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 listÂ
The list of some values form the series of that values uses list index as series index.
Python
# import pandas as pd import pandas as pd   # simple list lst = ['G','E','E','K','S','F',        'O','R','G','E','E','K','S']   # forming series s = pd.Series(lst)   # output 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 import pandas as pd   # simple dict dct = {'G':2,'E':4,'K':2,'S':2,        'F':1,'O':1,'R':1}   # forming series s = pd.Series(dct)   # output 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 pandas as pd   # import numpy as np import numpy as np   # numpy array arr = np.array(['G','E','E','K','S','F',                 'O','R','G','E','E','K','S'])   # forming series s = pd.Series(arr)   # output 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
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