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Python | Pandas Panel.cummin()

  • 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.cummin() function is used to returns a DataFrame or Series of the same size containing the cumulative minimum.

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Syntax: Panel.cummin(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: Cummin of DataFrame or Panel

Code #1:




# importing pandas module 
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}
  
# creating Panel 
panel = pd.Panel.from_dict(data, orient ='minor')
  
print(panel['b'])
  
print("\n", panel['b'].cummin(axis = 0))

Output:

 

Code #2:




# 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['b'])
  
  
df2 = pd.DataFrame({'b': [11, 12, 13]})
print("\n", panel['b'].cummin(axis = 0))

Output:




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