Create a Set From a Series in Pandas

We can create a set from a series of pandas by using set(), Series.unique() function. The set object is used to store multiple items which are heterogeneous. Just like a list, tuple, and dictionary, the set is another built-in data type in Python which is used to store elements without duplicates.

In this article, I will explain how to create a set from a series of pandas by using the set(), Series.unique() function.

Key Points –

• Use the pd.Series constructor to create a Series in Pandas.
• Transform the Series into a set using the set() function to obtain a unique collection of elements.
• Sets in Python are unordered, meaning they don’t have a specific order for elements.
• Sets eliminate duplicate values, ensuring only distinct elements are retained.
• The resulting set can be used for various set operations, such as union, intersection, and difference.
• Ensure that the Series contains elements that can be hashed, as sets in Python require hashable elements.

Quick Examples of Creating a Set From a Series

If you are in a hurry, below are some quick examples of how to create a set object from a series in pandas.

# Quick examples of creating a set from a series

# Example 1: using series.unique() & set() function
setObj = ser.unique()
ser2 = set(setObj)
print(ser2)

# Example 2: using set() function to create Set
ser2 = set(ser)
print(ser2)

Create Pandas Series

Pandas Series is a one-dimensional, Index-labeled data structure available only in the Pandas library. It can store all the data types such as strings, integers, float, and other python objects. We can access each element in the Series with the help of corresponding default indices.

Now, let’s create pandas series using a list of values.

import pandas as pd
# Create the Series
ser = pd.Series([20,25,15,10,5,20,30])
print(ser)

Yields below output.

# Output:
0    20
1    25
2    15
3    10
4     5
5    20
6    30
dtype: int64

Create a Set from Pandas Series

Set is used to store unique values hence it is best practice to remove duplicates from the pandas Servies using Series.unique() function and then create a Set object. Series.unique() function returns the NumPy Array object.

# use series.unique() function
ser2 = ser.unique()
print(ser2)

# Output
# [20 25 15 10  5 30]

# using series.unique() & set() function
ser.unique()
ser2 = set(ser)
print(ser2)

# Output
# {5, 10, 15, 20, 25, 30}

The set() function also removes all duplicate values and gets only unique values from Series. We can use this set() function to get unique values from Series. For examples.

Using set() function to create Set
ser2 = set(ser)
print(ser2)

Yields below output.

# Output
{5, 10, 15, 20, 25, 30}

Complete Example

import pandas as pd

# Create the Series
ser = pd.Series([20,25,15,10,5,20,30])
print(ser)

# use series.unique() function
ser2 = ser.unique()
print(ser2)

# using series.unique() & set() function
ser.unique()
ser2 = set(ser)
print(ser2)

# Using set() function to create Set
ser2 = set(ser.unique())
print(ser2)

Frequently Asked Questions on Creating a Set from a Series

Conclusion

In this article, I have explained how to create a set from a Pandas Series using set(), Series.unique() functions with examples.

Happy Learning !!

Related Articles

• Remove NaN From Pandas Series
• Pandas Series.max() Function
• Pandas Series.clip() Function 
• Pandas Series iloc[] Function
• Pandas Series.mean() Function 
• Pandas Series.dtype() Function
• Pandas Series round() Function 
• How to Get Index of Series in Pandas
• How to create Pandas Series in Python
• Convert Pandas Series to DataFrame 
• Pandas Series unique() Function with Examples 
• Pandas Get First Column of DataFrame as Series
• How to Get the Length of a Series in Pandas? 
• Convert GroupBy output from Series to DataFrame
• Pandas Series groupby() Function with Examples 
• Apply Multiple Filters to Pandas DataFrame or Series

References

• https://pandas.pydata.org/docs/reference/api/pandas.Series.to_list.html