Overview

Dataset statistics

Number of variables9
Number of observations966
Missing cells2604
Missing cells (%)30.0%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory70.9 KiB
Average record size in memory75.1 B

Variable types

Numeric3
DateTime1
Categorical2
Text3

Dataset

Description신용보증기금 투자기업 현황 정보를 제공합니다. 투자연도별 투자금액, 투자종류, 투자기업의 업종(코드 및 업종분류명), 소재지, 기업규모 구분을 확인할 수 있습니다.
URLhttps://www.data.go.kr/data/15002987/fileData.do

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
순서 is highly overall correlated with 투자연도High correlation
투자연도 is highly overall correlated with 순서High correlation
순서 has 372 (38.5%) missing valuesMissing
투자연도 has 372 (38.5%) missing valuesMissing
투자일자 has 372 (38.5%) missing valuesMissing
투자금액 has 372 (38.5%) missing valuesMissing
업종코드 has 372 (38.5%) missing valuesMissing
업종분류 has 372 (38.5%) missing valuesMissing
소재지 has 372 (38.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:45:35.888859
Analysis finished2023-12-12 13:45:37.768829
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순서
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct594
Distinct (%)100.0%
Missing372
Missing (%)38.5%
Infinite0
Infinite (%)0.0%
Mean297.5
Minimum1
Maximum594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T22:45:37.842742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30.65
Q1149.25
median297.5
Q3445.75
95-th percentile564.35
Maximum594
Range593
Interquartile range (IQR)296.5

Descriptive statistics

Standard deviation171.61731
Coefficient of variation (CV)0.5768649
Kurtosis-1.2
Mean297.5
Median Absolute Deviation (MAD)148.5
Skewness0
Sum176715
Variance29452.5
MonotonicityStrictly increasing
2023-12-12T22:45:38.334920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
392 1
 
0.1%
394 1
 
0.1%
395 1
 
0.1%
396 1
 
0.1%
397 1
 
0.1%
398 1
 
0.1%
399 1
 
0.1%
400 1
 
0.1%
401 1
 
0.1%
402 1
 
0.1%
Other values (584) 584
60.5%
(Missing) 372
38.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
594 1
0.1%
593 1
0.1%
592 1
0.1%
591 1
0.1%
590 1
0.1%
589 1
0.1%
588 1
0.1%
587 1
0.1%
586 1
0.1%
585 1
0.1%

투자연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)1.5%
Missing372
Missing (%)38.5%
Infinite0
Infinite (%)0.0%
Mean2019.0724
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T22:45:38.476225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2015
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.385979
Coefficient of variation (CV)0.0011817204
Kurtosis-0.85224556
Mean2019.0724
Median Absolute Deviation (MAD)2
Skewness-0.47764419
Sum1199329
Variance5.6928958
MonotonicityIncreasing
2023-12-12T22:45:38.612794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2022 112
 
11.6%
2021 110
 
11.4%
2019 85
 
8.8%
2017 62
 
6.4%
2020 62
 
6.4%
2018 61
 
6.3%
2016 45
 
4.7%
2015 30
 
3.1%
2014 27
 
2.8%
(Missing) 372
38.5%
ValueCountFrequency (%)
2014 27
 
2.8%
2015 30
 
3.1%
2016 45
4.7%
2017 62
6.4%
2018 61
6.3%
2019 85
8.8%
2020 62
6.4%
2021 110
11.4%
2022 112
11.6%
ValueCountFrequency (%)
2022 112
11.6%
2021 110
11.4%
2020 62
6.4%
2019 85
8.8%
2018 61
6.3%
2017 62
6.4%
2016 45
4.7%
2015 30
 
3.1%
2014 27
 
2.8%

투자일자
Date

MISSING 

Distinct418
Distinct (%)70.4%
Missing372
Missing (%)38.5%
Memory size7.7 KiB
Minimum2014-06-30 00:00:00
Maximum2022-12-29 00:00:00
2023-12-12T22:45:38.755683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.889489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

투자금액
Real number (ℝ)

MISSING 

Distinct273
Distinct (%)46.0%
Missing372
Missing (%)38.5%
Infinite0
Infinite (%)0.0%
Mean6.6198363 × 108
Minimum98416285
Maximum2 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T22:45:39.021909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98416285
5-th percentile99960000
Q13 × 108
median5 × 108
Q39.99985 × 108
95-th percentile1.4999765 × 109
Maximum2 × 109
Range1.9015837 × 109
Interquartile range (IQR)6.99985 × 108

