尊敬的stackoverflow社区，
过去的几周中，我通读了有关Python，Pandas和GeoPandas的文档和文章。可悲的是，编程对我来说仍然没有我想要的那么直观，而且由于我不是来自与GeoPandas接触的编程背景，所以对我来说纯粹是一场噩梦。
我有一个相当复杂的（至少对我来说）geopandas.GeoDataFrame，我需要对其进行转换以进行进一步的回归分析。遗憾的是，即使在这里进行了无数次关于stackoverflow和许多其他互联网页面的搜索之后，我仍然无法以合适的方式转换数据。
我的GeoDataFrame外观如下：
INCIDENTDATE CATEGORY_left CATEGORY_right \
POLYGON
1 2009 BURGLARY restaurant
1 2009 HOMICIDE restaurant
1 2010 ASSAULT restaurant
1 2011 ASSAULT restaurant
1 2012 LARCENY restaurant
1 2012 AGGRAVATED ASSAULT restaurant
1 2012 BURGLARY restaurant
1 2012 DAMAGE TO PROPERTY restaurant
1 2013 AGGRAVATED ASSAULT restaurant
1 2014 BURGLARY restaurant
3 2010 MURDER/INFORMATION crossing
3 2011 AGGRAVATED ASSAULT crossing
3 2011 BURGLARY crossing
3 2011 ASSAULT crossing
3 2012 AGGRAVATED ASSAULT crossing
3 2012 MURDER/INFORMATION crossing
3 2013 DANGEROUS DRUGS crossing
3 2014 DAMAGE TO PROPERTY crossing
3 2015 ASSAULT crossing
geometry shape_area
POLYGON
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
'CATEGORY_left'是geopandas.GeoDataFrame，它是使用geopandas.sjoin和几何点连接在一起的。它包含与犯罪相关的事件的不同类别，如下所示：
'CATEGORY_left' INCIDENTDATE CATEGORY geometry
0 2009 LARCENY POINT (-83.06870000000001 42.3516)
1 2009 ASSAULT POINT (-82.9504 42.4262)
2 2009 ASSAULT POINT (-83.2657 42.4371)
3 2009 DAMAGE TO PROPERTY POINT (-83.03189999999999 42.4381)
4 2009 STOLEN VEHICLE POINT (-83.1499 42.4094)
'CATEGORY_right'也是我与geopandas.GeoDataFrame一起加入的geopandas.sjoin。它包含不同的兴趣点，这些兴趣点仅取决于其'POLYGON'的位置。它们不会随时间变化。
'CATEGORY_right' CATEGORY geometry
13243 atm POINT (-83.06221670000002 42.32472120000001)
13244 atm POINT (-83.0711901 42.3213266)
13245 atm POINT (-83.0232692 42.34089829999999)
24624 supermarket POINT (-83.2400998 42.37158820000001)
24625 supermarket POINT (-82.9728123 42.3872246)
为了进行回归分析，我需要使其具有以下形状。
INCIDENTDATE TOTAL_CRIME_COUNT RESTAURANT_COUNT\
POLYGON
1 2009 4396 30
1 2010 6455 30
1 2011 7434 30
1 2012 3843 30
1 2013 5354 30
1 2014 3425 30
3 2010 4564 10
3 2011 3234 10
3 2012 8754 10
3 2013 4829 10
3 2014 9583 10
3 2015 4354 10
geometry shape_area
POLYGON
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
1 POLYGON ((-83.13630642653472 42.43895550416347... 3.959841e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
3 POLYGON ((-83.17870657596477 42.39269734838572... 3.918602e+06
重要注意事项：
'INCIDENTDATE'中用相同的值汇总行'TOTAL_CRIME_COUNT'列中即使对解决方案有丝毫暗示，我也很高兴。 我也愿意采用完全不同的方法来获得最终的DataFrame，因为我什至不确定我是否以正确的方式开始。
如果您能做到这一点，非常感谢！
查尔斯
P.S .：对于语法错误，我深表歉意。英语不是我的母语。