Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 KiB
Average record size in memory146.3 B

Variable types

Numeric1
Text3
Categorical14

Alerts

총 방류량 is highly overall correlated with Scope 3 배출량 and 7 other fieldsHigh correlation
2020년 CDP Water 등급 is highly overall correlated with Scope 3 배출량 and 7 other fieldsHigh correlation
재무보고서 상 물 관련 정보 공개 여부 is highly overall correlated with 기준연도 and 6 other fieldsHigh correlation
물 단위 is highly overall correlated with Scope 3 배출량 and 7 other fieldsHigh correlation
물 관련 목표 수립 여부 is highly overall correlated with 이행률 and 5 other fieldsHigh correlation
총 취수량 is highly overall correlated with Scope 3 배출량 and 7 other fieldsHigh correlation
2020년 CDP Climate 등급 is highly overall correlated with 감축목표 수립 방법론High correlation
Scope 3 배출량 is highly overall correlated with 보고된 Scope 3 배출원 수 and 8 other fieldsHigh correlation
보고된 Scope 3 배출원 수 is highly overall correlated with Scope 3 배출량 and 2 other fieldsHigh correlation
기준연도 is highly overall correlated with Scope 3 배출량 and 6 other fieldsHigh correlation
목표연도 is highly overall correlated with Scope 3 배출량 and 4 other fieldsHigh correlation
감축률 is highly overall correlated with Scope 3 배출량 and 9 other fieldsHigh correlation
이행률 is highly overall correlated with Scope 3 배출량 and 11 other fieldsHigh correlation
감축목표 수립 방법론 is highly overall correlated with 2020년 CDP Climate 등급 and 3 other fieldsHigh correlation
총 취수량 is highly imbalanced (71.4%)Imbalance
총 방류량 is highly imbalanced (71.4%)Imbalance
물 관련 목표 수립 여부 is highly imbalanced (58.2%)Imbalance
재무보고서 상 물 관련 정보 공개 여부 is highly imbalanced (80.6%)Imbalance
연번 has unique valuesUnique
기업명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:30:47.298533
Analysis finished2023-12-10 13:30:50.899944
Duration3.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:30:51.010209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T22:30:51.212037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

기업명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:30:51.593950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8.5
Mean length5.15
Min length2

Characters and Unicode

Total characters515
Distinct characters192
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowDGB금융지주
2nd rowDL이앤씨
3rd row하나금융지주
4th row현대자동차
5th row기업은행
ValueCountFrequency (%)
dgb금융지주 1
 
1.0%
에코프로비엠 1
 
1.0%
지역난방공사 1
 
1.0%
한국전력공사 1
 
1.0%
sk네트웍스 1
 
1.0%
sk 1
 
1.0%
ls산전 1
 
1.0%
lg하우시스 1
 
1.0%
hmm 1
 
1.0%
hdc현대산업개발 1
 
1.0%
Other values (91) 91
90.1%
2023-12-10T22:30:52.197998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 18
 
3.5%
이 14
 
2.7%
한 12
 
2.3%
대 12
 
2.3%
성 12
 
2.3%
지 11
 
2.1%
G 11
 
2.1%
S 10
 
1.9%
L 10
 
1.9%
삼 10
 
1.9%
Other values (182) 395
76.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 440
85.4%
Uppercase Letter 72
 
14.0%
Other Punctuation 2
 
0.4%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 18
 
4.1%
이 14
 
3.2%
한 12
 
2.7%
대 12
 
2.7%
성 12
 
2.7%
지 11
 
2.5%
삼 10
 
2.3%
전 9
 
2.0%
에 8
 
1.8%
금 8
 
1.8%
Other values (164) 326
74.1%
Uppercase Letter
ValueCountFrequency (%)
G 11
15.3%
S 10
13.9%
L 10
13.9%
K 7
9.7%
N 4
 
5.6%
H 4
 
5.6%
D 4
 
5.6%
B 4
 
5.6%
C 3
 
4.2%
M 3
 
4.2%
Other values (6) 12
16.7%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 440
85.4%
Latin 72
 
