Pandas: First Step For Data Analytics

pandas

Pandas: First Step For Data Analytics

In this Blog, we'll use the following shorthand:

df | Any pandas DataFrame object
s | Any pandas Series object

Also, a quick reminder — to make use of the commands listed below, you'll need to first import the relevant libraries like so:

import pandas as pd import numpy as np

Importing Data

Use these commands to import data from a variety of different sources and formats.

Numpy: Building block to Data analytics

numpy

NumPy is the library that gives Python its ability to work with data at speed. Originally, launched in 1995 as ‘Numeric,’ NumPy is the foundation on which many important Python data science libraries are built, including Pandas, SciPy, and sci-kit-learn.

It’s common when first learning NumPy to have trouble remembering all the functions and methods that you need.

 

Key and Imports

In this cheat sheet, we use the following shorthand:

arr | A NumPy Array object

You’ll also need to import NumPy to get started:

Java Is Still Free

TS stands for Long Term Support, this simply means (at least in terms of OpenJDK) that the vendor who is providing you with the JDK will support that version of JDK for longer than 6 moths. For OpenJDK Oracle will lead the way and for the first six months provide updates by producing OpenJDK builds, but after the initial six months will only provide updates for the Oracle JDK under a paid license. But Oracle will work with other vendors to work on the hand over of code base for OpenJDK LTS and allow them to continue to work on it to provide updates. It is up to the vendor if they want to provide updates and paid support for binaries. For example, the handover has already taken place for the code base of JAVA SE 8 and JAVA SE 11 with Red Hat.
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