatetimeIndex(['2014-07-04', '2014-08-04',\n",
|
||
" '2015-07-04', '2015-08-04'])\n",
|
||
"data = pd.Series([0, 1, 2, 3], index=index)\n",
|
||
"data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now that we have this data in a ``Series``, we can make use of any of the ``Series`` indexing patterns we discussed in previous sections, passing values that can be coerced into dates:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"2014-07-04 0\n",
|
||
"2014-08-04 1\n",
|
||
"2015-07-04 2\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data['2014-07-04':'2015-07-04']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"There are additional special date-only indexing operations, such as passing a year to obtain a slice of all data from that year:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"2015-07-04 2\n",
|
||
"2015-08-04 3\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data['2015']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Later, we will see additional examples of the convenience of dates-as-indices.\n",
|
||
"But first, a closer look at the available time series data structures."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Pandas Time Series Data Structures\n",
|
||
"\n",
|
||
"This section will introduce the fundamental Pandas data structures for working with time series data:\n",
|
||
"\n",
|
||
"- For *time stamps*, Pandas provides the ``Timestamp`` type. As mentioned before, it is essentially a replacement for Python's native ``datetime``, but is based on the more efficient ``numpy.datetime64`` data type. The associated Index structure is ``DatetimeIndex``.\n",
|
||
"- For *time Periods*, Pandas provides the ``Period`` type. This encodes a fixed-frequency interval based on ``numpy.datetime64``. The associated index structure is ``PeriodIndex``.\n",
|
||
"- For *time deltas* or *durations*, Pandas provides the ``Timedelta`` type. ``Timedelta`` is a more efficient replacement for Python's native ``datetime.timedelta`` type, and is based on ``numpy.timedelta64``. The associated index structure is ``TimedeltaIndex``."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The most fundamental of these date/time objects are the ``Timestamp`` and ``DatetimeIndex`` objects.\n",
|
||
"While these class objects can be invoked directly, it is more common to use the ``pd.to_datetime()`` function, which can parse a wide variety of formats.\n",
|
||
"Passing a single date to ``pd.to_datetime()`` yields a ``Timestamp``; passing a series of dates by default yields a ``DatetimeIndex``:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
|
||
" '2015-07-08'],\n",
|
||
" dtype='datetime64[ns]', freq=None)"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dates = pd.to_datetime([datetime(2015, 7, 3), '4th of July, 2015',\n",
|
||
" '2015-Jul-6', '07-07-2015', '20150708'])\n",
|
||
"dates"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Any ``DatetimeIndex`` can be converted to a ``PeriodIndex`` with the ``to_period()`` function with the addition of a frequency code; here we'll use ``'D'`` to indicate daily frequency:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"PeriodIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
|
||
" '2015-07-08'],\n",
|
||
" dtype='int64', freq='D')"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dates.to_period('D')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"A ``TimedeltaIndex`` is created, for example, when a date is subtracted from another:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"TimedeltaIndex(['0 days', '1 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq=None)"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dates - dates[0]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Regular sequences: ``pd.date_range()``\n",
|
||
"\n",
|
||
"To make the creation of regular date sequences more convenient, Pandas offers a few functions for this purpose: ``pd.date_range()`` for timestamps, ``pd.period_range()`` for periods, and ``pd.timedelta_range()`` for time deltas.\n",
|
||
"We've seen that Python's ``range()`` and NumPy's ``np.arange()`` turn a startpoint, endpoint, and optional stepsize into a sequence.\n",
|
||
"Similarly, ``pd.date_range()`` accepts a start date, an end date, and an optional frequency code to create a regular sequence of dates.\n",
|
||
"By default, the frequency is one day:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
|
||
" '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
|
