Chet  Lubowitz

Chet Lubowitz


5 Essential Pandas Tips For Easier Data Manipulation


Pandas for Python is a piece of software that needs no introduction. Whether you are entirely new to Data Science with Python, or you’ve been in the field for an extended period of time, it’s likely you’ve heard of the Pandas Python module. The library is widely used in the industry for data manipulation, and is a go to tool for any aspiring Data Scientist who wants to work with data-frames and Numpy. Many Data Scientists use Pandas every single day, and it is widely considered an essential tool for manipulating data with Python.

Although Pandas is rather easy to use, and has a lot of easy methods at its disposal, there are many parts to Pandas, some of which go entirely ignored most of the time. Pandas is a complex beast, and could take months, or even years to be used to its absolute highest level. That being said, there are some basic attributes Pandas claims that can be used effectively in most situations right now.

Conditional Masking

One attribute that most certainly pushes Pandas above its competitors and the dictionary type itself is conditional masking. Conditional masking allows the user to use a simple conditional statement to filter out values that don’t meet its requirements. This is incredibly convenient, and is leagues above what is available in Julia or R at this moment. Whereas in Julia for example, we would need to use the filter!() method with a conditional in order to manage our data, Pandas makes filtering data incredibly easy by using what is called a conditional mask.

A conditional mask iteratively loops through all of the data in the data-frame and compares the data to a preset condition. The return will be a filtered data-frame that will follow the condition set in the mask.

import pandas as pd
df = pd.DataFrame({"NA": [0,1,0,1,0,1], "Label": ["TN", "GA", "TN", "MN", "CA","CA"]})

ones_only = df[“NA”] == 1

 ![Image for post](*EaQuH7NlCeORemVbR42QNg.png)

#python #programming #data #data-science #machine-learning

What is GEEK

Buddha Community

5 Essential Pandas Tips For Easier Data Manipulation
 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

Gerhard  Brink

Gerhard Brink


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.


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

Kasey  Turcotte

Kasey Turcotte


Data Manipulation: SQL vs. Pandas

Which tool to use in your next data science project


Data cleaning and manipulation are essential steps in any data science project. Both **SQL **and **Pandas **are popular tools used by Data Analysts and Data Scientists nowadays.

Which tool to used depends on where the data is stored, what kind of data format, and how we want to use it.

Things to consider:

  • If the data you are working with is not in panel format yet and you will need to piece together data from various sources, Pandas might work better. For example, when processing text data or scraping data from websites, it is likely that data is in unstructured format, it would be very difficult to use SQL.
  • If you’re not familiar with data and would like explore the data, your database admin would appreciate that you do the work outside of the database with Pandas.
  • If you would like to do data visualization and implement statistical analysis and machine learning models, Pandas would work well with other libraries in Python, such as, Matplotlib, Scikit-Learn, **TensorFlow **and etc.
  • If you deal with large amount of data, you can use Pandas with other libraries, such as **Pyspark, Dask **and **Swifter **to fully utilize your hardware power.
  • If you’re very familiar with data and know exactly what steps to take to clean to data, such as, filtering, joining, calculation and etc, it should be easier to run SQL to process the data and export the final data for analysis tasks.
  • If you work on a front-end project and would like to access to the back-end database without complex data manipulations, you might be better off using SQL.

In following article, I am going to compare SQL and Pandas when implementing basis data manipulations. Hope it to be useful to someone who is familiar with SQL and would like to learn about Pandas, and vice versa.

#sql #pandas #data-manipulation #python #data-science #data manipulation: sql vs. pandas

Kasey  Turcotte

Kasey Turcotte


5 Pandas Presentation Tips You Should Know About

These tips will help you when you need to share your analysis with others

These tips will help you need to to share your analysis with others. Whether you are a Student, Data Scientist or a Ph.D. Researcher, each project ends with some kind of a report. May this be a post on Confluence, Readme on GitHub or a Scientific paper.

There is no need to copy-paste values one by one from a DataFrame to another software. Pandas with its formatting functions can convert a DataFrame to many formats.

#pandas #python #data-science #programming #5 pandas presentation tips you should know about #pandas presentation tips

Cyrus  Kreiger

Cyrus Kreiger


4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.

If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?

Let’s take a look at the most important things you need to know.

#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company