In this tutorial, we will look at how to get a list of unique values in a pandas dataframe column. Additionally, we will also look at how to get a count of each unique value within the column and the total unique count.

First, let’s create a sample dataframe that we will be using throughout this tutorial for demonstrating the usage.

import pandas as pd # dataframe of height and weight football players df = pd.DataFrame({ 'Height': [167, 175, 170, 186, 190, 188, 158, 169, 183, 180], 'Weight': [65, 70, 72, 80, 86, 94, 50, 58, 78, 85], 'Team': ['A', 'A', 'B', 'B', 'B', 'C', 'A', 'C', 'B', 'C'] }) # display the dataframe print(df)

Output:

Height Weight Team 0 167 65 A 1 175 70 A 2 170 72 B 3 186 80 B 4 190 86 B 5 188 94 C 6 158 50 A 7 169 58 C 8 183 78 B 9 180 85 C

Now we have a dataframe with 10 rows and three columns storing information on height, weight, and teams of top scorers in a football competition.

## 1. List of all unique values in a pandas dataframe column

You can use the pandas `unique()`

function to get the different unique values present in a column. It returns a numpy array of the unique values in the column. For example, let’s see what are the unique values present in the column “Team” of the dataframe “df” created above.

# unique values in column "Team" print(df['Team'].unique()) # check the return type print(type(df['Team'].unique()))

Output:

['A' 'B' 'C'] <class 'numpy.ndarray'>

You can see we get a numpy array of all the unique values present in the column “Team” – “A”, “B”, and “C”.

For more on the pandas unique() function, refer to its documentation.

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## 2. Count of unique values in a column

If you just need the count of unique values present in a pandas dataframe column, you can use the pandas `nunique()`

function. It returns the number of unique values present in the dataframe as an integer. For example, let’s count the number of unique values in the column “Team” of the dataframe “df”.

# count of unique values print(df['Team'].nunique())

Output:

3

We get `3`

as the output because there are three unique values present in the column “Team”. Note that you can use the nunique() function to count the unique values in a row as well.

## 3. Count of each unique value in a column

You can use the pandas `value_counts()`

function to get the number of times each unique value occurs in a column. For example, let’s find the what’s the count of each unique value in the “Team” column.

# value counts of each unique value print(df['Team'].value_counts())

Output:

B 4 A 3 C 3 Name: Team, dtype: int64

You can see that the value “B” occurs 4 times, and “A” and “C” occur 3 times each in the column “Team”. The value_counts() is a nice function to check the distribution of data points across a categorical field.

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 pandas version 1.0.5

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Tutorials on common column operations in pandas –