What are the AutoEDA tools commonly used in Python data mining
This article mainly explains "what are the AutoEDA tools commonly used in Python data mining". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn what are the AutoEDA tools commonly used in Python data mining.
1 、 Pandas Profiling
Pandas Profiling is a relatively mature tool, which can be directly input into DataFrame to complete the analysis process, and display the results in HTML format. At the same time, the analysis function is relatively powerful.
Functions: field type analysis, variable distribution analysis, correlation analysis, missing value analysis, duplicate row analysis
Time consuming: less
2 、 AutoViz
Https://github.com/AutoViML/AutoViz
AutoViz is a beautiful data analysis tool that saves the results in image format while visualizing.
Functions: correlation analysis, numerical variable box diagram, numerical variable distribution map
Time-consuming: more
3 、 Dataprep
Https://dataprep.ai/
Dataprep is a more flexible and powerful tool, and it is also my favorite. It can specify columns for analysis, as well as interactive analysis in Notebook.
Functions: field type analysis, variable distribution analysis, correlation analysis, missing value analysis, interactive analysis.
Time-consuming: more
4 、 SweetViz
Https://github.com/fbdesignpro/sweetviz
SweetViz is a powerful data analysis tool that can well analyze training sets and test sets, as well as the relationship between target tags and features.
Functions: data set comparative analysis, field type analysis, variable distribution analysis, target variable analysis
Time consuming: medium
5 、 D-Tale
Https://github.com/man-group/dtale
D-Tale is the most powerful data analysis tool, which supports the analysis process of single variable better.
Functions: field type analysis, variable distribution analysis, correlation analysis, missing value analysis, interactive analysis.
Time consuming: medium
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