Overview

Dataset statistics

Number of variables19
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory169.6 B

Variable types

Categorical18
Text1

Dataset

Description샘플 데이터
Author한국생산기술연구원
URLhttps://bigdata-region.kr/#/dataset/1385fc1e-9782-4778-b2f7-454adbd06035

Alerts

CAS_NO has constant value ""Constant
CAS_MTTR_NM has constant value ""Constant
IMXPRT_PRDLST_CODE has constant value ""Constant
IMXPRT_PRDLST_NM has constant value ""Constant
IMXPRT_YEAR has constant value ""Constant
IMXPRT_OCT_AMOUNT has constant value ""Constant
IMXPRT_JULY_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_JAN_AMOUNT is highly overall correlated with IMXPRT_FEB_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_MAR_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_APR_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_FEB_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_MAY_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_DEC_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_SEP_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_JUN_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_NOV_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_AUG_AMOUNT is highly overall correlated with IMXPRT_JAN_AMOUNT and 9 other fieldsHigh correlation
IMXPRT_JAN_AMOUNT is highly imbalanced (67.8%)Imbalance
IMXPRT_FEB_AMOUNT is highly imbalanced (63.2%)Imbalance
IMXPRT_MAR_AMOUNT is highly imbalanced (63.2%)Imbalance
IMXPRT_APR_AMOUNT is highly imbalanced (63.2%)Imbalance
IMXPRT_MAY_AMOUNT is highly imbalanced (63.2%)Imbalance
IMXPRT_JUN_AMOUNT is highly imbalanced (63.2%)Imbalance
IMXPRT_JULY_AMOUNT is highly imbalanced (67.8%)Imbalance
IMXPRT_AUG_AMOUNT is highly imbalanced (63.2%)Imbalance
IMXPRT_SEP_AMOUNT is highly imbalanced (63.2%)Imbalance
IMXPRT_NOV_AMOUNT is highly imbalanced (63.2%)Imbalance
IMXPRT_DEC_AMOUNT is highly imbalanced (63.2%)Imbalance

Reproduction

Analysis started2023-12-10 14:02:38.707156
Analysis finished2023-12-10 14:02:40.603137
Duration1.9 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CAS_NO
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
13463-67-7
29 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13463-67-7
2nd row13463-67-7
3rd row13463-67-7
4th row13463-67-7
5th row13463-67-7

Common Values

ValueCountFrequency (%)
13463-67-7 29
100.0%

Length

2023-12-10T23:02:40.702614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:40.828085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13463-67-7 29
100.0%

CAS_MTTR_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
Titanium dioxide
29 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTitanium dioxide
2nd rowTitanium dioxide
3rd rowTitanium dioxide
4th rowTitanium dioxide
5th rowTitanium dioxide

Common Values

ValueCountFrequency (%)
Titanium dioxide 29
100.0%

Length

2023-12-10T23:02:40.939671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:41.062157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
titanium 29
50.0%
dioxide 29
50.0%

IMXPRT_PRDLST_CODE
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
8108.90-9000
29 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8108.90-9000
2nd row8108.90-9000
3rd row8108.90-9000
4th row8108.90-9000
5th row8108.90-9000

Common Values

ValueCountFrequency (%)
8108.90-9000 29
100.0%

Length

2023-12-10T23:02:41.242269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:41.396478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8108.90-9000 29
100.0%

IMXPRT_PRDLST_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)
29 

Length

Max length89
Median length89
Mean length89
Min length89

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)
2nd rowOther article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)
3rd rowOther article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)
4th rowOther article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)
5th rowOther article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)

Common Values

ValueCountFrequency (%)
Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000) 29
100.0%

Length

2023-12-10T23:02:41.539278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:41.671365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
other 29
11.1%
article 29
11.1%
of 29
11.1%
titanium 29
11.1%
except 29
11.1%
8108.20 29
11.1%
8108.30-0000 29
11.1%
8108.90-1000 29
11.1%
8108.90-2000 29
11.1%

IMXPRT_TRGET_NATION_NM
Text

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-10T23:02:41.878952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length7
Mean length5.7586207
Min length2

