Overview

Dataset statistics

Number of variables7
Number of observations1787
Missing cells62
Missing cells (%)0.5%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory103.1 KiB
Average record size in memory59.1 B

Variable types

Numeric3
Categorical1
Text3

Dataset

Description환경책임투자 종합플랫폼에서 제공하는 2023년 기준 환경경영 관련 외부 보고서 목록(보고서 제목, 발행기관, 발행일, 키워드 등 )을 제공
Author한국환경산업기술원
URLhttps://www.data.go.kr/data/15105552/fileData.do

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
순번 is highly overall correlated with 파일번호 and 1 other fieldsHigh correlation
파일번호 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
발행연도 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
발행연도 has 28 (1.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:24:01.336742
Analysis finished2023-12-12 15:24:03.867242
Duration2.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION 

Distinct1781
Distinct (%)100.0%
Missing6
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1162.2173
Minimum90
Maximum2376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-12-13T00:24:03.946404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile207
Q1616
median1121
Q31699
95-th percentile2240
Maximum2376
Range2286
Interquartile range (IQR)1083

Descriptive statistics

Standard deviation646.41987
Coefficient of variation (CV)0.55619536
Kurtosis-1.1021457
Mean1162.2173
Median Absolute Deviation (MAD)532
Skewness0.17571789
Sum2069909
Variance417858.64
MonotonicityStrictly increasing
2023-12-13T00:24:04.100454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1474 1
 
0.1%
1485 1
 
0.1%
1484 1
 
0.1%
1483 1
 
0.1%
1482 1
 
0.1%
1481 1
 
0.1%
1480 1
 
0.1%
1479 1
 
0.1%
1478 1
 
0.1%
1477 1
 
0.1%
Other values (1771) 1771
99.1%
(Missing) 6
 
0.3%
ValueCountFrequency (%)
90 1
0.1%
91 1
0.1%
92 1
0.1%
93 1
0.1%
122 1
0.1%
123 1
0.1%
124 1
0.1%
125 1
0.1%
126 1
0.1%
127 1
0.1%
ValueCountFrequency (%)
2376 1
0.1%
2375 1
0.1%
2374 1
0.1%
2373 1
0.1%
2372 1
0.1%
2371 1
0.1%
2370 1
0.1%
2369 1
0.1%
2368 1
0.1%
2367 1
0.1%

분야
Categorical

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size14.1 KiB
<NA>
402 
분류체계
328 
평가체계
322 
정보공개
321 
녹색채권
314 
Other values (5)
100 

Length

Max length81
Median length4
Mean length4.1550084
Min length4

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 402
22.5%
분류체계 328
18.4%
평가체계 322
18.0%
정보공개 321
18.0%
녹색채권 314
17.6%
ESG규제 96
 
5.4%
ESG 규제 동향, 분류체계, 지속가능금융, ESG 공시, 기후 보고 규칙( 1
 
0.1%
탄소중립(Net-Zero), 산업부문(Industrial Sector), 온실가스 감축(Emission Reduction), 녹색전환 투자, EU 1
 
0.1%
ESG 투자, ESG 평가, 자산운용사, ESG 정보, 자산운용사 1
 
0.1%
녹색채권, 채권 표준, 기후 영향 보고서(impact reports) 1
 
0.1%

Length

2023-12-13T00:24:04.232843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:24:04.380741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 402
22.1%
분류체계 329
18.1%
평가체계 322
17.7%
정보공개 321
17.7%
녹색채권 315
17.3%
esg규제 96
 
5.3%
esg 5
 
0.3%
자산운용사 2
 
0.1%
투자 2
 
0.1%
기후 2
 
0.1%
Other values (21) 21
 
1.2%

파일번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1781
Distinct (%)100.0%
Missing6
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1319.0034
Minimum241
Maximum2565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-12-13T00:24:04.568259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum241
5-th percentile369
Q1765
median1266
Q31848
95-th percentile2413
Maximum2565
Range2324
Interquartile range (IQR)1083

Descriptive statistics

Standard deviation652.23869
Coefficient of variation (CV)0.49449358
Kurtosis-1.1008188
Mean1319.0034
Median Absolute Deviation (MAD)532
Skewness0.20645051
Sum2349145
Variance425415.31
MonotonicityStrictly increasing
2023-12-13T00:24:04.705792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1630 1
 
