전문지식 함양/TIL

[프로그래머스 겨울방학 인공지능 과정] 1주차 실습과제

샤프펜슬s 2022. 1. 9. 15:07

Week1_Mission

1주차 주말과제¶

Core Mission¶

비트코인 자료가 주어졌을 때 2016년 6월부터 2017년 6월 기간의 5-MA(Moving Average) 비트코인 가격 그래프를 그려주세요.

조건1. 선의 색깔은 노란색(#f2a900) 으로 해야합니다.
조건2. x-axis label과 y-axis label은 적절한 텍스트로 추가되어야 합니다.
조건3. 이동평균의 기준은 Open Column으로 계산합니다.
조건4. 이외에도 그래프를 알아보기 쉽게 하기 위한 정보(Text, Facet 등)을 추가하는 것도 좋습니다.

In [56]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline

In [57]:

# 5-MA : 5일 간 추이의 평균값을 출력한 것
# 데이터 호출
bitcoin = pd.read_csv('D:/workspaces/WinterStudy2022/datasetBitcoin/BitCoin.csv')

In [58]:

# 데이터 확인
bitcoin.head()

Out[58]:

	Unnamed: 0	Date	Open	High	Low	Close	Volume	Market Cap
0	0	2017-09-22	3628.02	3758.27	3553.53	3630.70	1.194830e+09	60152300000
1	1	2017-09-21	3901.47	3916.42	3613.63	3631.04	1.411480e+09	64677600000
2	2	2017-09-20	3916.36	4031.39	3857.73	3905.95	1.213830e+09	64918500000
3	3	2017-09-19	4073.79	4094.07	3868.87	3924.97	1.563980e+09	67520300000
4	4	2017-09-18	3591.09	4079.23	3591.09	4065.20	1.943210e+09	59514100000

In [59]:

# 데이터전처리
bitcoin.isnull()

bitcoin = bitcoin[(bitcoin['Date'] >= '2016-06-01') & (bitcoin['Date'] <= '2017-06-30')]

# sort_values를 이용한 오름차순 정렬(기준 : Date)
bitcoin_sorted = bitcoin.sort_values(by=['Date'], axis=0)
print(bitcoin_sorted.head())

print(bitcoin_sorted.tail())

     Unnamed: 0        Date    Open    High     Low   Close      Volume  \
625         625  2016-06-01  431.86  431.86  426.34  429.11  34042500.0   
594         594  2016-06-02  386.59  386.63  372.39  376.52  49249300.0   
565         565  2016-06-03  400.52  411.91  395.78  407.71  91212500.0   
534         534  2016-06-04  424.28  424.53  422.73  423.41  59091000.0   
504         504  2016-06-05  447.94  461.38  447.07  459.60  72796800.0   

     Market Cap  
625  6498830000  
594  5865740000  
565  6121400000  
534  6533260000  
504  6947800000  
    Unnamed: 0        Date     Open     High      Low    Close        Volume  \
88          88  2017-06-26  2590.57  2615.25  2376.29  2478.45  1.663280e+09   
87          87  2017-06-27  2478.45  2552.45  2332.99  2552.45  1.489790e+09   
86          86  2017-06-28  2553.03  2603.98  2484.42  2574.79  1.183870e+09   
85          85  2017-06-29  2567.56  2588.83  2510.48  2539.32  9.499790e+08   
84          84  2017-06-30  2539.24  2559.25  2478.43  2480.84  8.602730e+08   

     Market Cap  
88  42514000000  
87  40677900000  
86  41906700000  
85  42150300000  
84  41689100000

In [60]:

# bitcoin 원본파일 확인 결과 정상.
# 일자로만 계산한다면 394가 나와야 함.
len(bitcoin_sorted)

Out[60]:

In [61]:

# x축과 y축을 조건에 맞게 조절
# x축 : 범위를 이동하면서 5일마다 평균을 산출.
x = bitcoin_sorted['Date']

# x축의 index값을 재부여하기 위하여 신규 list 생성
# Series 데이터 가공에 유리하도록 만들기 위함.
bitcoin_index_list = []
for i in range(len(x)):
    bitcoin_index_list.append(i)
    
bitcoin_index_list

Out[61]:

In [62]:

# 0부터 순차적으로 부여된 index_list를 Series_x에 삽입
x.index = bitcoin_index_list
x

for i in range(len(x)):
    print(x[i])

In [63]:

# 참조된 데이터값이 5개씩 참고되어야 하지만
# 1 ~ 5, 2 ~ 6과 같이 범위가 유지된 채로 움직여야 한다.
# 그러기 위해서는 5개씩 참조가 잘 이루어지고 있는지를 확인해야 한다.
# temp_list = 5개씩 데이터가 참조되는지를 확인하는 리스트
temp_list = []

# new_x_list = 새로운 x축을 담당할 리스트
new_x_list = []
for i in range(len(x)):
    # temp_list에 x[i]원소를 넣는다.
    temp_list.append(x[i])
    # 만약 참조된 데이터가 5개라면
    if len(temp_list) == 5:
        # "a ~ b"라는 새로운 원소를 만든 후
        temp_element = "{0} ~ {1}".format(temp_list[0], temp_list[4])
        #  new_x_list에 추가한다.
        new_x_list.append(temp_element)
        del temp_list[0]
        
print(new_x_list)
print(len(new_x_list))