Characters and Unicode

Total characters167
Distinct characters55
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.4%

Sample

1st row우즈베키스탄
2nd row우즈베키스탄
3rd row슬로베니아
4th row슬로베니아
5th row알랜드아일랜드
ValueCountFrequency (%)
기타국가 4
 
10.3%
우즈베키스탄 2
 
5.1%
피에르도 2
 
5.1%
앤티가바부다 2
 
5.1%
아르헨티나 2
 
5.1%
안틸레스 2
 
5.1%
네덜란드령 2
 
5.1%
안길라 2
 
5.1%
북미 2
 
5.1%
미퀘론도 2
 
5.1%
Other values (9) 17
43.6%
2023-12-10T23:02:42.357155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
 
6.0%
드 8
 
4.8%
아 7
 
4.2%
스 6
 
3.6%
국 6
 
3.6%
미 6
 
3.6%
가 6
 
3.6%
바 5
 
3.0%
르 4
 
2.4%
기 4
 
2.4%
Other values (45) 105
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157
94.0%
Space Separator 10
 
6.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
드 8
 
5.1%
아 7
 
4.5%
스 6
 
3.8%
국 6
 
3.8%
미 6
 
3.8%
가 6
 
3.8%
바 5
 
3.2%
르 4
 
2.5%
기 4
 
2.5%
타 4
 
2.5%
Other values (44) 101
64.3%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 157
94.0%
Common 10
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
드 8
 
5.1%
아 7
 
4.5%
스 6
 
3.8%
국 6
 
3.8%
미 6
 
3.8%
가 6
 
3.8%
바 5
 
3.2%
르 4
 
2.5%
기 4
 
2.5%
타 4
 
2.5%
Other values (44) 101
64.3%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 157
94.0%
ASCII 10
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10
100.0%
Hangul
ValueCountFrequency (%)
드 8
 
5.1%
아 7
 
4.5%
스 6
 
3.8%
국 6
 
3.8%
미 6
 
3.8%
가 6
 
3.8%
바 5
 
3.2%
르 4
 
2.5%
기 4
 
2.5%
타 4
 
2.5%
Other values (44) 101
64.3%

IMPORT_XPORT_SE_NM
Categorical

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
수출
15 
수입
14 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수출
2nd row수입
3rd row수출
4th row수입
5th row수출

Common Values

ValueCountFrequency (%)
수출 15
51.7%
수입 14
48.3%

Length

2023-12-10T23:02:42.541993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:42.671659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수출 15
51.7%
수입 14
48.3%

IMXPRT_YEAR
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
2018
29 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 29
100.0%

Length

2023-12-10T23:02:42.826267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:42.953359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 29
100.0%

IMXPRT_JAN_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
26 
7802
 
1
31720
 
1
5883221
 
1

Length

Max length7
Median length1
Mean length1.4482759
Min length1

Unique

Unique3 ?
Unique (%)10.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26
89.7%
7802 1
 
3.4%
31720 1
 
3.4%
5883221 1
 
3.4%

Length

2023-12-10T23:02:43.090444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:43.225541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26
89.7%
7802 1
 
3.4%
31720 1
 
3.4%
5883221 1
 
3.4%

IMXPRT_FEB_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
25 
756
 
1
261
 
1
35927
 
1
6928618
 
1

Length

Max length7
Median length1
Mean length1.4827586
Min length1

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
86.2%
756 1
 
3.4%
261 1
 
3.4%
35927 1
 
3.4%
6928618 1
 
3.4%

Length

2023-12-10T23:02:43.394998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:43.559680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
86.2%
756 1
 
3.4%
261 1
 
3.4%
35927 1
 
3.4%
6928618 1
 
3.4%

IMXPRT_MAR_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
25 
2411
 
1
1327
 
1
75486
 
1
6015819
 
1

Length

Max length7
Median length1
Mean length1.5517241
Min length1

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
86.2%
2411 1
 
3.4%
1327 1
 
3.4%
75486 1
 
3.4%
6015819 1
 
3.4%

Length

2023-12-10T23:02:43.740579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:43.893384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
86.2%
2411 1
 
