How to Check Nan Values in Pandas

Null values are described as missing values in the original pandas manual. Since most programmers do, we may designate the null or missing data in pandas as NaN.

NaN, which means Not A Number, is one of the usual methods to show a value missing from a data set. It is a unique floating-point object and can only be changed to the floating-point datatype.

One of the main issues in data processing is the NaN value. Dealing with NaN is crucial if you want to fulfill your objectives.

Programmers can display empty entries in the dataframe by using either the NaN or None Python keywords. The fact that the pandas handle NaN and None equally is its most attractive feature. If the cell contains NaN or None, pandas.notnull will yield False to determine whether a component is missing. We can also use .isnull(), which is opposite to the .notnull function; hence it will return True if the value is null or NaN.

Therefore, we shall examine several techniques in this tutorial to determine if a particular cell or series or an entire dataframe contains the null value or not (NaN or None).

The several approaches that we'll talk about include:

1. isnull
2. isnan
3. isna
4. notnull

Using the .isnull Function

We can check which cell null values are present using this function. An example is shown below.

Code

Output:

    numbers
0     False
1     False
2     False
3     False
4     False
5     False
6      True
7     False
8     False
9      True
10    False
11    False
12    False
13     True

Also, using isnull.values.any(), we can check if a series of data frames contain any null value. It will give us a Boolean result.

Code

Output:

True

Using the same isnull() function, we can also get the sum of null values present in a series of the dataframe.

Code

Output:

The sum of NaN entries present in a series: 3

Using the following code, you can calculate the NaN values across the whole DataFrame.

Code

Output:

The sum of Nan values in the data frame: 8

Using the isnan() Method

Using the pandas.isnull function, we verified the NaN entries in the instance above. We will now employ a different technique termed isnan. This function is an in-built function of the numpy library. The code below shows an example of how to check the nan value in a particular cell.

Code

Output:

The particular value of the data frame is nan: True

In the preceding example, we looked for the NaN object in the cell of our dataframe. Additionally, we can determine whether a cell value in the pandas series is NaN or not. Let's look at how we can put it into practice.

Code

Output:

The value at series[3] is NaN: False

Using .isna Function of Pandas

We will show how to use .isna function of Pandas.

Code

Output:

checking for NaN value in the series numbers2 at the 6th index
True

Using .notnull Method

The notnull function is another way to determine whether a cell is NaN or a dataframe contains a null value. According to the program below, this function will give a boolean result of False if the given cell value is NaN or null.

Code

Output:

The cell does not has a null value:  False
The series does has not null value:  True
Number of no null value in the series of the dataframe:  numbers1    12
numbers2    10
dtype: int64