14.0%
Common 3
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 18
 
4.1%
이 14
 
3.2%
한 12
 
2.7%
대 12
 
2.7%
성 12
 
2.7%
지 11
 
2.5%
삼 10
 
2.3%
전 9
 
2.0%
에 8
 
1.8%
금 8
 
1.8%
Other values (164) 326
74.1%
Latin
ValueCountFrequency (%)
G 11
15.3%
S 10
13.9%
L 10
13.9%
K 7
9.7%
N 4
 
5.6%
H 4
 
5.6%
D 4
 
5.6%
B 4
 
5.6%
C 3
 
4.2%
M 3
 
4.2%
Other values (6) 12
16.7%
Common
ValueCountFrequency (%)
& 2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 440
85.4%
ASCII 75
 
14.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 18
 
4.1%
이 14
 
3.2%
한 12
 
2.7%
대 12
 
2.7%
성 12
 
2.7%
지 11
 
2.5%
삼 10
 
2.3%
전 9
 
2.0%
에 8
 
1.8%
금 8
 
1.8%
Other values (164) 326
74.1%
ASCII
ValueCountFrequency (%)
G 11
14.7%
S 10
13.3%
L 10
13.3%
K 7
9.3%
N 4
 
5.3%
H 4
 
5.3%
D 4
 
5.3%
B 4
 
5.3%
C 3
 
4.0%
M 3
 
4.0%
Other values (8) 15
20.0%

2020년 CDP Climate 등급
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F(무응답/불충분)
31 
Leadership A-
19 
Management B
14 
Leadership A
10 
F(무응답)
7 
Other values (6)
19 

Length

Max length13
Median length12
Mean length10.65
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowLeadership A-
2nd rowLeadership A
3rd rowLeadership A
4th rowLeadership A-
5th rowF(무응답/불충분)

Common Values

ValueCountFrequency (%)
F(무응답/불충분) 31
31.0%
Leadership A- 19
19.0%
Management B 14
14.0%
Leadership A 10
 
10.0%
F(무응답) 7
 
7.0%
Disclosure D 6
 
6.0%
Management B- 3
 
3.0%
응답 3
 
3.0%
Awareness C 3
 
3.0%
참여 거부 3
 
3.0%

Length

2023-12-10T22:30:52.391062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f(무응답/불충분 31
19.5%
leadership 29
18.2%
a 29
18.2%
management 17
10.7%
b 17
10.7%
f(무응답 7
 
4.4%
disclosure 7
 
4.4%
d 7
 
4.4%
응답 3
 
1.9%
awareness 3
 
1.9%
Other values (3) 9
 
5.7%

Scope 1 배출량
Text

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:30:52.613111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.31
Min length1

Characters and Unicode

Total characters431
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)58.0%

Sample

1st row3,835
2nd row35,543
3rd row8,932
4th row808,139
5th row-
ValueCountFrequency (%)
42
42.0%
8,680 1
 
1.0%
89 1
 
1.0%
80,911 1
 
1.0%
417,904 1
 
1.0%
4,048,198 1
 
1.0%
18,308 1
 
1.0%
2,258,326 1
 
1.0%
7,150 1
 
1.0%
13,335,813 1
 
1.0%
Other values (49) 49
49.0%
2023-12-10T22:30:53.054388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 69
16.0%
3 46
10.7%
- 42
9.7%
1 41
9.5%
7 33
7.7%
4 32
7.4%
8 31
7.2%
6 29
6.7%
0 28
6.5%
5 28
6.5%
Other values (2) 52
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320
74.2%
Other Punctuation 69
 
