# Pandas Series.unique()

This article explains the usage details of Pandas.Series.unique() in Python with few examples.

In Pandas, the Series class provides a member function unique(), which returns a numpy array of unique elements in the Series.

`Series.unique()`

Unique values in returned numpy array will be in the order of their appearance in the Series, which means these returned unique values will not be in any sorted order.

## Examples of Series.unique()

Let’s understand with an example,

First, we will create a Series object from a list,

```import pandas as pd

# Create Series object from List
seres_obj = pd.Series([11, 23, 4, 56, 34, 55, 11, 4, 56, 34])

print(seres_obj)```

Output:

```0    11
1    23
2     4
3    56
4    34
5    55
6    11
7     4
8    56
9    34
dtype: int64```

Our Series object contains many duplicate elements. Now let’s call the unique() function on this Series object,

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```# Get a Numpy Array of unique values in Series
unique_values = seres_obj.unique()

print(unique_values)```

Output:

`[11 23  4 56 34 55]`

It returned a numpy array containing all the unique values from the Series object. Also, the values returned are in the order of their appearance.

The complete example is as follows,

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```import pandas as pd

# Create Series object from List
seres_obj = pd.Series([11, 23, 4, 56, 34, 55, 11, 4, 56, 34])

print(seres_obj)

# Get a Numpy Array of unique values in Series
unique_values = seres_obj.unique()

print(unique_values)```

Output

```0    11
1    23
2     4
3    56
4    34
5    55
6    11
7     4
8    56
9    34
dtype: int64

[11 23  4 56 34 55]```

## Another example of Pandas.Series.unique()

Let’s see another example, where we will create a Pandas Series of strings and then fetch unique elements from Series using unique() function. For example,

```import pandas as pd

# Create Series object from List
names = pd.Series([ 'Ritika',
'John',
'Ritika',
'Shaun',
'John',
'Ritika',
'Mark',
'Shaun',
])

print(names)

# Get a Numpy Array of unique values in Series
unique_names = names.unique()

print(unique_names)```

Output:

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```0    Ritika
1      John
2    Ritika
3     Shaun
4      John
5    Ritika
6      Mark
7     Shaun
dtype: object

['Ritika' 'John' 'Shaun' 'Mark']```

Here, it gave us a numpy array of unique strings.

Summary:

Today we learned how to use the unique() function of the Pandas series.

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