# -*- coding:utf-8 -*-

# python for data analysis 8 Haiti data

import numpy as np

from pandas import DataFrame

import pandas as pd

from mpl_toolkits.basemap import Basemap

import matplotlib.pyplot as plt

path = 'C:/Users/dell/Desktop/pydata/datasets/haiti/Haiti.csv'

data = pd.read_csv(path)

data = data[(data.LATITUDE > 18) & (data.LATITUDE < 20) &

(data.LONGITUDE > -75) & (data.LONGITUDE < -70)

& data.CATEGORY.notnull()]

# 各个分类中又含有多个分类，需要将分类规整化

def to_cat_list(catstr):

stripped = (x.strip() for x in catstr.split(','))

return [x for x in stripped if x]

def get_all_categories(cat_serise):

cat_sets = (set(to_cat_list(x)) for x in cat_serise)

return sorted(set.union(*cat_sets))

def get_english(cat):

return code, names.strip()

# print get_english('2. Urgence Logistiques | Vital Lines')

all_cats = get_all_categories(data.CATEGORY)

english_mapping = dict(get_english(x) for x in all_cats)

def get_code(seq):

return [x.split('.')[0] for x in seq if x]

all_codes = get_code(all_cats)

code_index = pd.Index(np.unique(all_codes))

dummy_frame = DataFrame(np.zeros((len(data), len(code_index))),

index=data.index, columns=code_index)

for row, cat in zip(data.index, data.CATEGORY):

codes = get_code(to_cat_list(cat))

dummy_frame.ix[row, codes] = 1

data = data.join(dummy_frame.add_prefix('category_')) # 与原数据表建立对应关系

def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25,

return m

# 让返回的Basemap对象将坐标转换到画布上

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))

fig.subplots_adjust(hspace=0.05, wspace=0.05)

to_plot = ['2a', '1', '3c', '7a']

lllat=17.25; urlat=20.25; lllon=-75; urlon=-71

for code, ax in zip(to_plot, axes.flat):

m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,

lllon=lllon, urlon=urlon)

cat_data = data[data['category_%s' % code] == 1]

x, y = m(cat_data.LONGITUDE.values, cat_data.LATITUDE.values) # 添加values才能传递给Cython函数

m.plot(x, y, 'k.', alpha=0.5)

ax.set_title('%s: %s' % (code, english_mapping[code]))

plt.show()