数据清洗之缺失值处理-CFANZ编程社区

缺失值处理

缺失值首先需要根据实际情况定义
可以采取直接删除法
有时候需要使用替换法或者插值法
常用的替换法有均值替换、前向、后向替换和常数替换

import pandas as pd
import numpy as np
import

os.getcwd()

'D:\\Jupyter\\notebook\\Python数据清洗实战\\数据清洗之数据预处理'

os.chdir('D:\\Jupyter\\notebook\\Python数据清洗实战\\数据')

df = pd.read_csv('MotorcycleData.csv', encoding='gbk', na_values='Na')

def f(x):
    if '$' in str(x):
        x = str(x).strip('$')
        x = str(x).replace(',', '')
    else:
        x = str(x).replace(',', '')
    return float(x)

df['Price'] = df['Price'].apply(f)

df['Mileage'] = df['Mileage'].apply(f)

# 计算缺失比例
df.apply(lambda x: sum(x.isnull())/len(x), axis=0)

Condition         0.000000
Condition_Desc    0.778994
Price             0.000000
Location          0.000267
Model_Year        0.000534
Mileage           0.003470
Exterior_Color    0.095422
Make              0.000534
Warranty          0.318297
Model             0.016415
Sub_Model         0.676231
Type              0.197785
Vehicle_Title     0.964233
OBO               0.008808
Feedback_Perc     0.117710
Watch_Count       0.530629
N_Reviews         0.000801
Seller_Status     0.083411
Vehicle_Tile      0.007207
Auction           0.002269
Buy_Now           0.031630
Bid_Count         0.707727
dtype: float64

df.head(3)

	Condition	Condition_Desc	Price	Location	Model_Year	Mileage	Exterior_Color	Make	Warranty	Model	...	Vehicle_Title	OBO	Feedback_Perc	Watch_Count	N_Reviews	Seller_Status	Vehicle_Tile	Auction	Buy_Now	Bid_Count
0	Used	mint!!! very low miles	11412.0	McHenry, Illinois, United States	2013.0	16000.0	Black	Harley-Davidson	Unspecified	Touring	...	NaN	FALSE	8.1	NaN	2427	Private Seller	Clear	True	FALSE	28.0
1	Used	Perfect condition	17200.0	Fort Recovery, Ohio, United States	2016.0	60.0	Black	Harley-Davidson	Vehicle has an existing warranty	Touring	...	NaN	FALSE	100	17	657	Private Seller	Clear	True	TRUE	0.0
2	Used	NaN	3872.0	Chicago, Illinois, United States	1970.0	25763.0	Silver/Blue	BMW	Vehicle does NOT have an existing warranty	R-Series	...	NaN	FALSE	100	NaN	136	NaN	Clear	True	FALSE	26.0

3 rows × 22 columns

# how = 'all', 只有当前行都是缺失值才删除
# how = 'any', 只要当前行有一个缺失值就删除
df.dropna(how = 'any', axis=0)

	Condition	Condition_Desc	Price	Location	Model_Year	Mileage	Exterior_Color	Make	Warranty	Model	...	Vehicle_Title	OBO	Feedback_Perc	Watch_Count	N_Reviews	Seller_Status	Vehicle_Tile	Auction	Buy_Now	Bid_Count

0 rows × 22 columns

# subset 根据指定字段判断
# df.dropna(how='any', subset=['Condition', 'Price', 'Mileage'])

# 缺失值使用0填补
df.fillna(0).head(5)

	Condition	Condition_Desc	Price	Location	Model_Year	Mileage	Exterior_Color	Make	Warranty	Model	...	Vehicle_Title	OBO	Feedback_Perc	Watch_Count	N_Reviews	Seller_Status	Vehicle_Tile	Auction	Buy_Now	Bid_Count
0	Used	mint!!! very low miles	11412.0	McHenry, Illinois, United States	2013.0	16000.0	Black	Harley-Davidson	Unspecified	Touring	...	0	FALSE	8.1	0	2427	Private Seller	Clear	True	FALSE	28.0
1	Used	Perfect condition	17200.0	Fort Recovery, Ohio, United States	2016.0	60.0	Black	Harley-Davidson	Vehicle has an existing warranty	Touring	...	0	FALSE	100	17	657	Private Seller	Clear	True	TRUE	0.0
2	Used	0	3872.0	Chicago, Illinois, United States	1970.0	25763.0	Silver/Blue	BMW	Vehicle does NOT have an existing warranty	R-Series	...	0	FALSE	100	0	136	0	Clear	True	FALSE	26.0
3	Used	CLEAN TITLE READY TO RIDE HOME	6575.0	Green Bay, Wisconsin, United States	2009.0	33142.0	Red	Harley-Davidson	0	Touring	...	0	FALSE	100	0	2920	Dealer	Clear	True	FALSE	11.0
4	Used	0	10000.0	West Bend, Wisconsin, United States	2012.0	17800.0	Blue	Harley-Davidson	NO WARRANTY	Touring	...	0	FALSE	100	13	271	OWNER	Clear	True	TRUE	0.0

