Automatic Exploratory Data Analysis (EDA) using pandas-profiling package.
Warnings Tab is especially useful to spot data quality issues and remove unusable and strongly correlated features.

#!pip install pandas_profiling 
from pandas_profiling import ProfileReport

#Pandas Profiling
profile = ProfileReport(df,title="Pandas Profiling Report")
profile.to_file("pandas_profile.html",silent=False)

Group Pandas Data Frame Features by type:

feats_dtypes_dict = df.columns.groupby(df.dtypes.astype(str))
feats_dtypes_dict.keys()
  
num_feats = feats_dtypes_dict["float64"].to_list() + feats_dtypes_dict["int64"].to_list()
cat_feats = feats_dtypes_dict["object"].to_list()

Fetch all columns with missing values:

  df.isna().sum().sort_values(ascending=False).where(lambda x: x!=0).dropna()