Monday, 25 May 2020

Learning Python: Powerful Object-Oriented Programming Kindle Edition by Mark Lutz (Author) pdf

Author May 25, 2020 Books, Python No comments

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages.

Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code.

Explore Python’s major built-in object types such as numbers, lists, and dictionaries
Create and process objects with Python statements, and learn Python’s general syntax model
Use functions to avoid code redundancy and package code for reuse
Organize statements, functions, and other tools into larger components with modules
Dive into classes: Python’s object-oriented programming tool for structuring code
Write large programs with Python’s exception-handling model and development tools
Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing
Buy: Learning Python: Powerful Object-Oriented Programming Kindle Edition by Mark Lutz (Author)

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Logistic Regression in Python(part01) | python crash course_07

i am rider (iari) May 24, 2020 Python No comments

Logistic Regression in Python: part-01

Welcome to python crash course, Today we are going to start Logistic Regression. basically, in this post you will learn How to encoding data so let's start:

As the amount of available data, the strength of computing power, and the number of algorithmic improvements continue to rise, so does the importance of data science and machine learning. Classification is among the most important area of machine learning, and logistic regression is one of its basic methods. By the end of this tutorial, you will have learned about classification in general and the fundamentals of logistic regression in particular, as well as how to implement logistic regressions in Python.

Supervised machine learning algorithms define models that capture relationships among data. Classification is an area of supervised machine learning that tries to predict which class or category some entity belongs to, based on its features.

For example, you might analyze the employees of some company and try to establish a dependence on the features or variables, such as the level of education, number of years in a current position, age, salary, odds for being promoted. The features or variable can take one of two forms:

Independent variable, also called input or predictor, doesn’t depend on other features of interest (or at least you assume so for the purpose of the analysis).
The dependent variable, also called output or responses, depending on the independent variables.

Encoding Data


In [01]: # creating one hot encoding of categorical column.

data = pd.get_dummies(df, columns =['job', 'marital', 'default', 'housing', 'loan', 'poutcome'])

In [02]: data.head()

You will see the following outputs −

Dropping the “unknown”


In [03]: data.columns[12]

Out[03]: 'job_unknown'

In [04]: data.drop(data.columns[[12, 16, 18, 22, 24]], axis=1, inplace=True)

After dropping the undesired columns, you can see the final list of columns as shown in the output below −


In [05]: data.columns

Out[16]: Index(['y', 'job_admin.', 'job_bluecollar', 'jobentrepreneur', 

'jobhousemaid', 'job_management', 'job_retired', 'job_self-employed', 

'jobservices', 'job_student', 'job_technician', 'job_unemployed',

'marital_divorced', 'marital_married', 'marital_single', 'default_no', 

'default_yes', 'housingno', 'housing_yes', 'loan_no', 'loan_yes',

'poutcome_failure', 'poutcome_nonexistent', 'poutcomesuccess'], 

dtype='object')

our data is ready for model buildings.

In the next post, we will see how to split the data.

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File Handling | Python

Author May 22, 2020 Python No comments

Topics Discussed: 1)Opening a File 2)Reading from a file 3)Closing a file Python for beginners: https://www.youtube.com/watch?v=egq7Z...

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Rules for Python variables

Author May 22, 2020 Python No comments

Rules for Python variables:
A variable name must start with a letter or the underscore character
A variable name cannot start with a number
A variable name can only contain alpha-numeric characters and underscores (A-z, 0-9, and _ )
Variable names are case-sensitive (age, Age and AGE are three different variables)

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Export Pandas DataFrame to CSV

Author May 22, 2020 Python No comments

Topics discussed: 1)count the number of rows in a Pandas DataFrame in Python 2)count the number of columns in a Pandas DataFrame in Python 3)Extracting specific column in Pandas DataFrame in Python 4) Filter Pandas Dataframe 5)Export Pandas DataFrame to CSV Prerequisite: Read csv using pandas.read_csv() | Python | Castor Classes https://www.youtube.com/watch?v=Sgqry... Python for beginners: https://www.youtube.com/watch?v=egq7Z...