16.0%
Dash Punctuation 42
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 46
14.4%
1 41
12.8%
7 33
10.3%
4 32
10.0%
8 31
9.7%
6 29
9.1%
0 28
8.8%
5 28
8.8%
9 26
8.1%
2 26
8.1%
Other Punctuation
ValueCountFrequency (%)
, 69
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 69
16.0%
3 46
10.7%
- 42
9.7%
1 41
9.5%
7 33
7.7%
4 32
7.4%
8 31
7.2%
6 29
6.7%
0 28
6.5%
5 28
6.5%
Other values (2) 52
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 69
16.0%
3 46
10.7%
- 42
9.7%
1 41
9.5%
7 33
7.7%
4 32
7.4%
8 31
7.2%
6 29
6.7%
0 28
6.5%
5 28
6.5%
Other values (2) 52
12.1%

Scope 2 배출량
Text

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:30:53.477140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.45
Min length1

Characters and Unicode

Total characters445
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)58.0%

Sample

1st row16,871
2nd row258,781
3rd row60,025
4th row1,900,954
5th row-
ValueCountFrequency (%)
42
42.0%
16,522 1
 
1.0%
63,788 1
 
1.0%
161,466 1
 
1.0%
178,234 1
 
1.0%
8,532 1
 
1.0%
20,746 1
 
1.0%
2,338,546 1
 
1.0%
59,054 1
 
1.0%
65,331 1
 
1.0%
Other values (49) 49
49.0%
2023-12-10T22:30:53.907257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 70
15.7%
1 50
11.2%
2 44
9.9%
- 42
9.4%
5 37
8.3%
3 35
7.9%
9 33
7.4%
0 31
7.0%
4 27
 
6.1%
6 27
 
6.1%
Other values (2) 49
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 333
74.8%
Other Punctuation 70
 
15.7%
Dash Punctuation 42
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 50
15.0%
2 44
13.2%
5 37
11.1%
3 35
10.5%
9 33
9.9%
0 31
9.3%
4 27
8.1%
6 27
8.1%
8 26
7.8%
7 23
6.9%
Other Punctuation
ValueCountFrequency (%)
, 70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 445
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 70
15.7%
1 50
11.2%
2 44
9.9%
- 42
9.4%
5 37
8.3%
3 35
7.9%
9 33
7.4%
0 31
7.0%
4 27
 
6.1%
6 27
 
6.1%
Other values (2) 49
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 70
15.7%
1 50
11.2%
2 44
9.9%
- 42
9.4%
5 37
8.3%
3 35
7.9%
9 33
7.4%
0 31
7.0%
4 27
 
6.1%
6 27
 
6.1%
Other values (2) 49
11.0%

Scope 3 배출량
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
50 
비공개
 
3
83,660
 
1
46,873,098
 
1
1,357,792
 
1
Other values (44)
44 

Length

Max length14
Median length12.5
Mean length4.18
Min length1

Unique

Unique47 ?
Unique (%)47.0%

Sample

1st row60,874
2nd row5,140,365
3rd row17,014
4th row7,017,300
5th row-

Common Values

ValueCountFrequency (%)
- 50
50.0%
비공개 3
 
3.0%
83,660 1
 
1.0%
46,873,098 1
 
1.0%
1,357,792 1
 
1.0%
5,939,555 1
 
1.0%
17,014 1
 
1.0%
7,017,300 1
 
1.0%
16,029 1
 
1.0%
3,750,651 1
 
1.0%
Other values (39) 39
39.0%

Length

2023-12-10T22:30:54.205419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50
50.0%
비공개 3
 
3.0%
1,729,344 1
 
1.0%
2,098 1
 
1.0%
587,560 1
 
1.0%
109,739 1
 
1.0%
60,874 1
 
1.0%
5,747 1
 
1.0%
1,999,166 1
 
1.0%
249,991 1
 
1.0%
Other values (39) 39
39.0%

보고된 Scope 3 배출원 수
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
51 
9
8 
8
7 
5
6 
10
 
5
Other values (10)
23 

Length

Max length2
Median length1
Mean length1.12
Min length1

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row9
2nd row10
3rd row8
4th row11
5th row-

Common Values

ValueCountFrequency (%)
- 51
51.0%
9 8
 
8.0%
8 7
 
7.0%
5 6
 
6.0%
10 5
 
5.0%
3 5
 
5.0%
6 3
 
3.0%
13 3
 
3.0%
4 3
 
3.0%
2 2
 
2.0%
Other values (5) 7
 
7.0%

Length

2023-12-10T22:30:54.390684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
51
51.0%
9 8
 
8.0%
8 7
 
7.0%
5 6
 
6.0%
10 5
 
5.0%
3 5
 
5.0%
6 3
 
3.0%
13 3
 
3.0%
4 3
 
3.0%
2 2
 
2.0%
Other values (5) 7
 
7.0%

기준연도
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
45 
2015
10 
2018
7 
2016
6 
2017
6 
Other values (9)
26 

Length

Max length4
Median length4
Mean length2.65
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2019
2nd row2019
3rd row2015
4th row2016
5th row-

Common Values

ValueCountFrequency (%)
- 45
45.0%
2015 10
 
10.0%
2018 7
 
7.0%
2016 6
 
6.0%
2017 6
 
6.0%
2014 6
 
6.0%
2019 5
 
5.0%
2009 3
 
3.0%
2012 3
 
3.0%
2011 2
 
2.0%
Other values (4) 7
 
7.0%

Length

2023-12-10T22:30:54.573071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45
45.0%
2015 10
 
10.0%
2018 7
 
7.0%
2016 6
 
6.0%
2017 6
 
6.0%
2014 6
 
6.0%
2019 5
 
5.0%
2009 3
 
3.0%
2012 3
 
3.0%
2011 2
 
2.0%
Other values (4) 7
 
7.0%

목표연도
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
45 
2050
11 
2040
9 
2020
9 
2030
8 
Other values (11)
18 

Length

Max length9
Median length4
Mean length2.76
Min length1

Unique

Unique6 ?
Unique (%)6.0%

Sample

1st row2035
2nd row2050
3rd row2040
4th row2050
5th row-

Common Values

ValueCountFrequency (%)
- 45
45.0%
2050 11
 
11.0%
2040 9
 
9.0%
2020 9
 
9.0%
2030 8
 
8.0%
2025 3
 
3.0%
2019 3
 
3.0%
2035 2
 
2.0%
2036 2
 
2.0%
2021 2
 
2.0%
Other values (6) 6
 
6.0%

Length

2023-12-10T22:30:54.762258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
47
46.1%
2050 12
 
11.8%
2020 10
 
9.8%
2040 9
 
8.8%
2030 9
 
8.8%
2025 3
 
2.9%
2019 3
 
2.9%
2035 2
 
2.0%
2036 2
 
2.0%
2021 2
 
2.0%
Other values (3) 3
 
2.9%

감축률
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
45 
50%
5 
0%
 
3
55%
 
3
32%
 
2
Other values (31)
42 

Length

Max length4
Median length3
Mean length2
Min length1

Unique

Unique20 ?
Unique (%)20.0%

Sample

1st row30%
2nd row55%
3rd row53%
4th row51%
5th row-

Common Values

ValueCountFrequency (%)
- 45
45.0%
50% 5
 
5.0%
0% 3
 
3.0%
55% 3
 
3.0%
32% 2
 
2.0%
51% 2
 
2.0%
40% 2
 
2.0%
60% 2
 
2.0%
37% 2
 
2.0%
6% 2
 
2.0%
Other values (26) 32
32.0%

Length

2023-12-10T22:30:54.950592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45
45.0%
50 5
 
