This has been a tough year for working parents. Many people are attempting to do their jobs while overseeing children’s

Price of cryptocurrencies is irrelevant: One can make a Bitcoin payment at a Bitcoin price of $1 million, $10,000, or $0.01 a coin. Bitcoin doesn’t have an intrinsic value — its price is a pure function of supply and demand.

Just like Square is only investing 1% of its assets in Bitcoin and given their speculative nature, we are of the view that no one should be investing more than 5% of their net worth in Bitcoin or cryptocurrencies in general.

Let’s now look at the various capabilities of the PandasGUI library with an example. PandasGUI comes with some example datasets already. So we’ll use the Titanic dataset, which comes packaged with the library. Titanic is a fairly known ‘Hello World’ dataset in machine learning, where the task is to create a model that predicts which passengers survived the Titanic shipwreck.

Price of cryptocurrencies is irrelevant: One can make a Bitcoin payment at a Bitcoin price of $1 million, $10,000, or $0.01 a coin. Bitcoin doesn’t have an intrinsic value — its price is a pure function of supply and demand.

Tools that can perform basic EDA in two or three lines of code. These libraries essentially use pandas’ functions under the hood. Examples are SweetViz and Pandas profiling library.

Price of cryptocurrencies is irrelevant: One can make a Bitcoin payment at a Bitcoin price of $1 million, $10,000, or $0.01 a coin. Bitcoin doesn’t have an intrinsic value — its price is a pure function of supply and demand.

In this article, we looked at a GUI-based tool for analyzing pandas dataframes. This tool has a number of interesting features like filtering, sorting, visualizing, and even aggregating, which we saw in detail with an example dataset. Since PandasGUI is under active development, we might see some more features in the coming days. Such libraries can be a boon for people who are not very comfortable with coding or are looking for a low code environment. Whatever the ultimate goal is, it is always good to know about another open-source tool to add to our data science toolkit.

In this article, we looked at a GUI-based tool for analyzing pandas dataframes. This tool has a number of interesting features like filtering, sorting, visualizing, and even aggregating, which we saw in detail with an example dataset. Since PandasGUI is under active development, we might see some more features in the coming days. Such libraries can be a boon for people who are not very comfortable with coding or are looking for a low code environment. Whatever the ultimate goal is, it is always good to know about another open-source tool to add to our data science toolkit.

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aren’t just about a configuration change, they require you to structure your projects differently. So no, you can’t just run an npm command and normalize 10 projects at once. You can, however, after some re-thinking of their all structure, and the correct configuration changes, re-install the dependencies of 10 (or more) projects into a single place, allowing you to de-duplicate everything. And if you ask me, this is truly a huge improvement!

All cryptocurrencies are facing scalability challenges. In fact, the blockchain technology which is at the heart of all cryptocurrencies is hardly scalable and is not energy efficient. If blockchain technology doesn’t solve this issue, we struggle to see cryptocurrencies becoming sustainable means of payment.

Sometimes, there is a need to reshape the data to get more distinct insights. PandasGUI provides two functions — pivot and melts to achieve the same. Let’s see how to pivot the data via this tool.

If you have directly imported a dataframe onto the PandasGUI interface, you can access the same in the familiar juptyer notebook also, with just a few lines of code. From there, you can carry the data analysis process in the notebook itself, if you wish.

PandasGUI, as the name suggests, is a graphical user interface for analyzing Pandas’ dataframes. The project is still under active development and so can be subject to breaking changes, at times. PandasGUI comes with many useful features, which we shall cover in detail later in the article. Before that, let’s see how we can install the library and get it running.

Once you have explored the dataset, you can even filter the dataset based on some query expressions. Dataframe.query() is a method originally provided by pandas for performing filtering operations. It takes an expression in string form to filter data, makes changes to the original dataframe, and returns the filtered dataframe.

Recently, I came across another GUI based alternative to pandas called PandasGUI. One thing that struck was that it offered capabilities of plotting as well as reframing the dataframe. Also, the user has the freedom to perform custom operations too. This article will try and explain its various features and functionalities and how you could use it for your data.