Pandas Cookbook -- 08数据清理

Posted 2021-01-13 shiyushiyu

数据清理

简书大神SeanCheney的译作，我作了些格式调整和文章目录结构的变化，更适合自己阅读，以后翻阅是更加方便自己查找吧

import pandas as pd
import numpy as np

设定最大列数和最大行数

pd.set_option(‘max_columns‘,5 , ‘max_rows‘, 5)

1 宽格式转长格式

state_fruit = pd.read_csv(‘data/state_fruit.csv‘, index_col=0)

state_fruit

	Apple	Orange	Banana
Texas	12	10	40
Arizona	9	7	12
Florida	0	14	190

1.1 stack

DataFrame.stack(level=-1, dropna=True)

• Stack the prescribed level(s) from columns to index.
• 将列索引转化为行索引
  

• Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

  • if the columns have a single level, the output is a Series;
  • if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.

1.1.1 stack将列索引转化为行索引

stack方法可以将所有列名，转变为垂直的一级行索引

state_fruit.stack()

Texas    Apple      12
         Orange     10
                  ... 
Florida  Orange     14
         Banana    190
Length: 9, dtype: int64

使用reset_index()，将结果变为DataFrame

state_fruit_tidy = state_fruit.stack().reset_index()
state_fruit_tidy

	level_0	level_1	0
0	Texas	Apple	12
1	Texas	Orange	10
...	...	...	...
7	Florida	Orange	14
8	Florida	Banana	190

9 rows × 3 columns

重命名列名

state_fruit_tidy.columns = [‘state‘, ‘fruit‘, ‘weight‘]
state_fruit_tidy

	state	fruit	weight
0	Texas	Apple	12
1	Texas	Orange	10
...	...	...	...
7	Florida	Orange	14
8	Florida	Banana	190

9 rows × 3 columns

也可以使用rename_axis给不同的行索引层级命名

state_fruit.stack().rename_axis([‘state‘, ‘fruit‘])

state    fruit 
Texas    Apple      12
         Orange     10
                  ... 
Florida  Orange     14
         Banana    190
Length: 9, dtype: int64

再次使用reset_index方法

state_fruit.stack().rename_axis([‘state‘, ‘fruit‘])           .reset_index(name=‘weight‘)

	state	fruit	weight
0	Texas	Apple	12
1	Texas	Orange	10
...	...	...	...
7	Florida	Orange	14
8	Florida	Banana	190

9 rows × 3 columns

1.1.2 同时stack多组变量

即stack后将所有的列名，按照规则分为两列，或者多列

movie = pd.read_csv(‘data/movie.csv‘)
actor = movie[[
    ‘movie_title‘, ‘actor_1_name‘, ‘actor_2_name‘, 
    ‘actor_3_name‘, ‘actor_1_facebook_likes‘, 
    ‘actor_2_facebook_likes‘, ‘actor_3_facebook_likes‘]]

创建一个自定义函数，用来改变列名。wide_to_long要求分组的变量要有相同的数值结尾

def change_col_name(col_name):
    col_name = col_name.replace(‘_name‘, ‘‘)
    if ‘facebook‘ in col_name:
        fb_idx = col_name.find(‘facebook‘)
        col_name = col_name[:5] + col_name[fb_idx - 1:] + col_name[5:fb_idx-1]
    return col_name

actor2 = actor.rename(columns=change_col_name)
actor2.iloc[:5,:5]

	movie_title	actor_1	actor_2	actor_3	actor_facebook_likes_1
0	Avatar	CCH Pounder	Joel David Moore	Wes Studi	1000.0
1	Pirates of the Caribbean: At World‘s End	Johnny Depp	Orlando Bloom	Jack Davenport	40000.0
2	Spectre	Christoph Waltz	Rory Kinnear	Stephanie Sigman	11000.0
3	The Dark Knight Rises	Tom Hardy	Christian Bale	Joseph Gordon-Levitt	27000.0
4	Star Wars: Episode VII - The Force Awakens	Doug Walker	Rob Walker	NaN	131.0

