Edureka Fan

Edureka Fan

1598219340

How to Perform Exploratory Data Analysis Using Python

This Edureka video on the ‘How to perform Exploratory Data Analysis Using Python’ will help you understand how we can use Python to perform EDA for significant insights and data-driven conclusions.

#python #data-analysis #developer

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How to Perform Exploratory Data Analysis Using Python
Ray  Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

#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

Madaline  Mertz

Madaline Mertz

1621635960

Exploratory Data Analysis in Python: What You Need to Know?

Exploratory Data Analysis (EDA) is a very common and important practice followed by all data scientists. It is the process of looking at tables and tables of data from different angles in order to understand it fully. Gaining a good understanding of data helps us to clean and summarize it, which then brings out the insights and trends which were otherwise unclear.

EDA has no hard-core set of rules which are to be followed like in ‘data analysis’, for example. People who are new to the field always tend to confuse between the two terms, which are mostly similar but different in their purpose. Unlike EDA, data analysis is more inclined towards the implementation of probabilities and statistical methods to reveal facts and relationships among different variants.

Coming back, there is no right or wrong way to perform EDA. It varies from person to person however, there are some major guidelines commonly followed which are listed below.

  • Handling missing values: Null values can be seen when all the data may not have been available or recorded during collection.
  • Removing duplicate data: It is important to prevent any overfitting or bias created during training the machine learning algorithm using repeated data records
  • Handling outliers: Outliers are records that drastically differ from the rest of the data and don’t follow the trend. It can arise due to certain exceptions or inaccuracy during data collection
  • Scaling and normalizing: This is only done for numerical data variables. Most of the time the variables greatly differ in their range and scale which makes it difficult to compare them and find correlations.
  • Univariate and Bivariate analysis: Univariate analysis is usually done by seeing how one variable is affecting the target variable. Bivariate analysis is carried out between any 2 variables, it can either be numerical or categorical or both.

We will look at how some of these are implemented using a very famous ‘Home Credit Default Risk’ dataset available on Kaggle here. The data contains information about the loan applicant at the time of applying for the loan. It contains two types of scenarios:

  • The client with payment difficulties: he/she had late payment more than X days

on at least one of the first Y instalments of the loan in our sample,

  • All other cases: All other cases when the payment is paid on time.

We’ll be only working on the application data files for the sake of this article.

#data science #data analysis #data analysis in python #exploratory data analysis in python

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Exploratory Data Analysis using PandasGUI

Exploratory Data Analysis is the most crucial part, to begin with whenever we are working with a dataset. It allows us to analyze the data and let us explore the initial findings from data like how many rows and columns are there, what are the different columns, etc. EDA is an approach where we summarize the main characteristics of the data using different methods and mainly visualization.

EDA is an important and most crucial step if you are working with data. It takes up almost 30% of the total project timing to explore the data and find out what it is all about. EDA allows us and tells us how to preprocess the data before modeling. This is why EDA is most important but we can save this time by automating all the time taking EDA jobs and can use the time saved in modeling.

Pandasgui is an open-source python module/package which creates a GUI interface where we can analyze the pandas dataframe and use different functionalities in order to visualize and analyze data and perform exploratory data analysis.

In this article, we will explore Pandasgui and see how we can use it to automate the process of Exploratory Data Analysis and save our time and effort.

Installing Pandasgui

Like any other library, we can install pandasgui using pip.

pip install pandasgui

Loading dataset

A large variety of datasets are predefined in pandasgui we will use pandasgui to load one dataset named “IRIS” which is a very famous dataset and will explore it using the GUI interface of pandasgui. We will also import the “show” function which loads the dataset into the GUI.

from pandasgui.datasets import iris
#importing the show function
from pandasgui import show

#data-analysis #python #data-visualization #data-science #exploratory-data-analysis

Hertha  Walsh

Hertha Walsh

1603270800

Graphical Approach to Exploratory Data Analysis in Python

Exploratory Data Analysis (EDA) is one of the most important aspect in every data science or data analysis problem. It provides us greater understanding on our data and can possibly unravel hidden insights that aren’t that obvious to us. The first article I’ve wrote on Medium is also on performing EDA in R, you can check it out here. This post will focus more on graphical EDA in Python using matplotlib, regression line and even motion chart!

Dataset

The dataset we are using for this article can be obtained from Gapminder, and drilling down into _Population, Gender Equality in Education _and Income.

The _Population _data contains yearly data regarding the estimated resident population, grouped by countries around the world between 1800 and 2018.

The Gender Equality in Education data contains yearly data between 1970 and 2015 on the ratio between female to male in schools, among 25 to 34 years old which includes primary, secondary and tertiary education across different countries

The _Income _data contains yearly data of income per person adjusted for differences in purchasing power (in international dollars) across different countries around the world, for the period between 1800 and 2018.

EDA on Population

Let’s first plot the population data over time, and focus mainly on the three countries Singapore, United States and China. We will use matplotlib library to plot 3 different line charts on the same figure.

import pandas as pd
import matplotlib.pylab as plt
%matplotlib inline

## read in data
population = pd.read_csv('./population.csv')
## plot for the 3 countries
plt.plot(population.Year,population.Singapore,label="Singapore")
plt.plot(population.Year,population.China,label="China")
plt.plot(population.Year,population["United States"],label="United States")
## add legends, labels and title
plt.legend(loc='best')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Population Growth over time')
plt.show()

#exploratory-data-analysis #data-analysis #data-science #data-visualization #python