python - GeoPandas GeoDataFrame 转换
问题描述
亲爱的 stackoverflow 社区，
在过去的几周里，我阅读了有关 Python、Pandas 和 GeoPandas 的文档和文章。遗憾的是，编程对我来说仍然没有我希望的那么直观，而且由于我不是来自与 GeoPandas 打交道的编程背景，这对我来说是一场纯粹的噩梦。
我有一个相当复杂的（至少对我而言）geopandas.GeoDataFrame，我需要对其进行转换以进行进一步的回归分析。可悲的是，即使在 stackoverflow 和许多其他互联网页面上进行了无数次搜索后，我仍然无法以合适的方式转换我的数据。
我的 GeoDataFrame 如下所示：
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'是一个geopandas.GeoDataFrame使用geopandas.sjoin和几何点连接的。它包含不同类别的犯罪相关事件，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geopandas.GeoDataFrame我加入的一个geopandas.sjoin。它包含不同的兴趣点，这些兴趣点仅取决于'POLYGON'它们的输入。它们不会随着时间而改变。
'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，我需要它具有以下形状。
最后：
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
需要注意的是：
- 这些行由相同的值聚合
'INCIDENTDATE' - 列中取每个多边形每年的总犯罪事件总和
'TOTAL_CRIME_COUNT' - 每个多边形的不同兴趣点的总和。每个兴趣点都需要在自己的列中。
即使对解决方案有丝毫暗示，我也会很高兴。我也对完全不同的方法来达到我的最终 DataFrame 持开放态度，因为我什至不确定我是否以正确的方式开始。
如果你能做到这一点，非常感谢！
查尔斯
PS：我为语法错误道歉。英语不是我的第一语言。
解决方案
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