使用wide_to_long函数，同时stack两列actor和Facebook

stubs = [‘actor‘, ‘actor_facebook_likes‘]
actor2_tidy = pd.wide_to_long(actor2, 
    stubnames=stubs, 
    i=[‘movie_title‘], 
    j=‘actor_num‘, 
    sep=‘_‘)
actor2_tidy.head(10)

		actor	actor_facebook_likes
movie_title	actor_num
Avatar	1	CCH Pounder	1000.0
Pirates of the Caribbean: At World‘s End	1	Johnny Depp	40000.0
...	...	...	...
Avengers: Age of Ultron	1	Chris Hemsworth	26000.0
Harry Potter and the Half-Blood Prince	1	Alan Rickman	25000.0

10 rows × 2 columns

1.2 melt

pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name=‘value‘, col_level=None)

• This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.
• 这个函数是用来转化dataframe的格式，新的格式将满足一列或者多列作为id列，一列作为测量指标列，最后一列作为前面几列对应的测量值列
• id_vars : tuple, list, or ndarray, optional
  • Column(s) to use as identifier variables.
• value_vars : tuple, list, or ndarray, optional
  • Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
• var_name : scalar
  • Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
• value_name : scalar, default ‘value’
  • Name to use for the ‘value’ column.
• col_level : int or string, optional
  • If columns are a MultiIndex then use this level to melt.

读取state_fruit2数据集

state_fruit2 = pd.read_csv(‘data/state_fruit2.csv‘)
state_fruit2

	State	Apple	Orange	Banana
0	Texas	12	10	40
1	Arizona	9	7	12
2	Florida	0	14	190

melt可以将原先的列名作为变量，原先的值作为值

var_name和value_name可以用来重命名新生成的变量列和值的列

state_fruit2.melt(id_vars=[‘State‘],value_vars=[‘Apple‘, ‘Orange‘, ‘Banana‘])

	State	variable	value
0	Texas	Apple	12
1	Arizona	Apple	9
...	...	...	...
7	Arizona	Banana	12
8	Florida	Banana	190

9 rows × 3 columns

随意设定一个行索引

state_fruit2.index=list(‘abc‘)
state_fruit2.index.name = ‘letter‘
state_fruit2

	State	Apple	Orange	Banana
letter
a	Texas	12	10	40
b	Arizona	9	7	12
c	Florida	0	14	190

var_name和value_name可以用来重命名新生成的变量列和值的列
var_name对应value_vars 默认为variable
value_name对应原有表格中的值 默认为value

state_fruit2.melt(id_vars=[‘State‘],
    value_vars=[‘Apple‘, ‘Orange‘, ‘Banana‘],
    var_name=‘Fruit‘,
    value_name=‘Weight‘)

	State	Fruit	Weight
0	Texas	Apple	12
1	Arizona	Apple	9
...	...	...	...
7	Arizona	Banana	12
8	Florida	Banana	190

9 rows × 3 columns

如果想让所有值都位于一列，旧的列标签位于另一列，可以直接使用melt

state_fruit2.melt()

	variable	value
0	State	Texas
1	State	Arizona
...	...	...
10	Banana	12
11	Banana	190

12 rows × 2 columns

要指明id变量，只需使用id_vars参数

state_fruit2.melt(id_vars=‘State‘)

	State	variable	value
0	Texas	Apple	12
1	Arizona	Apple	9
...	...	...	...
7	Arizona	Banana	12
8	Florida	Banana	190

9 rows × 3 columns

2 长格式转宽格式

2.1 unstack

• DataFrame.unstack(level=-1, fill_value=None)
• level : int, string, or list of these, default -1 (last level)
  • Level(s) of index to unstack, can pass level name
  • 将行索引中的哪一级别展开
• fill_value : replace NaN with this value if the unstack produces
  • missing values
  • 缺失值怎样填充

读取college数据集，学校名作为行索引，只选取本科生的列

usecol_func = lambda x: ‘UGDS_‘ in x or x == ‘INSTNM‘
college = pd.read_csv(‘data/college.csv‘,index_col=‘INSTNM‘, usecols=usecol_func)

用stack方法，将所有水平列名，转化为垂直的行索引

college_stacked = college.stack()
college_stacked.head(18)

INSTNM                                         
Alabama A & M University             UGDS_WHITE    0.0333
                                     UGDS_BLACK    0.9353
                                                    ...  
University of Alabama at Birmingham  UGDS_NRA      0.0179
                                     UGDS_UNKN     0.0100
Length: 18, dtype: float64

unstack方法可以将其还原

college_stacked.unstack().head()

