Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory173.0 B

Variable types

Numeric13
Categorical3
Text3

Dataset

Description경락가격정보를 조회하기 위한 서비스로서 농산물, 축산물, 수산물, 화훼류, 산지공판장, 종합유통센터별로 경락가격 정보 제공
Author농림수산식품교육문화정보원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220207000000001703

Alerts

AUCNG_DE is highly overall correlated with NACF_DISTB_CNTER_CDHigh correlation
NACF_DISTB_CNTER_CD is highly overall correlated with AUCNG_DE and 1 other fieldsHigh correlation
CATGORY_CD is highly overall correlated with PRDLST_CD and 2 other fieldsHigh correlation
PRDLST_CD is highly overall correlated with CATGORY_CD and 2 other fieldsHigh correlation
SPCIES_CD is highly overall correlated with CATGORY_CD and 2 other fieldsHigh correlation
GRAD_CD is highly overall correlated with GRADHigh correlation
DELNGBUNDLE_QY is highly overall correlated with STNDRD_CDHigh correlation
STNDRD_CD is highly overall correlated with DELNGBUNDLE_QYHigh correlation
MUMM_AMT is highly overall correlated with MXMM_AMT and 1 other fieldsHigh correlation
MXMM_AMT is highly overall correlated with MUMM_AMT and 1 other fieldsHigh correlation
AVRG_AMT is highly overall correlated with MUMM_AMT and 1 other fieldsHigh correlation
NACF_DISTB_CNTER is highly overall correlated with NACF_DISTB_CNTER_CDHigh correlation
CATGORY_NM is highly overall correlated with CATGORY_CD and 2 other fieldsHigh correlation
GRAD is highly overall correlated with GRAD_CDHigh correlation
GRAD is highly imbalanced (59.1%)Imbalance
DELNG_QY is highly skewed (γ1 = 63.98803805)Skewed

Reproduction

Analysis started2023-12-11 03:34:08.470373
Analysis finished2023-12-11 03:34:37.289872
Duration28.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

AUCNG_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct1154
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20138238
Minimum20130101
Maximum20170606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:37.379768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20130101
5-th percentile20130119
Q120130317
median20130607
Q320150107
95-th percentile20160816
Maximum20170606
Range40505
Interquartile range (IQR)19790

Descriptive statistics

Standard deviation12345.406
Coefficient of variation (CV)0.00061303308
Kurtosis-0.16647138
Mean20138238
Median Absolute Deviation (MAD)388
Skewness1.201372
Sum2.0138238 × 1011
Variance1.5240904 × 108
MonotonicityNot monotonic
2023-12-11T12:34:37.571236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20130205 53
 
0.5%
20130206 53
 
0.5%
20130307 52
 
0.5%
20130202 52
 
0.5%
20130208 51
 
0.5%
20130607 50
 
0.5%
20130319 50
 
0.5%
20130313 50
 
0.5%
20130404 49
 
0.5%
20130608 49
 
0.5%
Other values (1144) 9491
94.9%
ValueCountFrequency (%)
20130101 19
0.2%
20130102 16
0.2%
20130103 28
0.3%
20130104 22
0.2%
20130105 22
0.2%
20130106 15
0.1%
20130107 32
0.3%
20130108 34
0.3%
20130109 35
0.4%
20130110 25
0.2%
ValueCountFrequency (%)
20170606 9
0.1%
20170605 4
 
< 0.1%
20170604 5
0.1%
20170603 11
0.1%
20170602 9
0.1%
20170601 8
0.1%
20170531 1
 
< 0.1%
20170524 8
0.1%
20170507 3
 
< 0.1%
20170506 7
0.1%

NACF_DISTB_CNTER
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
양재 유통센터
5966 
안성 물류센터
1935 
청주 유통센터
1142 
대전 유통센터
 
595
목포 유통센터
 
240
Other values (2)
 
122

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양재 유통센터
2nd row양재 유통센터
3rd row양재 유통센터
4th row청주 유통센터
5th row양재 유통센터

Common Values

ValueCountFrequency (%)
양재 유통센터 5966
59.7%
안성 물류센터 1935
 
19.4%
청주 유통센터 1142
 
11.4%
대전 유통센터 595
 
5.9%
목포 유통센터 240
 
2.4%
전주 유통센터 85
 
0.9%
달성 유통센터 37
 
0.4%

Length

2023-12-11T12:34:37.754009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:34:37.900588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유통센터 8065
40.3%
양재 5966
29.8%
안성 1935
 
9.7%
물류센터 1935
 
9.7%
청주 1142
 
5.7%
대전 595
 
3.0%
목포 240
 
1.2%
전주 85
 
0.4%
달성 37
 
0.2%

NACF_DISTB_CNTER_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99000467
Minimum99000101
Maximum99001501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:38.039347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99000101
5-th percentile99000101
Q199000101
median99000101
Q399000801
95-th percentile99001501
Maximum99001501
Range1400
Interquartile range (IQR)700

Descriptive statistics

Standard deviation556.33138
Coefficient of variation (CV)5.6194824 × 10-6
Kurtosis-0.49109433
Mean99000467
Median Absolute Deviation (MAD)0
Skewness1.1372404
Sum9.9000467 × 1011
Variance309504.61
MonotonicityNot monotonic
2023-12-11T12:34:38.183376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
99000101 5966
59.7%
99001501 1935
 
