def drop_multiple_col(col_names_list, df): '''AIM    -> Drop multiple columns based on their column names INPUT  -> List of column names, dfOUTPUT -> updated df with dropped columns ------'''df.drop(col_names_list, axis=1, inplace=True)return df

def change_dtypes(col_int, col_float, df): '''AIM    -> Changing dtypes to save memoryINPUT  -> List of column names (int, float), dfOUTPUT -> updated df with smaller memory  ------'''df[col_int] = df[col_int].astype('int32')df[col_float] = df[col_float].astype('float32')

def convert_cat2num(df):# Convert categorical variable to numerical variablenum_encode = {'col_1' : {'YES':1, 'NO':0},'col_2'  : {'WON':1, 'LOSE':0, 'DRAW':0}}  df.replace(num_encode, inplace=True)  

def check_missing_data(df):# check for any missing data in the df (display in descending order)return df.isnull().sum().sort_values(ascending=False)

def remove_col_str(df):# remove a portion of string in a dataframe column - col_1df['col_1'].replace('n', '', regex=True, inplace=True)# remove all the characters after &# (including &#) for column - col_1df['col_1'].replace(' &#.*', '', regex=True, inplace=True)

def remove_col_white_space(df):# remove white space at the beginning of string df[col] = df[col].str.lstrip()

def concat_col_str_condition(df):# concat 2 columns with strings if the last 3 letters of the first column are 'pil'mask = df['col_1'].str.endswith('pil', na=False)col_new = df[mask]['col_1'] + df[mask]['col_2']col_new.replace('pil', ' ', regex=True, inplace=True)  # replace the 'pil' with emtpy space

def convert_str_datetime(df): '''AIM    -> Convert datetime(String) to datetime(format we want)INPUT  -> dfOUTPUT -> updated df with new datetime format ------'''df.insert(loc=2, column='timestamp', value=pd.to_datetime(df.transdate, format='%Y-%m-%d %H:%M:%S.%f'))