머신러닝 프로그래밍 4주차 응용하기-strip(),csv,json등

strip() 종류

‘www.example.com’.strip(‘m’)

‘www.example.co’

‘www.example.com’.strip(‘w’)

‘.example.com’

‘www.example.com’.strip(‘cmowz.’)

‘example’

lstrip() 선행문자만 지울때 사용

url = ‘https://wikidocs.net’

strip() 을 사용했을 때, net 의 ‘t’도 생략됨.

url.strip(‘https://’)

‘wikidocs.ne’

lstrip() 을 사용했을 때,

url.lstrip(‘https://’)

‘wikidocs.net’

rstrip() 후행문자만 지울 때 사용함

url = ‘https://wikidocs.net’

url.rstrip(‘.net’)

‘https://wikidocs’

split 함수 예제

s = “a b c d e f g”

print(f’s :{s}’)

r = s.split()

print(f’s.split() : {r}’)

s : a b c d e f g

s.split() : [‘a’,’b’,’c’,’d’,’e’,’f’,’g’]

파라미터를 아무것도 사용하지 않고 문자열.split() 를 사용하게 되면 위 예제와 같이 띄어쓰기, 엔터를 구분하여 문자열을 나누게 됩니다.

s = "aa.bb.cc.dd.ee.ff.gg"

print(f's                : {s}')
 
r0 = s.split()

r1 = s.split('.')

r2 = s.split(sep='.')

print(f"s.split()        : {r0}")

print(f"s.split('.')     : {r1}")

print(f"s.split(sep='.') : {r2}")

s                : aa.bb.cc.dd.ee.ff.gg
s.split()        : ['aa.bb.cc.dd.ee.ff.gg']
s.split('.')     : ['aa', 'bb', 'cc', 'dd', 'ee', 'ff', 'gg']
s.split(sep='.') : ['aa', 'bb', 'cc', 'dd', 'ee', 'ff', 'gg']

# csv open 방법 1
data = []
f = open("california_housing_test.csv")
titles = f.readline().strip().split(',') #컬럼명 / strip() 문자열 및 공백 제거
for l in f.readlines():
    l1 = [float(x) for x in l.strip().split(',')]
    data.append(l1)

titles

['"longitude"',
 '"latitude"',
 '"housing_median_age"',
 '"total_rooms"',
 '"total_bedrooms"',
 '"population"',
 '"households"',
 '"median_income"',
 '"median_house_value"']

data[0]

[-122.05, 37.37, 27.0, 3885.0, 661.0, 1537.0, 606.0, 6.6085, 344700.0]

# csv open 방법 2 ( with문 사용하기)

file = open('california_housing_test.csv')
print(file.readline(),end="")
file.close()

"longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","median_house_value"

#아래처럼 바꿀 수 있다
with open('california_housing_test.csv') as file_data:
    #with문은 자동으로 close를 불러줍니다
    print(file_data.readline(),end='')

"longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","median_house_value"

쓰기 모드

with open(‘california_housing_test.csv’,’w’) as file_data:

file_data.write(“first\n”)

# 읽기 모드
# import csv

with open('california_housing_test.csv','r') as file_date:
    for line in file_date:
        print(line)

"longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","median_house_value"

-122.050000,37.370000,27.000000,3885.000000,661.000000,1537.000000,606.000000,6.608500,344700.000000

-118.300000,34.260000,43.000000,1510.000000,310.000000,809.000000,277.000000,3.599000,176500.000000

-117.810000,33.780000,27.000000,3589.000000,507.000000,1484.000000,495.000000,5.793400,270500.000000

-118.360000,33.820000,28.000000,67.000000,15.000000,49.000000,11.000000,6.135900,330000.000000

-119.670000,36.330000,19.000000,1241.000000,244.000000,850.000000,237.000000,2.937500,81700.000000

-119.560000,36.510000,37.000000,1018.000000,213.000000,663.000000,204.000000,1.663500,67000.000000

-121.430000,38.630000,43.000000,1009.000000,225.000000,604.000000,218.000000,1.664100,67000.000000

-120.650000,35.480000,19.000000,2310.000000,471.000000,1341.000000,441.000000,3.225000,166900.000000

-122.840000,38.400000,15.000000,3080.000000,617.000000,1446.000000,599.000000,3.669600,194400.000000

-118.020000,34.080000,31.000000,2402.000000,632.000000,2830.000000,603.000000,2.333300,164200.000000

-118.240000,33.980000,45.000000,972.000000,249.000000,1288.000000,261.000000,2.205400,125000.000000

-119.120000,35.850000,37.000000,736.000000,166.000000,564.000000,138.000000,2.416700,58300.000000

-121.930000,37.250000,36.000000,1089.000000,182.000000,535.000000,170.000000,4.690000,252600.000000

-117.030000,32.970000,16.000000,3936.000000,694.000000,1935.000000,659.000000,4.562500,231200.000000

-117.970000,33.730000,27.000000,2097.000000,325.000000,1217.000000,331.000000,5.712100,222500.000000

-117.990000,33.810000,42.000000,161.000000,40.000000,157.000000,50.000000,2.200000,153100.000000

-120.810000,37.530000,15.000000,570.000000,123.000000,189.000000,107.000000,1.875000,181300.000000

-121.200000,38.690000,26.000000,3077.000000,607.000000,1603.000000,595.000000,2.717400,137500.000000

-118.880000,34.210000,26.000000,1590.000000,196.000000,654.000000,199.000000,6.585100,300000.000000

-122.590000,38.010000,35.000000,8814.000000,1307.000000,3450.000000,1258.000000,6.172400,414300.000000

-122.150000,37.750000,40.000000,1445.000000,256.000000,849.000000,255.000000,3.891300,126300.000000

-121.370000,38.680000,36.000000,1775.000000,296.000000,937.000000,305.000000,3.178600,83400.000000

-118.160000,34.070000,47.000000,2994.000000,543.000000,1651.000000,561.000000,3.864400,241500.000000

-122.200000,37.790000,45.000000,2021.000000,528.000000,1410.000000,480.000000,2.778800,115400.000000

-117.280000,33.280000,13.000000,6131.000000,1040.000000,4049.000000,940.000000,3.815600,150700.000000

-118.030000,34.160000,36.000000,1401.000000,218.000000,667.000000,225.000000,7.161500,484700.000000

-122.420000,37.760000,52.000000,3587.000000,1030.000000,2259.000000,979.000000,2.540300,250000.000000

-118.390000,33.990000,32.000000,2612.000000,418.000000,1030.000000,402.000000,6.603000,369200.000000

-118.450000,34.070000,19.000000,4845.000000,1609.000000,3751.000000,1539.000000,1.583000,350000.000000

-118.480000,34.010000,30.000000,3078.000000,954.000000,1561.000000,901.000000,3.485200,425000.000000

-119.350000,36.330000,14.000000,1195.000000,220.000000,568.000000,229.000000,3.148600,105600.000000

-118.300000,33.910000,34.000000,1617.000000,493.000000,1530.000000,500.000000,2.618200,172600.000000

-121.130000,39.310000,17.000000,3442.000000,705.000000,1693.000000,619.000000,2.810200,128900.000000

-118.080000,34.550000,5.000000,16181.000000,2971.000000,8152.000000,2651.000000,4.523700,141800.000000

-118.320000,33.940000,38.000000,1067.000000,170.000000,499.000000,169.000000,4.638900,183800.000000

-118.110000,34.000000,33.000000,2886.000000,726.000000,2650.000000,728.000000,2.625000,178700.000000

-122.530000,37.970000,52.000000,1560.000000,451.000000,700.000000,419.000000,2.512500,270800.000000

-118.020000,33.920000,34.000000,1478.000000,251.000000,956.000000,277.000000,5.523800,185300.000000

-118.050000,33.930000,31.000000,894.000000,203.000000,883.000000,190.000000,3.677100,141500.000000

-119.010000,34.230000,11.000000,5785.000000,1035.000000,2760.000000,985.000000,4.693000,232200.000000

-119.320000,36.200000,15.000000,1562.000000,275.000000,961.000000,287.000000,3.423100,83300.000000

-116.920000,32.770000,16.000000,2770.000000,406.000000,1269.000000,429.000000,6.678300,275000.000000

-118.060000,34.150000,37.000000,1980.000000,226.000000,697.000000,226.000000,15.000100,500001.000000

-117.270000,34.090000,36.000000,848.000000,186.000000,737.000000,169.000000,0.983800,79300.000000

-118.230000,34.130000,48.000000,1308.000000,286.000000,835.000000,294.000000,4.289100,214800.000000

-117.240000,33.170000,4.000000,9998.000000,1874.000000,3925.000000,1672.000000,4.282600,237500.000000

-121.910000,37.440000,24.000000,1212.000000,251.000000,799.000000,242.000000,5.080800,212500.000000

-118.290000,33.940000,47.000000,1782.000000,338.000000,1003.000000,329.000000,2.539800,105700.000000

-121.350000,38.000000,6.000000,1649.000000,369.000000,732.000000,350.000000,3.423100,123800.000000

-117.990000,33.780000,19.000000,7399.000000,1698.000000,3554.000000,1593.000000,3.104900,173900.000000

-120.990000,37.700000,14.000000,9849.000000,1887.000000,4356.000000,1780.000000,3.587700,160900.000000

-119.420000,35.970000,21.000000,554.000000,121.000000,426.000000,122.000000,2.351600,47500.000000

-122.210000,37.800000,39.000000,2003.000000,500.000000,1109.000000,464.000000,3.068200,156500.000000

-118.170000,33.800000,26.000000,1589.000000,380.000000,883.000000,366.000000,3.531300,187500.000000

-117.900000,34.090000,39.000000,1726.000000,333.000000,892.000000,335.000000,4.340900,191800.000000

-117.990000,33.930000,36.000000,1287.000000,233.000000,779.000000,229.000000,4.852300,175800.000000

-121.420000,38.720000,10.000000,3054.000000,528.000000,1932.000000,510.000000,3.090300,91900.000000

-118.770000,34.260000,26.000000,3038.000000,468.000000,1825.000000,468.000000,5.638500,196900.000000

-121.930000,37.330000,44.000000,1449.000000,291.000000,676.000000,282.000000,3.575000,292200.000000

-121.820000,37.250000,16.000000,2650.000000,600.000000,1969.000000,586.000000,3.946100,194300.000000

-122.290000,37.560000,36.000000,805.000000,140.000000,445.000000,139.000000,5.822100,289400.000000

-121.780000,37.230000,18.000000,1747.000000,317.000000,1055.000000,285.000000,5.898000,229100.000000

-118.410000,34.000000,35.000000,1062.000000,305.000000,1026.000000,307.000000,2.715300,265500.000000

-121.670000,40.870000,31.000000,1581.000000,299.000000,776.000000,287.000000,2.906300,77800.000000

-118.000000,33.920000,26.000000,2830.000000,399.000000,1204.000000,404.000000,6.127300,289600.000000

-117.220000,32.730000,38.000000,3966.000000,768.000000,1640.000000,729.000000,3.840900,291400.000000

-121.080000,37.690000,19.000000,6473.000000,1212.000000,3559.000000,1123.000000,3.224600,129300.000000

-117.530000,33.920000,12.000000,2290.000000,319.000000,728.000000,228.000000,6.156100,233500.000000

-117.460000,34.080000,18.000000,3830.000000,750.000000,2767.000000,702.000000,3.660200,120700.000000

-117.970000,33.860000,35.000000,1691.000000,367.000000,1265.000000,378.000000,3.585500,174300.000000

-121.920000,37.330000,52.000000,2125.000000,382.000000,930.000000,387.000000,5.283100,299500.000000

-118.200000,34.040000,44.000000,1582.000000,544.000000,1998.000000,515.000000,1.688800,125000.000000

-118.060000,33.800000,22.000000,1892.000000,442.000000,1015.000000,404.000000,4.137900,212500.000000

-122.050000,37.360000,34.000000,2400.000000,419.000000,1017.000000,384.000000,4.136900,316900.000000

-123.790000,39.500000,24.000000,1421.000000,291.000000,588.000000,274.000000,2.325000,157300.000000

-120.790000,37.490000,44.000000,1186.000000,225.000000,687.000000,234.000000,3.416700,160700.000000

