Python关于excel和shp的使用在matplotlib

Python  /  管理员 发布于 7年前   323

关于excel和shp的使用在matplotlib

• 使用pandas 对excel进行简单操作
• 使用cartopy 读取shpfile 展示到matplotlib中
• 利用shpfile文件中的一些字段进行一些着色处理

#!/usr/bin/env python# -*- coding: utf-8 -*-# @File : map02.py# @Author: huifer# @Date : 2018/6/28import foliumimport pandas as pdimport requestsimport matplotlib.pyplot as pltimport cartopy.crs as ccrsimport zipfileimport cartopy.io.shapereader as shapereadfrom matplotlib import cmfrom cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatterimport osdataurl = "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):  """  根据url下载文件  :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):  """  度分转换成十进制  :param x: float  :return: integer float  """  integer = int(x)  integer = integer + (x - integer) * 1.66666667  return integerdef unzip(zip_path, out_path):  """  解压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 resultdef read_excel(path):  data = pd.read_excel(path)  # print(data.head(10)) # 获取几行  # print(data.ix[data['省份']=='浙江',:].shape[0]) # 计数工具  # print(data.sort_values('观测场拔海高度（米）',ascending=False).head(10))# 根据值排序  # 判断经纬度是什么格式(度分 、 十进制) 判断依据 %0.2f 是否大于60  # print(data['经度'].apply(lambda x:x-int(x)).sort_values(ascending=False).head()) # 结果判断为度分保存  # 坐标处理  data['经度'] = data['经度'].apply(degree_conversion_decimal)  data['纬度'] = data['纬度'].apply(degree_conversion_decimal)  ax = plt.axes(projection=ccrs.PlateCarree())  ax.set_extent([70, 140, 15, 55])  ax.stock_img()  ax.scatter(data['经度'], data['纬度'], s=0.3, c='g')  # shp = shaperead.Reader('ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp')  # # 抽取函数 州：国家  # 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 着色图  :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坐标标注  ax.set_yticks([35, 45, 55], crs=ccrs.PlateCarree()) # y 坐标标注  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()

总结

以上就是这篇文章的全部内容了，希望本文的内容对大家的学习或者工作具有一定的参考学习价值，谢谢大家的支持。如果你想了解更多相关内容请查看下面相关链接