Pandas count() : Count Values in Pandas Dataframe

Pandas count(): In this article, we will see how to count the number of observations on a given axis in a Pandas Dataframe.

Introduction

A pandas dataframe is a two-dimensional tabular data structure that can be modified in size with labeled axes that are commonly referred to as row and column labels, with different arithmetic operations aligned with the row and column labels.

The Pandas library, available on python, allows to import data and to make quick analysis on loaded data.

When working with data that you are not familiar with, it can be interesting to count the number of values in each column of your Pandas dataframe.

The count() function in the Pandas library allows you to count the number of values for each column or row.

Count() is also included within Pandas Describe.

In this tutorial we will see how :

• Count the number of values in each column.
• Count the number of values in each row.
• Count the number of null values
• Count the distinct values in our dataframe

First step, we will create a pandas dataframe to illustrate the different points:

import pandas as pd
import numpy as np

NaN = np.nan

school = [("Sam", 40, 40, 20),
           ("John", 50, NaN, NaN),
           ("Clark", 50, 70, 50),
           ("Hayley", 20, NaN, 70),
           ("Michelle", 10, 50, 70)
           ]

df = pd.DataFrame(school, columns=['Student', 'Math', 'Physics', 'Chemistry'])

print(df)

Output:

    Student  Math  Physics  Chemistry
0       Sam    40     40.0       20.0
1      John    50      NaN        NaN
2     Clark    50     70.0       50.0
3    Hayley    20      NaN       70.0
4  Michelle    10     50.0       70.0

Pandas count()

Pandas count() Syntax

The syntax of the count() function is as follows:

Syntax: 

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

We can see that the function can take 3 parameters :

Pandas Count Values in Each Column

To count the number of values in each column, by default the function counts the values on the axis=0. Therefore it is not necessary to use a parameter in the function :

# axis = 0

print(df.count())

Output:

Student      5
Math         5
Physics      3
Chemistry    4
dtype: int64

The example above shows us that the function does not count null values.

Pandas Count Values in Each Row

To count the number of values for each row of our dataframe, we need to specify the parameter axis= 1 or axis=’columns’ :

# row axis

print(df.count(axis = 1))

# Or print(df.count(axis = 'columns'))

Output:

0    4
1    2
2    4
3    3
4    4
dtype: int64

Count Null Values

We have seen that the count() function ignores null values. If you want to count the number of null values you can use the isnull() function combined with the sum() function:

# Count null values

print(df.isnull().sum())

Output:

Student      0
Math         0
Physics      2
Chemistry    1
dtype: int64

Pandas Count Uniques Values

The nunique() function counts the unique values in each row or column of our dataframe :

# Distinct Values

print(df.nunique())

Output:

Student      5
Math         4
Physics      3
Chemistry    3
dtype: int64

Conclusion

In this article we have how to count the number of values in our Pandas dataframe. I hope this function will have no more secrets for you :). It is a very useful function to explore and understand this data.

If you have any questions on this topic please let me know in the comments and I will be happy to answer them.

See you soon for new tutorials!

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