Get More Efficient in Your Data analysis with Pandas `query()` and `eval()` Methods

One of the common tasks in Exploratory data Analysis is asking questions of the data. This is usually made possible by filtering or slicing on columns, aggregation function and so on. Because Pandas is built on numpy , it allows you to carry out vectorized operations which are computationally efficient than using for loops .

#data-science #pandas #data-analysis

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Get More Efficient in Your Data analysis with Pandas `query()` and `eval()` Methods
Siphiwe  Nair

Siphiwe Nair

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

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Paula  Hall

Paula Hall

1623488340

3 Python Pandas Tricks for Efficient Data Analysis

Explained with examples.

Pandas is one of the predominant data analysis tools which is highly appreciated among data scientists. It provides numerous flexible and versatile functions to perform efficient data analysis.

In this article, we will go over 3 pandas tricks that I think will make you a more happy pandas user. It is better to explain these tricks with some examples. Thus, we start by creating a data frame to wok on.

The data frame contains daily sales quantities of 3 different stores. We first create a period of 10 days using the date_range function of pandas.

import numpy as np
import pandas as pd

days = pd.date_range("2020-01-01", periods=10, freq="D")

The days variable will be used as a column. We also need a sales quantity column which can be generated by the randint function of numpy. Then, we create a data frame with 3 columns for each store.

#machine-learning #data-science #python #python pandas tricks #efficient data analysis #python pandas tricks for efficient data analysis

Gerhard  Brink

Gerhard Brink

1624272463

How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

  • Building/collecting data
  • Cleaning/filtering data
  • Organizing data

#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different

Get More Efficient in Your Data analysis with Pandas `query()` and `eval()` Methods

One of the common tasks in Exploratory data Analysis is asking questions of the data. This is usually made possible by filtering or slicing on columns, aggregation function and so on. Because Pandas is built on numpy , it allows you to carry out vectorized operations which are computationally efficient than using for loops .

#data-science #pandas #data-analysis