Difference between size and count in Pandas?

When dealing with data frames, two commonly used methods are size() and count(). While they might seem similar at first glance, they serve different purposes and produce different results. In this article, we’ll explore the What’s the differences between size() and count() in Pandas and when to use each of them.

Difference between the size and count of Pandas?

Let’s see some of the key differences between size() and count() in Pandas.

Inclusion of NaN Values

• size() counts all elements, including NaN values.
• count() counts only non-null (valid) values, excluding NaN values.

Result Type

• size() returns a single integer representing the total number of elements.
• count() returns a Series with the count of non-null values for each column (if applied to a DataFrame) or a single integer (if applied to a Series).

When to Use size() or count() Methods

Knowing when to use size() or count() depends on your specific data analysis needs:

• Use Pandas size() when you want to understand the overall size of your dataset, including missing values. For example, you might use it to calculate proportions or ratios involving missing data.
• Use Pandas count() when you need to know how many valid data points you have in a specific column or when you want to filter out rows with missing values for further analysis.

What is the size of Pandas?

We use size() as the number of rows in a group returned as length. There is no differentiation between nan and non-null values.

Using size() with a Pandas Series

In this example, the size() method is applied to a Series, and it counts all elements, including the NaN value. The result is 5.

Python3

import pandas as pd

data = {'A': [1, 2, 3, None, 5]}
series = pd.Series(data['A'])

total_size = series.size
print(total_size)

Output:

5

Using size() with filtering

In this example, we first filter the DataFrame to include only rows where column ‘A’ is not null. Then, we use size() to count all elements in the filtered DataFrame, resulting in a size of 4.

Python3

import pandas as pd

data = {'A': [1, 2, 3, None, 5]}
df = pd.DataFrame(data)

# Filtering rows where column 'A' is not null
filtered_df = df[df['A'].notnull()]
filtered_size = filtered_df.size

print(filtered_df)

print(filtered_size)

Output

      A
 0  1.0
 1  2.0
 2  3.0
 4  5.0
 4

What is count in Pandas?

In count() it generally count the non -null values in Data frames. This method does differentiation between non-nan and nan value.

Using count() with a Pandas DataFrame

In this example, the count() method is applied to a DataFrame, and it counts the number of non-null values in each column. Both column ‘A’ and ‘B’ have 4 valid values.

Python3

import pandas as pd

data = {'A': [1, 2, 3, None, 5],
        'B': [6, 7, None, 9, 10]}
df = pd.DataFrame(data)

count_valid = df.count()
print(count_valid)

Output

A    4
B    4
dtype: int64

Using count() with filtering

Here, we filter the DataFrame to include only rows where column ‘A’ is not null, and then we use count() on that filtered column to count the valid values. The result is 4, which represents the count of non-null values in column ‘A’ after filtering.

Python3

import pandas as pd

data = {'A': [1, 2, 3, None, 5]}
df = pd.DataFrame(data)

# Filtering rows where column 'A' is not null
filtered_df = df[df['A'].notnull()]
count_filtered = filtered_df['A'].count()

print(filtered_df)


print(count_filtered)

Output

      A
 0  1.0
 1  2.0
 2  3.0
 4  5.0
 4

Difference between size() and count() in Pandas

Code to demonstration to show the difference between size and count in Pandas.

Python3

import pandas as pd

data = {'A': [1, 2, 3, None, 5],
        'B': [6, 7, 8, 9, 10]}

df = pd.DataFrame(data)

# Calculates the total number of elements in the DataFrame
total_size = df.size
# Output: 10 (5 elements in column 'A' + 5 elements in column 'B')
print(total_size)

# Counts non-null values in column 'A'
count_column_A = df['A'].count()
# Output: 4 (4 non-null values in column 'A')
print(count_column_A)

Output:

10
4