-121.890000,37.350000,47.000000,2879.000000,631.000000,2229.000000,606.000000,3.259900,183100.000000

-118.430000,34.200000,29.000000,3051.000000,694.000000,1942.000000,679.000000,3.111800,238100.000000

-118.750000,34.170000,18.000000,6217.000000,858.000000,2703.000000,834.000000,6.807500,325900.000000

-122.470000,37.990000,22.000000,7274.000000,1002.000000,2468.000000,957.000000,7.494000,439200.000000

-120.690000,37.400000,46.000000,860.000000,130.000000,496.000000,147.000000,3.516700,137500.000000

-118.280000,34.050000,44.000000,968.000000,384.000000,1805.000000,375.000000,1.480100,212500.000000

-118.440000,34.250000,35.000000,1583.000000,324.000000,1481.000000,351.000000,3.700000,176000.000000

-122.050000,38.260000,21.000000,7195.000000,1416.000000,3927.000000,1377.000000,3.091200,126300.000000

-121.990000,37.350000,18.000000,1712.000000,509.000000,972.000000,467.000000,4.397100,238900.000000

-121.020000,37.680000,28.000000,2875.000000,560.000000,1608.000000,558.000000,3.548900,106400.000000

-119.850000,36.740000,35.000000,1191.000000,190.000000,537.000000,182.000000,3.537500,96700.000000

-118.020000,34.080000,28.000000,2769.000000,631.000000,2452.000000,581.000000,2.607100,175900.000000

-123.520000,41.010000,17.000000,1564.000000,345.000000,517.000000,222.000000,2.154200,83800.000000

-122.400000,37.620000,44.000000,1619.000000,362.000000,1064.000000,335.000000,4.023800,224200.000000

-118.130000,34.150000,24.000000,1125.000000,341.000000,579.000000,321.000000,2.812500,141700.000000

-122.000000,37.980000,32.000000,1013.000000,169.000000,436.000000,173.000000,5.111800,226900.000000

-118.450000,34.250000,36.000000,1453.000000,270.000000,808.000000,275.000000,4.383900,204600.000000

-117.500000,33.870000,4.000000,6755.000000,1017.000000,2866.000000,850.000000,5.049300,239800.000000

-122.220000,37.840000,50.000000,2935.000000,473.000000,1031.000000,479.000000,7.500000,295200.000000

-119.820000,36.640000,30.000000,1694.000000,312.000000,1008.000000,321.000000,2.246600,96000.000000

-120.210000,36.770000,20.000000,1745.000000,348.000000,1093.000000,302.000000,2.319400,90600.000000

-120.970000,38.420000,16.000000,1748.000000,322.000000,4930.000000,287.000000,4.302900,121900.000000

-121.190000,38.870000,20.000000,3118.000000,500.000000,1405.000000,519.000000,6.000000,209400.000000

-118.200000,33.770000,52.000000,1375.000000,457.000000,1089.000000,317.000000,2.234400,200000.000000

-118.300000,34.020000,49.000000,2120.000000,483.000000,1522.000000,416.000000,1.850000,116800.000000

-122.230000,37.790000,43.000000,5963.000000,1344.000000,4367.000000,1231.000000,2.191700,112800.000000

-121.310000,38.620000,31.000000,3114.000000,430.000000,1121.000000,456.000000,6.244000,240000.000000

-117.250000,32.800000,35.000000,2281.000000,506.000000,1005.000000,496.000000,4.229600,275000.000000

-118.260000,33.990000,36.000000,2016.000000,505.000000,1807.000000,464.000000,1.690100,103500.000000

-119.390000,36.540000,34.000000,1590.000000,422.000000,1272.000000,407.000000,1.806800,59000.000000

-121.510000,38.520000,30.000000,3236.000000,588.000000,1167.000000,569.000000,4.097200,181400.000000

-119.180000,34.270000,6.000000,2307.000000,386.000000,910.000000,364.000000,5.215000,279500.000000

-118.180000,33.770000,30.000000,1418.000000,439.000000,720.000000,417.000000,2.637100,159400.000000

-122.430000,37.740000,52.000000,1514.000000,314.000000,724.000000,301.000000,5.329200,300900.000000

-117.930000,33.910000,24.000000,1698.000000,297.000000,676.000000,273.000000,5.201700,364600.000000

-124.160000,40.770000,35.000000,2141.000000,438.000000,1053.000000,434.000000,2.852900,85600.000000

-117.950000,33.630000,27.000000,2489.000000,481.000000,1082.000000,443.000000,5.877700,358800.000000

-118.050000,34.100000,36.000000,1606.000000,318.000000,889.000000,294.000000,4.793100,272600.000000

-116.970000,32.810000,19.000000,1573.000000,471.000000,844.000000,414.000000,2.142200,125000.000000

-118.850000,34.170000,42.000000,564.000000,96.000000,220.000000,81.000000,4.562500,318800.000000

-117.730000,33.630000,15.000000,2874.000000,592.000000,1382.000000,586.000000,5.513700,161800.000000

-122.070000,37.340000,30.000000,1851.000000,238.000000,631.000000,236.000000,10.100700,500001.000000

-117.180000,33.020000,15.000000,3540.000000,453.000000,1364.000000,425.000000,13.662300,500001.000000

-118.410000,34.000000,38.000000,324.000000,70.000000,268.000000,73.000000,2.550000,271400.000000

-121.960000,37.300000,20.000000,4228.000000,1006.000000,2334.000000,1007.000000,4.308100,227300.000000

-121.740000,38.550000,34.000000,2299.000000,579.000000,1300.000000,536.000000,1.643500,148500.000000

-118.210000,33.920000,28.000000,2949.000000,1003.000000,4551.000000,930.000000,1.902600,131900.000000

-121.900000,36.610000,29.000000,3412.000000,827.000000,1574.000000,759.000000,2.930900,217100.000000

-117.810000,33.840000,17.000000,4343.000000,515.000000,1605.000000,484.000000,10.598100,460100.000000

-118.190000,33.930000,42.000000,1829.000000,391.000000,1614.000000,377.000000,3.191200,146400.000000

-120.930000,37.730000,14.000000,2799.000000,618.000000,2294.000000,596.000000,2.634300,81500.000000

-122.020000,37.960000,25.000000,2615.000000,368.000000,935.000000,366.000000,6.672700,305100.000000

-122.470000,37.790000,52.000000,2844.000000,623.000000,1380.000000,596.000000,4.750000,500001.000000

-117.190000,34.030000,25.000000,2513.000000,340.000000,900.000000,320.000000,6.496200,182400.000000

-117.240000,32.800000,28.000000,1072.000000,331.000000,692.000000,321.000000,2.135700,187500.000000

-118.130000,34.100000,19.000000,2742.000000,756.000000,1396.000000,703.000000,2.566300,197500.000000

-122.420000,37.730000,50.000000,3426.000000,769.000000,2261.000000,671.000000,2.888000,246400.000000

-118.140000,34.710000,32.000000,1164.000000,248.000000,588.000000,270.000000,1.191700,86900.000000

-119.760000,36.750000,39.000000,2233.000000,563.000000,2031.000000,491.000000,1.864100,50800.000000

-122.340000,37.560000,39.000000,3562.000000,391.000000,1139.000000,391.000000,12.641700,500001.000000

-122.450000,40.460000,16.000000,2734.000000,501.000000,1413.000000,484.000000,2.808500,105700.000000

-118.290000,33.910000,31.000000,2025.000000,618.000000,2231.000000,593.000000,2.474100,151200.000000

-118.320000,33.910000,34.000000,3041.000000,677.000000,1920.000000,640.000000,4.530400,181300.000000

-122.040000,37.360000,26.000000,3298.000000,460.000000,1241.000000,472.000000,6.875300,403000.000000

-117.390000,34.100000,12.000000,7184.000000,1516.000000,4862.000000,1235.000000,2.449200,103800.000000

-122.250000,37.800000,36.000000,1678.000000,606.000000,1645.000000,543.000000,2.230300,116700.000000

-117.980000,34.100000,22.000000,5661.000000,1209.000000,5389.000000,1178.000000,3.772700,159700.000000

-120.060000,36.950000,24.000000,646.000000,134.000000,454.000000,149.000000,2.125000,61900.000000

-121.470000,39.490000,17.000000,1554.000000,242.000000,553.000000,230.000000,3.217400,91800.000000

-122.200000,37.790000,35.000000,1802.000000,459.000000,1009.000000,390.000000,2.303600,126000.000000

-117.230000,32.750000,23.000000,2415.000000,653.000000,1275.000000,596.000000,3.138900,101800.000000

-119.750000,36.740000,39.000000,1740.000000,351.000000,1098.000000,347.000000,1.895800,51300.000000

-117.920000,34.030000,35.000000,1341.000000,233.000000,898.000000,216.000000,4.111800,157300.000000

-121.640000,39.280000,25.000000,2857.000000,662.000000,2076.000000,685.000000,1.809500,64100.000000

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-122.410000,37.660000,34.000000,1075.000000,318.000000,906.000000,294.000000,3.005200,242500.000000

-117.230000,32.730000,36.000000,2052.000000,287.000000,699.000000,265.000000,7.555700,441400.000000

-118.330000,34.000000,47.000000,1671.000000,388.000000,895.000000,317.000000,2.205400,121500.000000

-117.430000,33.550000,8.000000,446.000000,62.000000,188.000000,68.000000,9.435600,465600.000000

-118.360000,34.080000,52.000000,1965.000000,480.000000,794.000000,451.000000,3.282400,304800.000000

-121.090000,38.970000,13.000000,1467.000000,221.000000,688.000000,231.000000,5.253600,191900.000000

-119.450000,35.150000,33.000000,5050.000000,964.000000,2293.000000,919.000000,3.159200,75400.000000

-121.270000,38.640000,22.000000,1597.000000,280.000000,657.000000,273.000000,4.309800,213500.000000

-118.000000,33.900000,35.000000,1758.000000,309.000000,972.000000,338.000000,4.383100,209800.000000

-118.210000,34.050000,45.000000,2146.000000,607.000000,2868.000000,625.000000,2.121000,144000.000000

-122.500000,37.770000,52.000000,2299.000000,441.000000,1252.000000,415.000000,5.056200,336700.000000

-122.310000,37.920000,38.000000,1250.000000,236.000000,631.000000,279.000000,3.724000,220100.000000

-118.300000,34.000000,40.000000,1131.000000,281.000000,859.000000,230.000000,1.180600,134600.000000

-121.840000,38.020000,46.000000,66.000000,22.000000,37.000000,21.000000,0.536000,87500.000000

-117.250000,32.800000,30.000000,2061.000000,631.000000,1007.000000,577.000000,2.581300,253100.000000

-124.140000,40.600000,27.000000,1148.000000,206.000000,521.000000,219.000000,4.025000,128100.000000

-118.180000,34.050000,52.000000,1070.000000,231.000000,925.000000,220.000000,1.825000,133000.000000

-119.780000,36.800000,34.000000,3426.000000,623.000000,1938.000000,647.000000,2.899400,66000.000000

-122.220000,38.080000,37.000000,2811.000000,539.000000,1574.000000,516.000000,3.105300,96700.000000

-118.500000,34.260000,33.000000,2831.000000,510.000000,1340.000000,504.000000,4.831600,237300.000000

-118.450000,34.180000,34.000000,1843.000000,442.000000,861.000000,417.000000,3.687500,246400.000000

-119.790000,36.310000,25.000000,4984.000000,1029.000000,2414.000000,961.000000,2.293700,72300.000000

-117.210000,32.740000,45.000000,3025.000000,583.000000,1980.000000,550.000000,2.298200,87500.000000

-122.080000,40.640000,14.000000,3099.000000,519.000000,1447.000000,494.000000,4.013200,141200.000000

-122.310000,37.520000,24.000000,2328.000000,335.000000,969.000000,354.000000,7.736400,435800.000000

-119.740000,36.760000,36.000000,912.000000,216.000000,842.000000,219.000000,1.476600,52800.000000

-118.280000,34.010000,52.000000,795.000000,308.000000,1118.000000,275.000000,1.217500,131300.000000

-118.270000,34.110000,39.000000,3825.000000,916.000000,1378.000000,746.000000,4.409400,352600.000000

-117.200000,33.160000,13.000000,4503.000000,1137.000000,3094.000000,1091.000000,2.315900,91600.000000

-122.330000,37.530000,25.000000,1729.000000,383.000000,769.000000,352.000000,4.041700,458500.000000

-120.860000,35.400000,21.000000,2787.000000,641.000000,1106.000000,501.000000,2.704300,186200.000000

-119.470000,35.400000,32.000000,2167.000000,421.000000,1301.000000,394.000000,1.971800,69800.000000

-117.270000,34.160000,32.000000,2894.000000,427.000000,1151.000000,446.000000,6.223600,159700.000000

-121.920000,38.020000,8.000000,2750.000000,479.000000,1526.000000,484.000000,5.102000,156500.000000

-121.450000,38.560000,51.000000,1250.000000,235.000000,452.000000,232.000000,2.625000,121200.000000

-117.910000,33.840000,16.000000,919.000000,253.000000,912.000000,249.000000,1.590300,165400.000000

-118.480000,35.610000,17.000000,4002.000000,930.000000,1614.000000,731.000000,1.623600,67300.000000

-118.030000,33.840000,28.000000,3857.000000,857.000000,2328.000000,830.000000,4.015600,196000.000000

-118.320000,34.040000,48.000000,1184.000000,328.000000,953.000000,311.000000,2.352600,156300.000000

-121.300000,38.890000,23.000000,1750.000000,297.000000,1012.000000,315.000000,3.470600,99300.000000

-117.690000,34.070000,34.000000,4055.000000,739.000000,2470.000000,753.000000,3.858600,136000.000000

-118.340000,33.940000,36.000000,2796.000000,1041.000000,4033.000000,944.000000,2.488600,160700.000000

-121.920000,36.620000,52.000000,2584.000000,599.000000,790.000000,444.000000,2.526300,286400.000000

-122.110000,37.410000,27.000000,5110.000000,1599.000000,2764.000000,1482.000000,3.419800,351900.000000

-117.650000,34.100000,44.000000,2808.000000,585.000000,1444.000000,550.000000,2.715900,139300.000000

-121.800000,38.010000,44.000000,3184.000000,581.000000,1399.000000,548.000000,2.723400,110200.000000

-122.660000,38.810000,22.000000,852.000000,176.000000,461.000000,142.000000,3.437500,83300.000000

-122.390000,37.780000,3.000000,3464.000000,1179.000000,1441.000000,919.000000,4.710500,275000.000000

-117.060000,34.870000,14.000000,3348.000000,619.000000,1756.000000,557.000000,3.598700,91400.000000

-121.340000,38.660000,16.000000,3154.000000,860.000000,1837.000000,793.000000,1.980500,92900.000000

-121.920000,36.950000,29.000000,3457.000000,699.000000,1327.000000,563.000000,3.659700,252300.000000

-122.590000,38.040000,25.000000,3412.000000,455.000000,1238.000000,406.000000,8.364600,397300.000000

-118.280000,34.110000,46.000000,1156.000000,203.000000,514.000000,213.000000,4.201900,352100.000000

-121.390000,38.600000,22.000000,5773.000000,1320.000000,2607.000000,1250.000000,2.523800,118800.000000

-122.330000,40.520000,23.000000,2801.000000,507.000000,1318.000000,454.000000,3.508100,116700.000000

-118.200000,34.040000,47.000000,1894.000000,408.000000,1629.000000,379.000000,3.761900,127600.000000

-121.960000,37.000000,20.000000,3847.000000,727.000000,1725.000000,737.000000,3.344700,305200.000000

-117.890000,33.870000,32.000000,1569.000000,422.000000,835.000000,386.000000,3.046500,148900.000000

-117.230000,32.880000,18.000000,5566.000000,1465.000000,6303.000000,1458.000000,1.858000,205000.000000

-122.000000,37.120000,17.000000,4413.000000,672.000000,1674.000000,608.000000,6.977200,383300.000000

-118.400000,34.280000,22.000000,3517.000000,810.000000,3134.000000,847.000000,2.665200,164800.000000

-122.460000,37.760000,52.000000,2236.000000,545.000000,1186.000000,532.000000,3.453100,414300.000000

-121.990000,37.540000,18.000000,3584.000000,715.000000,1673.000000,661.000000,3.944400,240100.000000

-117.230000,32.740000,16.000000,735.000000,139.000000,299.000000,134.000000,4.635400,179200.000000

-121.840000,37.290000,4.000000,2937.000000,648.000000,1780.000000,665.000000,4.385100,160400.000000

-118.150000,34.860000,10.000000,4597.000000,1009.000000,2227.000000,821.000000,2.614900,83500.000000

-118.330000,33.980000,38.000000,3063.000000,796.000000,2153.000000,721.000000,1.847200,149100.000000

-120.680000,35.510000,17.000000,1701.000000,298.000000,941.000000,293.000000,4.321800,209100.000000

-117.950000,33.790000,34.000000,2912.000000,520.000000,1625.000000,501.000000,4.466700,190600.000000

-117.970000,34.050000,33.000000,1452.000000,268.000000,1274.000000,278.000000,3.656300,162700.000000

-119.750000,36.870000,3.000000,13802.000000,2244.000000,5226.000000,1972.000000,5.094100,143700.000000

-122.080000,37.350000,35.000000,1347.000000,207.000000,548.000000,189.000000,7.706800,500001.000000

-122.320000,37.950000,36.000000,1425.000000,245.000000,573.000000,239.000000,4.350000,185000.000000

-122.220000,38.100000,38.000000,931.000000,181.000000,566.000000,207.000000,3.022100,93300.000000

-124.090000,40.550000,24.000000,2978.000000,553.000000,1370.000000,480.000000,2.764400,97300.000000

-121.500000,38.570000,41.000000,1124.000000,344.000000,807.000000,316.000000,1.471200,94600.000000

-118.110000,33.910000,19.000000,3056.000000,759.000000,1561.000000,740.000000,3.136900,196900.000000

-121.230000,37.960000,37.000000,2351.000000,564.000000,1591.000000,549.000000,1.656300,57200.000000

-121.890000,37.280000,35.000000,2418.000000,375.000000,988.000000,374.000000,6.093600,365400.000000

-122.480000,37.650000,39.000000,3348.000000,666.000000,1817.000000,668.000000,4.259300,227400.000000

-118.310000,34.090000,36.000000,2517.000000,842.000000,2446.000000,689.000000,2.152400,187500.000000

-123.020000,38.810000,35.000000,956.000000,213.000000,488.000000,215.000000,3.025000,140600.000000

-120.470000,34.650000,32.000000,2193.000000,430.000000,1074.000000,377.000000,2.333300,130200.000000

-122.100000,37.680000,37.000000,2116.000000,503.000000,1109.000000,448.000000,2.535000,174000.000000

-122.420000,37.790000,52.000000,3364.000000,1100.000000,2112.000000,1045.000000,2.134300,400000.000000

-122.640000,41.630000,19.000000,2722.000000,479.000000,1108.000000,430.000000,3.106200,100000.000000

-118.020000,33.910000,34.000000,2518.000000,429.000000,1309.000000,421.000000,4.786100,210700.000000

-119.020000,35.360000,48.000000,1833.000000,396.000000,947.000000,363.000000,2.282700,70000.000000

-121.330000,38.650000,23.000000,2446.000000,523.000000,1132.000000,513.000000,2.626600,198500.000000

-118.080000,33.950000,32.000000,1962.000000,387.000000,1274.000000,398.000000,4.830400,160600.000000

-118.080000,33.790000,34.000000,2840.000000,395.000000,1127.000000,396.000000,7.614400,376200.000000

-118.230000,33.910000,27.000000,1694.000000,393.000000,1890.000000,373.000000,3.034100,89100.000000

-118.290000,33.750000,37.000000,1319.000000,292.000000,766.000000,285.000000,2.703100,218900.000000

-118.020000,34.130000,34.000000,1966.000000,319.000000,980.000000,297.000000,7.730700,429000.000000

-117.890000,33.600000,36.000000,1496.000000,247.000000,441.000000,203.000000,7.816400,500001.000000

-118.230000,34.650000,17.000000,1827.000000,348.000000,766.000000,335.000000,3.567300,136300.000000

-118.310000,34.020000,45.000000,1423.000000,278.000000,822.000000,276.000000,2.451900,98100.000000

-118.070000,33.800000,34.000000,3486.000000,507.000000,1311.000000,503.000000,7.122100,384500.000000

-118.250000,33.940000,43.000000,1113.000000,378.000000,1305.000000,334.000000,1.143400,91300.000000

-122.440000,37.710000,52.000000,2711.000000,591.000000,1848.000000,524.000000,3.956700,251500.000000

-119.750000,34.500000,26.000000,3563.000000,579.000000,1479.000000,575.000000,5.952200,438400.000000

-117.940000,33.940000,26.000000,1962.000000,540.000000,1236.000000,520.000000,2.215600,145000.000000

-119.230000,34.170000,18.000000,6171.000000,1490.000000,2164.000000,1210.000000,3.687500,500001.000000

-118.110000,34.680000,6.000000,7430.000000,1184.000000,3489.000000,1115.000000,5.326700,140100.000000

-122.470000,37.770000,52.000000,2241.000000,443.000000,1042.000000,377.000000,4.163500,398400.000000

-120.930000,35.760000,11.000000,8997.000000,1698.000000,1825.000000,756.000000,3.230000,154300.000000

-118.140000,34.170000,52.000000,2667.000000,486.000000,1681.000000,504.000000,4.052400,173100.000000

-122.730000,38.460000,14.000000,4042.000000,1298.000000,2323.000000,1158.000000,2.065100,135400.000000

-117.060000,32.760000,37.000000,2356.000000,476.000000,1231.000000,499.000000,2.965000,155700.000000

-120.710000,35.500000,12.000000,3098.000000,453.000000,1433.000000,434.000000,5.250800,292900.000000

-118.310000,34.050000,35.000000,1692.000000,423.000000,1578.000000,406.000000,2.531300,305800.000000

-119.700000,36.750000,11.000000,3626.000000,779.000000,1819.000000,731.000000,2.495600,87500.000000

-121.340000,38.640000,17.000000,2761.000000,501.000000,1128.000000,482.000000,3.756200,139700.000000

-117.910000,34.090000,20.000000,4327.000000,1037.000000,2296.000000,963.000000,3.044100,185400.000000

-119.760000,36.790000,32.000000,2463.000000,468.000000,1261.000000,486.000000,3.328100,75100.000000

-120.660000,35.490000,17.000000,4422.000000,945.000000,2307.000000,885.000000,2.828500,171300.000000

-118.280000,34.080000,42.000000,1618.000000,522.000000,1454.000000,440.000000,3.160700,182000.000000

-122.540000,37.900000,48.000000,2491.000000,460.000000,937.000000,455.000000,4.437500,370000.000000

-117.590000,33.880000,13.000000,3239.000000,849.000000,2751.000000,813.000000,2.611100,107000.000000

-120.470000,34.940000,17.000000,1368.000000,308.000000,642.000000,303.000000,1.863300,109400.000000

-118.250000,33.930000,42.000000,819.000000,233.000000,899.000000,228.000000,1.134600,85400.000000

-121.970000,37.290000,25.000000,4096.000000,743.000000,2027.000000,741.000000,5.329400,300300.000000

-122.010000,36.970000,43.000000,2162.000000,509.000000,1208.000000,464.000000,2.541700,260900.000000

-122.020000,37.600000,32.000000,1295.000000,295.000000,1097.000000,328.000000,3.238600,149600.000000

-118.230000,34.090000,49.000000,1638.000000,456.000000,1500.000000,430.000000,2.692300,150000.000000

-117.170000,34.280000,13.000000,4867.000000,718.000000,780.000000,250.000000,7.199700,253800.000000

-122.330000,37.390000,52.000000,573.000000,102.000000,232.000000,92.000000,6.226300,500001.000000

-117.910000,33.600000,37.000000,2088.000000,510.000000,673.000000,390.000000,5.104800,500001.000000

-117.930000,33.860000,35.000000,931.000000,181.000000,516.000000,174.000000,5.586700,182500.000000

