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0    2
              1    5
              2    2
              3    1
              4    4
              5    2
              6    1
              7    5
              dtype: int64
                <ipython-input-109-ecdb615886f4>:2: FutureWarning: Dropping of nuisance columns in
              DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version
              this will raise TypeError.  Select only valid columns before calling the reduction.
                print(groceryDF.min(axis=1))
        Note that above warning has been caused by the presence of a mix of of string and numeric objects. Below, we find the
        minimium values across the row by including only the numeric columns from the DataFrame:
         >>> print("Minimum value in each row:")
         >>> print(groceryDF[['Quantity', 'Price']].min(axis = 1))
              Minimum value in each row:
              0    2
              1    5
              2    2
              3    1
              4    4
              5    2
              6    1
              7    5
              dtype: int64
        Next, we find the minimium values across the row by including only those columns from the DataFrame that comprise
        of the string objects :
         >>> print("Minimum value in each row:")
         >>> print(groceryDF[['Product', 'Category']].min(axis = 1))
              Minimum value in each row:
              0         Bread
              1          Food
              2       Biscuit
              3    Bourn-Vita
              4       Hygiene
              5         Brush
              6     Detergent
              7       Hygiene
              dtype: object
        You may be wondering why would someone find the minimum value across a row that includes attributes like Price
        and Quantity. It is equally meaningless to compare the values in the Product and Category columns. However,
        there are situations where comparison of data values across a row is indeed meaningful. For example, if the columns
        were to indicate the marks obtained by the students in different subjects, it would make definite sense to the know
        the maximum and minimum scores in a row, as illustrated below:
        Next, let us construct a DataFrame comprising the marks obtained by 4 students in 5 subjects. First, we create a
        dictionary marks that stores the marks of four students (RollNo: 301, 302, 303, 304) in different subjects (English,
        Mathematics, Economics, History, and Psychology) as lists of scores.
         >>> import pandas as pd
              students = {'RollNo': [301, 302, 303, 304],
                       'English': [78, 68, 57, 45],


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