['2016-06-01 ~ 2016-06-05', '2016-06-02 ~ 2016-06-06', '2016-06-03 ~ 2016-06-07', '2016-06-04 ~ 2016-06-08', '2016-06-05 ~ 2016-06-09', '2016-06-06 ~ 2016-06-10', '2016-06-07 ~ 2016-06-11', '2016-06-08 ~ 2016-06-12', '2016-06-09 ~ 2016-06-13', '2016-06-10 ~ 2016-06-14', '2016-06-11 ~ 2016-06-15', '2016-06-12 ~ 2016-06-16', '2016-06-13 ~ 2016-06-17', '2016-06-14 ~ 2016-06-18', '2016-06-15 ~ 2016-06-19', '2016-06-16 ~ 2016-06-20', '2016-06-17 ~ 2016-06-21', '2016-06-18 ~ 2016-06-22', '2016-06-19 ~ 2016-06-23', '2016-06-20 ~ 2016-06-24', '2016-06-21 ~ 2016-06-25', '2016-06-22 ~ 2016-06-26', '2016-06-23 ~ 2016-06-27', '2016-06-24 ~ 2016-06-28', '2016-06-25 ~ 2016-06-29', '2016-06-26 ~ 2016-06-30', '2016-06-27 ~ 2016-07-01', '2016-06-28 ~ 2016-07-02', '2016-06-29 ~ 2016-07-03', '2016-06-30 ~ 2016-07-04', '2016-07-01 ~ 2016-07-05', '2016-07-02 ~ 2016-07-06', '2016-07-03 ~ 2016-07-07', '2016-07-04 ~ 2016-07-08', '2016-07-05 ~ 2016-07-09', '2016-07-06 ~ 2016-07-10', '2016-07-07 ~ 2016-07-11', '2016-07-08 ~ 2016-07-12', '2016-07-09 ~ 2016-07-13', '2016-07-10 ~ 2016-07-14', '2016-07-11 ~ 2016-07-15', '2016-07-12 ~ 2016-07-16', '2016-07-13 ~ 2016-07-17', '2016-07-14 ~ 2016-07-18', '2016-07-15 ~ 2016-07-19', '2016-07-16 ~ 2016-07-20', '2016-07-17 ~ 2016-07-21', '2016-07-18 ~ 2016-07-22', '2016-07-19 ~ 2016-07-23', '2016-07-20 ~ 2016-07-24', '2016-07-21 ~ 2016-07-25', '2016-07-22 ~ 2016-07-26', '2016-07-23 ~ 2016-07-27', '2016-07-24 ~ 2016-07-28', '2016-07-25 ~ 2016-07-29', '2016-07-26 ~ 2016-07-30', '2016-07-27 ~ 2016-07-31', '2016-07-28 ~ 2016-08-01', '2016-07-29 ~ 2016-08-02', '2016-07-30 ~ 2016-08-03', '2016-07-31 ~ 2016-08-04', '2016-08-01 ~ 2016-08-05', '2016-08-02 ~ 2016-08-06', '2016-08-03 ~ 2016-08-07', '2016-08-04 ~ 2016-08-08', '2016-08-05 ~ 2016-08-09', '2016-08-06 ~ 2016-08-10', '2016-08-07 ~ 2016-08-11', '2016-08-08 ~ 2016-08-12', '2016-08-09 ~ 2016-08-13', '2016-08-10 ~ 2016-08-14', '2016-08-11 ~ 2016-08-15', '2016-08-12 ~ 2016-08-16', '2016-08-13 ~ 2016-08-17', '2016-08-14 ~ 2016-08-18', '2016-08-15 ~ 2016-08-19', '2016-08-16 ~ 2016-08-20', '2016-08-17 ~ 2016-08-21', '2016-08-18 ~ 2016-08-22', '2016-08-19 ~ 2016-08-23', '2016-08-20 ~ 2016-08-24', '2016-08-21 ~ 2016-08-25', '2016-08-22 ~ 2016-08-26', '2016-08-23 ~ 2016-08-27', '2016-08-24 ~ 2016-08-28', '2016-08-25 ~ 2016-08-29', '2016-08-26 ~ 2016-08-30', '2016-08-27 ~ 2016-08-31', '2016-08-28 ~ 2016-09-01', '2016-08-29 ~ 2016-09-02', '2016-08-30 ~ 2016-09-03', '2016-08-31 ~ 2016-09-04', '2016-09-01 ~ 2016-09-05', '2016-09-02 ~ 2016-09-06', '2016-09-03 ~ 2016-09-07', '2016-09-04 ~ 2016-09-08', '2016-09-05 ~ 2016-09-09', '2016-09-06 ~ 2016-09-10', '2016-09-07 ~ 2016-09-11', '2016-09-08 ~ 2016-09-12', '2016-09-09 ~ 2016-09-13', '2016-09-10 ~ 2016-09-14', '2016-09-11 ~ 2016-09-15', '2016-09-12 ~ 2016-09-16', '2016-09-13 ~ 2016-09-17', '2016-09-14 ~ 2016-09-18', '2016-09-15 ~ 2016-09-19', '2016-09-16 ~ 2016-09-20', '2016-09-17 ~ 2016-09-21', '2016-09-18 ~ 2016-09-22', '2016-09-19 ~ 2016-09-23', '2016-09-20 ~ 2016-09-24', '2016-09-21 ~ 2016-09-25', '2016-09-22 ~ 2016-09-26', '2016-09-23 ~ 2016-09-27', '2016-09-24 ~ 2016-09-28', '2016-09-25 ~ 2016-09-29', '2016-09-26 ~ 2016-09-30', '2016-09-27 ~ 2016-10-01', '2016-09-28 ~ 2016-10-02', '2016-09-29 ~ 2016-10-03', '2016-09-30 ~ 2016-10-04', '2016-10-01 ~ 2016-10-05', '2016-10-02 ~ 2016-10-06', '2016-10-03 ~ 2016-10-07', '2016-10-04 ~ 2016-10-08', '2016-10-05 ~ 2016-10-09', '2016-10-06 ~ 2016-10-10', '2016-10-07 ~ 2016-10-11', '2016-10-08 ~ 2016-10-12', '2016-10-09 ~ 2016-10-13', '2016-10-10 ~ 2016-10-14', '2016-10-11 ~ 2016-10-15', '2016-10-12 ~ 2016-10-16', '2016-10-13 ~ 2016-10-17', '2016-10-14 ~ 2016-10-18', '2016-10-15 ~ 2016-10-19', '2016-10-16 ~ 2016-10-20', '2016-10-17 ~ 2016-10-21', '2016-10-18 ~ 2016-10-22', '2016-10-19 ~ 2016-10-23', '2016-10-20 ~ 2016-10-24', '2016-10-21 ~ 2016-10-25', '2016-10-22 ~ 2016-10-26', '2016-10-23 ~ 2016-10-27', '2016-10-24 ~ 2016-10-28', '2016-10-25 ~ 2016-10-29', '2016-10-26 ~ 2016-10-30', '2016-10-27 ~ 2016-10-31', '2016-10-28 ~ 2016-11-01', '2016-10-29 ~ 2016-11-02', '2016-10-30 ~ 2016-11-03', '2016-10-31 ~ 2016-11-04', '2016-11-01 ~ 2016-11-05', '2016-11-02 ~ 2016-11-06', '2016-11-03 ~ 2016-11-07', '2016-11-04 ~ 2016-11-08', '2016-11-05 ~ 2016-11-09', '2016-11-06 ~ 2016-11-10', '2016-11-07 ~ 2016-11-11', '2016-11-08 ~ 2016-11-12', '2016-11-09 ~ 2016-11-13', '2016-11-10 ~ 2016-11-14', '2016-11-11 ~ 2016-11-15', '2016-11-12 ~ 2016-11-16', '2016-11-13 ~ 2016-11-17', '2016-11-14 ~ 2016-11-18', '2016-11-15 ~ 2016-11-19', '2016-11-16 ~ 2016-11-20', '2016-11-17 ~ 2016-11-21', '2016-11-18 ~ 2016-11-22', '2016-11-19 ~ 2016-11-23', '2016-11-20 ~ 2016-11-24', '2016-11-21 ~ 2016-11-25', '2016-11-22 ~ 2016-11-26', '2016-11-23 ~ 2016-11-27', '2016-11-24 ~ 2016-11-28', '2016-11-25 ~ 2016-11-29', '2016-11-26 ~ 2016-11-30', '2016-11-27 ~ 2016-12-01', '2016-11-28 ~ 2016-12-02', '2016-11-29 ~ 2016-12-03', '2016-11-30 ~ 2016-12-04', '2016-12-01 ~ 2016-12-05', '2016-12-02 ~ 2016-12-06', '2016-12-03 ~ 2016-12-07', '2016-12-04 ~ 2016-12-08', '2016-12-05 ~ 2016-12-09', '2016-12-06 ~ 2016-12-10', '2016-12-07 ~ 2016-12-11', '2016-12-08 ~ 2016-12-12', '2016-12-09 ~ 2016-12-13', '2016-12-10 ~ 2016-12-14', '2016-12-11 ~ 2016-12-15', '2016-12-12 ~ 2016-12-16', '2016-12-13 ~ 2016-12-17', '2016-12-14 ~ 2016-12-18', '2016-12-15 ~ 2016-12-19', '2016-12-16 ~ 2016-12-20', '2016-12-17 ~ 2016-12-21', '2016-12-18 ~ 2016-12-22', '2016-12-19 ~ 2016-12-23', '2016-12-20 ~ 2016-12-24', '2016-12-21 ~ 2016-12-25', '2016-12-22 ~ 2016-12-26', '2016-12-23 ~ 2016-12-27', '2016-12-24 ~ 2016-12-28', '2016-12-25 ~ 2016-12-29', '2016-12-26 ~ 2016-12-30', '2016-12-27 ~ 2016-12-31', '2016-12-28 ~ 2017-01-01', '2016-12-29 ~ 2017-01-02', '2016-12-30 ~ 2017-01-03', '2016-12-31 ~ 2017-01-04', '2017-01-01 ~ 2017-01-05', '2017-01-02 ~ 2017-01-06', '2017-01-03 ~ 2017-01-07', '2017-01-04 ~ 2017-01-08', '2017-01-05 ~ 2017-01-09', '2017-01-06 ~ 2017-01-13', '2017-01-07 ~ 2017-01-14', '2017-01-08 ~ 2017-01-15', '2017-01-09 ~ 2017-01-16', '2017-01-13 ~ 2017-01-17', '2017-01-14 ~ 2017-01-18', '2017-01-15 ~ 2017-01-19', '2017-01-16 ~ 2017-01-20', '2017-01-17 ~ 2017-01-21', '2017-01-18 ~ 2017-01-22', '2017-01-19 ~ 2017-01-23', '2017-01-20 ~ 2017-01-24', '2017-01-21 ~ 2017-01-25', '2017-01-22 ~ 2017-01-26', '2017-01-23 ~ 2017-01-27', '2017-01-24 ~ 2017-01-28', '2017-01-25 ~ 2017-01-29', '2017-01-26 ~ 2017-01-30', '2017-01-27 ~ 2017-01-31', '2017-01-28 ~ 2017-02-01', '2017-01-29 ~ 2017-02-02', '2017-01-30 ~ 2017-02-03', '2017-01-31 ~ 2017-02-04', '2017-02-01 ~ 2017-02-05', '2017-02-02 ~ 2017-02-06', '2017-02-03 ~ 2017-02-07', '2017-02-04 ~ 2017-02-08', '2017-02-05 ~ 2017-02-09', '2017-02-06 ~ 2017-02-13', '2017-02-07 ~ 2017-02-14', '2017-02-08 ~ 2017-02-15', '2017-02-09 ~ 2017-02-16', '2017-02-13 ~ 2017-02-17', '2017-02-14 ~ 2017-02-18', '2017-02-15 ~ 2017-02-19', '2017-02-16 ~ 2017-02-20', '2017-02-17 ~ 2017-02-21', '2017-02-18 ~ 2017-02-22', '2017-02-19 ~ 2017-02-23', '2017-02-20 ~ 2017-02-24', '2017-02-21 ~ 2017-02-25', '2017-02-22 ~ 2017-02-26', '2017-02-23 ~ 2017-02-27', '2017-02-24 ~ 2017-02-28', '2017-02-25 ~ 2017-03-01', '2017-02-26 ~ 2017-03-02', '2017-02-27 ~ 2017-03-03', '2017-02-28 ~ 2017-03-04', '2017-03-01 ~ 2017-03-05', '2017-03-02 ~ 2017-03-06', '2017-03-03 ~ 2017-03-07', '2017-03-04 ~ 2017-03-08', '2017-03-05 ~ 2017-03-09', '2017-03-06 ~ 2017-03-13', '2017-03-07 ~ 2017-03-14', '2017-03-08 ~ 2017-03-15', '2017-03-09 ~ 2017-03-16', '2017-03-13 ~ 2017-03-17', '2017-03-14 ~ 2017-03-18', '2017-03-15 ~ 2017-03-19', '2017-03-16 ~ 2017-03-20', '2017-03-17 ~ 2017-03-21', '2017-03-18 ~ 2017-03-22', '2017-03-19 ~ 2017-03-23', '2017-03-20 ~ 2017-03-24', '2017-03-21 ~ 2017-03-25', '2017-03-22 ~ 2017-03-26', '2017-03-23 ~ 2017-03-27', '2017-03-24 ~ 2017-03-28', '2017-03-25 ~ 2017-03-29', '2017-03-26 ~ 2017-03-30', '2017-03-27 ~ 2017-03-31', '2017-03-28 ~ 2017-04-01', '2017-03-29 ~ 2017-04-02', '2017-03-30 ~ 2017-04-03', '2017-03-31 ~ 2017-04-04', '2017-04-01 ~ 2017-04-05', '2017-04-02 ~ 2017-04-06', '2017-04-03 ~ 2017-04-07', '2017-04-04 ~ 2017-04-08', '2017-04-05 ~ 2017-04-09', '2017-04-06 ~ 2017-04-13', '2017-04-07 ~ 2017-04-14', '2017-04-08 ~ 2017-04-15', '2017-04-09 ~ 2017-04-16', '2017-04-13 ~ 2017-04-17', '2017-04-14 ~ 2017-04-18', '2017-04-15 ~ 2017-04-19', '2017-04-16 ~ 2017-04-20', '2017-04-17 ~ 2017-04-21', '2017-04-18 ~ 2017-04-22', '2017-04-19 ~ 2017-04-23', '2017-04-20 ~ 2017-04-24', '2017-04-21 ~ 2017-04-25', '2017-04-22 ~ 2017-04-26', '2017-04-23 ~ 2017-04-27', '2017-04-24 ~ 2017-04-28', '2017-04-25 ~ 2017-04-29', '2017-04-26 ~ 2017-04-30', '2017-04-27 ~ 2017-05-01', '2017-04-28 ~ 2017-05-02', '2017-04-29 ~ 2017-05-03', '2017-04-30 ~ 2017-05-04', '2017-05-01 ~ 2017-05-05', '2017-05-02 ~ 2017-05-06', '2017-05-03 ~ 2017-05-07', '2017-05-04 ~ 2017-05-08', '2017-05-05 ~ 2017-05-09', '2017-05-06 ~ 2017-05-13', '2017-05-07 ~ 2017-05-14', '2017-05-08 ~ 2017-05-15', '2017-05-09 ~ 2017-05-16', '2017-05-13 ~ 2017-05-17', '2017-05-14 ~ 2017-05-18', '2017-05-15 ~ 2017-05-19', '2017-05-16 ~ 2017-05-20', '2017-05-17 ~ 2017-05-21', '2017-05-18 ~ 2017-05-22', '2017-05-19 ~ 2017-05-23', '2017-05-20 ~ 2017-05-24', '2017-05-21 ~ 2017-05-25', '2017-05-22 ~ 2017-05-26', '2017-05-23 ~ 2017-05-27', '2017-05-24 ~ 2017-05-28', '2017-05-25 ~ 2017-05-29', '2017-05-26 ~ 2017-05-30', '2017-05-27 ~ 2017-05-31', '2017-05-28 ~ 2017-06-01', '2017-05-29 ~ 2017-06-02', '2017-05-30 ~ 2017-06-03', '2017-05-31 ~ 2017-06-04', '2017-06-01 ~ 2017-06-05', '2017-06-02 ~ 2017-06-06', '2017-06-03 ~ 2017-06-07', '2017-06-04 ~ 2017-06-08', '2017-06-05 ~ 2017-06-09', '2017-06-06 ~ 2017-06-13', '2017-06-07 ~ 2017-06-14', '2017-06-08 ~ 2017-06-15', '2017-06-09 ~ 2017-06-16', '2017-06-13 ~ 2017-06-17', '2017-06-14 ~ 2017-06-18', '2017-06-15 ~ 2017-06-19', '2017-06-16 ~ 2017-06-20', '2017-06-17 ~ 2017-06-21', '2017-06-18 ~ 2017-06-22', '2017-06-19 ~ 2017-06-23', '2017-06-20 ~ 2017-06-24', '2017-06-21 ~ 2017-06-25', '2017-06-22 ~ 2017-06-26', '2017-06-23 ~ 2017-06-27', '2017-06-24 ~ 2017-06-28', '2017-06-25 ~ 2017-06-29', '2017-06-26 ~ 2017-06-30']
373