-119.860000,34.420000,23.000000,1450.000000,642.000000,1258.000000,607.000000,1.179000,225000.000000

-118.140000,34.060000,27.000000,5257.000000,1082.000000,3496.000000,1036.000000,3.390600,237200.000000

-119.700000,36.300000,10.000000,956.000000,201.000000,693.000000,220.000000,2.289500,62000.000000

-117.120000,34.100000,40.000000,96.000000,14.000000,46.000000,14.000000,3.270800,162500.000000

-119.630000,34.420000,42.000000,1765.000000,263.000000,753.000000,260.000000,8.560800,500001.000000

# csv open 방법 3 (import csv)
import csv

#csvReader = csv.reader(open("california_housing_test.csv"))

f= open('california_housing_test.csv','r').readlines()
#  readlines(): 파일을 한 줄씩 전체를 읽어들여서 리스트로 반환

for line in f:
    print(line)
    
# f.close()

"longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","median_house_value"

-122.050000,37.370000,27.000000,3885.000000,661.000000,1537.000000,606.000000,6.608500,344700.000000

-118.300000,34.260000,43.000000,1510.000000,310.000000,809.000000,277.000000,3.599000,176500.000000

-117.810000,33.780000,27.000000,3589.000000,507.000000,1484.000000,495.000000,5.793400,270500.000000

-118.360000,33.820000,28.000000,67.000000,15.000000,49.000000,11.000000,6.135900,330000.000000

-119.670000,36.330000,19.000000,1241.000000,244.000000,850.000000,237.000000,2.937500,81700.000000

-119.560000,36.510000,37.000000,1018.000000,213.000000,663.000000,204.000000,1.663500,67000.000000

-121.430000,38.630000,43.000000,1009.000000,225.000000,604.000000,218.000000,1.664100,67000.000000

-120.650000,35.480000,19.000000,2310.000000,471.000000,1341.000000,441.000000,3.225000,166900.000000

-122.840000,38.400000,15.000000,3080.000000,617.000000,1446.000000,599.000000,3.669600,194400.000000

-118.020000,34.080000,31.000000,2402.000000,632.000000,2830.000000,603.000000,2.333300,164200.000000

-118.240000,33.980000,45.000000,972.000000,249.000000,1288.000000,261.000000,2.205400,125000.000000

-119.120000,35.850000,37.000000,736.000000,166.000000,564.000000,138.000000,2.416700,58300.000000

-121.930000,37.250000,36.000000,1089.000000,182.000000,535.000000,170.000000,4.690000,252600.000000

-117.030000,32.970000,16.000000,3936.000000,694.000000,1935.000000,659.000000,4.562500,231200.000000

-117.970000,33.730000,27.000000,2097.000000,325.000000,1217.000000,331.000000,5.712100,222500.000000

-117.990000,33.810000,42.000000,161.000000,40.000000,157.000000,50.000000,2.200000,153100.000000

-120.810000,37.530000,15.000000,570.000000,123.000000,189.000000,107.000000,1.875000,181300.000000

-121.200000,38.690000,26.000000,3077.000000,607.000000,1603.000000,595.000000,2.717400,137500.000000

-118.880000,34.210000,26.000000,1590.000000,196.000000,654.000000,199.000000,6.585100,300000.000000

-122.590000,38.010000,35.000000,8814.000000,1307.000000,3450.000000,1258.000000,6.172400,414300.000000

-122.150000,37.750000,40.000000,1445.000000,256.000000,849.000000,255.000000,3.891300,126300.000000

-121.370000,38.680000,36.000000,1775.000000,296.000000,937.000000,305.000000,3.178600,83400.000000

-118.160000,34.070000,47.000000,2994.000000,543.000000,1651.000000,561.000000,3.864400,241500.000000

-122.200000,37.790000,45.000000,2021.000000,528.000000,1410.000000,480.000000,2.778800,115400.000000

-117.280000,33.280000,13.000000,6131.000000,1040.000000,4049.000000,940.000000,3.815600,150700.000000

-118.030000,34.160000,36.000000,1401.000000,218.000000,667.000000,225.000000,7.161500,484700.000000

-122.420000,37.760000,52.000000,3587.000000,1030.000000,2259.000000,979.000000,2.540300,250000.000000

-118.390000,33.990000,32.000000,2612.000000,418.000000,1030.000000,402.000000,6.603000,369200.000000

-118.450000,34.070000,19.000000,4845.000000,1609.000000,3751.000000,1539.000000,1.583000,350000.000000

-118.480000,34.010000,30.000000,3078.000000,954.000000,1561.000000,901.000000,3.485200,425000.000000

-119.350000,36.330000,14.000000,1195.000000,220.000000,568.000000,229.000000,3.148600,105600.000000

-118.300000,33.910000,34.000000,1617.000000,493.000000,1530.000000,500.000000,2.618200,172600.000000

-121.130000,39.310000,17.000000,3442.000000,705.000000,1693.000000,619.000000,2.810200,128900.000000

-118.080000,34.550000,5.000000,16181.000000,2971.000000,8152.000000,2651.000000,4.523700,141800.000000

-118.320000,33.940000,38.000000,1067.000000,170.000000,499.000000,169.000000,4.638900,183800.000000

-118.110000,34.000000,33.000000,2886.000000,726.000000,2650.000000,728.000000,2.625000,178700.000000

-122.530000,37.970000,52.000000,1560.000000,451.000000,700.000000,419.000000,2.512500,270800.000000

-118.020000,33.920000,34.000000,1478.000000,251.000000,956.000000,277.000000,5.523800,185300.000000

-118.050000,33.930000,31.000000,894.000000,203.000000,883.000000,190.000000,3.677100,141500.000000

-119.010000,34.230000,11.000000,5785.000000,1035.000000,2760.000000,985.000000,4.693000,232200.000000

-119.320000,36.200000,15.000000,1562.000000,275.000000,961.000000,287.000000,3.423100,83300.000000

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-122.080000,37.350000,35.000000,1347.000000,207.000000,548.000000,189.000000,7.706800,500001.000000

-122.320000,37.950000,36.000000,1425.000000,245.000000,573.000000,239.000000,4.350000,185000.000000

-122.220000,38.100000,38.000000,931.000000,181.000000,566.000000,207.000000,3.022100,93300.000000

-124.090000,40.550000,24.000000,2978.000000,553.000000,1370.000000,480.000000,2.764400,97300.000000

-121.500000,38.570000,41.000000,1124.000000,344.000000,807.000000,316.000000,1.471200,94600.000000

-118.110000,33.910000,19.000000,3056.000000,759.000000,1561.000000,740.000000,3.136900,196900.000000

-121.230000,37.960000,37.000000,2351.000000,564.000000,1591.000000,549.000000,1.656300,57200.000000

-121.890000,37.280000,35.000000,2418.000000,375.000000,988.000000,374.000000,6.093600,365400.000000

-122.480000,37.650000,39.000000,3348.000000,666.000000,1817.000000,668.000000,4.259300,227400.000000

-118.310000,34.090000,36.000000,2517.000000,842.000000,2446.000000,689.000000,2.152400,187500.000000

-123.020000,38.810000,35.000000,956.000000,213.000000,488.000000,215.000000,3.025000,140600.000000

-120.470000,34.650000,32.000000,2193.000000,430.000000,1074.000000,377.000000,2.333300,130200.000000

-122.100000,37.680000,37.000000,2116.000000,503.000000,1109.000000,448.000000,2.535000,174000.000000

-122.420000,37.790000,52.000000,3364.000000,1100.000000,2112.000000,1045.000000,2.134300,400000.000000

-122.640000,41.630000,19.000000,2722.000000,479.000000,1108.000000,430.000000,3.106200,100000.000000

-118.020000,33.910000,34.000000,2518.000000,429.000000,1309.000000,421.000000,4.786100,210700.000000

-119.020000,35.360000,48.000000,1833.000000,396.000000,947.000000,363.000000,2.282700,70000.000000

-121.330000,38.650000,23.000000,2446.000000,523.000000,1132.000000,513.000000,2.626600,198500.000000

-118.080000,33.950000,32.000000,1962.000000,387.000000,1274.000000,398.000000,4.830400,160600.000000

-118.080000,33.790000,34.000000,2840.000000,395.000000,1127.000000,396.000000,7.614400,376200.000000

-118.230000,33.910000,27.000000,1694.000000,393.000000,1890.000000,373.000000,3.034100,89100.000000

-118.290000,33.750000,37.000000,1319.000000,292.000000,766.000000,285.000000,2.703100,218900.000000

-118.020000,34.130000,34.000000,1966.000000,319.000000,980.000000,297.000000,7.730700,429000.000000

-117.890000,33.600000,36.000000,1496.000000,247.000000,441.000000,203.000000,7.816400,500001.000000

-118.230000,34.650000,17.000000,1827.000000,348.000000,766.000000,335.000000,3.567300,136300.000000

-118.310000,34.020000,45.000000,1423.000000,278.000000,822.000000,276.000000,2.451900,98100.000000

-118.070000,33.800000,34.000000,3486.000000,507.000000,1311.000000,503.000000,7.122100,384500.000000

-118.250000,33.940000,43.000000,1113.000000,378.000000,1305.000000,334.000000,1.143400,91300.000000

-122.440000,37.710000,52.000000,2711.000000,591.000000,1848.000000,524.000000,3.956700,251500.000000

-119.750000,34.500000,26.000000,3563.000000,579.000000,1479.000000,575.000000,5.952200,438400.000000

-117.940000,33.940000,26.000000,1962.000000,540.000000,1236.000000,520.000000,2.215600,145000.000000

-119.230000,34.170000,18.000000,6171.000000,1490.000000,2164.000000,1210.000000,3.687500,500001.000000

-118.110000,34.680000,6.000000,7430.000000,1184.000000,3489.000000,1115.000000,5.326700,140100.000000

-122.470000,37.770000,52.000000,2241.000000,443.000000,1042.000000,377.000000,4.163500,398400.000000

-120.930000,35.760000,11.000000,8997.000000,1698.000000,1825.000000,756.000000,3.230000,154300.000000

-118.140000,34.170000,52.000000,2667.000000,486.000000,1681.000000,504.000000,4.052400,173100.000000

-122.730000,38.460000,14.000000,4042.000000,1298.000000,2323.000000,1158.000000,2.065100,135400.000000

-117.060000,32.760000,37.000000,2356.000000,476.000000,1231.000000,499.000000,2.965000,155700.000000

-120.710000,35.500000,12.000000,3098.000000,453.000000,1433.000000,434.000000,5.250800,292900.000000

-118.310000,34.050000,35.000000,1692.000000,423.000000,1578.000000,406.000000,2.531300,305800.000000

-119.700000,36.750000,11.000000,3626.000000,779.000000,1819.000000,731.000000,2.495600,87500.000000

-121.340000,38.640000,17.000000,2761.000000,501.000000,1128.000000,482.000000,3.756200,139700.000000

-117.910000,34.090000,20.000000,4327.000000,1037.000000,2296.000000,963.000000,3.044100,185400.000000

-119.760000,36.790000,32.000000,2463.000000,468.000000,1261.000000,486.000000,3.328100,75100.000000

-120.660000,35.490000,17.000000,4422.000000,945.000000,2307.000000,885.000000,2.828500,171300.000000

-118.280000,34.080000,42.000000,1618.000000,522.000000,1454.000000,440.000000,3.160700,182000.000000

-122.540000,37.900000,48.000000,2491.000000,460.000000,937.000000,455.000000,4.437500,370000.000000

-117.590000,33.880000,13.000000,3239.000000,849.000000,2751.000000,813.000000,2.611100,107000.000000

-120.470000,34.940000,17.000000,1368.000000,308.000000,642.000000,303.000000,1.863300,109400.000000

-118.250000,33.930000,42.000000,819.000000,233.000000,899.000000,228.000000,1.134600,85400.000000

-121.970000,37.290000,25.000000,4096.000000,743.000000,2027.000000,741.000000,5.329400,300300.000000

-122.010000,36.970000,43.000000,2162.000000,509.000000,1208.000000,464.000000,2.541700,260900.000000

-122.020000,37.600000,32.000000,1295.000000,295.000000,1097.000000,328.000000,3.238600,149600.000000

