Efficient way to compute prefix sum | Python

Prerequisite:
https://www.youtube.com/watch?v=ESVXW... Check this link for more detail explanation of this powerful algorithm: https://en.wikipedia.org/wiki/Prefix_sum Python for beginners: https://www.youtube.com/watch?v=egq7Z...

Code: l=eval(input()); i=1; output=[]; output.append(l[0]); prefixsum=l[0]; while(i<len(l)): prefixsum=prefixsum+l[i]; output.append(prefixsum); i=i+1; print(output)




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Prefix sum | Python

Check this link for more detail explanation of this powerful algorithm: https://en.wikipedia.org/wiki/Prefix_sum Python for beginners: https://www.youtube.com/watch?v=egq7Z...

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What is Seaborn in data visualization? | python crash course_04

What is Seaborn in data visualization?

Hello everyone,  In the Previous blog we see first part of data visualization (Matplotlib) This blog is related to Seaborn. so Let's start:

Keys Feature

1. Seaborns is a statistical plotting library
2. It has beautiful default style
3. It also is designed to work very well with Pandas dataframe object.

Installing and getting started:

To install the latest release of seaborn, you can use:

pip install seaborn

conda install seaborn

Alternatively, you can used pip to install the development version directly from github:

pip install git+https://github.com/mwaskom/seaborn.git

Another option would be to to clone the github repository and install from yours local copy:

pip install . Python 2.7 or 3.5+

Let's see examples of Bar plot:

Bar Plots

The barplots() shows the relation between a categorical variable and a continuous variable. The data is represented in rectangular bar where the length the bar represent the proportion of the data in that category.

Bar plots represents the estimate of central tendency. Let us use the ‘titanic’ dataset to learn bar plot.

Example

import pandas as pd

import seaborn as sb

from matplotlib import pyplot as plt
df = sb.load_dataset('titanic')

sb.barplot(x = "sex", y = "survived", hue = "class", data = df)

plt.show()

Output:

(Note: for detailed please click here )

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Reverse a string using Stack | Python

Prerequisite: Reverse First K elements of Queue using STACK | Python | Castor Classes https://www.youtube.com/watch?v=gqIA2... Python for beginners: https://www.youtube.com/watch?v=egq7Z...

Code: def reverse(string): #Add code here stack=[]; for i in string: stack.append(i); s=""; i=0; while(i<len(string)): s=s+stack.pop(-1); i=i+1; return s;





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Reverse First K elements of Queue using STACK | Python

Prerequisite: Stacks and Queues using List | Data Structures | Python | Castor Classes https://www.youtube.com/watch?v=bMVBW... Python for beginners: https://www.youtube.com/watch?v=egq7Z...
Code:


def reverseK(queue,k,n):
    # code here
    stacka=[];
    queue2=queue[k:];
    i=0;
    while(i<k):
        f=queue.pop(0);
        stacka.append(f);
        i=i+1;
    queue=[];
    i=0;
    while(i<k):
        f=stacka.pop(-1);
        queue.append(f);
        i=i+1;
    queue=queue+queue2;
    return queue


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What is Matplotlib in Data Visualization? | Python crash course_03

What is Matplotlib in Data Visualization?

Hello everyone, welcome to Python crash course. Today I am going to Explain What is Matplotlib in Data Visualization? Let's Start:

Introduction about Matplotlib:

A pictures is worth a thousand word, and with Python’s matplotlib library, it, fortunately, take far less than a thousand words of code to create a production-quality graphic.However, matplotlibs is also a massive library, and getting a plot to looks just right is often achieved through trial and error. Using one-liners to generate basic plot in matplotlib is fairly simple, but skillfully commanding the remaining 97% of the library can be daunting.

This articles is a beginner-to-intermediate-level walkthrough on matplotlib that mixe theory with example. While learning by example can be tremendously insightful, it helps to have even just a surface-level understanding of the library’s inner working and layout as well.

SOME EXAMPLE OF MATPLOTLIB:

In this chapter, we will learn how to create a simple plots with Matplotlib.

We shall now display a simple line plots of angle in radians. its sine values in Matplotlib. To begin with, the Pyplot module from Matplotlib package is imported, with an alias plot as a matter of convention.

import matplotlib.pyplot as plt

Next we need an array of number to plot. Various array function are defined in the Numpy library which is imported with the np alias.

import numpy as np

We now obtain the ndarray object of angle between 0 and 2π using the arange() function from the Numpy library.

x = np.arange(0, math.pi*2, 0.05)

The ndarray object serves as values on x axis of the graphs. The corresponding sine values of angles in x to be displayed on y axis are obtained by the following statements −

y = np.sin(x)

The value from two arrays are plotted using the plot() functions.

plt.plot(x,y)

You can set the plots title, and labels for x and y axes.

#You can set the plots title, and labels for x and y axes.

plt.xlabel("angle")

plt.ylabel("sine")

plt.title('sine wave')

The Plots viewer window is invoked by the show() function −

plt.show()

The complete programs is as follow −

from matplotlib import pyplot as plt

import numpy as np

import math #needed for definition of pi

x = np.arange(0, math.pi*2, 0.05)

y = np.sin(x)

plt.plot(x,y)

plt.xlabel("angle")

plt.ylabel("sine")




plt.title('sine wave')

plt.show()

DRIVE LINK: https://drive.google.com/open?id=1U2YiRrhPY-MpFU7qJTCycliW3TOy91G0

FOR DETAILED PLEASE OPEN BELOW LINK:

CLICK HERE 

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Reshape the Matrix | Python

Problem Statement:

In MATLAB, there is a very useful function called 'reshape', which can reshape a matrix into a new one with different size but keep its original data.

You're given a matrix represented by a two-dimensional array, and two positive integers r and c representing the row number and column number of the wanted reshaped matrix, respectively.

The reshaped matrix need to be filled with all the elements of the original matrix in the same row-traversing order as they were.

If the 'reshape' operation with given parameters is possible and legal, output the new reshaped matrix; Otherwise, output the original matrix.

Example 1:
Input:
nums =
[[1,2],
 [3,4]]
r = 1, c = 4
Output:
[[1,2,3,4]]
Explanation:
The row-traversing of nums is [1,2,3,4]. The new reshaped matrix is a 1 * 4 matrix, fill it row by row by using the previous list.
Example 2:
Input:
nums =
[[1,2],
 [3,4]]
r = 2, c = 4
Output:
[[1,2],
 [3,4]]
Explanation:
There is no way to reshape a 2 * 2 matrix to a 2 * 4 matrix. So output the original matrix.
Note:
The height and width of the given matrix is in range [1, 100].
The given r and c are all positive.

Code:

class Solution:
    def matrixReshape(self, nums: List[List[int]], r: int, c: int) -> List[List[int]]:
        k=len(nums);
        m=len(nums[0]);
        if(k*m==r*c):
            l=[];
            for i in nums:
                l=l+i;
            i=0;
            j=0;
            output=[];
            k=[];
            temp=0;
            while(i<r):
                while(j<c):
                    k.append(l[temp]);
                    temp+=1;
                    j=j+1;
                output.append(k);
                k=[];
                j=0;
                i=i+1;
            return output;
        else:
            return nums;

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All About Pandas in Data Science | python crash course_02

All About Pandas in Data Science:

Hello friend, Today I am going to explain What is pandas in Data Science? so Let start:

This tutorial has been prepared for those who seek to learn the basic and various functions of Pandas. It will specifically useful for people working with data cleansing and analysis. After completing this tutorial, you will find yourself at moderate level of expertise from where you can take yourself to higher level of expertise.
Pandas is an open-source, BSD-licen Python library providing high-performance, easy-to-use data structures and data analysis tool for the Python programming language. Python with Pandas is used in wide range of field including academic and commercial domain including finance, economic, Statistic, analytics, etc. In this tutorial, we will learn the various feature of Python Pandas and how to use them in a practice.

Pandas deals with the following three data structure 

Series

DataFrame

Panel

These data structure are built on top of Numpy array, which mean they are fast.

Standard Python distribution doesn not come bundled with Pandas module. A lightweight alternative is install Numpy using popular Python package installer, pip.

pip install pandas 

import pandas as pd

SOME EXAMPLE OF PANDAS LIBRARY:

There are two kind of sorting available in Pandas. They are 

• 1.By label
• 2.By Actual Value

import pandas as pd
import numpy as np

unsorted_df=pd.DataFrame(np.random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],colu
mns=['col2','col1'])
print unsorted_df

OUTPUT:

        col2       col1
1  -2.063177   0.537527
4   0.142932  -0.684884
6   0.012667  -0.389340
2  -0.548797   1.848743
3  -1.044160   0.837381
5   0.385605   1.300185
9   1.031425  -1.002967
8  -0.407374  -0.435142
0   2.237453  -1.067139
7  -1.445831  -1.701035

From 3D ndarray (FOR PANEL CREATION)

import pandas as pd
import numpy as np

data = np.random.rand(2,4,5)
p = pd.Panel(data)
print p

output:

<class 'pandas.core.panel.Panel'>
Dimensions: 2 (item) x 4 (major_axis) x 5 (minor_axis)
Items axis: 0 to 1
Major_axis axis: 0 to 3
Minor_axis axis: 0 to 4

FOR DETAIL PLEASE DRIVE LINK:
https://drive.google.com/open?id=1RKv0v0V9kfvMceWb2QHdWfavDffsGAgV

FOR MORE DETAILED ABOUT PANDAS PLEASE OPEN BELOW LINK:

CLICK HERE 

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What is Numpy library in python? | python crash course_01

What is the Numpy library in python?

