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) |
Output:
