安装方式：

!pip install git+git://github.com/AutoViML/AutoViz.git
!pip install xlrd

from autoviz.AutoViz_Class import AutoViz_Class

AV = AutoViz_Class()
dftc = AV.AutoViz(
    filename='', 
    sep='' , 
    depVar='target', 
    dfte=df, 
    header=0, 
    verbose=1, 
    lowess=False, 
    chart_format='png', 
    max_rows_analyzed=300000, 
    max_cols_analyzed=30
)

📊 Pandas Profiling 📚

from pandas_profiling import ProfileReport
df = pd.read_csv('/kaggle/input/titanic/train.csv')
report = ProfileReport(df)
# Start of Pandas Profiling process
start_time = dt.datetime.now()
print("Started at ", start_time)
report

!pip install sweetviz

import sweetviz as sv
df = pd.read_csv('/kaggle/input/credit-card-customers/BankChurners.csv').head(2000)
advert_report = sv.analyze([df, 'Data'])
advert_report.show_html()

print('SweetViz finished!!')
finish_time = dt.datetime.now()
print("Finished at ", finish_time)
elapsed = finish_time - start_time
print("Elapsed time: ", elapsed)

安装

!pip install dtale

import dtale
dtale.show(df)

官方链接：https://github.com/man-group/dtale

!pip install -U dataprep

实例

from dataprep.eda import plot, plot_correlation
plot(df)

plot_correlation(df)

plot(df, "Customer_Age")

plot(df, "Customer_Age", "Gender")

• Pandas Profiling GitHub - https://github.com/pandas-profiling/pandas-profiling
• Dan Roth, AutoViz: A New Tool for Automated Visualization - https://towardsdatascience.com/autoviz-a-new-tool-for-automated-visualization-ec9c1744a6ad
• George Vyshnya, PROs and CONs of Rapid EDA Tools - https://medium.com/sbc-group-blog/pros-and-cons-of-rapid-eda-tools-e1ccd159ab07
• SweetViz - https://towardsdatascience.com/sweetviz-automated-eda-in-python-a97e4cabacde
• DataPrep - https://sfu-db.github.io/dataprep/user_guide/eda/plot.html