8個數據清洗Python代碼，複製可用，最長11行

資料來源: https://mp.weixin.qq.com/s/iQgIhyr8XJKbqAQyu4P8rA

01.刪除多列

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

02.更改數據類型

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 )

03.將分類變量轉換為數字變量

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 )  

04.檢查缺失數據

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 )

05.刪除列中的字符串

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 )

 
06.刪除列中的空格

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

 
07.用字符串連接兩列（帶條件）

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

08.轉換時間戳（從字符串到日期時間格式）

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 ))