How to Handle Missing Values in a Dataset Using Pandas
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Condition for Handling Missing Values in a Dataset
Description:
In data analysis, missing values refer to the absence of data in certain cells of a dataset.
Handling missing values is crucial for ensuring the quality of data, as they can affect
statistical analyses and machine learning models.
Step-by-Step Process
Import Libraries: Import the required library (Pandas).
Load the Data: Load your dataset (from a CSV file, Excel, or other sources).
Find Missing Values: Use the isna() or isnull() method to identify missing values in the dataset.
Analyze Missing Data: Also, use sum() or describe() to check how many missing values are present in each column.
Drop Missing Values: Use dropna() to remove rows or columns with missing values.
# Drop rows with missing values
df_cleaned = df.dropna() # Drops rows where any column has a missing value
# Show the DataFrame after dropping rows with missing values
print("\nDataFrame after Dropping Rows with Missing Values:")
print(df_cleaned)
# Now drop columns with missing values
df_cleaned_columns = df.dropna(axis=1) # Drops columns with any missing value
# Show the DataFrame after dropping columns with missing values
print("\nDataFrame after Dropping Columns with Missing Values:")
print(df_cleaned_columns)