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Conditional operation on Pandas DataFrame columns
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Get unique values from a column in Pandas DataFrame

Last Updated : 28 Nov, 2024
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In Pandas, retrieving unique values from DataFrame is used for analyzing categorical data or identifying duplicates. Let's learn how to get unique values from a column in Pandas DataFrame.

Get the Unique Values of Pandas using unique()

The.unique()method returns a NumPy array. It is useful for identifying distinct values in a column, which can be helpful when working with categorical data or detecting outliers. The order of the unique values is preserved based on their first occurrence.

Syntax: DataFrame['column_name'].unique()

Consider the following example: we are retrieving and printing the unique values from the 'B' column using the unique() method.

Python 
# Import pandas package import pandas as pd  # create a dictionary with five fields each data = {     'A': ['A1', 'A2', 'A3', 'A4', 'A5'],     'B': ['B1', 'B2', 'B3', 'B4', 'B4'],     'C': ['C1', 'C2', 'C3', 'C3', 'C3'],     'D': ['D1', 'D2', 'D2', 'D2', 'D2'],     'E': ['E1', 'E1', 'E1', 'E1', 'E1']}  # Convert the dictionary into DataFrame df = pd.DataFrame(data) print("Pandas DataFrame:") display(df)  # Get the unique values of 'B' column unique_values = df['B'].unique()  # Print the unique values print("\nUnique values in 'B' column:") print(unique_values)

Output:

DataFrame-and-Unique-Values
Unique Values from 'B' Column in a Pandas Column

The unique values returned are ['B1', 'B2', 'B3', 'B4'].

Find the unique values in a column using nunique()

Let's use .nunique() method to get the count of unique values in each column of the above dataframe.

Python 
# Get number of unique values in column 'A' unique_values_A = df['A'].nunique() # Print the number of unique values print("Number of unique values in 'A' column:", unique_values_A)  # Get number of unique values in column 'B' unique_values_B = df['B'].nunique() # Print the number of unique values print("\nNumber of unique values in 'B' column:", unique_values_B)  # Get number of unique values in column 'C' unique_values_C = df['C'].nunique() # Print the number of unique values print("\nNumber of unique values in 'C' column:", unique_values_C)  # Get number of unique values in column 'D' unique_values_D = df['D'].nunique() # Print the number of unique values print("\nNumber of unique values in 'D' column:", unique_values_D)

Output: 

Number of unique values in 'A' column: 5
Number of unique values in 'B' column: 4
Number of unique values in 'C' column: 3
Number of unique values in 'D' column: 2

In addition to the .unique() method, there are other ways to retrieve unique values from a Pandas DataFrame, including:

Table of Content

  • Using .drop_duplicates()
  • Using .value_counts()
  • Using set()

Get Unique values from a Column in Pandas DataFrame using .drop_duplicates()

The .drop_duplicates() method removes duplicate values in the specified column, returning a DataFrame with only the unique values.

Syntax: DataFrame['column_name'].drop_duplicates()

Example: Get unique values from column 'C'

Python 
unique_values = df['C'].drop_duplicates()  print(unique_values)

Output: 

0    C1
1    C2
2    C3
Name: C, dtype: object

This method returns the unique values as a Series and preserves the index of the original DataFrame.

Extracting Unique values in Pandas DataFrame Using .value_counts()

The .value_counts() method counts the occurrences of each unique value in the column and returns the result as a Series.

Syntax: DataFrame['column_name'].value_counts()

Example: Get unique values from column 'D' along with their counts

Python 
unique_values_count = df['D'].value_counts()  print(unique_values_count)

Output: 

D
D2    4
D1    1
Name: count, dtype: int64

This method provides both the unique values and the frequency of each value. To extract just the unique values, you can use .index on the result.

Python 
unique_values = df['D'].value_counts().index  print(unique_values)

Output: 

Index(['D2', 'D1'], dtype='object', name='D')

Get Unique values from a column in Pandas DataFrame using set()

You can also use Python’s built-in set() function, which converts the column values into a set, automatically removing duplicates.

Syntax: set(DataFrame['column_name'])

Example: Get unique values from column 'D'

Python 
unique_values = set(df['D'])  print(unique_values)

Output: 

{'D1', 'D2'}

Using set() does not preserve the order of the unique values, but it is a quick way to get distinct values.

