pandas - 合并

学习目标

• 应用pd.concat实现数据的合并
• 应用pd.merge实现数据的合并

如果你的数据由多张表组成，那么有时候需要将不同的内容合并在一起分析

1 pd.concat实现数据合并

• pd.concat([data1, data2], axis=1)
  • 按照行或列进行合并

比如我们将刚才处理好的哑变量与原数据合并

pd.concat([data, dummaries], axis=1)

2 pd.merge

pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,left_index=False, right_index=False, sort=True,suffixes=('_x', '_y'), copy=True, indicator=False,validate=None)

• 可以指定按照两组数据的共同键值对合并或者左右各自
• left: A DataFrame object
• right: Another DataFrame object
• on: Columns (names) to join on. Must be found in both the left and right DataFrame objects.
• left_on=None, right_on=None：指定左右键

Merge method	SQL Join Name	Description
left	LEFT OUTER JOIN	Use keys from left frame only
right	RIGHT OUTER JOIN	Use keys from right frame only
outer	FULL OUTER JOIN	Use union of keys from both frames
inner	INNER JOIN	Use intersection of keys from both frames

3 案例：pd.merge合并

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
                        'key2': ['K0', 'K1', 'K0', 'K1'],
                        'A': ['A0', 'A1', 'A2', 'A3'],
                        'B': ['B0', 'B1', 'B2', 'B3']})

right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
                        'key2': ['K0', 'K0', 'K0', 'K0'],
                        'C': ['C0', 'C1', 'C2', 'C3'],
                        'D': ['D0', 'D1', 'D2', 'D3']})

# 默认内连接
result = pd.merge(left, right, on=['key1', 'key2'])

1、左连接

result = pd.merge(left, right, how='left', on=['key1', 'key2'])

2、右连接

result = pd.merge(left, right, how='right', on=['key1', 'key2'])

3、外链接

result = pd.merge(left, right, how='outer', on=['key1', 'key2'])