5 rows × 22 columns

# 针对一个变量进行缺失值判断，使用其均值进行填补
df.Mileage.fillna(df.Mileage.mean()).head(5)

0    16000.0
1       60.0
2    25763.0
3    33142.0
4    17800.0
Name: Mileage, dtype: float64

df.columns

Index(['Condition', 'Condition_Desc', 'Price', 'Location', 'Model_Year',
       'Mileage', 'Exterior_Color', 'Make', 'Warranty', 'Model', 'Sub_Model',
       'Type', 'Vehicle_Title', 'OBO', 'Feedback_Perc', 'Watch_Count',
       'N_Reviews', 'Seller_Status', 'Vehicle_Tile', 'Auction', 'Buy_Now',
       'Bid_Count'],
      dtype='object')

df[df['Exterior_Color'].isnull()].head(5)

	Condition	Condition_Desc	Price	Location	Model_Year	Mileage	Exterior_Color	Make	Warranty	Model	...	Vehicle_Title	OBO	Feedback_Perc	Watch_Count	N_Reviews	Seller_Status	Vehicle_Tile	Auction	Buy_Now	Bid_Count
14	Used	NaN	5500.0	Davenport, Iowa, United States	2008.0	22102.0	NaN	Harley-Davidson	Vehicle does NOT have an existing warranty	Touring	...	NaN	FALSE	9.3	NaN	244	Private Seller	Clear	True	FALSE	16.0
35	Used	NaN	7700.0	Roselle, Illinois, United States	2007.0	10893.0	NaN	Harley-Davidson	NaN	Other	...	NaN	FALSE	100	NaN	236	NaN	Clear	False	TRUE	NaN
41	Used	NaN	6800.0	Hampshire, Illinois, United States	2003.0	55782.0	NaN	Harley-Davidson	Vehicle does NOT have an existing warranty	Softail	...	NaN	TRUE	100	2<	298	Private Seller	Clear	False	TRUE	NaN
55	Used	NaN	29500.0	Parma, Michigan, United States	1950.0	8471.0	NaN	Harley-Davidson	NaN	Other	...	NaN	TRUE	100	24	216	NaN	Clear	False	TRUE	NaN
72	Used	NaN	6500.0	Bourbonnais, Illinois, United States	1986.0	55300.0	NaN	Harley-Davidson	NaN	Touring	...	NaN	TRUE	100	2<	1	Private Seller	Clear	False	TRUE	NaN

5 rows × 22 columns

# 求众数
df['Exterior_Color'].mode()[0]

'Black'

# 缺失用众数填补
df['Exterior_Color'].fillna(df['Exterior_Color'].mode()[0]).head(5)

0          Black
1          Black
2    Silver/Blue
3            Red
4           Blue
Name: Exterior_Color, dtype: object

df['Mileage'].median()

7083.0

# 对不同变量使用不同数据填补
# 不加inplace=True，不会对原数据生效
df.fillna(value={'Exterior_Color': df['Exterior_Color'].mode()[0], 
                'Mileage': df['Mileage'].median(),}).head(5)

	Condition	Condition_Desc	Price	Location	Model_Year	Mileage	Exterior_Color	Make	Warranty	Model	...	Vehicle_Title	OBO	Feedback_Perc	Watch_Count	N_Reviews	Seller_Status	Vehicle_Tile	Auction	Buy_Now	Bid_Count
0	Used	mint!!! very low miles	11412.0	McHenry, Illinois, United States	2013.0	16000.0	Black	Harley-Davidson	Unspecified	Touring	...	NaN	FALSE	8.1	NaN	2427	Private Seller	Clear	True	FALSE	28.0
1	Used	Perfect condition	17200.0	Fort Recovery, Ohio, United States	2016.0	60.0	Black	Harley-Davidson	Vehicle has an existing warranty	Touring	...	NaN	FALSE	100	17	657	Private Seller	Clear	True	TRUE	0.0
2	Used	NaN	3872.0	Chicago, Illinois, United States	1970.0	25763.0	Silver/Blue	BMW	Vehicle does NOT have an existing warranty	R-Series	...	NaN	FALSE	100	NaN	136	NaN	Clear	True	FALSE	26.0
3	Used	CLEAN TITLE READY TO RIDE HOME	6575.0	Green Bay, Wisconsin, United States	2009.0	33142.0	Red	Harley-Davidson	NaN	Touring	...	NaN	FALSE	100	NaN	2920	Dealer	Clear	True	FALSE	11.0
4	Used	NaN	10000.0	West Bend, Wisconsin, United States	2012.0	17800.0	Blue	Harley-Davidson	NO WARRANTY	Touring	...	NaN	FALSE	100	13	271	OWNER	Clear	True	TRUE	0.0

5 rows × 22 columns

# 前向填补
df['Exterior_Color'].fillna(method='ffill').tail(10)

7483      Purple
7484      Purple
7485       Black
7486       Black
7487        Gray
7488       Black
7489       Black
7490         Red
7491    TWO TONE
7492        Gray
Name: Exterior_Color, dtype: object

# 后向填补
df['Exterior_Color'].fillna(method='bfill').tail(10)

7483      Purple
7484       Black
7485       Black
7486       Black
7487        Gray
7488       Black
7489       Black
7490         Red
7491    TWO TONE
7492        Gray
Name: Exterior_Color, dtype: object

数据清洗之 缺失值处理

缺失值处理

数据清洗之缺失值处理