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  2. Python | Pandas Panel.clip_lower ()

Python | Pandas Panel.clip_lower ()



Panel.clip_lower() is used to return a copy of the input below the truncated threshold.

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

Parameters:
threshold: Minimum value allowed. All values ​​below threshold will be set to this value.
float: every value is compared to threshold.
array-like: The shape of threshold should match the object it`s compared to.
axis: Align self with threshold along the given axis.
inplace: Whether to perform the operation in place on the data.

Returns: same type as input.

Code # 1: Create a panel with using 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 (panel, "" )

Output:

Code # 2: Using clip_lower ()

# 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` : [ 11 , 12 , 13 ]})

print (panel [ `b` ]. clip_lower (df2 [ ` b` ] , axis = 0 ))

Output:

Code # 3:

# 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_lower (np.random.randn ( 7 ))

print (p)

Output: