This function will explain how we can convert a Series pandas to a array Numpy. Although it is very simple, the concept of this technique is very unique. Because we know the Series has an output index. Whereas in numy arrays, we only have elements in numpy arrays.

Syntax: Series.to_numpy ()

Parameters:
dtype: Data type which we are passing like str.
copy: [bool, default False] Ensures that the returned value is a not a view on another array.

To get a link to the CSV file, click on nba.csv

Code # 1:

Changing Series to Series.to_numpy () . Always remember that when dealing with a lot of data, you must cleanse the data first to get high accuracy. Although in this code we are using the first five values ​​of the Weight column with .head () .

# pandas import import pandas as pd    # reading CSV data = pd.read_csv ( "nba.csv" )    data.dropna (inplace = True )   # create a series form weight column gfg = pd.Series (data [ `Weight` ]. head ())   # using the to_numpy () function print ( type ( gfg.to_numpy ()))

Exit:

 [180. 235. 185. 235. 238.] 

Code # 2:
In this code, we just give parameters in the same code. So we provide dtype here.

# pandas import import pandas as pd    # read CSV file data = pd.read_csv ( "nba.csv" )    data.dropna (inplace = True )   # create a series form weight column gfg = pd.Series (data [ `Weight` ]. head ())    # providing dtype print ( gfg.to_numpy (dtype = `float32` ))

Output:

 [180. 235. 185. 235. 238.] 

Code # 3: Checking the array type after transformation.

# pandas import import pandas as pd    # reading CSV data = pd.read_csv ( "nba.csv" )    data.dropna ( inplace = True )   # create a series form weight column gfg = pd.Series (data [ ` Weight` ]. head ())   # using to_numpy () print ( type (gfg.to_numpy ()))

Output:

 & lt; class `numpy.ndarray` & gt;