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python数据清洗工具、方法、过程整理归纳（七、数据清洗之数据预处理（二）——异常值处理、数据离散化处理）

文章目录

数据预处理
- 7.3 异常值处理
- 7.4 数据离散化处理

数据预处理

7.3 异常值处理

异常值指那些偏离正常范围的值，不是错误值
异常值出现频率较低，但又会对实际项目分析造成偏差
异常值一般用箱线图法（分位差法）或者分布图法（标准差法）来判断
异常值往往采取盖帽法或者数据离散化来解决

df.head()

	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

x_bar = df['Price'].mean()

x_std = df['Price'].std()

df['Price'] > x_bar + 2.5*x_std#异常值：均值+2.5倍的标准差

0       False
1       False
2       False
3       False
4       False
5       False
6       False
7       False
8       False
9       False
10      False
11      False
12      False
13      False
14      False
15      False
16      False
17      False
18      False
19      False
20      False
21      False
22      False
23      False
24      False
25      False
26       True
27      False
28      False
29      False
30      False
31      False
32      False
33      False
34      False
35      False
36      False
37      False
38      False
39      False
40      False
41      False
42      False
43      False
44      False
45      False
46      False
47      False
48      False
49      False
        ...  
7443    False
7444    False
7445    False
7446    False
7447    False
7448    False
7449    False
7450    False
7451    False
7452    False
7453    False
7454    False
7455    False
7456    False
7457    False
7458    False
7459    False
7460    False
7461    False
7462    False
7463    False
7464    False
7465    False
7466    False
7467    False
7468    False
7469    False
7470    False
7471    False
7472    False
7473    False
7474    False
7475    False
7476    False
7477    False
7478    False
7479    False
7480    False
7481    False
7482    False
7483    False
7484    False
7485    False
7486    False
7487    False
7488    False
7489    False
7490    False
7491    False
7492    False
Name: Price, Length: 7493, dtype: bool

any(df['Price']>x_bar + 2.5*x_std)

True

any(df['Price']<x_bar-2.5*x_std)

False

df[df['Price'] > x_bar + 2.5*x_std]

