Check Values of Pandas Series is Unique

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  • Post last modified:October 5, 2023

Pandas Series.is_unique attribute is used to check every element or value present in the pandas series object holds unique values or not. It returns True if the elements present in the given series object is unique, otherwise, it returns False. In this article, I will explain Series.is_unique and use this function how to check if pandas series contains all unique values or not.

1. Quick Examples of Check Every Series Value is Unique

If you hurry below are quick examples of how to check every value in pandas series is unique or not.


# Below are quick examples

# Example 1: Usage of Series.is_unique
ser2 = ser.is_unique

# Example 2: Check the pandas series unique or not
ser = pd.Series(['Spark','PySpark','Pandas','NumPy']).is_unique

# Example 3: Use pandas.series() function to apply is_unique property
ser = pd.Series(['Spark','PySpark','Pandas','PySpark']).is_unique

# Example 4: Use is_unique attribute with nan values 
# To check unique values
ser = pd.Series(['Spark','PySpark','Pandas','NumPy',np.nan,np.nan]).is_unique

# Example 5: Use dropna() & is_unique attribute 
# With nan values to check unique values
ser = pd.Series(['Spark','PySpark','Pandas', np.nan, np.nan]).dropna().is_unique

# Example 6: Use nunique() function 
# To check series unique value
ser = pd.Series([1, 2, 3]).nunique()==len(pd.Series([1, 2, 3]))

2. Syntax of Pandas Series.is_unique

Following is the syntax of Series.is_unique attribute.


# Syntax of Series.is_unique
Series.is_unique

It returns a boolean value.

3. Check Pandas Series Contains Unique Values

Use the pandas Series.is_unique attribute to check whether every data or element in the given Series object is a unique value or not. If this attribute returns True it will indicate the given series object is unique.

Pandas Series is a one-dimensional, Index-labeled data structure available only in the Pandas library. It can store all the datatypes 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
import numpy as np
  
# Create the Series
ser = pd.Series(['Spark','PySpark','Pandas','NumPy'])
print(ser)

Yields below output.


# Output:
0      Spark
1    PySpark
2     Pandas
3      NumPy
dtype: object

Now, let’s use the Series.is_unique attribute to check if ti contains unique values.


# Usage Series.is_unique 
ser2 = ser.is_unique
print(ser2)

# Output:
# True

5. Check Pandas Series Unique or Not

Created a pandas series using a python list of strings, then apply is_unique attribute to the given series object. It will return the boolean value(either True or False). Let’s check whether the data given series object is unique or not.


# Check the pandas series unique or not
ser = pd.Series(['Spark','PySpark','Pandas','NumPy']).is_unique
print(ser)

# Output:
# True

# Use pandas.series() function to apply is_unique property
ser = pd.Series(['Spark','PySpark','Pandas','PySpark']).is_unique
print(ser)

# Output
# False

6. Use is_unique Attribute with NaN Values to Check Unique Values

Created a pandas series object with multiple NaN values, then call is_unique attribute, it returns the boolean value False, 


# Use is_unique attribute with nan values to check unique values
ser = pd.Series(['Spark','PySpark','Pandas','NumPy',np.nan,np.nan]).is_unique
print(ser)

# Output:
# False

To ignore the NaN values, first call dropna() function to drop all NaN values and then call the is_unique.


# Use dropna() & is_unique attribute with nan values to check unique values
ser = pd.Series(['Spark','PySpark','Pandas', np.nan, np.nan]).dropna().is_unique
print(ser)

# Output:
# True

7. Use nunique() Function to Check Series Unique Value

Alternatively, we can use nunique() function to check the values of the series objects are unique or not.


# Use nunique() function to check series unique value
ser = pd.Series([1, 2, 3]).nunique()==len(pd.Series([1, 2, 3]))
print(ser)

# Output:
# True

8. Conclusion

In this article, I have explained how to check every series value is unique by using pandas Series.is_unique attribute.

Happy Learning !!

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Malli

I am Mallikarjuna an experienced technical writer with a passion for translating complex Python concepts into clear, concise, and user-friendly documentation. Over the years, I have written hundreds of articles in Pandas, NumPy, Python, and I take pride in my ability to bridge the gap between technical experts and end-users by delivering well-structured, accessible, and informative content.

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