1604079360

# How to Run the Chi-Square Test in Python

We will provide a practical example of how we can run a Chi-Square Test in Python. Assume that we want to test if there is a statistically significant difference in **Genders **(M, F) population between **Smokers **and Non-Smokers. Let’s generate some sample data to work on it.

## Sample Data

``````mport pandas as pd
import numpy as np
from scipy.stats import chi2_contingency

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.DataFrame({'Gender' : ['M', 'M', 'M', 'F', 'F'] * 10,
'isSmoker' : ['Smoker', 'Smoker', 'Non-Smpoker', 'Non-Smpoker', 'Smoker'] * 10
})
``````

Output:

``````  Gender isSmoker
0 M Smoker
1 M Smoker
2 M Non-Smpoker
3 F Non-Smpoker
4 F Smoker
``````

## Contingency Table

To run the Chi-Square Test, the easiest way is to convert the data into a contingency table with frequencies. We will use the `crosstab` command from `pandas`.

``````contigency= pd.crosstab(df['Gender'], df['isSmoker'])
contigency
``````

Let’s say that we want to get the percentages by Gender (row)

``````contigency_pct = pd.crosstab(df['Gender'], df['isSmoker'], normalize='index')
contigency_pct
``````

If we want the percentages by column, then we should write normalize=’column’ and if we want the total percentage then we should write normalize=’all’

#statistical-analysis #chi-square-test #hypothesis-testing #python #statistics

1619510796

## Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

1626775355

## Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.

## 5 Reasons to Utilize Python for Programming Web Apps

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.

Utilized by the best

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.

Massive community support

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.

Progressive applications

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

### Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

1604079360

## How to Run the Chi-Square Test in Python

We will provide a practical example of how we can run a Chi-Square Test in Python. Assume that we want to test if there is a statistically significant difference in **Genders **(M, F) population between **Smokers **and Non-Smokers. Let’s generate some sample data to work on it.

## Sample Data

``````mport pandas as pd
import numpy as np
from scipy.stats import chi2_contingency

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.DataFrame({'Gender' : ['M', 'M', 'M', 'F', 'F'] * 10,
'isSmoker' : ['Smoker', 'Smoker', 'Non-Smpoker', 'Non-Smpoker', 'Smoker'] * 10
})
``````

Output:

``````  Gender isSmoker
0 M Smoker
1 M Smoker
2 M Non-Smpoker
3 F Non-Smpoker
4 F Smoker
``````

## Contingency Table

To run the Chi-Square Test, the easiest way is to convert the data into a contingency table with frequencies. We will use the `crosstab` command from `pandas`.

``````contigency= pd.crosstab(df['Gender'], df['isSmoker'])
contigency
``````

Let’s say that we want to get the percentages by Gender (row)

``````contigency_pct = pd.crosstab(df['Gender'], df['isSmoker'], normalize='index')
contigency_pct
``````

If we want the percentages by column, then we should write normalize=’column’ and if we want the total percentage then we should write normalize=’all’

#statistical-analysis #chi-square-test #hypothesis-testing #python #statistics

1602968400

## Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

### Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

``````>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName
>>> print(FirstName, LastName)
('Jordan', 'kalebu')
``````

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

1602666000

## How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

### Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips