NumPy_파일입출력

연습문제¶

• iris 데이터를 속성별로 정규화 하시오(평균이 0, 표준편차1)

In [1]:

import numpy as np
import matplotlib.pyplot as plt

In [2]:

f = open('iris.csv')

line = f.readline()
features = line.strip().split(',')[:4]

labels = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']

data = []
for line in f:
    l = line.strip().split(',')
    
    l[:4] = [float(i) for i in l[:4]]

    l[4] = labels.index(l[4])
    
    data.append(l)

f.close()

iris = np.array(data)

In [3]:

iris_norm = (iris - iris.mean(axis=0))/iris.std(axis=0)
iris_norm.shape

Out[3]:

(150, 5)

In [4]:

plt.plot(iris_norm[:,:4])

Out[4]:

[<matplotlib.lines.Line2D at 0xdfbdea9860>,
 <matplotlib.lines.Line2D at 0xdfbdea9a58>,
 <matplotlib.lines.Line2D at 0xdfbdea9ba8>,
 <matplotlib.lines.Line2D at 0xdfbdea9cf8>]

1. y=x**2 함수를 그리시오 (x값 범위는 -1~1)

In [5]:

x =np.arange(-1,1,0.01)
plt.plot(x**2)

Out[5]:

[<matplotlib.lines.Line2D at 0xdfbe002f28>]

In [6]:

plt.plot(x,x**2)

Out[6]:

[<matplotlib.lines.Line2D at 0xdfbe1ae828>]

In [7]:

plt.vlines([0], -0.1,1,linestyles=':') # vertical lines  [0]: x가 0에 긋 겠다. 리스트로 되어있는 이유는 여러군데 그을 수 있다.
plt.hlines([0], -1,1,linestyles=':')# horizontal lines
plt.text(0.1,0.8,'$y = x^2$', fontsize=20) # ^ 라텍스 기호
plt.xlabel('x')
plt.ylabel('y')

plt.plot(x,x**2)

Out[7]:

[<matplotlib.lines.Line2D at 0xdfbe254a58>]

Numpy 파일입출력¶

In [8]:

import numpy as np
import matplotlib.pyplot as plt

loadtxt()¶

help(loadtxt) >> 민감한 함수 , 제대로 적어줘야 오류가 안생겨

loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding='bytes')

csv(comma separate value), tsv(tap separate value)

In [9]:

X = np.loadtxt('iris.csv', skiprows = 1, delimiter = ',', usecols=[0,1,2,3])
X.shape

Out[9]:

(150, 4)

In [10]:

labels=['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']

Iris = np.loadtxt('iris.csv', skiprows=1, delimiter=',',
                 converters={4: lambda s: labels.index(s.decode())})   
# decode는 컴퓨터가 문자열을 불러올때 숫자형태로 불러오는데 그것을 우리가 아는 문자로 불러오기 위한 규칙 <> incode

'''converters = {0 : c0, 1 : c1, ...} 
각 속성마다 따로 함수를 지정해줄수 있다.'''

iris.shape

Out[10]:

(150, 5)

In [30]:

target={'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2}

Iris = np.loadtxt('iris.csv', skiprows=1, delimiter=',',
                 converters={4: lambda s: target[s.decode()]})   

iris.shape

Out[30]:

(150, 5)

In [11]:

labels=['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']

Iris = np.loadtxt('iris.csv', skiprows=1, delimiter=',',
                 converters={4: lambda s: labels.index(s)},
                  encoding = 'latin1')  

# 윈도우가 읽는 방법이랑 프로그램이 읽는 방법이랑 다르기 때문에 encoding을 이용하면 오류를 해결할수있다.
# cp949 윈도우 기본 인코딩, utf-8 파이썬, 메모장 기본 인코딩, ascii, latin1 등이 있다.


iris.shape

Out[11]:

(150, 5)

genfromtxt¶

• loadtxt보다 세밀한 기능 제공

help(np.genfromtxt)

genfromtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replacespace='', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None, encoding='bytes')

1,,3 = 1,None,3 = 1,NAN,3
값이 없을때 표현

In [12]:

labels=['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']

Iris = np.genfromtxt('iris.csv', skip_header=1, delimiter=',',
                 converters={4: lambda s: float(labels.index(s.decode()))})

iris.shape

Out[12]:

(150, 5)

savetxt()¶

help(np.savetxt)

savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', footer='', comments='# ', encoding=None)

In [13]:

np.savetxt('iris2.csv', iris, delimiter=',', fmt='%.2f', 
          header='SepalLength,SepalWidth,PetalLength,PetalWidth,Name')
# %.2f 소숫점 두자리까지 저장하겠다. iris2로 한 이유는 덮어쓰기 때문에.

In [14]:

np.savetxt('iris2.csv', iris, delimiter='조', fmt='%.2f')
# 분리 단위를 '조'로 바꿨다.

In [15]:

iris = np.loadtxt('iris2.csv',delimiter='조', encoding='cp949')
iris.shape

Out[15]:

(150, 5)

In [16]:

X = np.loadtxt('winequality-red.csv', skiprows =1, delimiter = ';')
X.shape

Out[16]:

(1599, 12)

기상 데이터 불러오기¶

In [31]:

s = '지점,일시,평균기온(°C),최저기온(°C),최저기온 시각(hhmi),최고기온(°C),최고기온 시각(hhmi),강수 계속시간(hr),10분 최다 강수량(mm),10분 최다강수량 시각(hhmi),1시간 최다강수량(mm),1시간 최다 강수량 시각(hhmi),일강수량(mm),최대 순간 풍속(m/s),최대 순간 풍속 풍향(16방위),최대 순간풍속 시각(hhmi),최대 풍속(m/s),최대 풍속 풍향(16방위),최대 풍속 시각(hhmi),평균 풍속(m/s),풍정합(100m),평균 이슬점온도(°C),최소 상대습도(%),최소 상대습도 시각(hhmi),평균 상대습도(%),평균 증기압(hPa),평균 현지기압(hPa),최고 해면기압(hPa),최고 해면기압 시각(hhmi),최저 해면기압(hPa),최저 해면기압 시각(hhmi),평균 해면기압(hPa),가조시간(hr),합계 일조 시간(hr),1시간 최다일사 시각(hhmi),1시간 최다일사량(MJ/m2),합계 일사(MJ/m2),일 최심신적설(cm),일 최심신적설 시각(hhmi),일 최심적설(cm),일 최심적설 시각(hhmi),합계 3시간 신적설(cm),평균 전운량(1/10),평균 중하층운량(1/10),평균 지면온도(°C),최저 초상온도(°C),평균 5cm 지중온도(°C),평균 10cm 지중온도(°C),평균 20cm 지중온도(°C),평균 30cm 지중온도(°C),0.5m 지중온도(°C),1.0m 지중온도(°C),1.5m 지중온도(°C),3.0m 지중온도(°C),5.0m 지중온도(°C),합계 대형증발량(mm),합계 소형증발량(mm),9-9강수(mm),기사,안개 계속시간(hr)'
cols = s.strip().split(',')
cols

Out[31]:

['지점',
 '일시',
 '평균기온(°C)',
 '최저기온(°C)',
 '최저기온 시각(hhmi)',
 '최고기온(°C)',
 '최고기온 시각(hhmi)',
 '강수 계속시간(hr)',
 '10분 최다 강수량(mm)',
 '10분 최다강수량 시각(hhmi)',
 '1시간 최다강수량(mm)',
 '1시간 최다 강수량 시각(hhmi)',
 '일강수량(mm)',
 '최대 순간 풍속(m/s)',
 '최대 순간 풍속 풍향(16방위)',
 '최대 순간풍속 시각(hhmi)',
 '최대 풍속(m/s)',
 '최대 풍속 풍향(16방위)',
 '최대 풍속 시각(hhmi)',
 '평균 풍속(m/s)',
 '풍정합(100m)',
 '평균 이슬점온도(°C)',
 '최소 상대습도(%)',
 '최소 상대습도 시각(hhmi)',
 '평균 상대습도(%)',
 '평균 증기압(hPa)',
 '평균 현지기압(hPa)',
 '최고 해면기압(hPa)',
 '최고 해면기압 시각(hhmi)',
 '최저 해면기압(hPa)',
 '최저 해면기압 시각(hhmi)',
 '평균 해면기압(hPa)',
 '가조시간(hr)',
 '합계 일조 시간(hr)',
 '1시간 최다일사 시각(hhmi)',
 '1시간 최다일사량(MJ/m2)',
 '합계 일사(MJ/m2)',
 '일 최심신적설(cm)',
 '일 최심신적설 시각(hhmi)',
 '일 최심적설(cm)',
 '일 최심적설 시각(hhmi)',
 '합계 3시간 신적설(cm)',
 '평균 전운량(1/10)',
 '평균 중하층운량(1/10)',
 '평균 지면온도(°C)',
 '최저 초상온도(°C)',
 '평균 5cm 지중온도(°C)',
 '평균 10cm 지중온도(°C)',
 '평균 20cm 지중온도(°C)',
 '평균 30cm 지중온도(°C)',
 '0.5m 지중온도(°C)',
 '1.0m 지중온도(°C)',
 '1.5m 지중온도(°C)',
 '3.0m 지중온도(°C)',
 '5.0m 지중온도(°C)',
 '합계 대형증발량(mm)',
 '합계 소형증발량(mm)',
 '9-9강수(mm)',
 '기사',
 '안개 계속시간(hr)']

In [32]:

len(cols)

Out[32]:

60

In [33]:

for i,c in enumerate(cols):
    print('%03d => %s' % (i,c))

000 => 지점
001 => 일시
002 => 평균기온(°C)
003 => 최저기온(°C)
004 => 최저기온 시각(hhmi)
005 => 최고기온(°C)
006 => 최고기온 시각(hhmi)
007 => 강수 계속시간(hr)
008 => 10분 최다 강수량(mm)
009 => 10분 최다강수량 시각(hhmi)
010 => 1시간 최다강수량(mm)
011 => 1시간 최다 강수량 시각(hhmi)
012 => 일강수량(mm)
013 => 최대 순간 풍속(m/s)
014 => 최대 순간 풍속 풍향(16방위)
015 => 최대 순간풍속 시각(hhmi)
016 => 최대 풍속(m/s)
017 => 최대 풍속 풍향(16방위)
018 => 최대 풍속 시각(hhmi)
019 => 평균 풍속(m/s)
020 => 풍정합(100m)
021 => 평균 이슬점온도(°C)
022 => 최소 상대습도(%)
023 => 최소 상대습도 시각(hhmi)
024 => 평균 상대습도(%)
025 => 평균 증기압(hPa)
026 => 평균 현지기압(hPa)
027 => 최고 해면기압(hPa)
028 => 최고 해면기압 시각(hhmi)
029 => 최저 해면기압(hPa)
030 => 최저 해면기압 시각(hhmi)
031 => 평균 해면기압(hPa)
032 => 가조시간(hr)
033 => 합계 일조 시간(hr)
034 => 1시간 최다일사 시각(hhmi)
035 => 1시간 최다일사량(MJ/m2)
036 => 합계 일사(MJ/m2)
037 => 일 최심신적설(cm)
038 => 일 최심신적설 시각(hhmi)
039 => 일 최심적설(cm)
040 => 일 최심적설 시각(hhmi)
041 => 합계 3시간 신적설(cm)
042 => 평균 전운량(1/10)
043 => 평균 중하층운량(1/10)
044 => 평균 지면온도(°C)
045 => 최저 초상온도(°C)
046 => 평균 5cm 지중온도(°C)
047 => 평균 10cm 지중온도(°C)
048 => 평균 20cm 지중온도(°C)
049 => 평균 30cm 지중온도(°C)
050 => 0.5m 지중온도(°C)
051 => 1.0m 지중온도(°C)
052 => 1.5m 지중온도(°C)
053 => 3.0m 지중온도(°C)
054 => 5.0m 지중온도(°C)
055 => 합계 대형증발량(mm)
056 => 합계 소형증발량(mm)
057 => 9-9강수(mm)
058 => 기사
059 => 안개 계속시간(hr)