||
" dtype='datetime64[ns]', freq='D')"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.date_range('2015-07-03', '2015-07-10')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Alternatively, the date range can be specified not with a start and endpoint, but with a startpoint and a number of periods:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
|
||
" '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
|
||
" dtype='datetime64[ns]', freq='D')"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.date_range('2015-07-03', periods=8)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The spacing can be modified by altering the ``freq`` argument, which defaults to ``D``.\n",
|
||
"For example, here we will construct a range of hourly timestamps:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"DatetimeIndex(['2015-07-03 00:00:00', '2015-07-03 01:00:00',\n",
|
||
" '2015-07-03 02:00:00', '2015-07-03 03:00:00',\n",
|
||
" '2015-07-03 04:00:00', '2015-07-03 05:00:00',\n",
|
||
" '2015-07-03 06:00:00', '2015-07-03 07:00:00'],\n",
|
||
" dtype='datetime64[ns]', freq='H')"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.date_range('2015-07-03', periods=8, freq='H')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"To create regular sequences of ``Period`` or ``Timedelta`` values, the very similar ``pd.period_range()`` and ``pd.timedelta_range()`` functions are useful.\n",
|
||
"Here are some monthly periods:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"PeriodIndex(['2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12',\n",
|
||
" '2016-01', '2016-02'],\n",
|
||
" dtype='int64', freq='M')"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.period_range('2015-07', periods=8, freq='M')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"And a sequence of durations increasing by an hour:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"TimedeltaIndex(['00:00:00', '01:00:00', '02:00:00', '03:00:00', '04:00:00',\n",
|
||
" '05:00:00', '06:00:00', '07:00:00', '08:00:00', '09:00:00'],\n",
|
||
" dtype='timedelta64[ns]', freq='H')"
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.timedelta_range(0, periods=10, freq='H')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"All of these require an understanding of Pandas frequency codes, which we'll summarize in the next section."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Frequencies and Offsets\n",
|
||
"\n",
|
||
"Fundamental to these Pandas time series tools is the concept of a frequency or date offset.\n",
|
||
"Just as we saw the ``D`` (day) and ``H`` (hour) codes above, we can use such codes to specify any desired frequency spacing.\n",
|
||
"The following table summarizes the main codes available:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"| Code | Description | Code | Description |\n",
|
||
"|--------|---------------------|--------|----------------------|\n",
|
||
"| ``D`` | Calendar day | ``B`` | Business day |\n",
|
||
"| ``W`` | Weekly | | |\n",
|
||
"| ``M`` | Month end | ``BM`` | Business month end |\n",
|
||
"| ``Q`` | Quarter end | ``BQ`` | Business quarter end |\n",
|
||
"| ``A`` | Year end | ``BA`` | Business year end |\n",
|
||
"| ``H`` | Hours | ``BH`` | Business hours |\n",
|
||
"| ``T`` | Minutes | | |\n",
|
||
"| ``S`` | Seconds | | |\n",
|
||
"| ``L`` | Milliseonds | | |\n",
|
||
"| ``U`` | Microseconds | | |\n",
|
||
"| ``N`` | nanoseconds | | |"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The monthly, quarterly, and annual frequencies are all marked at the end of the specified period.\n",
|
||
"By adding an ``S`` suffix to any of these, they instead will be marked at the beginning:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"| Code | Description || Code | Description |\n",
|
||
"|---------|------------------------||---------|------------------------|\n",
|
||
"| ``MS`` | Month start ||``BMS`` | Business month start |\n",
|
||
"| ``QS`` | Quarter start ||``BQS`` | Business quarter start |\n",
|
||
"| ``AS`` | Year start ||``BAS`` | Business year start |"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Additionally, you can change the month used to mark any quarterly or annual code by adding a three-letter month code as a suffix:\n",
|
||
"\n",
|
||
"- ``Q-JAN``, ``BQ-FEB``, ``QS-MAR``, ``BQS-APR``, etc.\n",
|
||
"- ``A-JAN``, ``BA-FEB``, ``AS-MAR``, ``BAS-APR``, etc.\n",
|
||
"\n",
|
||
"In the same way, the split-point of the weekly frequency can be modified by adding a three-letter weekday code:\n",
