In Python, you can count the total number of null or NaN (Not a Number) values in a DataFrame or Series using the `isna()`

method in combination with the `sum()`

method. Here’s an example:

```
import pandas as pd
# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, None, 4, None],
'B': [None, 2, 3, None, 5],
'C': [1, 2, 3, 4, 5]})
# Count the total number of null or NaN values in the DataFrame
null_count = df.isna().sum().sum()
print("Total number of null or NaN values: ", null_count)
```

In this example, we first create a sample DataFrame using the `pd.DataFrame()`

function from the pandas library. The DataFrame has three columns ‘A’, ‘B’, and ‘C’, and contains some null or NaN values.

We then use the `isna()`

method on the DataFrame `df`

to create a boolean mask where `True`

indicates the presence of a null or NaN value, and `False`

indicates the absence of a null or NaN value. The `isna()`

method returns a DataFrame of the same shape as `df`

with boolean values.

Next, we use the `sum()`

method twice to count the number of `True`

values in the boolean mask, once for each axis. The first `sum()`

method sums the values along the rows (axis=0), and the second `sum()`

method sums the values along the columns (axis=1). Finally, we use `sum()`

again to get the total count of null or NaN values across all columns in the DataFrame. The result is stored in the `null_count`

variable and printed.

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