II. Python for Data Science

Python Manipulation Using Pandas

1) Basics to DataFrames

File	Comments	Functionality
1. Getting Started with Pandas	Installing and importing from `Pandas`.	-
2. Core Data Structures	Understanding core data structures of `Pandas`: `DataFrames` and `Series`	-
3. Creating a DataFrame	Creating a DataFrame from lists, dictionaries, list of dictionaries, arrays, and loading data from an external file (e.g., CSV files) to a DataFrame.	Create New Object
4. Creating a Series	Using `pd.Series()`	Create New Object
5. Indexing a DataFrame	How to index by rows or columns, and subsetting the DataFrame using `[]` and `loc` and `iloc` attributes.	Filtering
6. Chained Indexing	Sequential indexing. NOT recommended.	Filtering
7. Boolean Masking	Filtering or subsetting a DataFrame based on a boolean condition (series). Alternative comparison and logical operators for DataFrames.	Filtering
8. Exporting a DataFrame	Exporting DataFrame as a CSV, Excel, JSON file.	-

2) Manipulating the Physical DataFrame Object

File	Comments	Functionality
1. Resetting the Index	Reset index of a DataFrame using `df.reset_index()` to drop or move the index data to axis=1 as a new column.	Index
2. Customize Index	Customize the index of DataFrame using `df.index` attribute.	Index
3. Setting New Index using `df.set_index()`	Set new index to DataFrame object with existing columns.	Index
4. Multi-Level Indexing	How to set, drop and query multi-index	Index
5. Sorting Rows by Column Values	Using `df.sort_values()`	Sorting
6. Reordering Columns	Manually reorder columns in desired order.	Sorting
7. Renaming Row Index and Column Names	-	Index
8. Transposing the DataFrame	Flipping columns to rows and rows as columns	Physical Structure
9. Pivoting the DataFrame	There are two build in functions: `pivot()` and `pivot_table()`.	Physical Structure
10. Merging DataFrames	Merging DataFrame Objects using: `merge()`, `join()`, and `concat()`.	Physical Structure
11. Stacking	Stacking using `concat()`, `append()`, and `stack()`.	Physical Structure

2) Manipulating the Data in the DataFrame Object

File	Comments	Functionality
1. Adding Data to a DataFrame and Series	Adding a row or column to a DataFrame. Adding a value to a series.	Add Data
2. Removing Data in a DataFrame and Series	Removing data in a pandas DataFrame, including removing rows and columns by index or name, replacing values with NaN, and handling missing data with `dropna()`.	Remove Data
4. How to Handle Missing Data	Checking for missing values with `isnull()` and `isna()` and handling missing values with `dropna()`.	Missing Data
5. Replacing Values	Using a dictionary with the `df.replace()` function to remap values in specific columns.	Replace Values
6. Converting DataTypes in a DataFrame	Using `astype()` to convert the data type of a specific column.	Change Data Type
7. Type Casting	Implicit vs explicit type conversions and using `df[col].astype()` to explicit change the data type of a column.	Change Data Type

3) Grouping and Aggregation

File	Comments	Type
1. Group By Aggregation	Basic aggregation operations, `.apply()` and `.agg()`.	Group By
2. Multi-Level Group By	-	Group By

4) Merging and Concatenation

5) Useful Functions

File	Comments	Functionality
1. Checking Data Types with `dtypes`	Check data types of each columns in a DataFrame object or Series.	-
2. Filter DataFrame Columns by `select_dtype()`	Filtering dataframe by datatype `select_dtype()`	Filtering

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