3.4%
1327 1
 
3.4%
75486 1
 
3.4%
6015819 1
 
3.4%

IMXPRT_APR_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
25 
4428
 
1
8506
 
1
213582
 
1
7534101
 
1

Length

Max length7
Median length1
Mean length1.5862069
Min length1

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
86.2%
4428 1
 
3.4%
8506 1
 
3.4%
213582 1
 
3.4%
7534101 1
 
3.4%

Length

2023-12-10T23:02:44.067845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:44.227790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
86.2%
4428 1
 
3.4%
8506 1
 
3.4%
213582 1
 
3.4%
7534101 1
 
3.4%

IMXPRT_MAY_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
25 
74
 
1
4874
 
1
28987
 
1
7293338
 
1

Length

Max length7
Median length1
Mean length1.4827586
Min length1

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
86.2%
74 1
 
3.4%
4874 1
 
3.4%
28987 1
 
3.4%
7293338 1
 
3.4%

Length

2023-12-10T23:02:44.398020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:44.574000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
86.2%
74 1
 
3.4%
4874 1
 
3.4%
28987 1
 
3.4%
7293338 1
 
3.4%

IMXPRT_JUN_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
25 
13153
 
1
250
 
1
43527
 
1
6622264
 
1

Length

Max length7
Median length1
Mean length1.5517241
Min length1

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
86.2%
13153 1
 
3.4%
250 1
 
3.4%
43527 1
 
3.4%
6622264 1
 
3.4%

Length

2023-12-10T23:02:44.869673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:45.045380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
86.2%
13153 1
 
3.4%
250 1
 
3.4%
43527 1
 
3.4%
6622264 1
 
3.4%

IMXPRT_JULY_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
26 
357
 
1
30764
 
1
6789243
 
1

Length

Max length7
Median length1
Mean length1.4137931
Min length1

Unique

Unique3 ?
Unique (%)10.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26
89.7%
357 1
 
3.4%
30764 1
 
3.4%
6789243 1
 
3.4%

Length

2023-12-10T23:02:45.231178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:45.401061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26
89.7%
357 1
 
3.4%
30764 1
 
3.4%
6789243 1
 
3.4%

IMXPRT_AUG_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
25 
312
 
1
2168
 
1
73515
 
1
7146544
 
1

Length

Max length7
Median length1
Mean length1.5172414
Min length1

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
86.2%
312 1
 
3.4%
2168 1
 
3.4%
73515 1
 
3.4%
7146544 1
 
3.4%

Length

2023-12-10T23:02:45.552449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:45.726465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
86.2%
312 1
 
3.4%
2168 1
 
3.4%
73515 1
 
3.4%
7146544 1
 
3.4%

IMXPRT_SEP_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
25 
13400
 
1
292
 
1
121579
 
1
4543388
 
1

Length

Max length7
Median length1
Mean length1.5862069
Min length1

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
86.2%
13400 1
 
3.4%
292 1
 
3.4%
121579 1
 
3.4%
4543388 1
 
3.4%

Length

2023-12-10T23:02:45.931003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:46.135393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
86.2%
13400 1
 
3.4%
292 1
 
3.4%
121579 1
 
3.4%
4543388 1
 
3.4%

IMXPRT_OCT_AMOUNT
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
29 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29
100.0%

Length

2023-12-10T23:02:46.297575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:46.431959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
100.0%

IMXPRT_NOV_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
25 
13385
 
1
1347
 
1
91536
 
1
6450021
 
1

Length

Max length7
Median length1
Mean length1.5862069
Min length1

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
86.2%
13385 1
 
3.4%
1347 1
 
3.4%
91536 1
 
3.4%
6450021 1
 
3.4%

Length

2023-12-10T23:02:46.581185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:46.766325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
86.2%
13385 1
 
3.4%
1347 1
 
3.4%
91536 1
 
3.4%
6450021 1
 
3.4%

IMXPRT_DEC_AMOUNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
25 
5280
 
1
697
 
1
33656
 
1
6623881
 
1

Length

Max length7
Median length1
Mean length1.5172414
Min length1

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
86.2%
5280 1
 
3.4%
697 1
 
3.4%
33656 1
 
3.4%
6623881 1
 
3.4%

Length

2023-12-10T23:02:46.957291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:47.162274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
86.2%
5280 1
 
