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Data Structure-SERIES |Pandas tutorial

this article aims to demonstrate what is series constructor, what are the parameters it accepts, how to create a series with various inputs like ndarray, dictionary , scalar value or a constant, how to access single series element or multiple series element without using label or with label.

Data Structure-SERIES


Pandas deals with 3 data structure, the higher dimensional data structure is a container of its lower-dimensional data structure. Through this article, I will cover ‘SERIES’ only.

1. SERIES

It is a one- dimensional labeled array capable of holding data of any type
(integers, float, python objects, strings, etc.) the axis labels are collectively called index.

A). Create Series:

You can create a series using constructor given below:

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


Parameters:
data: It can be of various forms like ndarray, list, constants.
index: it must be a unique and hashable, same length as data. If no index is passed, by default it takes np.arange(n).
dtype: It is for the data type, like string, integer, float, python objects, etc.
copy: copy data. By default it is False.

A series can be created using inputs like:
• Array
• Dictionary
• Scalar value or constant

B). Create an Empty Series.

Code: #import the pandas library and aliasing as pd

import pandas as pd
s = pd.Series()
print(s)

output:

Series([], dtype: float64)

C) Create a Series from ndarray without index.

Note: if data is a ndarray then, index must be of same length.
If no index is passes then it will be range(n) where n is array length.
Code:

import pandas as pd
import numpy as np
datainput = np.array(['x', 'y', 'z', 'w' ,'u'])
s=pd.Series(datainput)
print(s)

output:

#output:
'''
0 x
1 y
2 z
3 w
4 u
dtype: object'''

D) Create a Series from ndarray with index.

code:

import pandas as pd
import numpy as np
datainput = np.array(['x', 'y', 'z', 'w' ,'u'])
s=pd.Series(datainput, index=[70,71,72,73,74])
print(s)

output:

#output:
'''
70 x
71 y
72 z
73 w
74 u
dtype: object'''

E).Create a Series from ‘dict’ without index.

code:

import pandas as pd
import numpy as np
data ={'apple' : 1 ,'mango' :2 ,'orange':3 , 'grapes':4}
s=pd.Series(data)
print(s)

output:

#output:
'''apple 1
mango 2
orange 3
grapes 4
dtype: int64'''


F). Create a Series from ‘dict’ with index.

code:

import pandas as pd
import numpy as np
data ={'apple' : 100 ,'mango' :200 ,'orange':300 , 'grapes':400}
s=pd.Series(data, index=['apple' , 'orange' , 'grapes', 'mango','guava'])
print(s)

output:

#output:
'''apple 100.0
orange 300.0
grapes 400.0
mango 200.0
guava NaN
dtype: float64'''

Note: order is preserved.

G). Accessing Data from Series with the position.

Data in the series can be accessed similar to that in an array. Indexing starts from 0, i.e first element is stored at 0th position and so on.

#Retrieve first series element.

code:

import pandas as pd
import numpy as np
s=pd.Series([1,2,3,4,5], index=['a','b','c','d','e'])
print(s[0])

output:

 1


#Retrieve first three elements

code:

import pandas as pd
import numpy as np
s=pd.Series([1,2,3,4,5], index=['a','b','c','d','e'])
print(s[:3])

output: 

'''
a 1
b 2
c 3
dtype: int64'''


# Retrieve last three elements
code:

import pandas as pd
import numpy as np
s=pd.Series([1,2,3,4,5], index=['a','b','c','d','e'])
print(s[-3:])

output: 

'''
c 3
d 4
e 5
dtype: int64'''

#Retrieve single element using the label

code:

import pandas as pd
import numpy as np
s=pd.Series([1,2,3,4,5], index=['a','b','c','d','e'])
print(s['a'])

output:

1


#Retrieve multiple element using label

code:

import pandas as pd
import numpy as np
s=pd.Series([1,2,3,4,5], index=['a','b','c','d','e'])
print(s[['a','b','c']])

output:

'''
a 1
b 2
c 3
dtype: int64'''

#Retrieve element when the label is not contained in the index.
If the label is not contained in the index then an error will be raised.

code:

import pandas as pd
import numpy as np
s=pd.Series([1,2,3,4,5], index=['a','b','c','d','e'])
print(s['f'])
output:

KeyError: 'f'
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