Paula  Hall

Paula Hall

1638206700

How to Use A Time Series Forecast on Training Data using Numpy, Pandas

I created the program in Google Colab, a free online Jupyter Notebook. The great thing about Google Colab, and the entire range of Google products for that matter, is the fact that it is portable, which means that any code written using Google Colab can be called up on any computer that has an internet connection and a search engine. One thing that I don’t particularly like about Google Colab is the fact that is does not have an undo function, which means that if the user is not careful, valuable code could be overwritten or deleted.

#numpy #numpy #python #matplotlib 

What is GEEK

Buddha Community

How to Use A Time Series Forecast on Training Data using Numpy, Pandas
 iOS App Dev

iOS App Dev

1620466520

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

PANDAS: Most Used Functions in Data Science

Most useful functions for data preprocessing

When you get introduced to machine learning, the first step is to learn Python and the basic step of learning Python is to learn pandas library. We can install pandas library by pip install pandas. After installing we have to import pandas each time of the running session. The data used for example is from the UCI repository “https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records

  1. Read Data

2. Head and Tail

3. Shape, Size and Info

4. isna

#pandas: most used functions in data science #pandas #data science #function #used python data #most used functions in data science

Time Series Basics with Pandas

In my last post, I mentioned multiple selecting and filtering  in Pandas library. I will talk about time series basics with Pandas in this post. Time series data in different fields such as finance and economy is an important data structure. The measured or observed values over time are in a time series structure. Pandas is very useful for time series analysis. There are tools that we can easily analyze.

In this article, I will explain the following topics.

  • What is the time series?
  • What are time series data structures?
  • How to create a time series?
  • What are the important methods used in time series?

Before starting the topic, our Medium page includes posts on data science, artificial intelligence, machine learning, and deep learning. Please don’t forget to follow us on Medium 🌱 to see these posts and the latest posts.

Let’s get started.

#what-is-time-series #pandas #time-series-python #timeseries #time-series-data

Ray  Patel

Ray Patel

1623292080

Getting started with Time Series using Pandas

An introductory guide on getting started with the Time Series Analysis in Python

Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.

#data-analysis #time-series-analysis #exploratory-data-analysis #stock-market-analysis #financial-analysis #getting started with time series using pandas

Jamison  Fisher

Jamison Fisher

1620293605

Pandas Vs Numpy: Difference Between Pandas & Numpy [2021]

Python is undoubtedly one of the most popular programming languages in the software development and Data Science communities. The best part about this beginner-friendly language is that along with English-like syntax. It comes with a wide range of libraries. Pandas and NumPy are two of the most popular Python libraries.

Today’s post is all about exploring the differences between Pandas and NumPy to understand their features and aspects that make them unique.

Pandas vs. NumPy: What are they?

Pandas vs. NumPy: The core difference between Pandas and NumPy

#data science #comparison #difference between pandas and numpy #numpy #pandas #pandas vs numpy