Pandas count()

import pandas as pd

# sample DataFrame
data = {
    'A': [1, 2, 3, None],
    'B': [None, 5, None, 8],
    'C': [9, 10, 11, 12]
}
df = pd.DataFrame(data)

# use count() method
print(df.count())

'''
Output

A    3
B    2
C    4
dtype: int64
'''

count() Syntax

The syntax of the count() method in Pandas is:

df.count(axis=0, numeric_only=False)

count() Arguments

The count() method takes following arguments:

• axis (optional) - specifies if we want to count non-missing values by rows or columns
• numeric_only (optional) - if True, it will include only float, int, boolean columns. If False, it will count all columns including object type.

count() Return Value

The count() method returns the number of non-missing values for the specified axis.

Example1: Count Non-Missing Values Along Columns

import pandas as pd

# create a sample dataframe
data = {
    'A': [1, 2, 4, 4],
    'B': ['a', None, 'c', None],
    'C': [1.1, None, 3.3, 4.4]
}
df = pd.DataFrame(data)

# count non-missing values along columns
non_missing_counts = df.count()

print(non_missing_counts)

Output

A    4
B    2
C    3
dtype: int64

In the above example, we have used the count() method to count non-missing values along columns.

The displayed output shows the number of non-missing values in each column:

• Column A has 4 non-missing values.
• Column B has 2 non-missing values.
• Column C has 3 non-missing values.

Example 2: Count Non-Missing Values Along Rows

import pandas as pd

# create a sample dataframe
data = {
    'A': [1, 2, 4, 4],
    'B': ['a', None, 'c', None],
    'C': [1.1, None, 3.3, 4.4]
}
df = pd.DataFrame(data)

# count non-missing values along rows
non_missing_counts_rows = df.count(axis=1)

print(non_missing_counts_rows)

Output

0    3
1    1
2    3
3    2
dtype: int64

In this example, we have specified axis=1 inside count() to count non-missing values along each row.

Here,

1. Row 0 has 3 valid values: [1, 'a', 1.1].
2. Row 1 has 1 valid value: [2]. Both the other entries are None.
3. Row 2 has 3 valid values: [4, 'c', 3.3].
4. Row 3 has 2 valid values: [4, 4.4], with one entry as None.

Example 3: Count Only Numeric Columns

import pandas as pd

# create a sample dataframe with mixed data types
data = {
    'A': [1, 2, 4, 4],  
    'B': ['a', None, 'c', None],  
    'C': [1.1, None, 3.3, 4.4],  
    'D': [True, False, None, True]
}
df = pd.DataFrame(data)

# count non-missing values for only numeric columns
non_missing_counts_numeric = df.count(numeric_only=True)

print(non_missing_counts_numeric)

Output

A    4
C    3
dtype: int64

Here, we used the count(numeric_only=True) method on the df DataFrame. The method counts non-missing values, but it only considers numeric columns.

The output shows the count of non-missing values for only the numeric columns:

• Column 'A' has 4 non-missing values.
• Column 'C' has 3 non-missing values.