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11 분 소요

판다스(Pandas) 데이터프레임(DataFrame)의 출력결과 표기형식을 설정할 수 있는 다양한 옵션들에 대해 알아보겠습니다.

주요 내용

  • 최대, 최소 행/열 개수 지정
  • 반올림
  • 1,000 단위 콤마 구분자
  • 특정 값 이하 절삭
import numpy as np
import pandas as pd

# 샘플데이터 생성
df = pd.DataFrame(np.random.randn(100, 30))
df
0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29
0 -0.035806 0.540773 2.048162 0.987971 1.297979 -0.975736 1.511243 0.761065 0.466870 1.246932 ... 0.662477 -0.597404 -0.357058 -0.329638 0.070825 -0.836523 -0.229360 0.335023 0.602947 0.746151
1 0.981804 0.566098 -0.670318 1.900650 1.184195 1.185684 1.036443 0.147689 1.261201 -0.455557 ... -0.884224 -0.363598 1.959401 -1.197709 -0.630205 -0.062158 -1.478759 0.188979 0.293474 2.170450
2 -0.196254 -0.341994 0.568631 -0.977499 -1.818653 -0.756704 -2.174387 0.322598 0.692976 0.991518 ... -1.064358 -0.005801 -1.352274 1.679056 -1.716728 -1.346657 -0.887417 0.698803 -0.548383 0.999490
3 1.747521 -0.009455 0.029300 -0.055126 1.697121 -0.043711 -0.164797 -1.031167 -0.521485 0.759069 ... -0.600646 -0.027879 1.578827 0.387868 -0.949340 -0.875935 0.521120 -0.175317 -0.050255 1.424726
4 -1.372283 0.446507 1.324593 -0.093968 -1.102458 0.658459 1.537070 -2.330284 -0.427623 0.088559 ... 2.072393 -0.685572 1.167391 -0.920462 -0.378917 -0.505438 1.098358 0.462141 0.291459 -1.390486
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
95 0.440286 -1.159524 1.399405 1.882673 0.915369 -1.743549 -0.710229 1.536277 0.844029 -1.626463 ... -0.754897 -0.342387 -1.048288 -0.481786 0.548180 -2.110016 1.639998 0.937382 -0.092592 0.707600
96 0.681607 0.281317 -0.389708 0.943291 1.006331 0.432428 -0.719073 -1.737020 -1.418606 0.481165 ... 1.708825 0.563480 -1.000136 0.046754 1.107975 0.075705 -1.073683 1.184550 0.981149 -0.502977
97 0.409718 -0.559125 -0.622972 -0.131378 0.208289 0.887148 -0.093293 -0.003069 -1.861841 0.174812 ... 0.929058 -0.929894 -1.949187 -0.414730 -0.123279 0.266217 1.277206 -1.391318 -1.406094 1.060128
98 1.211452 0.339517 -0.288101 1.582239 0.581834 -0.536842 0.270649 -0.941815 0.752292 2.856031 ... 0.553618 -0.464397 -0.668322 -0.112496 1.534438 -2.455272 0.555450 -0.268942 -0.612532 0.209424
99 -0.117216 -0.231614 1.728668 1.116696 0.059390 -0.921383 1.488752 0.540287 -0.369421 0.134456 ... 0.363531 -1.614530 0.731306 0.024341 -0.897175 0.027369 -0.695695 -1.064006 -1.431332 -0.979335

100 rows × 30 columns

행, 열 출력

행(row)

  • display.max_rows에 최대 출력 행(row) 개수 지정. None 지정시 전체 행 출력.

  • display.min_rows에 최소 출력 행(row) 개수 지정.

# 최대 출력 행 개수 지정
pd.set_option('display.max_rows', 30)

# 최소 출력 행 개수 지정
pd.set_option('display.min_rows', 20)

# 출력결과
df
0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29
0 -0.035806 0.540773 2.048162 0.987971 1.297979 -0.975736 1.511243 0.761065 0.466870 1.246932 ... 0.662477 -0.597404 -0.357058 -0.329638 0.070825 -0.836523 -0.229360 0.335023 0.602947 0.746151
1 0.981804 0.566098 -0.670318 1.900650 1.184195 1.185684 1.036443 0.147689 1.261201 -0.455557 ... -0.884224 -0.363598 1.959401 -1.197709 -0.630205 -0.062158 -1.478759 0.188979 0.293474 2.170450
2 -0.196254 -0.341994 0.568631 -0.977499 -1.818653 -0.756704 -2.174387 0.322598 0.692976 0.991518 ... -1.064358 -0.005801 -1.352274 1.679056 -1.716728 -1.346657 -0.887417 0.698803 -0.548383 0.999490
3 1.747521 -0.009455 0.029300 -0.055126 1.697121 -0.043711 -0.164797 -1.031167 -0.521485 0.759069 ... -0.600646 -0.027879 1.578827 0.387868 -0.949340 -0.875935 0.521120 -0.175317 -0.050255 1.424726
4 -1.372283 0.446507 1.324593 -0.093968 -1.102458 0.658459 1.537070 -2.330284 -0.427623 0.088559 ... 2.072393 -0.685572 1.167391 -0.920462 -0.378917 -0.505438 1.098358 0.462141 0.291459 -1.390486
5 1.252676 0.520109 -0.086972 -1.390119 -1.328195 0.159062 -0.185403 1.136238 -2.323753 -0.153739 ... -0.586609 -1.454865 -1.141790 0.251067 -1.650410 -1.399671 0.053298 0.184873 -0.152222 -0.547457
6 0.748208 -0.601630 0.370751 1.508725 1.031906 0.684378 1.289777 0.382114 0.462913 -0.761836 ... -0.448044 -0.361551 -0.713991 -0.004631 -2.384194 -0.691138 -1.083416 0.315635 -0.477509 -0.736364
7 2.122694 1.648141 -0.551223 0.581174 -0.910993 0.090683 0.227400 -0.718582 0.515417 -0.111297 ... -0.479713 0.303648 1.164275 0.971229 -0.160322 3.029640 1.383594 -0.509485 0.505870 -1.039575
8 -0.002618 0.261833 -0.573321 1.177740 -1.238453 2.439766 0.959073 1.062197 0.217570 -0.025249 ... 0.903160 -0.538873 -0.085188 0.538790 2.007834 -0.532897 -1.078179 -1.165601 0.157725 -2.001069
9 -0.251036 -1.673364 0.373563 0.300650 0.823024 1.314862 -1.198112 -0.932139 1.025538 0.261294 ... -0.004391 -1.502177 -0.598474 -0.491475 -0.253052 0.070125 -1.021431 0.673204 -0.328213 -0.926225
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
90 1.012421 -0.844343 1.624287 0.457222 -0.869078 0.633851 0.674425 -1.283140 1.510371 1.683848 ... 1.431287 0.423220 1.485521 0.201107 -0.472796 0.359677 0.651251 -0.258083 3.026651 -0.137928
91 0.135754 0.698098 0.547223 0.484535 -0.116011 -0.769140 -0.591393 0.546883 -0.508868 0.524660 ... -0.946001 0.971161 0.646309 0.596603 0.862084 0.594964 0.369562 0.393629 -1.235659 -1.412415
92 0.862689 0.086395 0.942105 -0.896561 -0.382631 -0.540536 -1.475590 -1.462621 0.553185 -1.192904 ... 3.035245 0.346097 -0.743209 1.727618 -1.552566 0.817770 0.042594 0.644676 0.539964 0.910347
93 -0.825056 0.646993 0.311538 -0.507771 -0.798882 0.716366 0.045604 0.019914 0.122563 1.573202 ... -0.577820 -1.616715 -1.922380 -0.039266 1.651102 0.234814 0.816973 1.180786 -0.160160 -0.812621
94 -1.316661 -0.827844 1.541134 -1.600997 -1.548939 0.423432 -0.387452 0.897517 -0.082423 0.032062 ... 0.777377 1.288470 0.643530 -0.146580 0.340498 -0.949201 0.925776 -0.154046 -0.302537 0.750083
95 0.440286 -1.159524 1.399405 1.882673 0.915369 -1.743549 -0.710229 1.536277 0.844029 -1.626463 ... -0.754897 -0.342387 -1.048288 -0.481786 0.548180 -2.110016 1.639998 0.937382 -0.092592 0.707600
96 0.681607 0.281317 -0.389708 0.943291 1.006331 0.432428 -0.719073 -1.737020 -1.418606 0.481165 ... 1.708825 0.563480 -1.000136 0.046754 1.107975 0.075705 -1.073683 1.184550 0.981149 -0.502977
97 0.409718 -0.559125 -0.622972 -0.131378 0.208289 0.887148 -0.093293 -0.003069 -1.861841 0.174812 ... 0.929058 -0.929894 -1.949187 -0.414730 -0.123279 0.266217 1.277206 -1.391318 -1.406094 1.060128
98 1.211452 0.339517 -0.288101 1.582239 0.581834 -0.536842 0.270649 -0.941815 0.752292 2.856031 ... 0.553618 -0.464397 -0.668322 -0.112496 1.534438 -2.455272 0.555450 -0.268942 -0.612532 0.209424
99 -0.117216 -0.231614 1.728668 1.116696 0.059390 -0.921383 1.488752 0.540287 -0.369421 0.134456 ... 0.363531 -1.614530 0.731306 0.024341 -0.897175 0.027369 -0.695695 -1.064006 -1.431332 -0.979335