5.0%
0 3
 
3.0%
55 3
 
3.0%
12 2
 
2.0%
30 2
 
2.0%
2 2
 
2.0%
52 2
 
2.0%
20 2
 
2.0%
46 2
 
2.0%
Other values (26) 32
32.0%

이행률
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
45 
0%
6 
100%
 
3
신규목표
 
2
22%
 
2
Other values (39)
42 

Length

Max length6
Median length5
Mean length2.21
Min length1

Unique

Unique36 ?
Unique (%)36.0%

Sample

1st row신규목표
2nd row0%
3rd row20%
4th row10%
5th row-

Common Values

ValueCountFrequency (%)
- 45
45.0%
0% 6
 
6.0%
100% 3
 
3.0%
신규목표 2
 
2.0%
22% 2
 
2.0%
21% 2
 
2.0%
6% 2
 
2.0%
44% 2
 
2.0%
-24% 1
 
1.0%
-17% 1
 
1.0%
Other values (34) 34
34.0%

Length

2023-12-10T22:30:55.119712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45
45.0%
0 6
 
6.0%
100 3
 
3.0%
44 2
 
2.0%
10 2
 
2.0%
17 2
 
2.0%
24 2
 
2.0%
16 2
 
2.0%
6 2
 
2.0%
22 2
 
2.0%
Other values (30) 32
32.0%

감축목표 수립 방법론
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
43 
절대량
32 
절대량&원단위
16 
원단위
7 
F(무응답)
 
2

Length

Max length7
Median length6
Mean length2.84
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row절대량&원단위
2nd row절대량
3rd row절대량
4th row절대량
5th row-

Common Values

ValueCountFrequency (%)
- 43
43.0%
절대량 32
32.0%
절대량&원단위 16
 
16.0%
원단위 7
 
7.0%
F(무응답) 2
 
2.0%

Length

2023-12-10T22:30:55.297833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:30:55.435154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43
43.0%
절대량 32
32.0%
절대량&원단위 16
 
16.0%
원단위 7
 
7.0%
f(무응답 2
 
2.0%

2020년 CDP Water 등급
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
69 
무응답
15 
Leadership A
 
4
Management B
 
4
Management B-
 
4
Other values (2)
 
4

Length

Max length13
Median length1
Mean length3.03
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row-
2nd row-
3rd row-
4th rowLeadership A-
5th row-

Common Values

ValueCountFrequency (%)
- 69
69.0%
무응답 15
 
15.0%
Leadership A 4
 
4.0%
Management B 4
 
4.0%
Management B- 4
 
4.0%
Leadership A- 3
 
3.0%
응답 1
 
1.0%

Length

2023-12-10T22:30:55.577658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:30:55.718813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
69
60.0%
무응답 15
 
13.0%
management 8
 
7.0%
b 8
 
7.0%
leadership 7
 
6.1%
a 7
 
6.1%
응답 1
 
0.9%

총 취수량
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
86 
22,946
 
1
13
 
1
58,305
 
1
1,974,069
 
1
Other values (10)
10 

Length

Max length9
Median length1
Mean length1.64
Min length1

Unique

Unique14 ?
Unique (%)14.0%

Sample

1st row-
2nd row-
3rd row-
4th row22,946
5th row-

Common Values

ValueCountFrequency (%)
- 86
86.0%
22,946 1
 
1.0%
13 1
 
1.0%
58,305 1
 
1.0%
1,974,069 1
 
1.0%
88,187 1
 
1.0%
50,695 1
 
1.0%
3,868 1
 
1.0%
6,373 1
 
1.0%
90 1
 
1.0%
Other values (5) 5
 
5.0%

Length

2023-12-10T22:30:55.898923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
86
86.0%
22,946 1
 
1.0%
13 1
 
1.0%
58,305 1
 
1.0%
1,974,069 1
 
1.0%
88,187 1
 
1.0%
50,695 1
 
1.0%
3,868 1
 
1.0%
6,373 1
 
1.0%
90 1
 
1.0%
Other values (5) 5
 
5.0%

총 방류량
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
86 
9,561
 
1
7
 
1
14,809
 
1
1,866,329
 
1
Other values (10)
10 

Length

Max length9
Median length1
Mean length1.6
Min length1

Unique

Unique14 ?
Unique (%)14.0%

Sample

1st row-
2nd row-
3rd row-
4th row9,561
5th row-

Common Values

ValueCountFrequency (%)
- 86
86.0%
9,561 1
 
1.0%
7 1
 
1.0%
14,809 1
 
1.0%
1,866,329 1
 
1.0%
73,610 1
 
1.0%
37,875 1
 
1.0%
1,754 1
 
1.0%
1,756 1
 
1.0%
90 1
 
1.0%
Other values (5) 5
 
5.0%

Length

2023-12-10T22:30:56.094484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
86
86.0%
9,561 1
 