0.1%
1641 1
 
0.1%
1640 1
 
0.1%
1639 1
 
0.1%
1638 1
 
0.1%
1637 1
 
0.1%
1636 1
 
0.1%
1635 1
 
0.1%
1634 1
 
0.1%
1633 1
 
0.1%
Other values (1771) 1771
99.1%
(Missing) 6
 
0.3%
ValueCountFrequency (%)
241 1
0.1%
242 1
0.1%
243 1
0.1%
244 1
0.1%
282 1
0.1%
283 1
0.1%
284 1
0.1%
285 1
0.1%
286 1
0.1%
287 1
0.1%
ValueCountFrequency (%)
2565 1
0.1%
2564 1
0.1%
2563 1
0.1%
2562 1
0.1%
2561 1
0.1%
2560 1
0.1%
2559 1
0.1%
2558 1
0.1%
2557 1
0.1%
2556 1
0.1%

발행연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)0.9%
Missing28
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2021.7112
Minimum2004
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-12-13T00:24:04.826110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2019
Q12021
median2022
Q32023
95-th percentile2023
Maximum2023
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.713775
Coefficient of variation (CV)0.00084768536
Kurtosis22.924976
Mean2021.7112
Median Absolute Deviation (MAD)1
Skewness-3.7200398
Sum3556190
Variance2.9370247
MonotonicityNot monotonic
2023-12-13T00:24:04.935454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2023 620
34.7%
2022 587
32.8%
2021 309
17.3%
2020 136
 
7.6%
2019 38
 
2.1%
2018 35
 
2.0%
2017 9
 
0.5%
2011 5
 
0.3%
2016 5
 
0.3%
2010 3
 
0.2%
Other values (6) 12
 
0.7%
(Missing) 28
 
1.6%
ValueCountFrequency (%)
2004 1
 
0.1%
2007 1
 
0.1%
2009 3
 
0.2%
2010 3
 
0.2%
2011 5
0.3%
2013 2
 
0.1%
2014 2
 
0.1%
2015 3
 
0.2%
2016 5
0.3%
2017 9
0.5%
ValueCountFrequency (%)
2023 620
34.7%
2022 587
32.8%
2021 309
17.3%
2020 136
 
7.6%
2019 38
 
2.1%
2018 35
 
2.0%
2017 9
 
0.5%
2016 5
 
0.3%
2015 3
 
0.2%
2014 2
 
0.1%

발행기관명
Text

Distinct521
Distinct (%)29.3%
Missing6
Missing (%)0.3%
Memory size14.1 KiB
2023-12-13T00:24:05.187901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length46
Mean length7.8023582
Min length2

Characters and Unicode

Total characters13896
Distinct characters251
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)16.6%

Sample

1st rowUNEP FI
2nd rowLinklaters
3rd rowGID
4th rowBlackRock
5th row자본시장연구원
ValueCountFrequency (%)
cbi 62
 
2.7%
자본시장연구원 57
 
2.5%
adb 39
 
1.7%
cdp 38
 
1.6%
icma 37
 
1.6%
mdpi 29
 
1.3%
대외경제정책연구원 26
 
1.1%
climate 25
 
1.1%
에너지경제연구원 25
 
1.1%
russell 24
 
1.0%
Other values (610) 1952
84.4%
2023-12-13T00:24:05.589313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
536
 
3.9%
I 489
 
3.5%
C 459
 
3.3%
e 451
 
3.2%
i 433
 
3.1%
n 432
 
3.1%
a 423
 
3.0%
t 372
 
2.7%
E 348
 
2.5%
S 346
 
2.5%
Other values (241) 9607
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4699
33.8%
Lowercase Letter 4267
30.7%
Uppercase Letter 4254
30.6%
Space Separator 536
 
3.9%
Other Punctuation 82
 
0.6%
Decimal Number 20
 
0.1%
Open Punctuation 17
 
0.1%
Close Punctuation 16
 
0.1%
Dash Punctuation 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 331
 
7.0%
구 323
 
6.9%
연 309
 
6.6%
국 287
 
6.1%
한 246
 
5.2%
회 167
 
3.6%
경 143
 
3.0%
기 114
 
2.4%
업 113
 
2.4%
자 96
 
2.0%
Other values (173) 2570
54.7%
Lowercase Letter
ValueCountFrequency (%)
e 451
10.6%
i 433
10.1%
n 432
10.1%
a 423
9.9%
t 372
8.7%
o 327
 
7.7%
s 308
 
7.2%
l 278
 
6.5%
r 206
 
4.8%
u 155
 
3.6%
Other values (15) 882
20.7%
Uppercase Letter
ValueCountFrequency (%)
I 489
11.5%
C 459
10.8%
E 348
 
8.2%
S 346
 
8.1%
B 288
 
6.8%
F 277
 
6.5%
P 246
 
5.8%
D 242
 
5.7%
A 241
 
5.7%
M 228
 
5.4%
Other values (14) 1090
25.6%
Other Punctuation
ValueCountFrequency (%)
& 28
34.1%
/ 18
22.0%
, 16
19.5%
' 13
15.9%
· 4
 
4.9%
. 2
 
2.4%
: 1
 
1.2%
Decimal Number
ValueCountFrequency (%)
2 8
40.0%
0 4
20.0%
4 3
 
15.0%
1 2
 
10.0%
7 2
 
10.0%
3 1
 
5.0%
Space Separator
ValueCountFrequency (%)
536
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8521
61.3%
Hangul 4700
33.8%
Common 675
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 331
 
7.0%
구 323
 
6.9%
연 309
 
6.6%
국 287
 
6.1%
한 246
 
5.2%
회 167
 
3.6%
경 143
 
3.0%
기 114
 
2.4%
업 113
 
2.4%
자 96
 
2.0%
Other values (174) 2571
54.7%
Latin
ValueCountFrequency (%)
I 489
 
5.7%
C 459
 
5.4%
e 451
 
5.3%
i 433
 
5.1%
n 432
 
5.1%
a 423
 
5.0%
t 372
 
4.4%
E 348
 
4.1%
S 346
 
4.1%
o 327
 
3.8%
Other values (39) 4441
52.1%
Common
ValueCountFrequency (%)
536
79.4%
& 28
 
4.1%
/ 18
 
2.7%
( 17
 
2.5%
) 16
 
2.4%
, 16
 
2.4%
' 13
 
1.9%
2 8
 
1.2%
0 4
 
0.6%
· 4
 
0.6%
Other values (8) 15
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9192
66.1%
Hangul 4699
33.8%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
536
 
5.8%
I 489
 
5.3%
C 459
 
5.0%
e 451
 
4.9%
i 433
 
4.7%
n 432
 
4.7%
a 423
 
4.6%
t 372
 
4.0%
E 348
 
3.8%
S 346
 
3.8%
Other values (56) 4903
53.3%
Hangul
ValueCountFrequency (%)
원 331
 
7.0%
구 323
 
6.9%
연 309
 
6.6%
국 287
 
6.1%
한 246
 
5.2%
회 167
 
3.6%
경 143
 
3.0%
기 114
 
2.4%
업 113
 
2.4%
자 96
 
2.0%
Other values (173) 2570
54.7%
None
ValueCountFrequency (%)
· 4
80.0%
㈜ 1
 
20.0%

보고서 제목
Text

Distinct1769
Distinct (%)99.3%
Missing6
Missing (%)0.3%
Memory size14.1 KiB
2023-12-13T00:24:05.949459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length270
Median length118
Mean length49.827063
Min length6

Characters and Unicode

Total characters88742
Distinct characters529
Distinct categories18 ?
Distinct scripts4 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1757 ?
Unique (%)98.7%

Sample

1st rowFrom Disclosure to Action - Applying TCFD principles throughout financial institutions
2nd rowESG Legal Outlook 2021
3rd row2021 Spring ESG Newsletter
4th row2020 TCFD report
5th rowESG 평가 체계 개선 방향
ValueCountFrequency (%)
the 471
 
3.3%
esg 386
 
2.7%
and 353
 
2.5%
of 269
 
1.9%
green 237
 
1.7%
for 209
 
1.5%
185
 
1.3%
in 174
 
1.2%
sustainable 163
 
1.1%
to 156
 
1.1%
Other values (3615) 11608
81.7%
2023-12-13T00:24:06.550374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12500
 