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Read csv using pandas.read_csv() | Python

Author May 22, 2020 Python No comments

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Uncommon Words from Two Sentences | Python

Author May 22, 2020 Python No comments

Problem Statement: We are given two sentences A and B. (A sentence is a string of space separated words. Each word consists only of lowercase letters.) A word is uncommon if it appears exactly once in one of the sentences, and does not appear in the other sentence. Return a list of all uncommon words. You may return the list in any order. Example 1: Input: A = "this apple is sweet", B = "this apple is sour" Output: ["sweet","sour"] Example 2: Input: A = "apple apple", B = "banana" Output: ["banana"] Code is given in the comment section. Prerequisite: Counting the frequencies in a list using dictionary | Python | Castor Classes https://www.youtube.com/watch?v=yZKGU... Python for beginners: https://www.youtube.com/watch?v=egq7Z...

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Counting the frequencies in a list using dictionary | Python

Author May 22, 2020 Python No comments

Code:
num1=[1,1,2,3,2,5,7,5];
dict1={};
for num in num1:
if num in dict1:
dict1[num]=dict1[num]+1;
else:
dict1[num]=1;

Python for beginners:

https://www.youtube.com/watch?v=egq7Z...

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Dictionary Part 4 | Python

Author May 22, 2020 Python No comments

Prerequisite: Dictionary | Python | Castor Classes https://www.youtube.com/watch?v=yZTR5... Dictionary Part 2 | Python | Castor Classes https://www.youtube.com/watch?v=qU1dV... Dictionary Part 3 | Python | Castor Classes https://www.youtube.com/watch?v=nFfxX... Python for beginners: https://www.youtube.com/watch?v=egq7Z...

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QUIZ on Loops | Python

Author May 22, 2020 Python No comments

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Linear Regression in python | python crash course_06

i am rider (iari) May 22, 2020 Python No comments

Linear Regression in Machine learning :

Welcome to machine learning in python crash course, so in this section we will learn different machine learning algorithm. Let's start:

Linear Regressions is usually the first machine learning algorithm. It is a simple model but everyone need to master it as it lays the foundation for other machine learning algorithm.

Where can Linear Regressions be used?

It is a very powerful techniques and can be used to understand the factors that influence profitability. It can be used to forecast sale in the coming months by analyzing the sales data for previous month. It can also be used to gain various insights into customers behaviour. By the end of the blog, we will build a model which looks like the below picture i.e, determine a line which best fit the data.

Example

In this example, we will use Pima Indian Diabetes dataset to select four of the attributes having best features with the help of chi-square statistical test.


from pandas import read_csv

from numpy import set_printoptions

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import chi2

path = r'C:\pima-indians-diabetes.csv'

names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'age', 'class']

dataframe = read_csv(path, names=names)

array = dataframe.value

Next, we will separate array into inputs and outputs components −
X = array[:,0:8]

Y = array[:,8]
The following line of code will select the best features from dataset −
test = SelectKBest(score_func=chi2, k=4)

fit = test.fit(X,Y)
set_printoptions(precision=2)
print(fit.scores_)
featured_data = fit.transform(X)
print ("\nFeatured data:\n", featured_data[0:4])

OUTPUT:
[ 111.52 1411.89 17.61 53.11 2175.57 127.67 5.39 181.3
Featured data:
[[148. 0. 33.6 50.
[ 89. 94. 28.1 21. ]]
[ 85. 0. 26.6 31. ]
[ 183. 0. 23.3 32. ]
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Pandas Built in Data Visualization | python crash course_05

i am rider (iari) May 19, 2020 Python No comments

Pandas Built in Data Visualization:

Welcome to python crash course tutorial, today we will see the last topic Pandas Built in Data Visualization in the data science section.

SOME INTRODUCTION:

Data Visualizations is the presentation of data in graphical format. It help people understand the significance of data by summarizing and presenting a huge amount of data in a simple and easy-to-understand format and help communicate information clearly and effectively.

In this tutorial, we will learn about pandas built-in capabilities for data visualizations. It is built-off of matplotlib, but it baked into pandas for easier usage.

Let’s take a looks

Example:

Basic Plotting: plot

This functionality on Series and DataFrame is just a simple wrapper around the matplotlib libraries plot() methods.


import pandas as pd

import numpy as np


df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range('1/1/2000',

   periods=10), columns=list('ABCD'))


df.plot()

Output:

If the index consists of dates, it calls gct().autofmt_xdate() to format the x-axis as shown in the above illustration.

We can plots one column versus another using the x and y keywords.

Plotting method allow a handful of plot styles other than the default line plot. These method can be provided as the kind keyword argument to plots(). These include −