Page 192 - Robotics and AI class 10
P. 192

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

        Pandas(PANel Data)

        Panda is an open-source Python library used for data manipulation and data analysis. It provides a very strong
        feature of using three important data structures - Series (1-dimensional),DataFrame (2-dimensional) and Panel(3-
        -dimensional) for smooth processing and analysis of data, regardless of its origin. The data actually need not be
        labelled at all to be placed into a Pandas data structure.

        Pandas was created by Wes McKinney in 2008 and has derived its name from both "Panel Data", and "Python Data
        Analysis" which means using a statistical method of analysing the data taken from the observations about different
        cross sections over the period of time.

        Pandas libraries are built on NumPy so to work in Pandas the prerequisite is to get familiar with NumPy and install
        it. It gives us a single, convenient place to do most of our data analysis and visualisation work.

        Data required for Pandas can be taken as :
           • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
           • Ordered and unordered (not necessarily fixed-frequency) time series data.

           • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels.
           • Any other form of observational / statistical data sets.
        Installing Pandas

        To install Pandas from command line, we need to type in:

        pip install pandas
        All libraries including NumPy and Pandas can be installed only when Python is already installed on that system.




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