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Today, I will talk to you about how to use Streamlit for visual data analysis, many people may not know much about it. In order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

Stay at home more time, do a few data analysis, feel very easy to use, also to promote.

Previously, Jupyter Notebook has been used for data analysis and experimental results display.

Using Notebook to do data analysis has an advantage, that is, after the data load comes in, add cell to analyze what you want to analyze, and the results can be more friendly to display on the page, especially the display of dataframe and the plotting of various charts. A good notebook is a good data or experimental report.

Notebook is very inconvenient for me in several places.

First of all, writing python code on notebook is very troublesome for people with IDE dependence.

Second, all interactions have to be done by writing python code, for example, in a dataframe, if you want to see a row or column, or if you want to see data that conforms to a certain query, you have to write a dataframe location statement. If you don't want to read it, you have to delete it. In a word, the interaction of data analysis is not friendly.

Streamlit solves the above two problems. Of course, the emergence of streamlit is not to replace notebook, the original goal is to enable each data scientist to make a beautiful data analysis application in a few hours. Do not need to understand the server client architecture, do not need to be able to write pages such as CSS, HTML and so on. All you need is python.

Streamlit's API is very simple, you can read it in half an hour, and then look at a few examples to know how to use it.

Introduce several very convenient features:

With the support of Dataframe, the api of your call streamlit dataframe will be displayed on the page, but it does not simply display dataframe, but "Display a dataframe as an interactive table." for example, you can sort directly in this dataframe.

Widget . This is the best feature to use. For example, you can insert a drop-down order directly, and then the subsequent data analysis can be displayed according to the option of the drop-down order. This is so easy to use, you don't have to write all kinds of query like notebook. For example, a report card dataframe, plus a drop-down list-subject, and then you select the language, followed by only the language results (including a variety of follow-up charts). This function is too simple to mention, but it can be written with a python statement.

Support for all kinds of charts. In addition to supporting matplotlib charts, they also encapsulate some commonly used ones.

An essential part of app, progress bar, audio, video, map, placeholder and so on can be easily inserted.

Cache, which can cache the return value of the function, is very useful for data analysis app.

What about the convenience it claims to deploy? One order is done.

In short, when you plan to use streamlit for your data analysis application, you can think about what you want, and then you will find it really simple, convenient and beautiful.

After reading the above, do you have any further understanding of how to use Streamlit for visual data analysis? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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