!pip install pandas

# Contains all the powerful datasets such as titanic, tips, iris ..etc !pip install seaborn

!pip install dtale

import seaborn as sns df = sns.load_dataset('titanic') df.head()

import dtale import dtale.app as dtale_app dtale_app.USE_COLAB = True dtale.show(df)

!pip install pandas-profiling==2.7.1 # To avoid some issues used the specific version

import seaborn as sns df1 = sns.load_dataset('iris') df1.head()

from pandas_profiling import ProfileReport profile = ProfileReport(df1) #profile.to_widgets() --> to display the data in widgets profile.to_file('output.html')

# Code to display the html import IPython IPython.display.HTML(filename='/work/output.html')

import seaborn as sb df2 = sb.load_dataset('iris')

import sweetviz as sv report = sv.analyze(df2) report.show_html('Report.html')

# Code to display the html import IPython IPython.display.HTML(filename='/work/Report.html')

!pip install autoviz

from autoviz.AutoViz_Class import AutoViz_Class AV = AutoViz_Class() filename = "https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/MASS/Boston.csv" sep = "," dft = AV.AutoViz( filename, sep=",", depVar="", dfte=None, header=0, verbose=0, lowess=False, chart_format="svg", max_rows_analyzed=150000, max_cols_analyzed=30, )