1.0%
7 1
 
1.0%
14,809 1
 
1.0%
1,866,329 1
 
1.0%
73,610 1
 
1.0%
37,875 1
 
1.0%
1,754 1
 
1.0%
1,756 1
 
1.0%
90 1
 
1.0%
Other values (5) 5
 
5.0%

물 단위
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
86 
백만L/yr
14 

Length

Max length6
Median length1
Mean length1.7
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row백만L/yr
5th row-

Common Values

ValueCountFrequency (%)
- 86
86.0%
백만L/yr 14
 
14.0%

Length

2023-12-10T22:30:56.314898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:30:56.469236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
86
86.0%
백만l/yr 14
 
14.0%

물 관련 목표 수립 여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
85 
O
14 
X
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row-
2nd row-
3rd row-
4th rowO
5th row-

Common Values

ValueCountFrequency (%)
- 85
85.0%
O 14
 
14.0%
X 1
 
1.0%

Length

2023-12-10T22:30:56.619847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:30:56.762410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
85
85.0%
o 14
 
14.0%
x 1
 
1.0%

재무보고서 상 물 관련 정보 공개 여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
97 
O
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 97
97.0%
O 3
 
3.0%

Length

2023-12-10T22:30:56.909361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:30:57.047163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
97
97.0%
o 3
 