Descriptive statistics

Standard deviation4.1043201 × 108
Coefficient of variation (CV)0.62000326
Kurtosis-0.29563225
Mean6.6198363 × 108
Median Absolute Deviation (MAD)4.00001 × 108
Skewness0.46507464
Sum3.9321828 × 1011
Variance1.6845444 × 1017
MonotonicityNot monotonic
2023-12-12T22:45:39.220441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000000 63
 
6.5%
300000000 53
 
5.5%
500000000 41
 
4.2%
1500000000 16
 
1.7%
100000000 14
 
1.4%
200000000 12
 
1.2%
999900000 10
 
1.0%
999990000 10
 
1.0%
700000000 10
 
1.0%
999999000 8
 
0.8%
Other values (263) 357
37.0%
(Missing) 372
38.5%
ValueCountFrequency (%)
98416285 1
0.1%
98450000 1
0.1%
99600000 1
0.1%
99660141 1
0.1%
99668660 1
0.1%
99746640 1
0.1%
99750000 1
0.1%
99760000 1
0.1%
99796000 1
0.1%
99837460 1
0.1%
ValueCountFrequency (%)
2000000000 1
 
0.1%
1999998750 1
 
0.1%
1999728000 1
 
0.1%
1989596870 1
 
0.1%
1799920920 1
 
0.1%
1500000000 16
1.7%
1499998500 1
 
0.1%
1499995900 1
 
0.1%
1499995000 1
 
0.1%
1499994792 1
 
0.1%

투자종류
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
우선주
540 
<NA>
372 
전환사채
 
37
보통주
 
16
사채
 
1

Length

Max length4
Median length3
Mean length3.4223602
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row우선주
2nd row우선주
3rd row우선주
4th row우선주
5th row우선주

Common Values

ValueCountFrequency (%)
우선주 540
55.9%
<NA> 372
38.5%
전환사채 37
 
3.8%
보통주 16
 
1.7%
사채 1
 
0.1%

Length

2023-12-12T22:45:39.399195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:45:39.547964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우선주 540
55.9%
na 372
38.5%
전환사채 37
 
3.8%
보통주 16
 
1.7%
사채 1
 
0.1%

업종코드
Text

MISSING 

Distinct217
Distinct (%)36.5%
Missing372
Missing (%)38.5%
Memory size7.7 KiB
2023-12-12T22:45:39.831417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3564
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)20.4%

Sample

1st rowC22213
2nd rowJ62021
3rd rowC26429
4th rowC29199
5th rowC24212
ValueCountFrequency (%)
j58222 103
 
17.3%
j63991 28
 
4.7%
j58221 25
 
4.2%
c27199 15
 
2.5%
j62021 12
 
2.0%
c26429 10
 
1.7%
g47911 10
 
1.7%
g47912 9
 
1.5%
m70113 8
 
1.3%
c29299 7
 
1.2%
Other values (207) 367
61.8%
2023-12-12T22:45:40.330577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 943
26.5%
1 467
13.1%
9 367
 
10.3%
C 294
 
8.2%
0 211
 
5.9%
5 208
 
5.8%
3 202
 
5.7%
J 196
 
5.5%
8 175
 
4.9%
7 138
 
3.9%
Other values (14) 363
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2970
83.3%
Uppercase Letter 594
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 294
49.5%
J 196
33.0%
G 39
 
6.6%
M 37
 
6.2%
N 9
 
1.5%
S 5
 
0.8%
P 3
 
0.5%
K 2
 
0.3%
I 2
 
0.3%
F 2
 
0.3%
Other values (4) 5
 
0.8%
Decimal Number
ValueCountFrequency (%)
2 943
31.8%
1 467
15.7%
9 367
 
12.4%
0 211
 
7.1%
5 208
 
7.0%
3 202
 
6.8%
8 175
 
5.9%
7 138
 
4.6%
6 133
 
4.5%
4 126
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2970
83.3%
Latin 594
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 294
49.5%
J 196
33.0%
G 39
 
6.6%
M 37
 
6.2%
N 9
 
1.5%
S 5
 
0.8%
P 3
 
0.5%
K 2
 
0.3%
I 2
 
0.3%
F 2
 
0.3%
Other values (4) 5
 
0.8%
Common
ValueCountFrequency (%)
2 943
31.8%
1 467
15.7%
9 367
 
12.4%
0 211
 
7.1%
5 208
 
7.0%
3 202
 
6.8%
8 175
 
5.9%
7 138
 
4.6%
6 133
 
4.5%
4 126
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 943
26.5%
1 467
13.1%
9 367
 
