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By Kevin McAleer, 2 Minutes
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.
Pandas provides functions to identify and handle missing data:
# Identifying missing data missing_data = df.isnull()
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()
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)
Transform data by applying a function to each column or row:
# Applying a function df_transformed = df.apply(function)
Replace specific values in the DataFrame:
# Replacing values df_replaced = df.replace(original_value, new_value)
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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