Charles Kelly explains indexing and selection for Pandas data frames.
- [Instructor] In this video,…we'll examine techniques for indexing…and selecting…from data frames.…We'll examine these operations.…Select a column,…select a row by a label,…select a row by an integer,…select multiple rows…and select rows with a Boolean mask.…This is the syntax for each of these operations,…and this is the result of each of these operations.…If you'd like more information,…you can find it at this U-R-L.…The indexing file in your exercises file folder,…is pre-populated…with import statements for pandas and numpy,…and a data set in the form of a dictionary.…
Begin by placing your cursor in this cell,…execute the import statements…by pressing shift enter.…Do this same thing for the data set…and create the produce data frame.…In our first example of selection,…we'll select information from a data frame…using strings as an index.…As you can see, we've obtained…all the values in the fruits column.…Apple, bananas, pineapples, berries.…We can also use a list of strings…to select multiple columns from a data frame.…
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
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
SQL: Data Reporting and Analysiswith Emma Saunders2h 16m Intermediate
1. Introduction to Notebooks
2. Pandas Overview
4. Data Frames and Panels
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