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    Pandas DataFrame count() Function

    Pandas DataFrame count() Function

    Pandas DataFrame.count() function is used to count the number of non-NA cells for each column or row along with a specified axis. It works with non-floating type data as well. In order to get the row count you should use axis=1 or 'columns' and for column count, you should use axis=0 or 'index' as an argument to the count() function.

    In this article, I will explain pandas DataFrame.count() function and using this syntax, parameters of how we can return a number of non-NA cells for each column or row along with a specified axis.

    Key Points –

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    Quick Examples of Pandas DataFrame count() Function

    If you are in a hurry, below are some quick examples of how to use DataFrame count() function.

    
# Quick examples of Pandas DataFrame count()

# Example 1: Use dataframe.count() function
df2 = df.count()

# Example 2: Get count of each 
# Pandas dataframe column
df2 = df.count(axis = 0)

# Example 3: Get count of each 
# Pandas dataframe row
df2 =df.count(axis='columns')

# Example 4: Use dataframe.count() function 
# Along axis=1
df2 = df.count(axis = 1)

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    count() Function in Pandas

    The count() function in Pandas is used to count the number of non-missing or non-NA/null entries in each column or row of a DataFrame or Series. It excludes NaN (Not a Number) values by default. This function is particularly useful when you want to quickly get a sense of how much valid (non-null) data is available in your dataset for each column or row.

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    Syntax of Pandas DataFrame.count()

    Following is the syntax of the DataFrame.count() function.

    
# Syntax of DataFrame.count()
DataFrame.count(axis=0, level=None, numeric_only=False)

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    Parameters of count()

    Following are the parameters of pandas count() function.

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    Return value

    It returns pandas Series with count values of non-NA cells values or DataFrame if the level is specified.

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    Usage of Pandas DataFrame count() Function

    The count() function in Pandas is used to count the number of non-null values in each column or row of a DataFrame.

    Now, Let’s create Pandas DataFrame using data from a Python dictionary, where the columns are Courses, Fee, Duration and Discount.

    
# Create DataFrame
import pandas as pd
import numpy as np
technologies= ({
    'Courses':["Spark","PySpark","Hadoop",None,"Python","Pandas"],
    'Courses Fee' :[22000,25000,np.nan,23000,24000,26000],
    'Duration':['30days',np.nan,'50days','30days', None,np.nan],
    'Discount':[1000,None,2300,np.nan,1200,2500]
              })
df = pd.DataFrame(technologies)
print(df)

    Yields below output.

    
# Output:
   Courses  Courses Fee Duration  Discount
0    Spark      22000.0   30days    1000.0
1  PySpark      25000.0      NaN       NaN
2   Hadoop          NaN   50days    2300.0
3     None      23000.0   30days       NaN
4   Python      24000.0     None    1200.0
5   Pandas      26000.0      NaN    2500.0

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    Pandas DataFrame count() Function

    You can get the count of each column value other than the NaN values of DataFrame using DataFrame.count() function. In order to get the column values count you should pass axis=0 as an argument into this function. It will ignore all None & nan values and return the count values of each column.

    Note that the values None, NaN, NaT, and numpy.inf are considered as NA.

    
# Use dataframe.count() function
df2 = df.count()
print(df2)

# Get count of each pandas dataframe column
df2 = df.count(axis = 0)
print(df2)

    Yields below output.

    
# Output:
Courses        5
Courses Fee    5
Duration       3
Discount       4
dtype: int64

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    Get Count of Each Pandas DataFrame Row

    You can get the count of each row of DataFrame using DataFrame.count() function. In order to get the row count you should use axis='columns' or 1 as an argument to the count() function. Now, let’s run the DatFrame.count() to get the count of each row by ignoring None and Nan values.

    
# Get count of each pandas dataframe row
df2 =df.count(axis='columns')
print(df2)

# Use dataframe.count() function along axis=1
df2 = df.count(axis = 1)
print(df2)

    Yields below output.

    
# Output:
0    4
1    2
2    3
3    2
4    3
5    3
dtype: int64

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    Complete Example For Pandas DataFrame count() Function

    
# Complete Example For Pandas DataFrame count() Function
import pandas as pd
import numpy as np
technologies= ({
    'Courses':["Spark","PySpark","Hadoop",None,"Python","Pandas"],
    'Courses Fee' :[22000,25000,np.nan,23000,24000,26000],
    'Duration':['30days',np.nan,'50days','30days', None,np.nan],
    'Discount':[1000,None,2300,np.nan,1200,2500]
              })
df = pd.DataFrame(technologies)
print(df)

# Use dataframe.count() function
df2 = df.count()
print(df2)

# Get count of each pandas dataframe column
df2 = df.count(axis = 0)
print(df2)

# Get count of each pandas dataframe row
df2 =df.count(axis='columns')
print(df2)

# Use dataframe.count() function along axis=1
df2 = df.count(axis = 1)
print(df2)

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    FAQ on Pandas DataFrame count() Function

    The count() function is used to count the number of non-null/NaN entries in each column or row of a DataFrame.

    The count() function only counts non-null/NaN values. If a column has missing data, those entries are excluded from the count.

    By setting the axis parameter. Use axis=0 (default) to count along columns, or axis=1 to count along rows.

    The count() function returns a Series that contains the count of non-null entries for each column (or row, if axis=1 is set).

    You can count entries for specific columns by selecting those columns first and then applying the count() function.

    The count() function excludes NaN values, whereas the size attribute returns the total number of entries, including NaNs, in the DataFrame or Series.

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    Conclusion

    In this article, I have explained the pandas DataFrame count() function to get the count of column values and using this function how we can get the count values of each column or each row of a DataFrame along with a specified axis.

    Happy Learning !!

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