	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
26 	Used 	NaN 	45900.0 	South Haven, Michigan, United States 	2009.0 	40000.0 	Red 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	25 	199 	Private Seller 	Clear 	False 	TRUE 	NaN
62 	Used 	NaN 	34900.0 	Valparaiso, Indiana, United States 	2014.0 	12649.0 	Purple 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	NaN 	238 	Private Seller 	Clear 	False 	TRUE 	NaN
94 	New 	NaN 	36100.0 	Miami, Florida, United States 	2017.0 	949.0 	Blue black silver 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	6.1 	NaN 	157 	Private Seller 	Clear 	True 	FALSE 	21.0
122 	Used 	NaN 	47720.0 	Gastonia, North Carolina, United States 	2016.0 	3355.0 	SILVER 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	11 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
129 	Used 	NaN 	51552.0 	Gastonia, North Carolina, United States 	2017.0 	3335.0 	ORANGE 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	21 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
143 	Used 	NaN 	49441.0 	Gastonia, North Carolina, United States 	2017.0 	770.0 	SILVER 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	19 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
157 	Used 	NaN 	49441.0 	Gastonia, North Carolina, United States 	2017.0 	770.0 	SILVER 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	19 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
167 	Used 	NaN 	31999.0 	West Bloomfield, Michigan, United States 	2016.0 	300.0 	Silver 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	1< 	14 	Private Seller 	Clear 	False 	TRUE 	NaN
191 	Used 	NaN 	60000.0 	Alexandria, Louisiana, United States 	2004.0 	971.0 	Green 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	11 	290 	Dealer 	Clear 	False 	TRUE 	NaN
193 	Used 	NaN 	32888.0 	Gastonia, North Carolina, United States 	2017.0 	151.0 	SATIN BLACK &amp; RED 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	18 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
215 	Used 	NaN 	36908.0 	Gastonia, North Carolina, United States 	2014.0 	8180.0 	BLACK &amp; RED 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	13 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
221 	New 	NaN 	33000.0 	Sacramento, California, United States 	2017.0 	101.0 	Orange 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	4< 	75 	Private Seller 	Clear 	False 	TRUE 	NaN
222 	Used 	Brand new conversion on a 2016 harley davidson... 	48000.0 	Gilbert, Arizona, United States 	2016.0 	1100.0 	Silver 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	FALSE 	100 	18 	13 	NaN 	Clear 	False 	TRUE 	NaN
224 	Used 	NaN 	31991.0 	Gastonia, North Carolina, United States 	2015.0 	3555.0 	BLACK &amp; ORANGE 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	11 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
226 	Used 	NaN 	47720.0 	Gastonia, North Carolina, United States 	2016.0 	3355.0 	SILVER 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	8< 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
235 	Used 	NaN 	41444.0 	Gastonia, North Carolina, United States 	2016.0 	3998.0 	SATIN BLACK &amp; ORANGE 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	5< 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
267 	Used 	NaN 	33857.0 	Gastonia, North Carolina, United States 	2017.0 	2280.0 	WHITE 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	5< 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
272 	Used 	NaN 	51000.0 	Fontana, California, United States 	2016.0 	3500.0 	Silver 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	FALSE 	NaN 	4< 	3 	Private Seller 	Clear 	True 	TRUE 	0.0
294 	New 	NaN 	48900.0 	Lima, Ohio, United States 	2017.0 	0.0 	Red 	Honda 	Vehicle has an existing warranty 	Gold Wing 	... 	NaN 	TRUE 	100 	7< 	1645 	Dealer 	Clear 	False 	TRUE 	NaN
295 	Used 	ALL USED ITEMS, offered at BUY IT NOW PRICES! ... 	100000.0 	Lathrop, California, United States 	2000.0 	111111.0 	all 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Other 	... 	NaN 	FALSE 	9.8 	10 	3280 	Dealer 	Clear 	False 	TRUE 	NaN
296 	Used 	NaN 	70000.0 	Gilbert, Arizona, United States 	2010.0 	1863.0 	Black 	Harley-Davidson 	NaN 	Other 	... 	NaN 	TRUE 	NaN 	8< 	7 	Private Seller 	Clear 	False 	TRUE 	NaN
298 	Used 	NaN 	34000.0 	Riverside, California, United States 	2013.0 	7530.0 	Yellow 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	FALSE 	100 	NaN 	37 	Private Seller 	Clear 	True 	FALSE 	0.0
300 	Used 	NaN 	34000.0 	Riverside, California, United States 	2013.0 	7530.0 	Yellow 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	FALSE 	100 	NaN 	37 	Private Seller 	Clear 	True 	FALSE 	0.0
308 	Used 	NaN 	33857.0 	Gastonia, North Carolina, United States 	2017.0 	2280.0 	WHITE 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	7< 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
313 	Used 	NaN 	39919.0 	Gastonia, North Carolina, United States 	2017.0 	1770.0 	TITANIUM SILVER 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	12 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