-118.230000,34.090000,49.000000,1638.000000,456.000000,1500.000000,430.000000,2.692300,150000.000000

-117.170000,34.280000,13.000000,4867.000000,718.000000,780.000000,250.000000,7.199700,253800.000000

-122.330000,37.390000,52.000000,573.000000,102.000000,232.000000,92.000000,6.226300,500001.000000

-117.910000,33.600000,37.000000,2088.000000,510.000000,673.000000,390.000000,5.104800,500001.000000

-117.930000,33.860000,35.000000,931.000000,181.000000,516.000000,174.000000,5.586700,182500.000000

-119.860000,34.420000,23.000000,1450.000000,642.000000,1258.000000,607.000000,1.179000,225000.000000

-118.140000,34.060000,27.000000,5257.000000,1082.000000,3496.000000,1036.000000,3.390600,237200.000000

-119.700000,36.300000,10.000000,956.000000,201.000000,693.000000,220.000000,2.289500,62000.000000

-117.120000,34.100000,40.000000,96.000000,14.000000,46.000000,14.000000,3.270800,162500.000000

-119.630000,34.420000,42.000000,1765.000000,263.000000,753.000000,260.000000,8.560800,500001.000000

f[0].strip().split()

['"longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","median_house_value"']

f[1]

'-122.050000,37.370000,27.000000,3885.000000,661.000000,1537.000000,606.000000,6.608500,344700.000000\n'

# csv.dictreader => 항목의 이름을 key로, 그 값을 value로 갖게 되는 하나의 딕셔너리를 하나의 행으로 불러올 수 있게됩니다!
csvdict = csv.DictReader(f)

for row in csvdict:
    print(row)

