Generally, the data in each column represents a different feature of the 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 the some of the different use cases of 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 = { 'A': ['E1', 'E2', 'E3', 'E4', 'E5'], 'B': ['Male', 'Female', 'Female', 'Male', 'Male'], 'C': [27, 24, 29, 24, 25], 'D': ['Accounting', 'Sales', 'Accounting', np.nan, 'Sales'] } df = pd.DataFrame(data) # print the dataframe print(df)

Output:

A B C D 0 E1 Male 27 Accounting 1 E2 Female 24 Sales 2 E3 Female 29 Accounting 3 E4 Male 24 NaN 4 E5 Male 25 Sales

### 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.

print(df.nunique())

Output:

A 5 B 2 C 4 D 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 column `D`

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.

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 `B`

, you can use –

print(df['B'].value_counts())

Output:

Male 3 Female 2 Name: B, 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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