Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas. Pandas DataFrame.values attribute return a Numpy representation of the given DataFrame.

Syntax: DataFrame.values Parameter : None Returns : array

Example #1: Use DataFrame.values attribute to return the numpy representation of the given DataFrame. Python3

# importing pandas as pd
import pandas as pd

# Creating the DataFrame
df = pd.DataFrame({'Weight':[45, 88, 56, 15, 71],
                   'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],
                   'Age':[14, 25, 55, 8, 21]})

# Print the DataFrame
print(df)

Output : Now we will use DataFrame.values attribute to return the numpy representation of the given DataFrame. Python3 1==

# return the numpy representation of 
# this dataframe
result = df.values

# Print the result
print(result)

Output : As we can see in the output, the DataFrame.values attribute has successfully returned the numpy representation of the given DataFrame.   Example #2: Use DataFrame.values attribute to return the numpy representation of the given DataFrame. Python3

# importing pandas as pd
import pandas as pd

# Creating the DataFrame
df = pd.DataFrame({"A":[12, 4, 5, None, 1], 
                   "B":[7, 2, 54, 3, None], 
                   "C":[20, 16, 11, 3, 8], 
                   "D":[14, 3, None, 2, 6]}) 

# Print the DataFrame
print(df)

Output : Now we will use DataFrame.values attribute to return the numpy representation of the given DataFrame. Python3 1==

# return the numpy representation of 
# this dataframe
result = df.values

# Print the result
print(result)

Output : As we can see in the output, the DataFrame.values attribute has successfully returned the numpy representation of the given DataFrame.