Pandas is a two-dimensional data structure that allows you to store data in rows and columns format. It also provides a lot of API methods that can be used for easier data analysis. Two such methods are `isna()`

and `isnull()`

**Both isna() and isnull() functions are used to find the missing values in the pandas dataframe.**

`isnull()`

and `isna()`

literally does the same things. `isnull()`

is **just an alias** of the `isna()`

method as shown in pandas source code.

Missing values are used to denote the values which are null or do not have any actual values. You can use the` pd.NaT`

, `np.NaN`

or `None`

to denote the missing values in the dataframe.

## What is isna()

isna() is used to detect the missing values in the cells of the pandas dataframe.

It returns a dataframe of the same size with the values masked as `True`

for `na`

values and `False`

for non-NA values.

## What is isnull()

isnull() is also used to identify or detect the missing values in the dataframe. **It is just an alias for isna() method.**

It also returns a dataframe of the same size where the values such as `None`

or `Np.NaN`

are masked as `True`

and other values are masked as `False`

.

## Why Two Methods in Different Name

Pandas dataframe are based on R dataframes. In R, the `na`

values and `null`

values are different types. Hence, there are two different methods to check `na`

and `null`

. That’s why pandas have two method names.

On the other hand, in Python pandas is built on top of NumPy which doesn’t have `na`

or `null`

values. It uses `Np.NaN`

values to denote the missing values. Even `None`

values are considered as `Np.NaN`

.

You’ll see it in detail in the below examples.

## Finding Missing Values

In this section, you’ll use the `isna()`

and the `isnull()`

method to find the missing values in the sample dataframe.

The dataframe contains all the different types of the missing values `pd.naT`

, `None`

, `Np.NaN`

.

**Sample Dataframe**

```
import pandas as pd
import numpy as np
data = {"Product_Name":["Mouse", "Monitor", "CPU", "Speakers","Headset"],
"Unit_Price":[200, 5000.235, 10000.550, 250.50,
````None`],
"No_Of_Units":[5, 10, 20, 8, `pd.NaT`],
"Available_Quantity":[6,5,5, `pd.NaT`,`np.NaN`],
"Remarks":[`np.NaN`,`pd.NaT`,`pd.NaT`,`pd.NaT`,`pd.NaT`]
}
df = pd.DataFrame(data)
df

**Dataframe Will Look Like**

Product_Name | Unit_Price | No_Of_Units | Available_Quantity | Remarks | |
---|---|---|---|---|---|

0 | Mouse | 200.000 | 5 | 6 | NaT |

1 | Monitor | 5000.235 | 10 | 5 | NaT |

2 | CPU | 10000.550 | 20 | 5 | NaT |

3 | Speakers | 250.500 | 8 | NaT | NaT |

4 | Headset | NaN | NaT | NaN | NaT |

## Detect Missing Values Using isna()

You can use the below snippet to find the missing values in the dataframe using `isna()`

.

The values `None`

, `Np.NaN`

and the `pd.Nat`

will be identified as missing values when you use the `isna()`

function.

**Snippet**

`df.isna()`

The cells that have `True`

denote that has missing values and the cells that have `False`

denote that have a valid value.

**Dataframe Will Look Like**

Product_Name | Unit_Price | No_Of_Units | Available_Quantity | Remarks | |
---|---|---|---|---|---|

0 | False | False | False | False | True |

1 | False | False | False | False | True |

2 | False | False | False | False | True |

3 | False | False | False | True | True |

4 | False | True | True | True | True |

## Detect Missing Values Using isNull()

You can use the below snippet to find the missing values in the dataframe using `isnull()`

.

As the `isnull()`

method is just an alias of the `isna()`

method, it’ll also identify the values `None`

, `Np.NaN`

and the `pd.Nat`

as missing values.

**Snippet**

`df.isnull()`

The cells that have `True`

denote that have missing values and the cells that have `False`

denote that have a valid value.

**Dataframe Will look like**

Product_Name | Unit_Price | No_Of_Units | Available_Quantity | Remarks | |
---|---|---|---|---|---|

0 | False | False | False | False | True |

1 | False | False | False | False | True |

2 | False | False | False | False | True |

3 | False | False | False | True | True |

4 | False | True | True | True | True |

## Which One to Use

You can use the `isna()`

method to identify the missing values. Because it is the original method implemented and `isnull()`

is just an alias that internally calls the `isna()`

method.

## Conclusion

To summarize, you’ve learned the difference between `isnull()`

and isna()methods in the pandas dataframe. You’ve also learned which method needs to be used.

## You May Also Like

### Is ISNA and Isnull same?

Yes, both methods are used to detect the missing values. isnull() is just an alias of the isna() method and internally uses isna method.