14.1%
e 5946
 
6.7%
n 5548
 
6.3%
a 4634
 
5.2%
i 4612
 
5.2%
t 4308
 
4.9%
o 4148
 
4.7%
r 3614
 
4.1%
s 3361
 
3.8%
l 2047
 
2.3%
Other values (519) 38024
42.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51348
57.9%
Space Separator 12500
 
14.1%
Other Letter 11493
 
13.0%
Uppercase Letter 10202
 
11.5%
Decimal Number 1573
 
1.8%
Other Punctuation 723
 
0.8%
Dash Punctuation 378
 
0.4%
Open Punctuation 187
 
0.2%
Close Punctuation 187
 
0.2%
Final Punctuation 95
 
0.1%
Other values (8) 56
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 343
 
3.0%
기 278
 
2.4%
가 234
 
2.0%
시 225
 
2.0%
과 205
 
1.8%
제 195
 
1.7%
국 188
 
1.6%
지 182
 
1.6%
한 180
 
1.6%
사 171
 
1.5%
Other values (416) 9292
80.8%
Uppercase Letter
ValueCountFrequency (%)
S 1406
13.8%
E 1137
11.1%
G 987
 
9.7%
C 751
 
7.4%
I 624
 
6.1%
R 596
 
5.8%
A 572
 
5.6%
T 568
 
5.6%
F 458
 
4.5%
B 446
 
4.4%
Other values (17) 2657
26.0%
Lowercase Letter
ValueCountFrequency (%)
e 5946
11.6%
n 5548
10.8%
a 4634
9.0%
i 4612
9.0%
t 4308
 
8.4%
o 4148
 
8.1%
r 3614
 
7.0%
s 3361
 
6.5%
l 2047
 
4.0%
c 1743
 
3.4%
Other values (16) 11387
22.2%
Other Punctuation
ValueCountFrequency (%)
: 302
41.8%
, 216
29.9%
. 73
 
10.1%
' 37
 
5.1%
& 35
 
4.8%
· 28
 
3.9%
/ 12
 
1.7%
" 8
 
1.1%
… 3
 
0.4%
： 3
 
0.4%
Other values (4) 6
 
0.8%
Decimal Number
ValueCountFrequency (%)
2 706
44.9%
0 368
23.4%
1 227
 
14.4%
3 109
 
6.9%
5 43
 
2.7%
9 40
 
2.5%
8 23
 
1.5%
7 20
 
1.3%
6 19
 
1.2%
4 18
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 373
98.7%
― 4
 
1.1%
－ 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 167
89.3%
「 11
 
5.9%
[ 9
 
4.8%
Close Punctuation
ValueCountFrequency (%)
) 167
89.3%
」 11
 
5.9%
] 9
 
4.8%
Math Symbol
ValueCountFrequency (%)
+ 6
66.7%
| 2
 
22.2%
~ 1
 
11.1%
Final Punctuation
ValueCountFrequency (%)
’ 91
95.8%
” 4
 
4.2%
Initial Punctuation
ValueCountFrequency (%)
‘ 19
82.6%
“ 4
 
17.4%
Other Symbol
ValueCountFrequency (%)
° 3
75.0%
℃ 1
 
25.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
50.0%
Ⅰ 2
50.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
66.7%
€ 1
33.3%
Space Separator
ValueCountFrequency (%)
12500
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 11
100.0%
Other Number
ValueCountFrequency (%)
① 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61554
69.4%
Common 15695
 
17.7%
Hangul 11487
 
12.9%
Han 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 343
 
3.0%
기 278
 
2.4%
가 234
 
2.0%
시 225
 
2.0%
과 205
 
1.8%
제 195
 
1.7%
국 188
 
1.6%
지 182
 
1.6%
한 180
 
1.6%
사 171
 
1.5%
Other values (410) 9286
80.8%
Latin
ValueCountFrequency (%)
e 5946
 
9.7%
n 5548
 
9.0%
a 4634
 
7.5%
i 4612
 
7.5%
t 4308
 
7.0%
o 4148
 
6.7%
r 3614
 
5.9%
s 3361
 
5.5%
l 2047
 
3.3%
c 1743
 
2.8%
Other values (45) 21593
35.1%
Common
ValueCountFrequency (%)
12500
79.6%
2 706
 
