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:
