Importing libraries

# essentials
import pandas as pd
import numpy as np

# misc libraries
import random
import timeit
import math 
import collections 

# surpress warnings
import warnings
warnings.filterwarnings('ignore')

# Data Visualization
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style='darkgrid', color_codes=True)
plt.style.use('fivethirtyeight')
%matplotlib inline

# model building
import lightgbm as lgb
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

def printColumnTypes(df):
    non_num_df = df.select_dtypes(include=['object'])
    num_df = df.select_dtypes(exclude=['object'])
    '''separates non-numeric and numeric columns'''
    print("Non-Numeric columns:")
    for col in non_num_df:
        print(f"{col}")
    print("")
    print("Numeric columns:")
    for col in num_df:
        print(f"{col}")

def missing_cols(df):
    '''prints out columns with its amount of missing values with its %'''
    total = 0
    for col in df.columns:
        missing_vals = df[col].isnull().sum()
        pct = df[col].isna().mean() * 100
        total += missing_vals
        if missing_vals != 0:
          print('{} => {} [{}%]'.format(col, df[col].isnull().sum(), round(pct, 2)))
    
    if total == 0:
        print("no missing values")