4.5%
- 373
 
2.4%
0 368
 
2.3%
: 302
 
1.9%
1 227
 
1.4%
, 216
 
1.4%
( 167
 
1.1%
) 167
 
1.1%
3 109
 
0.7%
Other values (38) 560
 
3.6%
Han
ValueCountFrequency (%)
反 1
16.7%
美 1
16.7%
新 1
16.7%
中 1
16.7%
展 1
16.7%
進 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77059
86.8%
Hangul 11483
 
12.9%
Punctuation 125
 
0.1%
None 58
 
0.1%
CJK 6
 
< 0.1%
Compat Jamo 4
 
< 0.1%
Number Forms 4
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%
Currency Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12500
16.2%
e 5946
 
7.7%
n 5548
 
7.2%
a 4634
 
6.0%
i 4612
 
6.0%
t 4308
 
5.6%
o 4148
 
5.4%
r 3614
 
4.7%
s 3361
 
4.4%
l 2047
 
2.7%
Other values (75) 26341
34.2%
Hangul
ValueCountFrequency (%)
의 343
 
3.0%
기 278
 
2.4%
가 234
 
2.0%
시 225
 
2.0%
과 205
 
1.8%
제 195
 
1.7%
국 188
 
1.6%
지 182
 
1.6%
한 180
 
1.6%
사 171
 
1.5%
Other values (409) 9282
80.8%
Punctuation
ValueCountFrequency (%)
’ 91
72.8%
‘ 19
 
15.2%
― 4
 
3.2%
” 4
 
3.2%
“ 4
 
3.2%
… 3
 
2.4%
None
ValueCountFrequency (%)
· 28
48.3%
「 11
 
19.0%
」 11
 
19.0%
° 3
 
5.2%
： 3
 
5.2%
－ 1
 
1.7%
Ø 1
 
1.7%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
50.0%
Ⅰ 2
50.0%
CJK
ValueCountFrequency (%)
反 1
16.7%
美 1
16.7%
新 1
16.7%
中 1
16.7%
展 1
16.7%
進 1
16.7%
Letterlike Symbols
ValueCountFrequency (%)
℃ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
① 1
100.0%
Currency Symbols
ValueCountFrequency (%)
€ 1
100.0%

태그(보고서 관련 주요 키워드)
Text

Distinct1719
Distinct (%)96.7%
Missing10
Missing (%)0.6%
Memory size14.1 KiB
2023-12-13T00:24:06.905427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length199
Median length113
Mean length39.054024
Min length2

Characters and Unicode

Total characters69399
Distinct characters599
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1674 ?
Unique (%)94.2%

Sample

1st rowUN, 기후리스크, 금융
2nd rowESG, 전망, 법률
3rd rowESG, GID
4th rowTCFD, 기후리스크, 기업의무
5th row지속가능투자, ESG 평가체계
ValueCountFrequency (%)
esg 712
 
5.0%
및 348
 
2.4%
분석 173
 
1.2%
eu 169
 
1.2%
대한 162
 
1.1%
녹색채권 160
 
1.1%
위한 152
 
1.1%
기후 151
 
1.1%
관련 133
 
0.9%
투자 132
 
0.9%
Other values (4175) 12041
84.0%
2023-12-13T00:24:07.536929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12666
 
18.3%
, 2794
 
4.0%
S 1486
 
2.1%
E 1293
 
1.9%
기 1075
 
1.5%
G 1028
 
1.5%
가 1014
 
1.5%
의 997
 
1.4%
지 852
 
1.2%
( 786
 
1.1%
Other values (589) 45408
65.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37717
54.3%
Space Separator 12666
 
18.3%
Uppercase Letter 6842
 
9.9%
Lowercase Letter 6699
 
9.7%
Other Punctuation 2936
 
4.2%
Decimal Number 819
 
1.2%
Open Punctuation 789
 
1.1%
Close Punctuation 788
 
1.1%
Dash Punctuation 103
 
0.1%
Math Symbol 24
 
< 0.1%
Other values (4) 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 1075
 
2.9%
가 1014
 
2.7%
의 997
 
2.6%
지 852
 
2.3%
자 603
 
1.6%
시 573
 
1.5%
한 521
 
1.4%
권 512
 
1.4%
색 505
 
1.3%
녹 497
 
1.3%
Other values (500) 30568
81.0%
Uppercase Letter
ValueCountFrequency (%)
S 1486
21.7%
E 1293
18.9%
G 1028
15.0%
C 434
 
