```# Get the Numerical Data list to infer distribution plots

numerical = [var for var in df.columns if df[var].dtype!='O']
print('There are {} numerical variables\n'.format(len(numerical)))
print('The numerical variables are :', numerical)

# Get the Categorical Data list to infer distribution plots

categorical = [var for var in df.columns if df[var].dtype =='O']
print('There are {} Categorical variables\n'.format(len(categorical)))
print('The Categorical variables are :', categorical)```
```# Discretization
df3["Total_Amt_Chng_Q4_Q1_qcut"]=pd.qcut(df3["Total_Amt_Chng_Q4_Q1"],4)
df3["Total_Trans_Amt_qcut"]=pd.qcut(df3["Total_Trans_Amt"],4)
df3["Total_Ct_Chng_Q4_Q1_qcut"]=pd.qcut(df3["Total_Ct_Chng_Q4_Q1"],4)
```
```# Split the dataset 2 parts that categorical and numerical

cat_col=list(df1.select_dtypes(include="object").columns)
num_col=list(df1.select_dtypes(exclude="object").columns)
print("Categorical Features:",cat_col,sep="\n\n")
print("")
print("Numerical Features:",num_col,sep="\n\n")```
star

Mon Sep 05 2022 09:46:40 GMT+0000 (Coordinated Universal Time)

#python #pandas #dataset #numerical #categorical #eda
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Wed Jan 27 2021 07:43:36 GMT+0000 (Coordinated Universal Time)

#numerical #data #discretization #datamining
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Mon Jan 25 2021 07:21:19 GMT+0000 (Coordinated Universal Time)

#splitting #numerical #categorical