concat()函数的具体用法
pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
keys=None, levels=None, names=None, verify_integrity=False,
copy=True)
参数含义
In [1]: 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 [2]: 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 [3]: 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)
KEY参数
result = pd.concat(frames, keys=['x', 'y', 'z'])
JOIN参数
默认join = 'outer',为取并集的关系
In [8]: df4 = pd.DataFrame({'B': ['B2', 'B3', 'B6', 'B7'],
...: 'D': ['D2', 'D3', 'D6', 'D7'],
...: 'F': ['F2', 'F3', 'F6', 'F7']},
...: index=[2, 3, 6, 7])
...:
In [9]: result = pd.concat([df1, df4], axis=1)
结果：
当设置join = 'inner'，则说明为取交集
In [10]: result = pd.concat([df1, df4], axis=1, join='inner')
结果：
如果索引想从原始DataFrame重用确切索引：
In [11]: result = pd.concat([df1, df4], axis=1, join_axes=[df1.index]) #设置索引为df1的索引
pandas文档：http://pandas.pydata.org/pandas-docs/stable/
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。
