使用merge合并数据

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最新推荐文章于 2023-02-15 13:43:30 发布

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分类专栏： Python

本文链接：https://blog.youkuaiyun.com/chuan403082010/article/details/79442813

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这篇博客介绍了如何利用Python的Pandas库中的merge函数将两个数据集subway_df和weather_df按照日期、小时、纬度和经度进行内连接，从而合并数据。示例代码展示了具体的使用方法和合并后的数据。

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import pandas as pd

subway_df = pd.DataFrame({
    'UNIT': ['R003', 'R003', 'R003', 'R003', 'R003', 'R004', 'R004', 'R004',
             'R004', 'R004'],
    'DATEn': ['05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11',
              '05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11'],
    'hour': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'ENTRIESn': [ 4388333,  4388348,  4389885,  4391507,  4393043, 14656120,
                 14656174, 14660126, 14664247, 14668301],
    'EXITSn': [ 2911002,  2911036,  2912127,  2913223,  2914284, 14451774,
               14451851, 14454734, 14457780, 14460818],
    'latitude': [ 40.689945,  40.689945,  40.689945,  40.689945,  40.689945,
                  40.69132 ,  40.69132 ,  40.69132 ,  40.69132 ,  40.69132 ],
    'longitude': [-73.872564, -73.872564, -73.872564, -73.872564, -73.872564,
                  -73.867135, -73.867135, -73.867135, -73.867135, -73.867135]
})

weather_df = pd.DataFrame({
    'DATEn': ['05-01-11', '05-01-11', '05-02-11', '05-02-11', '05-03-11',
              '05-03-11', '05-04-11', '05-04-11', '05-05-11', '05-05-11'],
    'hour': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'latitude': [ 40.689945,  40.69132 ,  40.689945,  40.69132 ,  40.689945,
                  40.69132 ,  40.689945,  40.69132 ,  40.689945,  40.69132 ],
    'longitude': [-73.872564, -73.867135, -73.872564, -73.867135, -73.872564,
                  -73.867135, -73.872564, -73.867135, -73.872564, -73.867135],
    'pressurei': [ 30.24,  30.24,  30.32,  30.32,  30.14,  30.14,  29.98,  29.98,
                   30.01,  30.01],
    'fog': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'rain': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'tempi': [ 52. ,  52. ,  48.9,  48.9,  54. ,  54. ,  57.2,  57.2,  48.9,  48.9],
    'wspdi': [  8.1,   8.1,   6.9,   6.9,   3.5,   3.5,  15. ,  15. ,  15. ,  15. ]
})

def combine_dfs(subway_df, weather_df):
    return subway_df.merge(weather_df, on = ['DATEn','hour','latitude','longitude'], how = 'inner')
    
print combine_dfs(subway_df, weather_df)

      DATEn  ENTRIESn    EXITSn  UNIT  hour   latitude  longitude  fog  \
0  05-01-11   4388333   2911002  R003     0  40.689945 -73.872564    0   
1  05-02-11   4388348   2911036  R003     0  40.689945 -73.872564    0   
2  05-03-11   4389885   2912127  R003     0  40.689945 -73.872564    0   
3  05-04-11   4391507   2913223  R003     0  40.689945 -73.872564    0   
4  05-05-11   4393043   2914284  R003     0  40.689945 -73.872564    0   
5  05-01-11  14656120  14451774  R004     0  40.691320 -73.867135    0   
6  05-02-11  14656174  14451851  R004     0  40.691320 -73.867135    0   
7  05-03-11  14660126  14454734  R004     0  40.691320 -73.867135    0   
8  05-04-11  14664247  14457780  R004     0  40.691320 -73.867135    0   
9  05-05-11  14668301  14460818  R004     0  40.691320 -73.867135    0   

   pressurei  rain  tempi  wspdi  
0      30.24     0   52.0    8.1  
1      30.32     0   48.9    6.9  
2      30.14     0   54.0    3.5  
3      29.98     0   57.2   15.0  
4      30.01     0   48.9   15.0  
5      30.24     0   52.0    8.1  
6      30.32     0   48.9    6.9  
7      30.14     0   54.0    3.5  
8      29.98     0   57.2   15.0  
9      30.01     0   48.9   15.0

如果碰到名称不一样呢

subway_df.merge(weather_df, left_on = ['DATEn','hour','latitude','longitude'],
                            right_on = ['date','hour','latitude','longitude'],
                            how = 'inner')