	UGDS_WHITE	UGDS_BLACK	...	UGDS_NRA	UGDS_UNKN
INSTNM
Alabama A & M University	0.0333	0.9353	...	0.0059	0.0138
University of Alabama at Birmingham	0.5922	0.2600	...	0.0179	0.0100
Amridge University	0.2990	0.4192	...	0.0000	0.2715
University of Alabama in Huntsville	0.6988	0.1255	...	0.0332	0.0350
Alabama State University	0.0158	0.9208	...	0.0243	0.0137

5 rows × 9 columns

2.2 pivot

DataFrame.pivot(index=None, columns=None, values=None)

• Return reshaped DataFrame organized by given index / column values.

• 通过给的index和column返回一个重塑的dataframe

• index : string or object, optional
  • Column to use to make new frame’s index. If None, uses existing index.
  • 原有的dataframe中的哪些列的值作为新的datarame索引
• columns : string or object
  • Column to use to make new frame’s columns.
  • 原有的dataframe中的哪些列中的值作为新的datarame的列
• values : string, object or a list of the previous, optional
  • Column(s) to use for populating new frame’s values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.
  • 原有的datarame哪个列的值，作为新的表中的值

另一种方式是先用melt，再用pivot。先加载数据，不指定行索引名

college2 = pd.read_csv(‘data/college.csv‘, usecols=usecol_func)

college_melted = college2.melt(id_vars=‘INSTNM‘, var_name=‘Race‘,value_name=‘Percentage‘)
college_melted.head()

	INSTNM	Race	Percentage
0	Alabama A & M University	UGDS_WHITE	0.0333
1	University of Alabama at Birmingham	UGDS_WHITE	0.5922
2	Amridge University	UGDS_WHITE	0.2990
3	University of Alabama in Huntsville	UGDS_WHITE	0.6988
4	Alabama State University	UGDS_WHITE	0.0158

用pivot还原

melted_inv = college_melted.pivot(index=‘INSTNM‘,columns=‘Race‘,values=‘Percentage‘)
melted_inv.head()

Race	UGDS_2MOR	UGDS_AIAN	...	UGDS_UNKN	UGDS_WHITE
INSTNM
A & W Healthcare Educators	0.0000	0.0	...	0.0000	0.0000
A T Still University of Health Sciences	NaN	NaN	...	NaN	NaN
ABC Beauty Academy	0.0000	0.0	...	0.0000	0.0000
ABC Beauty College Inc	0.0000	0.0	...	0.0000	0.2895
AI Miami International University of Art and Design	0.0018	0.0	...	0.4644	0.0324

5 rows × 9 columns

3 透视表

3.1 使用pivot_table

pandas.pivot_table(data, values=None, index=None, columns=None, aggfunc=‘mean‘, fill_value=None, margins=False, dropna=True, margins_name=‘All‘)

Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame

类似pivot

• data : DataFrame
• values : column to aggregate, optional
• index : column, Grouper, array, or list of the previous
  • If an array is passed, it must be the same length as the data. The list can contain any of the other types (except list). Keys to group by on the pivot table index. If an array is passed, it is being used as the same manner as column values.
• columns : column, Grouper, array, or list of the previous
  • If an array is passed, it must be the same length as the data. The list can contain any of the other types (except list). Keys to group by on the pivot table column. If an array is passed, it is being used as the same manner as column values.
  • 当colomns传递的列表有多个元素时，也会建立入index的多级索引
• aggfunc : function, list of functions, dict, default numpy.mean
  • If list of functions passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves) If dict is passed, the key is column to aggregate and value is function or list of functions
  • 对所选取的值做聚合
• fill_value : scalar, default None
  • Value to replace missing values with

flights = pd.read_csv(‘data/flights.csv‘)
flights.head()

	MONTH	DAY	...	DIVERTED	CANCELLED
0	1	1	...	0	0
1	1	1	...	0	0
2	1	1	...	0	0
3	1	1	...	0	0
4	1	1	...	0	0

5 rows × 14 columns

用pivot_table方法求出每条航线每个始发地的被取消的航班总数

fp = flights.pivot_table(index=‘AIRLINE‘, 
    columns=‘ORG_AIR‘, 
    values=‘CANCELLED‘, 
    aggfunc=‘sum‘,
    fill_value=0).round(2)
fp.head()

