One thing I didn’t like was how streamlit didn’t have inbuilt functionality to view GeoDataFrames, so essentially that means I have to output geospatial data using the st.write() method, and that just results in some ugly, green-colored output, very much unlike the clean, tabular output you get when you use st.write() for displaying dataframes. It’s incredibly efficient and sort of extends native pandas commands to shapefile data, making everything much easier to work with. I used the geopandas library for dealing with shapefile data. However, one big advantage streamlit has is the fact that it is operated by the command line, making it much more efficient for a person like me who’s very much comfortable with typing out commands and running stuff, rather than dragging my pointer around to click objects. I think one distinct advantage Jupyter Notebook has is compartmentalisation, and how good code and markdown look next to each other, whereas streamlit seems to be more visually appealing. I was also introduced to streamlit, which provides, say, an alternative to Jupyter Notebooks but with more interaction and in my opinion, better visual appeal. This was very much new to me, so I found it fascinating to see how any sort of map can be broken down into polygons, lines, and points then played around with using code. Points are used to represent cities, landmarks, features of interest etc. Lines are used to represent boundaries, roads, railway lines etc. A polygon can represents certain shapes, so in the given context of maps and geospatial data, a polygon could act as a country, or perhaps an ocean and so on. shp file corresponds to either a polygon, a line, or a point. shp files, in the same way any other form of data is represented in say. I was recently introduced to geospatial data in python.
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