Learn to use pandas.unique() with Series/DataFrame

Using Pandas unique() with Series/DataFrame

unique() function is used to get the distinct / unique data from Series/DataFrames. It can be applied on the columns in the dataframe.

The unique() method returns a NumPy ndarray of unique values from the Series

In this tutorial we will discuss how to use unique() function with Series and DataFrame in Pandas.

 

Pandas Series.unique - Syntax:

 data.unique()

where, data is Series

 

Pandas DataFrame.unique() - Syntax:

 data['column'].unique()

where, data is the dataframe and column is the name of the column in the dataframe to get the unique values

We will discuss the following scenarios

• Get unique values from Series
• Get unique values from particular columns in DataFrame
• unique() with '.' operator in Series
• pandas.unique()

 

1. Using Pandas Series.unique()

In this scenario, we can get the unique data from the given series.

Example 1: In this example,  We are creating two series one with integer and another with strings and return the unique values from the first series.

# import pandas
import pandas 

# creating the series -1
data1 = pandas.Series([10,20,30,50,60,10,20,0,45,20])

# get the unique values 
print(data1.unique())

Output:

[10 20 30 50 60  0 45]

 

Example 2: In this example,  We are creating two series one with integer and another with strings and return the unique values from the second series.

# import pandas
import pandas 

# creating the series-2
data2 = pandas.Series(['Python','java','html','php','R','Python','java','html','php','R'])

# get the unique values 
print(data2.unique())

Output:

['Python' 'java' 'html' 'php' 'R']

 

2. Using Pandas DataFrame with unique()

In this case, we have to create the pandas dataframe and get the unique values from the dataframe columns by specifying the column names.

Example 1: In this example, we are going  to get the unique values from Subjects column in the dataframe. So the output will be all unique values from the Subjects column.

# import pandas
import pandas 

# creating the DataFrame 1
data1 = pandas.DataFrame({'Subjects':['java','java','python','html','html','php'],'marks':[80,98,76,87,90,100]})

# get the unique values 
print(data1['Subjects'].unique())

Output:

['java' 'python' 'html' 'php']

 

Example 2: In this example, we are going  to get the unique values from marks column in the dataframe. So the output will be all unique values from the marks column.

# import pandas
import pandas 

# creating the DataFrame 1
data1 = pandas.DataFrame({'Subjects':['java','java','python','html','html','php'],'marks':[80,98,76,87,90,100]})

# get the unique values 
print(data1['marks'].unique())

Output:

[ 80 98 76 87 90 100]

 

3. Using unique() with '.' operator in Series

Here we are going to create a dataframe and get the unique values from different columns. We have to specify dot(.) operator for specifying the column.

Syntax:

dataframe.column.unique()

where,

1. dataframe is the input dataframe
2. column is the column name to get unique values from this column.

 

Example 1: In this example, we are going  to get the unique values from Subjects column in the dataframe. So the output will be all unique values from the Subjects column through the "." dot operator.

# import pandas
import pandas 

# creating the DataFrame 1
data1 = pandas.DataFrame({'Subjects':['java','java','python','html','html','php'],'marks':[80,98,76,87,90,100]})

# get the unique values 
print(data1.Subjects.unique())

Output:

['java' 'python' 'html' 'php']

 

Example 2: In this example, we are going  to get the unique values from marks column in the dataframe. So the output will be all unique values from the marks column through the "." dot operator.

# import pandas
import pandas 

# creating the DataFrame 1
data1 = pandas.DataFrame({'Subjects':['java','java','python','html','html','php'],'marks':[80,98,76,87,90,100]})

# get the unique values 
print(data1.marks.unique())

Output:

[ 80 98 76 87 90 100]

 

4. Using pandas.unique()

Return unique values based on a hash table. Uniques are returned in order of appearance. This does NOT sort. Significantly faster than numpy.unique for long enough sequences. Includes NA values.

Syntax:

pandas.unique(dataframe['column'])

where,

1. dataframe is the input dataframe
2. column is the column name to get unique values from this column.

 

Example 1: In this example, we are going  to get the unique values from Subjects column in the dataframe.

# import pandas
import pandas 

# creating the DataFrame 1
data1 = pandas.DataFrame({'Subjects':['java','java','python','html','html','php'],'marks':[80,98,76,87,90,100]})

# get the unique values 
print(pandas.unique(dataframe['Subjects']))

Output:

['java' 'python' 'html' 'php']

 

Example 2: In this example, we are going  to get the unique values from marks column in the dataframe.

# import pandas
import pandas 

# creating the DataFrame 1
data1 = pandas.DataFrame({'Subjects':['java','java','python','html','html','php'],'marks':[80,98,76,87,90,100]})

# get the unique values 
print(pandas.unique(dataframe['marks']))

Output:

[ 80 98 76 87 90 100]

 

Summary

In this tutorial we discussed how to get the unique data from pandas Series and DataFrame using unique() function and we also several ways to get the unique data from the dataframe columns. Through the below implementations, we can het the unique values.

• unique() with '.' operator in Series
• pandas.unique()

unique() function is applied on machine learning projects/applications to know the duplicate data, with this we will get the accurate results.

 

References

Pandas - unique()