Anshu  Banga

Anshu Banga


Tutorial On Datacleaner - Python Tool to Speed-Up Data Cleaning Process

Data cleaning is an important part of data manipulation and analysis. We need to clean data with any null values, unknown characters, etc. Data cleaning is a time taking process which cannot be neglected  because when we are preparing data for the machine learning model the data should be cleaned otherwise we won’t be able to generate useful insights. Or predictions.

We can apply different functions on the pandas dataframe which can help us in cleaning the data which in  turn cleans the data, remove junk values, etc. But before that, we need to perform data analysis and know what all we need to do, what are the junk values, what are the datatypes of different columns in order to perform different operations for different datatypes. But what if we can automate this cleaning process? It can save a lot of time.

Datacleaner is an open-source python library which is used for automating the process of data cleaning. It is built  using Pandas Dataframe and scikit-learn data preprocessing features. The contributors are actively updating it with new features. Some of the current features are:

  • Dropping columns with null values
  • Replacing null values with a mean(numerical data) and median(categorical data)
  • Encoding non-numerical values with numerical equivalents.

In this article, we will see how datacleaner  automates the process of data cleaning to save time and effort.

#data analysis #data cleaning #python

What is GEEK

Buddha Community

Tutorial On Datacleaner - Python Tool to Speed-Up Data Cleaning Process
 iOS App Dev

iOS App Dev


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

Arvel  Parker

Arvel Parker


Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).


The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Ian  Robinson

Ian Robinson


Top 10 Big Data Tools for Data Management and Analytics

Introduction to Big Data

What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.

To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

List of Big Data Tools & Frameworks

The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
  2. Data Storage Tools
  3. Data Visualization Tools
  4. Big Data Processing Tools
  5. Data Preprocessing Tools
  6. Data Wrangling Tools
  7. Big Data Testing Tools
  8. Data Governance Tools
  9. Security Management Tools
  10. Real-Time Data Streaming Tools

#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics

Ray  Patel

Ray Patel


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

Data Science Tools Illustrated Study Guides

Twin brothers and educators Afshine and Shervine Amidi, creators of past fantastic machine learning and deep learning study resources, are back and at it again, this time with a set of illustrated study guides for an array of data science tools.

This set of illustrated study guides for data science tools was born out of an MIT class that Afshine is currently teaching, though the brothers created the resources in tandem.

What exactly is covered in these guides? They are broken up into four distinct categories, each category containing between one and three individual related guides. The below links redirect to the online versions of these guides; PDF versions are available further below.

Data retrieval


Concepts covered in this guide include: filtering, conditions and data types; types of joins; aggregations, window functions; table manipulation

Data manipulation


Concepts covered in these guides include: filtering, conditions and data types; types of joins; aggregations, window functions; data frame transformations; conversions made easy between R and Python

Data visualization


Concepts covered in these guides include: scatterplots, line plots, histograms; boxplots, maps; customized legend; conversion made easy between R and Python

#2020 aug tutorials # overviews #cheat sheet #data preprocessing #data processing #data science #data science tools #data visualization #python #r #sql