!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,
)