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Type            Function                                  Example

                  Row wise &         ARR.
                                                      [1]:  import numpy as np
                  column wise        min(axis=1)
                                                            ARR = np.array([[11,2,13,4],[3,4,5,6]])
                  minimum value      for row                print("Rowwise min :",ARR.min(axis=1))
                                     ARR.                   print("Column wise min :",ARR.min(axis=0))
                                     min(axis=0)            Rowwise min : [2 3]
                                     for column             Column wise min : [3 2 5 4]


                  Sum of all values   ARR.sum()
                                                      [1]:  import numpy as np
                  in the given array
                                                            ARR = np.array([[11,2,13,4],[3,4,5,6]])
                                                            print("Row Wise sum :",ARR.sum(axis=1))
                                                            print("Column wise sum :",ARR.sum(axis=0))
                                                            print("sum is :",ARR.sum())

                                                            Rowwise sum : [30 18]
                                                            Column wise sum : [14  6 18 10]
                                                            sum is : 48

                  Sorting the array  ARR.sort()
                                                      [1]:  a = np.array([12,4,-10,23,29,15, -1,45,33,37,-14])
                                                            #Creating a 1-D Numpy array
                                                            print(np.sort(a)) #Printing the sorted numpy array
                                                            #We can also sort array row wise and column wise!
                                                            b = np.array([[-9,5,18,9,12], [10,11,3,-5,-10]])
                                                            #Creating a 2-D Numpy array
                                                            print(np.sort(b, axis = 1)) #Axis = 1performs the
                                                            sorting function row-wise
                                                            print(np.sort(b, axis = 0)) #Axis = 0 performs the
                                                            sorting function columns-wise

                                                            [-14 -10  -1   4  12  15  23  29  33  37  45]
                                                            [[ -9   5   9  12  18]
                                                            [-10  -5   3  10  11]]
                                                            [[ -9   5   3  -5 -10]
                                                             [ 10  11  18   9  12]]



                         Pandas (PANel DAta)

                 Pandas is an open-source Python library used for data manipulation and analysis. It provides strong features for
                 working with three key data structures: Series (1-dimensional), DataFrame (2-dimensional), and Index (used for
                 label-based indexing). These structures allow smooth processing and analysis of data, regardless of its origin. In
                 Pandas, the data need not be labelled to be placed into a data structure.
                 Pandas was created by Wes McKinney in 2008. The name "Pandas" is derived from Panel Data (a term in statistics
                 for multidimensional data) and Python, as it was designed for data analysis in Python.
                 Pandas is built on top of NumPy, so while you don't need to be an expert in NumPy to use Pandas, familiarity
                 with NumPy can be helpful when performing operations on data.







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