In [20]:

np.loadtxt('기상관측_서울_20181004141633.csv', skiprows=1, delimiter=',',
           usecols=[2,3,5],
           converters = {2: lambda s: float(s) if s!='' else 0,
                        3: lambda s: float(s) if s!='' else 0,
                        5: lambda s: float(s) if s!='' else 0},
           encoding='cp949')

Out[20]:

array([[-7.7, -9.8, -4.3],
       [-6. , -9. , -1.9],
       [-2.7, -9.2,  3.1],
       ...,
       [15.4, 13. , 19.7],
       [15.9, 10.3, 22. ],
       [17.3, 11.2, 24.2]])

In [21]:

f = open('기상관측_서울_20181004141633.csv', encoding='cp949')

f.readline()

data = []

for line in f:
    l = line.strip().split(',')
    l2 = [int(i) for i in l[1].split('-')]
    data.append(l2)

f.close()

data

Out[21]:

[[2015, 1, 1],
 [2015, 1, 2],
 [2015, 1, 3],
 [2015, 1, 4],
 [2015, 1, 5],
 [2015, 1, 6],
 [2015, 1, 7],
 [2015, 1, 8],
 [2015, 1, 9],
 [2015, 1, 10],
 [2015, 1, 11],
 [2015, 1, 12],
 [2015, 1, 13],
 [2015, 1, 14],
 [2015, 1, 15],
 [2015, 1, 16],
 [2015, 1, 17],
 [2015, 1, 18],
 [2015, 1, 19],
 [2015, 1, 20],
 [2015, 1, 21],
 [2015, 1, 22],
 [2015, 1, 23],
 [2015, 1, 24],
 [2015, 1, 25],
 [2015, 1, 26],
 [2015, 1, 27],
 [2015, 1, 28],
 [2015, 1, 29],
 [2015, 1, 30],
 [2015, 1, 31],
 [2015, 2, 1],
 [2015, 2, 2],
 [2015, 2, 3],
 [2015, 2, 4],
 [2015, 2, 5],
 [2015, 2, 6],
 [2015, 2, 7],
 [2015, 2, 8],
 [2015, 2, 9],
 [2015, 2, 10],
 [2015, 2, 11],
 [2015, 2, 12],
 [2015, 2, 13],
 [2015, 2, 14],
 [2015, 2, 15],
 [2015, 2, 16],
 [2015, 2, 17],
 [2015, 2, 18],
 [2015, 2, 19],
 [2015, 2, 20],
 [2015, 2, 21],
 [2015, 2, 22],
 [2015, 2, 23],
 [2015, 2, 24],
 [2015, 2, 25],
 [2015, 2, 26],
 [2015, 2, 27],
 [2015, 2, 28],
 [2015, 3, 1],
 [2015, 3, 2],
 [2015, 3, 3],
 [2015, 3, 4],
 [2015, 3, 5],
 [2015, 3, 6],
 [2015, 3, 7],
 [2015, 3, 8],
 [2015, 3, 9],
 [2015, 3, 10],
 [2015, 3, 11],
 [2015, 3, 12],
 [2015, 3, 13],
 [2015, 3, 14],
 [2015, 3, 15],
 [2015, 3, 16],
 [2015, 3, 17],
 [2015, 3, 18],
 [2015, 3, 19],
 [2015, 3, 20],
 [2015, 3, 21],
 [2015, 3, 22],
 [2015, 3, 23],
 [2015, 3, 24],
 [2015, 3, 25],
 [2015, 3, 26],
 [2015, 3, 27],
