Understanding GroupBy Operations in Pandas

When working with groupby operations in pandas, it’s essential to understand how the ffill() and bfill() methods interact with each other. In this article, we’ll delve into the differences between using ffill().bfill() and bfill().ffill() on groups.

Introduction to GroupBy

Before we dive into the specifics of ffill() and bfill(), let’s quickly review how groupby works in pandas. The groupby() function splits a DataFrame into groups based on one or more columns, allowing us to perform aggregation operations on each group.

When using groupby(), we can access the resulting groups as DataFrameGroupBy objects, which have various methods available for performing different types of calculations.

What are ffill() and bfill()?

ffill() and bfill() are two popular methods used to fill missing values in a Series or DataFrame. The main difference between them is where the value is taken from: ffill() takes the previous row’s value, while bfill() takes the next row’s value.

## ffill() (Forward Fill)

`ffill()` performs forward filling, which means it takes values from the current row and moves up to fill missing values. If there are no previous rows with non-missing values, it will still attempt to fill the missing value based on the last available non-missing value.

```markdown
print(df.groupby('a')['b'].ffill())

bfill() (Backward Fill)

bfill() performs backward filling, which means it takes values from the current row and moves down to fill missing values. If there are no next rows with non-missing values, it will still attempt to fill the missing value based on the first available non-missing value.

print(df.groupby('a')['b'].bfill())

Why does .bfill().ffill() act differently than ffill().bfill() on groups?

In the provided example, we see that df.groupby('a')['b'].apply(lambda x: x.ffill().bfill()) and df.groupby('a')['b'].apply(lambda x: x.bfill().ffill()) produce different results.

This is because in the first case, we’re applying ffill() followed by bfill(), which means that DataframeGroupBy.ffill() (the method used for forward filling) is called on each group separately. In this scenario, since there are no groups within a Series, ffill() and bfill() operate independently.

On the other hand, in the second case, we’re applying bfill() followed by ffill(). This means that DataframeGroupBy.bfill() (the method used for backward filling) is called on each group separately. However, since a Series does not have groups, bfill() only fills missing values with the next available non-missing value.

Example

Let’s take a closer look at what happens when we apply these methods to a DataFrame:

# Sample DataFrame
df = pd.DataFrame({'a':[1,1,2,2,3,3], 'b':[5,np.nan, 6, np.nan, np.nan, np.nan]})

print(df.groupby('a')['b'].ffill())

Output:

# Applying ffill() followed by bfill()
print(df.groupby('a')['b'].apply(lambda x: x.ffill().bfill()))

Output:

# Applying bfill() followed by ffill()
print(df.groupby('a')['b'].apply(lambda x: x.bfill().ffill()))

Output:

Conclusion

In conclusion, when using ffill() and bfill() on groups in pandas, it’s essential to understand how these methods interact with each other. By applying ffill() followed by bfill(), we ensure that the value is filled with the next available non-missing value within the group. On the other hand, applying bfill() followed by ffill() can lead to unexpected results due to the lack of groups within a Series.

Additional Tips

• When working with missing values in pandas, it’s often useful to use the .isnull() method to identify rows or columns that contain missing data.
• The .notnull() method is also available for identifying non-missing values.
• For more advanced missing value handling techniques, consider using the pandas.DataFrame.fillna() function.

Last modified on 2023-06-15