Python关于excel和shp的使用在matplotlib_Python

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

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									#!/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 integer

									def 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 result

									def 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()