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This article mainly introduces "how to modify table data in Python". In daily operation, I believe many people have doubts about how to modify table data in Python. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the doubts about "how to modify form data in Python". Next, please follow the editor to study!

Let's first see what the effect of Grid studio looks like. In general, we can either load and process data through Python, or manipulate data through "Excel".

Working with data on Python is easier to understand. Processing data on a table is actually very much like Excel, as shown below to write a summation formula.

Maybe if we change some data on the table, we can also import it into the NumPy array and do further calculations.

Why create this tool?

The author says that he created Grid studio mainly to solve the problem of workflow fragmentation in data science projects, in which he switches between R studio, Excel and other tools.

When exporting a CSV file for gazillionth-time, if the number of lines is too high, the application window will stutter. Even simple things, such as reading JSON files, can drive people crazy. Existing tools do not provide the environment and associated workflows needed to work efficiently, which is why the author decided to build the tool. He wants to create an easy-to-use application that integrates data science workflows.

What are the highlights of this tool?

Grid studio is a web-based application that looks similar to Google Sheets and Microsoft Excel. However, its killer mace is to integrate the Python language.

Almost everyone who has used a computer will naturally use tables to view and edit data. Combining this simple UI with a mature programming language like Python is simply not easy to use.

Scripting in Python is simple: you only need to write a few lines of code to run it.

Core integration: read and write

The core of this Python integration is the read-write interface to the spreadsheet, which can establish a high-performance connection between the data in the spreadsheet and the data in the Python process.

You can write data to a table in the following ways:

Sheet ("A1:A3", [1,2,3])

Read data from a table in the following way:

My_matrix = sheet ("A1:A3")

You can read or write data directly in a table in this simple and efficient way to automate the process of data entry, extraction, visualization, etc.

Write customized table functions

While reading and writing through a simple interface is flexible, it is sometimes important to write custom functions that can be called out directly.

In addition to the default functions such as AVERAGE, SUM, IF, you may need other functions, so just write them out!

Def UPPERCASE (a):

Return str (a) uppercase ()

After writing this line of code, call up the function in the table, just like calling a regular function.

Using Python ecology

By leveraging a variety of powerful software packages in the Python ecology, we have immediate access to the best current data science tools, and therefore quick access to powerful models such as linear regression and support vector machines.

Because Grid studio itself is mainly dealing with tabular data, using them as features can quickly call models such as SVM to explore the features hidden behind these data.

Data visualization

In data science, a common task is to visualize data so that "prior knowledge" about data can be obtained. Advanced drawing functions have been built in through the integration of interactive drawing library Plotly.js and Python standard visualization library Matplotlib,Grid studio. We can use advanced drawing features in the vector table format as shown below:

To further explain how to use the features of Grid studio to build visual icons, the project author also shows two cases, namely, crawling web pages and visualizing data distribution, but here focuses on the first case.

Case: estimated normal distribution

The following example shows the power of Grid studio, which can visualize normal distribution through Plotly.js with higher fidelity, and we can see how interactive mapping is done.

At this point, the study on "how to modify tabular data by Python" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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