|
||
"\n",
|
||
"- ``W-SUN``, ``W-MON``, ``W-TUE``, ``W-WED``, etc.\n",
|
||
"\n",
|
||
"On top of this, codes can be combined with numbers to specify other frequencies.\n",
|
||
"For example, for a frequency of 2 hours 30 minutes, we can combine the hour (``H``) and minute (``T``) codes as follows:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"TimedeltaIndex(['00:00:00', '02:30:00', '05:00:00', '07:30:00', '10:00:00',\n",
|
||
" '12:30:00', '15:00:00', '17:30:00', '20:00:00'],\n",
|
||
" dtype='timedelta64[ns]', freq='150T')"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.timedelta_range(0, periods=9, freq=\"2H30T\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the ``pd.tseries.offsets`` module.\n",
|
||
"For example, we can create a business day offset directly as follows:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"DatetimeIndex(['2015-07-01', '2015-07-02', '2015-07-03', '2015-07-06',\n",
|
||
" '2015-07-07'],\n",
|
||
" dtype='datetime64[ns]', freq='B')"
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from pandas.tseries.offsets import BDay\n",
|
||
"pd.date_range('2015-07-01', periods=5, freq=BDay())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"For more discussion of the use of frequencies and offsets, see the [\"DateOffset\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects) of the Pandas documentation."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Resampling, Shifting, and Windowing\n",
|
||
"\n",
|
||
"The ability to use dates and times as indices to intuitively organize and access data is an important piece of the Pandas time series tools.\n",
|
||
"The benefits of indexed data in general (automatic alignment during operations, intuitive data slicing and access, etc.) still apply, and Pandas provides several additional time series-specific operations.\n",
|
||
"\n",
|
||
"We will take a look at a few of those here, using some stock price data as an example.\n",
|
||
"Because Pandas was developed largely in a finance context, it includes some very specific tools for financial data.\n",
|
||
"For example, the accompanying ``pandas-datareader`` package (installable via ``conda install pandas-datareader``), knows how to import financial data from a number of available sources, including Yahoo finance, Google Finance, and others.\n",
|
||
"Here we will load Google's closing price history:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Open</th>\n",
|
||
" <th>High</th>\n",
|
||
" <th>Low</th>\n",
|
||
" <th>Close</th>\n",
|
||
" <th>Volume</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Date</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>2004-08-19</th>\n",
|
||
" <td>49.96</td>\n",
|
||
" <td>51.98</td>\n",
|
||
" <td>47.93</td>\n",
|
||
" <td>50.12</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2004-08-20</th>\n",
|
||
" <td>50.69</td>\n",
|
||
" <td>54.49</td>\n",
|
||
" <td>50.20</td>\n",
|
||
" <td>54.10</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2004-08-23</th>\n",
|
||
" <td>55.32</td>\n",
|
||
" <td>56.68</td>\n",
|
||
" <td>54.47</td>\n",
|
||
" <td>54.65</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2004-08-24</th>\n",
|
||
" <td>55.56</td>\n",
|
||
" <td>55.74</td>\n",
|
||
" <td>51.73</td>\n",
|
||
" <td>52.38</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2004-08-25</th>\n",
|
||
" <td>52.43</td>\n",
|
||
" <td>53.95</td>\n",
|
||
" <td>51.89</td>\n",
|
||
" <td>52.95</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Open High Low Close Volume\n",
|
||
"Date \n",
|
||
"2004-08-19 49.96 51.98 47.93 50.12 NaN\n",
|
||
"2004-08-20 50.69 54.49 50.20 54.10 NaN\n",
|
||
"2004-08-23 55.32 56.68 54.47 54.65 NaN\n",
|
||
"2004-08-24 55.56 55.74 51.73 52.38 NaN\n",
|
||
"2004-08-25 52.43 53.95 51.89 52.95 NaN"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from pandas_datareader import data\n",
|
||
"\n",
|
||
"goog = data.DataReader('GOOG', start='2004', end='2016',\n",
|
||
" data_source='google')\n",
|
||
"goog.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"For simplicity, we'll use just the closing price:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"goog = goog['Close']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"We can visualize this using the ``plot()`` method, after the normal Matplotlib setup boilerplate (see [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn; seaborn.set()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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xwxKRGG/L2DUgMU48bglj7tnbMLfJbME7/zuMk2db8Obnh3y+9tS5Fq/t62oMyEREFDR9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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x1162f5e80>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"goog.plot();"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Resampling and converting frequencies\n",
