Charles Kelly explains how to change time interval within Pandas data structures by using resampling.
- [Narrator] The resampling file from your exercises…files folder is pre-populated with import statements…for Pandas and NumPy…it also includes a date range that will serve…as an index for a time series.…Begin by placing your cursor in this cell,…execute the cell by pressing shift + enter.…This changes the name space for Pandas to PD…and NumPy to NP.…If you'd like to get more information…about the reasons for and the uses of resampling,…you can navigate to this link in the data frame…information for the resample function.…
Let's begin by creating a new date range.…We can now display the information within my index.…You'll see that it contains nine periods…starting at midnight, one minute after, two minutes after,…three minutes after, et cetera.…Now let's create a time series…using Panda's series function.…The first argument to the function is a call…to NumPy's arange function.…It will return nine consecutive integers…between zero and eight.…
The second argument is my index,…the date-time range that we just created.…
Watch this course to gain an overview of Pandas. Charles Kelly helps you get started with time series, data frames, panels, plotting, and visualization. All you need is a copy of the free and interactive Jupyter Notebook app to practice and follow along.
- Using the Markdown language and Jupyter Notebook
- Creating objects
- Selecting objects
- Using operations
- Merging data
- Creating series
- Creating data frames
- Creating panels
- Annotating plots and data frame plots
Skill Level Intermediate
Introduction to Data Analysis with Pythonwith Michele Vallisneri2h 16m Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
SQL for Data Reporting and Analysiswith Emma Saunders2h 16m Intermediate
1. Introduction to Notebooks
2. Pandas Overview
4. Data Frames and Panels
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