今天小編就爲大家分享一篇關於Python關於excel和shp的使用在matplotlib,小編覺得內容挺不錯的,現在分享給大家,具有很好的參考價值,需要的朋友一起跟隨小編來看看吧
關於excel和shp的使用在matplotlib
- 使用pandas 對excel進行簡單操作
- 使用cartopy 讀取shpfile 展示到matplotlib中
- 利用shpfile文件中的一些字段進行一些着色處理
#!/usr/bin/env python # -*- coding: utf-8 -*- # @File : map02.py # @Author: huifer # @Date : 2018/6/28 import folium import pandas as pd import requests import matplotlib.pyplot as plt import cartopy.crs as ccrs import zipfile import cartopy.io.shapereader as shaperead from matplotlib import cm from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter import os dataurl = "http://image.data.cma.cn/static/doc/A/A.0012.0001/SURF_CHN_MUL_HOR_STATION.xlsx" shpurl = "http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip" def download_file(url): """ 根據url下載文件 :param url: str """ r = requests.get(url, allow_redirects=True) try: open(url.split('/')[-1], 'wb').write(r.content) except Exception as e: print(e) def degree_conversion_decimal(x): """ 度分轉換成十進制 :param x: float :return: integer float """ integer = int(x) integer = integer + (x - integer) * 1.66666667 return integer def unzip(zip_path, out_path): """ 解壓zip :param zip_path:str :param out_path: str :return: """ zip_ref = zipfile.ZipFile(zip_path, 'r') zip_ref.extractall(out_path) zip_ref.close() def get_record(shp, key, value): countries = shp.records() result = [country for country in countries if country.attributes[key] == value] countries = shp.records() return result def read_excel(path): data = pd.read_excel(path) # print(data.head(10)) # 獲取幾行 # print(data.ix[data['省份']=='浙江',:].shape[0]) # 計數工具 # print(data.sort_values('觀測場拔海高度(米)',ascending=False).head(10))# 根據值排序 # 判斷經緯度是什麼格式(度分 、 十進制) 判斷依據 %0.2f 是否大於60 # print(data['經度'].apply(lambda x:x-int(x)).sort_values(ascending=False).head()) # 結果判斷爲度分保存 # 座標處理 data['經度'] = data['經度'].apply(degree_conversion_decimal) data['緯度'] = data['緯度'].apply(degree_conversion_decimal) ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent([70, 140, 15, 55]) ax.stock_img() ax.scatter(data['經度'], data['緯度'], s=0.3, c='g') # shp = shaperead.Reader('ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp') # # 抽取函數 州:國家 # city_list = [country for country in countries if country.attributes['ADMIN'] == 'China'] # countries = shp.records() plt.savefig('test.png') plt.show() def gdp(shp_path): """ GDP 着色圖 :return: """ shp = shaperead.Reader(shp_path) cas = get_record(shp, 'SUBREGION', 'Central Asia') gdp = [r.attributes['GDP_MD_EST'] for r in cas] gdp_min = min(gdp) gdp_max = max(gdp) ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent([45, 90, 35, 55]) for r in cas: color = cm.Greens((r.attributes['GDP_MD_EST'] - gdp_min) / (gdp_max - gdp_min)) ax.add_geometries(r.geometry, ccrs.PlateCarree(), facecolor=color, edgecolor='black', linewidth=0.5) ax.text(r.geometry.centroid.x, r.geometry.centroid.y, r.attributes['ADMIN'], horizontalalignment='center', verticalalignment='center', transform=ccrs.Geodetic()) ax.set_xticks([45, 55, 65, 75, 85], crs=ccrs.PlateCarree()) # x座標標註 ax.set_yticks([35, 45, 55], crs=ccrs.PlateCarree()) # y 座標標註 lon_formatter = LongitudeFormatter(zero_direction_label=True) lat_formatter = LatitudeFormatter() ax.xaxis.set_major_formatter(lon_formatter) ax.yaxis.set_major_formatter(lat_formatter) plt.title('GDP TEST') plt.savefig("gdb.png") plt.show() def run_excel(): if os.path.exists("SURF_CHN_MUL_HOR_STATION.xlsx"): read_excel("SURF_CHN_MUL_HOR_STATION.xlsx") else: download_file(dataurl) read_excel("SURF_CHN_MUL_HOR_STATION.xlsx") def run_shp(): if os.path.exists("ne_10m_admin_0_countries"): gdp("ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp") else: download_file(shpurl) unzip('ne_10m_admin_0_countries.zip', "ne_10m_admin_0_countries") gdp("ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp") if __name__ == '__main__': # download_file(dataurl) # download_file(shpurl) # cas = get_record('SUBREGION', 'Central Asia') # print([r.attributes['ADMIN'] for r in cas]) # read_excel('SURF_CHN_MUL_HOR_STATION.xlsx') # gdp() run_excel() run_shp()
總結
以上就是這篇文章的全部內容了,希望本文的內容對大家的學習或者工作具有一定的參考學習價值,謝謝大家對神馬文庫的支持。如果你想了解更多相關內容請查看下面相關鏈接