324 	Used 	NaN 	32333.0 	Gastonia, North Carolina, United States 	2016.0 	1795.0 	GREY &amp; RED 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	11 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
344 	Used 	5,700 miles 	65000.0 	Bonney Lake, Washington, United States 	2015.0 	5200.0 	Green 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	3< 	266 	Private Seller 	Clear 	False 	TRUE 	NaN
359 	Used 	5,700 miles 	65000.0 	Bonney Lake, Washington, United States 	2015.0 	5200.0 	Green 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	3< 	266 	Private Seller 	Clear 	False 	TRUE 	NaN
371 	Used 	NaN 	31500.0 	Lee, New Hampshire, United States 	2016.0 	554.0 	NaN 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	2< 	352 	Dealer 	Clear 	False 	TRUE 	NaN
382 	New 	NaN 	48900.0 	Lima, Ohio, United States 	2017.0 	0.0 	Red 	Honda 	Vehicle has an existing warranty 	Gold Wing 	... 	NaN 	TRUE 	100 	7< 	1645 	Dealer 	Clear 	False 	TRUE 	NaN
399 	Used 	NaN 	36992.0 	Gastonia, North Carolina, United States 	2016.0 	4140.0 	SILVER 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	6< 	1016 	Dealer 	Clear 	False 	TRUE 	NaN
404 	Used 	MINT CONDITION 	49900.0 	Timmonsville, South Carolina, United States 	2013.0 	6500.0 	Green 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	21 	907 	Private Seller 	Clear 	False 	TRUE 	NaN
414 	Used 	NaN 	36499.0 	Brentwood, Tennessee, United States 	2016.0 	2200.0 	Red 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	6< 	222 	NaN 	Clear 	False 	TRUE 	NaN
528 	Used 	NaN 	32500.0 	Eagle River, Wisconsin, United States 	2016.0 	8000.0 	NaN 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	2< 	1553 	Private Seller 	Clear 	False 	TRUE 	NaN
568 	Used 	NaN 	32500.0 	Eagle River, Wisconsin, United States 	2016.0 	8000.0 	NaN 	Harley-Davidson 	Vehicle has an existing warranty 	Touring 	... 	NaN 	TRUE 	100 	2< 	1553 	Private Seller 	Clear 	False 	TRUE 	NaN
630 	Used 	only 4554 miles on bike with new independent s... 	35000.0 	Gilbert, Arizona, United States 	2015.0 	4554.0 	NaN 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	FALSE 	100 	6< 	13 	NaN 	Clear 	False 	TRUE 	NaN
632 	Used 	NaN 	45000.0 	Wichita Falls, Texas, United States 	2013.0 	2500.0 	Black 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	TRUE 	100 	1< 	42 	Private Seller 	Clear 	False 	TRUE 	NaN
649 	Used 	NaN 	32989.0 	Piedmont, South Carolina, United States 	2014.0 	4359.0 	TRIBAL ORANGE 	Harley-Davidson 	Unspecified 	Touring 	... 	NaN 	TRUE 	100 	9< 	174 	Private Seller 	Clear 	False 	TRUE 	NaN
860 	Used 	bike has 6027 miles and the conversion is new 	32500.0 	Gilbert, Arizona, United States 	2010.0 	6027.0 	NaN 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	FALSE 	100 	5< 	13 	NaN 	Clear 	False 	TRUE 	NaN
985 	Used 	SUPER CLEAN LIKE NEW CALIFORNIA SIDE CAR TRIKE... 	34900.0 	Lima, Ohio, United States 	2012.0 	7101.0 	BLUE / SILVER 	Honda 	Vehicle has an existing warranty 	Gold Wing 	... 	NaN 	TRUE 	100 	1< 	1645 	Dealer 	Clear 	False 	TRUE 	NaN
1092 	Used 	bike has 16954 miles. Q-TEC conversion is new 	37000.0 	Gilbert, Arizona, United States 	2014.0 	16954.0 	NaN 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	FALSE 	100 	3< 	13 	NaN 	Clear 	False 	TRUE 	NaN
1179 	Used 	bike has 16954 miles. Q-TEC conversion is new 	37000.0 	Gilbert, Arizona, United States 	2014.0 	16954.0 	NaN 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	FALSE 	100 	3< 	13 	NaN 	Clear 	False 	TRUE 	NaN
1217 	Used 	NaN 	39995.0 	Tempe, Arizona, United States 	2014.0 	1.0 	Silver 	Harley-Davidson 	NaN 	Road King? 	... 	TRUE 	>90 	12 	8 	Dealer 	Clear 	FALSE 	True 	NaN 	NaN
1235 	New 	NaN 	43900.0 	Lima, Ohio, United States 	2017.0 	0.0 	BLUE / BLACK 	Honda 	Vehicle has an existing warranty 	Gold Wing 	... 	NaN 	TRUE 	100 	1< 	1645 	Dealer 	Clear 	False 	TRUE 	NaN
1236 	Used 	NaN 	39000.0 	Toms River, New Jersey, United States 	2012.0 	6000.0 	Silver 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Touring 	... 	NaN 	FALSE 	100 	NaN 	108 	Private Seller 	Clear 	True 	FALSE 	0.0
1237 	Used 	NaN 	32000.0 	Parker, Colorado, United States 	2012.0 	2850.0 	Orange 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	TRUE 	100 	4< 	1599 	Private Seller 	Clear 	False 	TRUE 	NaN
1262 	New 	NaN 	43900.0 	Lima, Ohio, United States 	2017.0 	0.0 	BLUE / BLACK 	Honda 	Vehicle has an existing warranty 	Gold Wing 	... 	NaN 	TRUE 	100 	1< 	1645 	Dealer 	Clear 	False 	TRUE 	NaN
1311 	Used 	NaN 	39995.0 	Tempe, Arizona, United States 	2014.0 	1.0 	Silver 	Harley-Davidson 	NaN 	Road King? 	... 	TRUE 	>90 	12 	8 	Dealer 	Clear 	FALSE 	True 	NaN 	NaN
1487 	Used 	NaN 	42500.0 	Lubbock, Texas, United States 	2014.0 	5200.0 	Black 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Softail 	... 	NaN 	FALSE 	100 	NaN 	23 	Private Seller 	Clear 	True 	TRUE 	0.0
1550 	Used 	NaN 	42500.0 	Lubbock, Texas, United States 	2014.0 	5200.0 	Black 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Softail 	... 	NaN 	FALSE 	100 	NaN 	23 	Private Seller 	Clear 	True 	TRUE 	0.0
... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	... 	...
4858 	Used 	NaN 	57000.0 	West Bloomfield, Michigan, United States 	2016.0 	1706.0 	White 	Campagna 	NaN 	T-Rex16S-P 	... 	NaN 	TRUE 	100 	NaN 	14 	Private Seller 	Clear 	False 	TRUE 	NaN
4931 	Used 	NaN 	34900.0 	Lake Park, Georgia, United States 	2001.0 	2500.0 	Red 	Boss Hoss 	Vehicle does NOT have an existing warranty 	434 stroker 	... 	NaN 	FALSE 	100 	NaN 	38 	Private Seller 	Clear 	True 	FALSE 	4.0
5058 	Used 	NaN 	32450.0 	Dallas, Texas, United States 	2016.0 	1515.0 	Charcoal Slate and Carbon Dust 	Harley-Davidson 	Unspecified 	FLTRUSE - Screamin Eagle Road Glide Ultra CVO 	... 	NaN 	TRUE 	100 	6< 	532 	Dealer 	Clear 	False 	TRUE 	NaN
5104 	Used 	No defects, is in immaculate condition 	70000.0 	Whyteleafe, United Kingdom 	2006.0 	100.0 	NaN 	MV Agusta 	NaN 	MV Agusta F4 1000 Veltro Strada 	... 	NaN 	TRUE 	100 	4< 	228 	Private Seller 	Clear 	False 	TRUE 	NaN
5144 	Used 	NaN 	37000.0 	Brandon, Wisconsin, United States 	1990.0 	2220.0 	NaN 	Custom Built Motorcycles 	NaN 	Pro Street 	... 	NaN 	TRUE 	NaN 	NaN 	0 	NaN 	Clear 	False 	TRUE 	NaN
5157 	Used 	Spotless custom built show bike, best of every... 	34900.0 	Pittsburgh, Pennsylvania, United States 	2006.0 	1200.0 	Orange 	Custom Built Motorcycles 	Vehicle does NOT have an existing warranty 	Chopper 	... 	NaN 	FALSE 	NaN 	NaN 	0 	Private Seller 	Clear 	False 	TRUE 	NaN
5182 	New 	NaN 	59997.0 	Houston, Texas, United States 	2014.0 	0.0 	Red 	Ducati 	Unspecified 	1199 Superleggera 	... 	NaN 	TRUE 	100 	13 	246 	Dealer 	Clear 	False 	TRUE 	NaN
5184 	New 	NaN 	65000.0 	Marietta, Georgia, United States 	2010.0 	12.0 	Gray 	Custom Built Motorcycles 	Vehicle does NOT have an existing warranty 	Chopper 	... 	NaN 	FALSE 	NaN 	NaN 	0 	Private Seller 	Clear 	False 	TRUE 	NaN
5207 	Used 	NaN 	33977.0 	Dallas, Texas, United States 	2016.0 	1.0 	Black 	Big Dog 	Unspecified 	K-9 124 Level 3 Graphics BIG DOG IS BACK 	... 	NaN 	TRUE 	100 	NaN 	532 	Dealer 	Clear 	False 	TRUE 	NaN
5210 	Used 	NaN 	31488.0 	Dallas, Texas, United States 	2016.0 	208.0 	Gray 	Big Dog 	Unspecified 	Mastiff Limited Edition 	... 	NaN 	TRUE 	100 	1< 	532 	Dealer 	Clear 	False 	TRUE 	NaN
5231 	Used 	No expense spared in this beautiful museum gra... 	34000.0 	Miami, Florida, United States 	1968.0 	4708.0 	NaN 	Norton 	NaN 	NaN 	... 	NaN 	TRUE 	>97 	27 	115 	NaN 	Clear 	False 	TRUE 	NaN
5240 	Used 	NaN 	67447.0 	Pompano Beach, Florida, United States 	2017.0 	0.0 	Blue 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	2< 	1765 	Dealer 	Clear 	False 	TRUE 	NaN
5265 	Used 	NaN 	61447.0 	Pompano Beach, Florida, United States 	2017.0 	22.0 	Gray 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	1< 	1765 	Dealer 	Clear 	False 	TRUE 	NaN
5267 	Used 	NaN 	63446.0 	Pompano Beach, Florida, United States 	2017.0 	0.0 	Orange 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	1765 	Dealer 	Clear 	False 	TRUE 	NaN
5268 	New 	NaN 	50000.0 	Saint-Jean-Chrysostome, Quebec, Canada 	NaN 	NaN 	NaN 	NaN 	NaN 	NaN 	... 	NaN 	TRUE 	NaN 	13 	2 	NaN 	NaN 	False 	TRUE 	NaN
5270 	Used 	NaN 	67447.0 	Pompano Beach, Florida, United States 	2017.0 	21.0 	Red 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	2< 	1765 	Dealer 	Clear 	False 	TRUE 	NaN
5273 	Used 	NaN 	61447.0 	Pompano Beach, Florida, United States 	2017.0 	22.0 	Blue 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	1< 	1765 	Dealer 	Clear 	False 	TRUE 	NaN
5274 	Used 	NaN 	61447.0 	Pompano Beach, Florida, United States 	2017.0 	0.0 	White 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	1765 	Dealer 	Clear 	False 	TRUE 	NaN
5286 	Used 	NaN 	69446.0 	Pompano Beach, Florida, United States 	2017.0 	22.0 	Orange 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	1765 	Dealer 	Clear 	False 	TRUE 	NaN
5287 	Used 	NaN 	61447.0 	Pompano Beach, Florida, United States 	2017.0 	22.0 	Red 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	1765 	Dealer 	Clear 	False 	TRUE 	NaN
5388 	Used 	NaN 	32677.0 	Scott City, Missouri, United States 	2016.0 	4476.0 	Red 	Harley-Davidson 	Vehicle has an existing warranty 	Cvo Limited 	... 	NaN 	FALSE 	100 	NaN 	4 	Dealer 	Clear 	False 	FALSE 	NaN
5482 	New 	NaN 	52000.0 	Indianapolis, Indiana, United States 	2016.0 	1.0 	Gray 	Kawasaki 	NaN 	Ninja 	... 	NaN 	TRUE 	>80 	NaN 	375 	Dealer 	Clear 	False 	FALSE 	NaN
5484 	Used 	NaN 	49988.0 	Mooresville, North Carolina, United States 	2016.0 	3142.0 	Blue 	Harley-Davidson 	Unspecified 	STREET GLIDE SPECIAL (FLHXS) 	... 	NaN 	TRUE 	100 	NaN 	824 	Dealer 	Clear 	False 	FALSE 	NaN
5601 	New 	NaN 	31999.0 	Indianapolis, Indiana, United States 	2017.0 	1.0 	Blue Sapphire &amp; Brilliant Blue 	Indian 	NaN 	Roadmaster? 	... 	TRUE 	>80 	NaN 	375 	Dealer 	Clear 	FALSE 	False 	NaN 	NaN
5672 	Used 	NaN 	46988.0 	Mooresville, North Carolina, United States 	2014.0 	2707.0 	Orange 	Harley-Davidson 	Unspecified 	Street Glide Special FLHX 	... 	NaN 	TRUE 	100 	NaN 	824 	Dealer 	Clear 	False 	FALSE 	NaN
