# 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:

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