Overview

Dataset statistics

Number of variables12
Number of observations806
Missing cells394
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.8 KiB
Average record size in memory100.2 B

Variable types

Numeric4
Categorical3
Text4
Unsupported1

Dataset

DescriptionKOTRA의 무역투자데이터 분석을 통해 2021~2022년 기준, 우리나라의 주요 수출 10개국 대상 유망품목(HS코드, MTI코드 기준)을 추출하였습니다.
URLhttps://www.data.go.kr/data/15116652/fileData.do

Alerts

국가명 has constant value ""Constant
MTI코드 영문명(1자리 기준) is highly overall correlated with HS코드(10자리) and 1 other fieldsHigh correlation
MTI코드 영문명(2자리 기준) is highly overall correlated with HS코드(10자리) and 1 other fieldsHigh correlation
연번 is highly overall correlated with 분기평균성장률(%) * 2021년 1분기~2022년 4분기 기준High correlation
HS코드(10자리) is highly overall correlated with MTI코드 영문명(2자리 기준) and 1 other fieldsHigh correlation
분기평균성장률(%) * 2021년 1분기~2022년 4분기 기준 is highly overall correlated with 연번High correlation
HS코드 영문명 has 137 (17.0%) missing valuesMissing
MTI코드 영문명(세부) has 160 (19.9%) missing valuesMissing
MTI코드 영문명(4자리 기준) has 81 (10.0%) missing valuesMissing
MTI코드 영문명(3자리 기준) has 16 (2.0%) missing valuesMissing
연번 has unique valuesUnique
MTI코드(6자리) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 20:20:49.159411
Analysis finished2023-12-12 20:20:52.905002
Duration3.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct806
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402.5
Minimum0
Maximum805
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-13T05:20:53.001399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.25
Q1201.25
median402.5
Q3603.75
95-th percentile764.75
Maximum805
Range805
Interquartile range (IQR)402.5

Descriptive statistics

Standard deviation232.81645
Coefficient of variation (CV)0.57842597
Kurtosis-1.2
Mean402.5
Median Absolute Deviation (MAD)201.5
Skewness0
Sum324415
Variance54203.5
MonotonicityStrictly increasing
2023-12-13T05:20:53.195050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
542 1
 
0.1%
532 1
 
0.1%
533 1
 
0.1%
534 1
 
0.1%
535 1
 
0.1%
536 1
 
0.1%
537 1
 
0.1%
538 1
 
0.1%
539 1
 
0.1%
Other values (796) 796
98.8%
ValueCountFrequency (%)
0 1
0.1%
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%
ValueCountFrequency (%)
805 1
0.1%
804 1
0.1%
803 1
0.1%
802 1
0.1%
801 1
0.1%
800 1
0.1%
799 1
0.1%
798 1
0.1%
797 1
0.1%
796 1
0.1%

국가명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
China
806 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
China 806
100.0%

Length

2023-12-13T05:20:53.405475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:20:53.571549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
china 806
100.0%

HS코드(10자리)
Real number (ℝ)

HIGH CORRELATION 

Distinct802
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2395227 × 109
Minimum3.0229 × 108
Maximum9.701911 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-13T05:20:53.744570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0229 × 108
5-th percentile2.101959 × 109
Q13.913159 × 109
median6.5055545 × 109
Q38.508426 × 109
95-th percentile9.0308982 × 109
Maximum9.701911 × 109
Range9.399621 × 109
Interquartile range (IQR)4.595267 × 109

Descriptive statistics

Standard deviation2.4554436 × 109
Coefficient of variation (CV)0.39353067
Kurtosis-1.0880635
Mean6.2395227 × 109
Median Absolute Deviation (MAD)2.0217605 × 109
Skewness-0.46156258
Sum5.0290553 × 1012
Variance6.0292031 × 1018
MonotonicityNot monotonic
2023-12-13T05:20:53.921358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6209901000 2
 
