Python | Pandas DataFrame.values
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 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.
import pandas as pd
df = pd.DataFrame({ 'Weight' :[ 45 , 88 , 56 , 15 , 71 ],
'Name' :[ 'Sam' , 'Andrea' , 'Alex' , 'Robin' , 'Kia' ],
'Age' :[ 14 , 25 , 55 , 8 , 21 ]})
print (df)
|
Output :
Now we will use DataFrame.values
attribute to return the numpy representation of the given DataFrame.
result = df.values
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.
import pandas as pd
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 (df)
|
Output :
Now we will use DataFrame.values
attribute to return the numpy representation of the given DataFrame.
result = df.values
print (result)
|
Output :
As we can see in the output, the DataFrame.values
attribute has successfully returned the numpy representation of the given DataFrame.
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