Exploratory Data Analysis (EDA) using Apache Spark and Python

In this video we will walk through exploratory data analysis using Apache Spark, Databricks and Python. We will see various plots for uni-variate and multi-variate analysis and also understand which plots can help in what scenario. We will explore charts and plots like

• Bar chart
• Box Plot
• Scatter Plot
• Q-Q Plot
• Plotting in maps
• Pivot Table
• Histograms

Finally we will discuss on how to convert spark dataframe to pandas and challenges of it also use seaborn and matplot to plot simple graph

#python #datascience

What is GEEK

Buddha Community

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

Big Data Tools 1.0 Generally Available | The Big Data Tools Blog

A new update of the Big Data Tools plugin has been released. This is our first version for general use, after a year and a half of the Early Access Preview program.

Install the plugin from the JetBrains Plugin Repository or from inside your IDE to edit Zeppelin notebooks, upload files to cloud filesystems, and monitor Hadoop and Spark clusters. The following JetBrains IDEs support the plugin: IntelliJ IDEA Ultimate, PyCharm Professional Edition, and DataGrip.

In this release, we’ve added many useful features and addressed a variety of bugs. Let’s dive into the details.

#big data tools #newsletter #plugins #releases #apache #apache spark #apache zeppelin #big data #big data tools #precode #python #spark #spark-submit #zeppelin

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

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

Big Data Tools 1.0 Generally Available

A new update of the Big Data Tools plugin has been released. This is our first version for general use, after a year and a half of the Early Access Preview program.

Install the plugin from the JetBrains Plugin Repository or from inside your IDE to edit Zeppelin notebooks, upload files to cloud filesystems, and monitor Hadoop and Spark clusters. The following JetBrains IDEs support the plugin: IntelliJ IDEA Ultimate, PyCharm Professional Edition, and DataGrip.

In this release, we’ve added many useful features and addressed a variety of bugs. Let’s dive into the details.

#big data tools #newsletter #plugins #releases #apache #apache spark #apache zeppelin #big data #big data tools #precode #python #spark #spark-submit #zeppelin