Pandas Series unique() method is used to find and return the unique values in the order they appear, effectively filtering out duplicates.
This method is beneficial for categorical data. It preserves the order of the unique elements as they appear in the original Series.
It can be used on Series with any data type (e.g., numeric, string).
One thing to note is that NaN values are also considered unique values.
Syntax
Series.unique()
Parameters
This method does not take any arguments.
Return Value
It returns a NumPy array of unique values in the Series.
Example 1: Basic usage
import pandas as pd
srs = pd.Series([1, 2, 2, 3, 4, 4, 5])
print(srs.unique())
Output
[1 2 3 4 5]
This method returns the unique values from the given series.
Example 2: Usage with string values
import pandas as pd
srs = pd.Series(['apple', 'banana', 'apple', 'cherry', 'cherry'])
print(srs.unique())
Output
['apple' 'banana' 'cherry']
We get the unique string values in the series.
Example 3: Usage with NaN values
import pandas as pd
import numpy as np
srs = pd.Series([1, 2, np.nan, 4, np.nan])
print(srs.unique())
Output
[ 1. 2. nan 4.]
Example 4: Unique values in a Date Series
import pandas as pd
srs = pd.Series(pd.to_datetime(['2021-01-01', '2021-01-02', '2021-01-01']))
print(srs.unique())
Output
<DatetimeArray>
[ '2021-01-01 00:00:00',
'2021-01-02 00:00:00'
]
Length: 2, dtype: datetime64[ns]
In this code, we have shown unique dates in a series.
Example 5: Unique Values in a categorical series
import pandas as pd
srs = pd.Series(pd.Categorical(["test", "train", "test", "evaluate"]))
print(srs.unique())
Output
['test', 'train', 'evaluate']
Categories (3, object): ['evaluate', 'test', 'train']
In our code, this method returns the unique categories in a categorical series.
Ankit Lathiya is a Master of Computer Application by education and Android and Laravel Developer by profession and one of the authors of this blog. He is also expert in JavaScript and Python development.