- 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
To utilize the examples in this chapter, we will need to include the following imports and settings:
In [1]: # import pandas, numpy and datetime import numpy as np import pandas as pd import datetime # Set some pandas options for controlling output pd.set_option('display.notebook_repr_html', False) pd.set_option('display.max_columns', 10) pd.set_option('display.max_rows', 10)
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