|
||
"\n",
|
||
"One common need for time series data is resampling at a higher or lower frequency.\n",
|
||
"This can be done using the ``resample()`` method, or the much simpler ``asfreq()`` method.\n",
|
||
"The primary difference between the two is that ``resample()`` is fundamentally a *data aggregation*, while ``asfreq()`` is fundamentally a *data selection*.\n",
|
||
"\n",
|
||
"Taking a look at the Google closing price, let's compare what the two return when we down-sample the data.\n",
|
||
"Here we will resample the data at the end of business year:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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I/tXpWsrUde30BQghxCjkraqi5l9/QfXPfhoZh021mZg+KXVQ97EnGQf92vtr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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x1186afcf8>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"goog.plot(alpha=0.5, style='-')\n",
|
||
"goog.resample('BA').mean().plot(style=':')\n",
|
||
"goog.asfreq('BA').plot(style='--');\n",
|
||
"plt.legend(['input', 'resample', 'asfreq'],\n",
|
||
" loc='upper left');"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Notice the difference: at each point, ``resample`` reports the *average of the previous year*, while ``asfreq`` reports the *value at the end of the year*."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"For up-sampling, ``resample()`` and ``asfreq()`` are largely equivalent, though resample has many more options available.\n",
|
||
"In this case, the default for both methods is to leave the up-sampled points empty, that is, filled with NA values.\n",
|
||
"Just as with the ``pd.fillna()`` function discussed previously, ``asfreq()`` accepts a ``method`` argument to specify how values are imputed.\n",
|
||
"Here, we will resample the business day data at a daily frequency (i.e., including weekends):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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51bR+3HpVH6rrjLzw/kEOnSxXJIuDC7UelQqGSqEWQgjh5FQqFTOSYnjg5sHY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\n4cSkUAshhBBOTAq1EEII4cT+D31iM/ZIegn7AAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x11861cba8>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, ax = plt.subplots(2, sharex=True)\n",
|
||
"data = goog.iloc[:10]\n",
|
||
"\n",
|
||
"data.asfreq('D').plot(ax=ax[0], marker='o')\n",
|
||
"\n",
|
||
"data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o')\n",
|
||
"data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o')\n",
|
||
"ax[1].legend([\"back-fill\", \"forward-fill\"]);"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The top panel is the default: non-business days are left as NA values and do not appear on the plot.\n",
|
||
"The bottom panel shows the differences between two strategies for filling the gaps: forward-filling and backward-filling."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Time-shifts\n",
|
||
"\n",
|
||
"Another common time series-specific operation is shifting of data in time.\n",
|
||
"Pandas has two closely related methods for computing this: ``shift()`` and ``tshift()``\n",
|
||
"In short, the difference between them is that ``shift()`` *shifts the data*, while ``tshift()`` *shifts the index*.\n",
|
||
"In both cases, the shift is specified in multiples of the frequency.\n",
|
||
"\n",
|
||
"Here we will both ``shift()`` and ``tshift()`` by 900 days; "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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PZ8zRFHy9nGnT1Mvq869NmgI9U94zn/921sO9ad/cu8S+/v4eJCRdZsrCkvPl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yMQCJBOp2lqaqKzs/OHc77/5tjX18fo6Cj9/f1YrVaCwWCxL+HVFNq7UChEKpViamoKwzCoqKgg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\niIgJKJBFRERM4A/3oO5fXZCK2gAAAABJRU5ErkJggg==\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x11864e3c8>"
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]
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},
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"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, ax = plt.subplots(3, sharey=True)\n",
