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 :
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, "\n" )
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Output:
Code #1: Using clip_upper()
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, "\n" )
print (panel[ 'b' ], '\n' )
df2 = pd.DataFrame({ 'b' : np.random.randn( 5 )})
print (panel[ 'b' ].clip_upper(df2[ 'b' ], axis = 0 ))
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Output:
Code #2: Using clip_upper()
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)
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Output:
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