Python | Pandas Panel.rfloordiv()
Last Updated :
28 Jan, 2019
In Pandas, Panel is a very important container for three-dimensional data. The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data and, in particular, econometric analysis of panel data.
In Pandas Panel.rfloordiv()
function is used to get the integer division of series and dataframe/Panel.
Syntax: Panel.rfloordiv(other, axis=0)
Parameters:
other : DataFrame or Panel
axis : Axis to broadcast over
Returns: Panel
Code #1:
import pandas as pd
import numpy as np
df1 = pd.DataFrame({ 'a' : [ 'Geeks' , 'For' , 'geeks' , 'real' ],
'b' : [ 111 , 123 , 425 , 1333 ]})
df2 = pd.DataFrame({ 'a' : [ 'I' , 'am' , 'dataframe' , 'two' ],
'b' : [ 5 , 5 , 2 , 10 ]})
data = { 'item1' :df1, 'item2' :df2}
panel = pd.Panel.from_dict(data, orient = 'minor' )
print ( "panel['b'] is - \n\n" , panel[ 'b' ])
print ( "\nInteger Dividing panel['b'] with df2['b'] using rfloordiv() method - \n" )
print ( "\n" , panel[ 'b' ].rfloordiv(df2[ 'b' ], axis = 0 ))
|
Output:
panel['b'] is -
item1 item2
0 111 5
1 123 5
2 425 2
3 1333 10
Integer Dividing panel['b'] with df2['b'] using rfloordiv() method -
item1 item2
0 0 1
1 0 1
2 0 1
3 0 1
Code #2:
import pandas as pd
import numpy as np
df1 = pd.DataFrame({ 'a' : [ 'Geeks' , 'For' , 'geeks' , 'for' , 'real' ],
'b' : [ 11 , 1.025 , 333 , 114.48 , 1333 ]})
df2 = pd.DataFrame({ 'a' : [ 'I' , 'am' , 'DataFrame' , 'number' , 'two' ],
'b' : [ 3 , 3 , 3 , 13 , 27 ]})
data = { 'item1' :df1, 'item2' :df2}
panel = pd.Panel.from_dict(data, orient = 'minor' )
print ( "panel['b'] is - \n\n" , panel[ 'b' ], '\n' )
print ( "\nInteger Dividing panel['b']['item1'] with df2['b'] or panel['b']['item2'] using rfloordiv() method - \n" )
print ( "\n" , panel[ 'b' ][ 'item1' ].rfloordiv(df2[ 'b' ], axis = 0 ))
|
Output:
panel['b'] is -
item1 item2
0 11.000 3
1 1.025 3
2 333.000 3
3 114.480 13
4 1333.000 27
Integer Dividing panel['b']['item1'] with df2['b'] or panel['b']['item2'] using rfloordiv() method -
0 0
1 2
2 0
3 0
4 0
dtype: float64
Share your thoughts in the comments
Please Login to comment...