6.3%
I 275
 
4.0%
D 273
 
4.0%
F 267
 
3.9%
U 240
 
3.5%
B 238
 
3.5%
R 231
 
3.4%
Other values (17) 1077
15.7%
Lowercase Letter
ValueCountFrequency (%)
e 777
11.6%
n 726
10.8%
a 636
9.5%
i 604
9.0%
o 553
 
8.3%
r 505
 
7.5%
t 499
 
7.4%
s 430
 
6.4%
l 344
 
5.1%
c 240
 
3.6%
Other values (16) 1385
20.7%
Other Punctuation
ValueCountFrequency (%)
, 2794
95.2%
. 33
 
1.1%
& 27
 
0.9%
' 24
 
0.8%
· 19
 
0.6%
: 18
 
0.6%
/ 10
 
0.3%
" 6
 
0.2%
% 2
 
0.1%
; 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 348
42.5%
0 180
22.0%
1 124
 
15.1%
3 77
 
9.4%
5 23
 
2.8%
7 20
 
2.4%
9 16
 
2.0%
4 12
 
1.5%
6 11
 
1.3%
8 8
 
1.0%
Math Symbol
ValueCountFrequency (%)
+ 18
75.0%
~ 4
 
16.7%
→ 1
 
4.2%
> 1
 
4.2%
Open Punctuation
ValueCountFrequency (%)
( 786
99.6%
「 3
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 785
99.6%
」 3
 
0.4%
Other Symbol
ValueCountFrequency (%)
® 1
50.0%
° 1
50.0%
Space Separator
ValueCountFrequency (%)
12666
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 103
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 9
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 4
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37708
54.3%
Common 18140
26.1%
Latin 13542
 
19.5%
Han 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 1075
 
2.9%
가 1014
 
2.7%
의 997
 
2.6%
지 852
 
2.3%
자 603
 
1.6%
시 573
 
1.5%
한 521
 
1.4%
권 512
 
1.4%
색 505
 
1.3%
녹 497
 
1.3%
Other values (498) 30559
81.0%
Latin
ValueCountFrequency (%)
S 1486
 
11.0%
E 1293
 
9.5%
G 1028
 
7.6%
e 777
 
5.7%
n 726
 
5.4%
a 636
 
4.7%
i 604
 
4.5%
o 553
 
4.1%
r 505
 
3.7%
t 499
 
3.7%
Other values (44) 5435
40.1%
Common
ValueCountFrequency (%)
12666
69.8%
, 2794
 
15.4%
( 786
 
4.3%
) 785
 
4.3%
2 348
 
1.9%
0 180
 
1.0%
1 124
 
0.7%
- 103
 
0.6%
3 77
 
0.4%
. 33
 
0.2%
Other values (25) 244
 
1.3%
Han
ValueCountFrequency (%)
美 5
55.6%
社 4
44.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37706
54.3%
ASCII 31638
45.6%
None 28
 
< 0.1%
Punctuation 14
 
< 0.1%
CJK 9
 
< 0.1%
Compat Jamo 2
 
< 0.1%
Number Forms 1
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12666
40.0%
, 2794
 
8.8%
S 1486
 
4.7%
E 1293
 
4.1%
G 1028
 
3.2%
( 786
 
2.5%
) 785
 
2.5%
e 777
 
2.5%
n 726
 
2.3%
a 636
 
2.0%
Other values (68) 8661
27.4%
Hangul
ValueCountFrequency (%)
기 1075
 
2.9%
가 1014
 
2.7%
의 997
 
2.6%
지 852
 
2.3%
자 603
 
1.6%
시 573
 
1.5%
한 521
 
1.4%
권 512
 
1.4%
색 505
 
1.3%
녹 497
 
1.3%
Other values (497) 30557
81.0%
None
ValueCountFrequency (%)
· 19
67.9%
」 3
 
10.7%
「 3
 
10.7%
® 1
 
3.6%
° 1
 
3.6%
Ｃ 1
 
3.6%
Punctuation
ValueCountFrequency (%)
’ 9
64.3%
‘ 4
28.6%
… 1
 
7.1%
CJK
ValueCountFrequency (%)
美 5
55.6%
社 4
44.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 2
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

