Page 200 - Robotics and AI class 10
P. 200

• Deleting a Column
           f2=f2.drop('Marks', axis=1)

          print(f2)
                    Name    Section

          11        Riya            A
          33       Sneha            E
          44       Nitin            C

                    Task                                                           #Coding & Computational Thinking



             Find out what happens when you add the following statements in a DataFrame created earlier:
             a.  f2.loc[22]
             b.  f2[:] = 0

             c.  f2.loc[33]=0



        Python  with  Pandas  is  used  in  a  wide  range  of  fields  including  academic  and  commercial  domains  including
        finance, economics, Statistics, analytics, etc.
        Pandas can perform some of the important functions like:
           • It provides a fast and efficient way to manage and explore data using Series,DataFrames and Panels which
           makes it really fast and suitable for data science.

           • Missing Data represented as NaN and of any data type is very easily and efficiently handled.
           • Columns can be easily inserted, modified and deleted from DataFrame and Panel.

           • Objects can be explicitly aligned by the users to a set of labels, or automatically handled by data structures for
           smooth processing and data analysis.
           • Organisation and labelling of data are perfectly taken care of by the intelligent methods of slicing, alignment
           and indexing.
           • It provides support for cleaning of data needed for processing.

           • It provides strong support for reshaping, merging and joining various datasets with extreme efficiency.


        SciPy(Scientific Python)
        SciPy is a free, open source Python library used for scientific and technical Computation. It finds its great importance
        in the field of physics, engineering, mathematics, biology, and data science. SciPy is built on the Numpy extension
        of Python.
        SciPy provides a wide range of modules and submodules that helps in the area of scientific computing. Some of
        the important modules in SciPy are:

           • scipy.optimize:  This  module  has  functions  for  optimisation  problems,  including  root  finding,  curve  fitting,
           minimisation, and linear programming.

           • scipy.integrate: It provides mathematical integration techniques by using functions for integrating ordinary
           differential equations (ODEs), solving differential algebraic  equations (DAEs), and  performing numerical
           quadrature.

              198     Touchpad Robotics & Artificial Intelligence-X
   195   196   197   198   199   200   201   202   203   204   205