100 rows × 30 columns

설정 초기화

초기 설정 값으로 되돌리기 위해서는 pd.reset_option(옵션명)을 호출하면 됩니다.

# 원래 설정으로 초기화
pd.reset_option('display.max_rows')
pd.reset_option('display.min_rows')

열(column)

  • display.max_columns에 최대 출력 열(column) 개수 지정. None 지정시 전체 출력.
# 최대 출력 컬럼 개수 지정
pd.set_option('display.max_columns', None)

# 출력결과
df
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
0 -0.035806 0.540773 2.048162 0.987971 1.297979 -0.975736 1.511243 0.761065 0.466870 1.246932 0.041226 -0.309691 0.358050 -1.313568 -0.776349 1.710610 0.969274 -1.399644 0.395019 1.053263 0.662477 -0.597404 -0.357058 -0.329638 0.070825 -0.836523 -0.229360 0.335023 0.602947 0.746151
1 0.981804 0.566098 -0.670318 1.900650 1.184195 1.185684 1.036443 0.147689 1.261201 -0.455557 -0.291492 -0.497445 1.296869 1.410124 -1.920911 -0.378394 1.447838 -1.454872 -0.446637 -1.355824 -0.884224 -0.363598 1.959401 -1.197709 -0.630205 -0.062158 -1.478759 0.188979 0.293474 2.170450
2 -0.196254 -0.341994 0.568631 -0.977499 -1.818653 -0.756704 -2.174387 0.322598 0.692976 0.991518 -0.117331 0.862454 -1.449798 2.176193 0.243537 0.411133 0.533833 -0.393785 -0.928627 -0.393218 -1.064358 -0.005801 -1.352274 1.679056 -1.716728 -1.346657 -0.887417 0.698803 -0.548383 0.999490
3 1.747521 -0.009455 0.029300 -0.055126 1.697121 -0.043711 -0.164797 -1.031167 -0.521485 0.759069 -1.148656 -0.895544 0.784258 1.556413 -1.345823 -0.353388 -0.128010 -0.109613 1.234353 -0.698380 -0.600646 -0.027879 1.578827 0.387868 -0.949340 -0.875935 0.521120 -0.175317 -0.050255 1.424726
4 -1.372283 0.446507 1.324593 -0.093968 -1.102458 0.658459 1.537070 -2.330284 -0.427623 0.088559 -0.854313 -0.665910 1.368841 -0.642099 0.934470 -0.953041 -0.245948 -1.082086 0.089842 1.113693 2.072393 -0.685572 1.167391 -0.920462 -0.378917 -0.505438 1.098358 0.462141 0.291459 -1.390486
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
95 0.440286 -1.159524 1.399405 1.882673 0.915369 -1.743549 -0.710229 1.536277 0.844029 -1.626463 -0.460458 1.067152 -1.573902 -1.210941 0.827637 -1.033014 -1.972978 -1.811644 1.555603 0.277433 -0.754897 -0.342387 -1.048288 -0.481786 0.548180 -2.110016 1.639998 0.937382 -0.092592 0.707600
96 0.681607 0.281317 -0.389708 0.943291 1.006331 0.432428 -0.719073 -1.737020 -1.418606 0.481165 1.172533 -0.401827 -1.951013 -2.207138 2.045194 1.839619 1.157589 -0.082741 0.060264 -0.793476 1.708825 0.563480 -1.000136 0.046754 1.107975 0.075705 -1.073683 1.184550 0.981149 -0.502977
97 0.409718 -0.559125 -0.622972 -0.131378 0.208289 0.887148 -0.093293 -0.003069 -1.861841 0.174812 -0.191320 0.039081 -0.749582 -0.258455 0.070604 1.428898 0.172877 0.368134 0.444407 0.808398 0.929058 -0.929894 -1.949187 -0.414730 -0.123279 0.266217 1.277206 -1.391318 -1.406094 1.060128
98 1.211452 0.339517 -0.288101 1.582239 0.581834 -0.536842 0.270649 -0.941815 0.752292 2.856031 -0.075809 1.824439 -0.619107 1.148403 1.167635 0.519472 0.271008 -0.234025 0.052374 -0.534476 0.553618 -0.464397 -0.668322 -0.112496 1.534438 -2.455272 0.555450 -0.268942 -0.612532 0.209424
99 -0.117216 -0.231614 1.728668 1.116696 0.059390 -0.921383 1.488752 0.540287 -0.369421 0.134456 0.118990 -0.206389 -1.544280 0.792406 0.180844 -0.548614 -0.704487 1.327546 -1.553650 0.216136 0.363531 -1.614530 0.731306 0.024341 -0.897175 0.027369 -0.695695 -1.064006 -1.431332 -0.979335