In short:

  • The .unique() method returns a NumPy array of unique values, preserving their order of appearance.
  • The .drop_duplicates() method returns a Series with unique values, preserving the original index.
  • The .value_counts() method provides both the unique values and their frequency count.
  • The set() function quickly returns unique values but does not preserve their order.

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Conditional operation on Pandas DataFrame columns

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    Prerequisite: Regular Expression in Python In this article, we will see how to extract punctuation used in the specified column of the Dataframe using Regex. Firstly, we are making regular expression that contains all the punctuation: [!"\$%&\'()*+,\-.\/:;=#@?\[\\\]^_`{|}~]* Then we are passing
    2 min read
    Replace missing white spaces in a string with the least frequent character using Pandas
    Let's create a program in python which will replace the white spaces in a string with the character that occurs in the string very least using the Pandas library.  Example 1:  String S = "akash loves gfg" here: 'g' comes: 2 times 's' comes: 2 times 'a' comes: 2 times 'h' comes: 1 time  'o' comes: 1
    2 min read
    How to Convert Floats to Strings in Pandas DataFrame?
    In this post, we'll see different ways to Convert Floats to Strings in Pandas Dataframe? Pandas Dataframe provides the freedom to change the data type of column values. We can change them from Integers to Float type, Integer to String, String to Integer, Float to String, etc. There are three methods
    4 min read

    Accessing and Manipulating Data in DataFrame

    Access Index of Last Element in pandas DataFrame in Python
    In this article, we are going to see how to access an index of the last element in the pandas Dataframe. To achieve this, we can use Dataframe.iloc, Dataframe.iget, and Dataframe.index. let's go through all of them one by one.   Dataframe.iloc -  Pandas Dataframe.iloc is used to retrieve data by spe
    3 min read
    Replace Characters in Strings in Pandas DataFrame
    In this article, we are going to see how to replace characters in strings in pandas dataframe using Python.  We can replace characters using str.replace() method is basically replacing an existing string or character in a string with a new one. we can replace characters in strings is for the entire
    3 min read
    Replace values of a DataFrame with the value of another DataFrame in Pandas
    In this article, we will learn how we can replace values of a DataFrame with the value of another DataFrame using pandas.  It can be done using the DataFrame.replace() method. It is used to replace a regex, string,  list, series, number, dictionary, etc. from a DataFrame, Values of the DataFrame met
    4 min read
    Replace negative values with latest preceding positive value in Pandas DataFrame
    In this article, we will discuss how to replace the negative value in Pandas DataFrame Column with the latest preceding positive value. While doing this there may arise two situations -  Value remains unmodified if no proceeding positive value existsValue update to 0 if no proceeding positive value
    3 min read
    How to add column from another DataFrame in Pandas ?
    In this discussion, we will explore the process of adding a column from another data frame in Pandas. Pandas is a powerful data manipulation library for Python, offering versatile tools for handling and analyzing structured data. Add column from another DataFrame in Pandas There are various ways to
    6 min read

    DataFrame Visualization and Exporting

    How to render Pandas DataFrame as HTML Table?
    Pandas in Python can convert a Pandas DataFrame to a table in an HTML web page. The pandas.DataFrame.to_html() method is used to render a Pandas DataFrame into an HTML format, allowing for easy display of data in web applications. In this article, we will understand how to use the Styler Object and
    6 min read
    Exporting Pandas DataFrame to JSON File
    Pandas a powerful Python library for data manipulation provides the to_json() function to convert a DataFrame into a JSON file and the read_json() function to read a JSON file into a DataFrame.In this article we will explore how to export a Pandas DataFrame to a JSON file with detailed explanations
    2 min read
    Create and display a one-dimensional array-like object using Pandas in Python
    Series() is a function present in the Pandas library that creates a one-dimensional array and can hold any type of objects or data in it. In this article, let us learn the syntax, create and display one-dimensional array-like object containing an array of data using Pandas library. pandas.Series() S
    2 min read
    Export Pandas dataframe to a CSV file
    When working on a Data Science project one of the key tasks is data management which includes data collection, cleaning and storage. Once our data is cleaned and processed it’s essential to save it in a structured format for further analysis or sharing.A CSV (Comma-Separated Values) file is a widely
    2 min read
    Display the Pandas DataFrame in Heatmap style
    Pandas library in the Python programming language is widely used for its ability to create various kinds of data structures and it also offers many operations to be performed on numeric and time-series data. By displaying a panda dataframe in Heatmap style, the user gets a visualisation of the numer
    6 min read
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