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: