pandas 之 concat

本文摘自：http://pandas.pydata.org/pandas-docs/stable/merging.html

前提：

ide：

liuqian@ubuntu:~$ ipython 

准备：

In [1]: import pandas as pd
 
In [2]: df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
   ...:                     'B': ['B0', 'B1', 'B2', 'B3'],
   ...:                     'C': ['C0', 'C1', 'C2', 'C3'],
   ...:                     'D': ['D0', 'D1', 'D2', 'D3']},
   ...:                     index=[0, 1, 2, 3])
   ...: 
 
In [3]: df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
   ...:                     'B': ['B4', 'B5', 'B6', 'B7'],
   ...:                     'C': ['C4', 'C5', 'C6', 'C7'],
   ...:                     'D': ['D4', 'D5', 'D6', 'D7']},
   ...:                      index=[4, 5, 6, 7])
   ...: 
 
In [4]: df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],
   ...:                     'B': ['B8', 'B9', 'B10', 'B11'],
   ...:                     'C': ['C8', 'C9', 'C10', 'C11'],
   ...:                     'D': ['D8', 'D9', 'D10', 'D11']},
   ...:                     index=[8, 9, 10, 11])  ...: In [4]: frames = [df1, df2, df3] In [5]: result = pd.concat(frames), 11])
   ...: 
 
In [5]: frames = [df1, df2, df3] # 不要忘了

, 11]) ...: In [4]: frames = [df1, df2, df3] In [5]: result = pd.concat(frames)

语法：

pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, copy=True)

实例1：

In [6]: result = pd.concat(frames)    # 等价于 result = df1.append([df2, df3])

In [7]: result = pd.concat(frames, axis=1)

实例2：

In [8]: result = pd.concat(frames, keys=['x', 'y', 'z'])

In [9]: result = pd.concat(frames, keys=['x', 'y', 'z'], axis=1)

实例3：

In [10]: df4 = pd.DataFrame({'B': ['B2', 'B3', 'B6', 'B7'],
   ...:                   'D': ['D2', 'D3', 'D6', 'D7'],
   ...:                   'F': ['F2', 'F3', 'F6', 'F7']},
   ...:                  index=[2, 3, 6, 7])
   ...: 

In [11]: result = pd.concat([df1, df4], axis=1, join='inner')

In [12]: result = pd.concat([df1, df4], join='inner')

实例4：

In [13]: result = pd.concat([df1, df4], axis=1, join_axes=[df1.index])

In [14]: result = pd.concat([df1, df4],  join_axes=[df1.columns])

实例5：

In [15]: result = pd.concat([df1, df4], ignore_index=True)   # 等价于 df1.append(df4, ignore_index=True)

In [16]: result = pd.concat([df1, df4], axis=1, ignore_index=True)

总结：

 1， axis=0, 对行操作    axis=1， 对列操作
 2. join='outer', 连接各个数据    join='inner',只取各个数据的公共部分
 3. join_axes=[df1.index], 保留与df1的行标签一样的数据，配合axis=1一起用    
    join_axes=[df1.columns]，保留与df1的列标签一样的数据，不要添加axis=1
 4. ignore_index=False, 保留原索引    ignore_index=True,忽略原索引并生成新索引
 5. keys=['x', 'y', 'z'] 对组成的每个df重新添加个索引