Hello everyone,

I am doing data exploration on a data set in which I have 23 continuous variables and a target variable ‘total_cases’. In one of the columns ‘precipit’ of my data frame, I have 192 missing values. There are no missing values in my target variable. I want to find out the mean of total_cases corresponding to the rows which have NaN in ‘precip’ column. I am working on pythom.

# I want to find mean of those rows in target variable corresponding to which there are null values in a particular column

**vikas_10**#1

**pavscorp1911**#2

@vikas_10 From what I have understood, you wish to calculate the mean of **total_cases** for those rows where the **precipit** has null values.

So, assuming that your dataframe’s name is ‘**data**’, try doing the following :

```
import pandas
precipit_is_null = pandas.isnull(data["precipit"])
null_precipit_dataset = data[precipit_is_null]
mean_total_cases = null_precipit_dataset["total_cases"].mean()
```

Hoping that this works, all the best!

**Arihant**#3

Please try this :

import pandas as pd

col_null = pd.isnull(df[“precipit”])

col_null_dataset = df[col_null]

mean_find = col_null_dataset[“total_cases”].mean()

where df is your dataframe