Overview

Dataset statistics

Number of variables13
Number of observations4867
Missing cells21
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory508.7 KiB
Average record size in memory107.0 B

Variable types

Categorical6
Numeric2
Text5

Dataset

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

Alerts

INTRL_CORPR_CSTMS is highly overall correlated with ORDR and 5 other fieldsHigh correlation
MRKT_ACCES_TAXRT is highly overall correlated with BASS_TAXRT and 3 other fieldsHigh correlation
OPN_SE is highly overall correlated with MRKT_ACCES_TAXRT and 3 other fieldsHigh correlation
OPN_YEAR is highly overall correlated with INTRL_CORPR_CSTMS and 1 other fieldsHigh correlation
FEXTARF is highly overall correlated with ORDR and 5 other fieldsHigh correlation
ORDR is highly overall correlated with INTRL_CORPR_CSTMS and 1 other fieldsHigh correlation
BASS_TAXRT is highly overall correlated with MRKT_ACCES_TAXRT and 2 other fieldsHigh correlation
YEAR is highly overall correlated with FEXTARFHigh correlation
MRKT_ACCES_TAXRT is highly imbalanced (71.1%)Imbalance
INTRL_CORPR_CSTMS is highly imbalanced (91.3%)Imbalance
FEXTARF is highly imbalanced (92.1%)Imbalance
OPN_SE is highly imbalanced (61.7%)Imbalance
OPN_YEAR is highly imbalanced (70.5%)Imbalance
BASS_TAXRT has 321 (6.6%) zerosZeros

Reproduction

Analysis started2023-12-11 03:44:24.201674
Analysis finished2023-12-11 03:44:26.161691
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

YEAR
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
2016
1623 
2014
1622 
2015
1622 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2016 1623
33.3%
2014 1622
33.3%
2015 1622
33.3%

Length

2023-12-11T12:44:26.227522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:44:26.356267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 1623
33.3%
2014 1622
33.3%
2015 1622
33.3%

ORDR
Real number (ℝ)

HIGH CORRELATION 

Distinct1623
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean811.66674
Minimum1
Maximum1623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2023-12-11T12:44:26.486040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile82
Q1406
median812
Q31217
95-th percentile1541.7
Maximum1623
Range1622
Interquartile range (IQR)811

Descriptive statistics

Standard deviation468.37544
Coefficient of variation (CV)0.57705389
Kurtosis-1.1999996
Mean811.66674
Median Absolute Deviation (MAD)406
Skewness8.7807447 × 10-7
Sum3950382
Variance219375.56
MonotonicityNot monotonic
2023-12-11T12:44:26.650841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
273 3
 
0.1%
1148 3
 
0.1%
1158 3
 
0.1%
1157 3
 
0.1%
1156 3
 
0.1%
1155 3
 
0.1%
1154 3
 
0.1%
1153 3
 
0.1%
1152 3
 
0.1%
1151 3
 
0.1%
Other values (1613) 4837
99.4%
ValueCountFrequency (%)
1 3
0.1%
2 3
0.1%
3 3
0.1%
4 3
0.1%
5 3
0.1%
6 3
0.1%
7 3
0.1%
8 3
0.1%
9 3
0.1%
10 3
0.1%
ValueCountFrequency (%)
1623 1
 
< 0.1%
1622 3
0.1%
1621 3
0.1%
1620 3
0.1%
1619 3
0.1%
1618 3
0.1%
1617 3
0.1%
1616 3
0.1%
1615 3
0.1%
1614 3
0.1%

HSK
Text

Distinct1628
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
2023-12-11T12:44:26.960600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters58404
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

Unique6 ?
Unique (%)0.1%

Sample

1st row0506.90.9000
2nd row0507.10.1000
3rd row0507.10.2000
4th row0507.10.9000
5th row0507.90.1110
ValueCountFrequency (%)
0506.90.9000 3
 