In [64]:

# Y값 가공 시작
y = bitcoin_sorted['Open']
y

Out[64]:

625     431.86
594     386.59
565     400.52
534     424.28
504     447.94
        ...   
88     2590.57
87     2478.45
86     2553.03
85     2567.56
84     2539.24
Name: Open, Length: 377, dtype: float64

In [65]:

# 역시 index가 제대로 부여되지 않았으므로
# index를 재부여하여 차후 데이터 가공을 쉽도록 만든다.
y.index = bitcoin_index_list
y

Out[65]:

0       431.86
1       386.59
2       400.52
3       424.28
4       447.94
        ...   
372    2590.57
373    2478.45
374    2553.03
375    2567.56
376    2539.24
Name: Open, Length: 377, dtype: float64

In [66]:

# 1 ~ 5 일차의 Open 값 평균을 내고, 2 ~ 6일차의 Open 값 평균을 낸다.
# 역시 temp_list와 new_y_list를 통해 해결한다.
# temp_list : index값이 5개씩 참조가 되었는지를 확인하는 리스트
# new_y_list : 5개가 확인된 리스트의 평균을 추가한 리스트
temp_list = []
new_y_list = []

for i in range(len(y)):
    # temp_list에 y값을 추가한다.
    temp_list.append(y[i])
    # 만약 temp_list에 참조된 값 개수가 5개라면?
    if len(temp_list) == 5:
        # temp_list 내부의 값을 평균낸 후 temp_Value에 저장한 
        temp_value = sum(temp_list)/len(temp_list)
        new_y_list.append(temp_value)
        del temp_list[0]
        
print(new_y_list)
print(len(new_y_list))

