Overview

Dataset statistics

Number of variables7
Number of observations9114
Missing cells7296
Missing cells (%)11.4%
Duplicate rows989
Duplicate rows (%)10.9%
Total size in memory525.3 KiB
Average record size in memory59.0 B

Variable types

Text3
Numeric2
Categorical2

Dataset

Description한국가스안전공사에 신고된 고압가스수입신고 내역에 대한 데이터로 수입가스 종류 및 수입량과 용도 등의 항목으로 구성되어 있는 데이터입니다.
Author한국가스안전공사
URLhttps://www.data.go.kr/data/15039469/fileData.do

Alerts

Dataset has 989 (10.9%) duplicate rowsDuplicates
수입량(압축가스㎥) is highly overall correlated with 수입량(액화가스kg)High correlation
수입량(액화가스kg) is highly overall correlated with 수입량(압축가스㎥)High correlation
공급처 has 7295 (80.0%) missing valuesMissing
수입량(액화가스kg) is highly skewed (γ1 = 23.51944104)Skewed
수입량(압축가스㎥) has 7181 (78.8%) zerosZeros
수입량(액화가스kg) has 1950 (21.4%) zerosZeros

Reproduction

Analysis started2023-12-12 13:26:42.382172
Analysis finished2023-12-12 13:26:43.964827
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가스종류
Text

Distinct242
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
2023-12-12T22:26:44.218112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length30
Mean length5.9470046
Min length2

Characters and Unicode

Total characters54201
Distinct characters150
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)0.5%

Sample

1st rowLPG
2nd rowLPG
3rd rowLPG
4th rowLPG
5th rowLPG
ValueCountFrequency (%)
삼불화질소 734
 
7.3%
혼합가스 643
 
6.4%
헬륨 570
 
5.7%
lpg 424
 
4.2%
프로필렌 386
 
3.9%
염화수소 352
 
3.5%
r-410a 339
 
3.4%
암모니아 286
 
2.9%
프레온 259
 
2.6%
r-134a 241
 
2.4%
Other values (245) 5756
57.6%
2023-12-12T22:26:44.735950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 2646
 
4.9%
- 1974
 
3.6%
화 1920
 
3.5%
소 1709
 
3.2%
2 1671
 
3.1%
1 1514
 
2.8%
4 1488
 
2.7%
F 1449
 
2.7%
C 1372
 
2.5%
E 1296
 
2.4%
Other values (140) 37162
68.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24588
45.4%
Uppercase Letter 15619
28.8%
Decimal Number 7419
 
13.7%
Dash Punctuation 1974
 
3.6%
Lowercase Letter 1903
 
3.5%
Space Separator 879
 
1.6%
Other Punctuation 690
 
1.3%
Open Punctuation 547
 
1.0%
Close Punctuation 542
 
1.0%
Math Symbol 40
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 1920
 
7.8%
소 1709
 
7.0%
불 1175
 
4.8%
로 977
 
4.0%
프 956
 
3.9%
삼 917
 
3.7%
스 800
 
3.3%
질 781
 
3.2%
혼 718
 
2.9%
합 718
 
2.9%
Other values (74) 13917
56.6%
Uppercase Letter
ValueCountFrequency (%)
R 2646
16.9%
F 1449
 
9.3%
C 1372
 
8.8%
E 1296
 
8.3%
O 1141
 
7.3%
H 983
 
6.3%
A 769
 
4.9%
N 750
 
4.8%
P 725
 
4.6%
L 703
 
4.5%
Other values (13) 3785
24.2%
Lowercase Letter
ValueCountFrequency (%)
a 856
45.0%
e 189
 
9.9%
r 154
 
8.1%
o 123
 
6.5%
l 92
 
4.8%
n 63
 
3.3%
c 61
 
3.2%
u 60
 
3.2%
t 59
 
3.1%
m 59
 
3.1%
Other values (11) 187
 
9.8%
Decimal Number
ValueCountFrequency (%)
2 1671
22.5%
1 1514
20.4%
4 1488
20.1%
3 1143
15.4%
0 794
10.7%
5 338
 
4.6%
6 198
 
2.7%
7 148
 
2.0%
8 123
 
1.7%
9 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
, 365
52.9%
/ 213
30.9%
% 78
 
11.3%
. 34
 
4.9%
Open Punctuation
ValueCountFrequency (%)
( 546
99.8%
[ 1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 541
99.8%
] 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
+ 39
97.5%
~ 1
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 1974
100.0%
Space Separator
ValueCountFrequency (%)
879
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24588
45.4%
Latin 17522
32.3%
Common 12091
22.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 1920
 
7.8%
소 1709
 
7.0%
불 1175
 
4.8%
로 977
 
4.0%
프 956
 
3.9%
삼 917
 
3.7%
스 800
 
3.3%
질 781
 
3.2%
혼 718
 
2.9%
합 718
 
2.9%
Other values (74) 13917
56.6%
Latin
ValueCountFrequency (%)
R 2646
15.1%
F 1449
 
8.3%
C 1372
 
7.8%
E 1296
 
7.4%
O 1141
 
6.5%
H 983
 
5.6%
a 856
 
4.9%
A 769
 
4.4%
N 750
 
4.3%
P 725
 
4.1%
Other values (34) 5535
31.6%
Common
ValueCountFrequency (%)
- 1974
16.3%
2 1671
13.8%
1 1514
12.5%
4 1488
12.3%
3 1143
9.5%
879
7.3%
0 794
6.6%
( 546
 
4.5%
) 541
 
4.5%
, 365
 
3.0%
Other values (12) 1176
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29613
54.6%
Hangul 24588
45.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 2646
 
8.9%
- 1974
 
6.7%
2 1671
 
5.6%
1 1514
 
5.1%
4 1488
 
5.0%
F 1449
 
4.9%
C 1372
 
4.6%
E 1296
 
4.4%
3 1143
 
3.9%
O 1141
 
3.9%
Other values (56) 13919
47.0%
Hangul
ValueCountFrequency (%)
화 1920
 
7.8%
소 1709
 
7.0%
불 1175
 
4.8%
로 977
 
4.0%
프 956
 
3.9%
삼 917
 
3.7%
스 800
 
3.3%
질 781
 
3.2%
혼 718
 
2.9%
합 718
 
2.9%
Other values (74) 13917
56.6%

수입량(압축가스㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct992
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43176.811
Minimum0
Maximum5679469
Zeros7181
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T22:26:45.220514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile40700
Maximum5679469
Range5679469
Interquartile range (IQR)0

Descriptive statistics

Standard deviation272858.51
Coefficient of variation (CV)6.3195614
Kurtosis92.04382
Mean43176.811
Median Absolute Deviation (MAD)0
Skewness8.5592385
Sum3.9351346 × 108
Variance7.4451768 × 1010
MonotonicityNot monotonic
2023-12-12T22:26:45.363730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7181
78.8%
18000.0 56
 
0.6%
36000.0 56
 
0.6%
54000.0 50
 
0.5%
2700.0 41
 
0.4%
17000.0 37
 
0.4%
9000.0 32
 
0.4%
6000.0 31
 
0.3%
72000.0 24
 
0.3%
15000.0 20
 
0.2%
Other values (982) 1586
 
17.4%
ValueCountFrequency (%)
0.0 7181
78.8%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 6
 
0.1%
0.04 5
 
0.1%
0.05 3
 
< 0.1%
0.06 18
 
0.2%
0.07 1
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
ValueCountFrequency (%)
5679469.0 1
< 0.1%
5028037.0 1
< 0.1%
4132635.0 1
< 0.1%
3570346.0 1
< 0.1%
3508030.0 1
< 0.1%
3500487.0 1
< 0.1%
3499985.0 1
< 0.1%
3301139.0 1
< 0.1%
3301036.0 1
< 0.1%
3300464.0 1
< 0.1%

수입량(액화가스kg)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3148
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223235.6
Minimum0
Maximum1.1420703 × 108
Zeros1950
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T22:26:45.511844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median2281.5
Q314000
95-th percentile68000
Maximum1.1420703 × 108
Range1.1420703 × 108
Interquartile range (IQR)13996.25

Descriptive statistics

Standard deviation2123007.6
Coefficient of variation (CV)9.510166
Kurtosis979.63439
Mean223235.6
Median Absolute Deviation (MAD)2281.5
Skewness23.519441
Sum2.0345693 × 109
Variance4.5071614 × 1012
MonotonicityNot monotonic
2023-12-12T22:26:45.651044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1950
 
21.4%
18000.0 261
 
2.9%
16000.0 198
 
2.2%
8000.0 182
 
2.0%
36000.0 180
 
2.0%
12000.0 126
 
1.4%
6000.0 118
 
1.3%
32000.0 84
 
0.9%
19152.0 73
 
0.8%
3000.0 73
 
0.8%
Other values (3138) 5869
64.4%
ValueCountFrequency (%)
0.0 1950
21.4%
0.03 1
 
< 0.1%
0.04 2
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 1
 
< 0.1%
0.11 2
 
< 0.1%
0.12 2
 
< 0.1%
ValueCountFrequency (%)
114207028.0 1
< 0.1%
33671284.65 1
< 0.1%
30000000.0 1
< 0.1%
28140000.0 1
< 0.1%
27200000.0 1
< 0.1%
25739045.0 1
< 0.1%
25711643.0 1
< 0.1%
25594489.0 1
< 0.1%
25590276.0 1
< 0.1%
25590098.0 1
< 0.1%

수출국
Categorical

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
중국
3030 
일본
2107 
미국
1945 
대만
379 
독일
 
253
Other values (35)
1400 

Length

Max length11
Median length2
Mean length2.2533465
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row일본
2nd row중국
3rd row중국
4th row일본
5th row일본

Common Values

ValueCountFrequency (%)
중국 3030
33.2%
일본 2107
23.1%
미국 1945
21.3%
대만 379
 
4.2%
독일 253
 
2.8%
싱가포르 209
 
2.3%
한국 155
 
1.7%
사우디아라비아 108
 
1.2%
벨기에 100
 
1.1%
우크라이나 84
 
0.9%
Other values (30) 744
 
8.2%

Length

2023-12-12T22:26:45.775924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중국 3030
33.2%
일본 2107
23.1%
미국 1945
21.3%
대만 379
 
4.2%
독일 253
 
2.8%
싱가포르 209
 
2.3%
한국 155
 
1.7%
사우디아라비아 108
 
1.2%
벨기에 100
 
1.1%
우크라이나 84
 
0.9%
Other values (32) 748
 
8.2%

공급처
Text

MISSING 

Distinct63
Distinct (%)3.5%
Missing7295
Missing (%)80.0%
Memory size71.3 KiB
2023-12-12T22:26:45.985075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length36
Mean length8.5634964
Min length2

Characters and Unicode

Total characters15577
Distinct characters139
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.9%

Sample

1st row바이어스도르프코리아
2nd row바이어스도르프코리아
3rd row바이어스도르프코리아
4th row바이어스도르프코리아
5th row(주)레미경주지점
ValueCountFrequency (%)
한국메티슨가스 449
17.0%
products 342
12.9%
air 342
12.9%
상동 303
11.5%
gas 107
 
4.0%
rec 105
 
4.0%
주)린데코리아 69
 
2.6%
liming 64
 
2.4%
ltd 58
 
2.2%
인테그리스코리아 55
 
2.1%
Other values (95) 751
28.4%
2023-12-12T22:26:46.365883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 856
 
5.5%
826
 
5.3%
I 682
 
4.4%
C 677
 
4.3%
스 606
 
3.9%
A 605
 
3.9%
S 547
 
3.5%
T 525
 
3.4%
한 517
 
3.3%
D 498
 
3.2%
Other values (129) 9238
59.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7221
46.4%
Other Letter 6498
41.7%
Space Separator 826
 
5.3%
Lowercase Letter 599
 
3.8%
Open Punctuation 164
 
1.1%
Close Punctuation 163
 
1.0%
Other Punctuation 105
 
0.7%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 606
 
9.3%
한 517
 
8.0%
국 468
 
7.2%
슨 458
 
7.0%
티 453
 
7.0%
가 451
 
6.9%
메 449
 
6.9%
동 303
 
4.7%
상 303
 
4.7%
리 258
 
4.0%
Other values (76) 2232
34.3%
Uppercase Letter
ValueCountFrequency (%)
R 856
11.9%
I 682
9.4%
C 677
9.4%
A 605
8.4%
S 547
 
7.6%
T 525
 
7.3%
D 498
 
6.9%
O 479
 
6.6%
P 455
 
6.3%
E 448
 
6.2%
Other values (15) 1449
20.1%
Lowercase Letter
ValueCountFrequency (%)
i 124
20.7%
a 118
19.7%
c 72
12.0%
s 61
10.2%
n 58
9.7%
o 38
 
6.3%
f 32
 
5.3%
t 18
 
3.0%
e 17
 
2.8%
v 16
 
2.7%
Other values (12) 45
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 82
78.1%
, 23
 
21.9%
Space Separator
ValueCountFrequency (%)
826
100.0%
Open Punctuation
ValueCountFrequency (%)
( 164
100.0%
Close Punctuation
ValueCountFrequency (%)
) 163
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7820
50.2%
Hangul 6498
41.7%
Common 1259
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 606
 
9.3%
한 517
 
8.0%
국 468
 
7.2%
슨 458
 
7.0%
티 453
 
7.0%
가 451
 
6.9%
메 449
 
6.9%
동 303
 
4.7%
상 303
 
4.7%
리 258
 
4.0%
Other values (76) 2232
34.3%
Latin
ValueCountFrequency (%)
R 856
10.9%
I 682
 
8.7%
C 677
 
8.7%
A 605
 
7.7%
S 547
 
7.0%
T 525
 
6.7%
D 498
 
6.4%
O 479
 
6.1%
P 455
 
5.8%
E 448
 
5.7%
Other values (37) 2048
26.2%
Common
ValueCountFrequency (%)
826
65.6%
( 164
 
13.0%
) 163
 
12.9%
. 82
 
6.5%
, 23
 
1.8%
- 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9079
58.3%
Hangul 6498
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 856
 
9.4%
826
 
9.1%
I 682
 
7.5%
C 677
 
7.5%
A 605
 
6.7%
S 547
 
6.0%
T 525
 
5.8%
D 498
 
5.5%
O 479
 
5.3%
P 455
 
5.0%
Other values (43) 2929
32.3%
Hangul
ValueCountFrequency (%)
스 606
 
9.3%
한 517
 
8.0%
국 468
 
7.2%
슨 458
 
7.0%
티 453
 
7.0%
가 451
 
6.9%
메 449
 
6.9%
동 303
 
4.7%
상 303
 
4.7%
리 258
 
4.0%
Other values (76) 2232
34.3%

용도
Text

Distinct339
Distinct (%)3.7%
Missing1
Missing (%)< 0.1%
Memory size71.3 KiB
2023-12-12T22:26:46.615970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length25
Mean length5.0513552
Min length1

Characters and Unicode

Total characters46033
Distinct characters273
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)1.2%

Sample

1st row헤어용품
2nd row화장품
3rd row화장품
4th row화장품
5th row화장품
ValueCountFrequency (%)
반도체 1141
 
9.8%
냉매용 915
 
7.8%
판매 772
 
6.6%
산업용 659
 
5.6%
판매용 633
 
5.4%
반도체용 523
 
4.5%
반도체산업용 395
 
3.4%
원료 363
 
3.1%
반도체제조 343
 
2.9%
제조용 281
 
2.4%
Other values (343) 5672
48.5%
2023-12-12T22:26:47.099669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 5121
 