5729 	Used 	NaN 	35900.0 	Bear, Delaware, United States 	2008.0 	9500.0 	Red 	Harley-Davidson 	NaN 	Touring 	... 	NaN 	FALSE 	100 	15 	46 	Private Seller 	Rebuilt, Rebuildable &amp; Reconstructed 	True 	TRUE 	0.0
5798 	Used 	NaN 	69446.0 	Pompano Beach, Florida, United States 	2017.0 	22.0 	Orange 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	1765 	Dealer 	Clear 	False 	FALSE 	NaN
5824 	Used 	NaN 	61447.0 	Pompano Beach, Florida, United States 	2017.0 	22.0 	Blue 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	1765 	Dealer 	Clear 	False 	FALSE 	NaN
5934 	Used 	NaN 	60000.0 	Red Deer, Alberta, Canada 	2002.0 	8266.0 	Black 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Road Glide 	... 	NaN 	TRUE 	100 	2< 	3 	Private Seller 	NaN 	False 	TRUE 	NaN
6232 	Used 	NaN 	51995.0 	Charlotte, North Carolina, United States 	2012.0 	6685.0 	NaN 	Harley-Davidson 	Vehicle has an existing warranty 	Other 	... 	NaN 	TRUE 	8.2 	NaN 	80 	Dealer 	Clear 	False 	FALSE 	NaN
6345 	Used 	Mike Hailwood was one of motorcycle racings al... 	42900.0 	Saint Louis, Missouri, United States 	1980.0 	12083.0 	Red/Green 	Ducati 	Vehicle does NOT have an existing warranty 	Other 	... 	NaN 	TRUE 	8.6 	NaN 	2251 	Dealer 	Clear 	False 	FALSE 	NaN
6394 	Used 	NaN 	38995.0 	Omaha, Nebraska, United States 	1958.0 	61.0 	Red 	Harley-Davidson 	Vehicle does NOT have an existing warranty 	Touring 	... 	NaN 	TRUE 	3.4 	NaN 	738 	Dealer 	Clear 	False 	FALSE 	NaN
6483 	Used 	NaN 	37000.0 	McKinney, Texas, United States 	2014.0 	15698.0 	Black 	*Harley-Davidson* Martin Bros 	Unspecified 	Custom Road Glide 	... 	NaN 	TRUE 	>50 	NaN 	397 	Dealer 	Clear 	False 	FALSE 	NaN
6588 	Used 	NaN 	41500.0 	Saint Louis, Missouri, United States 	1981.0 	1.0 	Red 	Ducati 	Unspecified 	Other 	... 	NaN 	TRUE 	8.6 	NaN 	2251 	Dealer 	Clear 	False 	FALSE 	NaN
6806 	Used 	NaN 	41500.0 	Saint Louis, Missouri, United States 	1980.0 	12083.0 	Green 	Ducati 	Unspecified 	Other 	... 	NaN 	TRUE 	8.6 	NaN 	2251 	Dealer 	Clear 	False 	FALSE 	NaN
6917 	Used 	NaN 	35999.0 	Delbarton, West Virginia, United States 	2016.0 	14327.0 	Blue 	Harley-Davidson 	Unspecified 	FLHTKSE - CVO?Limited 	... 	NaN 	TRUE 	100 	NaN 	28 	Dealer 	Clear 	False 	FALSE 	NaN
6924 	Used 	NaN 	33588.0 	Scott City, Missouri, United States 	2016.0 	3960.0 	Red 	Harley-Davidson 	Vehicle has an existing warranty 	Cvo Road Glide Ultra 	... 	NaN 	FALSE 	100 	NaN 	4 	Dealer 	Clear 	False 	FALSE 	NaN
6970 	Used 	NaN 	38999.0 	Elmhurst, Illinois, United States 	2015.0 	6970.0 	Black 	Boss Hoss 	Unspecified 	BHC-9 Coupe Trike 	... 	NaN 	TRUE 	100 	NaN 	152 	Dealer 	Clear 	False 	FALSE 	NaN
6988 	Used 	NaN 	35450.0 	Dallas, Texas, United States 	2017.0 	2864.0 	Black 	Harley-Davidson 	Unspecified 	FLHXSE - Screamin Eagle Street Glide CVO 	... 	NaN 	TRUE 	100 	NaN 	532 	Dealer 	Clear 	False 	FALSE 	NaN
6999 	Used 	NaN 	37500.0 	Poland, Maine, United States 	2017.0 	2571.0 	Silver/Black 	Harley-Davidson 	NaN 	Tri Glide 	... 	NaN 	TRUE 	6.4 	NaN 	992 	Private Seller 	Clear 	False 	FALSE 	NaN
7001 	Used 	NaN 	37500.0 	Poland, Maine, United States 	2017.0 	2571.0 	Silver/Black 	Harley-Davidson 	NaN 	Tri Glide 	... 	NaN 	TRUE 	6.4 	NaN 	992 	Private Seller 	Clear 	False 	FALSE 	NaN
7020 	New 	NaN 	54900.0 	Glen Ellyn, Illinois, United States 	2016.0 	1.0 	Yellow 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	573 	Dealer 	Clear 	False 	FALSE 	NaN
7045 	New 	NaN 	52897.0 	Glen Ellyn, Illinois, United States 	2016.0 	0.0 	Red 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	573 	Dealer 	Clear 	False 	FALSE 	NaN
7050 	New 	NaN 	52897.0 	Glen Ellyn, Illinois, United States 	2016.0 	0.0 	Red 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	573 	Dealer 	Clear 	False 	FALSE 	NaN
7064 	New 	NaN 	54897.0 	Glen Ellyn, Illinois, United States 	2016.0 	1.0 	Gray 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	573 	Dealer 	Clear 	False 	FALSE 	NaN
7138 	Used 	NaN 	42500.0 	Phoenix, Arizona, United States 	1999.0 	700.0 	Blue 	ADVANTAGE 	Vehicle does NOT have an existing warranty 	32 Victory 	... 	NaN 	TRUE 	100 	NaN 	1452 	Dealer 	Clear 	False 	FALSE 	NaN
7221 	Used 	NaN 	67447.0 	Pompano Beach, Florida, United States 	2017.0 	0.0 	Black 	Campagna 	Unspecified 	T-Rex 	... 	NaN 	TRUE 	100 	NaN 	1765 	Dealer 	Clear 	False 	FALSE 	NaN
7249 	Used 	NaN 	34900.0 	Greensboro, North Carolina, United States 	2017.0 	2341.0 	Black 	Harley-Davidson 	Unspecified 	FLHXSE - CVO?Street Glide? 	... 	TRUE 	NaN 	NaN 	0 	Dealer 	Clear 	FALSE 	False 	NaN 	NaN
7337 	Used 	NaN 	32999.0 	Greensboro, North Carolina, United States 	2016.0 	10.0 	Black 	Indian 	Unspecified 	Chief?Vintage Jack Daniel&#039;s?Limited Edition 	... 	NaN 	TRUE 	100 	NaN 	312 	Dealer 	Clear 	False 	FALSE 	NaN
7340 	Used 	NaN 	32877.0 	Scott City, Missouri, United States 	2016.0 	5100.0 	Black 	Harley-Davidson 	Vehicle has an existing warranty 	FLHXSE - CVO Street Glide 	... 	NaN 	FALSE 	100 	NaN 	4 	Dealer 	Clear 	False 	FALSE 	NaN