[418.238, 446.78599999999994, 503.552, 538.4540000000001, 574.9, 607.8059999999999, 633.6479999999999, 651.308, 670.6320000000001, 690.23, 704.872, 703.414, 705.368, 720.79, 731.228, 746.8779999999999, 754.75, 734.234, 703.97, 677.7479999999999, 658.018, 644.028, 636.716, 648.848, 652.5559999999999, 647.618, 600.434, 549.866, 499.79799999999994, 455.698, 419.508, 450.5959999999999, 510.912, 546.914, 584.3040000000001, 614.898, 639.9560000000001, 657.1800000000001, 672.586, 681.0559999999999, 690.368, 680.976, 660.5319999999999, 663.5339999999999, 667.4979999999999, 670.2259999999999, 670.516, 671.1020000000001, 665.286, 661.8199999999999, 659.51, 657.31, 654.652, 655.4040000000001, 655.3439999999999, 654.494, 654.6679999999999, 615.85, 560.3039999999999, 512.174, 465.354, 426.02, 449.946, 502.73199999999997, 538.3879999999999, 576.732, 608.514, 633.6980000000001, 659.176, 658.1, 652.2920000000001, 642.9220000000001, 615.7520000000001, 577.6880000000001, 574.958, 572.708, 573.826, 576.766, 577.476, 580.088, 581.9019999999999, 582.722, 581.8839999999999, 581.512, 578.124, 576.2560000000001, 575.0420000000001, 575.0100000000001, 549.796, 510.514, 478.47799999999995, 447.81800000000004, 423.942, 449.70599999999996, 508.29800000000006, 543.728, 584.836, 617.028, 642.554, 663.266, 666.662, 663.1600000000001, 661.4440000000001, 640.9280000000001, 608.384, 608.0360000000001, 608.242, 607.9739999999999, 607.242, 605.2540000000001, 603.2379999999999, 601.856, 600.5559999999999, 600.0, 602.148, 604.156, 604.568, 605.162, 574.6479999999999, 528.2740000000001, 489.974, 452.88800000000003, 423.84799999999996, 449.36800000000005, 504.258, 538.8399999999999, 579.508, 610.768, 640.168, 664.652, 674.3280000000001, 677.144, 681.8420000000001, 665.0899999999999, 638.85, 639.5260000000001, 639.75, 637.8199999999999, 636.17, 634.434, 638.0759999999999, 641.882, 646.55, 651.92, 660.934, 667.01, 673.636, 685.66, 694.6519999999999, 648.7499999999999, 587.5719999999999, 532.9620000000001, 474.512, 424.15600000000006, 450.15, 503.424, 538.4, 578.71, 612.3860000000001, 639.7620000000001, 665.016, 687.632, 703.348, 720.658, 719.7819999999999, 713.808, 720.9100000000001, 730.876, 740.094, 744.1120000000001, 743.0640000000001, 745.2700000000001, 743.8279999999999, 741.5400000000001, 743.5899999999999, 742.7500000000001, 738.8979999999999, 737.24, 736.408, 677.742, 606.592, 544.418, 481.7200000000001, 424.954, 457.254, 510.9719999999999, 544.4100000000001, 581.242, 618.926, 640.3399999999999, 664.692, 703.062, 737.662, 765.5020000000001, 777.9440000000001, 780.97, 783.042, 785.1800000000001, 787.6160000000001, 791.952, 801.7539999999999, 816.53, 842.8279999999999, 864.308, 883.5600000000001, 898.394, 912.382, 922.972, 937.548, 950.2940000000001, 961.356, 968.5779999999999, 1009.5599999999998, 1029.396, 1106.93, 1371.864, 1676.196, 2014.4480000000003, 2740.4579999999996, 2631.5460000000003, 2338.908, 2004.016, 1594.112, 819.95, 841.076, 853.716, 869.722, 884.476, 902.7280000000001, 905.9540000000001, 910.4219999999999, 909.1719999999999, 910.54, 909.7699999999999, 908.632, 910.9100000000001, 916.5559999999999, 920.2700000000001, 936.3220000000001, 950.36, 1010.882, 1042.974, 1142.9879999999998, 1424.0700000000002, 1713.35, 2013.8400000000001, 2778.002, 2693.5719999999997, 2411.114, 2125.076, 1781.1799999999998, 1006.1200000000001, 1016.186, 1028.79, 1037.286, 1051.612, 1069.3480000000002, 1082.9599999999998, 1106.5500000000002, 1130.8939999999998, 1143.8919999999998, 1153.6879999999999, 1166.3780000000002, 1136.1560000000002, 1104.3660000000002, 1125.654, 1113.4879999999998, 1168.1, 1462.524, 1759.944, 2051.714, 2748.178, 2701.7780000000002, 2449.466, 2197.786, 1906.14, 1225.118, 1200.7, 1149.614, 1109.03, 1069.836, 1057.934, 1048.0059999999999, 1060.35, 1040.21, 1023.9399999999999, 994.22, 993.126, 994.652, 1015.7860000000001, 1026.3120000000001, 1040.782, 1038.132, 1084.402, 1105.0639999999999, 1197.88, 1498.558, 1804.4919999999997, 2110.392, 2799.614, 2741.674, 2466.1820000000002, 2187.442, 1860.578, 1178.9019999999998, 1177.452, 1185.8120000000001, 1194.568, 1205.9299999999998, 1213.822, 1221.452, 1220.952, 1228.826, 1236.1399999999999, 1247.974, 1265.138, 1286.7800000000002, 1301.0639999999999, 1279.212, 1231.5400000000002, 1218.85, 1182.1100000000001, 1225.91, 1497.0439999999999, 1808.914, 2137.582, 2756.4120000000003, 2792.862, 2650.554, 2491.6680000000006, 2260.482, 1760.17, 1779.286, 1798.5880000000002, 1833.748, 1898.8139999999999, 1962.1060000000002, 2036.6779999999999, 2121.478, 2213.878, 2264.65, 2295.266, 2267.77, 2235.382, 2197.206, 2170.4660000000003, 1934.0600000000002, 1728.9239999999998, 1550.5320000000002, 1324.622, 1198.5459999999998, 1533.866, 1849.806, 2247.834, 2897.9900000000002, 3122.8100000000004, 3128.018, 3106.3140000000003, 2948.706, 2576.19, 2571.0780000000004, 2537.5080000000003, 2555.844, 2603.8160000000003, 2639.2200000000003, 2649.6180000000004, 2687.5160000000005, 2690.714, 2666.942, 2624.4260000000004, 2593.5640000000003, 2559.372, 2545.77]
373

In [67]:

len(new_x_list) == len(new_y_list)

Out[67]:

True

In [68]:

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

# 제목 설정
plt.title("BitCoin Price(5-MA, Open)", fontsize = 15)

# x, y축 이름 설정
plt.xlabel("X = Date")
plt.ylabel("Y = Price")

# y축 눈금 설정
plt.yticks([i for i in range(0, 4000, 250)])
plt.xticks([i for i in range(0, len(new_x_list), 183)])

# 그래프 격자
plt.grid()


# 범례
plt.plot(new_x_list, new_y_list, color = '#f2a900', label = "BitCoin")
plt.legend()

plt.show()

Bonus Mission¶

비트코인 데이터와 더불어 이더리움 데이터도 주어졌을 때, 2016.6 ~ 2017.6 기간에 해당하는 비트코인의 5-MA 그래프와 이더리움의 5-MA 그래프를 동시에 그리시오.