3.0%

Interactions

2023-12-10T22:30:50.206579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:30:57.144352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번기업명2020년 CDP Climate 등급Scope 1 배출량Scope 2 배출량Scope 3 배출량보고된 Scope 3 배출원 수기준연도목표연도감축률이행률감축목표 수립 방법론2020년 CDP Water 등급총 취수량총 방류량물 단위물 관련 목표 수립 여부재무보고서 상 물 관련 정보 공개 여부
연번1.0001.0000.5440.4370.4370.4160.3400.4730.4100.4540.4200.3770.3830.1470.1470.3850.2650.000
기업명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2020년 CDP Climate 등급0.5441.0001.0000.9180.9180.0000.5540.6370.7420.7390.6620.7610.1190.0000.0000.3770.3540.330
Scope 1 배출량0.4371.0000.9181.0001.0001.0001.0001.0001.0001.0001.0000.8850.9931.0001.0001.0001.0001.000
Scope 2 배출량0.4371.0000.9181.0001.0001.0001.0001.0001.0001.0001.0000.8850.9931.0001.0001.0001.0001.000
Scope 3 배출량0.4161.0000.0001.0001.0001.0000.9930.9920.9680.9950.9970.8180.9950.9950.9950.9700.4020.699
보고된 Scope 3 배출원 수0.3401.0000.5541.0001.0000.9931.0000.7710.7930.9450.9810.7650.6800.9090.9090.4780.4300.301
기준연도0.4731.0000.6371.0001.0000.9920.7711.0000.8660.9520.9880.8300.7360.7710.7710.7150.6040.789
목표연도0.4101.0000.7421.0001.0000.9680.7930.8661.0000.9540.9790.8370.5850.6890.6890.5820.4780.168
감축률0.4541.0000.7391.0001.0000.9950.9450.9520.9541.0000.9940.8350.9220.9640.9640.8470.8440.824
이행률0.4201.0000.6621.0001.0000.9970.9810.9880.9790.9941.0000.9040.9420.9930.9930.9790.8850.945
감축목표 수립 방법론0.3771.0000.7610.8850.8850.8180.7650.8300.8370.8350.9041.0000.2840.5560.5560.2980.3020.309
2020년 CDP Water 등급0.3831.0000.1190.9930.9930.9950.6800.7360.5850.9220.9420.2841.0000.9230.9230.9000.8030.558
총 취수량0.1471.0000.0001.0001.0000.9950.9090.7710.6890.9640.9930.5560.9231.0001.0001.0001.0001.000
총 방류량0.1471.0000.0001.0001.0000.9950.9090.7710.6890.9640.9930.5560.9231.0001.0001.0001.0001.000
물 단위0.3851.0000.3771.0001.0000.9700.4780.7150.5820.8470.9790.2980.9001.0001.0001.0000.7210.507
물 관련 목표 수립 여부0.2651.0000.3541.0001.0000.4020.4300.6040.4780.8440.8850.3020.8031.0001.0000.7211.0000.401
재무보고서 상 물 관련 정보 공개 여부0.0001.0000.3301.0001.0000.6990.3010.7890.1680.8240.9450.3090.5581.0001.0000.5070.4011.000
2023-12-10T22:30:57.746510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총 방류량감축률Scope 3 배출량이행률2020년 CDP Water 등급재무보고서 상 물 관련 정보 공개 여부보고된 Scope 3 배출원 수목표연도물 단위2020년 CDP Climate 등급물 관련 목표 수립 여부기준연도총 취수량감축목표 수립 방법론
총 방류량1.0000.6280.7190.7390.7160.9310.4620.3020.9310.0000.9130.3921.0000.253
감축률0.6281.0000.7520.7640.5730.5530.5570.5770.5730.2920.4870.5890.6280.467
Scope 3 배출량0.7190.7521.0000.8270.6710.4250.7030.5560.6620.0000.1410.6850.7190.391
이행률0.7390.7640.8271.0000.5760.6310.6540.6380.6780.1970.5330.7020.7390.521
2020년 CDP Water 등급0.7160.5730.6710.5761.0000.5840.3720.2970.9410.0470.7410.3500.7160.182
재무보고서 상 물 관련 정보 공개 여부0.9310.5530.4250.6310.5841.0000.2530.1160.3380.3000.6280.5970.9310.370
보고된 Scope 3 배출원 수0.4620.5570.7030.6540.3720.2531.0000.4020.4060.2410.2010.3910.4620.412
목표연도0.3020.5770.5560.6380.2970.1160.4021.0000.4260.3790.2750.5170.3020.584
물 단위0.9310.5730.6620.6780.9410.3380.4060.4261.0000.3430.9550.5340.9310.358
2020년 CDP Climate 등급0.0000.2920.0000.1970.0470.3000.2410.3790.3431.0000.2100.3040.0000.532
물 관련 목표 수립 여부0.9130.4870.1410.5330.7410.6280.2010.2750.9550.2101.0000.3920.9130.236
기준연도0.3920.5890.6850.7020.3500.5970.3910.5170.5340.3040.3921.0000.3920.591
총 취수량1.0000.6280.7190.7390.7160.9310.4620.3020.9310.0000.9130.3921.0000.253
감축목표 수립 방법론0.2530.4670.3910.5210.1820.3700.4120.5840.3580.5320.2360.5910.2531.000
2023-12-10T22:30:58.001086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번2020년 CDP Climate 등급Scope 3 배출량보고된 Scope 3 배출원 수기준연도목표연도감축률이행률감축목표 수립 방법론2020년 CDP Water 등급총 취수량총 방류량물 단위물 관련 목표 수립 여부재무보고서 상 물 관련 정보 공개 여부
연번1.0000.2640.0910.1240.2040.1630.1350.1040.1580.1990.0370.0370.2820.1550.000
2020년 CDP Climate 등급0.2641.0000.0000.2410.3040.3790.2920.1970.5320.0470.0000.0000.3430.2100.300
Scope 3 배출량0.0910.0001.0000.7030.6850.5560.7520.8270.3910.6710.7190.7190.6620.1410.425
보고된 Scope 3 배출원 수0.1240.2410.7031.0000.3910.4020.5570.6540.4120.3720.4620.4620.4060.2010.253
기준연도0.2040.3040.6850.3911.0000.5170.5890.7020.5910.3500.3920.3920.5340.3920.597
목표연도0.1630.3790.5560.4020.5171.0000.5770.6380.5840.2970.3020.3020.4260.2750.116
감축률0.1350.2920.7520.5570.5890.5771.0000.7640.4670.5730.6280.6280.5730.4870.553
이행률0.1040.1970.8270.6540.7020.6380.7641.0000.5210.5760.7390.7390.6780.5330.631
감축목표 수립 방법론0.1580.5320.3910.4120.5910.5840.4670.5211.0000.1820.2530.2530.3580.2360.370
2020년 CDP Water 등급0.1990.0470.6710.3720.3500.2970.5730.5760.1821.0000.7160.7160.9410.7410.584
총 취수량0.0370.0000.7190.4620.3920.3020.6280.7390.2530.7161.0001.0000.9310.9130.931
총 방류량0.0370.0000.7190.4620.3920.3020.6280.7390.2530.7161.0001.0000.9310.9130.931
물 단위0.2820.3430.6620.4060.5340.4260.5730.6780.3580.9410.9310.9311.0000.9550.338
물 관련 목표 수립 여부0.1550.2100.1410.2010.3920.2750.4870.5330.2360.7410.9130.9130.9551.0000.628
재무보고서 상 물 관련 정보 공개 여부0.0000.3000.4250.2530.5970.1160.5530.6310.3700.5840.9310.9310.3380.6281.000

