Pandas Library

5.

Data Cleaning and Preparation

5.1.

Handling Missing Data

5.1.1.

Identifying Missing Values

5.1.1.1.

isnull() Method

5.1.1.2.

notnull() Method

5.1.2.

Dropping Missing Values

5.1.2.1.

dropna() Method

5.1.2.2.

Dropping Rows with Missing Values

5.1.2.3.

Dropping Columns with Missing Values

5.1.2.4.

Setting Thresholds for Dropping

5.1.3.

Filling Missing Values

5.1.3.1.

fillna() Method

5.1.3.2.

Filling with Constants

5.1.3.3.

5.1.3.4.

5.1.3.5.

Filling with Statistical Measures

5.1.4.

Interpolating Missing Values

5.1.4.1.

interpolate() Method

5.1.4.2.

Linear Interpolation

5.1.4.3.

Time-based Interpolation

5.2.

Handling Duplicate Data

5.2.1.

Identifying Duplicates

5.2.1.1.

duplicated() Method

5.2.1.2.

Duplicates in Rows

5.2.1.3.

Duplicates in Specific Columns

5.2.2.

Dropping Duplicates

5.2.2.1.

drop_duplicates() Method

5.2.2.2.

Keeping First or Last Occurrence

5.3.

Data Transformation

5.3.1.

Applying Functions

5.3.1.1.

5.3.1.2.

5.3.1.3.

applymap() Method

5.3.2.

Replacing Values

5.3.2.1.

replace() Method

5.3.2.2.

Replacing Single Values

5.3.2.3.

Replacing Multiple Values

5.3.2.4.

Replacing with Regular Expressions

5.3.3.

Renaming Labels

5.3.3.1.

rename() Method

5.3.3.2.

Renaming Columns

5.3.3.3.

5.3.4.

Changing Data Types

5.3.4.1.

astype() Method

5.3.4.2.

Converting to Numeric Types

5.3.4.3.

Converting to Categorical Types

5.3.4.4.

Converting to Datetime Types

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6. Combining and Reshaping Data