ORG_AIR	ATL	DEN	...	PHX	SFO
AIRLINE
AA	3	4	...	4	2
AS	0	0	...	0	0
B6	0	0	...	0	1
DL	28	1	...	1	2
EV	18	6	...	0	0

5 rows × 10 columns

3.2 使用groupby方法

groupby聚合不能直接复现这张表,需要先按所有index和columns的列聚合.

fg = flights.groupby([‘AIRLINE‘, ‘ORG_AIR‘])[‘CANCELLED‘].sum()
fg.head()

AIRLINE  ORG_AIR
AA       ATL         3
         DEN         4
         DFW        86
         IAH         3
         LAS         3
Name: CANCELLED, dtype: int64

再使用unstack，将ORG_AIR这层索引作为列名

fg_unstack = fg.unstack(‘ORG_AIR‘, fill_value=0)
fg_unstack.head()

ORG_AIR	ATL	DEN	...	PHX	SFO
AIRLINE
AA	3	4	...	4	2
AS	0	0	...	0	0
B6	0	0	...	0	1
DL	28	1	...	1	2
EV	18	6	...	0	0

5 rows × 10 columns

3.3 两种方式的比较

fg_unstack = fg.unstack(‘ORG_AIR‘, fill_value=0)
fp.equals(fg_unstack)

True

fp2 = flights.pivot_table(index=[‘AIRLINE‘, ‘MONTH‘],
    columns=[‘ORG_AIR‘, ‘CANCELLED‘],
    values=[‘DEP_DELAY‘, ‘DIST‘],
    aggfunc=[np.mean, np.sum],
    fill_value=0)
fp2

		mean		...	sum
		DEP_DELAY		...	DIST
	ORG_AIR	ATL		...	SFO
	CANCELLED	0	1	...	0	1
AIRLINE	MONTH
AA	1	-3.250000	0	...	33483	0
	2	-3.000000	0	...	32110	2586
...	...	...	...	...	...	...
WN	11	5.932203	0	...	23235	784
	12	15.691589	0	...	30508	0

149 rows × 80 columns

用groupby和unstack复现上面的方法

flights.groupby([‘AIRLINE‘, ‘MONTH‘, ‘ORG_AIR‘, ‘CANCELLED‘])[‘DEP_DELAY‘, ‘DIST‘]     .agg([‘mean‘, ‘sum‘])     .unstack([‘ORG_AIR‘, ‘CANCELLED‘], fill_value=0)     .swaplevel(0, 1, axis=‘columns‘)     .head()

		mean		...	sum
		DEP_DELAY		...	DIST
	ORG_AIR	ATL		...	SFO
	CANCELLED	0	1	...	0	1
AIRLINE	MONTH
AA	1	-3.250000	NaN	...	33483.0	NaN
	2	-3.000000	NaN	...	32110.0	2586.0
	3	-0.166667	NaN	...	43580.0	NaN
	4	0.071429	NaN	...	51054.0	NaN
	5	5.777778	NaN	...	40233.0	NaN

5 rows × 80 columns

4 数据清理TIPS

一些数据分析的案例和技巧

4.1 为了更容易reshaping，重新命名索引层

读取college数据集，分组后，统计本科生的SAT数学成绩信息

college = pd.read_csv(‘data/college.csv‘)

cg = college.groupby([‘STABBR‘, ‘RELAFFIL‘])[‘UGDS‘, ‘SATMTMID‘].agg([‘count‘, ‘min‘, ‘max‘]).head(6)
cg

		UGDS		...	SATMTMID
		count	min	...	min	max
STABBR	RELAFFIL
AK	0	7	109.0	...	NaN	NaN
	1	3	27.0	...	503.0	503.0
...	...	...	...	...	...	...
AR	0	68	18.0	...	427.0	565.0
	1	14	20.0	...	495.0	600.0

6 rows × 6 columns

行索引的两级都有名字，而列索引没有名字。用rename_axis给列索引的两级命名

cg = cg.rename_axis([‘AGG_COLS‘, ‘AGG_FUNCS‘], axis=‘columns‘)
cg

	AGG_COLS	UGDS		...	SATMTMID
	AGG_FUNCS	count	min	...	min	max
STABBR	RELAFFIL
AK	0	7	109.0	...	NaN	NaN
	1	3	27.0	...	503.0	503.0
...	...	...	...	...	...	...
AR	0	68	18.0	...	427.0	565.0
	1	14	20.0	...	495.0	600.0