|
||
"\n",
|
||
"# apply a frequency to the data\n",
|
||
"goog = goog.asfreq('D', method='pad')\n",
|
||
"\n",
|
||
"goog.plot(ax=ax[0])\n",
|
||
"goog.shift(900).plot(ax=ax[1])\n",
|
||
"goog.tshift(900).plot(ax=ax[2])\n",
|
||
"\n",
|
||
"# legends and annotations\n",
|
||
"local_max = pd.to_datetime('2007-11-05')\n",
|
||
"offset = pd.Timedelta(900, 'D')\n",
|
||
"\n",
|
||
"ax[0].legend(['input'], loc=2)\n",
|
||
"ax[0].get_xticklabels()[2].set(weight='heavy', color='red')\n",
|
||
"ax[0].axvline(local_max, alpha=0.3, color='red')\n",
|
||
"\n",
|
||
"ax[1].legend(['shift(900)'], loc=2)\n",
|
||
"ax[1].get_xticklabels()[2].set(weight='heavy', color='red')\n",
|
||
"ax[1].axvline(local_max + offset, alpha=0.3, color='red')\n",
|
||
"\n",
|
||
"ax[2].legend(['tshift(900)'], loc=2)\n",
|
||
"ax[2].get_xticklabels()[1].set(weight='heavy', color='red')\n",
|
||
"ax[2].axvline(local_max + offset, alpha=0.3, color='red');"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"We see here that ``shift(900)`` shifts the *data* by 900 days, pushing some of it off the end of the graph (and leaving NA values at the other end), while ``tshift(900)`` shifts the *index values* by 900 days.\n",
|
||
"\n",
|
||
"A common context for this type of shift is in computing differences over time. For example, we use shifted values to compute the one-year return on investment for Google stock over the course of the dataset:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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TN8ADznlo7vBi8YV6WdsUOrwfYu3cMRHDejrLm2a0c14UPXSDcw8KKnYSeMfO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\n3AkrUPtYNzSZQnoN8GpH8aR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YZW6ttfXABazdVYovfj0hfTDbNqtkgBJaohgMzF4SiX4O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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x118a41160>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"ROI = 100 * (goog.tshift(-365) / goog - 1)\n",
|
||
"ROI.plot()\n",
|
||
"plt.ylabel('% Return on Investment');"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"This helps us to see the overall trend in Google stock: thus far, the most profitable times to invest in Google have been (unsurprisingly, in retrospect) shortly after its IPO, and in the middle of the 2009 recession."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Rolling windows\n",
|
||
"\n",
|
||
"Rolling statistics are a third type of time series-specific operation implemented by Pandas.\n",
|
||
"These can be accomplished via the ``rolling()`` attribute of ``Series`` and ``DataFrame`` objects, which returns a view similar to what we saw with the ``groupby`` operation (see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)).\n",
|
||
"This rolling view makes available a number of aggregation operations by default.\n",
|
||
"\n",
|
||
"For example, here is the one-year centered rolling mean and standard deviation of the Google stock prices:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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LsXTpUjZu3IiiKGzZsoXNmzej6zpbt26dkKIRw3u34/29ENntqfjdnl544a+sWHEun/70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\nX4vfgTqYSFNT4+Jwoier9nFKOB4hkoj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xs3ZDctpMWdnjUHx7vXfpu+kLe9jTvY9/ffE7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\nrkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x11924aa58>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"rolling = goog.rolling(365, center=True)\n",
|
||
"\n",
|
||
"data = pd.DataFrame({'input': goog,\n",
|
||
" 'one-year rolling_mean': rolling.mean(),\n",
|
||
" 'one-year rolling_std': rolling.std()})\n",
|
||
"ax = data.plot(style=['-', '--', ':'])\n",
|
||
"ax.lines[0].set_alpha(0.3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"As with group-by operations, the ``aggregate()`` and ``apply()`` methods can be used for custom rolling computations."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Where to Learn More\n",
|
||
"\n",
|
||
"This section has provided only a brief summary of some of the most essential features of time series tools provided by Pandas; for a more complete discussion, you can refer to the [\"Time Series/Date\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html) of the Pandas online documentation.\n",
|
||
"\n",
|
||
"Another excellent resource is the textbook [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney (OReilly, 2012).\n",
