Infographic Note on Correlation Analysis using corr() function over a Pandas dataframe in Python

 

Infographic Note on Correlation Analysis of numerical attributes of a Pandas Dataframe in Python

 

Infographic Note on Python Code Analysis of a Sample Outliers Detection function over a numerical attribute in a pandas dataframe

 

Infographic Note on Outliers Detection , finding Inter-Quartile ranges and Interpreting Box-Plot diagrams over Pandas Dataframe in Python

 

Infographic Note on steps to perform Count , filter , group and aggregate numerical values over a dataset using Numpy and Pandas in Python using Jupyter Notebook IDE

 

Infographic Note on steps to formulate and perform a Hypothesis Test using Numpy , Pandas and Scipy Libraries of Python

 

Infographic Note on Numpy Arrays in Python with Questions and Answers on Array and Matrix Creation and Data Retrieval

 

Q1) Print range between 1 to 20 and show 5 integer random numbers

Q2) Print range between 1 to 100 and show 10 random integer numbers

Q3) Print range between 1 to 1000 and show 20 random integer numbers

Q4) Print a matrix with range between random number - 5 rows and 5 cols integer random numbers

Q5) Print a matrix range between random number - 10 rows and 6 cols integer random numbers in a             matrix format

An Infographic Note on Arrays in Numpy in Python with standard questions and answers on Array Creation and associated Operations

 

Some Basic Questions on Numpy Package in Python for Interviews

 

Comparision of Arrays with respect to their method of usage and handling in Numpy and SciPy Libraries of Python

 

Concepts related to Array datatype object handling and manipulation using Numpy and Scipy Libraries of Python - A Descriptive Article

 

Numpy and SciPy Libraries Basic Functionality in Python

 

Lists and Dictionaries in Python Dictionaries in Python

 When should one use dictionaries and when should one use lists in Python

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 *  Lists are the most widely used data when the data is stored in the form of integral indexes with some starting position to some given position within the Python list

 *  Lists are also mostly used when the data that needs to be maintained in a certain specific manner or  where the data needs to be sorted where finding  some data is only good enough when the user of the given set of code is knowing the exact index or position of the item element where the list item needs to be searched or retrieved

 *  Lists are also used when the ordering of items within the List item object is irrelevant as any given element within a list can be searched and retrieved just by using some conditional loops which will iterate over the list item object and then the item could be put to use directly without any form of replacement or elimination within the data structure

 *  One of the main highlighting important points about Lists as a data structure for item storage is that Lists can be used for storage of other lists and tuples as a data storage within it because of which the main list containing the sub-lists within it could act as a parent list object which can keep within it other child objects for storage and retrieval as well

 *   Now coming to dictionaries and what they are used for :-

 *  Dictionaries are perfect storage objects when the data is to be stored on the basis of positonal indexes coupled with an adjoining value associated for that index position . Here also the ordering of the data is not of great matter and does not need to sort the stored data . Finding any associated data of the dictionary object needs only a key value of that object 

*   Example where a List is used for data storage is an example of a shopping cart where any stored item object's storage does not matter .

*  Example where a Dictionary Item object is used as a storage object is that of a an examination roll / rank number sheet ( parikhya patra ) where people are arranged on the basis of their marks obtained in some examination from Rank 1 to the last rank in the exam . Usually the person who obtains the highest marks in a given exam is attributted the 1st rank and the first roll number for that examination is also alloted to the first rank holder after the results are out , the successive role numbers are also attributed according the net marks obtained  by an examiner in the considered case of the examination till the last rank/roll number of the examiner .

 

*  A dictionary is very useful data storage object where one may need to access a given data based on the item that is present over a given index position within the storage object of the data structure .

 *  As a measure of how important the dictionaries are - every module , class , function and method written in Python in the form of dictionaries where all the attribute names are in the form of Keys and all the attribute values there in can be found quickly .

 *  Therefore when one wants to decide upon which data structure to be used for a certain type of data object one might need to consider the best use cases of both these data structure objects : Lists and Dictionary Items .

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To sum up once again for Lists and Dictionaries

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*  Lists are ordered data structure objects that means that the program has control over the order in which the data is stored . This data is also sortable that means that the program can sort the data into whatever order when the data is needed to be fetched and retrieved , the list is also containing a list of iterable items i,e items that can be accessed one item at a time when the list object is iterated or executed within a loop .

 

*  Dictionaries are like the partially ordered data structures that can control the order or the manner in which the data items are inserted and stored within the object in the form of keys and attributes . Dictionaries are non-sortable and iterable in pairs of both keys and associated value objects .

 

 

 

 

 

 

 

 

 

Last modified: 18:49

Advanced Matrix Operations – A theoretical view

                     

  Advanced Matrix Operations – A theoretical view               ========================================

 

* One may encounter some important matrix operations using algorithmic formulations

 

* The advanced matrix operations are formulating the transpose and inverse of any given matrix form of dataset

 

* Transposition occurs when a matrix of shape n x m is transformed into a matrix in the form of m x n by exchanging the rows with the columns

 

* Most of the tests indicate the operation using the superscript T in the form of A( transpose )

 

* One can apply " matrix inversion " over matrices of shape m x m , which are square matrices that have the same number of rows and columns . In mathematical language , this form of square ordering of matrices is said that the matrix has m rows and m columns .

 

* The above operation is important for the sake of finding the immediate resolution of the various equations which involve matrix multiplication such as y = bX where one has to discover the values in the vector b . More on Matrix multiplications with more conceptual examples would be showcased in another article in which I shall try to cover how the Matrix Multiplication of different Matrices occur and how this Multiplication is used to solve more important / complex problems .

 

 

* Since most scalar numbers (exceptions including zero) have a number whose multiplication results in a value of 1 , the idea is to find a matrix inverse whose multiplication would result in a special matrix called the identity matrix whose elements are zero , except the diagonal elements

 ( the elements in positions where the index 1 is equal to the index j)

* Now , if one wants to find the inverse of a scalar quantity , then one can do so by finding the inverse of a scalar . (The scalar number n has an inverse value that is n to the power minus 1 which can be represented by 1/n that is 1 upon n )

 

* Sometimes, finding the inverse of a matrix is impossible and hence the inverse of a matrix A is indicated as A to the power minus 1

 

* When a matrix cannot be inverted, it is referred to "singular matrix" or a "degenerate matrix" . Singular matrices are usually not found in isolation, rather are quite rare to occur and generalise .

13 - Sample Space of Events and Basic Operations like Union , Intersection , Complement etc

 

12 - Events - Mutually Exclusive Events ( A sample Representation )

 

09 - Types of Sample Spaces ( Finite , Countably Infinite , Non Countably Infinite , Non-Discrete )

 

Lists and Tuples in Python with slicing and indexing operations and examples

 

Lists and Tuples in Python with example of Slicing and Indexing Operations over them

Method to Process Lists in Python