How to Count the Total Number of Null and ISNA Values in Python?

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.