
The values property in pandas returns the numpy representation of the dataframe. Only the values in the DataFrame will be returned and the axes labels will be removed.
Syntax –
dataframe.values
Examples –
1 . All the columns are of same type –
When all the columns are of same type (e.g., int64) results in an array of the same type.
Let’s create a dataframe.
import pandas as pd
data = {'Apple':[89, 89, 90, 110, 125, 84, 131, 123, 123, 140, 145, 145],
'Orange': [46, 46, 50, 65, 63, 48, 110, 120, 60, 42, 47, 62],
'Banana': [26, 30, 30, 25, 38, 22, 22, 36, 20, 27, 23, 34 ],
'Mango': [80, 80, 90, 125, 130, 150, 140, 140, 135, 135, 80, 90]}
index = ['Jan','Feb','Mar','Apr','May','June','Jul','Aug','Sep','Oct','Nov','Dec']
df = pd.DataFrame(data, index=index)
df

Now, we can use the values property to get the numpy representation of this dataframe.
df.values

2. Columns are Mixed Type –
A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray of the broadest type that accommodates these mixed types (e.g., object).
Let’s create a dataframe of mixed types.
import pandas as pd
data = {'Name': ['Eleven','Mike','Lucas','Will','Max'],
'Age': [18, 20, 20, 18, 19],
'Sex': ['F', 'M', 'M', 'M', 'F'],
'Marks': [99, 85, 82, 70, 80]}
df = pd.DataFrame(data)
df

Now, let’s use the values property.
df.values