{'longitude': '-122.050000', 'latitude': '37.370000', 'housing_median_age': '27.000000', 'total_rooms': '3885.000000', 'total_bedrooms': '661.000000', 'population': '1537.000000', 'households': '606.000000', 'median_income': '6.608500', 'median_house_value': '344700.000000'}
{'longitude': '-118.300000', 'latitude': '34.260000', 'housing_median_age': '43.000000', 'total_rooms': '1510.000000', 'total_bedrooms': '310.000000', 'population': '809.000000', 'households': '277.000000', 'median_income': '3.599000', 'median_house_value': '176500.000000'}
{'longitude': '-117.810000', 'latitude': '33.780000', 'housing_median_age': '27.000000', 'total_rooms': '3589.000000', 'total_bedrooms': '507.000000', 'population': '1484.000000', 'households': '495.000000', 'median_income': '5.793400', 'median_house_value': '270500.000000'}
{'longitude': '-118.360000', 'latitude': '33.820000', 'housing_median_age': '28.000000', 'total_rooms': '67.000000', 'total_bedrooms': '15.000000', 'population': '49.000000', 'households': '11.000000', 'median_income': '6.135900', 'median_house_value': '330000.000000'}
{'longitude': '-119.670000', 'latitude': '36.330000', 'housing_median_age': '19.000000', 'total_rooms': '1241.000000', 'total_bedrooms': '244.000000', 'population': '850.000000', 'households': '237.000000', 'median_income': '2.937500', 'median_house_value': '81700.000000'}
{'longitude': '-119.560000', 'latitude': '36.510000', 'housing_median_age': '37.000000', 'total_rooms': '1018.000000', 'total_bedrooms': '213.000000', 'population': '663.000000', 'households': '204.000000', 'median_income': '1.663500', 'median_house_value': '67000.000000'}
{'longitude': '-121.430000', 'latitude': '38.630000', 'housing_median_age': '43.000000', 'total_rooms': '1009.000000', 'total_bedrooms': '225.000000', 'population': '604.000000', 'households': '218.000000', 'median_income': '1.664100', 'median_house_value': '67000.000000'}
{'longitude': '-120.650000', 'latitude': '35.480000', 'housing_median_age': '19.000000', 'total_rooms': '2310.000000', 'total_bedrooms': '471.000000', 'population': '1341.000000', 'households': '441.000000', 'median_income': '3.225000', 'median_house_value': '166900.000000'}
{'longitude': '-122.840000', 'latitude': '38.400000', 'housing_median_age': '15.000000', 'total_rooms': '3080.000000', 'total_bedrooms': '617.000000', 'population': '1446.000000', 'households': '599.000000', 'median_income': '3.669600', 'median_house_value': '194400.000000'}
{'longitude': '-118.020000', 'latitude': '34.080000', 'housing_median_age': '31.000000', 'total_rooms': '2402.000000', 'total_bedrooms': '632.000000', 'population': '2830.000000', 'households': '603.000000', 'median_income': '2.333300', 'median_house_value': '164200.000000'}
{'longitude': '-118.240000', 'latitude': '33.980000', 'housing_median_age': '45.000000', 'total_rooms': '972.000000', 'total_bedrooms': '249.000000', 'population': '1288.000000', 'households': '261.000000', 'median_income': '2.205400', 'median_house_value': '125000.000000'}
{'longitude': '-119.120000', 'latitude': '35.850000', 'housing_median_age': '37.000000', 'total_rooms': '736.000000', 'total_bedrooms': '166.000000', 'population': '564.000000', 'households': '138.000000', 'median_income': '2.416700', 'median_house_value': '58300.000000'}
{'longitude': '-121.930000', 'latitude': '37.250000', 'housing_median_age': '36.000000', 'total_rooms': '1089.000000', 'total_bedrooms': '182.000000', 'population': '535.000000', 'households': '170.000000', 'median_income': '4.690000', 'median_house_value': '252600.000000'}
{'longitude': '-117.030000', 'latitude': '32.970000', 'housing_median_age': '16.000000', 'total_rooms': '3936.000000', 'total_bedrooms': '694.000000', 'population': '1935.000000', 'households': '659.000000', 'median_income': '4.562500', 'median_house_value': '231200.000000'}
{'longitude': '-117.970000', 'latitude': '33.730000', 'housing_median_age': '27.000000', 'total_rooms': '2097.000000', 'total_bedrooms': '325.000000', 'population': '1217.000000', 'households': '331.000000', 'median_income': '5.712100', 'median_house_value': '222500.000000'}
{'longitude': '-117.990000', 'latitude': '33.810000', 'housing_median_age': '42.000000', 'total_rooms': '161.000000', 'total_bedrooms': '40.000000', 'population': '157.000000', 'households': '50.000000', 'median_income': '2.200000', 'median_house_value': '153100.000000'}
{'longitude': '-120.810000', 'latitude': '37.530000', 'housing_median_age': '15.000000', 'total_rooms': '570.000000', 'total_bedrooms': '123.000000', 'population': '189.000000', 'households': '107.000000', 'median_income': '1.875000', 'median_house_value': '181300.000000'}
{'longitude': '-121.200000', 'latitude': '38.690000', 'housing_median_age': '26.000000', 'total_rooms': '3077.000000', 'total_bedrooms': '607.000000', 'population': '1603.000000', 'households': '595.000000', 'median_income': '2.717400', 'median_house_value': '137500.000000'}
{'longitude': '-118.880000', 'latitude': '34.210000', 'housing_median_age': '26.000000', 'total_rooms': '1590.000000', 'total_bedrooms': '196.000000', 'population': '654.000000', 'households': '199.000000', 'median_income': '6.585100', 'median_house_value': '300000.000000'}
{'longitude': '-122.590000', 'latitude': '38.010000', 'housing_median_age': '35.000000', 'total_rooms': '8814.000000', 'total_bedrooms': '1307.000000', 'population': '3450.000000', 'households': '1258.000000', 'median_income': '6.172400', 'median_house_value': '414300.000000'}
{'longitude': '-122.150000', 'latitude': '37.750000', 'housing_median_age': '40.000000', 'total_rooms': '1445.000000', 'total_bedrooms': '256.000000', 'population': '849.000000', 'households': '255.000000', 'median_income': '3.891300', 'median_house_value': '126300.000000'}
{'longitude': '-121.370000', 'latitude': '38.680000', 'housing_median_age': '36.000000', 'total_rooms': '1775.000000', 'total_bedrooms': '296.000000', 'population': '937.000000', 'households': '305.000000', 'median_income': '3.178600', 'median_house_value': '83400.000000'}
{'longitude': '-118.160000', 'latitude': '34.070000', 'housing_median_age': '47.000000', 'total_rooms': '2994.000000', 'total_bedrooms': '543.000000', 'population': '1651.000000', 'households': '561.000000', 'median_income': '3.864400', 'median_house_value': '241500.000000'}
{'longitude': '-122.200000', 'latitude': '37.790000', 'housing_median_age': '45.000000', 'total_rooms': '2021.000000', 'total_bedrooms': '528.000000', 'population': '1410.000000', 'households': '480.000000', 'median_income': '2.778800', 'median_house_value': '115400.000000'}
{'longitude': '-117.280000', 'latitude': '33.280000', 'housing_median_age': '13.000000', 'total_rooms': '6131.000000', 'total_bedrooms': '1040.000000', 'population': '4049.000000', 'households': '940.000000', 'median_income': '3.815600', 'median_house_value': '150700.000000'}
{'longitude': '-118.030000', 'latitude': '34.160000', 'housing_median_age': '36.000000', 'total_rooms': '1401.000000', 'total_bedrooms': '218.000000', 'population': '667.000000', 'households': '225.000000', 'median_income': '7.161500', 'median_house_value': '484700.000000'}
{'longitude': '-122.420000', 'latitude': '37.760000', 'housing_median_age': '52.000000', 'total_rooms': '3587.000000', 'total_bedrooms': '1030.000000', 'population': '2259.000000', 'households': '979.000000', 'median_income': '2.540300', 'median_house_value': '250000.000000'}
{'longitude': '-118.390000', 'latitude': '33.990000', 'housing_median_age': '32.000000', 'total_rooms': '2612.000000', 'total_bedrooms': '418.000000', 'population': '1030.000000', 'households': '402.000000', 'median_income': '6.603000', 'median_house_value': '369200.000000'}
{'longitude': '-118.450000', 'latitude': '34.070000', 'housing_median_age': '19.000000', 'total_rooms': '4845.000000', 'total_bedrooms': '1609.000000', 'population': '3751.000000', 'households': '1539.000000', 'median_income': '1.583000', 'median_house_value': '350000.000000'}
{'longitude': '-118.480000', 'latitude': '34.010000', 'housing_median_age': '30.000000', 'total_rooms': '3078.000000', 'total_bedrooms': '954.000000', 'population': '1561.000000', 'households': '901.000000', 'median_income': '3.485200', 'median_house_value': '425000.000000'}
{'longitude': '-119.350000', 'latitude': '36.330000', 'housing_median_age': '14.000000', 'total_rooms': '1195.000000', 'total_bedrooms': '220.000000', 'population': '568.000000', 'households': '229.000000', 'median_income': '3.148600', 'median_house_value': '105600.000000'}
{'longitude': '-118.300000', 'latitude': '33.910000', 'housing_median_age': '34.000000', 'total_rooms': '1617.000000', 'total_bedrooms': '493.000000', 'population': '1530.000000', 'households': '500.000000', 'median_income': '2.618200', 'median_house_value': '172600.000000'}
{'longitude': '-121.130000', 'latitude': '39.310000', 'housing_median_age': '17.000000', 'total_rooms': '3442.000000', 'total_bedrooms': '705.000000', 'population': '1693.000000', 'households': '619.000000', 'median_income': '2.810200', 'median_house_value': '128900.000000'}
{'longitude': '-118.080000', 'latitude': '34.550000', 'housing_median_age': '5.000000', 'total_rooms': '16181.000000', 'total_bedrooms': '2971.000000', 'population': '8152.000000', 'households': '2651.000000', 'median_income': '4.523700', 'median_house_value': '141800.000000'}
{'longitude': '-118.320000', 'latitude': '33.940000', 'housing_median_age': '38.000000', 'total_rooms': '1067.000000', 'total_bedrooms': '170.000000', 'population': '499.000000', 'households': '169.000000', 'median_income': '4.638900', 'median_house_value': '183800.000000'}
{'longitude': '-118.110000', 'latitude': '34.000000', 'housing_median_age': '33.000000', 'total_rooms': '2886.000000', 'total_bedrooms': '726.000000', 'population': '2650.000000', 'households': '728.000000', 'median_income': '2.625000', 'median_house_value': '178700.000000'}
{'longitude': '-122.530000', 'latitude': '37.970000', 'housing_median_age': '52.000000', 'total_rooms': '1560.000000', 'total_bedrooms': '451.000000', 'population': '700.000000', 'households': '419.000000', 'median_income': '2.512500', 'median_house_value': '270800.000000'}
{'longitude': '-118.020000', 'latitude': '33.920000', 'housing_median_age': '34.000000', 'total_rooms': '1478.000000', 'total_bedrooms': '251.000000', 'population': '956.000000', 'households': '277.000000', 'median_income': '5.523800', 'median_house_value': '185300.000000'}
{'longitude': '-118.050000', 'latitude': '33.930000', 'housing_median_age': '31.000000', 'total_rooms': '894.000000', 'total_bedrooms': '203.000000', 'population': '883.000000', 'households': '190.000000', 'median_income': '3.677100', 'median_house_value': '141500.000000'}
{'longitude': '-119.010000', 'latitude': '34.230000', 'housing_median_age': '11.000000', 'total_rooms': '5785.000000', 'total_bedrooms': '1035.000000', 'population': '2760.000000', 'households': '985.000000', 'median_income': '4.693000', 'median_house_value': '232200.000000'}
{'longitude': '-119.320000', 'latitude': '36.200000', 'housing_median_age': '15.000000', 'total_rooms': '1562.000000', 'total_bedrooms': '275.000000', 'population': '961.000000', 'households': '287.000000', 'median_income': '3.423100', 'median_house_value': '83300.000000'}
{'longitude': '-116.920000', 'latitude': '32.770000', 'housing_median_age': '16.000000', 'total_rooms': '2770.000000', 'total_bedrooms': '406.000000', 'population': '1269.000000', 'households': '429.000000', 'median_income': '6.678300', 'median_house_value': '275000.000000'}
{'longitude': '-118.060000', 'latitude': '34.150000', 'housing_median_age': '37.000000', 'total_rooms': '1980.000000', 'total_bedrooms': '226.000000', 'population': '697.000000', 'households': '226.000000', 'median_income': '15.000100', 'median_house_value': '500001.000000'}
{'longitude': '-117.270000', 'latitude': '34.090000', 'housing_median_age': '36.000000', 'total_rooms': '848.000000', 'total_bedrooms': '186.000000', 'population': '737.000000', 'households': '169.000000', 'median_income': '0.983800', 'median_house_value': '79300.000000'}
{'longitude': '-118.230000', 'latitude': '34.130000', 'housing_median_age': '48.000000', 'total_rooms': '1308.000000', 'total_bedrooms': '286.000000', 'population': '835.000000', 'households': '294.000000', 'median_income': '4.289100', 'median_house_value': '214800.000000'}
{'longitude': '-117.240000', 'latitude': '33.170000', 'housing_median_age': '4.000000', 'total_rooms': '9998.000000', 'total_bedrooms': '1874.000000', 'population': '3925.000000', 'households': '1672.000000', 'median_income': '4.282600', 'median_house_value': '237500.000000'}
{'longitude': '-121.910000', 'latitude': '37.440000', 'housing_median_age': '24.000000', 'total_rooms': '1212.000000', 'total_bedrooms': '251.000000', 'population': '799.000000', 'households': '242.000000', 'median_income': '5.080800', 'median_house_value': '212500.000000'}
{'longitude': '-118.290000', 'latitude': '33.940000', 'housing_median_age': '47.000000', 'total_rooms': '1782.000000', 'total_bedrooms': '338.000000', 'population': '1003.000000', 'households': '329.000000', 'median_income': '2.539800', 'median_house_value': '105700.000000'}
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{'longitude': '-121.840000', 'latitude': '37.290000', 'housing_median_age': '4.000000', 'total_rooms': '2937.000000', 'total_bedrooms': '648.000000', 'population': '1780.000000', 'households': '665.000000', 'median_income': '4.385100', 'median_house_value': '160400.000000'}
{'longitude': '-118.150000', 'latitude': '34.860000', 'housing_median_age': '10.000000', 'total_rooms': '4597.000000', 'total_bedrooms': '1009.000000', 'population': '2227.000000', 'households': '821.000000', 'median_income': '2.614900', 'median_house_value': '83500.000000'}
{'longitude': '-118.330000', 'latitude': '33.980000', 'housing_median_age': '38.000000', 'total_rooms': '3063.000000', 'total_bedrooms': '796.000000', 'population': '2153.000000', 'households': '721.000000', 'median_income': '1.847200', 'median_house_value': '149100.000000'}
{'longitude': '-120.680000', 'latitude': '35.510000', 'housing_median_age': '17.000000', 'total_rooms': '1701.000000', 'total_bedrooms': '298.000000', 'population': '941.000000', 'households': '293.000000', 'median_income': '4.321800', 'median_house_value': '209100.000000'}
{'longitude': '-117.950000', 'latitude': '33.790000', 'housing_median_age': '34.000000', 'total_rooms': '2912.000000', 'total_bedrooms': '520.000000', 'population': '1625.000000', 'households': '501.000000', 'median_income': '4.466700', 'median_house_value': '190600.000000'}
{'longitude': '-117.970000', 'latitude': '34.050000', 'housing_median_age': '33.000000', 'total_rooms': '1452.000000', 'total_bedrooms': '268.000000', 'population': '1274.000000', 'households': '278.000000', 'median_income': '3.656300', 'median_house_value': '162700.000000'}
{'longitude': '-119.750000', 'latitude': '36.870000', 'housing_median_age': '3.000000', 'total_rooms': '13802.000000', 'total_bedrooms': '2244.000000', 'population': '5226.000000', 'households': '1972.000000', 'median_income': '5.094100', 'median_house_value': '143700.000000'}
{'longitude': '-122.080000', 'latitude': '37.350000', 'housing_median_age': '35.000000', 'total_rooms': '1347.000000', 'total_bedrooms': '207.000000', 'population': '548.000000', 'households': '189.000000', 'median_income': '7.706800', 'median_house_value': '500001.000000'}
{'longitude': '-122.320000', 'latitude': '37.950000', 'housing_median_age': '36.000000', 'total_rooms': '1425.000000', 'total_bedrooms': '245.000000', 'population': '573.000000', 'households': '239.000000', 'median_income': '4.350000', 'median_house_value': '185000.000000'}
{'longitude': '-122.220000', 'latitude': '38.100000', 'housing_median_age': '38.000000', 'total_rooms': '931.000000', 'total_bedrooms': '181.000000', 'population': '566.000000', 'households': '207.000000', 'median_income': '3.022100', 'median_house_value': '93300.000000'}
{'longitude': '-124.090000', 'latitude': '40.550000', 'housing_median_age': '24.000000', 'total_rooms': '2978.000000', 'total_bedrooms': '553.000000', 'population': '1370.000000', 'households': '480.000000', 'median_income': '2.764400', 'median_house_value': '97300.000000'}
{'longitude': '-121.500000', 'latitude': '38.570000', 'housing_median_age': '41.000000', 'total_rooms': '1124.000000', 'total_bedrooms': '344.000000', 'population': '807.000000', 'households': '316.000000', 'median_income': '1.471200', 'median_house_value': '94600.000000'}
{'longitude': '-118.110000', 'latitude': '33.910000', 'housing_median_age': '19.000000', 'total_rooms': '3056.000000', 'total_bedrooms': '759.000000', 'population': '1561.000000', 'households': '740.000000', 'median_income': '3.136900', 'median_house_value': '196900.000000'}
{'longitude': '-121.230000', 'latitude': '37.960000', 'housing_median_age': '37.000000', 'total_rooms': '2351.000000', 'total_bedrooms': '564.000000', 'population': '1591.000000', 'households': '549.000000', 'median_income': '1.656300', 'median_house_value': '57200.000000'}
{'longitude': '-121.890000', 'latitude': '37.280000', 'housing_median_age': '35.000000', 'total_rooms': '2418.000000', 'total_bedrooms': '375.000000', 'population': '988.000000', 'households': '374.000000', 'median_income': '6.093600', 'median_house_value': '365400.000000'}
{'longitude': '-122.480000', 'latitude': '37.650000', 'housing_median_age': '39.000000', 'total_rooms': '3348.000000', 'total_bedrooms': '666.000000', 'population': '1817.000000', 'households': '668.000000', 'median_income': '4.259300', 'median_house_value': '227400.000000'}
{'longitude': '-118.310000', 'latitude': '34.090000', 'housing_median_age': '36.000000', 'total_rooms': '2517.000000', 'total_bedrooms': '842.000000', 'population': '2446.000000', 'households': '689.000000', 'median_income': '2.152400', 'median_house_value': '187500.000000'}
{'longitude': '-123.020000', 'latitude': '38.810000', 'housing_median_age': '35.000000', 'total_rooms': '956.000000', 'total_bedrooms': '213.000000', 'population': '488.000000', 'households': '215.000000', 'median_income': '3.025000', 'median_house_value': '140600.000000'}
{'longitude': '-120.470000', 'latitude': '34.650000', 'housing_median_age': '32.000000', 'total_rooms': '2193.000000', 'total_bedrooms': '430.000000', 'population': '1074.000000', 'households': '377.000000', 'median_income': '2.333300', 'median_house_value': '130200.000000'}
{'longitude': '-122.100000', 'latitude': '37.680000', 'housing_median_age': '37.000000', 'total_rooms': '2116.000000', 'total_bedrooms': '503.000000', 'population': '1109.000000', 'households': '448.000000', 'median_income': '2.535000', 'median_house_value': '174000.000000'}
{'longitude': '-122.420000', 'latitude': '37.790000', 'housing_median_age': '52.000000', 'total_rooms': '3364.000000', 'total_bedrooms': '1100.000000', 'population': '2112.000000', 'households': '1045.000000', 'median_income': '2.134300', 'median_house_value': '400000.000000'}
{'longitude': '-122.640000', 'latitude': '41.630000', 'housing_median_age': '19.000000', 'total_rooms': '2722.000000', 'total_bedrooms': '479.000000', 'population': '1108.000000', 'households': '430.000000', 'median_income': '3.106200', 'median_house_value': '100000.000000'}
{'longitude': '-118.020000', 'latitude': '33.910000', 'housing_median_age': '34.000000', 'total_rooms': '2518.000000', 'total_bedrooms': '429.000000', 'population': '1309.000000', 'households': '421.000000', 'median_income': '4.786100', 'median_house_value': '210700.000000'}
{'longitude': '-119.020000', 'latitude': '35.360000', 'housing_median_age': '48.000000', 'total_rooms': '1833.000000', 'total_bedrooms': '396.000000', 'population': '947.000000', 'households': '363.000000', 'median_income': '2.282700', 'median_house_value': '70000.000000'}
{'longitude': '-121.330000', 'latitude': '38.650000', 'housing_median_age': '23.000000', 'total_rooms': '2446.000000', 'total_bedrooms': '523.000000', 'population': '1132.000000', 'households': '513.000000', 'median_income': '2.626600', 'median_house_value': '198500.000000'}
{'longitude': '-118.080000', 'latitude': '33.950000', 'housing_median_age': '32.000000', 'total_rooms': '1962.000000', 'total_bedrooms': '387.000000', 'population': '1274.000000', 'households': '398.000000', 'median_income': '4.830400', 'median_house_value': '160600.000000'}
{'longitude': '-118.080000', 'latitude': '33.790000', 'housing_median_age': '34.000000', 'total_rooms': '2840.000000', 'total_bedrooms': '395.000000', 'population': '1127.000000', 'households': '396.000000', 'median_income': '7.614400', 'median_house_value': '376200.000000'}
{'longitude': '-118.230000', 'latitude': '33.910000', 'housing_median_age': '27.000000', 'total_rooms': '1694.000000', 'total_bedrooms': '393.000000', 'population': '1890.000000', 'households': '373.000000', 'median_income': '3.034100', 'median_house_value': '89100.000000'}
{'longitude': '-118.290000', 'latitude': '33.750000', 'housing_median_age': '37.000000', 'total_rooms': '1319.000000', 'total_bedrooms': '292.000000', 'population': '766.000000', 'households': '285.000000', 'median_income': '2.703100', 'median_house_value': '218900.000000'}
{'longitude': '-118.020000', 'latitude': '34.130000', 'housing_median_age': '34.000000', 'total_rooms': '1966.000000', 'total_bedrooms': '319.000000', 'population': '980.000000', 'households': '297.000000', 'median_income': '7.730700', 'median_house_value': '429000.000000'}
{'longitude': '-117.890000', 'latitude': '33.600000', 'housing_median_age': '36.000000', 'total_rooms': '1496.000000', 'total_bedrooms': '247.000000', 'population': '441.000000', 'households': '203.000000', 'median_income': '7.816400', 'median_house_value': '500001.000000'}
{'longitude': '-118.230000', 'latitude': '34.650000', 'housing_median_age': '17.000000', 'total_rooms': '1827.000000', 'total_bedrooms': '348.000000', 'population': '766.000000', 'households': '335.000000', 'median_income': '3.567300', 'median_house_value': '136300.000000'}
{'longitude': '-118.310000', 'latitude': '34.020000', 'housing_median_age': '45.000000', 'total_rooms': '1423.000000', 'total_bedrooms': '278.000000', 'population': '822.000000', 'households': '276.000000', 'median_income': '2.451900', 'median_house_value': '98100.000000'}
{'longitude': '-118.070000', 'latitude': '33.800000', 'housing_median_age': '34.000000', 'total_rooms': '3486.000000', 'total_bedrooms': '507.000000', 'population': '1311.000000', 'households': '503.000000', 'median_income': '7.122100', 'median_house_value': '384500.000000'}
{'longitude': '-118.250000', 'latitude': '33.940000', 'housing_median_age': '43.000000', 'total_rooms': '1113.000000', 'total_bedrooms': '378.000000', 'population': '1305.000000', 'households': '334.000000', 'median_income': '1.143400', 'median_house_value': '91300.000000'}
{'longitude': '-122.440000', 'latitude': '37.710000', 'housing_median_age': '52.000000', 'total_rooms': '2711.000000', 'total_bedrooms': '591.000000', 'population': '1848.000000', 'households': '524.000000', 'median_income': '3.956700', 'median_house_value': '251500.000000'}
{'longitude': '-119.750000', 'latitude': '34.500000', 'housing_median_age': '26.000000', 'total_rooms': '3563.000000', 'total_bedrooms': '579.000000', 'population': '1479.000000', 'households': '575.000000', 'median_income': '5.952200', 'median_house_value': '438400.000000'}
{'longitude': '-117.940000', 'latitude': '33.940000', 'housing_median_age': '26.000000', 'total_rooms': '1962.000000', 'total_bedrooms': '540.000000', 'population': '1236.000000', 'households': '520.000000', 'median_income': '2.215600', 'median_house_value': '145000.000000'}
{'longitude': '-119.230000', 'latitude': '34.170000', 'housing_median_age': '18.000000', 'total_rooms': '6171.000000', 'total_bedrooms': '1490.000000', 'population': '2164.000000', 'households': '1210.000000', 'median_income': '3.687500', 'median_house_value': '500001.000000'}
{'longitude': '-118.110000', 'latitude': '34.680000', 'housing_median_age': '6.000000', 'total_rooms': '7430.000000', 'total_bedrooms': '1184.000000', 'population': '3489.000000', 'households': '1115.000000', 'median_income': '5.326700', 'median_house_value': '140100.000000'}
{'longitude': '-122.470000', 'latitude': '37.770000', 'housing_median_age': '52.000000', 'total_rooms': '2241.000000', 'total_bedrooms': '443.000000', 'population': '1042.000000', 'households': '377.000000', 'median_income': '4.163500', 'median_house_value': '398400.000000'}
{'longitude': '-120.930000', 'latitude': '35.760000', 'housing_median_age': '11.000000', 'total_rooms': '8997.000000', 'total_bedrooms': '1698.000000', 'population': '1825.000000', 'households': '756.000000', 'median_income': '3.230000', 'median_house_value': '154300.000000'}
{'longitude': '-118.140000', 'latitude': '34.170000', 'housing_median_age': '52.000000', 'total_rooms': '2667.000000', 'total_bedrooms': '486.000000', 'population': '1681.000000', 'households': '504.000000', 'median_income': '4.052400', 'median_house_value': '173100.000000'}
{'longitude': '-122.730000', 'latitude': '38.460000', 'housing_median_age': '14.000000', 'total_rooms': '4042.000000', 'total_bedrooms': '1298.000000', 'population': '2323.000000', 'households': '1158.000000', 'median_income': '2.065100', 'median_house_value': '135400.000000'}
{'longitude': '-117.060000', 'latitude': '32.760000', 'housing_median_age': '37.000000', 'total_rooms': '2356.000000', 'total_bedrooms': '476.000000', 'population': '1231.000000', 'households': '499.000000', 'median_income': '2.965000', 'median_house_value': '155700.000000'}
{'longitude': '-120.710000', 'latitude': '35.500000', 'housing_median_age': '12.000000', 'total_rooms': '3098.000000', 'total_bedrooms': '453.000000', 'population': '1433.000000', 'households': '434.000000', 'median_income': '5.250800', 'median_house_value': '292900.000000'}
{'longitude': '-118.310000', 'latitude': '34.050000', 'housing_median_age': '35.000000', 'total_rooms': '1692.000000', 'total_bedrooms': '423.000000', 'population': '1578.000000', 'households': '406.000000', 'median_income': '2.531300', 'median_house_value': '305800.000000'}
{'longitude': '-119.700000', 'latitude': '36.750000', 'housing_median_age': '11.000000', 'total_rooms': '3626.000000', 'total_bedrooms': '779.000000', 'population': '1819.000000', 'households': '731.000000', 'median_income': '2.495600', 'median_house_value': '87500.000000'}
{'longitude': '-121.340000', 'latitude': '38.640000', 'housing_median_age': '17.000000', 'total_rooms': '2761.000000', 'total_bedrooms': '501.000000', 'population': '1128.000000', 'households': '482.000000', 'median_income': '3.756200', 'median_house_value': '139700.000000'}
{'longitude': '-117.910000', 'latitude': '34.090000', 'housing_median_age': '20.000000', 'total_rooms': '4327.000000', 'total_bedrooms': '1037.000000', 'population': '2296.000000', 'households': '963.000000', 'median_income': '3.044100', 'median_house_value': '185400.000000'}
{'longitude': '-119.760000', 'latitude': '36.790000', 'housing_median_age': '32.000000', 'total_rooms': '2463.000000', 'total_bedrooms': '468.000000', 'population': '1261.000000', 'households': '486.000000', 'median_income': '3.328100', 'median_house_value': '75100.000000'}
{'longitude': '-120.660000', 'latitude': '35.490000', 'housing_median_age': '17.000000', 'total_rooms': '4422.000000', 'total_bedrooms': '945.000000', 'population': '2307.000000', 'households': '885.000000', 'median_income': '2.828500', 'median_house_value': '171300.000000'}
{'longitude': '-118.280000', 'latitude': '34.080000', 'housing_median_age': '42.000000', 'total_rooms': '1618.000000', 'total_bedrooms': '522.000000', 'population': '1454.000000', 'households': '440.000000', 'median_income': '3.160700', 'median_house_value': '182000.000000'}
{'longitude': '-122.540000', 'latitude': '37.900000', 'housing_median_age': '48.000000', 'total_rooms': '2491.000000', 'total_bedrooms': '460.000000', 'population': '937.000000', 'households': '455.000000', 'median_income': '4.437500', 'median_house_value': '370000.000000'}
{'longitude': '-117.590000', 'latitude': '33.880000', 'housing_median_age': '13.000000', 'total_rooms': '3239.000000', 'total_bedrooms': '849.000000', 'population': '2751.000000', 'households': '813.000000', 'median_income': '2.611100', 'median_house_value': '107000.000000'}
{'longitude': '-120.470000', 'latitude': '34.940000', 'housing_median_age': '17.000000', 'total_rooms': '1368.000000', 'total_bedrooms': '308.000000', 'population': '642.000000', 'households': '303.000000', 'median_income': '1.863300', 'median_house_value': '109400.000000'}
{'longitude': '-118.250000', 'latitude': '33.930000', 'housing_median_age': '42.000000', 'total_rooms': '819.000000', 'total_bedrooms': '233.000000', 'population': '899.000000', 'households': '228.000000', 'median_income': '1.134600', 'median_house_value': '85400.000000'}
{'longitude': '-121.970000', 'latitude': '37.290000', 'housing_median_age': '25.000000', 'total_rooms': '4096.000000', 'total_bedrooms': '743.000000', 'population': '2027.000000', 'households': '741.000000', 'median_income': '5.329400', 'median_house_value': '300300.000000'}
{'longitude': '-122.010000', 'latitude': '36.970000', 'housing_median_age': '43.000000', 'total_rooms': '2162.000000', 'total_bedrooms': '509.000000', 'population': '1208.000000', 'households': '464.000000', 'median_income': '2.541700', 'median_house_value': '260900.000000'}
{'longitude': '-122.020000', 'latitude': '37.600000', 'housing_median_age': '32.000000', 'total_rooms': '1295.000000', 'total_bedrooms': '295.000000', 'population': '1097.000000', 'households': '328.000000', 'median_income': '3.238600', 'median_house_value': '149600.000000'}
{'longitude': '-118.230000', 'latitude': '34.090000', 'housing_median_age': '49.000000', 'total_rooms': '1638.000000', 'total_bedrooms': '456.000000', 'population': '1500.000000', 'households': '430.000000', 'median_income': '2.692300', 'median_house_value': '150000.000000'}
{'longitude': '-117.170000', 'latitude': '34.280000', 'housing_median_age': '13.000000', 'total_rooms': '4867.000000', 'total_bedrooms': '718.000000', 'population': '780.000000', 'households': '250.000000', 'median_income': '7.199700', 'median_house_value': '253800.000000'}
{'longitude': '-122.330000', 'latitude': '37.390000', 'housing_median_age': '52.000000', 'total_rooms': '573.000000', 'total_bedrooms': '102.000000', 'population': '232.000000', 'households': '92.000000', 'median_income': '6.226300', 'median_house_value': '500001.000000'}
{'longitude': '-117.910000', 'latitude': '33.600000', 'housing_median_age': '37.000000', 'total_rooms': '2088.000000', 'total_bedrooms': '510.000000', 'population': '673.000000', 'households': '390.000000', 'median_income': '5.104800', 'median_house_value': '500001.000000'}
{'longitude': '-117.930000', 'latitude': '33.860000', 'housing_median_age': '35.000000', 'total_rooms': '931.000000', 'total_bedrooms': '181.000000', 'population': '516.000000', 'households': '174.000000', 'median_income': '5.586700', 'median_house_value': '182500.000000'}
{'longitude': '-119.860000', 'latitude': '34.420000', 'housing_median_age': '23.000000', 'total_rooms': '1450.000000', 'total_bedrooms': '642.000000', 'population': '1258.000000', 'households': '607.000000', 'median_income': '1.179000', 'median_house_value': '225000.000000'}
{'longitude': '-118.140000', 'latitude': '34.060000', 'housing_median_age': '27.000000', 'total_rooms': '5257.000000', 'total_bedrooms': '1082.000000', 'population': '3496.000000', 'households': '1036.000000', 'median_income': '3.390600', 'median_house_value': '237200.000000'}
{'longitude': '-119.700000', 'latitude': '36.300000', 'housing_median_age': '10.000000', 'total_rooms': '956.000000', 'total_bedrooms': '201.000000', 'population': '693.000000', 'households': '220.000000', 'median_income': '2.289500', 'median_house_value': '62000.000000'}
{'longitude': '-117.120000', 'latitude': '34.100000', 'housing_median_age': '40.000000', 'total_rooms': '96.000000', 'total_bedrooms': '14.000000', 'population': '46.000000', 'households': '14.000000', 'median_income': '3.270800', 'median_house_value': '162500.000000'}
{'longitude': '-119.630000', 'latitude': '34.420000', 'housing_median_age': '42.000000', 'total_rooms': '1765.000000', 'total_bedrooms': '263.000000', 'population': '753.000000', 'households': '260.000000', 'median_income': '8.560800', 'median_house_value': '500001.000000'}

