Python | Pandas Panel.clip()
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()
function is used to trim values at input threshold. Thresholds can be singular values or array_like.
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: Creating a Panel using from_dict()
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}
panel = pd.Panel.from_dict(data, orient = 'minor' )
print (panel)
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Output:
Code #2: Using clip() function
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}
panel = pd.Panel.from_dict(data, orient = 'minor' )
print (panel[ 'b' ], '\n' )
df2 = pd.DataFrame({ 'b' : [ 11 , 12 , 13 ]})
print (panel[ 'b' ].clip(df2[ 'b' ], axis = 0 ))
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Output:
Code #3: Using clip() function
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}
panel = pd.Panel.from_dict(data, orient = 'minor' )
print (panel[ 'b' ], '\n' )
print (panel[ 'b' ].clip( - 4 , 6 ))
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Output:
Last Updated :
01 Jan, 2019
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