Series is a one-dimensional Data Structure of Pandas, it is used for data analysis. It can contain a heterogeneous types of values. Series type Object has two main components:

- An array of actual
**data** - An associated array of
**indexes**or**data labels**.

Fig-1 Series |

Like list, there are indexes in Series, but in series, we can assign our own indexes (Data labels). The

**labels need not be unique**but must be a hashable type.

## How to create pandas series?

To create the Series Object a list of data to be passed in

**Series() Function.**This function is available inside the Pandas Module. Hence before creating Series Object we have to import pandas library using:import pandas

Following is the syntax for Series Creation:

<Series Object> = pandas.Series(data)

<Series Object> = pandas.Series(data=None, index=None, dtype=None, name=None, copy=False)

**data**: data parameter provides data for Series creation, it can be a Sequence or Scalar Value

**index**: This is used to change the default index of Series

**dtype**: It is used to change the default datatype of Series

**name**: To give a name to Series

**copy**: To copy input Data, A boolean Value by default is false

By default, the index of series will be integers starting from 0,1,2...etc. as shown above in

*Fig-1 Series.*### Use of different types of Data

In Series Data Structure, we can use our own data for creating the series. This data can be of different types.

### <Series Object> = pandas.Series(**data=None**, index=None, dtype=None, name=None, copy=False, fastpath=False)

The

**data**can be:- Without Data (Empty Series)
- A scalar value
- A python sequence (ex:- list, tuple, dictionary etc.)
- A ndarray

Let us discuss each one by one with Pandas Series Examples.

**a) Without Data (Empty Series)**

If we do not provide any data then python pandas will create an empty series. The

**default data type**that pandas provide to series is "**float64**". In the following example, you can see that I have not provided any parameter in**Series()**function. This will create an empty series.**import pandas as pd
s = pd.Series()
print(s) ## Series([], dtype: float64)
print(type(s)) ## <class 'pandas.core.series.Series'>**

**b) A Scalar Value**

A Pandas Series can be created with only single Parameters. This will create a Series Object with only a single value. In the following example, you can see that I have provided a value

**20**only. A Series Object**'****s'**has been created by Pandas with value**20.**import pandas as pd s = pd.Series(20) print(s) print(type(s)) ''' ## Output ----------- 0 20 dtype: int64 <class 'pandas.core.series.Series'> '''

**c) A python sequence (ex:- list, tuple, dictionary etc.)**

A Python sequence can be used for the creation of Series Object. Any Python Sequence like list, tuple or dictionary can be used for this purpose. Next, we will create a series using

**List**and**Dictionary**.**Using List:**When we use list fr creation of Series, it takes the index of Series same as the index of the list. As shown in the example the list

*myList*is shaving Three values, these values can be of any type.

import pandas as pd myList = ['A','B',2] s = pd.Series(myList) print(s) print(type(s)) ''' Output: ------- 0 A 1 B 2 2 dtype: object <class 'pandas.core.series.Series'> '''

**Using Dictionary:**When a dictionary is used for Series creation, the

*values*of that dictionary is used as

**data**of Series and

*Keys*are used as

**data labels (index)**of Series.

**import pandas as pd
d = {'A':1, 'B':2, 'C':3}
print(d)
s = pd.Series(d)
print(s)
print(type(s))
'''
Output:
------
{'A': 1, 'B': 2, 'C': 3}
A 1
B 2
C 3
dtype: int64
<class 'pandas.core.series.Series'>
'''**

**d) A ndarray**

Numpy is a core library for numerical and scientific computation. We can use a one-dimensional Numpy array for Series creation. The Index or data labels of Series will come as 0,1,2....etc. by default.

import numpy as np import pandas as pd arr = np.array([1,2,3,4]) print(arr) s = pd.Series(arr) print(s) print(type(s)) ''' Output: ------- [1 2 3 4] 0 1 1 2 2 3 3 4 dtype: int32 <class 'pandas.core.series.Series'> '''

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