I have a dataframe of coordinates in which I perform point-in-polygon and it returns the set of points in the polygon.
df1 - Original coordinates
0 POINT (-97.96192929999999 29.8929939)
1 POINT (-97.98886109999999 29.8230438)
2 POINT (-97.6573715 30.15241810000001)
3 POINT (-97.68809509999998 30.3590794)
4 POINT (-97.37609860000001 31.0930271)
5 POINT (-97.66625980000001 30.3466492)
6 POINT (-97.6666412 30.3455753)
...
df2 - Results
4 POINT (-97.37609860000001 31.0930271)
1496 POINT (-97.64907839999999 30.3872128)
445 POINT (-97.64907839999999 30.3872128)
2822 POINT (-97.649353 30.387228)
1369 POINT (-97.6488342 30.3873215)
6 POINT (-97.6666412 30.3455753)
2303 POINT (-97.6492767 30.38755039999999)
...
How would I add an "area" column in df1 and set the values = "area1" for the row indexes that are in both dfs? In the example above, rows 4 and 6 are in the results so I'd like to have an area column = "area1" for those rows in df1
If you are talking about the index, cause the pandas is index/column sensitive, which mean it will assign the new value base on the index matched.
So what you can do
df1['area']=df2['Value']
#df2['New']='are1' then df1['area']=df2['New']
Toy data
df1=pd.DataFrame({'d1':[1,2,3]},index=[1,2,3])
df2=pd.DataFrame({'d2':[1,2,3]},index=[2,6,1])
df1['New']=df2.d2
df1
Out[724]:
d1 New
1 1 3.0
2 2 1.0
3 3 NaN