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.
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Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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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.
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:
on at least one of the first Y instalments of the loan in our sample,
We’ll be only working on the application data files for the sake of this article.
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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.
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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.
Like any other library, we can install pandasgui using pip.
pip install pandasgui
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
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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!
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.
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()
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