Thank you so much for this playlist! Went through all of it today and learned a lot. I am very experienced in Pandas and there's something I would like to simplify in your process for any new learners here: After you perform your spatial join (I've changed some variables names here) ``` states_power = gpd.sjoin(states, powerplants, how='inner', predicate='contains') ``` You can easily map the powerplant count by converting that count into a dictionary and then mapping it to the desired dataframe using the `map` function of geopandas (also in regular pandas): ``` # convert count to dictionary state_map = states_power.name.value_counts().to_dict() # map powerplant count to states states['count'] = states.name.map(state_map) ``` And just like that! For instances that do not have a match, it will have a NaN count. I know this an old video, but I hope this helps!
You are such an amazing teacher. The series is so well organized and you really know how to explain and clarify the main points. Thank you so much for making the videos.
Amazing. I do my PhD in hydrogeology and I strongly focus on GIS (so I guess 'slightly' different from your spec) but I found your channel looking for geopandas tutorials. Very helpful! I hope you will do more geopandas/pandas videos...
Glad you enjoyed it! I have content mostly here and sometimes at littlecolumns.com. I'll be posting a big new secret project in a few weeks that I'll email out about from tinyletter.com/jsoma
Thank you so much for this playlist! Went through all of it today and learned a lot. I am very experienced in Pandas and there's something I would like to simplify in your process for any new learners here:
After you perform your spatial join (I've changed some variables names here)
```
states_power = gpd.sjoin(states, powerplants, how='inner', predicate='contains')
```
You can easily map the powerplant count by converting that count into a dictionary and then mapping it to the desired dataframe using the `map` function of geopandas (also in regular pandas):
```
# convert count to dictionary
state_map = states_power.name.value_counts().to_dict()
# map powerplant count to states
states['count'] = states.name.map(state_map)
```
And just like that! For instances that do not have a match, it will have a NaN count.
I know this an old video, but I hope this helps!
Went through complete series. Great practical series, with useful examples and informative Videos. Kindly Continue making such videos.
You are such an amazing teacher. The series is so well organized and you really know how to explain and clarify the main points. Thank you so much for making the videos.
I learn so much from your videos. I watched several tutorials, but yours taught me the most. Thank you so much!!
Make it a full course and upload it to Udemy or edx for a small fee. This is clear and to the point. Good job.
Great tutorial. Up to the point!
fantastic video, super detailed with every possible explain. Really learned a lot, thx!!!!!
Amazing. I do my PhD in hydrogeology and I strongly focus on GIS (so I guess 'slightly' different from your spec) but I found your channel looking for geopandas tutorials. Very helpful! I hope you will do more geopandas/pandas videos...
loved this series! please make more series on various topics
thank you for the amazing tutorial series. looking forward for more, where do you publish your tutorials?
Glad you enjoyed it! I have content mostly here and sometimes at littlecolumns.com. I'll be posting a big new secret project in a few weeks that I'll email out about from tinyletter.com/jsoma
Please. We do not want to stop till choropleths and joins. We want to learn more of it. Thank you for this amazing series.
Thanks for your tutorial but how i joint shp and csv file in python and make Choropleths?
Just thank you