europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo', 'australia':'vienna'}

print(europe.keys())
print(europe['norway'])

europe['italy'] = 'rome'    # add or update
europe['poland'] = 'warsaw'
del(europe['australia'])    # Remove

print(europe)               # 순서는 고정 안됨

print('italy' in europe)

# Dictionary of dictionaries
europe = {'spain': {'capital':'madrid', 'population':46.77},
          'france': {'capital':'paris', 'population':66.03},
          'germany': {'capital':'berlin', 'population':80.62},
          'norway': {'capital':'oslo', 'population':5.084}}

print(europe['france']['capital'])

# Create sub-dictionary data
data = {'capital':'rome', 'population':59.83}
europe['italy'] = data

print(europe)

import pandas as pd
import numpy as np

names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]

my_dict = {
    'country':names,
    'drives_right':dr,
    'cars_per_cap':cpc
}

# Build a DataFrame
cars = pd.DataFrame(my_dict)
cars.index = ['US', 'AUS', 'JAP', 'IN', 'RU', 'MOR', 'EG']
cars

cars = pd.read_csv('data/cars.csv', index_col = 0)
cars

print("\n{}".format(cars[1:4]))
print("\n{}".format(cars["country"]))      # output as Pandas Series
print("\n{}".format(cars[["country"]]))    # output as Pandas DataFrame
print("\n{}".format(cars[["country", "drives_right"]]))

# loc & iloc
print(cars.loc[['RU', 'AUS']])
print(cars.iloc[[4, 1]])

print(cars.loc['MOR', 'drives_right'])
print(cars.loc[['RU','MOR'], ['country', 'drives_right']])  # sub-DataFrame

print(cars.loc[:, 'drives_right'])      # Series
print(cars.loc[:, ['drives_right']])    # DataFrame
print(cars.loc[:, ['cars_per_cap', 'drives_right']])

# Filtering pandas dataframe
cars[cars['drives_right'] == True]

many_cars = cars["cars_per_cap"] > 500
cars[many_cars]

car = pd.read_csv('data/automobile.csv')
print(car.shape)
car.head()

car.loc[car.wheels == '4wd']

# symboling : 차량 안전등급 지수
car.loc[car.wheels == '4wd', 'symboling']

a1 = car.loc[car.wheels == '4wd', 'symboling'].mean()
a2 = car.loc[car.wheels == 'fwd', 'symboling'].mean()
print(a1);print(a2)

grouped = car.groupby('wheels')
grouped.get_group('4wd')

grouped['symboling'].mean()

print(grouped['symboling'].agg([np.mean, np.sum]))
print(grouped['symboling'].agg({'평균': np.mean, '합계': np.sum}))

# Ordered Dictionary
from collections import OrderedDict

d = OrderedDict([('평균', np.mean), ('합계', np.sum)])
d

d['평균']

grouped['symboling'].agg(OrderedDict([('평균', np.mean), ('합계', np.sum)]))

distance = [11.25, 18.0, 20.0, 10.75, 9.50]
for d in distance :
    print(d)

for index, a in enumerate(distance) :
    print("room " + str(index + 1) + " : " + str(a))

distance = [["London", 11.25],
            ["Rome", 18.0],
            ["Oslo", 20.0],
            ["Paris", 10.75],
            ["Madrid", 9.50]]

for city in distance:
    print("the " + city[0] + " : " + str(city[1]) + " km")

europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn', 'norway':'oslo', 'italy':'rome', 
          'poland':'warsaw', 'australia':'vienna'}

for key, value in europe.items():
    print("the capital of " + key.upper() + " is " + value)

height = [74, 79, 72, 77, 73, 69, 67, 71, 76]
np_height = np.array(height)

for x in np_height :                # 1D array
    print(str(x) + " inches")

people = [[74, 180], [74, 215], [72, 210], [72, 210], [73, 188], [69, 176]]
np_people = np.array(people)

for x in np_people :   # 2D array
    print(x)

for x in np.nditer(np_people) :   # 2D array
    print(x)

cars = pd.read_csv('data/cars.csv', index_col = 0)

for lab, row in cars.iterrows() :
    print(lab + " --- " + row['country'] + " : " + str(row['cars_per_cap']))

# adds COUNTRY column
for lab, row in cars.iterrows() :
    cars.loc[lab, 'COUNTRY'] = row['country'].upper()

cars

np.random.seed(9999)
print(np.random.rand())             # random float
print(np.random.randint(1, 7))      # random int (1~6 범위)

# Random Walk
all_walks = []
for i in range(1000) :
    random_walk = [0]

    for x in range(100) :
        # step에 마지막 숫자 설정
        step = random_walk[-1]

        # 주사위 던지기
        dice = np.random.randint(1,7)

        # 다음 step 결정.
        # 주사위 2 이하이면 -1. 3에서 5 사이이면 +1.
        if dice <= 2:
            step = max(0, step - 1)     # 음수값 되면 0 리턴
        elif dice <= 5:
            step += 1
        else:
            step += np.random.randint(1, 7)

        # append next_step to random_walk
        random_walk.append(step)
        
    # random_walk 결과를 전체 결과 array에 추가
    all_walks.append(random_walk)

np_all_walks = np.array(all_walks)
np_all_walks

np_aw_t = np.transpose(np_all_walks)
np_aw_t

%matplotlib inline
import matplotlib.pyplot as plt

# setting plot defatult size
%pylab inline
pylab.rcParams['figure.figsize'] = (12, 6)

plt.plot(np_aw_t)
plt.show()

ends = np_aw_t[-1]
ends[0:50]

plt.hist(ends)
plt.show()