10.3%
C 294
 
8.2%
0 211
 
5.9%
5 208
 
5.8%
3 202
 
5.7%
J 196
 
5.5%
8 175
 
4.9%
7 138
 
3.9%
Other values (14) 363
 
10.2%

업종분류
Text

MISSING 

Distinct230
Distinct (%)38.7%
Missing372
Missing (%)38.5%
Memory size7.7 KiB
2023-12-12T22:45:40.735147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length15.777778
Min length3

Characters and Unicode

Total characters9372
Distinct characters273
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)22.7%

Sample

1st row플라스틱 시트 및 판 제조업
2nd row컴퓨터시스템 통합 자문 및 구축 서비스업
3rd row기타 무선 통신장비 제조업
4th row그 외 기타 일반목적용 기계 제조업
5th row알루미늄 제련, 정련 및 합금 제조업
ValueCountFrequency (%)
및 346
 
12.7%
제조업 283
 
10.4%
기타 152
 
5.6%
공급업 135
 
5.0%
소프트웨어 135
 
5.0%
개발 134
 
4.9%
응용 103
 
3.8%
외 53
 
1.9%
그 53
 
1.9%
서비스업 45
 
1.7%
Other values (419) 1281
47.1%
2023-12-12T22:45:41.278513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2126
22.7%
업 607
 
6.5%
제 403
 
4.3%
및 346
 
3.7%
조 327
 
3.5%
기 320
 
3.4%
용 206
 
2.2%
공 194
 
2.1%
개 171
 
1.8%
소 170
 
1.8%
Other values (263) 4502
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7198
76.8%
Space Separator 2126
 
22.7%
Other Punctuation 48
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 607
 
8.4%
제 403
 
5.6%
및 346
 
4.8%
조 327
 
4.5%
기 320
 
4.4%
용 206
 
2.9%
공 194
 
2.7%
개 171
 
2.4%
소 170
 
2.4%
발 164
 
2.3%
Other values (261) 4290
59.6%
Space Separator
ValueCountFrequency (%)
2126
100.0%
Other Punctuation
ValueCountFrequency (%)
, 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7198
76.8%
Common 2174
 
23.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 607
 
8.4%
제 403
 
5.6%
및 346
 
4.8%
조 327
 
4.5%
기 320
 
4.4%
용 206
 
2.9%
공 194
 
2.7%
개 171
 
2.4%
소 170
 
2.4%
발 164
 
2.3%
Other values (261) 4290
59.6%
Common
ValueCountFrequency (%)
2126
97.8%
, 48
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7189
76.7%
ASCII 2174
 
23.2%
Compat Jamo 9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2126
97.8%
, 48
 
2.2%
Hangul
ValueCountFrequency (%)
업 607
 
8.4%
제 403
 
5.6%
및 346
 
4.8%
조 327
 
4.5%
기 320
 
4.5%
용 206
 
2.9%
공 194
 
2.7%
개 171
 
2.4%
소 170
 
2.4%
발 164
 
2.3%
Other values (260) 4281
59.5%
Compat Jamo
ValueCountFrequency (%)
ㆍ 9
100.0%

소재지
Text

MISSING 

Distinct125
Distinct (%)21.0%
Missing372
Missing (%)38.5%
Memory size7.7 KiB
2023-12-12T22:45:41.566340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.013468
Min length5

Characters and Unicode

Total characters3572
Distinct characters89
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)8.9%

Sample

1st row경기 광주시
2nd row대전 유성구
3rd row경기 화성시
4th row전북 군산시
5th row광주 광산구
ValueCountFrequency (%)
서울 211
 
17.8%
경기 150
 
12.6%
강남구 66
 
5.6%
성남시 52
 
4.4%
인천 30
 
2.5%
대전 30
 
2.5%
부산 29
 
2.4%
유성구 27
 
2.3%
마포구 25
 
2.1%
충남 23
 
1.9%
Other values (112) 545
45.9%
2023-12-12T22:45:41.977973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
594
16.6%
구 352
 
9.9%
시 256
 
7.2%
서 254
 
7.1%
울 233
 
6.5%
경 197
 
5.5%
남 183
 
5.1%
기 166
 
4.6%
성 126
 
3.5%
강 84
 
2.4%
Other values (79) 1127
31.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2978
83.4%
Space Separator 594
 
16.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 352
 
11.8%
시 256
 
8.6%
서 254
 
8.5%
울 233
 
7.8%
경 197
 
6.6%
남 183
 
6.1%
기 166
 
5.6%
성 126
 
4.2%
강 84
 
2.8%
산 73
 
2.5%
Other values (78) 1054
35.4%
Space Separator
ValueCountFrequency (%)
594
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2978
83.4%
Common 594
 