0.1%
1902.11.1000 3
 
0.1%
1904.90.1090 3
 
0.1%
1904.90.1010 3
 
0.1%
1904.30.0000 3
 
0.1%
1904.20.9000 3
 
0.1%
1904.20.1000 3
 
0.1%
1904.10.9000 3
 
0.1%
1904.10.3000 3
 
0.1%
1904.10.2000 3
 
0.1%
Other values (1618) 4837
99.4%
2023-12-11T12:44:27.383417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24204
41.4%
. 9734
16.7%
1 7553
 
12.9%
2 4870
 
8.3%
9 4526
 
7.7%
3 1881
 
3.2%
4 1456
 
2.5%
5 1400
 
2.4%
6 1023
 
1.8%
7 990
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48670
83.3%
Other Punctuation 9734
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24204
49.7%
1 7553
 
15.5%
2 4870
 
10.0%
9 4526
 
9.3%
3 1881
 
3.9%
4 1456
 
3.0%
5 1400
 
2.9%
6 1023
 
2.1%
7 990
 
2.0%
8 767
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 9734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58404
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24204
41.4%
. 9734
16.7%
1 7553
 
12.9%
2 4870
 
8.3%
9 4526
 
7.7%
3 1881
 
3.2%
4 1456
 
2.5%
5 1400
 
2.4%
6 1023
 
1.8%
7 990
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24204
41.4%
. 9734
16.7%
1 7553
 
12.9%
2 4870
 
8.3%
9 4526
 
7.7%
3 1881
 
3.2%
4 1456
 
2.5%
5 1400
 
2.4%
6 1023
 
1.8%
7 990
 
1.7%

KOREAN_PRDNM
Text

Distinct1676
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
2023-12-11T12:44:27.737157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length49
Mean length12.584754
Min length1

Characters and Unicode

Total characters61250
Distinct characters597
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

Unique76 ?
Unique (%)1.6%

Sample

1st row뼈와 혼코어·분(기타)
2nd row상아(분과 웨이스트)
3rd row서각(분과 웨이스트)
4th row기타 아이보리(분과 웨이스트)
5th row녹용(전지)
ValueCountFrequency (%)
것 616
 
5.7%
기타 289
 
2.7%
또는 288
 
2.7%
및 188
 
1.7%
그 103
 
1.0%
종자 87
 
0.8%
안한 84
 
0.8%
분획물 66
 
0.6%
도메스티쿠스종에 63
 
0.6%
동물의 57
 
0.5%
Other values (2135) 8973
83.0%
2023-12-11T12:44:28.312676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6022
 
9.8%
( 3597
 
5.9%
) 3593
 
5.9%
기 2125
 
3.5%
타 1873
 
3.1%
/ 1517
 
2.5%
의 928
 
1.5%
조 924
 
1.5%
리 908
 
1.5%
스 906
 
1.5%
Other values (587) 38857
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44506
72.7%
Space Separator 6022
 
9.8%
Open Punctuation 3597
 
5.9%
Close Punctuation 3593
 
5.9%
Other Punctuation 2086
 
3.4%
Decimal Number 1047
 
1.7%
Dash Punctuation 222
 
0.4%
Lowercase Letter 171
 
0.3%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 2125
 
4.8%
타 1873
 
4.2%
의 928
 
2.1%
조 924
 
2.1%
리 908
 
2.0%
스 906
 
2.0%
유 841
 
1.9%
것 794
 
1.8%
이 750
 
1.7%
제 741
 
1.7%
Other values (561) 33716
75.8%
Decimal Number
ValueCountFrequency (%)
0 313
29.9%
1 190
18.1%
2 124
 
11.8%
5 108
 
10.3%
6 66
 
6.3%
4 61
 
5.8%
8 60
 
5.7%
9 56
 
5.3%
3 54
 
5.2%
7 15
 
1.4%
Other Punctuation
ValueCountFrequency (%)
/ 1517
72.7%
, 347
 
16.6%
. 90
 
4.3%
· 62
 
3.0%
? 34
 
1.6%
% 33
 
1.6%
: 3
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
g 75
43.9%
m 48
28.1%
k 48
28.1%
Math Symbol
ValueCountFrequency (%)
< 3
50.0%
> 3
50.0%
Space Separator
ValueCountFrequency (%)
6022
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3597
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3593
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44476
72.6%
Common 16573
 
27.1%
Latin 171
 
0.3%
Han 30
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 2125
 
4.8%
타 1873
 
4.2%
의 928
 
2.1%
조 924
 
2.1%
리 908
 
2.0%
스 906
 
2.0%
유 841
 
1.9%
것 794
 
1.8%
이 750
 
1.7%
제 741
 
1.7%
Other values (551) 33686
75.7%
Common
ValueCountFrequency (%)
6022
36.3%
( 3597
21.7%
) 3593
21.7%
/ 1517
 
9.2%
, 347
 
2.1%
0 313
 
1.9%
- 222
 
1.3%
1 190
 
1.1%
2 124
 
0.7%
5 108
 
0.7%
Other values (13) 540
 
3.3%
Han
ValueCountFrequency (%)
黃 3
10.0%
川 3
10.0%
芎 3
10.0%
當 3
10.0%
歸 3
10.0%
五 3
10.0%
味 3
10.0%
子 3
10.0%
芍 3
10.0%
藥 3
10.0%
Latin
ValueCountFrequency (%)
g 75
43.9%
m 48
28.1%
k 48
28.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44476
72.6%
ASCII 16682
 
27.2%
None 62
 
0.1%
CJK 30
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6022
36.1%
( 3597
21.6%
) 3593
21.5%
/ 1517
 
9.1%
, 347
 
2.1%
0 313
 
1.9%
- 222
 
1.3%
1 190
 
1.1%
2 124
 
0.7%
5 108
 
0.6%
Other values (15) 649
 
3.9%
Hangul
ValueCountFrequency (%)
기 2125
 
4.8%
타 1873
 
4.2%
의 928
 
2.1%
조 924
 
2.1%
리 908
 
2.0%
스 906
 
2.0%
유 841
 
1.9%
것 794
 
1.8%
이 750
 
1.7%
제 741
 
1.7%
Other values (551) 33686
75.7%
None
ValueCountFrequency (%)
· 62
100.0%
CJK
ValueCountFrequency (%)
黃 3
10.0%
川 3
10.0%
芎 3
10.0%
當 3
10.0%
歸 3
10.0%
五 3
10.0%
味 3
10.0%
子 3
10.0%
芍 3
10.0%
藥 3
10.0%

ENGL_PRDNM
Text

Distinct1642
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
2023-12-11T12:44:28.744636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length150
Median length85
Mean length32.009246
Min length4

Characters and Unicode

Total characters155789
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

Unique56 ?
Unique (%)1.2%

Sample

1st rowOther bones and powder of bones
2nd rowIvory of elephant(powder, waste)
3rd rowRhinoceros horns(powder, waste)
4th rowOther(powder, waste)
5th rowYoung antlers(In whole)
ValueCountFrequency (%)
or 1103
 
5.5%
of 1032
 
5.1%
other 927
 
4.6%
and 725
 
3.6%
meat 350
 
1.7%
chilled 240
 
1.2%
preserved 219
 
1.1%
the 193
 
1.0%
offal 169
 
0.8%
by 168
 
0.8%
Other values (1916) 15096
74.7%
2023-12-11T12:44:29.361150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 17238
 
11.1%
15373
 
9.9%
r 11782
 
7.6%
o 9584
 
6.2%
a 9405
 
6.0%
s 8888
 
5.7%
t 8824
 
5.7%
i 7830
 
5.0%
n 7268
 
4.7%
d 5616
 
3.6%
Other values (65) 53981
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 122924
78.9%
Space Separator 15373
 
9.9%
Uppercase Letter 7646
 
4.9%
Open Punctuation 3274
 
2.1%
Close Punctuation 3270
 
2.1%
Other Punctuation 2208
 
1.4%
Decimal Number 765
 
0.5%
Dash Punctuation 314
 
0.2%
Other Symbol 15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17238
14.0%
r 11782
 
9.6%
o 9584
 
7.8%
a 9405
 
7.7%
s 8888
 
7.2%
t 8824
 
7.2%
i 7830
 
6.4%
n 7268
 
5.9%
d 5616
 
4.6%
l 5318
 
4.3%
Other values (16) 31171
25.4%
Uppercase Letter
ValueCountFrequency (%)
O 2157
28.2%
C 718
 
9.4%
S 632
 
8.3%
M 510
 
6.7%
P 509
 
6.7%
F 380
 
5.0%
B 365
 
4.8%
R 330
 
4.3%
G 318
 
4.2%
L 267
 
3.5%
Other values (16) 1460
19.1%
Decimal Number
ValueCountFrequency (%)
0 232
30.3%
5 123
16.1%
1 118
15.4%
2 91
 
11.9%
4 55
 
7.2%
9 47
 
6.1%
8 39
 
5.1%
3 33
 
4.3%
6 18
 
2.4%
7 9
 
1.2%
Other Punctuation
ValueCountFrequency (%)
/ 1002
45.4%
, 664
30.1%
. 240
 
10.9%
: 123
 
5.6%
% 111
 
5.0%
' 48
 
2.2%
; 20
 
0.9%
Other Symbol
ValueCountFrequency (%)
° 12
80.0%
ⓚ 3
 
20.0%
Space Separator
ValueCountFrequency (%)
15373
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3274
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3270
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130570
83.8%
Common 25219
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17238
13.2%
r 11782
 
9.0%
o 9584
 
7.3%
a 9405
 
7.2%
s 8888
 
6.8%
t 8824
 
6.8%
i 7830
 
6.0%
n 7268
 
5.6%
d 5616
 
4.3%
l 5318
 
4.1%
Other values (42) 38817
29.7%
Common
ValueCountFrequency (%)
15373
61.0%
( 3274
 
13.0%
) 3270
 
13.0%
/ 1002
 
4.0%
, 664
 
2.6%
- 314
 
1.2%
. 240
 
1.0%
0 232
 
0.9%
: 123
 
0.5%
5 123
 
0.5%
Other values (13) 604
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155774
> 99.9%
None 12
 
< 0.1%
Enclosed Alphanum 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17238
 
11.1%
15373
 
9.9%
r 11782
 
7.6%
o 9584
 
6.2%
a 9405
 
6.0%
s 8888
 
5.7%
t 8824
 
5.7%
i 7830
 
5.0%
n 7268
 
4.7%
d 5616
 
3.6%
Other values (63) 53966
34.6%
None
ValueCountFrequency (%)
° 12
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
ⓚ 3
100.0%

BASS_TAXRT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.583727
Minimum0
Maximum50
Zeros321
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2023-12-11T12:44:29.569501image/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 deviation13.686404
Coefficient of variation (CV)0.82529118
Kurtosis-0.51447343
Mean16.583727
Median Absolute Deviation (MAD)8
Skewness0.74879831
Sum80713
Variance187.31765
MonotonicityNot monotonic
2023-12-11T12:44:29.707161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8.0 1367
28.1%
30.0 610
12.5%
5.0 428
 
8.8%
20.0 385
 
7.9%
3.0 366
 
7.5%
0.0 321
 
6.6%
27.0 306
 
6.3%
45.0 174
 
3.6%
18.0 162
 
3.3%
50.0 138
 
2.8%
Other values (17) 610
12.5%
ValueCountFrequency (%)
0.0 321
6.6%
1.0 6
 
0.1%
1.8 12
 
0.2%
2.0 84
 
1.7%
3.0 366
7.5%
4.0 3
 
0.1%
4.2 9
 
0.2%
5.0 428
8.8%
5.4 18
 
0.4%
7.0 3
 
0.1%
ValueCountFrequency (%)
50.0 138
 
2.8%
45.0 174
 
3.6%
40.0 138
 
2.8%
36.0 111
 
2.3%
32.8 3
 
0.1%
30.0 610
12.5%
27.0 306
6.3%
25.0 60
 
1.2%
24.0 6
 
0.1%
22.5 112
 
2.3%

CONCESSTDR_TAXRT
Text

Distinct86
Distinct (%)1.8%
Missing9
Missing (%)0.2%
Memory size38.2 KiB
2023-12-11T12:44:29.985698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.5131741
Min length1

Characters and Unicode

Total characters12209
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

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row30
3rd row30
4th row30
5th row40
ValueCountFrequency (%)
30 849
17.5%
20 705
14.5%
10 429
 
8.8%
60 294
 
6.1%
40 222
 
4.6%
50 198
 
4.1%
100 177
 
3.6%
25 156
 
3.2%
59.2 144
 
3.0%
35 120
 
2.5%
Other values (76) 1564
32.2%
2023-12-11T12:44:30.410136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3354
27.5%
2 1480
12.1%
3 1380
11.3%
5 1229
 
10.1%
1 1049
 
8.6%
. 955
 
7.8%
4 768
 
6.3%
6 589
 
4.8%
9 544
 
4.5%
7 464
 
3.8%
Other values (2) 397
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11238
92.0%
Other Punctuation 955
 
7.8%
Dash Punctuation 16
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3354
29.8%
2 1480
13.2%
3 1380
12.3%
5 1229
 
10.9%
1 1049
 
9.3%
4 768
 
6.8%
6 589
 
5.2%
9 544
 
4.8%
7 464
 
4.1%
8 381
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 955
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12209
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3354
27.5%
2 1480
12.1%
3 1380
11.3%
5 1229
 
10.1%
1 1049
 
8.6%
. 955
 
7.8%
4 768
 
6.3%
6 589
 
4.8%
9 544
 
4.5%
7 464
 
3.8%
Other values (2) 397
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3354
27.5%
2 1480
12.1%
3 1380
11.3%
5 1229
 
10.1%
1 1049
 
8.6%
. 955
 
7.8%
4 768
 
6.3%
6 589
 
4.8%
9 544
 
4.5%
7 464
 
3.8%
Other values (2) 397
 
3.3%

THSYY_CONCES_CSTMS
Text

Distinct102
Distinct (%)2.1%
Missing12
Missing (%)0.2%
Memory size38.2 KiB
2023-12-11T12:44:30.659198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.7466529
Min length1

Characters and Unicode

Total characters13335
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

Unique0 ?
Unique (%)0.0%

Sample

1st row6.6
2nd row19.7
3rd row19.7
4th row19.7
5th row36
ValueCountFrequency (%)
27 504
 
10.4%
18 375
 
7.7%
19.7 369
 
7.6%
54 364
 
7.5%
13.1 327
 
6.7%
45 279
 
5.7%
36 213
 
4.4%
6.6 171
 
3.5%
22.5 164
 
3.4%
30 163
 
3.4%
Other values (92) 1926
39.7%
2023-12-11T12:44:31.076121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1897
14.2%
1 1854
13.9%
5 1537
11.5%
2 1534
11.5%
3 1207
9.1%
7 1194
9.0%
4 1153
8.6%
6 857
6.4%
9 699
 
5.2%
0 697
 
5.2%
Other values (2) 706
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11422
85.7%
Other Punctuation 1897
 
14.2%
Dash Punctuation 16
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1854
16.2%
5 1537
13.5%
2 1534
13.4%
3 1207
10.6%
7 1194
10.5%
4 1153
10.1%
6 857
7.5%
9 699
 
6.1%
0 697
 
6.1%
8 690
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 1897
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13335
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1897
14.2%
1 1854
13.9%
5 1537
11.5%
2 1534
11.5%
3 1207
9.1%
7 1194
9.0%
4 1153
8.6%
6 857
6.4%
9 699
 
5.2%
0 697
 
5.2%
Other values (2) 706
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1897
14.2%
1 1854
13.9%
5 1537
11.5%
2 1534
11.5%
3 1207
9.1%
7 1194
9.0%
4 1153
8.6%
6 857
6.4%
9 699
 
5.2%
0 697
 
5.2%
Other values (2) 706
 
5.3%

MRKT_ACCES_TAXRT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
<NA>
4051 
20
 
219
5
 
129
0
 
123
8
 
72
Other values (12)
 
273

Length

Max length4
Median length4
Mean length3.5826998
Min length1

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> 4051
83.2%
20 219
 
4.5%
5 129
 
2.7%
0 123
 
2.5%
8 72
 
1.5%
50 60
 
1.2%
40 48
 
1.0%
- 45
 
0.9%
3 42
 
0.9%
30 39
 
0.8%
Other values (7) 39
 
0.8%

Length

2023-12-11T12:44:31.216293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4051
83.2%
20 219
 
4.5%
5 129
 
2.7%
0 123
 
2.5%
8 72
 
1.5%
50 60
 
1.2%
40 48
 
1.0%
45
 
0.9%
3 42
 
0.9%
30 39
 
0.8%
Other values (7) 39
 
0.8%

INTRL_CORPR_CSTMS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
<NA>
4713 
15
 
66
20
 
36
35
 
15
40
 
12
Other values (4)
 
25

Length

Max length13
Median length4
Mean length3.9716458
Min length1

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> 4713
96.8%
15 66
 
1.4%
20 36
 
0.7%
35 15
 
0.3%
40 12
 
0.2%
특긴:41,583원/kg 10
 
0.2%
5 6
 
0.1%
특긴:31,017원/kg 6
 
0.1%
30 3
 
0.1%

Length

2023-12-11T12:44:31.339689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:44:31.482080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4713
96.8%
15 66
 
1.4%
20 36
 
0.7%
35 15
 
0.3%
40 12
 
0.2%
특긴:41,583원/kg 10
 
0.2%
5 6
 
0.1%
특긴:31,017원/kg 6
 
0.1%
30 3
 
0.1%

FEXTARF
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct36
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
<NA>
4663 
할당0 (12월말)
 
41
할당:0%
 
37
할당0(12월말)
 
37
특긴:429,056톤/684%/145원/kg
 
15
Other values (31)
 
74

Length

Max length24
Median length4
Mean length4.2436819
Min length4

Unique

Unique16 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4663
95.8%
할당0 (12월말) 41
 
0.8%
할당:0% 37
 
0.8%
할당0(12월말) 37
 
0.8%
특긴:429,056톤/684%/145원/kg 15
 
0.3%
조정45% 14
 
0.3%
조정:45% 7
 
0.1%
특긴:415톤/1,067% 6
 
0.1%
특긴:41,583원/kg 5
 
0.1%
할당25 (6월말) 4
 
0.1%
Other values (26) 38
 
0.8%

Length

2023-12-11T12:44:31.630922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4663
94.6%
12월말 44
 
0.9%
할당0 41
 
0.8%
할당:0 37
 
0.8%
할당0(12월말 37
 
0.8%
특긴:429,056톤/684%/145원/kg 15
 
0.3%
조정45 14
 
0.3%
6월말 10
 
0.2%
조정:45 7
 
0.1%
특긴:415톤/1,067 6
 
0.1%
Other values (28) 55
 
1.1%

OPN_SE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
<NA>
3994 
TC
 
216
TM
 
153
BM
 
153
BC
 
147
Other values (3)
 
204

Length

Max length4
Median length4
Mean length3.6412574
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> 3994
82.1%
TC 216
 
4.4%
TM 153
 
3.1%
BM 153
 
3.1%
BC 147
 
3.0%
BX 120
 
2.5%
ST 48
 
1.0%
TX 36
 
0.7%

Length

2023-12-11T12:44:31.758247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:44:31.875752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3994
82.1%
tc 216
 
4.4%
tm 153
 
3.1%
bm 153
 
3.1%
bc 147
 
3.0%
bx 120
 
2.5%
st 48
 
1.0%
tx 36
 
0.7%

OPN_YEAR
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
<NA>
3994 
95.1
591 
97.7
 
117
96.7
 
66
01.1
 
28
Other values (5)
 
71

Length

Max length7
Median length4
Mean length4.0024656
Min length1

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> 3994
82.1%
95.1 591
 
12.1%
97.7 117
 
2.4%
96.7 66
 
1.4%
01.1 28
 
0.6%
15.1.1 22
 
0.5%
- 16
 
0.3%
1.1 14
 
0.3%
‘15.1.1 10
 
0.2%
96.1 9
 
0.2%

Length

2023-12-11T12:44:32.204039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:44:32.304016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3994
82.1%
95.1 591
 
12.1%
97.7 117
 
2.4%
96.7 66
 
1.4%
01.1 28
 
0.6%
15.1.1 22
 
0.5%
16
 
0.3%
1.1 14
 
0.3%
‘15.1.1 10
 
0.2%
96.1 9
 
0.2%

Interactions

2023-12-11T12:44:25.398585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:25.223158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:25.496109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:44:25.311669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:44:32.388803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YEARORDRBASS_TAXRTCONCESSTDR_TAXRTMRKT_ACCES_TAXRTINTRL_CORPR_CSTMSFEXTARFOPN_SEOPN_YEAR
YEAR1.0000.0000.0000.0000.4030.3090.9900.0000.470
ORDR0.0001.0000.7710.8800.8010.9920.9070.6910.652
BASS_TAXRT0.0000.7711.0000.9180.8920.8520.9070.6930.839
CONCESSTDR_TAXRT0.0000.8800.9181.0000.9860.9340.9510.9860.961
MRKT_ACCES_TAXRT0.4030.8010.8920.9861.000NaN0.7860.7920.791
INTRL_CORPR_CSTMS0.3090.9920.8520.934NaN1.000NaNNaNNaN
FEXTARF0.9900.9070.9070.9510.786NaN1.0000.8930.743
OPN_SE0.0000.6910.6930.9860.792NaN0.8931.0000.731
OPN_YEAR0.4700.6520.8390.9610.791NaN0.7430.7311.000
2023-12-11T12:44:32.532644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INTRL_CORPR_CSTMSYEARMRKT_ACCES_TAXRTOPN_SEOPN_YEARFEXTARF
INTRL_CORPR_CSTMS1.0000.2021.0001.0001.0001.000
YEAR0.2021.0000.2390.0000.2350.882
MRKT_ACCES_TAXRT1.0000.2391.0000.5180.4040.515
OPN_SE1.0000.0000.5181.0000.5030.706
OPN_YEAR1.0000.2350.4040.5031.0000.498
FEXTARF1.0000.8820.5150.7060.4981.000
2023-12-11T12:44:32.655385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ORDRBASS_TAXRTYEARMRKT_ACCES_TAXRTINTRL_CORPR_CSTMSFEXTARFOPN_SEOPN_YEAR
ORDR1.000-0.2170.0000.4730.8630.5820.4410.368
BASS_TAXRT-0.2171.0000.0000.6860.6640.5590.3950.413
YEAR0.0000.0001.0000.2390.2020.8820.0000.235
MRKT_ACCES_TAXRT0.4730.6860.2391.0001.0000.5150.5180.404
INTRL_CORPR_CSTMS0.8630.6640.2021.0001.0001.0001.0001.000
FEXTARF0.5820.5590.8820.5151.0001.0000.7060.498
OPN_SE0.4410.3950.0000.5181.0000.7061.0000.503
OPN_YEAR0.3680.4130.2350.4041.0000.4980.5031.000

Missing values

2023-12-11T12:44:25.645808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:44:25.889119image/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.
2023-12-11T12:44:26.066818image/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

YEARORDRHSKKOREAN_PRDNMENGL_PRDNMBASS_TAXRTCONCESSTDR_TAXRTTHSYY_CONCES_CSTMSMRKT_ACCES_TAXRTINTRL_CORPR_CSTMSFEXTARFOPN_SEOPN_YEAR
020142730506.90.9000뼈와 혼코어·분(기타)Other bones and powder of bones3.0106.6<NA><NA><NA><NA><NA>
120142740507.10.1000상아(분과 웨이스트)Ivory of elephant(powder, waste)8.03019.7<NA><NA><NA><NA><NA>
220142750507.10.2000서각(분과 웨이스트)Rhinoceros horns(powder, waste)8.03019.7<NA><NA><NA><NA><NA>
320142760507.10.9000기타 아이보리(분과 웨이스트)Other(powder, waste)8.03019.7<NA><NA><NA><NA><NA>
420142770507.90.1110녹용(전지)Young antlers(In whole)20.04036<NA><NA><NA><NA><NA>
520142780507.90.1190녹용(기타)Young antlers(Other)20.04036<NA><NA><NA><NA><NA>
620142790507.90.1200녹각Antlers20.04036<NA><NA><NA><NA><NA>
720142800507.90.2010귀갑과 귀판Tortoise shells and plates8.03019.7<NA><NA><NA><NA><NA>
820142810507.90.2030천산갑Pangolin shells and scales8.03019.7<NA><NA><NA><NA><NA>
920142820507.90.2040발굽과 발톱Hooves and claws(including nails)8.03019.7<NA><NA><NA><NA><NA>
YEARORDRHSKKOREAN_PRDNMENGL_PRDNMBASS_TAXRTCONCESSTDR_TAXRTTHSYY_CONCES_CSTMSMRKT_ACCES_TAXRTINTRL_CORPR_CSTMSFEXTARFOPN_SEOPN_YEAR
4857201614232306.90.1000참깨의 것(오일케이크 및 유박)Oil-cake and other solid residues(Of sesamum seeds)5.070635<NA><NA>TM95.1
4858201614242306.90.2000들깨의 것(오일케이크 및 유박)Oil-cake and other solid residues(Of perilla seeds)5.0109<NA><NA><NA><NA><NA>
4859201614252306.90.3000옥수수배의 것(오일케이크 및 유박)Oil-cake and other solid residues(Of Maize germ)5.0106.6<NA><NA><NA><NA><NA>
4860201614262306.90.9000기타(오일케이크 및 유박)Oil-cake and other solid residues(Other)5.0106.6<NA><NA><NA><NA><NA>
4861201614272307.00.0000포도주박과 생주석Wine less; argol5.0106.6<NA><NA><NA><NA><NA>
4862201614282308.00.1000도토리(사료용, 식물성 웨이스트 등)Acorns(vegetable materials and vegetable waste, residues and by-products)5.010090<NA><NA><NA><NA><NA>
4863201614292308.00.2000마로니에열매(사료용, 식물성 웨이스트용)Horse-chestnuts(vegetable materials and vegetable waste, residues and by-products)5.02013.1<NA><NA><NA><NA><NA>
4864201614302308.00.3000면실피Cotton seed hulls(vegetable materials and vegetable waste, residues and by-products)5.02018<NA><NA>할당:0%<NA><NA>
4865201614312308.00.9000사료용 식물성 부산물 기타Other vegetable materials and vegetable waste, residues and by-products5.051.646.45<NA>할당:0%TM95.1
4866201614322309.10.1000개 사료Dog food(for retail sale)5.02018<NA><NA><NA><NA><NA>