6 rows × 6 columns

将AGG_FUNCS列移到行索引

cg.stack(‘AGG_FUNCS‘).head()

		AGG_COLS	UGDS	SATMTMID
STABBR	RELAFFIL	AGG_FUNCS
AK	0	count	7.0	0.0
		min	109.0	NaN
		max	12865.0	NaN
	1	count	3.0	1.0
		min	27.0	503.0

stack默认是将列放到行索引的最内层，可以使用swaplevel改变层级

cg.stack(‘AGG_FUNCS‘).swaplevel(‘AGG_FUNCS‘, ‘STABBR‘, axis=‘index‘).head()

		AGG_COLS	UGDS	SATMTMID
AGG_FUNCS	RELAFFIL	STABBR
count	0	AK	7.0	0.0
min	0	AK	109.0	NaN
max	0	AK	12865.0	NaN
count	1	AK	3.0	1.0
min	1	AK	27.0	503.0

在此前的基础上再做sort_index

cg.stack(‘AGG_FUNCS‘)     .swaplevel(‘AGG_FUNCS‘, ‘STABBR‘, axis=‘index‘)     .sort_index(level=‘RELAFFIL‘, axis=‘index‘)     .sort_index(level=‘AGG_COLS‘, axis=‘columns‘).head(6)

		AGG_COLS	SATMTMID	UGDS
AGG_FUNCS	RELAFFIL	STABBR
count	0	AK	0.0	7.0
		AL	13.0	71.0
...	...	...	...	...
min	0	AL	420.0	12.0
		AR	427.0	18.0

6 rows × 2 columns

对一些列做stack，对其它列做unstack

cg.stack(‘AGG_FUNCS‘).unstack([‘RELAFFIL‘, ‘STABBR‘])

AGG_COLS	UGDS		...	SATMTMID
RELAFFIL	0	1	...	0	1
STABBR	AK	AK	...	AR	AR
AGG_FUNCS
count	7.0	3.0	...	9.0	7.0
min	109.0	27.0	...	427.0	495.0
max	12865.0	275.0	...	565.0	600.0

3 rows × 12 columns

对所有列做stack，会返回一个Series

cg.stack([‘AGG_FUNCS‘, ‘AGG_COLS‘]).head(12)

STABBR  RELAFFIL  AGG_FUNCS  AGG_COLS
AK      0         count      UGDS         7.0
                             SATMTMID     0.0
                                         ... 
AL      0         count      UGDS        71.0
                             SATMTMID    13.0
Length: 12, dtype: float64

删除行和列索引所有层级的名称

cg.rename_axis([None, None], axis=‘index‘).rename_axis([None, None], axis=‘columns‘)

		UGDS		...	SATMTMID
		count	min	...	min	max
AK	0	7	109.0	...	NaN	NaN
	1	3	27.0	...	503.0	503.0
...	...	...	...	...	...	...
AR	0	68	18.0	...	427.0	565.0
	1	14	20.0	...	495.0	600.0

6 rows × 6 columns

4.2 当多个变量被存储为列名时进行清理

当多个变量被存储为列名时进行清理

读取weightlifting数据集

weightlifting = pd.read_csv(‘data/weightlifting_men.csv‘)
weightlifting

	Weight Category	M35 35-39	...	M75 75-79	M80 80+
0	56	137	...	62	55
1	62	152	...	67	57
...	...	...	...	...	...
6	105	210	...	95	80
7	105+	217	...	100	85

8 rows × 11 columns

用melt方法，将sex_age放入一个单独的列

wl_melt = weightlifting.melt(id_vars=‘Weight Category‘, 
    var_name=‘sex_age‘, 
    value_name=‘Qual Total‘)
wl_melt.head()

	Weight Category	sex_age	Qual Total
0	56	M35 35-39	137
1	62	M35 35-39	152
2	69	M35 35-39	167
3	77	M35 35-39	182
4	85	M35 35-39	192

用split方法将sex_age列分为两列

sex_age = wl_melt[‘sex_age‘].str.split(expand=True)
sex_age.head()

	0	1
0	M35	35-39
1	M35	35-39
2	M35	35-39
3	M35	35-39
4	M35	35-39

sex_age.columns = [‘Sex‘, ‘Age Group‘]
sex_age.head()

	Sex	Age Group
0	M35	35-39
1	M35	35-39
2	M35	35-39
3	M35	35-39
4	M35	35-39

只取出字符串中的M

sex_age[‘Sex‘] = sex_age[‘Sex‘].str[0]
sex_age.head()

	Sex	Age Group
0	M	35-39
1	M	35-39
2	M	35-39
3	M	35-39
4	M	35-39

用concat方法，将sex_age,与wl_cat_total连接起来

wl_cat_total = wl_melt[[‘Weight Category‘, ‘Qual Total‘]]
wl_tidy = pd.concat([sex_age, wl_cat_total], axis=‘columns‘)
wl_tidy.head()

	Sex	Age Group	Weight Category	Qual Total
0	M	35-39	56	137
1	M	35-39	62	152
2	M	35-39	69	167
3	M	35-39	77	182
4	M	35-39	85	192

上面的结果也可以如下实现

cols = [‘Weight Category‘, ‘Qual Total‘]
sex_age[cols] = wl_melt[cols]

也可以通过assign的方法，动态加载新的列

age_group = wl_melt.sex_age.str.extract(‘(d{2}[-+](?:d{2})?)‘, expand=False)
sex = wl_melt.sex_age.str[0]
new_cols = {‘Sex‘:sex,‘Age Group‘: age_group}
wl_tidy2 = wl_melt.assign(**new_cols).drop(‘sex_age‘, axis=‘columns‘)
wl_tidy2.head()

	Weight Category	Qual Total	Sex	Age Group
0	56	137	M	35-39
1	62	152	M	35-39
2	69	167	M	35-39
3	77	182	M	35-39
4	85	192	M	35-39

4.3 当多个变量被存储为列的值时进行清理

读取restaurant_inspections数据集，将Date列的数据类型变为datetime64

inspections = pd.read_csv(‘data/restaurant_inspections.csv‘, parse_dates=[‘Date‘])
inspections.head(10)

	Name	Date	Info	Value
0	E & E Grill House	2017-08-08	Borough	MANHATTAN
1	E & E Grill House	2017-08-08	Cuisine	American
...	...	...	...	...
8	PIZZA WAGON	2017-04-12	Grade	A
9	PIZZA WAGON	2017-04-12	Score	10.0

10 rows × 4 columns

4.3.1 stack方式

inspections.set_index([‘Name‘,‘Date‘, ‘Info‘]).unstack(‘Info‘).head()

		Value
	Info	Borough	Cuisine	Description	Grade	Score
Name	Date
3 STAR JUICE CENTER	2017-05-10	BROOKLYN	Juice, Smoothies, Fruit Salads	Facility not vermin proof. Harborage or condit...	A	12.0
A & L PIZZA RESTAURANT	2017-08-22	BROOKLYN	Pizza	Facility not vermin proof. Harborage or condit...	A	9.0
AKSARAY TURKISH CAFE AND RESTAURANT	2017-07-25	BROOKLYN	Turkish	Plumbing not properly installed or maintained;...	A	13.0
ANTOJITOS DELI FOOD	2017-06-01	BROOKLYN	Latin (Cuban, Dominican, Puerto Rican, South &...	Live roaches present in facility‘s food and/or...	A	10.0
BANGIA	2017-06-16	MANHATTAN	Korean	Covered garbage receptacle not provided or ina...	A	9.0

用reset_index方法，使行索引层级与列索引相同

insp_tidy = inspections.set_index([‘Name‘,‘Date‘, ‘Info‘])     .unstack(‘Info‘)     .reset_index(col_level=-1)
insp_tidy.head()

			...	Value
Info	Name	Date	...	Grade	Score
0	3 STAR JUICE CENTER	2017-05-10	...	A	12.0
1	A & L PIZZA RESTAURANT	2017-08-22	...	A	9.0
2	AKSARAY TURKISH CAFE AND RESTAURANT	2017-07-25	...	A	13.0
3	ANTOJITOS DELI FOOD	2017-06-01	...	A	10.0
4	BANGIA	2017-06-16	...	A	9.0

5 rows × 7 columns

除掉列索引的最外层，重命名行索引的层为None

insp_tidy.columns = insp_tidy.columns.droplevel(0).rename(None)
insp_tidy.head()