0.2%
2930909090 2
 
0.2%
9403700000 2
 
0.2%
8524911000 2
 
0.2%
6004100000 1
 
0.1%
9101290000 1
 
0.1%
5607500000 1
 
0.1%
8529909642 1
 
0.1%
8537102010 1
 
0.1%
8207300000 1
 
0.1%
Other values (792) 792
98.3%
ValueCountFrequency (%)
302290000 1
0.1%
303550000 1
0.1%
303591000 1
0.1%
303630000 1
0.1%
303670000 1
0.1%
303892000 1
0.1%
303899099 1
0.1%
306149090 1
0.1%
307919000 1
0.1%
307929000 1
0.1%
ValueCountFrequency (%)
9701911000 1
0.1%
9616100000 1
0.1%
9612109000 1
0.1%
9607199000 1
0.1%
9607191000 1
0.1%
9606220000 1
0.1%
9606100000 1
0.1%
9603290000 1
0.1%
9507901000 1
0.1%
9506910000 1
0.1%

HS코드 영문명
Text

MISSING 

Distinct325
Distinct (%)48.6%
Missing137
Missing (%)17.0%
Memory size6.4 KiB
2023-12-13T05:20:54.206427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length232
Median length145
Mean length20.189836
Min length4

Characters and Unicode

Total characters13507
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique291 ?
Unique (%)43.5%

Sample

1st rowOther
2nd rowTrade advertising material, commercial catalogues and the like
3rd rowOf television cameras, digital cameras or video camera recorders
4th rowOf paper or paperboard
5th rowOf other polyesters
ValueCountFrequency (%)
other 315
 
15.2%
of 166
 
8.0%
and 70
 
3.4%
or 62
 
3.0%
for 56
 
2.7%
not 19
 
0.9%
the 19
 
0.9%
a 18
 
0.9%
in 18
 
0.9%
with 16
 
0.8%
Other values (730) 1308
63.3%
2023-12-13T05:20:54.673881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1404
 
10.4%
e 1362
 
10.1%
r 1053
 
7.8%
t 1038
 
7.7%
a 833
 
6.2%
i 807
 
6.0%
s 739
 
5.5%
o 732
 
5.4%
n 703
 
5.2%
h 574
 
4.2%
Other values (61) 4262
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10910
80.8%
Space Separator 1404
 
10.4%
Uppercase Letter 732
 
5.4%
Decimal Number 207
 
1.5%
Other Punctuation 169
 
1.3%
Dash Punctuation 41
 
0.3%
Open Punctuation 22
 
0.2%
Close Punctuation 22
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1362
12.5%
r 1053
9.7%
t 1038
 
9.5%
a 833
 
7.6%
i 807
 
7.4%
s 739
 
6.8%
o 732
 
6.7%
n 703
 
6.4%
h 574
 
5.3%
c 466
 
4.3%
Other values (16) 2603
23.9%
Uppercase Letter
ValueCountFrequency (%)
O 406
55.5%
P 49
 
6.7%
C 35
 
4.8%
F 32
 
4.4%
M 24
 
3.3%
D 23
 
3.1%
S 22
 
3.0%
A 16
 
2.2%
E 15
 
2.0%
W 14
 
1.9%
Other values (14) 96
 
13.1%
Decimal Number
ValueCountFrequency (%)
0 53
25.6%
1 37
17.9%
8 22
10.6%
4 20
 
9.7%
5 19
 
9.2%
3 14
 
6.8%
7 13
 
6.3%
9 11
 
5.3%
6 10
 
4.8%
2 8
 
3.9%
Other Punctuation
ValueCountFrequency (%)
, 115
68.0%
. 34
 
20.1%
? 14
 
8.3%
% 2
 
1.2%
/ 2
 
1.2%
; 1
 
0.6%
' 1
 
0.6%
Space Separator
ValueCountFrequency (%)
1404
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11642
86.2%
Common 1865
 
13.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1362
11.7%
r 1053
 
9.0%
t 1038
 
8.9%
a 833
 
7.2%
i 807
 
6.9%
s 739
 
6.3%
o 732
 
6.3%
n 703
 
6.0%
h 574
 
4.9%
c 466
 
4.0%
Other values (40) 3335
28.6%
Common
ValueCountFrequency (%)
1404
75.3%
, 115
 
6.2%
0 53
 
2.8%
- 41
 
2.2%
1 37
 
2.0%
. 34
 
1.8%
( 22
 
1.2%
) 22
 
1.2%
8 22
 
1.2%
4 20
 
1.1%
Other values (11) 95
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13507
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1404
 
10.4%
e 1362
 
10.1%
r 1053
 
7.8%
t 1038
 
7.7%
a 833
 
6.2%
i 807
 
6.0%
s 739
 
5.5%
o 732
 
5.4%
n 703
 
5.2%
h 574
 
4.2%
Other values (61) 4262
31.6%
Distinct802
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22800187
Minimum12430.62
Maximum2.8533172 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-13T05:20:54.895158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12430.62
5-th percentile110523
Q1468980.03
median1602779
Q36120641.1
95-th percentile54090830
Maximum2.8533172 × 109
Range2.8533048 × 109
Interquartile range (IQR)5651661.1

Descriptive statistics

Standard deviation1.371105 × 108
Coefficient of variation (CV)6.0135691
Kurtosis250.31393
Mean22800187
Median Absolute Deviation (MAD)1356232
Skewness14.079789
Sum1.8376951 × 1010
Variance1.879929 × 1016
MonotonicityNot monotonic
2023-12-13T05:20:55.049465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143671.25 2
 
0.2%
2643089.0 2
 
0.2%
293250.5 2
 
0.2%
130201913.75 2
 
0.2%
14052707.12 1
 
0.1%
960702.75 1
 
0.1%
1738905.38 1
 
0.1%
2822433.75 1
 
0.1%
647338.5 1
 
0.1%
8705025.25 1
 
0.1%
Other values (792) 792
98.3%
ValueCountFrequency (%)
12430.62 1
0.1%
15197.88 1
0.1%
17937.25 1
0.1%
19408.25 1
0.1%
20814.38 1
0.1%
24357.38 1
0.1%
27271.25 1
0.1%
29199.71 1
0.1%
33883.75 1
0.1%
37134.38 1
0.1%
ValueCountFrequency (%)
2853317239.38 1
0.1%
1470386568.12 1
0.1%
1129908466.5 1
0.1%
900731069.5 1
0.1%
829656925.5 1
0.1%
642203422.5 1
0.1%
639624456.0 1
0.1%
468358947.0 1
0.1%
451134038.62 1
0.1%
402026098.25 1
0.1%
Distinct296
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0484367
Minimum0.01
Maximum255.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-13T05:20:55.190040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.2
median0.56
Q31.345
95-th percentile8.7
Maximum255.47
Range255.46
Interquartile range (IQR)1.145

Descriptive statistics

Standard deviation16.29804
Coefficient of variation (CV)5.34636
Kurtosis183.86844
Mean3.0484367
Median Absolute Deviation (MAD)0.41
Skewness12.853612
Sum2457.04
Variance265.62611
MonotonicityDecreasing
2023-12-13T05:20:55.345148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 16
 
2.0%
0.15 15
 
1.9%
0.01 15
 
1.9%
0.03 15
 
1.9%
0.08 15
 
1.9%
0.26 14
 
1.7%
0.02 12
 
1.5%
0.66 12
 
1.5%
0.1 12
 
1.5%
0.13 12
 
1.5%
Other values (286) 668
82.9%
ValueCountFrequency (%)
0.01 15
1.9%
0.02 12
1.5%
0.03 15
1.9%
0.04 10
1.2%
0.05 9
1.1%
0.06 7
0.9%
0.07 4
 
0.5%
0.08 15
1.9%
0.09 8
1.0%
0.1 12
1.5%
ValueCountFrequency (%)
255.47 1
0.1%
243.19 1
0.1%
237.38 1
0.1%
99.33 1
0.1%
68.17 1
0.1%
56.15 1
0.1%
56.12 1
0.1%
47.69 1
0.1%
44.12 1
0.1%
42.72 1
0.1%

MTI코드(6자리)
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size6.4 KiB
Distinct268
Distinct (%)41.5%
Missing160
Missing (%)19.9%
Memory size6.4 KiB
2023-12-13T05:20:55.545573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length38
Mean length21.097523
Min length3

Characters and Unicode

Total characters13629
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)21.5%

Sample

1st rowTV Camera
2nd rowother printed matter
3rd rowother part of radio communication apparatus
4th rowother articles of plastic
5th rowother integrated circuit semiconductor
ValueCountFrequency (%)
other 188
 
10.5%
of 114
 
6.4%
fabrics 47
 
2.6%
articles 46
 
2.6%
material 41
 
2.3%
chemical 38
 
2.1%
fine 38
 
2.1%
part 35
 
2.0%
steel 30
 
1.7%
polyester 22
 
1.2%
Other values (327) 1186
66.4%
2023-12-13T05:20:55.931514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1515
11.1%
r 1185
 
