Data Archive

Handling Missing Data

Missing data can provide insights into the data collection process. It’s important to determine whether the missing data is randomly distributed or specific to certain features. Filling in data is a type of Data Transformation.

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Identifying Missing Data

How do you find which features have the most missing data?

To find where missing values (NA) are located in your dataset, use the following commands:

df.isnull().sum()
df.isna().sum()
df[df.columns[df.isnull().sum() > 0].tolist()].info()

Treating Missing Values (Imputation Techniques)

There are two main strategies for handling missing values: removing them or replacing them.

Remove Missing Values:

  • dropna: Drops rows with missing values.
  • df.dropna(inplace=True): Drops rows with NA values and updates the DataFrame in place.
  • df.reset_index(inplace=True, drop=True): Resets the index after dropping rows.

Replace Missing Values:

  • fillna: Fills missing values with specified values.
    • Example: df['var1'] = df['var1'].fillna(df['var1'].mean()) fills missing values in var1 with the column’s average.
  • isnull: Checks for missing values.
  • df.reindex: Reindexes the DataFrame.
  • Imputation methods for filling in missing data so that it has a higher likelyhood of being true.

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