不平等加入了熊猫？

Question

我通常使用Dataframe.merge组合熊猫中的数据框。据我了解，这仅适用于平等联接。使用其他类型的检查（例如，不等式）来联接两个数据框的惯用方式是什么？

Answer 1

熊猫合并（）允许outer，left，right连接（而不仅仅是inner连接）两个数据帧之间，这样你就可以返回匹配的记录。此外，merge()甚至可以泛化为返回交叉联接（两个数据帧之间的所有组合匹配），并且随后进行过滤可以返回不匹配的记录。还有，还有isin（） pandas方法。

考虑下面的演示。下面是我们喜欢的两种数据框架， 计算机语言
。如图所示，第一数据帧是第二数据帧的子集。外部联接返回的记录都包含NaN不匹配的列，以后可以将其过滤掉。交叉联接返回可以过滤的完整完整行，并isin()在列中搜索值：

import pandas as pd

df1 = pd.DataFrame({'Languages': ['C++', 'C', 'Java', 'C#', 'Python', 'PHP'],
                    'Uses': ['computing', 'computing', 'application', 'application', 'application', 'web'], 
                    'Type': ['Proprietary', 'Proprietary', 'Proprietary', 'Proprietary', 'Open-Source', 'Open-Source']})

df2 = pd.DataFrame({'Languages': ['C++', 'C', 'Java', 'C#', 'Python', 'PHP',
                                 'Perl', 'R', 'Ruby', 'VB.NET', 'Javascript', 'Matlab'],
                    'Uses': ['computing', 'computing', 'application', 'application', 'application', 'web',
                            'application', 'computing', 'web', 'application', 'web', 'computing'],
                    'Type': ['Proprietary', 'Proprietary', 'Proprietary', 'Proprietary', 'Open-Source',
                            'Open-Source', 'Open-Source', 'Open-Source', 'Open-Source', 'Proprietary',
                            'Open-Source', 'Proprietary']})

# OUTER JOIN 
mergedf = pd.merge(df1, df2, on=['Languages'], how='outer')
# FILTER OUT LANGUAGES IN SMALLER THAT IS NULL
mergedf = mergedf[pd.isnull(mergedf['Type_x'])][['Languages', 'Uses_y', 'Type_y']]

#     Languages       Uses_y       Type_y
#6         Perl  application  Open-Source
#7            R    computing  Open-Source
#8         Ruby          web  Open-Source
#9       VB.NET  application  Proprietary
#10  Javascript          web  Open-Source
#11      Matlab    computing  Proprietary


# ISIN COMPARISON, RETURNING RECORDS IN LARGER NOT IN SMALLER
unequaldf = df2[~df2.Languages.isin(df1['Languages'])]

#     Languages         Type         Uses
#6         Perl  Open-Source  application
#7            R  Open-Source    computing
#8         Ruby  Open-Source          web
#9       VB.NET  Proprietary  application
#10  Javascript  Open-Source          web
#11      Matlab  Proprietary    computing


# CROSS JOIN 
df1['key'] = 1                 # (REQUIRES A JOIN KEY OF SAME VALUE)
df2['key'] = 1                    
crossjoindf = pd.merge(df1, df2, on=['key'])
# FILTER FOR LANGUAGES IN LARGER NOT IN SMALLER (ALSO USING ISIN)
crossjoindf = crossjoindf[~crossjoindf['Languages_y'].isin(crossjoindf['Languages_x'])]\
                    [['Languages_y', 'Uses_y', 'Type_y']].drop_duplicates()

#   Languages_y       Uses_y       Type_y
#6         Perl  application  Open-Source
#7            R    computing  Open-Source
#8         Ruby          web  Open-Source
#9       VB.NET  application  Proprietary
#10  Javascript          web  Open-Source
#11      Matlab    computing  Proprietary

诚然，交叉连接在这里可能是多余且冗长的，但是如果您无与伦比的需求需要跨数据帧进行排列，那么它会很方便。