Ukubonwa kweGeodata sisixhobo esinamandla esisivumela ukuba siqonde iipateni ezinzima kunye nobudlelwane phakathi kwejografi kunye nezinye iinkcukacha. Inceda ekwenzeni izigqibo ezizisiweyo kunye nokunikezela ngedatha ngendlela efikelelekayo nebandakanyayo. Kweli nqaku, siza kuphonononga ukuba ungafezekiswa njani umbono we-geodata kusetyenziswa iPython, enye yezona lwimi zisebenza ngeendlela ezininzi ezikhoyo namhlanje. Siza kuphonononga amathala eencwadi ahlukeneyo, imisebenzi, kunye neendlela ezisetyenziswayo ukusombulula iingxaki eziqhelekileyo kule ndawo, siqinisekisa ukuba unesiseko esiluqilima sokwakhela phezu kwaso.
Ukwazisa iGeodata Visualization kwiPython
IPython ibonelela ngeelayibrari ezininzi eziyilelwe ngokukodwa ukubonwa kwe-geodata. Ezinye zezona zidumileyo ziquka GeoPandas, I-Folium, yaye Ngobuqili. Ithala leencwadi ngalinye lisebenzela injongo yalo ekhethekileyo, libonelela ngemisebenzi enokuthi isetyenziswe ukwenza iimephu ezinamandla nezisebenzisanayo, iitshathi, kunye nezicwangciso ezinxulumene ne-geodata. Njengomphuhlisi kunye nengcaphephe kwiPython, kubalulekile ukuqonda la mathala, iimpawu zawo, kunye nemida yawo ukwenza imbonakalo ye-geodata esebenzayo kunye nesebenziseka lula.
- GeoPandas lithala elakhiwe phezu kwePandas, eyilelwe ngokucacileyo ukuphatha idatha ye-geospatial. Inokufunda kwaye ibhale iifomathi ezahlukeneyo zedatha, yenza imisebenzi ye-geospatial, kwaye idibanise ngokulula namanye amathala eencwadi ePython njengeMatplotlib yokubonwa kwedatha.
- I-Folium lilayibrari elenza iimephu ezisebenzisanayo zisebenzisa iLeaflet JavaScript ilayibrari, ilungele ukusebenzisana iimephu zechoropleth kunye neemephu zobushushu. Ibonelela ngojongano olulula lokwenza iimephu ezinemigangatho eyahlukeneyo (abamakishi, ii-popups, njl.), iyenza ibe lolona khetho lufanelekileyo kwabo bangezongcali abafuna ukwenza iimephu ezinzima.
- Ngobuqili lithala leencwadi elinamandla neliguquguqukayo lokwenza iigrafu, iitshathi, kunye neemephu ezilungele ukupapashwa. IPlotly Express lujongano olukwinqanaba eliphezulu lokwenza oku kubonwa ngokukhawuleza, ngelixa i-API ebandakanyekayo `yegraph_objects` ivumela ukwenza zonke iinkcukacha zokubonwayo.
Isisombululo kwiNgxaki: Ukubona iGeodata usebenzisa iPython
Masithathele ingqalelo imeko efanayo apho sifuna ukuba nomfanekiso ngqondweni wokusasazwa koxinaniso lwabemi kumazwe ahlukeneyo. Siza kusebenzisa isethi yedatha equlethe imida yejografi kwifomathi ye-GeoJSON kunye noxinaniso lwabemi kwifomathi ye-CSV. Okokuqala, kufuneka sifunde, siqhube, kwaye sidibanise le datha. Emva koko, siya kudala imephu ye-choropleth ukujonga uxinaniso kunye nezikali zombala ezifanelekileyo.
1. Funda kwaye Uqhubekise iDatha
Siza kuqala ngokufunda idatha sisebenzisa i-GeoPandas yedatha yendawo kunye neePandas zokuxinana kwabantu. Emva koko, siya kudibanisa ezi zimbini zedata ezisekelwe kwiqhosha eliqhelekileyo (umzekelo, ikhowudi yelizwe).
import geopandas as gpd
import pandas as pd
# Read the GeoJSON file
world_map = gpd.read_file("world_map.geojson")
# Read the CSV file with population densities
density_data = pd.read_csv("population_density.csv")
# Merge the dataframes based on the common key (country code)
merged_data = world_map.merge(density_data, on="country_code")
2. Yenza iMaphu yeChoropleth
Ngokusebenzisa i-GeoPandas kunye neMatplotlib, sinokwenza imephu ye-choropleth ukubonisa ukuxinana kwabemi ngezikali zombala.
import matplotlib.pyplot as plt # Create a choropleth map using population density data fig, ax = plt.subplots(1, figsize=(10, 6)) merged_data.plot(column="population_density", cmap="Blues", linewidth=0.8, ax=ax) plt.show()
Inkcazo yenyathelo ngenyathelo leKhowudi yePython
Ngoku ukuba sinesisombululo sethu, masiye kwikhowudi inyathelo ngenyathelo ukuqonda indawo nganye. Siqala ngokungenisa ngaphandle amathala eencwadi ayimfuneko:
import geopandas as gpd import pandas as pd import matplotlib.pyplot as plt
Emva koko, sifunda ifayile yeGeoJSON usebenzisa iGeoPandas kunye nefayile yeCSV usebenzisa iPandas.
world_map = gpd.read_file("world_map.geojson")
density_data = pd.read_csv("population_density.csv")
Emva koko, sidibanisa iifreyimu zedatha ngeqhosha eliqhelekileyo, kule meko, ikhowudi yelizwe.
merged_data = world_map.merge(density_data, on="country_code")
Ekugqibeleni, senza imephu ye-choropleth usebenzisa i-GeoPandas kunye ne-Matplotlib, ichaza ikholamu yokujonga (ubuninzi babantu) kunye nemephu yombala (Blues).
fig, ax = plt.subplots(1, figsize=(10, 6)) merged_data.plot(column="population_density", cmap="Blues", linewidth=0.8, ax=ax) plt.show()
Oku kugqiba ukuphonononga kwethu ukubonwa kwe-geodata kwiPython. Sixoxe ngamathala eencwadi ahlukeneyo, anje GeoPandas, I-Folium, yaye Ngobuqili, kunye nokusebenza kwazo ekudaleni imbonakalo ye-geodata enamandla nesebenzayo. Ngolu lwazi, ngoku kufuneka uxhotyiswe ngcono ukujongana nemisebenzi enzima yokujonga i-geodata kwaye uphuhlise izisombululo ezisebenzayo ngakumbi.
