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    Create a Set From a Series in Pandas

    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 –

    <a id="h-quick-examples-of-creating-a-set-from-a-series"></a>

    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)

    <a id="h-create-pandas-series"></a>

    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

    <a id="h-create-a-set-from-pandas-series"></a>

    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}

    <a id="h-complete-example"></a>

    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)

    <a id="h-frequently-asked-questions-on-creating-a-set-from-a-series"></a>

    Frequently Asked Questions on Creating a Set from a Series

    Creating a set from a Pandas Series is useful for obtaining a unique collection of elements. Sets automatically eliminate duplicates, allowing for efficient identification of unique values within the Series.

    Sets store only unique elements, automatically discarding duplicates. This ensures that the resulting set contains distinct values from the original Series.

    Sets support common set operations such as union, intersection, and difference. This allows for easy analysis and manipulation of data based on set principles.

    The conversion to a set is still possible. However, it's important to be aware that sets store elements in an unordered manner, and the resulting set will only include unique elements, disregarding the original order.

    The conversion is efficient for handling large datasets with duplicate values. It provides a quick way to identify unique values and simplifies tasks such as filtering and analysis based on distinct elements.

    Sets are mutable, allowing for the addition or removal of elements. This can be useful for dynamically updating the set based on changing requirements.

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    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 !!

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    References