|
||
"Although it is now a few years old, it is an invaluable resource on the use of Pandas.\n",
|
||
"In particular, this book emphasizes time series tools in the context of business and finance, and focuses much more on particular details of business calendars, time zones, and related topics.\n",
|
||
"\n",
|
||
"As always, you can also use the IPython help functionality to explore and try further options available to the functions and methods discussed here. I find this often is the best way to learn a new Python tool."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Example: Visualizing Seattle Bicycle Counts\n",
|
||
"\n",
|
||
"As a more involved example of working with some time series data, let's take a look at bicycle counts on Seattle's [Fremont Bridge](http://www.openstreetmap.org/#map=17/47.64813/-122.34965).\n",
|
||
"This data comes from an automated bicycle counter, installed in late 2012, which has inductive sensors on the east and west sidewalks of the bridge.\n",
|
||
"The hourly bicycle counts can be downloaded from http://data.seattle.gov/; here is the [direct link to the dataset](https://data.seattle.gov/Transportation/Fremont-Bridge-Hourly-Bicycle-Counts-by-Month-Octo/65db-xm6k).\n",
|
||
"\n",
|
||
"As of summer 2016, the CSV can be downloaded as follows:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Once this dataset is downloaded, we can use Pandas to read the CSV output into a ``DataFrame``.\n",
|
||
"We will specify that we want the Date as an index, and we want these dates to be automatically parsed:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Fremont Bridge West Sidewalk</th>\n",
|
||
" <th>Fremont Bridge East Sidewalk</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>2012-10-03 00:00:00</th>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>9.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-10-03 01:00:00</th>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-10-03 02:00:00</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-10-03 03:00:00</th>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-10-03 04:00:00</th>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Fremont Bridge West Sidewalk \\\n",
|
||
"Date \n",
|
||
"2012-10-03 00:00:00 4.0 \n",
|
||
"2012-10-03 01:00:00 4.0 \n",
|
||
"2012-10-03 02:00:00 1.0 \n",
|
||
"2012-10-03 03:00:00 2.0 \n",
|
||
"2012-10-03 04:00:00 6.0 \n",
|
||
"\n",
|
||
" Fremont Bridge East Sidewalk \n",
|
||
"Date \n",
|
||
"2012-10-03 00:00:00 9.0 \n",
|
||
"2012-10-03 01:00:00 6.0 \n",
|
||
"2012-10-03 02:00:00 1.0 \n",
|
||
"2012-10-03 03:00:00 3.0 \n",
|
||
"2012-10-03 04:00:00 1.0 "
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True)\n",
|
||
"data.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"For convenience, we'll further process this dataset by shortening the column names and adding a \"Total\" column:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"data.columns = ['West', 'East']\n",
|
||
"data['Total'] = data.eval('West + East')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now let's take a look at the summary statistics for this data:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>West</th>\n",
|
||
" <th>East</th>\n",
|
||
" <th>Total</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>35752.000000</td>\n",
|
||
" <td>35752.000000</td>\n",
|
||
" <td>35752.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>61.470267</td>\n",
|
||
" <td>54.410774</td>\n",
|
||
" <td>115.881042</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>82.588484</td>\n",
|
||
" <td>77.659796</td>\n",
|
||
" <td>145.392385</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>8.000000</td>\n",
|
||
" <td>7.000000</td>\n",
|
||
" <td>16.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>33.000000</td>\n",
|
||
" <td>28.000000</td>\n",
|
||
" <td>65.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>79.000000</td>\n",
|
||
" <td>67.000000</td>\n",
|
||
" <td>151.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>825.000000</td>\n",
|
||
" <td>717.000000</td>\n",
|
||
" <td>1186.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" West East Total\n",
|
||
"count 35752.000000 35752.000000 35752.000000\n",
|
||
"mean 61.470267 54.410774 115.881042\n",
|
||
"std 82.588484 77.659796 145.392385\n",
|
||
"min 0.000000 0.000000 0.000000\n",
|
||
"25% 8.000000 7.000000 16.000000\n",
|
||
"50% 33.000000 28.000000 65.000000\n",
|
||
"75% 79.000000 67.000000 151.000000\n",
