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Python is an incredibly versatile language, useful for a wide variety of tasks in a wide range of disciplines. One such discipline is statistical analysis on datasets, and along with SPSS, Python is one of the most common tools for statistics.
Python’s user-friendly and intuitive nature makes running statistical tests and implementing analytical techniques easy, especially through the use of the statsmodels library.
The statsmodels
library is a module for Python that gives easy access to a variety of statistical tools for carrying out statistical tests and exploring data. There are a number of statistical tests and functions that the library grants access to, including ordinary least squares (OLS) regressions, generalized linear models, logit models, Principal Component Analysis (PCA), and Autoregressive Integrated Moving Average (ARIMA) models.
The results of the models are constantly tested against other statistical packages to ensure that the models are accurate. When combined with SciPy and Pandas, it’s simple to visualize data, run statistical tests, and check relationships for significance.
Before we can practice statistics with Python, we need to select a dataset. We’ll be making use of a dataset compiled by the Gapminder Foundation.
The Gapminder Dataset tracks many variables used to assess the general health and wellness of populations in countries around the world. We’ll be using the dataset because it is very well documented, standardized, and complete. We won’t have to do much in the way of preprocessing in order to make use of it.
There are a few things we’ll want to do just to get the dataset ready to run regressions, ANOVAs, and other tests, but by and large the dataset is ready to work with.
The starting point for our statistical analysis of the Gapminder dataset is exploratory data analysis. We’ll use some graphing and plotting functions from Matplotlib and Seaborn to visualize some interesting relationships and get an idea of what variable relationships we may want to explore.
We’ll start off by visualizing some possible relationships. Using Seaborn and Pandas we can do some regressions that look at the strength of correlations between the variables in our dataset to get an idea of which variable relationships are worth studying.
We’ll import those two and any other libraries we’ll be using here:
import statsmodels.formula.api as smf
import statsmodels.stats.multicomp as multi
import scipy
from scipy.stats import pearsonr
import pandas as pd
from seaborn import regplot
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
There isn’t much preprocessing we have to do, but we do need to do a few things. First, we’ll check for missing any missing or null
data and convert any non-numeric entries to numeric. We’ll also make a copy of the transformed dataframe that we’ll work with:
# Check for missing data
def check_missing_values(df, cols):
for col in cols:
print("Column {} is missing:".format(col))
print((df[col].values == ' ').sum())
print()
# Convert to numeric
def convert_numeric(dataframe, cols):
for col in cols:
dataframe[col] = pd.to_numeric(dataframe[col], errors='coerce')
df = pd.read_csv("gapminder.csv")
print("Null values:")
print(df.isnull().values.any())
cols = ['lifeexpectancy', 'breastcancerper100th', 'suicideper100th']
norm_cols = ['internetuserate', 'employrate']
df2 = df.copy()
check_missing_values(df, cols)
check_missing_values(df, norm_cols)
convert_numeric(df2, cols)
convert_numeric(df2, norm_cols)
Here are the outputs:
Null values:
Column lifeexpectancy is missing:
22
Column breastcancerper100th is missing:
40
Column suicideper100th is missing:
22
Column internetuserate is missing:
21
Column employrate is missing:
35
#python #data science #machine learning #programming
1619518440
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
…
#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners
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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
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.
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.
Simple to read and compose
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.
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
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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.
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
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.
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
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
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
#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips