Overview

Dataset statistics

Number of variables12
Number of observations1622
Missing cells2976
Missing cells (%)15.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory161.7 KiB
Average record size in memory102.1 B

Variable types

Numeric6
Text3
Categorical3

Dataset

Description우리나라 농축산물의 WTO 양허관세율과 기본세율 자료
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220217000000002067

Alerts

일련 번호 is highly overall correlated with 탄력관세High correlation
기본세율 is highly overall correlated with 양허기준세율 and 3 other fieldsHigh correlation
양허기준세율 is highly overall correlated with 기본세율 and 1 other fieldsHigh correlation
2015양허관세 is highly overall correlated with 기본세율 and 1 other fieldsHigh correlation
시장접근세율 is highly overall correlated with 기본세율High correlation
탄력관세 is highly overall correlated with 일련 번호 and 1 other fieldsHigh correlation
개방구분 is highly overall correlated with 개방연도High correlation
개방연도 is highly overall correlated with 개방구분High correlation
탄력관세 is highly imbalanced (92.0%)Imbalance
개방구분 is highly imbalanced (61.7%)Imbalance
개방연도 is highly imbalanced (65.9%)Imbalance
시장접근세율 has 1395 (86.0%) missing valuesMissing
국제협력관세 has 1576 (97.2%) missing valuesMissing
일련 번호 has unique valuesUnique
H S K has unique valuesUnique
기본세율 has 107 (6.6%) zerosZeros
양허기준세율 has 31 (1.9%) zerosZeros
2015양허관세 has 31 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-11 03:44:44.860652
Analysis finished2023-12-11 03:44:51.176984
Duration6.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련 번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1622
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean811.5
Minimum1
Maximum1622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-12-11T12:44:51.238520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile82.05
Q1406.25
median811.5
Q31216.75
95-th percentile1540.95
Maximum1622
Range1621
Interquartile range (IQR)810.5

Descriptive statistics

Standard deviation468.37538
Coefficient of variation (CV)0.57717238
Kurtosis-1.2
Mean811.5
Median Absolute Deviation (MAD)405.5
Skewness0
Sum1316253
Variance219375.5
MonotonicityStrictly increasing
2023-12-11T12:44:51.382470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1080 1
 
0.1%
1090 1
 
0.1%
1089 1
 
0.1%
1088 1
 
0.1%
1087 1
 
0.1%
1086 1
 
0.1%
1085 1
 
0.1%
1084 1
 
0.1%
1083 1
 
0.1%
Other values (1612) 1612
99.4%
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 (%)
1622 1
0.1%
1621 1
0.1%
1620 1
0.1%
1619 1
0.1%
1618 1
0.1%
1617 1
0.1%
1616 1
0.1%
1615 1
0.1%
1614 1
0.1%
1613 1
0.1%

H S K
Text

UNIQUE 

Distinct1622
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
2023-12-11T12:44:51.657150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters19464
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1622 ?
Unique (%)100.0%

Sample

1st row0101.21.1000
2nd row0101.21.9000
3rd row0101.29.1000
4th row0101.29.9000
5th row0101.30.1000
ValueCountFrequency (%)
0101.21.1000 1
 
0.1%
1703.10.1000 1
 
0.1%
1801.00.1000 1
 
0.1%
1704.90.9000 1
 
0.1%
1704.90.2090 1
 
0.1%
1704.90.2020 1
 
0.1%
1704.90.2010 1
 
0.1%
1704.90.1000 1
 
0.1%
1704.10.0000 1
 
0.1%
1703.90.9000 1
 
0.1%
Other values (1612) 1612
99.4%
2023-12-11T12:44:52.111951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8069
41.5%
. 3244
16.7%
1 2518
 
12.9%
2 1623
 
8.3%
9 1508
 
7.7%
3 624
 
3.2%
4 485
 
2.5%
5 466
 
2.4%
6 342
 
1.8%
7 330
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16220
83.3%
Other Punctuation 3244
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8069
49.7%
1 2518
 
15.5%
2 1623
 
10.0%
9 1508
 
9.3%
3 624
 
3.8%
4 485
 
3.0%
5 466
 
2.9%
6 342
 
2.1%
7 330
 
2.0%
8 255
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 3244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19464
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8069
41.5%
. 3244
16.7%
1 2518
 
12.9%
2 1623
 
8.3%
9 1508
 
7.7%
3 624
 
3.2%
4 485
 
2.5%
5 466
 
2.4%
6 342
 
1.8%
7 330
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8069
41.5%
. 3244
16.7%
1 2518
 
12.9%
2 1623
 
8.3%
9 1508
 
7.7%
3 624
 
3.2%
4 485
 
2.5%
5 466
 
2.4%
6 342
 
1.8%
7 330
 
1.7%

한 글 품 명
Text

Distinct1602
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
2023-12-11T12:44:52.474898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length49
Mean length12.606042
Min length1

Characters and Unicode

Total characters20447
Distinct characters596
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1589 ?
Unique (%)98.0%

Sample

1st row말(번식용/농가사육용)
2nd row말(번식용/기타)
3rd row말(기타/경주말)
4th row말(기타/기타)
5th row당나귀(번식용)
ValueCountFrequency (%)
것 204
 
5.6%
기타 103
 
2.8%
또는 97
 
2.7%
및 63
 
1.7%
그 34
 
0.9%
종자 29
 
0.8%
안한 28
 
0.8%
분획물 22
 
0.6%
도메스티쿠스종에 21
 
0.6%
100분의 19
 
0.5%
Other values (2071) 3001
82.9%
2023-12-11T12:44:53.048448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2026
 
9.9%
( 1200
 
5.9%
) 1199
 
5.9%
기 709
 
3.5%
타 624
 
3.1%
/ 506
 
2.5%
의 311
 
1.5%
조 306
 
1.5%
리 303
 
1.5%
스 302
 
1.5%
Other values (586) 12961
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14850
72.6%
Space Separator 2026
 
9.9%
Open Punctuation 1200
 
5.9%
Close Punctuation 1199
 
5.9%
Other Punctuation 696
 
3.4%
Decimal Number 343
 
1.7%
Dash Punctuation 74
 
0.4%
Lowercase Letter 57
 
0.3%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 709
 
4.8%
타 624
 
4.2%
의 311
 
2.1%
조 306
 
2.1%
리 303
 
2.0%
스 302
 
2.0%
유 282
 
1.9%
것 264
 
1.8%
이 250
 
1.7%
제 244
 
1.6%
Other values (560) 11255
75.8%
Decimal Number
ValueCountFrequency (%)
0 102
29.7%
1 63
18.4%
2 41
12.0%
5 35
 
10.2%
6 22
 
6.4%
4 20
 
5.8%
8 20
 
5.8%
9 18
 
5.2%
3 17
 
5.0%
7 5
 
1.5%
Other Punctuation
ValueCountFrequency (%)
/ 506
72.7%
, 117
 
16.8%
· 31
 
4.5%
. 29
 
4.2%
% 11
 
1.6%
: 1
 
0.1%
? 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
g 25
43.9%
m 16
28.1%
k 16
28.1%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Space Separator
ValueCountFrequency (%)
2026
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1200
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1199
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14840
72.6%
Common 5540
 
27.1%
Latin 57
 
0.3%
Han 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 709
 
4.8%
타 624
 
4.2%
의 311
 
2.1%
조 306
 
2.1%
리 303
 
2.0%
스 302
 
2.0%
유 282
 
1.9%
것 264
 
1.8%
이 250
 
1.7%
제 244
 
1.6%
Other values (550) 11245
75.8%
Common
ValueCountFrequency (%)
2026
36.6%
( 1200
21.7%
) 1199
21.6%
/ 506
 
9.1%
, 117
 
2.1%
0 102
 
1.8%
- 74
 
1.3%
1 63
 
1.1%
2 41
 
0.7%
5 35
 
0.6%
Other values (13) 177
 
3.2%
Han
ValueCountFrequency (%)
當 1
10.0%
歸 1
10.0%
五 1
10.0%
黃 1
10.0%
味 1
10.0%
子 1
10.0%
芍 1
10.0%
藥 1
10.0%
川 1
10.0%
芎 1
10.0%
Latin
ValueCountFrequency (%)
g 25
43.9%
m 16
28.1%
k 16
28.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14840
72.6%
ASCII 5566
 
27.2%
None 31
 
0.2%
CJK 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2026
36.4%
( 1200
21.6%
) 1199
21.5%
/ 506
 
9.1%
, 117
 
2.1%
0 102
 
1.8%
- 74
 
1.3%
1 63
 
1.1%
2 41
 
0.7%
5 35
 
0.6%
Other values (15) 203
 
3.6%
Hangul
ValueCountFrequency (%)
기 709
 
4.8%
타 624
 
4.2%
의 311
 
2.1%
조 306
 
2.1%
리 303
 
2.0%
스 302
 
2.0%
유 282
 
1.9%
것 264
 
1.8%
이 250
 
1.7%
제 244
 
1.6%
Other values (550) 11245
75.8%
None
ValueCountFrequency (%)
· 31
100.0%
CJK
ValueCountFrequency (%)
當 1
10.0%
歸 1
10.0%
五 1
10.0%
黃 1
10.0%
味 1
10.0%
子 1
10.0%
芍 1
10.0%
藥 1
10.0%
川 1
10.0%
芎 1
10.0%

영 문 품 명
Text

Distinct1592
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
2023-12-11T12:44:53.530110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length150
Median length82
Mean length32.002466
Min length4

Characters and Unicode

Total characters51908
Distinct characters75
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1579 ?
Unique (%)97.3%

Sample

1st rowHorses: Pure-bred breeding anmials(For farm breeding)
2nd rowHorses: Pure-bred breeding anmials(Other)
3rd rowHorses: Other(Horses for racing)
4th rowHorses: Other(Other)
5th rowAsses: Pure-bred breeding animals
ValueCountFrequency (%)
or 369
 
5.5%
of 343
 
5.1%
other 308
 
4.6%
and 245
 
3.6%
meat 118
 
1.7%
chilled 80
 
1.2%
preserved 73
 
1.1%
the 63
 
0.9%
offal 57
 
0.8%
by 56
 
0.8%
Other values (1883) 5042
74.7%
2023-12-11T12:44:54.278949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5741
 
11.1%
5140
 
9.9%
r 3923
 
7.6%
o 3195
 
6.2%
a 3135
 
6.0%
s 2965
 
5.7%
t 2938
 
5.7%
i 2605
 
5.0%
n 2419
 
4.7%
d 1870
 
3.6%
Other values (65) 17977
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40945
78.9%
Space Separator 5140
 
9.9%
Uppercase Letter 2548
 
4.9%
Open Punctuation 1090
 
2.1%
Close Punctuation 1089
 
2.1%
Other Punctuation 737
 
1.4%
Decimal Number 249
 
0.5%
Dash Punctuation 105
 
0.2%
Other Symbol 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5741
14.0%
r 3923
 
9.6%
o 3195
 
7.8%
a 3135
 
7.7%
s 2965
 
7.2%
t 2938
 
7.2%
i 2605
 
6.4%
n 2419
 
5.9%
d 1870
 
4.6%
l 1778
 
4.3%
Other values (16) 10376
25.3%
Uppercase Letter
ValueCountFrequency (%)
O 717
28.1%
C 239
 
9.4%
S 211
 
8.3%
M 170
 
6.7%
P 169
 
6.6%
F 127
 
5.0%
B 122
 
4.8%
R 110
 
4.3%
G 106
 
4.2%
L 89
 
3.5%
Other values (16) 488
19.2%
Decimal Number
ValueCountFrequency (%)
0 75
30.1%
5 40
16.1%
1 39
15.7%
2 30
 
12.0%
4 18
 
7.2%
9 15
 
6.0%
8 13
 
5.2%
3 10
 
4.0%
6 6
 
2.4%
7 3
 
1.2%
Other Punctuation
ValueCountFrequency (%)
/ 334
45.3%
, 223
30.3%
. 79
 
10.7%
: 41
 
5.6%
% 37
 
5.0%
' 16
 
2.2%
; 7
 
0.9%
Other Symbol
ValueCountFrequency (%)
° 4
80.0%
ⓚ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
5140
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1090
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1089
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43493
83.8%
Common 8415
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5741
13.2%
r 3923
 
9.0%
o 3195
 
7.3%
a 3135
 
7.2%
s 2965
 
6.8%
t 2938
 
6.8%
i 2605
 
6.0%
n 2419
 
5.6%
d 1870
 
4.3%
l 1778
 
4.1%
Other values (42) 12924
29.7%
Common
ValueCountFrequency (%)
5140
61.1%
( 1090
 
13.0%
) 1089
 
12.9%
/ 334
 
4.0%
, 223
 
2.7%
- 105
 
1.2%
. 79
 
0.9%
0 75
 
0.9%
: 41
 
0.5%
5 40
 
0.5%
Other values (13) 199
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51903
> 99.9%
None 4
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5741
 
11.1%
5140
 
9.9%
r 3923
 
7.6%
o 3195
 
6.2%
a 3135
 
6.0%
s 2965
 
5.7%
t 2938
 
5.7%
i 2605
 
5.0%
n 2419
 
4.7%
d 1870
 
3.6%
Other values (63) 17972
34.6%
None
ValueCountFrequency (%)
° 4
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
ⓚ 1
100.0%

기본세율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.585697
Minimum0
Maximum50
Zeros107
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-12-11T12:44:54.462848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median8
Q327
95-th percentile45
Maximum50
Range50
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.688249
Coefficient of variation (CV)0.82530441
Kurtosis-0.51263131
Mean16.585697
Median Absolute Deviation (MAD)8
Skewness0.74917662
Sum26902
Variance187.36815
MonotonicityNot monotonic
2023-12-11T12:44:54.621536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8.0 456
28.1%
30.0 203
12.5%
5.0 142
 
8.8%
20.0 128
 
7.9%
3.0 122
 
7.5%
0.0 107
 
6.6%
27.0 102
 
6.3%
45.0 58
 
3.6%
18.0 54
 
3.3%
40.0 46
 
2.8%
Other values (17) 204
12.6%
ValueCountFrequency (%)
0.0 107
6.6%
1.0 2
 
0.1%
1.8 4
 
0.2%
2.0 28
 
1.7%
3.0 122
7.5%
4.0 1
 
0.1%
4.2 3
 
0.2%
5.0 142
8.8%
5.4 6
 
0.4%
7.0 1
 
0.1%
ValueCountFrequency (%)
50.0 46
 
2.8%
45.0 58
 
3.6%
40.0 46
 
2.8%
36.0 37
 
2.3%
32.8 1
 
0.1%
30.0 203
12.5%
27.0 102
6.3%
25.0 20
 
1.2%
24.0 2
 
0.1%
22.5 38
 
2.3%

양허기준세율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)5.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean85.705861
Minimum0
Maximum986
Zeros31
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-12-11T12:44:54.773245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q120
median30
Q360
95-th percentile428.6
Maximum986
Range986
Interquartile range (IQR)40

Descriptive statistics

Standard deviation160.87982
Coefficient of variation (CV)1.8771157
Kurtosis12.86833
Mean85.705861
Median Absolute Deviation (MAD)14.5
Skewness3.5674913
Sum138929.2
Variance25882.316
MonotonicityNot monotonic
2023-12-11T12:44:54.980346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 283
17.4%
20.0 236
14.5%
10.0 143
 
8.8%
60.0 98
 
6.0%
40.0 74
 
4.6%
50.0 66
 
4.1%
100.0 59
 
3.6%
25.0 52
 
3.2%
59.2 48
 
3.0%
35.0 40
 
2.5%
Other values (75) 522
32.2%
ValueCountFrequency (%)
0.0 31
 
1.9%
10.0 143
8.8%
11.8 11
 
0.7%
20.0 236
14.5%
23.7 33
 
2.0%
25.0 52
 
3.2%
28.5 1
 
0.1%
29.6 32
 
2.0%
30.0 283
17.4%
35.0 40
 
2.5%
ValueCountFrequency (%)
986.0 5
 
0.3%
889.2 12
0.7%
866.0 1
 
0.1%
838.1 16
1.0%
700.0 3
 
0.2%
679.4 2
 
0.1%
675.0 2
 
0.1%
629.8 2
 
0.1%
616.4 4
 
0.2%
571.0 16
1.0%

2015양허관세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)6.2%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean71.359271
Minimum0
Maximum887.4
Zeros31
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-12-11T12:44:55.155490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q118
median27
Q345
95-th percentile385.7
Maximum887.4
Range887.4
Interquartile range (IQR)27

Descriptive statistics

Standard deviation146.1159
Coefficient of variation (CV)2.0476092
Kurtosis12.885795
Mean71.359271
Median Absolute Deviation (MAD)13.9
Skewness3.583761
Sum115459.3
Variance21349.857
MonotonicityNot monotonic
2023-12-11T12:44:55.314371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.0 168
 
10.4%
18.0 125
 
7.7%
19.7 123
 
7.6%
54.0 121
 
7.5%
13.1 110
 
6.8%
45.0 93
 
5.7%
36.0 71
 
4.4%
6.6 57
 
3.5%
22.5 55
 
3.4%
30.0 54
 
3.3%
Other values (90) 641
39.5%
ValueCountFrequency (%)
0.0 31
1.9%
1.8 5
 
0.3%
2.0 30
1.8%
4.2 4
 
0.2%
5.0 38
2.3%
5.4 14
 
0.9%
6.6 57
3.5%
8.0 3
 
0.2%
9.0 34
2.1%
10.0 4
 
0.2%
ValueCountFrequency (%)
887.4 5
 
0.3%
800.3 12
0.7%
779.4 1
 
0.1%
754.3 16
1.0%
630.0 3
 
0.2%
611.5 2
 
0.1%
607.5 2
 
0.1%
566.8 2
 
0.1%
554.8 4
 
0.2%
513.6 2
 
0.1%

시장접근세율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)6.6%
Missing1395
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean17.798238
Minimum0
Maximum50
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-12-11T12:44:55.475568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q15
median20
Q320
95-th percentile50
Maximum50
Range50
Interquartile range (IQR)15

Descriptive statistics

Standard deviation14.703031
Coefficient of variation (CV)0.82609474
Kurtosis-0.19574366
Mean17.798238
Median Absolute Deviation (MAD)12
Skewness0.86990023
Sum4040.2
Variance216.17911
MonotonicityNot monotonic
2023-12-11T12:44:55.625204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20.0 73
 
4.5%
5.0 43
 
2.7%
8.0 24
 
1.5%
50.0 20
 
1.2%
40.0 16
 
1.0%
3.0 14
 
0.9%
30.0 13
 
0.8%
0.0 11
 
0.7%
1.8 4
 
0.2%
5.4 2
 
0.1%
Other values (5) 7
 
0.4%
(Missing) 1395
86.0%
ValueCountFrequency (%)
0.0 11
 
0.7%
1.8 4
 
0.2%
2.0 1
 
0.1%
3.0 14
 
0.9%
4.2 1
 
0.1%
5.0 43
2.7%
5.4 2
 
0.1%
8.0 24
1.5%
9.0 2
 
0.1%
11.0 1
 
0.1%
ValueCountFrequency (%)
50.0 20
 
1.2%
40.0 16
 
1.0%
30.0 13
 
0.8%
24.0 2
 
0.1%
20.0 73
4.5%
11.0 1
 
0.1%
9.0 2
 
0.1%
8.0 24
 
1.5%
5.4 2
 
0.1%
5.0 43
2.7%

국제협력관세
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)13.0%
Missing1576
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean20.543478
Minimum5
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-12-11T12:44:55.750300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile15
Q115
median15
Q320
95-th percentile40
Maximum40
Range35
Interquartile range (IQR)5

Descriptive statistics

Standard deviation9.2031553
Coefficient of variation (CV)0.44798428
Kurtosis0.10630103
Mean20.543478
Median Absolute Deviation (MAD)5
Skewness1.017925
Sum945
Variance84.698068
MonotonicityNot monotonic
2023-12-11T12:44:55.895361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
15 22
 
1.4%
20 12
 
0.7%
35 5
 
0.3%
40 4
 
0.2%
5 2
 
0.1%
30 1
 
0.1%
(Missing) 1576
97.2%
ValueCountFrequency (%)
5 2
 
0.1%
15 22
1.4%
20 12
0.7%
30 1
 
0.1%
35 5
 
0.3%
40 4
 
0.2%
ValueCountFrequency (%)
40 4
 
0.2%
35 5
 
0.3%
30 1
 
0.1%
20 12
0.7%
15 22
1.4%
5 2
 
0.1%

탄력관세
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
<NA>
1566 
할당0(12월말)
 
38
조정45%
 
9
조정40%,1,625원/kg
 
2
할당10(12월말)
 
1
Other values (6)
 
6

Length

Max length15
Median length4
Mean length4.1535142
Min length3

Unique

Unique7 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1566
96.5%
할당0(12월말) 38
 
2.3%
조정45% 9
 
0.6%
조정40%,1,625원/kg 2
 
0.1%
할당10(12월말) 1
 
0.1%
할당5 1
 
0.1%
할당5(12월말) 1
 
0.1%
조정26%,206원/kg 1
 
0.1%
조정50% 1
 
0.1%
조정35% 1
 
0.1%

Length

2023-12-11T12:44:56.072994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1566
96.5%
할당0(12월말 38
 
2.3%
조정45 9
 
0.6%
조정40%,1,625원/kg 2
 
0.1%
할당10(12월말 1
 
0.1%
할당5 1
 
0.1%
할당5(12월말 1
 
0.1%
조정26%,206원/kg 1
 
0.1%
조정50 1
 
0.1%
조정35 1
 
0.1%

개방구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
<NA>
1331 
TC
 
72
TM
 
51
BM
 
51
BC
 
49
Other values (3)
 
68

Length

Max length4
Median length4
Mean length3.6411837
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1331
82.1%
TC 72
 
4.4%
TM 51
 
3.1%
BM 51
 
3.1%
BC 49
 
3.0%
BX 40
 
2.5%
ST 16
 
1.0%
TX 12
 
0.7%

Length

2023-12-11T12:44:56.263216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:44:56.458298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1331
82.1%
tc 72
 
4.4%
tm 51
 
3.1%
bm 51
 
3.1%
bc 49
 
3.0%
bx 40
 
2.5%
st 16
 
1.0%
tx 12
 
0.7%

개방연도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
<NA>
1331 
95.1
197 
97.7
 
39
96.7
 
22
‘15.1.1
 
16
Other values (2)
 
17

Length

Max length7
Median length4
Mean length4.0209618
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1331
82.1%
95.1 197
 
12.1%
97.7 39
 
2.4%
96.7 22
 
1.4%
‘15.1.1 16
 
1.0%
1.1 14
 
0.9%
96.1 3
 
0.2%

Length

2023-12-11T12:44:56.635838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:44:56.781598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1331
82.1%
95.1 197
 
12.1%
97.7 39
 
2.4%
96.7 22
 
1.4%
‘15.1.1 16
 
1.0%
1.1 14
 
0.9%
96.1 3
 
0.2%

Interactions

2023-12-11T12:44:49.813280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:45.764209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:46.617520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:47.450181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:48.261000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:49.066833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:49.906655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:45.902561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:46.763895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:47.612770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:48.417773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:49.210901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:50.028187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:46.050460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:46.914609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:47.739047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:48.577692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:49.342922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:50.140523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:46.195309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:47.035827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:47.888874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:48.711434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:49.467798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:50.269010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:46.338195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:47.175304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:48.002274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:48.829611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:49.607089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:50.350626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:46.492599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:47.309668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:48.118531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:48.943885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:49.718152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:44:56.908043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련 번호기본세율양허기준세율2015양허관세시장접근세율국제협력관세탄력관세개방구분개방연도
일련 번호1.0000.7640.5740.5460.6670.9120.7990.6740.680
기본세율0.7641.0000.5190.4260.9020.9200.8480.6770.777
양허기준세율0.5740.5191.0000.9990.5910.9850.4060.7130.631
2015양허관세0.5460.4260.9991.0000.584NaN0.6580.7060.610
시장접근세율0.6670.9020.5910.5841.000NaN0.0000.6500.444
국제협력관세0.9120.9200.985NaNNaN1.000NaNNaNNaN
탄력관세0.7990.8480.4060.6580.000NaN1.0000.0000.000
개방구분0.6740.6770.7130.7060.650NaN0.0001.0000.730
개방연도0.6800.7770.6310.6100.444NaN0.0000.7301.000
2023-12-11T12:44:57.099451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개방연도개방구분탄력관세
개방연도1.0000.5450.000
개방구분0.5451.0000.000
탄력관세0.0000.0001.000
2023-12-11T12:44:57.234883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련 번호기본세율양허기준세율2015양허관세시장접근세율국제협력관세탄력관세개방구분개방연도
일련 번호1.000-0.2160.077-0.030-0.241-0.1740.5200.4230.441
기본세율-0.2161.0000.5510.5690.878-0.0520.5790.3770.473
양허기준세율0.0770.5511.0000.939-0.043-0.2480.1620.4630.394
2015양허관세-0.0300.5690.9391.000-0.0580.0560.3100.4550.375
시장접근세율-0.2410.878-0.043-0.0581.000NaN0.0000.4940.322
국제협력관세-0.174-0.052-0.2480.056NaN1.000NaN0.0000.000
탄력관세0.5200.5790.1620.3100.000NaN1.0000.0000.000
개방구분0.4230.3770.4630.4550.4940.0000.0001.0000.545
개방연도0.4410.4730.3940.3750.3220.0000.0000.5451.000

Missing values

2023-12-11T12:44:50.483809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:44:50.981250image/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.
2023-12-11T12:44:51.106035image/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

일련 번호H S K한 글 품 명영 문 품 명기본세율양허기준세율2015양허관세시장접근세율국제협력관세탄력관세개방구분개방연도
010101.21.1000말(번식용/농가사육용)Horses: Pure-bred breeding anmials(For farm breeding)0.020.013.1<NA><NA><NA><NA><NA>
120101.21.9000말(번식용/기타)Horses: Pure-bred breeding anmials(Other)8.020.013.1<NA><NA><NA><NA><NA>
230101.29.1000말(기타/경주말)Horses: Other(Horses for racing)8.020.013.1<NA><NA><NA><NA><NA>
340101.29.9000말(기타/기타)Horses: Other(Other)8.020.013.1<NA><NA><NA><NA><NA>
450101.30.1000당나귀(번식용)Asses: Pure-bred breeding animals8.020.013.1<NA><NA><NA><NA><NA>
560101.30.9000당나귀(기타)Asses: Other8.020.013.1<NA><NA><NA><NA><NA>
670101.90.0000기타(기타)Other8.020.013.1<NA><NA><NA><NA><NA>
780102.21.1000축우(번식용/젖소)Cattle: Pure-bred breeding animals(For milk)0.099.089.10.0<NA><NA>TM95.1
890102.21.2000축우(번식용/육우)Cattle: Pure-bred breeding animals(For meat)0.099.089.10.0<NA><NA>TM95.1
9100102.21.9000축우(번식용/기타)Cattle: Pure-bred breeding animals(Other)0.099.089.10.0<NA><NA>TM95.1
일련 번호H S K한 글 품 명영 문 품 명기본세율양허기준세율2015양허관세시장접근세율국제협력관세탄력관세개방구분개방연도
161216135203.00.0000면(카드 또는 코움한 것)Cotton, carded or combed0.010.02.0<NA><NA><NA><NA><NA>
161316145301.10.0000생아마 또는 침지아마Flax, raw or retted2.010.02.0<NA><NA><NA><NA><NA>
161416155301.21.0000아마(쇄경 또는 타마한 것)Flax(broken or scutched)2.010.02.0<NA><NA><NA><NA><NA>
161516165301.29.0000아마(기타)Other flax2.010.02.0<NA><NA><NA><NA><NA>
161616175301.30.1000아마의 토우Flax tow2.010.02.0<NA><NA><NA><NA><NA>
161716185301.30.2000아마의 웨이스트Flax waste2.010.02.0<NA><NA><NA><NA><NA>
161816195302.10.0000생대마 또는 침지대마True hemp, raw or retted2.010.02.0<NA><NA><NA><NA><NA>
161916205302.90.1000쇄경, 탐, 핵클 또는 기타의 방법으로 가공한 대마True hemp, broken, scutched, hackled or other wise processed2.010.02.0<NA><NA><NA><NA><NA>
162016215302.90.2010대마의 토우Tow of true hemp2.010.02.0<NA><NA><NA><NA><NA>
162116225302.90.2020대마의 웨이스트Waste of true hemp2.010.02.0<NA><NA><NA><NA><NA>