# How to find missing values in Python

Missing values is a very big problem in data science projects. Missing values can occur when no information is provided for one or more items or for a whole unit. It is necessary to find out whether there are missings.

The search for missings is usually one of the first steps in data analysis. At the beginning, the question is whether there are any missings at all and, if so, how many there are. As is often the case, Pandas offers several ways to determine the number of missings.

Missing values can be handled in different ways depending on if the missing values are continuous or categorical.

In Pandas missing data is represented by two value: None and NaN. Pandas treat `None`

and `NaN`

as essentially interchangeable for indicating missing or null values.

In order to check missing values in Pandas DataFrame, we use a function `isnull()`

and `notnull()`

. Both function help in checking whether a value is `NaN`

or not. These function can also be used in Pandas Series in order to find null values in a series.

`isnull()`

In order to check null values in Pandas DataFrame, we use `isnull()`

function this function return dataframe of Boolean values which are True for NaN values.

Example:

import pandas as pd import numpy as np di = {'Name': ['abi', 'hari', np.nan, 'dev'], 'Age': [21, 20, 21, np.nan], 'Grade': [np.nan, 'london', 'delhi', 'mumbai']} df = pd.DataFrame(di) print(df.isnull())

Output:

Name Age Grade 0 False False True 1 False False False 2 True False False 3 False True False

#### `notnull()`

In order to check null values in Pandas Dataframe, we use notnull() function this function return dataframe of Boolean values which are False for NaN values.

Example:

import pandas as pd import numpy as np di = {'Name': ['abi', 'hari', np.nan, 'dev'], 'Age': [21, 20, 21, np.nan], 'Grade': [np.nan, 'london', 'delhi', 'mumbai'], 'Gender': [np.nan, np.nan, np.nan, np.nan]} df = pd.DataFrame(di) print(df.notnull())

Output:

Name Age Grade Gender 0 True True False False 1 True True True False 2 False True True False 3 True False True False