csvdict.fieldnames

['longitude',
 'latitude',
 'housing_median_age',
 'total_rooms',
 'total_bedrooms',
 'population',
 'households',
 'median_income',
 'median_house_value']

import pandas as pd

df = pd.read_csv('california_housing_test.csv')

df

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value
0	-122.05	37.37	27.0	3885.0	661.0	1537.0	606.0	6.6085	344700.0
1	-118.30	34.26	43.0	1510.0	310.0	809.0	277.0	3.5990	176500.0
2	-117.81	33.78	27.0	3589.0	507.0	1484.0	495.0	5.7934	270500.0
3	-118.36	33.82	28.0	67.0	15.0	49.0	11.0	6.1359	330000.0
4	-119.67	36.33	19.0	1241.0	244.0	850.0	237.0	2.9375	81700.0
...	...	...	...	...	...	...	...	...	...
2995	-119.86	34.42	23.0	1450.0	642.0	1258.0	607.0	1.1790	225000.0
2996	-118.14	34.06	27.0	5257.0	1082.0	3496.0	1036.0	3.3906	237200.0
2997	-119.70	36.30	10.0	956.0	201.0	693.0	220.0	2.2895	62000.0
2998	-117.12	34.10	40.0	96.0	14.0	46.0	14.0	3.2708	162500.0
2999	-119.63	34.42	42.0	1765.0	263.0	753.0	260.0	8.5608	500001.0

