Python | Pandas Panel.clip_upper()

Last Updated : 01 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. Panel.clip_upper() function is used to return copy of input with values above given value(s) truncated.

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.

Creating a Panel :

Python3 1==

# importing pandas module 
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}

# creating Panel 
panel = pd.Panel.from_dict(data, orient ='minor')
print(panel, "\n")

Output:

Code #1: Using clip_upper()

Python3 1==

# importing pandas module 
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}

# creating Panel 
panel = pd.Panel.from_dict(data, orient ='minor')
print(panel, "\n")
print(panel['b'], '\n')


df2 = pd.DataFrame({'b': np.random.randn(5)})
print(panel['b'].clip_upper(df2['b'], axis = 0))

Output:

Code #2: Using clip_upper()

Python3 1==

# creating 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'], '\n')

p = pen['Item1'][0].clip_upper(np.random.randn(7))
print(p)

Output:

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Python | Pandas Panel.clip_upper()

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