100 rows × 30 columns

# 최대 출력 컬럼 개수 지정
pd.set_option('display.max_columns', 10)

# 출력결과
df
0 1 2 3 4 ... 25 26 27 28 29
0 -0.035806 0.540773 2.048162 0.987971 1.297979 ... -0.836523 -0.229360 0.335023 0.602947 0.746151
1 0.981804 0.566098 -0.670318 1.900650 1.184195 ... -0.062158 -1.478759 0.188979 0.293474 2.170450
2 -0.196254 -0.341994 0.568631 -0.977499 -1.818653 ... -1.346657 -0.887417 0.698803 -0.548383 0.999490
3 1.747521 -0.009455 0.029300 -0.055126 1.697121 ... -0.875935 0.521120 -0.175317 -0.050255 1.424726
4 -1.372283 0.446507 1.324593 -0.093968 -1.102458 ... -0.505438 1.098358 0.462141 0.291459 -1.390486
... ... ... ... ... ... ... ... ... ... ... ...
95 0.440286 -1.159524 1.399405 1.882673 0.915369 ... -2.110016 1.639998 0.937382 -0.092592 0.707600
96 0.681607 0.281317 -0.389708 0.943291 1.006331 ... 0.075705 -1.073683 1.184550 0.981149 -0.502977
97 0.409718 -0.559125 -0.622972 -0.131378 0.208289 ... 0.266217 1.277206 -1.391318 -1.406094 1.060128
98 1.211452 0.339517 -0.288101 1.582239 0.581834 ... -2.455272 0.555450 -0.268942 -0.612532 0.209424
99 -0.117216 -0.231614 1.728668 1.116696 0.059390 ... 0.027369 -0.695695 -1.064006 -1.431332 -0.979335

100 rows × 30 columns

반올림(float_format)

# 소수점 2째자리 이하에서 반올림
pd.set_option('display.float_format', lambda x: f'{x:.2f}') 

# 출력결과
df
0 1 2 3 4 ... 25 26 27 28 29
0 -0.04 0.54 2.05 0.99 1.30 ... -0.84 -0.23 0.34 0.60 0.75
1 0.98 0.57 -0.67 1.90 1.18 ... -0.06 -1.48 0.19 0.29 2.17
2 -0.20 -0.34 0.57 -0.98 -1.82 ... -1.35 -0.89 0.70 -0.55 1.00
3 1.75 -0.01 0.03 -0.06 1.70 ... -0.88 0.52 -0.18 -0.05 1.42
4 -1.37 0.45 1.32 -0.09 -1.10 ... -0.51 1.10 0.46 0.29 -1.39
... ... ... ... ... ... ... ... ... ... ... ...
95 0.44 -1.16 1.40 1.88 0.92 ... -2.11 1.64 0.94 -0.09 0.71
96 0.68 0.28 -0.39 0.94 1.01 ... 0.08 -1.07 1.18 0.98 -0.50
97 0.41 -0.56 -0.62 -0.13 0.21 ... 0.27 1.28 -1.39 -1.41 1.06
98 1.21 0.34 -0.29 1.58 0.58 ... -2.46 0.56 -0.27 -0.61 0.21
99 -0.12 -0.23 1.73 1.12 0.06 ... 0.03 -0.70 -1.06 -1.43 -0.98

