Ukubukwa kwe-geodata kuyithuluzi elinamandla elisivumela ukuthi siqonde amaphethini ayinkimbinkimbi nobudlelwano phakathi kwendawo nenye idatha. Kuyasiza ekwenzeni izinqumo ezinolwazi kanye nokwethula idatha ngendlela efinyeleleka kakhudlwana nebandakanyayo. Kulesi sihloko, sizocubungula ukuthi ukubonwa kwe-geodata kungafinyelelwa kanjani kusetshenziswa i-Python, olunye lwezilimi zokuhlela ezisebenza ngezindlela eziningi ezitholakalayo namuhla. Sizohlola amalabhulali ahlukene, imisebenzi, namasu asetshenziswa ukuxazulula izinkinga ezijwayelekile kule ndawo, siqinisekise ukuthi unesisekelo esiqinile ongakhela phezu kwaso.
Sethula Ukubonwa Kwe-Geodata ku-Python
I-Python inikeza imitapo yolwazi eminingana eklanyelwe ukubonwa kwe-geodata. Ezinye ezidume kakhulu zihlanganisa I-GeoPandas, I-Folium, Futhi Ngobuhle. Ilabhulali ngayinye isebenzisa injongo yayo eyingqayizivele, ihlinzeka ngemisebenzi engasetshenziswa ukudala amamephu anamandla nasebenzisanayo, amashadi, neziqephu ezihlobene ne-geodata. Njengonjiniyela nochwepheshe kuPython, kubalulekile ukuqonda le mitapo yolwazi, izici zayo, kanye nemikhawulo yayo ukuze udale ukubonwa kwe-geodata okusebenzayo nokusebenziseka kalula.
- I-GeoPandas iwumtapo wolwazi owakhiwe phezu kwama-Panda, oklanyelwe ngokusobala ukuphatha idatha ye-geospatial. Ingakwazi ukufunda nokubhala amafomethi edatha ahlukahlukene, yenze imisebenzi ye-geospatial, futhi ihlanganise kalula neminye imitapo yolwazi ye-Python efana ne-Matplotlib ukuze kubonwe idatha.
- I-Folium iyilabhulali ekhiqiza amamephu asebenzisanayo kusetshenziswa umtapo wezincwadi we-Leaflet JavaScript, afanele amamephu e-choropleth asebenzisanayo namamephu okushisa. Inikeza isixhumi esibonakalayo esilula sokudala amamephu anezendlalelo ezihlukene (omaka, izigelekeqe, njll.), okwenza kube ukukhetha okuhle kwabangebona ochwepheshe abafuna ukudala amamephu ayinkimbinkimbi.
- Ngobuhle iwumtapo wezincwadi onamandla noguquguqukayo wokudala amagrafu, amashadi, namamephu asebenzisanayo futhi alungele ukushicilelwa. I-Plotly Express iyisixhumi esibonakalayo esisezingeni eliphezulu sokudala lezi zithombe ngokushesha, kuyilapho i-API ethi `graph_objects` ebandakanyeka kakhulu ivumela ukwenza ngokwezifiso yonke imininingwane yokuboniswa.
Isixazululo Senkinga: Ukubona I-Geodata Usebenzisa I-Python
Ake sicabangele isimo esivamile lapho sifuna ukubona ngeso lengqondo ukusatshalaliswa kokuminyana kwabantu emazweni ahlukahlukene. Sizosebenzisa idathasethi equkethe imingcele yendawo ngefomethi ye-GeoJSON nokuminyana kwabantu ngefomethi ye-CSV. Okokuqala, sidinga ukufunda, ukucubungula, futhi sihlanganise le datha. Bese, sizodala imephu ye-choropleth ukuze sibone ngeso lengqondo ukuminyana ngezikali zombala ezifanele.
1. Funda futhi Ucubungule Idatha
Sizoqala ngokufunda idatha sisebenzisa i-GeoPandas yedatha yendawo kanye namaPanda ngokuminyana kwabantu. Bese, sizohlanganisa lawa mafayela edatha amabili ngokusekelwe kukhiye ovamile (isb, ikhodi yezwe).
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. Dala imephu ye-Choropleth
Sisebenzisa i-GeoPandas ne-Matplotlib, singakha imephu ye-choropleth ukuze sibonise ukuminyana kwabantu 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()
Incazelo yesinyathelo ngesinyathelo yekhodi yePython
Manje njengoba sinesixazululo sethu, ake sidlule kukhodi isinyathelo ngesinyathelo ukuze siqonde ingxenye ngayinye. Siqala ngokungenisa imitapo yolwazi edingekayo:
import geopandas as gpd import pandas as pd import matplotlib.pyplot as plt
Okulandelayo, sifunda ifayela le-GeoJSON sisebenzisa i-GeoPandas nefayela le-CSV sisebenzisa i-Pandas.
world_map = gpd.read_file("world_map.geojson")
density_data = pd.read_csv("population_density.csv")
Ngemva kwalokho, sihlanganisa amafreyimu edatha ngokhiye ovamile, kulokhu, ikhodi yezwe.
merged_data = world_map.merge(density_data, on="country_code")
Ekugcineni, sakha imephu ye-choropleth sisebenzisa i-GeoPandas ne-Matplotlib, sicacisa ikholomu okufanele uyibone ngeso lengqondo (ukuminyana kwabantu) kanye 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()
Lokhu kuphetha ukuhlola kwethu ukubonwa kwe-geodata ku-Python. Sixoxe ngemitapo yolwazi ehlukene, njenge I-GeoPandas, I-Folium, Futhi Ngobuhle, kanye nokusebenza kwazo ekudaleni ukubonwa kwe-geodata okunamandla nokusebenzisanayo. Ngalolu lwazi, kufanele manje ukuhlomele kangcono ukubhekana nemisebenzi eyinkimbinkimbi yokubuka i-geodata futhi uthuthukise izixazululo ezisebenza kahle kakhulu.