|
||
"max 825.000000 717.000000 1186.000000"
|
||
]
|
||
},
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data.dropna().describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Visualizing the data\n",
|
||
"\n",
|
||
"We can gain some insight into the dataset by visualizing it.\n",
|
||
"Let's start by plotting the raw data:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"import seaborn; seaborn.set()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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tj8t7zgtGo2U41IqszWJH7s3W28BIHoyFBRDKO+bx0otbqe9dTrXe8N6kSRM8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\neHt7Y8KECfDz88P8+fMxatQo+Pv7IyAgoMr7XVn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|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x1192479e8>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"data.plot()\n",
|
||
"plt.ylabel('Hourly Bicycle Count');"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The ~25,000 hourly samples are far too dense for us to make much sense of.\n",
|
||
"We can gain more insight by resampling the data to a coarser grid.\n",
|
||
"Let's resample by week:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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TTwMAHA4HGIbBzTffjL179wIA3n33XYwfPx4TJkzArl27EIvF0N7ejkOHDmH06NGYNGkS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NKz6nnpwC4AphcNvlj73Zv70etFZtUXD1j1tnb72Hush5pyu9afutHdzPMNL3LU1cS4ns9z95cv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\nAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x118a35240>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"weekly = data.resample('W').sum()\n",
|
||
"weekly.plot(style=[':', '--', '-'])\n",
|
||
"plt.ylabel('Weekly bicycle count');"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"This shows us some interesting seasonal trends: as you might expect, people bicycle more in the summer than in the winter, and even within a particular season the bicycle use varies from week to week (likely dependent on weather; see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) where we explore this further).\n",
|
||
"\n",
|
||
"Another way that comes in handy for aggregating the data is to use a rolling mean, utilizing the ``pd.rolling_mean()`` function.\n",
|
||
"Here we'll do a 30 day rolling mean of our data, making sure to center the window:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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YWIjhw4ejsLAQOTk5kMvlEIvFqKioQFpaGvbu3Yv8/HwIhUKsW7cOjz76KKqqqsBxnI3F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\n4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9NLSBzs3yJ5Tui49oLl82Lb4YoVK3j00Ucxm80MGjSIWbNmIUkSN910E4sXL8Zut7Ns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\n/wfxJKUnYTfWmQAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x11ad10908>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"daily = data.resample('D').sum()\n",
|
||
"daily.rolling(30, center=True).sum().plot(style=[':', '--', '-'])\n",
|
||
"plt.ylabel('mean hourly count');"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The jaggedness of the result is due to the hard cutoff of the window.\n",
|
||
"We can get a smoother version of a rolling mean using a window function–for example, a Gaussian window.\n",
|
||
"The following code specifies both the width of the window (we chose 50 days) and the width of the Gaussian within the window (we chose 10 days):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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eXYN18pR+zeeKoEBbKwQCGLJz4tYGY1YW9uklOLdspnPfXqwTJ8WtLamg/b1N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\nGbuLW6hrOVa7+lntV+w+ut/nlZ5GJSMqRINEIiAIAumhCwhWTr6+NBC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2N+1BQGB17BkB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IlpWu37ceX3b7Mz+2vui/0vBrfu7+pyPVpdfepXNaVt48aNNG3alOeee47i4mKGDx/OjTfeyNSp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\n1EVERDyEQl1ERMRDKNRFREQ8xP8D2G7R4lwJBEEAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x118a9fb38>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"daily.rolling(50, center=True,\n",
|
||
" win_type='gaussian').sum(std=10).plot(style=[':', '--', '-']);"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Digging into the data\n",
|
||
"\n",
|
||
"While these smoothed data views are useful to get an idea of the general trend in the data, they hide much of the interesting structure.\n",
|
||