3.4%
697 1
 
3.4%
33656 1
 
3.4%
6623881 1
 
3.4%

Correlations

2023-12-10T23:02:47.295581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IMXPRT_TRGET_NATION_NMIMPORT_XPORT_SE_NMIMXPRT_JAN_AMOUNTIMXPRT_FEB_AMOUNTIMXPRT_MAR_AMOUNTIMXPRT_APR_AMOUNTIMXPRT_MAY_AMOUNTIMXPRT_JUN_AMOUNTIMXPRT_JULY_AMOUNTIMXPRT_AUG_AMOUNTIMXPRT_SEP_AMOUNTIMXPRT_NOV_AMOUNTIMXPRT_DEC_AMOUNT
IMXPRT_TRGET_NATION_NM1.0000.0000.2510.4020.4020.4020.4020.4020.2510.4020.4020.4020.402
IMPORT_XPORT_SE_NM0.0001.0000.1040.0170.0170.0170.0170.0170.0000.0170.0170.0170.017
IMXPRT_JAN_AMOUNT0.2510.1041.0001.0001.0001.0001.0001.0000.9771.0001.0001.0001.000
IMXPRT_FEB_AMOUNT0.4020.0171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMXPRT_MAR_AMOUNT0.4020.0171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMXPRT_APR_AMOUNT0.4020.0171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMXPRT_MAY_AMOUNT0.4020.0171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMXPRT_JUN_AMOUNT0.4020.0171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMXPRT_JULY_AMOUNT0.2510.0000.9771.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMXPRT_AUG_AMOUNT0.4020.0171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMXPRT_SEP_AMOUNT0.4020.0171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMXPRT_NOV_AMOUNT0.4020.0171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IMXPRT_DEC_AMOUNT0.4020.0171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T23:02:47.538219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IMXPRT_JULY_AMOUNTIMXPRT_JAN_AMOUNTIMXPRT_MAR_AMOUNTIMXPRT_APR_AMOUNTIMXPRT_FEB_AMOUNTIMPORT_XPORT_SE_NMIMXPRT_MAY_AMOUNTIMXPRT_DEC_AMOUNTIMXPRT_SEP_AMOUNTIMXPRT_JUN_AMOUNTIMXPRT_NOV_AMOUNTIMXPRT_AUG_AMOUNT
IMXPRT_JULY_AMOUNT1.0000.7920.9800.9800.9800.0000.9800.9800.9800.9800.9800.980
IMXPRT_JAN_AMOUNT0.7921.0000.9800.9800.9800.0240.9800.9800.9800.9800.9800.980
IMXPRT_MAR_AMOUNT0.9800.9801.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
IMXPRT_APR_AMOUNT0.9800.9801.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
IMXPRT_FEB_AMOUNT0.9800.9801.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
IMPORT_XPORT_SE_NM0.0000.0240.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
IMXPRT_MAY_AMOUNT0.9800.9801.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
IMXPRT_DEC_AMOUNT0.9800.9801.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
IMXPRT_SEP_AMOUNT0.9800.9801.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
IMXPRT_JUN_AMOUNT0.9800.9801.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
IMXPRT_NOV_AMOUNT0.9800.9801.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
IMXPRT_AUG_AMOUNT0.9800.9801.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
2023-12-10T23:02:47.800240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IMPORT_XPORT_SE_NMIMXPRT_JAN_AMOUNTIMXPRT_FEB_AMOUNTIMXPRT_MAR_AMOUNTIMXPRT_APR_AMOUNTIMXPRT_MAY_AMOUNTIMXPRT_JUN_AMOUNTIMXPRT_JULY_AMOUNTIMXPRT_AUG_AMOUNTIMXPRT_SEP_AMOUNTIMXPRT_NOV_AMOUNTIMXPRT_DEC_AMOUNT
IMPORT_XPORT_SE_NM1.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
IMXPRT_JAN_AMOUNT0.0241.0000.9800.9800.9800.9800.9800.7920.9800.9800.9800.980
IMXPRT_FEB_AMOUNT0.0000.9801.0001.0001.0001.0001.0000.9801.0001.0001.0001.000
IMXPRT_MAR_AMOUNT0.0000.9801.0001.0001.0001.0001.0000.9801.0001.0001.0001.000
IMXPRT_APR_AMOUNT0.0000.9801.0001.0001.0001.0001.0000.9801.0001.0001.0001.000
IMXPRT_MAY_AMOUNT0.0000.9801.0001.0001.0001.0001.0000.9801.0001.0001.0001.000
IMXPRT_JUN_AMOUNT0.0000.9801.0001.0001.0001.0001.0000.9801.0001.0001.0001.000
IMXPRT_JULY_AMOUNT0.0000.7920.9800.9800.9800.9800.9801.0000.9800.9800.9800.980
IMXPRT_AUG_AMOUNT0.0000.9801.0001.0001.0001.0001.0000.9801.0001.0001.0001.000
IMXPRT_SEP_AMOUNT0.0000.9801.0001.0001.0001.0001.0000.9801.0001.0001.0001.000
IMXPRT_NOV_AMOUNT0.0000.9801.0001.0001.0001.0001.0000.9801.0001.0001.0001.000
IMXPRT_DEC_AMOUNT0.0000.9801.0001.0001.0001.0001.0000.9801.0001.0001.0001.000