3000 rows × 9 columns

df.head()

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value
0	-122.05	37.37	27.0	3885.0	661.0	1537.0	606.0	6.6085	344700.0
1	-118.30	34.26	43.0	1510.0	310.0	809.0	277.0	3.5990	176500.0
2	-117.81	33.78	27.0	3589.0	507.0	1484.0	495.0	5.7934	270500.0
3	-118.36	33.82	28.0	67.0	15.0	49.0	11.0	6.1359	330000.0
4	-119.67	36.33	19.0	1241.0	244.0	850.0	237.0	2.9375	81700.0

df.tail()

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value
2995	-119.86	34.42	23.0	1450.0	642.0	1258.0	607.0	1.1790	225000.0
2996	-118.14	34.06	27.0	5257.0	1082.0	3496.0	1036.0	3.3906	237200.0
2997	-119.70	36.30	10.0	956.0	201.0	693.0	220.0	2.2895	62000.0
2998	-117.12	34.10	40.0	96.0	14.0	46.0	14.0	3.2708	162500.0
2999	-119.63	34.42	42.0	1765.0	263.0	753.0	260.0	8.5608	500001.0

df.shape

(3000, 9)

index_col . 특정 열(Column)을 인덱스로 지정하고 싶을 때 사용한다.

-.만약 위 Dataframe에서 ‘이름’ 열을 인덱스로 하려면 2가지 방법이 있다.

index_col = “이름” 또는 index_col = 0

-.0을 입력하는 이유는 파이썬 인덱싱이 첫번째가 0부터 시작하기 때문이다.(이름열이 Dataframe에서 첫번째임)

df1= pd.read_csv('california_housing_test.csv',index_col='latitude')

df1

	longitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value
latitude
37.37	-122.05	27.0	3885.0	661.0	1537.0	606.0	6.6085	344700.0
34.26	-118.30	43.0	1510.0	310.0	809.0	277.0	3.5990	176500.0
33.78	-117.81	27.0	3589.0	507.0	1484.0	495.0	5.7934	270500.0
33.82	-118.36	28.0	67.0	15.0	49.0	11.0	6.1359	330000.0
36.33	-119.67	19.0	1241.0	244.0	850.0	237.0	2.9375	81700.0
...	...	...	...	...	...	...	...	...
34.42	-119.86	23.0	1450.0	642.0	1258.0	607.0	1.1790	225000.0
34.06	-118.14	27.0	5257.0	1082.0	3496.0	1036.0	3.3906	237200.0
36.30	-119.70	10.0	956.0	201.0	693.0	220.0	2.2895	62000.0
34.10	-117.12	40.0	96.0	14.0	46.0	14.0	3.2708	162500.0
34.42	-119.63	42.0	1765.0	263.0	753.0	260.0	8.5608	500001.0

3000 rows × 8 columns

usecols

-. usecols는 엑셀 파일에서 특정 열(Column)들만 골라서 Dataframe화 할 때 사용한다.

-. 위 엑셀파일에서 이름이랑 영어점수만 Dataframe으로 생성하고 싶다면 usecols = “A, C” 를 입력한다.

-. 알파벳말고 숫자 인덱싱으로도 접근 가능하다. A,C열이면 usecols = [0, 2]를 입력해도 같은 결과이다.

df = pd.read_csv('california_housing_test.csv',usecols=[0,3])

df

	longitude	total_rooms
0	-122.05	3885.0
1	-118.30	1510.0
2	-117.81	3589.0
3	-118.36	67.0
4	-119.67	1241.0
...	...	...
2995	-119.86	1450.0
2996	-118.14	5257.0
2997	-119.70	956.0
2998	-117.12	96.0
2999	-119.63	1765.0

3000 rows × 2 columns

불러올 행 제한

nrows : 행 개수 제한

skiprows : 처음부터 n번째 행 제외

pd.read_csv('california_housing_test.csv',skiprows=1)

	-122.050000	37.370000	27.000000	3885.000000	661.000000	1537.000000	606.000000	6.608500	344700.000000
0	-118.30	34.26	43.0	1510.0	310.0	809.0	277.0	3.5990	176500.0
1	-117.81	33.78	27.0	3589.0	507.0	1484.0	495.0	5.7934	270500.0
2	-118.36	33.82	28.0	67.0	15.0	49.0	11.0	6.1359	330000.0
3	-119.67	36.33	19.0	1241.0	244.0	850.0	237.0	2.9375	81700.0
4	-119.56	36.51	37.0	1018.0	213.0	663.0	204.0	1.6635	67000.0
...	...	...	...	...	...	...	...	...	...
2994	-119.86	34.42	23.0	1450.0	642.0	1258.0	607.0	1.1790	225000.0
2995	-118.14	34.06	27.0	5257.0	1082.0	3496.0	1036.0	3.3906	237200.0
2996	-119.70	36.30	10.0	956.0	201.0	693.0	220.0	2.2895	62000.0
2997	-117.12	34.10	40.0	96.0	14.0	46.0	14.0	3.2708	162500.0
2998	-119.63	34.42	42.0	1765.0	263.0	753.0	260.0	8.5608	500001.0

2999 rows × 9 columns

pd.read_csv('california_housing_test.csv',nrows=3)

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value
0	-122.05	37.37	27.0	3885.0	661.0	1537.0	606.0	6.6085	344700.0
1	-118.30	34.26	43.0	1510.0	310.0	809.0	277.0	3.5990	176500.0
2	-117.81	33.78	27.0	3589.0	507.0	1484.0	495.0	5.7934	270500.0

pd.read_csv('california_housing_test.csv', skipfooter=3) #뒤에서 n개 제외

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value
0	-122.05	37.37	27.0	3885.0	661.0	1537.0	606.0	6.6085	344700.0
1	-118.30	34.26	43.0	1510.0	310.0	809.0	277.0	3.5990	176500.0
2	-117.81	33.78	27.0	3589.0	507.0	1484.0	495.0	5.7934	270500.0
3	-118.36	33.82	28.0	67.0	15.0	49.0	11.0	6.1359	330000.0
4	-119.67	36.33	19.0	1241.0	244.0	850.0	237.0	2.9375	81700.0
...	...	...	...	...	...	...	...	...	...
2992	-122.33	37.39	52.0	573.0	102.0	232.0	92.0	6.2263	500001.0
2993	-117.91	33.60	37.0	2088.0	510.0	673.0	390.0	5.1048	500001.0
2994	-117.93	33.86	35.0	931.0	181.0	516.0	174.0	5.5867	182500.0
2995	-119.86	34.42	23.0	1450.0	642.0	1258.0	607.0	1.1790	225000.0
2996	-118.14	34.06	27.0	5257.0	1082.0	3496.0	1036.0	3.3906	237200.0

2997 rows × 9 columns

df = pd.read_csv('california_housing_test.csv', skipfooter=3) #뒤에서 n개 제외

C:\Users\MyCom\anaconda3\lib\site-packages\pandas\util\_decorators.py:311: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.
  return func(*args, **kwargs)

JSON (JavaScript Object Notation)은 JavaScript 객체 리터럴 문법에서 영감을 얻은 경량 데이터 교환 형식입니다

df.to_json("data2.json") # json으로 담기

dfjson = df.to_json("data2.json") # json으로 담기

pd.read_json("data2.json") #json 으로 일기

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value
0	-122.05	37.37	27	3885	661	1537	606	6.6085	344700
1	-118.30	34.26	43	1510	310	809	277	3.5990	176500
2	-117.81	33.78	27	3589	507	1484	495	5.7934	270500
3	-118.36	33.82	28	67	15	49	11	6.1359	330000
4	-119.67	36.33	19	1241	244	850	237	2.9375	81700
...	...	...	...	...	...	...	...	...	...
2992	-122.33	37.39	52	573	102	232	92	6.2263	500001
2993	-117.91	33.60	37	2088	510	673	390	5.1048	500001
2994	-117.93	33.86	35	931	181	516	174	5.5867	182500
2995	-119.86	34.42	23	1450	642	1258	607	1.1790	225000
2996	-118.14	34.06	27	5257	1082	3496	1036	3.3906	237200

2997 rows × 9 columns

json 형태 문자열과 파일 읽기

#예제
import json

data = '{"title": "Book1", "ISBN": "12345", "author": [{"name": "autho1", "age": 30}, {"name": "autho2", "age": 25}]}'
json_data = json.loads(data)
#JSON 형태의 문자열을 읽기 위해 loads()를 사용합니다.
print(json_data['title'])
print(json_data['ISBN'])

for author in json_data['author']:
    print(author['name'])
    print(author['age'])

Book1
12345
autho1
30
autho2
25

HDF5 구성

HDF5 파일은 object 형태의 데이터셋 또는 다른 그룹들을 포함하는 그룹으로 구 성됩니다. 그리고 object 의 메타 데이터를 저장할 수 있는 attrs 으로 구성됩니다.

간단히 비유를 하여 설명하면 그룹은 디렉터리로 비유할 수 있고, 데이터셋은 numpy 배열로 생각하시면 됩니다.

df.to_hdf("data.h5","data") # 바이너리 형태로 저장되있음

!pip install h5py

Requirement already satisfied: h5py in c:\users\mycom\anaconda3\lib\site-packages (3.2.1)
Requirement already satisfied: numpy>=1.16.0 in c:\users\mycom\anaconda3\lib\site-packages (from h5py) (1.20.3)


WARNING: Ignoring invalid distribution -onlpy (c:\users\mycom\appdata\roaming\python\python38\site-packages)
WARNING: Ignoring invalid distribution -pype1 (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ensorflow-gpu (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -onlpy (c:\users\mycom\appdata\roaming\python\python38\site-packages)
WARNING: Ignoring invalid distribution -pype1 (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ensorflow-gpu (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -onlpy (c:\users\mycom\appdata\roaming\python\python38\site-packages)
WARNING: Ignoring invalid distribution -pype1 (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ensorflow-gpu (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -onlpy (c:\users\mycom\appdata\roaming\python\python38\site-packages)
WARNING: Ignoring invalid distribution -pype1 (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ensorflow-gpu (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -onlpy (c:\users\mycom\appdata\roaming\python\python38\site-packages)
WARNING: Ignoring invalid distribution -pype1 (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ensorflow-gpu (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -onlpy (c:\users\mycom\appdata\roaming\python\python38\site-packages)
WARNING: Ignoring invalid distribution -pype1 (c:\users\mycom\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ensorflow-gpu (c:\users\mycom\anaconda3\lib\site-packages)

import h5py

f = h5py.File("data.h5")

f["data"]

<HDF5 group "/data" (4 members)>

f["data"].keys()

<KeysViewHDF5 ['axis0', 'axis1', 'block0_items', 'block0_values']>

strip() 종류

split 함수 예제

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