Python | Pandas Index.is_monotonic_increasing

Pandas Index is an immutable ndarray implementing an ordered, sliceable set. It is the basic object which stores the axis labels for all pandas objects.
Pandas Index.is_monotonic_increasing attribute return True if the underlying data in the given Index object is monotonically increasing else it return False.
Syntax: Index.is_monotonic_increasing
Parameter : None
Returns : boolean
Example #1:  Use Index.is_monotonic_increasing attribute to find out if the underlying data in the given Index object is monotonically increasing or not.
| # importing pandas as pd importpandas as pd  # Creating the index idx =pd.Index([100, 200, 420, 888, 924])  # Print the index print(idx)  | 
Output : 
Now we will use Index.is_monotonic_increasing attribute to find out if the underlying data in the given Index object is monotonically increasing or not.
| # check if the values in the Index # are monotonically increasing result =idx.is_monotonic_increasing  # Print the result print(result)  | 
Output : 
As we can see in the output, the Index.is_monotonic_increasing attribute has returned True indicating that the underlying data of the given Index object is monotonically increasing.
 
Example #2 :  Use Index.is_monotonic_increasing attribute to find out if the underlying data in the given Index object is monotonically increasing or not.
| # importing pandas as pd importpandas as pd  # Creating the index idx =pd.Index(['2012-12-12', None, '2002-1-10', None])  # Print the index print(idx)  | 
Output : 
Now we will use Index.is_monotonic_increasing attribute to find out if the underlying data in the given Index object is monotonically increasing or not.
| # check if the values in the Index # are monotonically increasing result =idx.is_monotonic_increasing  # Print the result print(result)  | 
Output : 
As we can see in the output, the Index.is_monotonic_increasing attribute has returned False indicating that the underlying data of the given Index object is not monotonically increasing.
 
				 
					



