How to Filter in NaN Pandas

As a data scientist or software engineer, you are often faced with the task of cleaning and processing large datasets. One common issue you might encounter is missing data, represented in Pandas as NaN (Not a Number). In this article, we will discuss how to filter NaN values in a Pandas DataFrame.

Table of Contents

1. What is NaN?
2. Understanding NaN values in Pandas
3. Filtering NaN values in a Pandas DataFrame
4. Common Errors and How to Handle Them
5. Conclusion

What is NaN?

NaN is a special floating-point value used to represent missing or undefined data in Pandas. It can arise due to a variety of reasons, such as incomplete data, errors in data collection, or data corruption. NaN can also be generated as a result of mathematical operations involving missing values.

Understanding NaN values in Pandas

Before we dive into filtering NaN values, it is essential to understand how Pandas handles them. NaN values are considered to be neither greater than nor less than any other value, including other NaN values. This means that NaN cannot be compared using standard comparison operators like < or >. Instead, we use special functions provided by Pandas to handle NaN values.

Filtering NaN values in a Pandas DataFrame

To filter NaN values in a Pandas DataFrame, we use the isna() or isnull() functions. These functions return a boolean mask that indicates whether each element in the DataFrame is NaN or not. We can then use this boolean mask to filter out rows or columns with NaN values.

Filtering rows with NaN values

To filter rows with NaN values, we use the dropna() function. This function removes any row with a NaN value and returns a new DataFrame with the filtered rows. By default, dropna() removes any row with at least one NaN value.

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5],
                   'B': [6, 7, pd.np.nan, 9, 10],
                   'C': [11, pd.np.nan, 13, 14, 15]})

# Filtering rows with NaN values
filtered_df = df.dropna()
print(filtered_df)

Output:

   A     B     C
0  1   6.0  11.0
3  4   9.0  14.0
4  5  10.0  15.0

As you can see, the rows with NaN values in column B and C have been removed.

Filtering columns with NaN values

To filter columns with NaN values, we use the dropna() function with the axis parameter set to 1. This function removes any column with a NaN value and returns a new DataFrame with the filtered columns. By default, dropna() removes any column with at least one NaN value.

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5],
                   'B': [6, 7, pd.np.nan, 9, 10],
                   'C': [11, pd.np.nan, 13, 14, 15]})

# Filtering columns with NaN values
filtered_df = df.dropna(axis=1)
print(filtered_df)

Output:

   A
0  1
1  2
2  3
3  4
4  5

As you can see, the column with NaN values has been removed.

Filling NaN values

Using fillna()

In some cases, it might be preferable to fill NaN values with a specific value instead of removing them. To fill NaN values, we use the fillna() function. This function replaces NaN values with the specified value and returns a new DataFrame with the filled values.

import numpy as np
import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5],
                   'B': [6, 7, np.nan, 9, 10],
                   'C': [11, np.nan, 13, 14, 15]})

# Filling NaN values with 0
filled_df = df.fillna(0)
print(filled_df)

Output:

   A     B     C
0  1   6.0  11.0
1  2   7.0   0.0
2  3   0.0  13.0
3  4   9.0  14.0
4  5  10.0  15.0

As you can see, the NaN values have been replaced with 0.

Using interpolate()

Another solution to replace NaN is to use interpolate(). The interpolate() method is useful when you want to fill NaN values with interpolated values, making it suitable for time-series data.

# Interpolate NaN values
df_interpolated = df.interpolate()
print(df_interpolated)

Output:

   A     B     C
0  1   6.0  11.0
1  2   7.0  12.0
2  3   8.0  13.0
3  4   9.0  14.0
4  5  10.0  15.0

Common Errors and How to Handle Them

• Setting inplace parameter: When using methods like dropna() or fillna(), be cautious with the inplace parameter. Not setting it to True might lead to unexpected results.

# Incorrect usage without setting inplace=True
df.dropna()  # This does not modify the original DataFrame

To avoid this, either set inplace=True or assign the result back to the original DataFrame:

# Correct usage
df.dropna(inplace=True)  # Modifies the original DataFrame
# or
df = df.dropna()  # Assigns the result back to the original DataFrame

Conclusion

In this article, we discussed how to filter NaN values in a Pandas DataFrame. We learned that Pandas provides two functions, isna() and isnull(), to detect NaN values and the dropna() and fillna() functions to filter or replace them. By understanding how to handle NaN values in Pandas, data scientists and software engineers can clean and process large datasets with ease.

SHARE:

About Saturn Cloud

Saturn Cloud is your all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, and more. Request a demo today to learn more.