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import pandas as pd

percentiles = df['arrival_delay_deciles'].apply(pd.Series)
percentiles.rename(columns = lambda x : '{0}%'.format(x*10), inplace=True)
percentiles.head()

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# function to calculate feature engineering by aggregating integer columns
def get_agg_by_cols(data, col, agg_list, agg_cols):
    """
    data : dataframe to calculate FE on
    col : list of columns to aggregate by (the object datatype)
    agg_list : a list of statistical measure e.g mean, median
    agg_cols : a list of numerical datatype columns
    """
    for cols in col:
        for i in agg_list:

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from sklearn.model_selection import KFold, StratifiedKFold
from catboost import CatBoostClassifier
from sklearn.metrics import roc_auc_score
kfold, scores, y_pred_totcb = StratifiedKFold(n_splits=5, shuffle=True, random_state=221), list(), []
for train, test in kfold.split(X, y):
    x_train, x_test = X.iloc[train], X.iloc[test]
    y_train, y_test = y.iloc[train], y.iloc[test]
    
    model = CatBoostClassifier(random_state=27,  n_estimators=3000, cat_features = cat_columns,
                               max_depth=7, verbose=500, learning_rate=0.102, eval_metric='AUC')

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def remove_system_generated_msgs(uploaded_file: str) -> list:
  """Remove system generated messages like::
      1. +234 was added
      2. +234 left etc

      uploaded_file: str: path of dataset

	   Returns:
     --------
     list

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 #removing emojis
def remove_emoji(text: str) -> str:
    """
    remove emojis from chats i think idont need
    
    args:
    ----
    text: a single list of message
    
    Returns:

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import pandas as pd

def processChatMsgs(wh_chat: list) -> pd.DataFrame:
  """
	process the uploaded chat data by removing unwanted
	entries and returns a dataframe

	args: 
	-----
	wh_chat: whatsapp chat data

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def get_total_msg(user:str, df:pd.DataFrame) -> str:
  """ 
  Return the total msgs 'user' has sent 
  Args:
  ----
  user: the user to extract his/her total count of messages
  df: pd.DataFrame: dataframe that stores information about each user and message sent

  Returns:
  --------

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from nltk.corpus import stopwords
import matplotlib.pyplot as plt
from wordcloud import WordCloud 

def draw_wordcloud(msgs: list):
	"""
	Draw wordcloud for visualization of the most used words 
	during conversation
	args: 
	-----

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def pie_chart(user):
  fig, ax = plt.subplots(figsize=(15, 8))
  explodex = []
  for i in np.arange(len(user)):
      explodex.append(0.005)
  ax = user.plot(kind='pie', colors=['red', 'green', 'cyan', 'lime', 'gold'], 
                 fontsize=12, autopct='%1.1f%%', startangle=180,
                pctdistance=0.85, explode=explodex)
  inner_circle = plt.Circle((0,0), 0.50, fc='white')
  fig = plt.gcf()