Box addresses from our dataframe (you can do this before creating the “Map Address” column as well): df_copy = df.str.contains("P.O. import pandas as pd df = pd.read_csv(r"C:\napa_wine_full.csv", encoding= 'unicode_escape') df If one decides to use the Napa Wine Project website, keep in mind that several bad addresses or non-California addresses are taken out in the Github link version, which also contains a slightly edited version of the spreadsheet as well. One can either download the spreadsheet that contains the data here, or one can visit the Napa Wine Project website, and download the spreadsheet there as well. In this example we will use Pandas, an often used Python library for data manipulation and analysis, to import our csv file of Napa Valley wineries. Importing and manipulating our dataĪ basic step in many projects like this is simply importing your data. The guide linked here in installing Geopandas (not Geopy) may be helpful. I used the Anaconda command prompt to do this. I used Jupyter Notebooks in this example, but if one wants to use Jupyter Notebooks, you will need to create your own environment in order for Geopy and Folium to be installed and work. The following example is a rudimentary example of what one can do using Python and geocoding. One useful quality of the following example is that given a list of valid addresses, one can create all sorts of customized maps they may like, such as a map of local businesses or local industries, or extend further into more expansive national or global maps. In this article we will create a simple script and map of Napa Valley wineries using Python and geocoding.
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