Missing values

2023-12-10T23:02:40.061385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:02:40.469453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CAS_NOCAS_MTTR_NMIMXPRT_PRDLST_CODEIMXPRT_PRDLST_NMIMXPRT_TRGET_NATION_NMIMPORT_XPORT_SE_NMIMXPRT_YEARIMXPRT_JAN_AMOUNTIMXPRT_FEB_AMOUNTIMXPRT_MAR_AMOUNTIMXPRT_APR_AMOUNTIMXPRT_MAY_AMOUNTIMXPRT_JUN_AMOUNTIMXPRT_JULY_AMOUNTIMXPRT_AUG_AMOUNTIMXPRT_SEP_AMOUNTIMXPRT_OCT_AMOUNTIMXPRT_NOV_AMOUNTIMXPRT_DEC_AMOUNT
013463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)우즈베키스탄수출2018000000000000
113463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)우즈베키스탄수입2018000000000000
213463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)슬로베니아수출2018000000000000
313463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)슬로베니아수입2018000000000000
413463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)알랜드아일랜드수출2018000000000000
513463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)알랜드아일랜드수입2018000000000000
613463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)스발바드수출2018000000000000
713463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)스발바드수입2018000000000000
813463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)잔메이엔수출2018000000000000
913463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)잔메이엔수입2018000000000000
CAS_NOCAS_MTTR_NMIMXPRT_PRDLST_CODEIMXPRT_PRDLST_NMIMXPRT_TRGET_NATION_NMIMPORT_XPORT_SE_NMIMXPRT_YEARIMXPRT_JAN_AMOUNTIMXPRT_FEB_AMOUNTIMXPRT_MAR_AMOUNTIMXPRT_APR_AMOUNTIMXPRT_MAY_AMOUNTIMXPRT_JUN_AMOUNTIMXPRT_JULY_AMOUNTIMXPRT_AUG_AMOUNTIMXPRT_SEP_AMOUNTIMXPRT_OCT_AMOUNTIMXPRT_NOV_AMOUNTIMXPRT_DEC_AMOUNT
1913463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)북미 기타국가수입2018000000000000
2013463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)안길라수출2018000000000000
2113463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)안길라수입2018000000000000
2213463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)네덜란드령 안틸레스수출2018000000000000
2313463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)네덜란드령 안틸레스수입2018000000000000
2413463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)아르헨티나수출2018000000000000
2513463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)아르헨티나수입2018000000000000
2613463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)앤티가바부다수출2018000000000000
2713463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)앤티가바부다수입2018000000000000
2813463-67-7Titanium dioxide8108.90-9000Other article of titanium (except 8108.20-****; 8108.30-0000; 8108.90-1000; 8108.90-2000)아루바수출2018000000000000