indicator:将一列添加到名为_merge的输出DataFrame,其中包含有关每行源的信息。_merge是分类类型,并且对于其合并键仅出现在“左”DataFrame中的观察值,取得值为left_only,对于其合并键仅出现在“右”DataFrame中的观察值为right_only,并且如果在两者中都找到观察点的合并键,则为left_only。 【实例】 代码语言:javas...
pd.merge(left, # 待合并的2个数据框 right, how='inner', # ‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’ on=None, # 连接的键,默认是相同的键 left_on=None, # 指定不同的连接字段:键不同,但是键的取值有相同的内容 right_on=None, left_index=False, # 根据索引来连接 right_index...
pd.merge(df6, df7, how='left') 1. 输出: 5. suffixes参数 如果输出结果中有两个重复的列名,因此pd.merge()函数会自动为它们增加后缀 _x 或 _y,当然也可以通过suffixes参数自定义后缀名。 df8 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'], 'rank': [1, 2, 3, 4]}) df9...
merge: 合并数据集, 通过left, right确定连接字段,默认是两个数据集相同的字段 参数 说明 left 参与合并的左侧DataFrame right 参与合并的右侧DataFrame how 连接方式:‘inner’(默认);还有,‘outer’、‘left’、‘right’ on 用于连接的列名,必须同时存在于左右两个DataFrame对象中,如果位指定,则以left和right列名...
pd.merge(left, right, left_on='lkey', right_on='rkey') iii) 连接方式(默认为inner) pd.merge(left, right, on='key', how='outer') iv) 连接键为多列 pd.merge(left, right, on=['key1','key2']) v) 重复列名的处理 pd.merge(left, right, on='key', suffixes=['_left','_right...
To merge two pandas DataFrames on multiple columns, you can use the merge() function and specify the columns to join on using the on parameter. This
merge函数left_on是什么？pandas库的pd.merge函数left_on是什么？pandas库的pd.merge函数left_on是左侧...
In [93]: result = left.join(right, on="key") In [94]: result =pd.merge( ...: left, right, left_on="key", right_index=True, how="left", sort=False ...: ) ...: To join on multiple keys, the passed DataFrame must have aMultiIndex: In [...
In Example 2, I’ll show how to combine multiple pandas DataFrames using an outer join (also called full join). To do this, we have to set the how argument within the merge function to be equal to “outer”: After executing the previous Python syntax the horizontally appended pandas Data...
In [41]: result = pd.merge(left, right, on="key") Here is a more complicated example with multiple join keys. Only the keys appearing inleftandrightare present (the intersection), sincehow='inner'by default. In [42]: left =pd.DataFrame( ...