# Navigating Missing Values: Understanding NaN in Numpy and Pandas Dataframe

## NaN in Numpy and Pandas Dataframe: Understanding Missing Values

Have you ever faced a situation where your data is missing? Missing data is a common issue in data analysis, and it can lead to biased results.

In Numpy and Pandas Dataframe, NaN represents missing or undefined values. It stands for “Not a Number,” which is a numeric data type.

In this article, we will explore NaN in Numpy and Pandas Dataframe. We will discuss what NaN is, how it affects mathematical operations, how to ignore NaN values while performing operations, and how to handle NaN values in Pandas Dataframe.

## NaN in Numpy

### What is NaN?

NaN is an abbreviation for “Not a Number.” Still, in Numpy, it is used to represent undefined values or missing values. These undefined values occur when arithmetic operations produce results that are not suitable for a floating-point number, such as dividing zero by zero, or logarithms of negative numbers.

NaN is a concept used throughout computing to indicate an undefined value.

### Mathematical operations on a Numpy array with NaN

When performing mathematical operations on an array that contains a NaN value, the result is also NaN. If we perform a mathematical operation, such as the sum or the max, on a Numpy array that contains a NaN value, it will return NaN as the output.

### How to ignore NaN values while performing Mathematical operations on a Numpy array

Sometimes, NaN values can be a hindrance when performing mathematical operations on NumPy arrays. However, we can use the `np.nansum()` and `np.nanmax()` functions to solve this problem.

These functions can ignore the NaN values and perform the mathematical operations on the remaining elements of the array.

### Checking for NaN values

It’s essential to check for NaN values in your Numpy array or Pandas DataFrame. We can use the `np.isnan()` function to identify NaN values in Numpy arrays.

If we use this function on a Numpy array containing NaN values, it will return a Boolean array with True values for NaN and False values for valid data.

### Equating two nans

Equating two NaN values is not as straightforward as comparing two numbers in Numpy. If we compare two NaN values using ==, it will return False as the result, because NaN values are not equal to each other, even if they appear identical; this is because NaN values represent undefined values.

## NaN in Pandas Dataframe

### Checking for NaN values

The `isnull()` function is used for detecting NaN values in a Pandas dataframe. It returns a Boolean value for each value in a data frame, with True values for NaN and False values for valid values.

### Replacing NaN values

We can use the `fillna()` function to replace NaN values present in a data frame with a specified value. The `fillna()` function can replace either NaN values with a specified value or use techniques such as interpolation to estimate the value of the NaN values.

### Drop rows containing NaN values

If you have NaN values in your dataframe, you could decide to drop the rows containing the NaNs. The `dropna()` function can be used to remove rows with NaN values from the dataframe.

## Conclusion

In this article, we have discussed NaN in Numpy and Pandas Dataframe. We’ve talked about what NaN is, how it affects mathematical operations and how to ignore NaN values while performing operations on Numpy arrays.

We’ve also described how to handle NaN values in Pandas Dataframe by checking for NaN values, replacing NaN values with desired values, and dropping rows containing NaN values. NaN is a crucial concept for data analyst to understand when working with datasets that contain undefined values.