# How to Count Missing Values in a Pandas DataFrame

Often you may be interested in counting the number of missing values in a pandas DataFrame.

This tutorial shows several examples of how to count missing values using the following DataFrame:

```import pandas as pd
import numpy as np

#create DataFrame with some missing values
df = pd.DataFrame({'a': [4, np.nan, np.nan, 7, 8, 12],
'b': [np.nan, 6, 8, 14, 29, np.nan],
'c': [11, 8, 10, 6, 6, np.nan]})

#view DataFrame
print(df)

a     b     c
0   4.0   NaN  11.0
1   NaN   6.0   8.0
2   NaN   8.0  10.0
3   7.0  14.0   6.0
4   8.0  29.0   6.0
5  12.0   NaN   NaN
```

### Count the Total Missing Values in Entire DataFrame

The following code shows how to calculate the total number of missing values in the entire DataFrame:

```df.isnull().sum().sum()

5```

This tells us that there are 5 total missing values.

### Count the Total Missing Values per Column

The following code shows how to calculate the total number of missing values in each column of the DataFrame:

```df.isnull().sum()

a    2
b    2
c    1
```

This tells us:

• Column ‘a’ has missing values.
• Column ‘b’ has missing values.
• Column ‘c’ has 1 missing value.

You can also display the number of missing values as a percentage of the entire column:

```df.isnull().sum()/len(df)*100

a    33.333333
b    33.333333
c    16.666667
```

This tells us:

• 33.33% of values in Column ‘a’ are missing.
• 33.33% of values in Column ‘b’ are missing.
• 16.67% of values in Column ‘c’ are missing.

### Count the Total Missing Values per Row

The following code shows how to calculate the total number of missing values in each row of the DataFrame:

```df.isnull().sum(axis=1)

0    1
1    1
2    1
3    0
4    0
5    2```

This tells us:

• Row 1 has 1 missing value.
• Row 2 has 1 missing value.
• Row 3 has 1 missing value.
• Row 4 has 0 missing values.
• Row 5 has 0 missing values.
• Row 6 has 2 missing values.