Size Matters, But So Does Data Validity: A Guide to size and count in pandas

Understanding size and count:

• size:

  • Counts all elements in the object, including missing values (NaN).
  • Returns a single integer representing the total number of elements.
  • Example: df.size returns 8 for a DataFrame with 4 rows and 2 columns.

• count:

  • Counts only non-null (valid) values, excluding missing values (NaN).
  • Returns:
    • A Series with the count of non-null values for each column if used on a DataFrame.
    • A single integer representing the count of non-null values if used on a Series.
  • Example: df.count() returns a Series with values 4 for both 'A' and 'B' columns.

Key Differences:

Example:

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, np.nan, 8]})
print("Shape of the dataframe:", df.shape)
print("Size of the dataframe:", df.size)
print("Count of the dataframe:")
print(df.count())

This code outputs:

Shape of the dataframe: (4, 2)
Size of the dataframe: 8
Count of the dataframe:
A    3
B    3
dtype: int64

As you can see, size is 8 because it counts all elements, including the NaN value in column 'B'. On the other hand, count excludes NaN values, resulting in a Series where both 'A' and 'B' have counts of 3.

Choosing between size and count:

• Use size when you want to know the total number of elements, regardless of missing values. This can be useful for tasks like iterating over all elements.
• Use count when you want to know the number of valid (non-null) values. This is helpful for understanding how many usable data points you have in each column.

I hope this explanation clarifies the difference between size and count in pandas!