조건1. 선의 색깔은 비트코인 : 노란색(#f2a900), 이더리움 : 검은색(#3c3c3d) 으로 한다
조건2. x-axis label과 y-axis label은 적절한 텍스트로 추가하라.
조건3. 이동평균의 기준은 Open Column으로 계산하라.
이외에도 그래프를 알아보기 쉽게 하기 위한 정보(Text, Facet 등)을 추가하라 (선택)

In [69]:

# .rolling을 활용한 이동평균 산출
bitcoin_sorted_open = bitcoin_sorted['Open']

bitcoin_sorted_MA = bitcoin_sorted_open.rolling(window = 5).mean()
print(bitcoin_sorted_MA)

bitcoin_index_list = [i for i in range(len(bitcoin_sorted_MA))]
bitcoin_sorted.index = bitcoin_index_list

print(bitcoin_sorted_open)

0           NaN
1           NaN
2           NaN
3           NaN
4       418.238
         ...   
372    2666.942
373    2624.426
374    2593.564
375    2559.372
376    2545.770
Name: Open, Length: 377, dtype: float64
0       431.86
1       386.59
2       400.52
3       424.28
4       447.94
        ...   
372    2590.57
373    2478.45
374    2553.03
375    2567.56
376    2539.24
Name: Open, Length: 377, dtype: float64

In [70]:

# 이더리움 데이터 호출 및 가공
ethereum = pd.read_csv('D:/workspaces/WinterStudy2022/datasetBitcoin/ETH_day.csv')
ethereum.head()

Out[70]:

	Date	Symbol	Open	High	Low	Close	Volume ETH	Volume USD
0	2020-04-15	ETHUSD	158.61	158.61	158.61	158.61	0.00	0.00
1	2020-04-14	ETHUSD	156.97	162.15	155.74	158.61	18061.58	2872210.44
2	2020-04-13	ETHUSD	158.56	159.51	150.12	156.97	15698.32	2416728.28
3	2020-04-12	ETHUSD	158.66	165.37	155.21	158.56	12877.33	2082804.05
4	2020-04-11	ETHUSD	158.26	161.49	154.25	158.66	13761.72	2172914.57

In [71]:

# 2016년 6월 1일부터 2017년 6월 30일 외 자료는 모두 삭제한다.
ethereum = ethereum[(ethereum['Date'] >= '2016-06-01') & (ethereum['Date'] <= '2017-06-30')]
ethereum_sorted = ethereum.sort_values(by = ['Date'], axis = 0)

ethereum_sorted.head()

Out[71]:

	Date	Symbol	Open	High	Low	Close	Volume ETH	Volume USD
1414	2016-06-01	ETHUSD	14.18	14.35	13.54	13.95	6390.06	89027.81
1413	2016-06-02	ETHUSD	13.95	14.13	13.82	13.90	942.85	13180.38
1412	2016-06-03	ETHUSD	13.90	16.05	13.50	14.02	11426.53	157342.19
1411	2016-06-04	ETHUSD	14.02	14.16	13.64	13.86	4330.50	59977.39
1410	2016-06-05	ETHUSD	13.86	13.92	13.70	13.91	787.49	10869.67

In [72]:

ethereum_sorted.tail()

Out[72]:

	Date	Symbol	Open	High	Low	Close	Volume ETH	Volume USD
1024	2017-06-26	ETHUSD	278.73	299.90	210.00	253.00	249076.24	60950252.99
1023	2017-06-27	ETHUSD	253.00	288.17	210.02	286.21	173179.40	42085473.60
1022	2017-06-28	ETHUSD	286.21	324.95	260.49	317.59	184030.56	54573461.77
1021	2017-06-29	ETHUSD	317.59	322.84	285.23	293.80	152271.32	46521350.06
1020	2017-06-30	ETHUSD	293.80	304.94	270.77	280.80	146545.22	42337469.59

In [73]:

# bitcoin_sorted와 ethereuem_sorted를 활용한 데이터셋 확인
print(bitcoin_sorted['Date'])

print(ethereum_sorted['Date'])

0      2016-06-01
1      2016-06-02
2      2016-06-03
3      2016-06-04
4      2016-06-05
          ...    
372    2017-06-26
373    2017-06-27
374    2017-06-28
375    2017-06-29
376    2017-06-30
Name: Date, Length: 377, dtype: object
1414    2016-06-01
1413    2016-06-02
1412    2016-06-03
1411    2016-06-04
1410    2016-06-05
           ...    
1024    2017-06-26
1023    2017-06-27
1022    2017-06-28
1021    2017-06-29
1020    2017-06-30
Name: Date, Length: 395, dtype: object

In [74]:

temp_date_list = []

# bitcoin과 ethereum의 날짜 개수가 맞지 않으므로
# ethereum의 날짜 개수와 bitcoin의 날짜 개수를 맞추도록 한다.
# 먼저 bitcoin에 포함되지 않은 날짜를 파악한다.
not_in_bitcoin = ethereum_sorted[~ethereum_sorted['Date'].isin(bitcoin_sorted['Date'])]
print(not_in_bitcoin)
print(len(not_in_bitcoin))

