Python | Pandas Panel.cummax()
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.cummax()
function is used to returns a DataFrame or Series of the same size containing the cumulative maximum.
Syntax: Panel.cummax(axis=None, skipna=True, *args, **kwargs)
Parameters:
axis : The index or the name of the axis. 0 is equivalent to None or ‘index’.
skipna : Exclude NA/null values. If an entire row/column is NA, the result will be NA.
Returns: Cummax of DataFrame or Panel
Code #1:
import pandas as pd
import numpy as np
df1 = pd.DataFrame({ 'a' : [ 'Geeks' , 'For' , 'geeks' , 'for' , 'real' ],
'b' : [ 11 , 1.025 , 333 , 114.48 , 1333 ]})
data = { 'item1' :df1, 'item2' :df1}
panel = pd.Panel.from_dict(data, orient = 'minor' )
print (panel, "\n" )
print (panel[ 'b' ])
print ( "\n" , panel[ 'b' ].cummax(axis = 0 ))
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Output:
Code #2:
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' ])
df2 = pd.DataFrame({ 'b' : [ 11 , 12 , 13 ]})
print ( "\n" , panel[ 'b' ].cummax(axis = 0 ))
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Output:
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