	Name	Date	...	Grade	Score
0	3 STAR JUICE CENTER	2017-05-10	...	A	12.0
1	A & L PIZZA RESTAURANT	2017-08-22	...	A	9.0
2	AKSARAY TURKISH CAFE AND RESTAURANT	2017-07-25	...	A	13.0
3	ANTOJITOS DELI FOOD	2017-06-01	...	A	10.0
4	BANGIA	2017-06-16	...	A	9.0

5 rows × 7 columns

4.3.2 pivot_table方式

pivot_table需要传入聚合函数，才能产生一个单一值

inspections.pivot_table(index=[‘Name‘, ‘Date‘], 
         columns=‘Info‘, 
         values=‘Value‘, 
         aggfunc=‘first‘)     .reset_index()    .rename_axis(None, axis=‘columns‘)

	Name	Date	...	Grade	Score
0	3 STAR JUICE CENTER	2017-05-10	...	A	12.0
1	A & L PIZZA RESTAURANT	2017-08-22	...	A	9.0
...	...	...	...	...	...
98	WANG MANDOO HOUSE	2017-08-29	...	A	12.0
99	XIAOYAN YABO INC	2017-08-29	...	Z	49.0

100 rows × 7 columns

# inspections.pivot(index=[‘Name‘, ‘Date‘], columns=‘Info‘, values=‘Value‘)
# 运行pivot会报错，因为没有聚合函数，通过[‘Name‘, ‘Date‘]索引和columns=‘Info‘对应的是多个值

4.4 当两个或多个值存储于一个单元格时进行清理

读取texas_cities数据集

cities = pd.read_csv(‘data/texas_cities.csv‘)
cities

	City	Geolocation
0	Houston	29.7604° N, 95.3698° W
1	Dallas	32.7767° N, 96.7970° W
2	Austin	30.2672° N, 97.7431° W

将Geolocation分解为四个单独的列

geolocations = cities.Geolocation.str.split(pat=‘. ‘, expand=True)
geolocations.columns = [‘latitude‘, ‘latitude direction‘, ‘longitude‘, ‘longitude direction‘]
geolocations

	latitude	latitude direction	longitude	longitude direction
0	29.7604	N	95.3698	W
1	32.7767	N	96.7970	W
2	30.2672	N	97.7431	W

转变数据类型

geolocations = geolocations.astype({‘latitude‘:‘float‘, ‘longitude‘:‘float‘})
geolocations.dtypes

latitude               float64
latitude direction      object
longitude              float64
longitude direction     object
dtype: object

将新列与原先的city列连起来

cities_tidy = pd.concat([cities[‘City‘], geolocations], axis=‘columns‘)
cities_tidy

	City	latitude	latitude direction	longitude	longitude direction
0	Houston	29.7604	N	95.3698	W
1	Dallas	32.7767	N	96.7970	W
2	Austin	30.2672	N	97.7431	W

函数to_numeric可以将每列自动变为整数或浮点数

temp = geolocations.apply(pd.to_numeric, errors=‘ignore‘)
temp

	latitude	latitude direction	longitude	longitude direction
0	29.7604	N	95.3698	W
1	32.7767	N	96.7970	W
2	30.2672	N	97.7431	W

|符，可以对多个标记进行分割

 cities.Geolocation.str.split(pat=‘° |, ‘, expand=True)

	0	1	2	3
0	29.7604	N	95.3698	W
1	32.7767	N	96.7970	W
2	30.2672	N	97.7431	W

更复杂的提取方式

cities.Geolocation.str.extract(‘([0-9.]+). (N|S), ([0-9.]+). (E|W)‘, expand=True)

	0	1	2	3
0	29.7604	N	95.3698	W
1	32.7767	N	96.7970	W
2	30.2672	N	97.7431	W

4.5 当多个变量被存储为列名和列值时进行清理

读取sensors数据集

sensors = pd.read_csv(‘data/sensors.csv‘)
sensors

	Group	Property	...	2015	2016
0	A	Pressure	...	973	870
1	A	Temperature	...	1036	1042
...	...	...	...	...	...
4	B	Temperature	...	1002	1013
5	B	Flow	...	824	873