"For example, we might want to look at the average traffic as a function of the time of day.\n",
|
||
"We can do this using the GroupBy functionality discussed in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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0n1RC80xPyCJy3iTd0BUkG7uSz7AJ+e677+buu+8+7fH169ef9tjy5ctZvny5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\ngg/4f1unlzCpFZdIAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x10192d5c0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"by_time = data.groupby(data.index.time).mean()\n",
|
||
"hourly_ticks = 4 * 60 * 60 * np.arange(6)\n",
|
||
"by_time.plot(xticks=hourly_ticks, style=[':', '--', '-']);"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The hourly traffic is a strongly bimodal distribution, with peaks around 8:00 in the morning and 5:00 in the evening.\n",
|
||
"This is likely evidence of a strong component of commuter traffic crossing the bridge.\n",
|
||
"This is further evidenced by the differences between the western sidewalk (generally used going toward downtown Seattle), which peaks more strongly in the morning, and the eastern sidewalk (generally used going away from downtown Seattle), which peaks more strongly in the evening.\n",
|
||
"\n",
|
||
"We also might be curious about how things change based on the day of the week. Again, we can do this with a simple groupby:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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xuEpKmjaQy6FpXIJVk57hW4I1wIPhtoRTPwUb+yow7KfA9NhTvr7v2ltMTAyWLl2KJUuW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\n8BD/A/9r7TmuhSCKAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x119484d68>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"by_weekday = data.groupby(data.index.dayofweek).mean()\n",
|
||
"by_weekday.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
|
||
"by_weekday.plot(style=[':', '--', '-']);"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"This shows a strong distinction between weekday and weekend totals, with around twice as many average riders crossing the bridge on Monday through Friday than on Saturday and Sunday.\n",
|
||
"\n",
|
||
"With this in mind, let's do a compound GroupBy and look at the hourly trend on weekdays versus weekends.\n",
|
||
"We'll start by grouping by both a flag marking the weekend, and the time of day:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"weekend = np.where(data.index.weekday < 5, 'Weekday', 'Weekend')\n",
|
||
"by_time = data.groupby([weekend, data.index.time]).mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now we'll use some of the Matplotlib tools described in [Multiple Subplots](04.08-Multiple-Subplots.ipynb) to plot two panels side by side:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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L0xFCCBFjIvU6paiqtvfwKisbtTy8JrKybHLecSIezxk6ft6uygr23/1zbOdMpMdNtwBQ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\nCAt7cXHruiB5eYH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Ht2eWL5+xzOew42tpCVvxv58pJxcAV7kkx0KI6Ne05XOcB0qxjZtA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fUE102J7+midaqcohqWr81i8vBwxg85Mz5+YvhYthTsZE/xXvaWHGCYve3BsSRJhN5+FzmFhVSu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\nRhAEQRAEQRCEPun/A1rsNddykhZ+AAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x11b2305c0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
|
||
"by_time.ix['Weekday'].plot(ax=ax[0], title='Weekdays',\n",
|
||
" xticks=hourly_ticks, style=[':', '--', '-'])\n",
|
||
"by_time.ix['Weekend'].plot(ax=ax[1], title='Weekends',\n",
|
||
" xticks=hourly_ticks, style=[':', '--', '-']);"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The result is very interesting: we see a bimodal commute pattern during the work week, and a unimodal recreational pattern during the weekends.\n",
|
||
"It would be interesting to dig through this data in more detail, and examine the effect of weather, temperature, time of year, and other factors on people's commuting patterns; for further discussion, see my blog post [\"Is Seattle Really Seeing an Uptick In Cycling?\"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/), which uses a subset of this data.\n",
|
||
"We will also revisit this dataset in the context of modeling in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<!--NAVIGATION-->\n",
|
||
"< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) >"
|
||
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