Related Articles
Create Pandas Series using NumPy functions
• Last Updated : 18 Dec, 2018

Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.).

Let’s see how can we create a Pandas Series using different `numpy `functions.

Code #1: Using numpy.linspace()

 `# import pandas and numpy``import` `pandas as pd``import` `numpy as np`` ` `# series with numpy linspace() ``ser1 ``=` `pd.Series(np.linspace(``3``, ``33``, ``3``))``print``(ser1)`` ` `# series with numpy linspace()``ser2 ``=` `pd.Series(np.linspace(``1``, ``100``, ``10``))``print``(``"\n"``, ser2)`` `

Output:

Code #2: Using np.random.normal() and random.rand() method.

 `# import pandas and numpy``import` `pandas as pd``import` `numpy as np`` ` `# series with numpy random.normal``ser3 ``=` `pd.Series(np.random.normal())``print``(ser3)`` ` `# series with numpy random.normal``ser4 ``=` `pd.Series(np.random.normal(``0.0``, ``1.0``, ``5``))``print``(``"\n"``, ser4)`` ` `# series with numpy random.rand``ser5 ``=` `pd.Series(np.random.rand(``10``))``print``(``"\n"``, ser5)`

Output:

Code #3: Using numpy.repeat()

 `# import pandas and numpy``import` `pandas as pd``import` `numpy as np`` ` ` ` `# series with numpy random.repeat``ser ``=` `pd.Series(np.repeat(``0.08``, ``7``))``print``(``"\n"``, ser)`

Output:

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up