unique(): Using unique() and nunique() in Pandas DataFrames

";require "templates/body_start.php";?> We will get unique values and its frequency as series.

Parameters

values: 1-D array input.

Examples with Parameters

The name of classes ( unique )

import pandas as pd my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],         'ID':[1,2,3,4,5,6],         'GAME':['CRICKET','TENNIS','CRICKET','HOCKEY','CRICKET','TENNIS'],         'CLASS1':['Four','Three','Three','Four','Five','Three']}my_data = pd.DataFrame(data=my_dict)print(my_data['CLASS1'].unique())

Output

['Four' 'Three' 'Five']

We will get unique games played by students.

print(my_data['GAME'].unique())

Output

['CRICKET' 'TENNIS' 'HOCKEY']

What are the number of unique data ( use nunique() ).

print(my_data['CLASS1'].nunique())

Output

3

Example: Finding Unique Values Across Multiple Columns

We can use `unique()` to get distinct values across different columns. This is helpful when analyzing multiple categories.

import pandas as pdmy_data = pd.DataFrame({    'A': [1, 2, 2, 3, 4],    'B': [4, 4, 3, 3, 2]})unique_values = pd.concat([my_data['A'], my_data['B']]).unique()print(unique_values)  

[1 2 3 4]  

Use Case: Comparing Unique Values in Different DataFrames

When working with multiple datasets, you may want to compare unique values to identify overlaps or discrepancies.

import pandas as pd  df1 = pd.DataFrame({'City': ['NY', 'LA', 'SF', 'NY']})df2 = pd.DataFrame({'City': ['NY', 'DC', 'SF', 'Chicago']})unique_cities_df1 = df1['City'].unique()unique_cities_df2 = df2['City'].unique()common_cities = set(unique_cities_df1).intersection(unique_cities_df2)print(common_cities)

{'NY', 'SF'}

Advanced Feature: Using `unique()` with Categorical Data

Pandas can work more efficiently by converting columns to categorical types before using `unique()`, especially with repeated data.

my_data['Category'] = my_data['A'].astype('category')unique_categories = my_data['Category'].unique()print(unique_categories)

Example: Counting Unique Values Using `nunique()`

Alongside `unique()`, we can use `nunique()` to quickly count distinct entries in a column.

import pandas as pd  data = pd.Series(['apple', 'orange', 'apple', 'banana', 'orange'])unique_count = data.nunique()print(unique_count)

3 

« value_counts()

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