Panel.clip() is used to trim values ​​at the input threshold. Thresholds can be single or massive.

Syntax: Panel.clip (lower = None, upper = None, axis = None, inplace = False, * args, ** kwargs)

Parameters: Parameters:
lower: Minimum threshold value. All values ​​below this threshold will be set to it.
upper: Maximum threshold value. All values ​​above this threshold will be set to it.
axis: Align object with lower and upper along the given axis.
inplace: Whether to perform the operation in place on the data.

Returns: [Series or DataFrame] Same type as calling object with the values ​​outside the clip boundaries replaced.

Code # 1: Create a panel with from_dict ()

# 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 < / code> (panel)

Output:

Code # 2: Using the clip () function

# 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 [ `b` ], `` )     df2 = pd.DataFrame ({ `b` : [ 11 , 12 , 13 ]}) print (panel [ `b` ]. clip (df2 [ `b` ], axis = 0 ))

Output:

Code # 3: Using the clip () function

# pandas module import import pandas as pd  import numpy as np   df1 = pd.DataFrame ({ `a` : [ `Geeks` , ` For` , `geeks` , `real` ],    `b` : [ - 11 , + 1.025 , - 114.48 , 1333 ]})   data = { `item1` : df1, ` item2` : df1}   # create a panel panel = pd.Panel.from_dict (data, orient = `minor` )   print (panel [ `b` ], ` `  )   print (panel [ ` b` ]. clip ( - 4 , 6 ))

Output: