python之pandas&&DataFrame(二)

简单操作

Python-层次聚类-Hierarchical clustering

>>> data = pd.Series(np.random.randn(10),index=[['a','a','a','b','b','c','c','d','d','d'],[1,2,3,1,2,1,2,3,1,2]])
>>> data
a  1   -0.168871
   2    0.828841
   3    0.786215
b  1    0.506081
   2   -2.304898
c  1    0.864875
   2    0.183091
d  3   -0.678791
   1   -1.241735
   2    0.778855
dtype: float64

Hierarchical与DataFrame之间的转换

>>> data.unstack()
          1         2         3
a -0.168871  0.828841  0.786215
b  0.506081 -2.304898       NaN
c  0.864875  0.183091       NaN
d -1.241735  0.778855 -0.678791
>>> type(data.unstack())
<class 'pandas.core.frame.DataFrame'>

Merge,join,Concatenate

>>> df2 = pd.DataFrame({'apts':[55000,60000],'cars':[15000,12000]},index=['hangzhou','najing'])
>>> df1 = pd.DataFrame({'apts':[55000,60000],'cars':[20000,30000]},index=['shanghai','beijing'])
>>> df3 = pd.DataFrame({'apts':[55000,60000],'cars':[15000,12000]},index=['guangzhou','chongqing'])
>>> [df1,df2,df3]
[           apts   cars
shanghai  55000  20000
beijing   60000  30000,            apts   cars
hangzhou  55000  15000
najing    60000  12000,             apts   cars
guangzhou  55000  15000
chongqing  60000  12000]
>>> pd.concat([df1,df2,df3])
            apts   cars
shanghai   55000  20000
beijing    60000  30000
hangzhou   55000  15000
najing     60000  12000
guangzhou  55000  15000
chongqing  60000  12000
frames = [df1,df2,df3]
>>> result2 = pd.concat(frames,keys=['x','y','z'])
>>> result2
              apts   cars
x shanghai   55000  20000
  beijing    60000  30000
y hangzhou   55000  15000
  najing     60000  12000
z guangzhou  55000  15000
  chongqing  60000  12000

进行拼接concat

>>> df4 = pd.DataFrame({"salaries":[10000,30000,30000,20000,15000]},index=['suzhou','beijing','shanghai','guanghzou','tianjin'])
>>> result3 = pd.concat([result,df4],axis=1)
>>> result3
              apts     cars  salaries
beijing    60000.0  30000.0   30000.0
chongqing  60000.0  12000.0       NaN
guanghzou      NaN      NaN   20000.0
guangzhou  55000.0  15000.0       NaN
hangzhou   55000.0  15000.0       NaN
najing     60000.0  12000.0       NaN
shanghai   55000.0  20000.0   30000.0
suzhou         NaN      NaN   10000.0
tianjin        NaN      NaN   15000.0

合并两个DataFrame，并且只是交集

>>> result3 = pd.concat([result,df4],axis=1,join='inner')
>>> result3
           apts   cars  salaries
shanghai  55000  20000     30000
beijing   60000  30000     30000

Series和DataFrame一起Concatenate

>>> s1 = pd.Series([60,50],index=['shanghai','beijing'],name='meal')
>>> s1
shanghai    60
beijing     50
Name: meal, dtype: int64
>>> type(s1)
<class 'pandas.core.series.Series'>
>>> df1
           apts   cars
shanghai  55000  20000
beijing   60000  30000
>>> type(df1)
<class 'pandas.core.frame.DataFrame'>
>>> pd.concat([df1,s1],axis=1)
           apts   cars  meal
shanghai  55000  20000    60
beijing   60000  30000    50
>>> 

Series可以使用append进行行添加也可以列添加，但是concat不可以

>>> s2 = pd.Series([18000,12000],index=['apts','cars'],name='xiamen')
>>> s2
apts    18000
cars    12000
Name: xiamen, dtype: int64
>>> df1.append(s2)
           apts   cars
shanghai  55000  20000
beijing   60000  30000
xiamen    18000  12000
>>> pd.concat([df1,s2],axis=0)
                0     apts     cars
shanghai      NaN  55000.0  20000.0
beijing       NaN  60000.0  30000.0
apts      18000.0      NaN      NaN
cars      12000.0      NaN      NaN
>>> pd.concat([df1,s2],axis=1)
             apts     cars   xiamen
apts          NaN      NaN  18000.0
beijing   60000.0  30000.0      NaN
cars          NaN      NaN  12000.0
shanghai  55000.0  20000.0      NaN
>>> 

merge合并

>>> df1 = pd.DataFrame({"salaries":[10000,30000,30000,20000,15000],'cities':['suzhou','beijing','shanghai','guanghzou','tianjin']})
>>> df4 = pd.DataFrame({'apts':[55000,60000],'cars':[15000,12000],'cities':['shanghai','beijing']})
>>> result = pd.merge(df1,df4,on='cities') #on表示合并的列                                      
>>> result
     cities  salaries   apts   cars
0   beijing     30000  60000  12000
1  shanghai     30000  55000  15000

>>> result = pd.merge(df1,df4,on='cities',how='right')
>>> result
     cities  salaries   apts   cars
0   beijing     30000  60000  12000
1  shanghai     30000  55000  15000
>>> result = pd.merge(df1,df4,on='cities',how='left')
>>> result
      cities  salaries     apts     cars
0     suzhou     10000      NaN      NaN
1    beijing     30000  60000.0  12000.0
2   shanghai     30000  55000.0  15000.0
3  guanghzou     20000      NaN      NaN
4    tianjin     15000      NaN      NaN