8.7%
1139
 
8.4%
t 1115
 
8.2%
o 958
 
7.0%
a 957
 
7.0%
i 906
 
6.6%
s 756
 
5.5%
c 708
 
5.2%
n 662
 
4.9%
Other values (36) 3728
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12369
90.8%
Space Separator 1139
 
8.4%
Other Punctuation 71
 
0.5%
Uppercase Letter 44
 
0.3%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1515
12.2%
r 1185
 
9.6%
t 1115
 
9.0%
o 958
 
7.7%
a 957
 
7.7%
i 906
 
7.3%
s 756
 
6.1%
c 708
 
5.7%
n 662
 
5.4%
l 618
 
5.0%
Other values (16) 2989
24.2%
Uppercase Letter
ValueCountFrequency (%)
W 10
22.7%
B 7
15.9%
G 5
11.4%
M 4
 
9.1%
R 3
 
6.8%
T 2
 
4.5%
V 2
 
4.5%
C 2
 
4.5%
D 2
 
4.5%
P 2
 
4.5%
Other values (4) 5
11.4%
Other Punctuation
ValueCountFrequency (%)
, 39
54.9%
& 26
36.6%
; 4
 
5.6%
/ 2
 
2.8%
Space Separator
ValueCountFrequency (%)
1139
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12413
91.1%
Common 1216
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1515
12.2%
r 1185
 
9.5%
t 1115
 
9.0%
o 958
 
7.7%
a 957
 
7.7%
i 906
 
7.3%
s 756
 
6.1%
c 708
 
5.7%
n 662
 
5.3%
l 618
 
5.0%
Other values (30) 3033
24.4%
Common
ValueCountFrequency (%)
1139
93.7%
, 39
 
3.2%
& 26
 
2.1%
- 6
 
0.5%
; 4
 
0.3%
/ 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1515
11.1%
r 1185
 
8.7%
1139
 
8.4%
t 1115
 
8.2%
o 958
 
7.0%
a 957
 
7.0%
i 906
 
6.6%
s 756
 
5.5%
c 708
 
5.2%
n 662
 
4.9%
Other values (36) 3728
27.4%
Distinct212
Distinct (%)29.2%
Missing81
Missing (%)10.0%
Memory size6.4 KiB
2023-12-13T05:20:56.175731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length37
Mean length20.848276
Min length3

Characters and Unicode

Total characters15115
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)10.9%

Sample

1st rowTV Camera and Brauntube
2nd rowaccessory/personal ornament
3rd rowotherprintedmatter
4th rowPart of Wireless Communication Apparatus
5th rowaccessory/personal ornament
ValueCountFrequency (%)
other 180
 
9.2%
of 118
 
6.0%
fabrics 64
 
3.3%
articles 60
 
3.1%
garments 50
 
2.6%
chemical 49
 
2.5%
fine 47
 
2.4%
material 42
 
2.1%
knitfabrics 36
 
1.8%
and 36
 
1.8%
Other values (268) 1275
65.2%
2023-12-13T05:20:56.598843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1639
10.8%
r 1297
 
8.6%
t 1253
 
8.3%
1232
 
8.2%
a 1105
 
7.3%
i 1084
 
7.2%
o 945
 
6.3%
c 868
 
5.7%
n 838
 
5.5%
s 828
 
5.5%
Other values (29) 4026
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13779
91.2%
Space Separator 1232
 
8.2%
Uppercase Letter 63
 
0.4%
Other Punctuation 27
 
0.2%
Dash Punctuation 14
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1639
11.9%
r 1297
 
9.4%
t 1253
 
9.1%
a 1105
 
8.0%
i 1084
 
7.9%
o 945
 
6.9%
c 868
 
6.3%
n 838
 
6.1%
s 828
 
6.0%
l 626
 
4.5%
Other values (16) 3296
23.9%
Uppercase Letter
ValueCountFrequency (%)
W 13
20.6%
C 13
20.6%
P 10
15.9%
A 9
14.3%
S 6
9.5%
T 3
 
4.8%
V 3
 
4.8%
B 3
 
4.8%
R 3
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 18
66.7%
/ 9
33.3%
Space Separator
ValueCountFrequency (%)
1232
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13842
91.6%
Common 1273
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1639
11.8%
r 1297
 
