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Python | Pandas DataFrame.values - GeeksforGeeks

Last Updated : 20 Feb, 2019

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

attribute return a Numpy representation of the given DataFrame.

Syntax: DataFrame.values Parameter : None Returns : array

Use

attribute to return the numpy representation of the given DataFrame.

# 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)

Now we will use

attribute to return the numpy representation of the given DataFrame.

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

# Print the result
print(result)

As we can see in the output, the

attribute has successfully returned the numpy representation of the given DataFrame.

Use

attribute to return the numpy representation of the given DataFrame.

# 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)

Now we will use

attribute to return the numpy representation of the given DataFrame.

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

# Print the result
print(result)

As we can see in the output, the

attribute has successfully returned the numpy representation of the given DataFrame.

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