Interactions

2023-12-13T00:24:03.194339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:02.469097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:02.860130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:03.286280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:02.590056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:02.973089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:03.371542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:02.711792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:03.080730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:24:07.995205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번분야파일번호발행연도
순번1.0000.2960.9990.517
분야0.2961.0000.3270.116
파일번호0.9990.3271.0000.518
발행연도0.5170.1160.5181.000
2023-12-13T00:24:08.089221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번파일번호발행연도분야
순번1.0001.0000.8580.186
파일번호1.0001.0000.8580.207
발행연도0.8580.8581.0000.043
분야0.1860.2070.0431.000

Missing values

2023-12-13T00:24:03.513308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:24:03.650842image/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.
2023-12-13T00:24:03.779839image/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

순번분야파일번호발행연도발행기관명보고서 제목태그(보고서 관련 주요 키워드)
090<NA>2412020UNEP FIFrom Disclosure to Action - Applying TCFD principles throughout financial institutionsUN, 기후리스크, 금융
191<NA>2422021LinklatersESG Legal Outlook 2021ESG, 전망, 법률
292<NA>2432021GID2021 Spring ESG NewsletterESG, GID
393<NA>2442020BlackRock2020 TCFD reportTCFD, 기후리스크, 기업의무
4122<NA>2822021자본시장연구원ESG 평가 체계 개선 방향지속가능투자, ESG 평가체계
5123<NA>2832021자본시장연구원ESG 평가 체계 현황과 특성 분석ESG, 이슈보고서, 평가체계
6124<NA>2842021자본시장연구원ESG채권의 특성 분석과 활성화 방안ESG 채권
7125<NA>2852022자본시장연구원경영진 보상에 대한 ESG 요소 반영 추세ESG 경영, ESG 성과
8126<NA>2862020자본시장연구원국내 ESG 펀드의 현황 및 특징 분석ESG 펀드, 동향
9127<NA>2872021한국기업지배구조원KCGS 리포트 제11권 1호_ESG 동향ESG 동향, 국내기업
순번분야파일번호발행연도발행기관명보고서 제목태그(보고서 관련 주요 키워드)
17772367녹색채권25562023대신증권세계는 지금 금융상품의 ESG워싱 규제 강화 중ESG펀드 규제, ESG워싱, ESG 자산
17782368분류체계25572023한국지방행정연구원시민참여를 통한 탄소중립 이행 : 탄소중립 그린도시,‘수원특례시’탄소중립, 온실가스 배출량, 그린도시
17792369분류체계25582023한국무역협회수출 기업의 기후변화 대응 현황 및 시사점기후변화 대응, 수출 기업, 환경 규제
17802370분류체계25592023유진투자증권EU, 풍력지원 패키지 발표 풍력산업에서 트럼프 발작은 없어풍력지원 패키지, 트럼프 정권, 해상풍력
17812371평가체계25602023서울대학교 대학원ESG Score and Analyst Forecast Accuracy: Evidence from KoreaESG 점수, 이익 예측 정확도, 산업별 ESG 우선순위
17822372평가체계25612023한국무역통상학회물류기업의 ESG 활동이 경영성과에 미치는 영향에서 조직몰입의 매개효과ESG 활동, ESG 성과, 물류기업, 경영성과
17832373평가체계25622023한국무역연구원기업의 ESG 활동이 고객태도에 미치는 영향: 지속가능성의 매개효과를 중심으로ESG 활동, ESG 성과, 기업 지속가능성
17842374정보공개25632023EYTechnical Line - How the climate-related disclosures under the SEC proposal, the ESRS and the ISSB standards compareSEC 제안(SEC proposal), 유럽지속가능성보고표준(ESRS), ISSB 표준
17852375정보공개25642023UK FinanceUK adoption of international sustainability disclosuresUK Finance, 유럽금융시장협회(Association for Financial Markets in Europe, AFME), 지속가능성공시, 영국
17862376정보공개25652023Financial Markets AuthorityGuidance for keeping proper climate-related disclosure records기후공시, 기후공시 기록, 중대성(materiality) 기준

Duplicate rows

Most frequently occurring

순번분야파일번호발행연도발행기관명보고서 제목태그(보고서 관련 주요 키워드)# duplicates
0<NA><NA><NA><NA><NA><NA><NA>2