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Filling the Missing Values

Now that we know that there are two missing values in the data, we have to fill those missing values.


Pandas provides some built-in methods to do this job.

  • bfill - used to backward fill or use the next valid observation to fill the missing values in the dataset.
  • ffill - used to forward fill or use the previous valid observation to fill the missing values in the dataset.
  • using fillna(metohd='bfill') is another way of achieving the job using backward filling. Similarly, one could use ffill or any other way like mean().

Let us use bfill() to fill the two missing values.

  • First let us see the number of missing values in each column in modified_data using isna().sum().

    print("Before filling missing values:\n", modified_df.<< your code comes here >>)
  • Use bfill method on modified_df and put axis=rows.

    modified_df = modified_df.<< your code comes here >>(axis ='rows')
  • Now, let us again check the column-wise null values in modified_df. Now we expect all the values to be 0 as we have filled them.

    print("\nAfter filling missing values:\n",modified_df.isna().sum())

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