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Computer Science
Data Science
Pandas Library
1. Introduction to Pandas
2. Core Data Structures
3. Data Loading and Saving
4. Indexing and Data Selection
5. Data Cleaning and Preparation
6. Combining and Reshaping Data
7. Grouping and Aggregation
8. Working with Text Data
9. Working with Time Series Data
10. Multi-level Indexing
11. Data Visualization
12. Advanced Topics and Performance
10.
Multi-level Indexing
10.1.
Creating MultiIndex
10.1.1.
From Arrays
10.1.2.
From Tuples
10.1.3.
From Product of Levels
10.1.4.
from_arrays() Method
10.1.5.
from_tuples() Method
10.1.6.
from_product() Method
10.2.
Indexing and Slicing MultiIndex DataFrames
10.2.1.
Selecting Data at Different Levels
10.2.2.
Slicing with MultiIndex
10.2.3.
Cross-section Selection
10.2.3.1.
xs() Method
10.3.
MultiIndex Operations
10.3.1.
Swapping Levels
10.3.1.1.
swaplevel() Method
10.3.2.
Sorting by Index Levels
10.3.2.1.
sort_index() Method
10.3.3.
Dropping Index Levels
10.3.3.1.
droplevel() Method
10.4.
Stacking and Unstacking
10.4.1.
stack() Method
10.4.2.
unstack() Method
10.4.3.
Reshaping with Multiple Levels
10.5.
Pivot Tables and Cross-tabulations
10.5.1.
pivot_table() Function
10.5.1.1.
Aggregation in Pivot Tables
10.5.1.2.
Multi-level Columns in Pivot Tables
10.5.2.
crosstab() Function
10.5.2.1.
Frequency Tables
10.5.2.2.
Normalizing Cross-tabulations
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9. Working with Time Series Data
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11. Data Visualization