160 rows × 22 columns

df['Price'].describe()

count      7493.000000
mean       9968.811557
std        8497.326850
min           0.000000
25%        4158.000000
50%        7995.000000
75%       13000.000000
max      100000.000000
Name: Price, dtype: float64

Q1 = df['Price'].quantile(q = 0.25)#四分之一分位数

Q3 = df['Price'].quantile(q = 0.75)#四分之三分位数

IQR = Q3 - Q1#分位差

any(df['Price'] > Q3 + 1.5*IQR)#上离群点

True

any(df['Price'] < Q1 - 1.5*IQR)#下离群点

False

import matplotlib.pyplot as plt

%matplotlib inline

 #确保图能在jupyter notebook里面运行出来

df['Price'].plot(kind = 'box')#箱图（盒图）

<matplotlib.axes._subplots.AxesSubplot at 0x1f698c8f198>

#分布图

plt.style.use('seaborn')#使用seaborn绘图风格，好看

df.Price.plot(kind = 'hist', bins = 30, density = True)#bins是柱状个数；density是否绘制成概率密度形式

df.Price.plot(kind = 'kde')#核密度图

plt.show()

P99 = df['Price'].quantile(q = 0.99)

P1 = df['Price'].quantile(q = 0.01)

df['Price_new'] = df['Price']

df.loc[df['Price_new'] > P99, 'Price_new'] = P99#   盖帽法

df.loc[df['Price'] < P1, 'Price_new'] = P1

df[['Price', 'Price_new']].describe()

	Price 	Price_new
count 	7493.000000 	7493.000000
mean 	9968.811557 	9821.220873
std 	8497.326850 	7737.092537
min 	0.000000 	100.000000
25% 	4158.000000 	4158.000000
50% 	7995.000000 	7995.000000
75% 	13000.000000 	13000.000000
max 	100000.000000 	39995.320000

df['Price_new'].plot(kind = 'box')

<matplotlib.axes._subplots.AxesSubplot at 0x1f69acf1eb8>

7.4 数据离散化处理

数据离散化就是分箱
一般常用分箱方法是等频分箱或者等宽分箱
一般使用pd.cut或者pd.qcut函数

等宽分箱

df['Price_bin'] = pd.cut(df['Price_new'], bins = 5, labels = range(5))

df['Price_bin']