9.4%
t 1253
 
9.1%
a 1105
 
8.0%
i 1084
 
7.8%
o 945
 
6.8%
c 868
 
6.3%
n 838
 
6.1%
s 828
 
6.0%
l 626
 
4.5%
Other values (25) 3359
24.3%
Common
ValueCountFrequency (%)
1232
96.8%
, 18
 
1.4%
- 14
 
1.1%
/ 9
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1639
10.8%
r 1297
 
8.6%
t 1253
 
8.3%
1232
 
8.2%
a 1105
 
7.3%
i 1084
 
7.2%
o 945
 
6.3%
c 868
 
5.7%
n 838
 
5.5%
s 828
 
5.5%
Other values (29) 4026
26.6%
Distinct115
Distinct (%)14.6%
Missing16
Missing (%)2.0%
Memory size6.4 KiB
2023-12-13T05:20:56.875875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length36
Mean length21.291139
Min length3

Characters and Unicode

Total characters16820
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)3.0%

Sample

1st rowWireless Communication apparatus
2nd rowportable goods
3rd rowotherprintedmatter
4th rowWireless Communication apparatus
5th rowportable goods
ValueCountFrequency (%)
of 95
 
4.9%
articles 92
 
4.7%
other 83
 
4.3%
or 68
 
3.5%
fabrics 64
 
3.3%
garments/clothes 60
 
3.1%
chemical 49
 
2.5%
fine 47
 
2.4%
apparatus 47
 
2.4%
material 43
 
2.2%
Other values (166) 1295
66.6%
2023-12-13T05:20:57.334042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1786
 
10.6%
a 1384
 
8.2%
t 1375
 
8.2%
r 1304
 
7.8%
1153
 
6.9%
i 1131
 
6.7%
c 1034
 
6.1%
o 1030
 
6.1%
s 1009
 
6.0%
l 871
 
5.2%
Other values (22) 4743
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15415
91.6%
Space Separator 1153
 
6.9%
Other Punctuation 161
 
1.0%
Dash Punctuation 57
 
0.3%
Uppercase Letter 34
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1786
11.6%
a 1384
 
9.0%
t 1375
 
8.9%
r 1304
 
8.5%
i 1131
 
7.3%
c 1034
 
6.7%
o 1030
 
6.7%
s 1009
 
6.5%
l 871
 
5.7%
n 857
 
5.6%
Other values (15) 3634
23.6%
Other Punctuation
ValueCountFrequency (%)
, 97
60.2%
/ 61
37.9%
? 3
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
W 17
50.0%
C 17
50.0%
Space Separator
ValueCountFrequency (%)
1153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15449
91.8%
Common 1371
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1786
11.6%
a 1384
 
9.0%
t 1375
 
8.9%
r 1304
 
8.4%
i 1131
 
7.3%
c 1034
 
6.7%
o 1030
 
6.7%
s 1009
 
6.5%
l 871
 
5.6%
n 857
 
5.5%
Other values (17) 3668
23.7%
Common
ValueCountFrequency (%)
1153
84.1%
, 97
 
7.1%
/ 61
 
4.4%
- 57
 
4.2%
? 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1786
 
10.6%
a 1384
 
8.2%
t 1375
 
8.2%
r 1304
 
7.8%
1153
 
6.9%
i 1131
 
6.7%
c 1034
 
6.1%
o 1030
 
6.1%
s 1009
 
6.0%
l 871
 
5.2%
Other values (22) 4743
28.2%

MTI코드 영문명(2자리 기준)
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
industrial electronic articles
89 
precision chemistry
80 
made-up textiles
69 
fabrics
64 
miscellaneous goods/sundries
53 
Other values (34)
451 

Length

Max length47
Median length33
Mean length22.084367
Min length4

Unique

Unique4 ?
Unique (%)0.5%

Sample

1st rowindustrial electronic articles
2nd rowmiscellaneous goods/sundries
3rd rowprintedmatter
4th rowindustrial electronic articles
5th rowmiscellaneous goods/sundries