 Hello everyone, welcome to my python crash course. In the previous blog post, I released the python syllabus so it's time to start the tutorial. Let's start:

Numpy

What is a Python NumPy?

Numpy is Python package that stands for ‘Numerical Python’. It is the core library for scientific computing, which contain a powerful n-dimensional array object, provide tool for integrating C, C++, etc. It is also useful in linear algebra, random number capability. Numpy array can also used as an efficient multi-dimensional container for generic data. Now, let me tell you what exactly is python Numpy array.

NumPy Array: Numpy array is powerful N-dimensional array object which is in the form of row and column. We can initialize numpy arrays from nested Python lists and access it element. In order to perform these numpy operation, the next question which will come in your mind is:

How do I install NumPy?

To install Python Numpy, go to your command prompt and type “pip install Numpy”. Once the installation is completed, go to your IDE and simply import it by typing: “Import Numpy as np”
Moving ahead in python numpy tutorials, let us understand what exactly is a multi-dimensional Numpy array.

Trigonometric Function

Numpy has standard trigonometric function which return trigonometric ratios for a given angle in radian.

Example

import numpy as np 
a = np.array([0,30,45,60,90]) 

print 'Sine of different angles:' 
# Convert to radian by multiplying with pi/180 
print np.sin(a*np.pi/180) 
print '\n'  

print 'Cosine values for angles in array:' 
print np.cos(a*np.pi/180) 
print '\n'  

print 'Tangent values for given angles:' 
print np.tan(a*np.pi/180) 

output is:

Sine of different angles:
[ 0.          0.5         0.70710678  0.8660254   1.        ]

Cosine values for angle in array:
[  1.00000000e+00   8.66025404e-01   7.07106781e-01   5.00000000e-01
   6.12323400e-17]                                                            

Tangent values for given angle:
[  0.00000000e+00   5.77350269e-01   1.00000000e+00   1.73205081e+00
   1.63312394e+16]

for detail information please open below  drive link:
https://drive.google.com/open?id=1tiE_cc8O8yRBYNKrSnhd8TO2mdZKdHtW
FOR DETAILED PLEASE CLICK BELOW:

CLICK HERE 
(NOTE: If any question then comment)
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Print first n numbers made up of some particular digits | Queue Application | Python

Prerequisite: Stacks and Queues using List | Data Structures | Python | Castor Classes https://www.youtube.com/watch?v=bMVBW... Python for beginners: https://www.youtube.com/watch?v=egq7Z...

Code:
x=int(input('Enter the first digit:'));
y=int(input('Enter the second digit:'));
l=[];
q=[];
n=int(input("How many numbers you want?"));
i=0;
q.append(str(x));
q.append(str(y));
while(i<n):
    es=q.pop(0);
    l.append(int(es));
    ex=es+str(x);
    q.append(ex);
    ey=es+str(y);
    q.append(ey);
    i=i+1;
print(l)

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IN operator as a shorthand for multiple OR conditions in Python

Prerequisite: List membership Testing using in | Part 8 | Python | Castor Classes https://www.youtube.com/watch?v=ggmBT... Python for beginners: https://www.youtube.com/watch?v=egq7Z...

Telegram: https://t.me/clcoding_python https://www.facebook.com/pirawenpython/ https://www.facebook.com/groups/piraw...

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what is python? | overview about python language

Hello everyone, welcome to my blog post, today we have to start python crash course so first upon required to know the basics of this programming language hence let's start.

INTRODUCTION TO PYTHON:

(Befor the starting the section please read the this post: CLICK HERE)

Python was conceived in the late 1980s as a successor to the ABC language. Python 2.0, released in 2000, introduced features like list comprenstion and a garbage collection system capable of collecting reference cycle. Python 3.0, released in 2008, was a major revision of the language that is not completely backward, and much Python 2 code does not run unmodified on Python 3.

The Python 2 language was officially discontinued in 2020 (first planned for 2015), and "Python 2.7.18 is the last Python 2.7 release and therefore the last Python 2 release. No more security patches or other improvements will be released for it. With Python 2's end of life, only Python 3.5.x and later are supported.

Python is a general purpose and high level programming language. You can use Python for developing desktop GUI applications, websites and web applications. Also, Python, as a high level programming language, allows you to focus on core functionality of the application by taking care of common programming tasks.

Python is an object-oriented programming language created by Guido Rossum in 1989. It is ideally designed for rapid prototyping of complex applications. Python programming is widely used in Artificial Intelligence, Natural Language Generation, Neural Networks and other advanced fields of Computer Science.

(Note: If you want to read more introduction then click on Read more)
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