 [2015, 3, 28],
 [2015, 3, 29],
 [2015, 3, 30],
 [2015, 3, 31],
 [2015, 4, 1],
 [2015, 4, 2],
 [2015, 4, 3],
 [2015, 4, 4],
 [2015, 4, 5],
 [2015, 4, 6],
 [2015, 4, 7],
 [2015, 4, 8],
 [2015, 4, 9],
 [2015, 4, 10],
 [2015, 4, 11],
 [2015, 4, 12],
 [2015, 4, 13],
 [2015, 4, 14],
 [2015, 4, 15],
 [2015, 4, 16],
 [2015, 4, 17],
 [2015, 4, 18],
 [2015, 4, 19],
 [2015, 4, 20],
 [2015, 4, 21],
 [2015, 4, 22],
 [2015, 4, 23],
 [2015, 4, 24],
 [2015, 4, 25],
 [2015, 4, 26],
 [2015, 4, 27],
 [2015, 4, 28],
 [2015, 4, 29],
 [2015, 4, 30],
 [2015, 5, 1],
 [2015, 5, 2],
 [2015, 5, 3],
 [2015, 5, 4],
 [2015, 5, 5],
 [2015, 5, 6],
 [2015, 5, 7],
 [2015, 5, 8],
 [2015, 5, 9],
 [2015, 5, 10],
 [2015, 5, 11],
 [2015, 5, 12],
 [2015, 5, 13],
 [2015, 5, 14],
 [2015, 5, 15],
 [2015, 5, 16],
 [2015, 5, 17],
 [2015, 5, 18],
 [2015, 5, 19],
 [2015, 5, 20],
 [2015, 5, 21],
 [2015, 5, 22],
 [2015, 5, 23],
 [2015, 5, 24],
 [2015, 5, 25],
 [2015, 5, 26],
 [2015, 5, 27],
 [2015, 5, 28],
 [2015, 5, 29],
 [2015, 5, 30],
 [2015, 5, 31],
 [2015, 6, 1],
 [2015, 6, 2],
 [2015, 6, 3],
 [2015, 6, 4],
 [2015, 6, 5],
 [2015, 6, 6],
 [2015, 6, 7],
 [2015, 6, 8],
 [2015, 6, 9],
 [2015, 6, 10],
 [2015, 6, 11],
 [2015, 6, 12],
 [2015, 6, 13],
 [2015, 6, 14],
 [2015, 6, 15],
 [2015, 6, 16],
 [2015, 6, 17],
 [2015, 6, 18],
 [2015, 6, 19],
 [2015, 6, 20],
 [2015, 6, 21],
 [2015, 6, 22],
 [2015, 6, 23],
 [2015, 6, 24],
 [2015, 6, 25],
 [2015, 6, 26],
 [2015, 6, 27],
 [2015, 6, 28],
 [2015, 6, 29],
 [2015, 6, 30],
 [2015, 7, 1],
 [2015, 7, 2],
 [2015, 7, 3],
 [2015, 7, 4],
 [2015, 7, 5],
 [2015, 7, 6],
 [2015, 7, 7],
 [2015, 7, 8],
 [2015, 7, 9],
 [2015, 7, 10],
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list(range(50)).remove

어레이를 바이너리 파일로 저장하기¶

txt 파일은 용량이 너무 커져서 바이너리가 좋다.

In [22]:

np.save('iris.npy', iris)

In [23]:

iris2 = np.load('iris.npy')
iris2.shape

Out[23]:

(150, 5)

In [24]:

np.savez('iris.npz', X=iris[:,:4],y=iris[:,4]) 

savez 압축해서 저장, 범위 지정해서 저장할 수 있다.
여기서는 X,y로 저장했기 때문에 다음의 방법으로 열어야 한다.

In [25]:

arch = np.load('iris.npz')

In [ ]:

arch['X'].shape, arch['y'].shape