Common Values

ValueCountFrequency (%)
industrial electronic articles 89
 
11.0%
precision chemistry 80
 
9.9%
made-up textiles 69
 
8.6%
fabrics 64
 
7.9%
miscellaneous goods/sundries 53
 
6.6%
electronic components 39
 
4.8%
heavy electric equipment 37
 
4.6%
petrochemicals 35
 
4.3%
mechanical elements, tools and metallic pattern 32
 
4.0%
agriculturalproducts/farmproduce 31
 
3.8%
Other values (29) 277
34.4%

Length

2023-12-13T05:20:57.483136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
articles 201
 
10.1%
electronic 148
 
7.4%
precision 103
 
5.2%
of 100
 
5.0%
industrial 89
 
4.5%
chemistry 80
 
4.0%
made-up 69
 
3.5%
textiles 69
 
3.5%
fabrics 64
 
3.2%
or 61
 
3.1%
Other values (61) 1007
50.6%

MTI코드 영문명(1자리 기준)
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
electrical articles & electronic articles
185 
textile & apparel
141 
chemical industry manufactures
141 
machinery
112 
livingware
65 
Other values (6)
162 

Length

Max length41
Median length37
Mean length26.483871
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowelectrical articles & electronic articles
2nd rowlivingware
3rd rowmiscellaneousarticles/sundries
4th rowelectrical articles & electronic articles
5th rowlivingware

Common Values

ValueCountFrequency (%)
electrical articles & electronic articles 185
23.0%
textile & apparel 141
17.5%
chemical industry manufactures 141
17.5%
machinery 112
13.9%
livingware 65
 
8.1%
articles of iron or steel & metals 57
 
7.1%
agricultural & forest & marineproducts 48
 
6.0%
articles of plastic rubber or leather 34
 
4.2%
mineralproduct 13
 
1.6%
miscellaneousarticles/sundries 9
 
1.1%

Length

2023-12-13T05:20:57.621488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
479
17.0%
articles 461
16.4%
electrical 185
 
6.6%
electronic 185
 
6.6%
textile 141
 
5.0%
apparel 141
 
5.0%
chemical 141
 
5.0%
industry 141
 
5.0%
manufactures 141
 
5.0%
machinery 112
 
4.0%
Other values (15) 687
24.4%

Interactions

2023-12-13T05:20:51.788990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:49.743748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.361290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.919975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.920759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:49.892822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.508174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.081292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:52.029645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.032506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.646374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.220255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:52.161572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.235487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.783693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.677788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:20:57.719075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번HS코드(10자리)분기별 평균 수출금액(USD) * 2021년 1분기~2022년 4분기 기준분기평균성장률(%) * 2021년 1분기~2022년 4분기 기준MTI코드 영문명(2자리 기준)MTI코드 영문명(1자리 기준)
연번1.0000.2660.0000.3850.2970.180
HS코드(10자리)0.2661.0000.0000.2090.9600.951
분기별 평균 수출금액(USD)\n* 2021년 1분기~2022년 4분기 기준0.0000.0001.0000.5180.0000.000
분기평균성장률(%)\n* 2021년 1분기~2022년 4분기 기준0.3850.2090.5181.0000.0000.187
MTI코드 영문명(2자리 기준)0.2970.9600.0000.0001.0001.000
MTI코드 영문명(1자리 기준)0.1800.9510.0000.1871.0001.000
2023-12-13T05:20:57.878760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MTI코드 영문명(1자리 기준)MTI코드 영문명(2자리 기준)
MTI코드 영문명(1자리 기준)1.0000.982
MTI코드 영문명(2자리 기준)0.9821.000
2023-12-13T05:20:57.960191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번HS코드(10자리)분기별 평균 수출금액(USD) * 2021년 1분기~2022년 4분기 기준분기평균성장률(%) * 2021년 1분기~2022년 4분기 기준MTI코드 영문명(2자리 기준)MTI코드 영문명(1자리 기준)
연번1.000-0.0170.180-1.0000.1040.056
HS코드(10자리)-0.0171.0000.0160.0170.7490.626
분기별 평균 수출금액(USD)\n* 2021년 1분기~2022년 4분기 기준0.1800.0161.000-0.1800.0000.000
분기평균성장률(%)\n* 2021년 1분기~2022년 4분기 기준-1.0000.017-0.1801.0000.0000.078
MTI코드 영문명(2자리 기준)0.1040.7490.0000.0001.0000.982
MTI코드 영문명(1자리 기준)0.0560.6260.0000.0780.9821.000

