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')