11.1%
매 3095
 
6.7%
2585
 
5.6%
도 2573
 
5.6%
체 2538
 
5.5%
반 2536
 
5.5%
제 1867
 
4.1%
냉 1660
 
3.6%
판 1542
 
3.3%
산 1206
 
2.6%
Other values (263) 21310
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40106
87.1%
Space Separator 2585
 
5.6%
Uppercase Letter 2193
 
4.8%
Close Punctuation 304
 
0.7%
Open Punctuation 304
 
0.7%
Other Punctuation 211
 
0.5%
Decimal Number 202
 
0.4%
Lowercase Letter 100
 
0.2%
Math Symbol 27
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 5121
 
12.8%
매 3095
 
7.7%
도 2573
 
6.4%
체 2538
 
6.3%
반 2536
 
6.3%
제 1867
 
4.7%
냉 1660
 
4.1%
판 1542
 
3.8%
산 1206
 
3.0%
조 1138
 
2.8%
Other values (206) 16830
42.0%
Uppercase Letter
ValueCountFrequency (%)
N 374
17.1%
A 368
16.8%
I 224
10.2%
E 192
8.8%
C 161
7.3%
P 134
 
6.1%
H 127
 
5.8%
T 113
 
5.2%
Y 97
 
4.4%
X 93
 
4.2%
Other values (13) 310
14.1%
Lowercase Letter
ValueCountFrequency (%)
a 17
17.0%
t 13
13.0%
e 12
12.0%
m 12
12.0%
l 9
9.0%
o 8
8.0%
v 6
 
6.0%
s 5
 
5.0%
n 5
 
5.0%
i 5
 
5.0%
Other values (6) 8
8.0%
Decimal Number
ValueCountFrequency (%)
2 119
58.9%
4 37
 
18.3%
0 20
 
9.9%
1 9
 
4.5%
6 8
 
4.0%
5 6
 
3.0%
3 2
 
1.0%
9 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
, 69
32.7%
* 66
31.3%
/ 36
17.1%
% 27
 
12.8%
. 13
 
6.2%
Space Separator
ValueCountFrequency (%)
2585
100.0%
Close Punctuation
ValueCountFrequency (%)
) 304
100.0%
Open Punctuation
ValueCountFrequency (%)
( 304
100.0%
Math Symbol
ValueCountFrequency (%)
+ 27
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40106
87.1%
Common 3634
 
7.9%
Latin 2293
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 5121
 
12.8%
매 3095
 
7.7%
도 2573
 
6.4%
체 2538
 
6.3%
반 2536
 
6.3%
제 1867
 
4.7%
냉 1660
 
4.1%
판 1542
 
3.8%
산 1206
 
3.0%
조 1138
 
2.8%
Other values (206) 16830
42.0%
Latin
ValueCountFrequency (%)
N 374
16.3%
A 368
16.0%
I 224
9.8%
E 192
8.4%
C 161
 
7.0%
P 134
 
5.8%
H 127
 
5.5%
T 113
 
4.9%
Y 97
 
4.2%
X 93
 
4.1%
Other values (29) 410
17.9%
Common
ValueCountFrequency (%)
2585
71.1%
) 304
 
8.4%
( 304
 
8.4%
2 119
 
3.3%
, 69
 
1.9%
* 66
 
1.8%
4 37
 
1.0%
/ 36
 
1.0%
% 27
 
0.7%
+ 27
 
0.7%
Other values (8) 60
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40106
87.1%
ASCII 5927
 
12.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 5121
 
12.8%
매 3095
 
7.7%
도 2573
 
6.4%
체 2538
 
6.3%
반 2536
 
6.3%
제 1867
 
4.7%
냉 1660
 
4.1%
판 1542
 
3.8%
산 1206
 
3.0%
조 1138
 
2.8%
Other values (206) 16830
42.0%
ASCII
ValueCountFrequency (%)
2585
43.6%
N 374
 
6.3%
A 368
 
6.2%
) 304
 
5.1%
( 304
 
5.1%
I 224
 
3.8%
E 192
 
3.2%
C 161
 
2.7%
P 134
 
2.3%
H 127
 
2.1%
Other values (47) 1154
19.5%

신고연도
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
2017
3441 
2018
3310 
2019
2363 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 3441
37.8%
2018 3310
36.3%
2019 2363
25.9%

Length

2023-12-12T22:26:47.228137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:26:47.318002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 3441
37.8%
2018 3310
36.3%
2019 2363
25.9%

Interactions

2023-12-12T22:26:43.321098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.103967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.446204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.206779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:26:47.379452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수입량(압축가스㎥)수입량(액화가스kg)수출국공급처신고연도
수입량(압축가스㎥)1.0000.0000.2390.1790.061
수입량(액화가스kg)0.0001.0000.550NaN0.002
수출국0.2390.5501.0000.9590.313
공급처0.179NaN0.9591.0000.650
신고연도0.0610.0020.3130.6501.000
2023-12-12T22:26:47.476487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수출국신고연도
수출국1.0000.152
신고연도0.1521.000
2023-12-12T22:26:47.570230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수입량(압축가스㎥)수입량(액화가스kg)수출국신고연도
수입량(압축가스㎥)1.000-0.7030.0840.036
수입량(액화가스kg)-0.7031.0000.3110.002
수출국0.0840.3111.0000.152
신고연도0.0360.0020.1521.000

Missing values

2023-12-12T22:26:43.580894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:26:43.750810image/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.
2023-12-12T22:26:43.902523image/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

가스종류수입량(압축가스㎥)수입량(액화가스kg)수출국공급처용도신고연도
0LPG0.03531.6일본<NA>헤어용품2017
1LPG0.09370.08중국<NA>화장품2017
2LPG0.011923.2중국<NA>화장품2017
3LPG0.04148.4일본<NA>화장품2017
4LPG0.05358.96일본<NA>화장품2017
5LPG0.013076.64중국<NA>화장품2017
6디메틸에테르0.0777.27미국<NA>스프레이용2017
7디메틸에테르0.016027.0캐나다<NA>스프레이용2017
8부탄0.0213.0영국<NA>자전거 세척/윤활2017
9R-23(HFC-23)0.06080.0중국<NA>냉동기용2017
가스종류수입량(압축가스㎥)수입량(액화가스kg)수출국공급처용도신고연도
9104R134A0.018000.0중국<NA>내수판매용2019
9105R-125(CHF2CF3)0.036000.0중국<NA>내수판매용2019
9106R-227EA0.015000.0중국<NA>내수판매용2019
9107R-134a0.036000.0중국<NA>내수판매용2019
9108R-134a0.018000.0중국<NA>내수판매용2019
9109R-134a0.036000.0중국<NA>내수판매용2019
9110R134A0.018000.0중국<NA>수출 및 내수판매용2019
9111R-134a0.090000.0중국<NA>내수판매용2019
9112R-404a0.06600.0중국<NA>내수판매용2019
9113디메틸에테르0.05229.0독일<NA>건축자재용2019

Duplicate rows

Most frequently occurring

가스종류수입량(압축가스㎥)수입량(액화가스kg)수출국공급처용도신고연도# duplicates
408R-410a0.016000.0중국<NA>냉매용201834
726염화수소0.06000.0일본<NA>전자재료공정201831
741염화수소6000.00.0일본<NA>에칭(ECHING)201928
753육불화황0.012000.0중국LIMING용기충전용201728
48CF-I 65 ECO0.019152.0벨기에<NA>건축용201827
709염화수소0.03000.0일본<NA>전자재료공정201727
47CF-I 65 ECO0.019152.0벨기에<NA>건축용201726
460디메틸아민(DMA)0.01000.0일본<NA>반도체 고유전막재료 원료201723
723염화수소0.06000.0일본한국메티슨가스반도체201823
262R-152a0.018000.0중국<NA>판매201721