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Python Pandas - Series

In the Python Pandas library, a Series is one of the primary data structures, that offers a convenient way to handle and manipulate one-dimensional data. It is similar to a column in a spreadsheet or a single column in a database table. In this tutorial you will learn more about Pandas Series and use Series effectively for data manipulation and analysis.

A Series in Pandas is a one-dimensional labeled array capable of holding data of any type, including integers, floats, strings, and Python objects. It consists of two main components −

• Data: The actual values stored in the Series.
• Index: The labels or indices that correspond to each data value.

A Series is similar to a one-dimensional ndarray (NumPy array) but with labels, which are also known as indices. These labels can be used to access the data within the Series. By default, the index values are integers starting from 0 to the length of the Series minus one, but you can also manually set the index labels.

A pandas Series can be created using the following constructor −

class pandas.Series(data, index, dtype, name, copy)

The parameters of the constructor are as follows −

data

Data takes various forms like ndarray, list, or constants.

index

Index values must be unique and hashable, with the same length as data. Default is np.arange(n) if no index is passed.

dtype

Data type. If None, data type will be inferred.

copy

Copy data. Default is False.

A series object can be created using various inputs like −

• List
• ndarray
• Dict
• Scalar value or constant

If no data is provided to the Series constructor pandas.Series() it will create a basic empty series object.

Following is the example demonstrating creating the empty Series.

#import the pandas library and aliasing as pd
import pandas as pd
s = pd.Series()

# Display the result
print('Resultant Empty Series:\n',s)

Its output is as follows −

Resultant Empty Series: 
Series([], dtype: object)

An ndarray is provided as an input data to the Series constructor, then it will create series with that data. If you want specify the custom index then index passed must be of the same length of input data. If no index is specified, then Pandas will automatically generate a default index from staring 0 to length of the input data, i.e., [0,1,2,3. range(len(array))-1].

Here's the example creating a Pandas Series using an ndarray.

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data)
print(s)

Its output is as follows −

0   a
1   b
2   c
3   d
dtype: object

We did not pass any index, so by default, it assigned the indexes ranging from 0 to len(data)-1, i.e., 0 to 3.

This example demonstrates applying the custom index to the series object while creating.

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data,index=[100,101,102,103])
print("Output:\n",s)

Its output is as follows −

Output:
100  a
101  b
102  c
103  d
dtype: object

In this example we have provided the index values. Now we can observe the customized indexed values in the output.

A dictionary can be passed as input to the pd.Series() constructor to create a series with the dictionary values. If no index is specified, then the dictionary keys are taken in a sorted order to construct the series index. If index is passed, the values in data corresponding to the labels in the index will be pulled out.

Here is the basic example of creating the Series object using a Python dictionary.

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd.Series(data)
print(s)

Its output is as follows −

a 0.0
b 1.0
c 2.0
dtype: float64

Observe − Dictionary keys are used to construct index.

In this example a Series object is created with Python dictionary by explicitly specifying the index labels.

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd.Series(data,index=['b','c','x','a'])
print(s)

Its output is as follows −

b 1.0
c 2.0
d NaN
a 0.0
dtype: float64

Observe − Index order is persisted and the missing element is filled with NaN (Not a Number).

If you provide a single scalar value as data to the Pd.Series() constructor with specified index labels. Then that single value will be repeated to match the length of provided index object.

Following is the example that demonstrates creating a Series object using a single scalar value.

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
s = pd.Series(5, index=[0, 1, 2, 3])
print(s)

Its output is as follows −

0  5
1  5
2  5
3  5
dtype: int64

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