Creating Series from list, dictionary, and numpy array in Pandas
Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index. Pandas Series is nothing but a column in an excel sheet. In this article, we will see various ways of creating a series using different data types.
Creating Series from list
The list of some values form the series of that values uses list index as series index.
Python
import pandas as pd
lst = [ 'G' , 'E' , 'E' , 'K' , 'S' , 'F' ,
'O' , 'R' , 'G' , 'E' , 'E' , 'K' , 'S' ]
s = pd.Series(lst)
print (s)
|
Output :
0 G
1 E
2 E
3 K
4 S
5 F
6 O
7 R
8 G
9 E
10 E
11 K
12 S
dtype: object
Creating Series from dictionary
Dictionary of some key and value pair for the series of values taking keys as index of series.
Python3
import pandas as pd
dct = { 'G' : 2 , 'E' : 4 , 'K' : 2 , 'S' : 2 ,
'F' : 1 , 'O' : 1 , 'R' : 1 }
s = pd.Series(dct)
print (s)
|
Output :
G 2
E 4
K 2
S 2
F 1
O 1
R 1
dtype: int64
Creating Series from Numpy array
The 1-D Numpy array of some values form the series of that values uses array index as series index.
Python3
import pandas as pd
import numpy as np
arr = np.array([ 'G' , 'E' , 'E' , 'K' , 'S' , 'F' ,
'O' , 'R' , 'G' , 'E' , 'E' , 'K' , 'S' ])
s = pd.Series(arr)
print (s)
|
Output :
0 G
1 E
2 E
3 K
4 S
5 F
6 O
7 R
8 G
9 E
10 E
11 K
12 S
dtype: object
Last Updated :
08 Jun, 2020
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