0       1
1       2
2       0
3       0
4       1
5       0
6       3
7       0
8       0
9       0
10      0
11      1
12      0
13      0
14      0
15      1
16      1
17      1
18      2
19      1
20      1
21      1
22      0
23      0
24      3
25      1
26      4
27      1
28      1
29      2
30      3
31      2
32      1
33      1
34      2
35      0
36      0
37      1
38      0
39      2
40      0
41      0
42      0
43      1
44      1
45      0
46      0
47      2
48      1
49      0
       ..
7443    0
7444    0
7445    0
7446    0
7447    2
7448    0
7449    0
7450    0
7451    0
7452    0
7453    1
7454    0
7455    0
7456    0
7457    1
7458    1
7459    0
7460    0
7461    0
7462    0
7463    0
7464    0
7465    0
7466    0
7467    0
7468    1
7469    1
7470    0
7471    0
7472    0
7473    0
7474    0
7475    0
7476    1
7477    0
7478    1
7479    0
7480    0
7481    1
7482    0
7483    0
7484    0
7485    0
7486    1
7487    0
7488    0
7489    1
7490    0
7491    0
7492    1
Name: Price_bin, Length: 7493, dtype: category
Categories (5, int64): [0 < 1 < 2 < 3 < 4]

df['Price_bin'].value_counts()

0    3873
1    2331
2     866
3     277
4     146
Name: Price_bin, dtype: int64

df['Price_bin'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1f697d6b710>

df['Price_bin'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x1f69adb5a58>

#自己定义分段标准
w = [100, 1000, 5000, 10000, 20000, 100000]

df['Price_bin'] = pd.cut(df['Price_new'], bins = w, labels = range(5))

df[['Price_new','Price_bin']]

	Price_new 	Price_bin
0 	11412.00 	3
1 	17200.00 	3
2 	3872.00 	1
3 	6575.00 	2
4 	10000.00 	2
5 	1500.00 	1
6 	24900.00 	4
7 	1400.00 	1
8 	5100.00 	2
9 	8000.00 	2
10 	2125.00 	1
11 	11100.00 	3
12 	1125.00 	1
13 	3550.00 	1
14 	5500.00 	2
15 	9000.00 	2
16 	8100.00 	2
17 	14000.00 	3
18 	20000.00 	3
19 	13000.00 	3
20 	10900.00 	3
21 	9999.00 	2
22 	5700.00 	2
23 	7400.00 	2
24 	26500.00 	4
25 	9850.00 	2
26 	39995.32 	4
27 	10600.00 	3
28 	12000.00 	3
29 	20000.00 	3
30 	25000.00 	4
31 	20000.00 	3
32 	11000.00 	3
33 	8500.00 	2
34 	18000.00 	3
35 	7700.00 	2
36 	2750.00 	1
37 	10895.00 	3
38 	6700.00 	2
39 	18000.00 	3
40 	7300.00 	2
41 	6800.00 	2
42 	5000.00 	1
43 	13200.00 	3
44 	11400.00 	3
45 	3250.00 	1
46 	8000.00 	2
47 	17800.00 	3
48 	8500.00 	2
49 	4050.00 	1
... 	... 	...
7443 	7900.00 	2
7444 	5900.00 	2
7445 	3900.00 	1
7446 	7900.00 	2
7447 	23499.00 	4
7448 	6500.00 	2
7449 	6500.00 	2
7450 	3900.00 	1
7451 	5200.00 	2
7452 	3900.00 	1
7453 	8300.00 	2
7454 	7400.00 	2
7455 	4700.00 	1
7456 	4900.00 	1
7457 	8500.00 	2
7458 	9200.00 	2
7459 	3900.00 	1
7460 	7500.00 	2
7461 	4200.00 	1
7462 	6500.00 	2
7463 	7800.00 	2
7464 	4400.00 	1
7465 	7900.00 	2
7466 	6500.00 	2
7467 	7400.00 	2
7468 	8800.00 	2
7469 	13570.00 	3
7470 	7900.00 	2
7471 	6200.00 	2
7472 	7500.00 	2
7473 	5500.00 	2
7474 	3400.00 	1
7475 	3900.00 	1
7476 	8500.00 	2
7477 	4900.00 	1
7478 	9900.00 	2
7479 	2900.00 	1
7480 	6500.00 	2
7481 	10500.00 	3
7482 	7200.00 	2
7483 	7500.00 	2
7484 	5000.00 	1
7485 	5000.00 	1
7486 	8900.00 	2
7487 	4900.00 	1
7488 	3900.00 	1
7489 	8900.00 	2
7490 	7800.00 	2
7491 	7900.00 	2
7492 	12970.00 	3

7493 rows × 2 columns

df['Price_bin'].hist() 

<matplotlib.axes._subplots.AxesSubplot at 0x1f699bfaac8>

df['Price_bin'] = pd.cut(df['Price_new'], 5)

df['Price_bin']

