Python數(shù)據(jù)分析筆記-09

1.DataFrame的合并

1)未指定基于哪一列合并

>>> import numpy as np

>>> import pandas as pd

>>> frame1=pd.DataFrame({'id':['ball','pencil','pen','mug','ashtray'],'price':[12.33,11.44,33.21,13.23,33.62]})

>>> frame1

????id?price

0???ball?12.33

1??pencil?11.44

2???pen?33.21

3???mug?13.23

4?ashtray?33.62

>>> frame2=pd.DataFrame({'id':['pencil','pencil','ball','pen'],'color':['white','red','red','black']})

>>> frame2

??color???id

0?white?pencil

1??red?pencil

2??red??ball

3?black???pen

>>> pd.merge(frame1,frame2)? #frame1放到前，則依據(jù)frame1去合并经窖，字段順序也是先frame1的 后frame2的呜师，且只保留二者都有的

????id?price?color

0??ball?12.33??red

1?pencil?11.44?white

2?pencil?11.44??red

3???pen?33.21?black

>>> pd.merge(frame2,frame1)

??color???id?price

0?white?pencil?11.44

1??red?pencil?11.44

2??red??ball?12.33

3?black???pen?33.21

2）指定基于哪一列進(jìn)行合并：當(dāng)兩個DataFrame對象有不止一個列名相同的時候竭恬，合并的時候需用指定依據(jù)哪個列進(jìn)行合并

>>> import numpy as np

>>> import pandas as pd

>>> frame1=pd.DataFrame({'id':['ball','pencil','pen','mug','ashtray'],'color':['white','red','red','black','green'],'brand':['OMG','ABC','ABC','POD','POD']})

>>> frame1

?brand?color????id

0??OMG?white???ball

1??ABC??red??pencil

2??ABC??red???pen

3??POD?black???mug

4??POD?green?ashtray

>>> frame2=pd.DataFrame({'id':['pencil','pencil','ball','pen'],'brand':['OMG','POD','ABD','POD']})

>>> frame2

?brand???id

0??OMG?pencil

1??POD?pencil

2??ABD??ball

3??POD???pen

>>> pd.merge(frame1,frame2)

Empty DataFrame

Columns: [brand, color, id]

Index: []

>>> pd.merge(frame1,frame2,on='id')

?brand_x?color???id brand_y

0???OMG?white??ball???ABD

1???ABC??red?pencil???OMG

2???ABC??red?pencil???POD

3???ABC??red???pen???POD

>>> pd.merge(frame1,frame2,on='brand')

?brand?color???id_x??id_y

0??OMG?white???ball?pencil

1??POD?black???mug?pencil

2??POD?black???mug???pen

3??POD?green?ashtray?pencil

4??POD?green?ashtray???pen

>>> pd.merge(frame2,frame1,on='id')

?brand_x???id brand_y?color

0???OMG?pencil???ABC??red

1???POD?pencil???ABC??red

2???ABD??ball???OMG?white

3???POD???pen???ABC??red

>>> pd.merge(frame2,frame1,on='brand')

?brand??id_x?color???id_y

0??OMG?pencil?white???ball

1??POD?pencil?black???mug

2??POD?pencil?green?ashtray

3??POD???pen?black???mug

4??POD???pen?green?ashtray

3）基于左對標(biāo)和基于右對標(biāo)：當(dāng)兩個DataFrame的列名不同時候，用left_on和right_on指定第一個dataframe用來對標(biāo)的列和第二個dataframe用來對標(biāo)的列腺办，兩列進(jìn)行對標(biāo)

>>> frame1

?brand?color????id

0??OMG?white???ball

1??ABC??red??pencil

2??ABC??red???pen

3??POD?black???mug

4??POD?green?ashtray

>>> frame2

?brand???id

0??OMG?pencil

1??POD?pencil

2??ABD??ball

3??POD???pen

>>> frame2.columns=['brand','sid']

>>> frame2

?brand???sid

0??OMG?pencil

1??POD?pencil

2??ABD??ball

3??POD???pen

>>> pd.merge(frame1,frame2,left_on='id',right_on='sid')

?brand_x?color???id brand_y???sid

0???OMG?white??ball???ABD??ball

1???ABC??red?pencil???OMG?pencil

2???ABC??red?pencil???POD?pencil

3???ABC??red???pen???POD???pen

4）外鏈接：相當(dāng)于把所有列聚合倒一起粘招，還有左鏈接和右鏈接

>>> frame2

?brand???sid

0??OMG?pencil

1??POD?pencil

2??ABD??ball

3??POD???pen

>>> frame2.columns=['brand','id']

>>> frame2

?brand???id

0??OMG?pencil

1??POD?pencil

2??ABD??ball

3??POD???pen

>>> pd.merge(frame1,frame2,on='id')

?brand_x?color???id brand_y

0???OMG?white??ball???ABD

1???ABC??red?pencil???OMG

2???ABC??red?pencil???POD

3???ABC??red???pen???POD

>>> pd.merge(frame1,frame2,on='id',how='outer')

?brand_x?color????id brand_y

0???OMG?white???ball???ABD

1???ABC??red??pencil???OMG

2???ABC??red??pencil???POD

3???ABC??red???pen???POD

4???POD?black???mug???NaN

5???POD?green?ashtray???NaN

>>> pd.merge(frame1,frame2,on='id',how='left')

?brand_x?color????id brand_y

0???OMG?white???ball???ABD

1???ABC??red??pencil???OMG

2???ABC??red??pencil???POD

3???ABC??red???pen???POD

4???POD?black???mug???NaN

5???POD?green?ashtray???NaN

>>> pd.merge(frame1,frame2,on='id',how='right')

?brand_x?color???id brand_y

0???OMG?white??ball???ABD

1???ABC??red?pencil???OMG

2???ABC??red?pencil???POD

3???ABC??red???pen???POD

>>> pd.merge(frame1,frame2,on=['id','brand'],how='outer')

?brand?color????id

0??OMG?white???ball

1??ABC??red??pencil

2??ABC??red???pen

3??POD?black???mug

4??POD?green?ashtray

5??OMG??NaN??pencil

6??POD??NaN??pencil

7??ABD??NaN???ball

8??POD??NaN???pen

5）根據(jù)索引合并

>>> frame1

?brand?color????id

0??OMG?white???ball

1??ABC??red??pencil

2??ABC??red???pen

3??POD?black???mug

4??POD?green?ashtray

>>> frame2

?brand???id

0??OMG?pencil

1??POD?pencil

2??ABD??ball

3??POD???pen

>>> pd.merge(frame1,frame2,left_index=True,right_index=True)

?brand_x?color??id_x brand_y??id_y

0???OMG?white??ball???OMG?pencil

1???ABC??red?pencil???POD?pencil

2???ABC??red???pen???ABD??ball

3???POD?black???mug???POD???pen

6)join函數(shù)更適合利用所以進(jìn)行合并索引相同列不一致的dataframe對象

列名相同的報錯

>>> frame1

?brand?color????id

0??OMG?white???ball

1??ABC??red??pencil

2??ABC??red???pen

3??POD?black???mug

4??POD?green?ashtray

>>> frame2

?brand???id

0??OMG?pencil

1??POD?pencil

2??ABD??ball

3??POD???pen

>>> frame1.join(frame2)#由于列名有重復(fù)的報錯

Traceback (most recent call last):

?File "", line 1, in

?File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/frame.py", line 5316, in join

??rsuffix=rsuffix, sort=sort)

?File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/frame.py", line 5331, in _join_compat

??suffixes=(lsuffix, rsuffix), sort=sort)

?File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/reshape/merge.py", line 58, in merge

??return op.get_result()

?File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/reshape/merge.py", line 588, in get_result

??rdata.items, rsuf)

?File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/internals.py", line 5026, in items_overlap_with_suffix

??to_rename)

ValueError: columns overlap but no suffix specified: Index(['brand', 'id'], dtype='object')

修改frame2的列名后利用join函數(shù)進(jìn)行相同索引不同列名的

>>> frame1

?brand?color????id

0??OMG?white???ball

1??ABC??red??pencil

2??ABC??red???pen

3??POD?black???mug

4??POD?green?ashtray

>>> frame2

?brand???id

0??OMG?pencil

1??POD?pencil

2??ABD??ball

3??POD???pen

>>> frame2.columns=['brand2','id2']

>>> frame1.join(frame2)

?brand?color????id brand2???id2

0??OMG?white???ball??OMG?pencil

1??ABC??red??pencil??POD?pencil

2??ABC??red???pen??ABD??ball

3??POD?black???mug??POD???pen

4??POD?green?ashtray??NaN???NaN

>>> frame2.join(frame1)

?brand2???id2 brand?color???id

0??OMG?pencil??OMG?white??ball

1??POD?pencil??ABC??red?pencil

2??ABD??ball??ABC??red???pen

3??POD???pen??POD?black???mug

2.拼接

1）numpy的nparray對象進(jìn)行拼接而涉，利用np.concatenate函數(shù)

>>> import numpy as np

>>> array1=([0,1,2],[3,4,5],[6,7,8])

>>> array1

([0, 1, 2], [3, 4, 5], [6, 7, 8])

>>> array2=np.arange(9).reshape(3,3)

>>> array2

array([[0, 1, 2],

????[3, 4, 5],

????[6, 7, 8]])

>>> np.concatenate([array1,array2],axis=1)

array([[0, 1, 2, 0, 1, 2],

????[3, 4, 5, 3, 4, 5],

????[6, 7, 8, 6, 7, 8]])

>>> np.concatenate([array1,array2],axis=0)

array([[0, 1, 2],

????[3, 4, 5],

????[6, 7, 8],

????[0, 1, 2],

????[3, 4, 5],

????[6, 7, 8]])

2）pandas及他的Series和DataFrame等數(shù)據(jù)結(jié)構(gòu)實(shí)現(xiàn)了帶編號的軸，pandas的concat函數(shù)實(shí)現(xiàn)了按照軸進(jìn)行拼接

Series對象拼接

>>> ser1=pd.Series(np.random.rand(4),index=[1,2,3,4])

>>> ser1

1??0.835800

2??0.421464

3??0.859449

4??0.974251

dtype: float64

>>> ser2=pd.Series(np.random.rand(4),index=[5,6,7,8])

>>> ser2

5??0.296453

6??0.692925

7??0.098937

8??0.324647

dtype: float64

>>> pd.concat([ser1,ser2])#默認(rèn)按照0軸（行）進(jìn)行拼接

1??0.835800

2??0.421464

3??0.859449

4??0.974251

5??0.296453

6??0.692925

7??0.098937

8??0.324647

dtype: float64

>>> pd.concat([ser1,ser2],axis=1)

?????0?????1

1?0.835800????NaN

2?0.421464????NaN

3?0.859449????NaN

4?0.974251????NaN

5????NaN?0.296453

6????NaN?0.692925

7????NaN?0.098937

8????NaN?0.324647

>>> ser3=pd.concat([ser1,ser2],axis=1)

>>> ser3

?????0?????1

1?0.835800????NaN

2?0.421464????NaN

3?0.859449????NaN

4?0.974251????NaN

5????NaN?0.296453

6????NaN?0.692925

7????NaN?0.098937

8????NaN?0.324647

>>> ser1

1??0.835800

2??0.421464

3??0.859449

4??0.974251

dtype: float64

>>> pd.concat([ser1,ser3],axis=1,join='inner')

?????0?????0??1

1?0.835800?0.835800 NaN

2?0.421464?0.421464 NaN

3?0.859449?0.859449 NaN

4?0.974251?0.974251 NaN

>>> pd.concat([ser1,ser2],keys=[1,2])#在結(jié)果上創(chuàng)建等級

1?1??0.835800

??2??0.421464

??3??0.859449

??4??0.974251

2?5??0.296453

??6??0.692925

??7??0.098937

??8??0.324647

dtype: float64

>>> pd.concat([ser1,ser2],axis=1,keys=[1,2])#在1軸上拼接瞳秽，生成DDAtaFrame對象

?????1?????2

1?0.835800????NaN

2?0.421464????NaN

3?0.859449????NaN

4?0.974251????NaN

5????NaN?0.296453

6????NaN?0.692925

7????NaN?0.098937

8????NaN?0.324647

DataFrame對象拼接

>>> frame1=pd.DataFrame(np.random.rand(9).reshape(3,3),index=[1,2,3])

>>> frame2=pd.DataFrame(np.random.rand(9).reshape(3,3),index=[4,5,6])

>>> frame1

?????0?????1?????2

1?0.687920?0.176828?0.050595

2?0.174212?0.315637?0.780156

3?0.352718?0.613909?0.447205

>>> frame2

?????0?????1?????2

4?0.216871?0.389312?0.363118

5?0.253215?0.312054?0.760887

6?0.437853?0.847273?0.300629

>>> pd.concat([frame1,frame2],axis=1)

?????0?????1?????2?????0?????1?????2

1?0.687920?0.176828?0.050595????NaN????NaN????NaN

2?0.174212?0.315637?0.780156????NaN????NaN????NaN

3?0.352718?0.613909?0.447205????NaN????NaN????NaN

4????NaN????NaN????NaN?0.216871?0.389312?0.363118

5????NaN????NaN????NaN?0.253215?0.312054?0.760887

6????NaN????NaN????NaN?0.437853?0.847273?0.300629

>>> pd.concat([frame1,frame2])

?????0?????1?????2

1?0.687920?0.176828?0.050595

2?0.174212?0.315637?0.780156

3?0.352718?0.613909?0.447205

4?0.216871?0.389312?0.363118

5?0.253215?0.312054?0.760887

6?0.437853?0.847273?0.300629

3.組合

無法通過合并和拼接的辦法進(jìn)行組合數(shù)據(jù)瓣履，兩個數(shù)據(jù)集索引完全或者部分重合

cmbine_first函數(shù)可以用來組合Series對象，同時對齊數(shù)據(jù)

>>> ser1=pd.Series(np.random.rand(5),index=[1,2,3,4,5])

>>> ser1

1??0.428280

2??0.350504

3??0.832672

4??0.746660

5??0.698320

dtype: float64

>>> ser2=pd.Series(np.random.rand(4),index=[2,4,5,6])

>>> ser2

2??0.173355

4??0.668513

5??0.766825

6??0.683407

dtype: float64

>>> ser1.combine_first(ser2)

1??0.428280

2??0.350504

3??0.832672

4??0.746660

5??0.698320

6??0.683407

dtype: float64

>>> ser2.combine_first(ser1)

1??0.428280

2??0.173355

3??0.832672

4??0.668513

5??0.766825

6??0.683407

dtype: float64

>>> ser1

1??0.428280

2??0.350504

3??0.832672

4??0.746660

5??0.698320

dtype: float64

>>> ser2

2??0.173355

4??0.668513

5??0.766825

6??0.683407

dtype: float64

>>> ser1[:3].combine_first(ser2[:3])

1??0.428280

2??0.350504

3??0.832672

4??0.668513

5??0.766825

dtype: float64

4.按等級索引旋轉(zhuǎn)

1）入棧：旋轉(zhuǎn)數(shù)據(jù)結(jié)構(gòu)把列轉(zhuǎn)換為行

2)出棧：把行轉(zhuǎn)換為列

>>> frame1=pd.DataFrame(np.arange(9).reshape(3,3),index=['white','black','red'],columns=['ball','pen','pencil'])

>>> frame1

????ball?pen?pencil

white???0??1????2

black???3??4????5

red????6??7????8

>>> frame1.stack()

white?ball???0

???? ? ? ?pen????1

???? ? ? ?pencil??2

black?ball???3

????? ? ? pen????4

????pencil??5

red?? ?ball???6

????pen????7

????pencil??8

dtype: int64

>>> ser5=frame1.stack()

>>> ser5

white?ball???0

???? ? ? ?pen????1

???? ? ? ?pencil??2

black?ball???3

????? ? ? pen????4

????pencil??5

red?? ?ball???6

????pen????7

????pencil??8

dtype: int64

>>> ser5.unstack()

????ball?pen?pencil

white???0??1????2

black???3??4????5

red????6??7????8

>>> ser5.unstack(0)

????white?black?red

ball????0???3??6

pen?????1???4??7

pencil???2???5??8

5.從長格式向?qū)捀袷睫D(zhuǎn)化

1)長格式數(shù)據(jù)

>>> longframe=pd.DataFrame({'color':['white','white','white','red','red','red','black','black','black'],'item':['ball','pen','mug','ball','pen','mug','ball','pen','mug'],'value':np.random.rand(9)})

>>> longframe

??color?item???value

0?white?ball?0.825575

1?white??pen?0.595834

2?white??mug?0.228163

3??red?ball?0.543954

4??red??pen?0.150930

5??red??mug?0.285771

6?black?ball?0.092283

7?black??pen?0.961326

8?black??mug?0.027581

2）轉(zhuǎn)換成寬格式

>>> wideframe=longframe.pivot('color','item')

>>> wideframe

?????value

item????ball???? ? ? ? ?mug????? ? pen

color

black?0.092283?0.027581?0.961326

red??0.543954?0.285771?0.150930

white?0.825575?0.228163?0.595834

6.刪除

>>> frame1=pd.DataFrame(np.arange(9).reshape(3,3),index=['white','black','red'],columns=['ball','pen','pencil'])

>>> frame1

??? ? ??ball?pen?pencil

white???0??1????2

black???3??4????5

red????6??7????8

>>> del frame1['ball'] #刪除一列

>>> frame1

????pen?pencil

white??1????2

black??4????5

red???7????8

>>> frame1.drop('white') #刪除行

????pen?pencil

black??4????5

red???7????8

7.刪除重復(fù)元素

>>> import numpy as np

>>> import pandas as pd

>>> dframe=pd.DataFrame({'color':['white','white','red','red','white'],'value':[2,1,3,3,2]})

>>> dframe

??color?value

0?white?????2

1?white?????1

2???red?????3

3???red?????3

4?white?????2

>>> dframe.duplicated()

0???False

1???False

2???False

3????True

4????True

dtype: bool

>>> dframe[dframe.duplicated()]

??color?value

3???red?????3

4?white?????2

>>> dframe.drop_duplicates()

??color?value

0?white?????2

1?white?????1

2???red?????3

8.映射

以下函數(shù)都以dict對象作為參數(shù)练俐，也就是用能夠承載映射的ditct字典對象作為參數(shù)

1）replace函數(shù)：替換元素

2）map函數(shù)：新建一列

3）rename函數(shù)：替換索引

用映射替換元素

>>> import numpy as np

>>> import pandas as pd

>>> frame=pd.DataFrame()

>>> frame=pd.DataFrame({'item':['ball','mug','pen','pencil','ashtray'],'color':['white','rosso','verde','black','yellow'],'price':[5.56,4.20,1.30,0.56,2.75]})

>>> frame

???color????item?price

0??white????ball??5.56

1??rosso?????mug??4.20

2??verde?????pen??1.30

3??black??pencil??0.56

4?yellow?ashtray??2.75

>>> newcolors={'rosso':'red','verde':'green'}

>>> frame.replace(newcolors)

???color????item?price

0??white????ball??5.56

1????red?????mug??4.20

2??green?????pen??1.30

3??black??pencil??0.56

4?yellow?ashtray??2.75

>>> ser=pd.Series([1,3,np.nan,4,6,np.nan,3])

>>> ser

0???1.0

1???3.0

2???NaN

3???4.0

4???6.0

5???NaN

6???3.0

dtype: float64

>>> ser.replace(np.nan,0)

0???1.0

1???3.0

2???0.0

3???4.0

4???6.0

5???0.0

6???3.0

dtype: float64

用映射添加元素

>>> frame=pd.DataFrame({'item':['ball','mug','pen','pencil','ashtray'],'color':['white','red','freen','black','yellow']})

>>> frame

???color????item

0??white????ball

1????red?????mug

2??freen?????pen

3??black??pencil

4?yellow?ashtray

>>> price={'ball':5.56,'mug':4.20,'bottle':1.30,'scissors':3.41,'pen':1.30,'pencil':0.56,'ashtray':2.75}

>>> frame['price']=frame['item'].map(price)

>>> frame

???color????item?price

0??white????ball??5.56

1????red?????mug??4.20

2??freen?????pen??1.30

3??black??pencil??0.56

4?yellow?ashtray??2.75

重命名軸索引

>>> frame

???color????item?price

0??white????ball??5.56

1????red?????mug??4.20

2??freen?????pen??1.30

3??black??pencil??0.56

4?yellow?ashtray??2.75

>>> reindex={0:'frist',1:'second',2:'third',3:'fourth',4:'fifth'}

>>> frame.rename(reindex)

????????color????item?price

frist???white????ball??5.56

second????red?????mug??4.20

third???freen?????pen??1.30

fourth??black??pencil??0.56

fifth??yellow?ashtray??2.75

>>> recolumns={'item':'object','price':'value'}

>>> frame.rename(index=reindex,columns=recolumns)

????????color??object?value

frist???white????ball??5.56

second????red?????mug??4.20

third???freen?????pen??1.30

fourth??black??pencil??0.56

fifth??yellow?ashtray??2.75

>>> frame.rename(columns=recolumns)

???color??object?value

0??white????ball??5.56

1????red?????mug??4.20

2??freen?????pen??1.30

3??black??pencil??0.56

4?yellow?ashtray??2.75

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