Code template for running exploratory data analysis in sweetviz.
Link to website : None
Link to repository : https://github.com/fbdesignpro/sweetviz
Tag: Exploratory Data Analysis
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Exploratory Data Analysis – pandas-profiling
Code template for running exploratory data analysis in pandas-profiling.
Link to website : https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/
Link to repository : https://github.com/pandas-profiling/pandas-profiling
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Exploratory Data Analysis – lux
Code template for running exploratory data analysis in lux.
Link to website : None
Link to repository : https://github.com/lux-org/lux
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Exploratory Data Analysis – dtale
Code template for running exploratory data analysis in dtale.
Link to website : None
Link to repository : https://github.com/man-group/dtale
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Exploratory Data Analysis – dataprep
Code template for running exploratory data analysis in dataprep.
Link to website : https://dataprep.ai/
Link to repository : https://github.com/sfu-db/dataprep
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Exploratory Data Analysis – autoviz
Code template for running exploratory data analysis in autoviz.
Link to website : None
Link to repository : https://github.com/AutoViML/AutoViz
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Comparing Exploratory Data Analysis Libraries
I have tested and reviewed a few Python packages for data processing and/or exploratory data analysis (EDA). Most of these packages attempt to automate parts of the data processing and/or EDA process, or provide a suite of functions to manipulate and visualize data.
The main objective here is to review and explore Python packages that will shorten the time needed for data processing and/or exploratory data analysis.
In summary, sweetviz may be the best option under a business setting (with focus on business understanding) while autoviz/pandas-profiling are solid choices under an R&D setting (with focus on deep dive analysis).
Comparison
Package sweetviz pandas-profiling autoviz lux-api dtale dataprep Version 2.1.3 3.0.0 0.0.83 0.3.2 1.56.0 0.3.0 Recommended for exploration Yes No Yes No No Yes Recommended for production No No No No No No Ease of use Yes Yes Yes Yes Yes Yes Computation speed Fast Medium Fast Fast Fast Fast Installation complexity Low Low Low Low Medium Low Target variable-centric Yes No Yes Yes No Yes Missing data check No Yes No No Yes Yes Per variable summary statistics Yes Yes No No Yes Yes AutoEDA focus No Yes Yes No No Yes Score 4 3 2 2 1 1 Table comparing autoEDA libraries for exploratory data analysis. Unique Points of Each Library
sweetviz
- Beautiful and simple visualisation that is good for business explanation.
- Limited features beyond simple visualisation.
pandas-profiling
- Good for generic data quality check.
- No option to specify target variable for tailored EDA.
autoviz
- Holistic visualisations that are good for deep dive analysis.
- Claims to do smart selection of plots/analyses but have yet to see the impact.
lux-api
- Generate very simple single or pair variables distribution plots.
- Plot of interest selection is manual.
- Charts are not integrated into notebook (as widgets only).
dtale
- Very good as a Python-based data manipulation tool with GUI.
- Contains a full suite of tools for data manipulation/analysis/visualisation, almost matching similar commercial data analytics tools.
- Too much manual effort required to setup all analyses/plots required.
- No dashboard deployment support.
- No option to specify target variable for tailored EDA.
- Complex dependecies on many libraries.
dataprep
- EDA module is like an extension to pandas-profiling. Very comprehensive.
- Contains 3 modules to : collect data, explore data and clean data.
- Potentially faster on large data due to use of Dask.
- Possible to deep dive investigate selected columns/variables.