There is no effective mining of information efficiently, if we do not pass the information in the best way, tracing data is very important and can help in making decisions. Repair the chart below
the graph above gives some information, but it is presented in a way that does not support decision making.When we talk about decision making, they often need to be taken agilely and margin for error.
Let's see the correct process for grouping, organizing, and displaying the chart above
in [1] import libraries for calculations
in [3] import map library
in [23] loading dataset
in [26] presenting the first 5 data
in[15]we request the counting of all occurrences
Creating a new dataset with the occurrences involving cyclists
when requesting the size of the dataset with len () I confirm the size and the same of the list in the image above.
in [27] we have created the LAT variable with the 42 latitude values of the acCiclistas dataset, and we do the same for longitude.
in [28] I use the folium library to start the variable mapa with the coordinates of Recife, already with the appropriate Zoom.
in [29] I run a loop to index the latitude and longitude variables within the map object
out [30] we show the map, see how it is possible to map the areas with the highest number of cyclists, the most dangerous avenues, we can still map accident schedules using different colors for the blue arrows, but that's for a next post.
