- Learning pandas
- Table of Contents
- Learning pandas
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. A Tour of pandas
- 2. Installing pandas
- 3. NumPy for pandas
- Installing and importing NumPy
- Benefits and characteristics of NumPy arrays
- Creating NumPy arrays and performing basic array operations
- Selecting array elements
- Logical operations on arrays
- Slicing arrays
- Reshaping arrays
- Combining arrays
- Splitting arrays
- Useful numerical methods of NumPy arrays
- Summary
- 4. The pandas Series Object
- 5. The pandas DataFrame Object
- Creating DataFrame from scratch
- Example data
- Selecting columns of a DataFrame
- Selecting rows and values of a DataFrame using the index
- Selecting rows of a DataFrame by Boolean selection
- Modifying the structure and content of DataFrame
- Arithmetic on a DataFrame
- Resetting and reindexing
- Hierarchical indexing
- Summarized data and descriptive statistics
- Summary
- 6. Accessing Data
- 7. Tidying Up Your Data
- 8. Combining and Reshaping Data
- 9. Grouping and Aggregating Data
- 10. Time-series Data
- 11. Visualization
- 12. Applications to Finance
- Index
Visualizing pandas data is incredibly simple as pandas is built with tight integration with the matplotlib framework. To demonstrate how simple it is to visualize data with pandas, the following code plots the stock data we just retrieved from Yahoo! Finance:
In [40]: # plot the Adj Close values we just read in goog.plot(y='Adj Close'),
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