Kasey  Turcotte

Kasey Turcotte

1623945720

How Grouping Works with Python Pandas vs R Data Table

Explained with examples

What are the average house prices in different cities of the US? What are the total sales amounts of different product groups in a store? What are the average salaries in different companies?

All these questions can be answered by using a grouping operation given that we have proper data. Most data analysis libraries and frameworks implement a function to perform such operations.

In this article, we will compare two of the most popular data analysis libraries with regards to tasks that involve grouping. The first one is Python Pandas and the other is R data table.

We will be using the Melbourne housing dataset available on Kaggle for the examples. We first import the libraries and read the dataset.

## pandas
import pandas as pd
melb = pd.read_csv("/content/melb_data.csv")

## data.table
library(data.table)
melb <- fread("datasets/melb_data.csv")

#python #data-science #r #how grouping works with python pandas vs r data table #grouping works #data table

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How Grouping Works with Python Pandas vs R Data Table
Kasey  Turcotte

Kasey Turcotte

1623945720

How Grouping Works with Python Pandas vs R Data Table

Explained with examples

What are the average house prices in different cities of the US? What are the total sales amounts of different product groups in a store? What are the average salaries in different companies?

All these questions can be answered by using a grouping operation given that we have proper data. Most data analysis libraries and frameworks implement a function to perform such operations.

In this article, we will compare two of the most popular data analysis libraries with regards to tasks that involve grouping. The first one is Python Pandas and the other is R data table.

We will be using the Melbourne housing dataset available on Kaggle for the examples. We first import the libraries and read the dataset.

## pandas
import pandas as pd
melb = pd.read_csv("/content/melb_data.csv")

## data.table
library(data.table)
melb <- fread("datasets/melb_data.csv")

#python #data-science #r #how grouping works with python pandas vs r data table #grouping works #data table

Paula  Hall

Paula Hall

1623471960

5 Examples to Compare Python Pandas and R data.table

A practical guide for both

Python and R are the two predominant languages in the data science ecosystem. Both of them offer a rich selection of libraries that expedite and improve data science workflow.

In this article, we will compare pandas and data.table, two popular data analysis and manipulation libraries for Python and R, respectively. We will not be trying to declare one as superior to the other. Instead, the focus is to demonstrate how both libraries provide efficient and flexible methods for data wrangling.

The examples we will cover are the common data analysis and manipulation operations. Thus, you are likely to use them a lot.

We will be using the Melbourne housing dataset available on Kaggle for the examples. I will be using Google Colab (for pandas) and RStudio (for data.table) as IDE. Let’s first import the libraries and read the dataset.

## pandas
import pandas as pd
melb = pd.read_csv("/content/melb_data.csv")

## data.table
library(data.table)
melb <- fread("datasets/melb_data.csv")

#python #data-science #programming #r #examples to compare python pandas and r data.table #python pandas and r data.table

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

How to work with Pandas in Python

The complete guide to Pandas for beginners

When we talk about data science, we usually refer to the data analysis through summarization, visualizations, sophisticated algorithms that learn patterns in data (machine learning), and other fancy tools. When we discuss the term with software developers, we also hear a lot of Python, the popular programming language.

But why is Python so popular and special in the data science world? There are many reasons, and an important one is the Python ecosystem and libraries that make data science seem natural to Python.

One of these libraries is pandas , which every data science in the world uses, used, or at least heard of (if you are a data scientist who never used pandas, scream in comments).

Pandas is an essential part of the ecosystem that many other data science tools build on top or provide specific functionalities for pandas.

This guide introduces pandas for developers and aims to cover the what, why, and how of pandas’ most commonly used features.

Before we get started, if you want to access the full source code for this project to follow along, you can download the project’s source code from GitHub .

#how to work with pandas in python #python #pandas #work #pandas in python

August  Larson

August Larson

1624422360

R vs Python: What Should Beginners Learn?

Let go of any doubts or confusion, make the right choice and then focus and thrive as a data scientist.

I currently lead a research group with data scientists who use both R and Python. I have been in this field for over 14 years. I have witnessed the growth of both languages over the years and there is now a thriving community behind both.

I did not have a straightforward journey and learned many things the hard way. However, you can avoid making the mistakes I made and lead a more focussed, more rewarding journey and reach your goals quicker than others.

Before I dive in, let’s get something out of the way. R and Python are just tools to do the same thing. Data Science. Neither of the tools is inherently better than the other. Both the tools have been evolving over years (and will likely continue to do so).

Therefore, the short answer on whether you should learn Python or R is: it depends.

The longer answer, if you can spare a few minutes, will help you focus on what really matters and avoid the most common mistakes most enthusiastic beginners aspiring to become expert data scientists make.

#r-programming #python #perspective #r vs python: what should beginners learn? #r vs python #r