6 rows × 7 columns

用melt清理数据

sensors.melt(id_vars=[‘Group‘, ‘Property‘], var_name=‘Year‘).head(6)

	Group	Property	Year	value
0	A	Pressure	2012	928
1	A	Temperature	2012	1026
...	...	...	...	...
4	B	Temperature	2012	1008
5	B	Flow	2012	887

6 rows × 4 columns

用pivot_table，将Property列转化为新的列名

sensors.melt(id_vars=[‘Group‘, ‘Property‘], var_name=‘Year‘)     .pivot_table(index=[‘Group‘, ‘Year‘], columns=‘Property‘, values=‘value‘)     .reset_index()     .rename_axis(None, axis=‘columns‘)

	Group	Year	Flow	Pressure	Temperature
0	A	2012	819	928	1026
1	A	2013	806	873	1038
...	...	...	...	...	...
8	B	2015	824	806	1002
9	B	2016	873	942	1013

10 rows × 5 columns

用stack和unstack实现上述方法

sensors.set_index([‘Group‘, ‘Property‘])     .stack()     .unstack(‘Property‘)     .rename_axis([‘Group‘, ‘Year‘], axis=‘index‘)     .rename_axis(None, axis=‘columns‘)     .reset_index()

	Group	Year	Flow	Pressure	Temperature
0	A	2012	819	928	1026
1	A	2013	806	873	1038
...	...	...	...	...	...
8	B	2015	824	806	1002
9	B	2016	873	942	1013

10 rows × 5 columns

4.6 当多个观察单位被存储于同一张表时进行清理

就是将一张表拆成多张表

读取movie_altered数据集

movie = pd.read_csv(‘data/movie_altered.csv‘)
movie.head()

	title	rating	...	actor_fb_likes_2	actor_fb_likes_3
0	Avatar	PG-13	...	936.0	855.0
1	Pirates of the Caribbean: At World‘s End	PG-13	...	5000.0	1000.0
2	Spectre	PG-13	...	393.0	161.0
3	The Dark Knight Rises	PG-13	...	23000.0	23000.0
4	Star Wars: Episode VII - The Force Awakens	NaN	...	12.0	NaN

5 rows × 12 columns

插入新的列，用来标识每一部电影

movie.insert(0, ‘id‘, np.arange(len(movie)))

用wide_to_long，将所有演员放到一列，将所有Facebook likes放到一列

stubnames = [‘director‘, ‘director_fb_likes‘, ‘actor‘, ‘actor_fb_likes‘]

movie_long = pd.wide_to_long(movie, stubnames=stubnames, i=‘id‘, j=‘num‘, sep=‘_‘).reset_index()
movie_long[‘num‘] = movie_long[‘num‘].astype(int)
movie_long.head(9)

	id	num	...	actor	actor_fb_likes
0	0	1	...	CCH Pounder	1000.0
1	0	2	...	Joel David Moore	936.0
...	...	...	...	...	...
7	2	2	...	Rory Kinnear	393.0
8	2	3	...	Stephanie Sigman	161.0

9 rows × 10 columns

movie.columns

Index([‘id‘, ‘title‘, ‘rating‘, ‘year‘, ‘duration‘, ‘director_1‘,
       ‘director_fb_likes_1‘, ‘actor_1‘, ‘actor_2‘, ‘actor_3‘,
       ‘actor_fb_likes_1‘, ‘actor_fb_likes_2‘, ‘actor_fb_likes_3‘],
      dtype=‘object‘)

movie_long.columns

Index([‘id‘, ‘num‘, ‘year‘, ‘duration‘, ‘rating‘, ‘title‘, ‘director‘,
       ‘director_fb_likes‘, ‘actor‘, ‘actor_fb_likes‘],
      dtype=‘object‘)

将这个数据分解成多个小表

movie_table = movie_long[[‘id‘,‘title‘, ‘year‘, ‘duration‘, ‘rating‘]]
director_table = movie_long[[‘id‘, ‘director‘, ‘num‘, ‘director_fb_likes‘]]
actor_table = movie_long[[‘id‘, ‘actor‘, ‘num‘, ‘actor_fb_likes‘]]

做一些去重和去除缺失值的工作

movie_table = movie_table.drop_duplicates().reset_index(drop=True)
director_table = director_table.dropna().reset_index(drop=True)
actor_table = actor_table.dropna().reset_index(drop=True)