import os
import sys
os.chdir('/home/park/DataScience/data/')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from matplotlib import font_manager, rc
plt.rcParams['axes.unicode_minus'] = False
path = '/usr/share/fonts/truetype/nanum/NanumGothic.ttf'
font_name = font_manager.FontProperties(fname=path).get_name()
rc('font', family=font_name)

CCTV_Seoul = pd.read_csv('../data/01. CCTV_in_Seoul.csv', encoding='utf-8')
CCTV_Seoul.head()
CCTV_Seoul.rename(columns={CCTV_Seoul.columns[0] : '구별'}, inplace=True)
pop_Seoul = pd.read_excel('01. population_in_Seoul.xls',
                          header=2,
                          usecols = 'B, D, G, J, N',
                          encoding='utf-8')

pop_Seoul.rename(columns={pop_Seoul.columns[0] : '구별',
                          pop_Seoul.columns[1] : '인구수',
                          pop_Seoul.columns[2] : '한국인',
                          pop_Seoul.columns[3] : '외국인',
                          pop_Seoul.columns[4] : '고령자'}, inplace=True)
pop_Seoul.drop([0], inplace=True)
pop_Seoul['구별'].unique()
pop_Seoul.drop([26], inplace=True)
pop_Seoul.head()

CCTV_Seoul['최근증가율'] = (CCTV_Seoul['2016년'] +  CCTV_Seoul['2015년'] + \
                            CCTV_Seoul['2014년']) / CCTV_Seoul['2013년도 이전'] * 100
CCTV_Seoul.sort_values(by='최근증가율', ascending=False).head(5)


pop_Seoul['외국인비율'] = pop_Seoul['외국인'] / pop_Seoul['인구수'] * 100
pop_Seoul['고령자비율'] = pop_Seoul['고령자'] / pop_Seoul['인구수'] * 100
pop_Seoul.head()

import platform

from matplotlib import font_manager, rc
plt.rcParams['axes.unicode_minus'] = False

if platform.system() == 'Darwin':
    rc('font', family ='AppleGothic')
elif platform.system() == 'Windows':
    path = "c:/Windows/Fonts/malgun.ttf"
    font_name = font_manager.FontProperties(fname=path).get_name()
    rc('font', family=font_name)
else:
    print("Unknown system...  sorry~~~")

data_result = pd.merge(CCTV_Seoul, pop_Seoul, on = '구별')
data_result.head()

del data_result['2013년도 이전']
del data_result['2014년']
del data_result['2015년']
del data_result['2016년']
data_result.head()

data_result.set_index('구별', inplace=True)
data_result.head()

data_result['소계'].sort_values().plot(kind='barh', grid=True, figsize=(10,10))
plt.show()

data_result['CCTV비율'] = data_result['소계'] / data_result['인구수'] * 100
data_result['CCTV비율'].sort_values().plot(kind='barh', grid=True, figsize=(10,10))
plt.show()

plt.figure(figsize=(6,6))
plt.scatter(data_result['인구수'], data_result['소계'], s=50)
plt.xlabel('인구수')
plt.ylabel('CCTV')
plt.grid()
plt.show()

fp1 = np.polyfit(data_result['인구수'], data_result['소계'], 1)
fp1

f1 = np.poly1d(fp1)
fx = np.linspace(100000, 700000, 100)

plt.figure(figsize=(6,6))
plt.scatter(data_result['인구수'], data_result['소계'], s=50)
plt.plot(fx, f1(fx), ls='dashed', lw=3, color='g')
plt.xlabel('인구수')
plt.ylabel('CCTV')
plt.grid()
plt.show()

fp1 = np.polyfit(data_result['인구수'], data_result['소계'], 1)

f1 = np.poly1d(fp1)
fx = np.linspace(100000, 700000, 100)

data_result['오차'] = np.abs(data_result['소계'] - f1(data_result['인구수']))

df_sort = data_result.sort_values(by='오차', ascending=False)
df_sort.head()

df_sort['인구수'][0]*1.02
df_sort['소계'][0]*0.98

plt.figure(figsize=(14,10))

plt.scatter(data_result['인구수'], data_result['소계'],
            c=data_result['오차'], s=50)
plt.plot(fx, f1(fx), ls='dashed', lw=3, color='g')

for n in range(10):
    plt.text(df_sort['인구수'][n]*1.000001, df_sort['소계'][n]*1.0000001, df_sort.index[n], fontsize=15)

plt.xlabel('인구수')
plt.ylabel('인구당비율')

plt.colorbar()
plt.grid()
plt.show()