0        (8079.064, 16058.128]
1       (16058.128, 24037.192]
2           (60.105, 8079.064]
3           (60.105, 8079.064]
4        (8079.064, 16058.128]
5           (60.105, 8079.064]
6       (24037.192, 32016.256]
7           (60.105, 8079.064]
8           (60.105, 8079.064]
9           (60.105, 8079.064]
10          (60.105, 8079.064]
11       (8079.064, 16058.128]
12          (60.105, 8079.064]
13          (60.105, 8079.064]
14          (60.105, 8079.064]
15       (8079.064, 16058.128]
16       (8079.064, 16058.128]
17       (8079.064, 16058.128]
18      (16058.128, 24037.192]
19       (8079.064, 16058.128]
20       (8079.064, 16058.128]
21       (8079.064, 16058.128]
22          (60.105, 8079.064]
23          (60.105, 8079.064]
24      (24037.192, 32016.256]
25       (8079.064, 16058.128]
26       (32016.256, 39995.32]
27       (8079.064, 16058.128]
28       (8079.064, 16058.128]
29      (16058.128, 24037.192]
30      (24037.192, 32016.256]
31      (16058.128, 24037.192]
32       (8079.064, 16058.128]
33       (8079.064, 16058.128]
34      (16058.128, 24037.192]
35          (60.105, 8079.064]
36          (60.105, 8079.064]
37       (8079.064, 16058.128]
38          (60.105, 8079.064]
39      (16058.128, 24037.192]
40          (60.105, 8079.064]
41          (60.105, 8079.064]
42          (60.105, 8079.064]
43       (8079.064, 16058.128]
44       (8079.064, 16058.128]
45          (60.105, 8079.064]
46          (60.105, 8079.064]
47      (16058.128, 24037.192]
48       (8079.064, 16058.128]
49          (60.105, 8079.064]
                 ...          
7443        (60.105, 8079.064]
7444        (60.105, 8079.064]
7445        (60.105, 8079.064]
7446        (60.105, 8079.064]
7447    (16058.128, 24037.192]
7448        (60.105, 8079.064]
7449        (60.105, 8079.064]
7450        (60.105, 8079.064]
7451        (60.105, 8079.064]
7452        (60.105, 8079.064]
7453     (8079.064, 16058.128]
7454        (60.105, 8079.064]
7455        (60.105, 8079.064]
7456        (60.105, 8079.064]
7457     (8079.064, 16058.128]
7458     (8079.064, 16058.128]
7459        (60.105, 8079.064]
7460        (60.105, 8079.064]
7461        (60.105, 8079.064]
7462        (60.105, 8079.064]
7463        (60.105, 8079.064]
7464        (60.105, 8079.064]
7465        (60.105, 8079.064]
7466        (60.105, 8079.064]
7467        (60.105, 8079.064]
7468     (8079.064, 16058.128]
7469     (8079.064, 16058.128]
7470        (60.105, 8079.064]
7471        (60.105, 8079.064]
7472        (60.105, 8079.064]
7473        (60.105, 8079.064]
7474        (60.105, 8079.064]
7475        (60.105, 8079.064]
7476     (8079.064, 16058.128]
7477        (60.105, 8079.064]
7478     (8079.064, 16058.128]
7479        (60.105, 8079.064]
7480        (60.105, 8079.064]
7481     (8079.064, 16058.128]
7482        (60.105, 8079.064]
7483        (60.105, 8079.064]
7484        (60.105, 8079.064]
7485        (60.105, 8079.064]
7486     (8079.064, 16058.128]
7487        (60.105, 8079.064]
7488        (60.105, 8079.064]
7489     (8079.064, 16058.128]
7490        (60.105, 8079.064]
7491        (60.105, 8079.064]
7492     (8079.064, 16058.128]
Name: Price_bin, Length: 7493, dtype: category
Categories (5, interval[float64]): [(60.105, 8079.064] < (8079.064, 16058.128] < (16058.128, 24037.192] < (24037.192, 32016.256] < (32016.256, 39995.32]]

df.Price_bin.value_counts()

(60.105, 8079.064]        3873
(8079.064, 16058.128]     2331
(16058.128, 24037.192]     866
(24037.192, 32016.256]     277
(32016.256, 39995.32]      146
Name: Price_bin, dtype: int64

#等频分箱

k = 5

w = [i/k for i in range(k+1)]

w

[0.0, 0.2, 0.4, 0.6, 0.8, 1.0]

df['Price_bin'] = pd.qcut(df['Price_new'], q = w, labels = range(5))

df['Price_bin'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x1f699b6a6a0>

#找分位点

k = 5

w1 = df['Price_new'].quantile([i/k for i in range(k+1)])

w1

0.0      100.00
0.2     3500.00
0.4     6491.00
0.6     9777.00
0.8    14999.00
1.0    39995.32
Name: Price_new, dtype: float64

#让最小值再小一点，最大值再大一点

w1[0] = w1[0] * 0.95

w1[1.0] = w1[1.0] * 1.1

w1

0.0       95.000
0.2     3500.000
0.4     6491.000
0.6     9777.000
0.8    14999.000
1.0    43994.852
Name: Price_new, dtype: float64

#按照这个分段标准用等宽分箱来分，分出来的一定是等频

df['Price_bin'] = pd.cut(df['Price_new'], bins = w1, labels = range(5))

df['Price_bin'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x1f699c2ee80>

欢迎阅读数据清洗系列文章：python数据清洗工具、方法、过程整理归纳

一、数据清洗之常用工具——numpy，pandas
二、数据清洗之文件读写——读取csv、Excel和MySQL数据
三、数据清洗之数据表操作——数据筛选、增加删除、查找修改、数据整理和层次化索引
四、数据清洗之数据转换——日期格式数据处理、高阶函数数据处理、字符串数据处理
五、数据清洗之数据统计——数据分组运算、聚合函数使用、分组对象和apply函数、透视图与交叉表
六、数据清洗之数据预处理（一）——重复值处理、缺失值处理
七、数据清洗之数据预处理（二）——异常值处理、数据离散化处理
八、总结

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