In this tutorial, we will explore how to test for ‘Not a Number’ (NaN) values in Python. NaN values usually arise from undefined or unrepresentable data like 0/0.

They can cause a variety of problems if not properly handled when performing calculations or data analysis.

Python provides several methods for identifying these NaN values within a dataset, so let’s get started!

### Step 1: Import Necessary Libraries

The primary library we will use for this tutorial is the *pandas* library. This library provides the *isnull* function, which returns True for each NaN or None value in a given series or dataframe.

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import pandas as pd |

### Step 2: Create a DataFrame with NaN Values

Let’s create an exemplar dataframe with NaN values to work on. We can create NaN values using the numpy library.

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import numpy as np data = {'A': [1,2,np.nan], 'B': [5,np.nan,1], 'C': [1,2,3]} df = pd.DataFrame(data) |

### Step 3: Use the pandas isnull Function

Now we will use the *isnull* function, which returns a Boolean value for each element in the DataFrame or Series. If the element is NaN or None, *isnull* returns True; otherwise, it returns False.

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print(df.isnull()) |

### Full Code

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import pandas as pd import numpy as np # Create a DataFrame data = {'A': [1,2,np.nan], 'B': [5,np.nan,1], 'C': [1,2,3]} df = pd.DataFrame(data) # Use isnull function to check for NaN values print(df.isnull()) |

## Output

A B C 0 False False False 1 False True False 2 True False False

From the output above, the *True* values represent NaN or None values found in the DataFrame.

## Conclusion

This tutorial has shown you how to test for NaN values in Python using the pandas *isnull* function. Remember, always handling these kinds of values in your datasets is crucial as they can cause unexpected results during your data analysis or when developing machine learning models.

A proper understanding and handling of NaN values are necessary for developing more accurate models.