Missing values

2023-12-13T05:20:52.359618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:20:52.610321image/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.
2023-12-13T05:20:52.816573image/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

연번국가명HS코드(10자리)HS코드 영문명분기별 평균 수출금액(USD) * 2021년 1분기~2022년 4분기 기준분기평균성장률(%) * 2021년 1분기~2022년 4분기 기준MTI코드(6자리)MTI코드 영문명(세부)MTI코드 영문명(4자리 기준)MTI코드 영문명(3자리 기준)MTI코드 영문명(2자리 기준)MTI코드 영문명(1자리 기준)
00China8525801090<NA>639624456.0255.47812410TV CameraTV Camera and BrauntubeWireless Communication apparatusindustrial electronic articleselectrical articles & electronic articles
11China4820900000Other27271.25243.19515190<NA>accessory/personal ornamentportable goodsmiscellaneous goods/sundrieslivingware
22China4911100000Trade advertising material, commercial catalogues and the like1176044.62237.38919000other printed matterotherprintedmatterotherprintedmatterprintedmattermiscellaneousarticles/sundries
33China8529903020Of television cameras, digital cameras or video camera recorders900731069.599.33812890other part of radio communication apparatusPart of Wireless Communication ApparatusWireless Communication apparatusindustrial electronic articleselectrical articles & electronic articles
44China4910001000Of paper or paperboard29199.7168.17515190<NA>accessory/personal ornamentportable goodsmiscellaneous goods/sundrieslivingware
55China3920690000Of other polyesters1885537.7556.15310900other articles of plasticother articles of plasticarticles of plasticarticles of plastic rubber or leatherarticles of plastic rubber or leather
66China8542394090<NA>14254213.556.12831190other integrated circuit semiconductorintegrated circuit semiconductorsemiconductorelectronic componentselectrical articles & electronic articles
77China8535909000Other1292676.047.69841390<NA><NA>rotary electric equipmentheavy electric equipmentelectrical articles & electronic articles
88China6202201000Overcoats, raincoats, car-coats, capes, cloaks and similar articles551004.544.12441201coats & jacketsknitfabrics garmentsgarments/clothesmade-up textilestextile & apparel
99China6110190000Other108715.2542.72441108sweatersknitted garmentsgarments/clothesmade-up textilestextile & apparel
연번국가명HS코드(10자리)HS코드 영문명분기별 평균 수출금액(USD) * 2021년 1분기~2022년 4분기 기준분기평균성장률(%) * 2021년 1분기~2022년 4분기 기준MTI코드(6자리)MTI코드 영문명(세부)MTI코드 영문명(4자리 기준)MTI코드 영문명(3자리 기준)MTI코드 영문명(2자리 기준)MTI코드 영문명(1자리 기준)
796796China5402110000Of aramids8039334.880.01422100nylon filament yarnnylon filament yarnman-made filament yarnyarntextile & apparel
797797China7410229000Other1093242.750.01622250copper foillow grade processing copperarticles of coppernonferrous metal productsarticles of iron or steel & metals
798798China2710199000Other2678090.880.01133600lubricating oillubricating oilarticles of petroleummineral fuelsmineralproduct
799799China6204629000Other1521972.750.01441208trousers,& skirts , WGknitfabrics garmentsgarments/clothesmade-up textilestextile & apparel
800800China3206411000Ultramarine106420.00.01221200pigmentpigmentdye or pigmentprecision chemistrychemical industry manufactures
801801China2007991000Jams, fruit jellies and marmalades65318.750.0116900other processed agricultural productsother processed agricultural productsprocessed agricultural productsagriculturalproducts/farmproduceagricultural & forest & marineproducts
802802China8538909000Other128726036.120.01836110liquiddeviceflatdisplayflatdisplayandsensorelectronic componentselectrical articles & electronic articles
803803China6202990000<NA>1650432.50.01441201coats & jacketsknitfabrics garmentsgarments/clothesmade-up textilestextile & apparel
804804China9027892090Other911551.50.01815510physical chemistry analyzeranalysis testermeter, controller, analysis instrumentindustrial electronic articleselectrical articles & electronic articles
805805China2923202000Other phosphoaminolipids855175.750.01228900other fine chemical materialother fine chemical materialfine chemical materialprecision chemistrychemical industry manufactures