100 rows × 30 columns

# 설정 초기화
pd.reset_option('display.float_format')

출력 형식에 콤마 구분자(comma separation) 추가

  • 금융 데이터 분석 및 보고서 작성에 주로 활용
# 소수점 2째자리 이하에서 반올림
# 1000 자리수마다 , 구분자 추가
pd.set_option('display.float_format', lambda x: f'{x:,.2f}') 

# 출력결과
df * 1000000
0 1 2 3 4 ... 25 26 27 28 29
0 -35,806.46 540,772.90 2,048,161.99 987,971.01 1,297,978.51 ... -836,522.70 -229,359.65 335,023.45 602,947.07 746,151.45
1 981,803.57 566,097.83 -670,318.26 1,900,649.55 1,184,195.02 ... -62,158.31 -1,478,759.17 188,979.42 293,474.27 2,170,449.66
2 -196,254.37 -341,994.26 568,631.29 -977,498.95 -1,818,652.91 ... -1,346,656.94 -887,416.74 698,803.28 -548,383.15 999,490.35
3 1,747,520.77 -9,455.15 29,300.34 -55,125.88 1,697,121.09 ... -875,934.87 521,120.23 -175,316.54 -50,255.35 1,424,726.29
4 -1,372,283.03 446,506.95 1,324,592.72 -93,967.97 -1,102,457.96 ... -505,438.16 1,098,358.06 462,141.14 291,459.06 -1,390,485.88
... ... ... ... ... ... ... ... ... ... ... ...
95 440,286.01 -1,159,524.49 1,399,404.51 1,882,672.92 915,368.70 ... -2,110,016.45 1,639,998.04 937,382.45 -92,591.77 707,600.31
96 681,607.33 281,317.08 -389,707.58 943,290.59 1,006,331.23 ... 75,704.64 -1,073,683.29 1,184,549.99 981,149.35 -502,977.15
97 409,718.35 -559,125.32 -622,972.50 -131,378.25 208,288.96 ... 266,217.01 1,277,206.46 -1,391,317.87 -1,406,093.94 1,060,128.26
98 1,211,451.53 339,516.75 -288,100.50 1,582,239.23 581,833.55 ... -2,455,271.54 555,450.19 -268,942.36 -612,532.22 209,423.89
99 -117,216.34 -231,614.45 1,728,667.90 1,116,695.97 59,390.22 ... 27,368.98 -695,695.09 -1,064,006.46 -1,431,331.50 -979,334.87

100 rows × 30 columns

만약 소수점 이하는 절삭하고 싶은 경우

# 소수점은 반올림하여 정수형
# 1000 자리수마다 , 구분자 추가
pd.set_option('display.float_format', lambda x: f'{x:,.0f}') 

# 출력결과
df * 1000000
0 1 2 3 4 ... 25 26 27 28 29
0 -35,806 540,773 2,048,162 987,971 1,297,979 ... -836,523 -229,360 335,023 602,947 746,151
1 981,804 566,098 -670,318 1,900,650 1,184,195 ... -62,158 -1,478,759 188,979 293,474 2,170,450
2 -196,254 -341,994 568,631 -977,499 -1,818,653 ... -1,346,657 -887,417 698,803 -548,383 999,490
3 1,747,521 -9,455 29,300 -55,126 1,697,121 ... -875,935 521,120 -175,317 -50,255 1,424,726
4 -1,372,283 446,507 1,324,593 -93,968 -1,102,458 ... -505,438 1,098,358 462,141 291,459 -1,390,486
... ... ... ... ... ... ... ... ... ... ... ...
95 440,286 -1,159,524 1,399,405 1,882,673 915,369 ... -2,110,016 1,639,998 937,382 -92,592 707,600
96 681,607 281,317 -389,708 943,291 1,006,331 ... 75,705 -1,073,683 1,184,550 981,149 -502,977
97 409,718 -559,125 -622,972 -131,378 208,289 ... 266,217 1,277,206 -1,391,318 -1,406,094 1,060,128
98 1,211,452 339,517 -288,101 1,582,239 581,834 ... -2,455,272 555,450 -268,942 -612,532 209,424
99 -117,216 -231,614 1,728,668 1,116,696 59,390 ... 27,369 -695,695 -1,064,006 -1,431,332 -979,335

