详解pandas数据合并与重塑（pd.concat篇）

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

# 现将表构成list，然后在作为concat的输入
In [4]: frames = [df1, df2, df3]
 
In [5]: result = pd.concat(frames)

In [6]: result = pd.concat(frames, keys=['x', 'y', 'z'])

In [9]: result = pd.concat([df1, df4], axis=1)

In [10]: result = pd.concat([df1, df4], axis=1, join='inner')

In [11]: result = pd.concat([df1, df4], axis=1, join_axes=[df1.index])

In [12]: result = df1.append(df2)

In [27]: result = pd.concat(frames, keys=['x', 'y', 'z'])

In [28]: pieces = {'x': df1, 'y': df2, 'z': df3}
In [29]: result = pd.concat(pieces)

In [34]: s2 = pd.Series(['X0', 'X1', 'X2', 'X3'], index=['A', 'B', 'C', 'D'])
 
In [35]: result = df1.append(s2, ignore_index=True)

In [36]: dicts = [{'A': 1, 'B': 2, 'C': 3, 'X': 4},
  ....:     {'A': 5, 'B': 6, 'C': 7, 'Y': 8}]
  ....: 
 
In [37]: result = df1.append(dicts, ignore_index=True)