Python | Pandas Panel.clip_upper ()

Michael Zippo 18.07.2021

Panel.clip_upper() is used to return a copy of the input with truncated values above the specified values.

Syntax: Panel.clip_upper (threshold, axis = None, inplace = False)

Parameters:
threshold: float or array_like
axis: Align object with threshold along the given axis.
inplace: Whether to perform the operation in place on the data

Returns: same type as input.

Panel creation:

# pandas module import

import pandas as pd

import numpy as np

df1 = pd.DataFrame ({ ’a’ : [ ’Geeks’ , ’ For’ , ’geeks’ ],

   ’b’  : np.random.randn (  3  )}) 
   
   data   =   { ’ item1’  : df1,   ’item2’  : df1} 
   
  # create a panel  
   panel   =   pd.Panel.from_dic t (data, orient   =   ’minor’  )  
   print   (panel,   ""  )

Exit :

Code # 1: Using clip_upper ()

# pandas module import

import pandas as pd

import numpy as np

df1 = pd.DataFrame ({ ’a’ : [ ’Geeks’ , ’ For’ , ’geeks’ ],

’b’ : np.random.randn ( 3 )})

data = { ’item1’ : df1, ’item2’ : df1}

# create a panel

panel = pd.Panel.from_dict (data, orient = ’minor’ )

print (panel, "" )

print (panel [ ’b’ ], ’’ )

df2 = pd.DataFrame ({ ’b’ : np.random.randn ( 5 )})

print (panel [ ’b’ ] .clip_upper (df2 [ ’b’ ], axis = 0 ))

Output:

Code # 2: Using clip_upper ()

# create an empty panel

import pandas as pd

import numpy as np

data = { ’ Item1’ : pd.DataFrame (np. random.randn ( 7 , 4 )),

’Item2’ : pd.DataFrame (np.random.randn ( 4 , 5 ))}

   
   pen   =   pd.Panel (data) 
   print   (pen [  ’Item1’  ],  ’ ’  ) 
   
   p   =   pen [ ’ Item1’  ] [  0  ]. clip_upper (np.random.randn (  7  )) 
   print   (p)