# 날짜가 올바르게 뽑혔는지 확인
len(bitcoin_sorted) + len(not_in_bitcoin) == len(ethereum_sorted) # True

            Date  Symbol    Open    High     Low   Close  Volume ETH  \
1191  2017-01-10  ETHUSD   10.24   10.74   10.24   10.68     6278.68   
1190  2017-01-11  ETHUSD   10.68   10.74    9.50    9.87     8385.73   
1189  2017-01-12  ETHUSD    9.87   10.47    9.40    9.76    11446.77   
1160  2017-02-10  ETHUSD   11.06   11.44   10.87   11.31     6767.39   
1159  2017-02-11  ETHUSD   11.31   11.39   11.28   11.39     2766.97   
1158  2017-02-12  ETHUSD   11.39   11.41   11.36   11.36     3642.93   
1132  2017-03-10  ETHUSD   18.00   19.78   16.47   19.10   332830.87   
1131  2017-03-11  ETHUSD   19.10   21.53   18.51   21.50    70708.17   
1130  2017-03-12  ETHUSD   21.50   24.00   21.40   23.60    19543.48   
1101  2017-04-10  ETHUSD   43.87   45.65   42.80   43.92     5523.11   
1100  2017-04-11  ETHUSD   43.92   44.68   43.73   43.87     3870.56   
1099  2017-04-12  ETHUSD   43.87   47.99   43.33   46.54    26082.30   
1071  2017-05-10  ETHUSD   87.59   91.86   86.51   89.15    11930.97   
1070  2017-05-11  ETHUSD   89.15   90.48   84.50   88.27   107095.97   
1069  2017-05-12  ETHUSD   88.27   91.01   83.93   85.38   101938.70   
1040  2017-06-10  ETHUSD  280.21  344.01  279.99  331.22    93171.47   
1039  2017-06-11  ETHUSD  331.22  346.99  309.00  339.00    58874.67   
1038  2017-06-12  ETHUSD  339.00  418.50  310.00  394.90   156872.57   

       Volume USD  
1191     66110.04  
1190     84613.14  
1189    113401.47  
1160     75345.73  
1159     31376.45  
1158     41457.80  
1132   6211148.53  
1131   1412310.09  
1130    437684.88  
1101    241127.33  
1100    170588.45  
1099   1167010.32  
1071   1063082.82  
1070   9147344.84  
1069   8768077.71  
1040  28459166.17  
1039  19758981.83  
1038  56835613.91  
18

Out[74]:

True

In [75]:

# 포함되지 않은 데이터 값이 옳게 추출되었다는 것을 검증했으므로
# ethereum_sorted를 bitcoin_sorted의 행 개수와 맞도록 재정의한다.
ethereum_sorted = ethereum_sorted[ethereum_sorted['Date'].isin(bitcoin_sorted['Date'])]

# 개수를 통한 값 추출 확인
len(ethereum_sorted) == len(bitcoin_sorted) # True

# index기준으로 값을 산출하므로
# index num 재부여
index_list_ethereum = [i for i in range(len(ethereum_sorted))]
print(index_list_ethereum)

ethereum_sorted.index = index_list_ethereum
print(ethereum_sorted)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376]
           Date  Symbol    Open    High     Low   Close  Volume ETH  \
0    2016-06-01  ETHUSD   14.18   14.35   13.54   13.95     6390.06   
1    2016-06-02  ETHUSD   13.95   14.13   13.82   13.90      942.85   
2    2016-06-03  ETHUSD   13.90   16.05   13.50   14.02    11426.53   
3    2016-06-04  ETHUSD   14.02   14.16   13.64   13.86     4330.50   
4    2016-06-05  ETHUSD   13.86   13.92   13.70   13.91      787.49   
..          ...     ...     ...     ...     ...     ...         ...   
372  2017-06-26  ETHUSD  278.73  299.90  210.00  253.00   249076.24   
373  2017-06-27  ETHUSD  253.00  288.17  210.02  286.21   173179.40   
374  2017-06-28  ETHUSD  286.21  324.95  260.49  317.59   184030.56   
375  2017-06-29  ETHUSD  317.59  322.84  285.23  293.80   152271.32   
376  2017-06-30  ETHUSD  293.80  304.94  270.77  280.80   146545.22   

      Volume USD  
0       89027.81  
1       13180.38  
2      157342.19  
3       59977.39  
4       10869.67  
..           ...  
372  60950252.99  
373  42085473.60  
374  54573461.77  
375  46521350.06  
376  42337469.59  

[377 rows x 8 columns]

In [76]:

# rolling을 활용한 5MA 산출
ethereum_sorted_MA = ethereum_sorted['Open'].rolling(window = 5).mean()
ethereum_sorted_MA

Out[76]:

0          NaN
1          NaN
2          NaN
3          NaN
4       13.982
        ...   
372    310.850
373    296.592
374    289.898
375    288.066
376    285.866
Name: Open, Length: 377, dtype: float64

In [77]:

# 그래프 크기 설정
plt.figure(figsize = (10, 10))

# 제목 설정
plt.title("BitCoin & Ethereum Price(5-MA, Open)", fontsize = 15)

# x, y축 이름 설정
plt.xlabel("X = Date")
plt.ylabel("Y = Price")

# y축 눈금 설정
plt.yticks([i for i in range(0, 4000, 250)])

# x축의 경우, bitcoin과 ethereum의 날짜가 서로 다르므로
# x축의 의미가 없다.
# grid표시를 위하여 축만 표시하고
# 숫자는 숨기도록 한다.
plt.xticks([i for i in range(0, len(new_x_list), 30)], color = 'w')

# 그래프 격자
plt.grid()


# 그래프 그리기 및 범례 그리기
plt.plot(ethereum_sorted_MA, color = '#3c3c3d', label = 'ethereum')
plt.plot(bitcoin_sorted_MA, color = '#f2a900', label = 'bitcoin')
plt.legend()

plt.show()

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