Data Cleaning and Preparation

Learn the essentials of data cleaning and preparation in Pandas, including handling missing data, data transformation, and data filtering for effective data analysis.

By Kevin McAleer,    2 Minutes

Cover photo


In this lesson, we delve into Data Cleaning and Preparation with Pandas. Effective data analysis often requires thorough cleaning and preparation of datasets. This lesson covers key techniques like handling missing data, transforming data, and filtering to prepare your datasets for analysis.

Handling Missing Data

Identifying Missing Data

Pandas provides functions to identify and handle missing data:

# Identifying missing data
missing_data = df.isnull()

Filling Missing Data

You can fill missing data with a specific value or interpolated values:

# Filling missing data with a specific value
df_filled = df.fillna(value)

# Interpolating missing values
df_interpolated = df.interpolate()

Dropping Missing Data

Alternatively, you can choose to drop rows or columns with missing values:

# Dropping rows with missing data
df_dropped_rows = df.dropna()

# Dropping columns with missing data
df_dropped_columns = df.dropna(axis=1)

Data Transformation

Applying Functions

Transform data by applying a function to each column or row:

# Applying a function
df_transformed = df.apply(function)

Replacing Values

Replace specific values in the DataFrame:

# Replacing values
df_replaced = df.replace(original_value, new_value)

Data Filtering

Filter data based on conditions or values:

# Filtering data
filtered_data = df[df['ColumnName'] > value]


This lesson covered essential techniques in data cleaning and preparation using Pandas. Handling missing data, transforming data, and filtering are critical steps in preparing your dataset for analysis.

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