Dealing with Duplicate or Unwanted Rows in a Pandas DataFrame

Understanding the Problem

When working with data in pandas DataFrames, it’s not uncommon to encounter duplicate or unwanted rows that need to be removed. In this article, we’ll explore how to delete rows based on certain conditions, specifically when the number of non-null values in a row exceeds a threshold.

A Sample Use Case

Suppose you have a long DataFrame containing data for your project, and you want to remove the rows that contain more than two cells with null values. The original DataFrame might look like this:

         A          B           C           D          E   F
0   9012_1  :2683_1_0         NaN         NaN        NaN NaN
1   9044_0  :2680_1_0         NaN         NaN        NaN NaN
2   9007_1     9007_2   :8487_3_0   :8487_4_0  :2675_1_0 NaN
3   8814_2  :8374_1_2         NaN         NaN        NaN NaN
4  77114_0    77114_1  :53453_1_0  :53453_1_1        NaN NaN

Your goal is to remove the rows that contain more than two cells with null values, resulting in a DataFrame like this:

         A          B           C           D          E   F
0   9012_1  :2683_1_0         NaN         NaN        NaN NaN
1   9044_0  :2680_1_0         NaN         NaN        NaN NaN
3   8814_2  :8374_1_2         NaN         NaN        NaN NaN

The Solution

To achieve this, you can use the notna() method to select rows with non-null values and then filter those rows based on the desired condition (in this case, fewer than or equal to two null values). Here’s the relevant code snippet:

print(df[df.notna().sum(1) <= 2])

This line of code works as follows:

notna(): This method returns a boolean mask indicating whether each value in the DataFrame is not null.
.sum(1): This applies the sum function along the rows (axis=1), effectively counting the number of non-null values in each row. The result is a Series containing the count for each row.
<= 2: This filters the rows to include only those with two or fewer non-null values.

Explanation and Example

Let’s break down the code further and provide additional explanations:

Using .notna(): The .notna() method is a vectorized operation that allows you to efficiently check for null values in the DataFrame. This is particularly useful when working with large DataFrames.
Applying .sum(1): By using axis=1 (which means along rows), we’re counting the number of non-null values in each row. The result is a Series that contains the count for each row.
Filtering with <=: To filter the rows, we use the comparison operator <= (less than or equal to). This selects only those rows where the count of non-null values is two or fewer.

Here’s an example of how this works:

Suppose we have a DataFrame df like this:

   A    B
0  1.0  2.0
1  3.0  NaN
2  NaN  4.0
3  5.0  NaN
4  NaN  NaN

If we apply the filter df[df.notna().sum(1) <= 2], we get:

   A    B
0  1.0  2.0
1  3.0  NaN
2  NaN  4.0

As you can see, the row with a null value in column B is included because it has only one non-null value.

Conclusion

Dealing with duplicate or unwanted rows in a pandas DataFrame can be challenging, but using techniques like .notna() and filtering based on conditions can help. By understanding how to effectively use these methods, you’ll become more proficient at working with DataFrames and efficiently processing large datasets.

In the next article, we’ll explore other useful techniques for data manipulation and analysis in pandas DataFrames.

Last modified on 2023-12-03