Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.ptp() function returns the difference between the maximum value and the
minimum value in the object. This is the equivalent of the numpy.ndarray method ptp.
Syntax: Series.ptp(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameter :
axis : Axis for the function to be applied on.
skipna : Exclude NA/null values when computing the result.
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
**kwargs : Additional keyword arguments to be passed to the function.Returns : ptp : scalar or Series (if level specified)
Example #1: Use Series.ptp() function to return the difference between the maximum and the minimum value of the underlying data in the given Series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([10, 25, 3, 11, 24, 6]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.ptp() function to find the difference between the max and min value in the given series object.
# return the difference between the # maximum and the minimum value result = sr.ptp() # Print the result print(result) |
Output :
As we can see in the output, the Series.ptp() function has successfully returned the difference between the maximum and the minimum value of the underlying data in the given series object.
Example #2: Use Series.ptp() function to return the difference between the maximum and the minimum value of the underlying data in the given Series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([11, 21, 8, 18, 65, 84, 32, 10, 5, 24, 32]) # Print the series print(sr) |
Output :
Now we will use Series.ptp() function to find the difference between the max and min value in the given series object.
# return the difference between the # maximum and the minimum value result = sr.ptp() # Print the result print(result) |
Output :
As we can see in the output, the Series.ptp() function has successfully returned the difference between the maximum and the minimum value of the underlying data in the given series object.
Example #3: Use Series.ptp() function to return the difference between the maximum and the minimum value of the underlying data in the given Series object. The given series object contains some missing values in it.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([19.5, 16.8, None, 22.78, None, 20.124, None, 18.1002, None]) # Print the series print(sr) |
Output :
Now we will use Series.ptp() function to find the difference between the max and min value in the given series object. we are going to skip the missing values in the calculation.
# return the difference between the # maximum and the minimum value result = sr.ptp(skipna = True) # Print the result print(result) |
Output :
As we can see in the output, the Series.ptp() function has successfully returned the difference between the maximum and the minimum value of the underlying data in the given series object.