Missing values

2023-12-10T22:30:50.416790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:30:50.788061image/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.

Sample

연번기업명2020년 CDP Climate 등급Scope 1 배출량Scope 2 배출량Scope 3 배출량보고된 Scope 3 배출원 수기준연도목표연도감축률이행률감축목표 수립 방법론2020년 CDP Water 등급총 취수량총 방류량물 단위물 관련 목표 수립 여부재무보고서 상 물 관련 정보 공개 여부
01DGB금융지주Leadership A-3,83516,87160,87492019203530%신규목표절대량&원단위------
12DL이앤씨Leadership A35,543258,7815,140,365102019205055%0%절대량------
23하나금융지주Leadership A8,93260,02517,01482015204053%20%절대량------
34현대자동차Leadership A-808,1391,900,9547,017,300112016205051%10%절대량Leadership A-22,9469,561백만L/yrO-
45기업은행F(무응답/불충분)---------------
56KB금융Leadership A-18,980111,73116,02962017204038%2%절대량------
67기아자동차Leadership A368,746903,4683,750,651102016204040%27%절대량Leadership A137백만L/yrO-
78LG화학Management B5,544,0455,315,9121,172,5259201920500%0%절대량&원단위Leadership A-58,30514,809백만L/yrO-
89포스코Management B79,447,000795,00013,138,0006200920209%45%원단위Management B1,974,0691,866,329백만L/yrOO
910삼성에스디에스F(무응답/불충분)---------------
연번기업명2020년 CDP Climate 등급Scope 1 배출량Scope 2 배출량Scope 3 배출량보고된 Scope 3 배출원 수기준연도목표연도감축률이행률감축목표 수립 방법론2020년 CDP Water 등급총 취수량총 방류량물 단위물 관련 목표 수립 여부재무보고서 상 물 관련 정보 공개 여부
9091NHNF(무응답/불충분)---------------
9192KTLeadership A-36,0871,098,220587,560132007204050%22%절대량------
9293LG유플러스Leadership A7,9971,090,913101,71592015204041%-16%절대량------
9394솔브레인홀딩스F(무응답)---------------
9495동부화재F(무응답)---------------
9596맥쿼리인프라F(무응답)---------------
9697메리츠금융지주F(무응답)---------------
9798메리츠종금증권F(무응답)---------------
9899메리츠화재F(무응답)---------------
99100삼성생명F(무응답)---------------