Generally, the data in each column represents a different feature of a pandas dataframe. It may be continuous, categorical, or something totally different like distinct texts. If you’re not sure about the nature of the values you’re dealing with, it might be a good exploratory step to know about the count of distinct values. In this tutorial, we’ll look at how to get the count of unique values in each column of a pandas dataframe.

## The `nunique()`

function

To count the unique values of each column of a dataframe, you can use the pandas dataframe `nunique()`

function. The following is the syntax:

counts = df.nunique()

Here, `df`

is the dataframe for which you want to know the unique counts. It returns a pandas Series of counts. By default, the pandas dataframe `nunique()`

function counts the distinct values along `axis=0`

, that is, row-wise which gives you the count of distinct values in each column.

## Examples

Let’s look at some of the different use cases for getting unique counts through some examples. First, we’ll create a sample dataframe that we’ll be using throughout this tutorial.

import pandas as pd import numpy as np # create a sample dataframe data = { 'EmpCode': ['E1', 'E2', 'E3', 'E4', 'E5'], 'Gender': ['Male', 'Female', 'Female', 'Male', 'Male'], 'Age': [27, 24, 29, 24, 25], 'Department': ['Accounting', 'Sales', 'Accounting', np.nan, 'Sales'] } df = pd.DataFrame(data) # display the dataframe df

Output:

Here, we created a dataframe with information about some employees in an office. The dataframe has the following columns – “EmpCode”, “Gender”, “Age”, and the “Department”.

### 1. Count of unique values in each column

Using the pandas dataframe `nunique()`

function with default parameters gives a count of all the distinct values in each column.

# count of unique values in each column print(df.nunique())

Output:

EmpCode 5 Gender 2 Age 4 Department 2 dtype: int64

In the above example, the `nunique()`

function returns a pandas Series with counts of distinct values in each column. Note that, for the `Department`

column we only have two distinct values as the `nunique()`

function, by default, ignores all NaN values.

### 2. Count of unique values in each row

You can also get the count of distinct values in each row by setting the `axis`

parameter to `1`

or `'columns'`

in the `nunique()`

function.

# count of unique values in each row print(df.nunique(axis=1))

Output:

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

In the above example, you can see that we have 4 distinct values in each row except for the row with index `3`

which has 3 unique values due to the presence of a NaN value.

For more on the pandas dataframe `nunique()`

function, refer to its official documentation.

## Good to know

In case you want to know the count of each of the distinct values of a specific column, you can use the pandas `value_counts()`

function. In the above dataframe `df`

, if you want to know the count of each distinct value in the column `Gender`

, you can use –

# count of each unique value in the "Gender" column print(df['Gender'].value_counts())

Output:

Male 3 Female 2 Name: Gender, dtype: int64

In the above example, the pandas series `value_counts()`

function is used to get the counts of `'Male'`

and `'Female'`

, the distinct values in the column `B`

of the dataframe `df`

.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5 and pandas version 1.0.5

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