8个用于数据清洗的Python代码

def drop_multiple_col(col_names_list, df):
 '''
 AIM -> Drop multiple columns based on their column names
 INPUT -> List of column names, df
 OUTPUT -> 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 memory
 INPUT -> List of column names (int, float), df
 OUTPUT -> 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 variable
 num_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_1
 df['col_1'].replace('
', '', regex=True, inplace=True)
 # remove all the characters after  (including ) for column - col_1
 df['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 -> df
 OUTPUT -> 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'))