100 rows × 30 columns

# 설정 초기화
pd.reset_option('display.float_format')

과학 표기법(scientific notation) 자릿수 변환

# 소수3째자리 이하는 과학표기법으로 표현
pd.set_option('display.precision', 3)

소수 3째자리 이하는 과학표기법(scientific notation) 으로 변환됨을 확인할 수 있습니다.

df / 1000
0 1 2 3 4 ... 25 26 27 28 29
0 -3.581e-05 5.408e-04 2.048e-03 9.880e-04 1.298e-03 ... -8.365e-04 -2.294e-04 3.350e-04 6.029e-04 7.462e-04
1 9.818e-04 5.661e-04 -6.703e-04 1.901e-03 1.184e-03 ... -6.216e-05 -1.479e-03 1.890e-04 2.935e-04 2.170e-03
2 -1.963e-04 -3.420e-04 5.686e-04 -9.775e-04 -1.819e-03 ... -1.347e-03 -8.874e-04 6.988e-04 -5.484e-04 9.995e-04
3 1.748e-03 -9.455e-06 2.930e-05 -5.513e-05 1.697e-03 ... -8.759e-04 5.211e-04 -1.753e-04 -5.026e-05 1.425e-03
4 -1.372e-03 4.465e-04 1.325e-03 -9.397e-05 -1.102e-03 ... -5.054e-04 1.098e-03 4.621e-04 2.915e-04 -1.390e-03
... ... ... ... ... ... ... ... ... ... ... ...
95 4.403e-04 -1.160e-03 1.399e-03 1.883e-03 9.154e-04 ... -2.110e-03 1.640e-03 9.374e-04 -9.259e-05 7.076e-04
96 6.816e-04 2.813e-04 -3.897e-04 9.433e-04 1.006e-03 ... 7.570e-05 -1.074e-03 1.185e-03 9.811e-04 -5.030e-04
97 4.097e-04 -5.591e-04 -6.230e-04 -1.314e-04 2.083e-04 ... 2.662e-04 1.277e-03 -1.391e-03 -1.406e-03 1.060e-03
98 1.211e-03 3.395e-04 -2.881e-04 1.582e-03 5.818e-04 ... -2.455e-03 5.555e-04 -2.689e-04 -6.125e-04 2.094e-04
99 -1.172e-04 -2.316e-04 1.729e-03 1.117e-03 5.939e-05 ... 2.737e-05 -6.957e-04 -1.064e-03 -1.431e-03 -9.793e-04

100 rows × 30 columns

# 설정 초기화
pd.reset_option('display.precision')

특정 값이하 절삭(chop_threshold)

# 값이 1보다 작으면 0으로 절삭
pd.set_option('display.chop_threshold', 1)

# 출력결과
df
0 1 2 3 4 ... 25 26 27 28 29
0 0.000000 0.000000 2.048162 0.000000 1.297979 ... 0.000000 0.000000 0.000000 0.000000 0.000000
1 0.000000 0.000000 0.000000 1.900650 1.184195 ... 0.000000 -1.478759 0.000000 0.000000 2.170450
2 0.000000 0.000000 0.000000 0.000000 -1.818653 ... -1.346657 0.000000 0.000000 0.000000 0.000000
3 1.747521 0.000000 0.000000 0.000000 1.697121 ... 0.000000 0.000000 0.000000 0.000000 1.424726
4 -1.372283 0.000000 1.324593 0.000000 -1.102458 ... 0.000000 1.098358 0.000000 0.000000 -1.390486
... ... ... ... ... ... ... ... ... ... ... ...
95 0.000000 -1.159524 1.399405 1.882673 0.000000 ... -2.110016 1.639998 0.000000 0.000000 0.000000
96 0.000000 0.000000 0.000000 0.000000 1.006331 ... 0.000000 -1.073683 1.184550 0.000000 0.000000
97 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 1.277206 -1.391318 -1.406094 1.060128
98 1.211452 0.000000 0.000000 1.582239 0.000000 ... -2.455272 0.000000 0.000000 0.000000 0.000000
99 0.000000 0.000000 1.728668 1.116696 0.000000 ... 0.000000 0.000000 -1.064006 -1.431332 0.000000

100 rows × 30 columns

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참고

poetry 의 거의 모든것 (튜토리얼)

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