# Create Pandas Series using NumPy functions

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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