16.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 352
 
11.8%
시 256
 
8.6%
서 254
 
8.5%
울 233
 
7.8%
경 197
 
6.6%
남 183
 
6.1%
기 166
 
5.6%
성 126
 
4.2%
강 84
 
2.8%
산 73
 
2.5%
Other values (78) 1054
35.4%
Common
ValueCountFrequency (%)
594
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2978
83.4%
ASCII 594
 
16.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
594
100.0%
Hangul
ValueCountFrequency (%)
구 352
 
11.8%
시 256
 
8.6%
서 254
 
8.5%
울 233
 
7.8%
경 197
 
6.6%
남 183
 
6.1%
기 166
 
5.6%
성 126
 
4.2%
강 84
 
2.8%
산 73
 
2.5%
Other values (78) 1054
35.4%

기업구분
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
소기업
526 
<NA>
372 
중기업
67 
중견기업
 
1

Length

Max length4
Median length3
Mean length3.3861284
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row중기업
2nd row중기업
3rd row소기업
4th row소기업
5th row중견기업

Common Values

ValueCountFrequency (%)
소기업 526
54.5%
<NA> 372
38.5%
중기업 67
 
6.9%
중견기업 1
 
0.1%

Length

2023-12-12T22:45:42.136452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:45:42.265365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소기업 526
54.5%
na 372
38.5%
중기업 67
 
6.9%
중견기업 1
 
0.1%

Interactions

2023-12-12T22:45:36.996360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.346202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.689255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.091791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.438982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.788619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.197813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.564534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.895726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:45:42.335886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서투자연도투자금액투자종류기업구분
순서1.0000.9500.6070.3700.452
투자연도0.9501.0000.4730.4420.566
투자금액0.6070.4731.0000.1420.278
투자종류0.3700.4420.1421.0000.217
기업구분0.4520.5660.2780.2171.000
2023-12-12T22:45:42.453355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업구분투자종류
기업구분1.0000.206
투자종류0.2061.000
2023-12-12T22:45:42.574884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서투자연도투자금액투자종류기업구분
순서1.0000.990-0.3430.2280.303
투자연도0.9901.000-0.3300.2100.350
투자금액-0.343-0.3301.0000.0930.172
투자종류0.2280.2100.0931.0000.206
기업구분0.3030.3500.1720.2061.000

Missing values

2023-12-12T22:45:37.349964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:45:37.500464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T22:45:37.658237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

순서투자연도투자일자투자금액투자종류업종코드업종분류소재지기업구분
0120142014-06-301000000000우선주C22213플라스틱 시트 및 판 제조업경기 광주시중기업
1220142014-06-30900000000우선주J62021컴퓨터시스템 통합 자문 및 구축 서비스업대전 유성구중기업
2320142014-07-15999982500우선주C26429기타 무선 통신장비 제조업경기 화성시소기업
3420142014-07-151000000000우선주C29199그 외 기타 일반목적용 기계 제조업전북 군산시소기업
4520142014-08-041000000000우선주C24212알루미늄 제련, 정련 및 합금 제조업광주 광산구중견기업
5620142014-08-06999990000우선주C20499그 외 기타 분류 안된 화학제품 제조업경기 시흥시중기업
6720142014-08-141000000000우선주C11112맥아 및 맥주 제조업강원 횡성군소기업
7820142014-09-22999900000우선주J58221시스템 소프트웨어 개발 및 공급업서울 강남구중기업
8920142014-09-23600000000우선주C14192근무복, 작업복 및 유사의복 제조업서울 송파구중기업
91020142014-09-26999600000우선주C26299그 외 기타 전자부품 제조업경북 구미시중기업
순서투자연도투자일자투자금액투자종류업종코드업종분류소재지기업구분
956<NA><NA><NA><NA><NA><NA><NA><NA><NA>
957<NA><NA><NA><NA><NA><NA><NA><NA><NA>
958<NA><NA><NA><NA><NA><NA><NA><NA><NA>
959<NA><NA><NA><NA><NA><NA><NA><NA><NA>
960<NA><NA><NA><NA><NA><NA><NA><NA><NA>
961<NA><NA><NA><NA><NA><NA><NA><NA><NA>
962<NA><NA><NA><NA><NA><NA><NA><NA><NA>
963<NA><NA><NA><NA><NA><NA><NA><NA><NA>
964<NA><NA><NA><NA><NA><NA><NA><NA><NA>
965<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

순서투자연도투자일자투자금액투자종류업종코드업종분류소재지기업구분# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>372