19.4%
99000301 1142
 
11.4%
99000801 595
 
5.9%
99001001 163
 
1.6%
99000401 85
 
0.9%
99001401 77
 
0.8%
99001101 37
 
0.4%
ValueCountFrequency (%)
99000101 5966
59.7%
99000301 1142
 
11.4%
99000401 85
 
0.9%
99000801 595
 
5.9%
99001001 163
 
1.6%
99001101 37
 
0.4%
99001401 77
 
0.8%
99001501 1935
 
19.4%
ValueCountFrequency (%)
99001501 1935
 
19.4%
99001401 77
 
0.8%
99001101 37
 
0.4%
99001001 163
 
1.6%
99000801 595
 
5.9%
99000401 85
 
0.9%
99000301 1142
 
11.4%
99000101 5966
59.7%

CATGORY_NM
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
엽경채류
2784 
양채류
1413 
버섯류
1291 
조미채소류
1212 
과실류
863 
Other values (14)
2437 

Length

Max length6
Median length5
Mean length3.6925
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row엽경채류
2nd row양채류
3rd row산채류
4th row버섯류
5th row서류

Common Values

ValueCountFrequency (%)
엽경채류 2784
27.8%
양채류 1413
14.1%
버섯류 1291
12.9%
조미채소류 1212
12.1%
과실류 863
 
8.6%
과일과채류 808
 
8.1%
산채류 570
 
5.7%
근채류 355
 
3.5%
서류 198
 
2.0%
농림가공 184
 
1.8%
Other values (9) 322
 
3.2%

Length

2023-12-11T12:34:38.389484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
엽경채류 2784
27.8%
양채류 1413
14.1%
버섯류 1291
12.9%
조미채소류 1212
12.1%
과실류 863
 
8.6%
과일과채류 808
 
8.1%
산채류 570
 
5.7%
근채류 355
 
3.5%
서류 198
 
2.0%
농림가공 184
 
1.8%
Other values (10) 323
 
3.2%

CATGORY_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.7009
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:38.547214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median11
Q313
95-th percentile17
Maximum91
Range90
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.206257
Coefficient of variation (CV)0.88231992
Kurtosis41.009243
Mean12.7009
Median Absolute Deviation (MAD)2
Skewness6.2653802
Sum127009
Variance125.5802
MonotonicityNot monotonic
2023-12-11T12:34:38.733754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10 2784
27.8%
13 1413
14.1%
17 1291
12.9%
12 1212
12.1%
6 863
 
8.6%
8 808
 
8.1%
14 570
 
5.7%
11 355
 
3.5%
5 198
 
2.0%
91 184
 
1.8%
Other values (9) 322
 
3.2%
ValueCountFrequency (%)
1 3
 
< 0.1%
3 18
 
0.2%
4 6
 
0.1%
5 198
 
2.0%
6 863
 
8.6%
7 99
 
1.0%
8 808
 
8.1%
9 133
 
1.3%
10 2784
27.8%
11 355
 
3.5%
ValueCountFrequency (%)
91 184
 
1.8%
76 1
 
< 0.1%
19 5
 
0.1%
17 1291
12.9%
16 51
 
0.5%
15 6
 
0.1%
14 570
5.7%
13 1413
14.1%
12 1212
12.1%
11 355
 
3.5%

PRDLST_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct153
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1284.5848
Minimum103
Maximum9110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:38.931799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile601
Q11005
median1103
Q31325
95-th percentile1711
Maximum9110
Range9007
Interquartile range (IQR)320

Descriptive statistics

Standard deviation1122.0767
Coefficient of variation (CV)0.87349364
Kurtosis40.683188
Mean1284.5848
Median Absolute Deviation (MAD)201
Skewness6.2295287
Sum12845848
Variance1259056
MonotonicityNot monotonic
2023-12-11T12:34:39.119604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1005 469
 
4.7%
1711 465
 
4.7%
601 352
 
3.5%
1303 300
 
3.0%
1205 296
 
3.0%
1202 292
 
2.9%
1799 249
 
2.5%
1016 246
 
2.5%
1017 240
 
2.4%
1399 232
 
2.3%
Other values (143) 6859
68.6%
ValueCountFrequency (%)
103 3
 
< 0.1%
301 2
 
< 0.1%
304 11
 
0.1%
305 5
 
0.1%
401 1
 
< 0.1%
403 2
 
< 0.1%
407 1
 
< 0.1%
499 2
 
< 0.1%
501 71
0.7%
502 117
1.2%
ValueCountFrequency (%)
9110 184
 
1.8%
7609 1
 
< 0.1%
1907 5
 
0.1%
1799 249
2.5%
1719 4
 
< 0.1%
1718 2
 
< 0.1%
1711 465
4.7%
1709 2
 
< 0.1%
1707 17
 
0.2%
1706 21
 
0.2%
Distinct273
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:34:39.402202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length4.1908
Min length1

Characters and Unicode

Total characters41908
Distinct characters254
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)0.4%

Sample

1st row기타
2nd row피망(단고추)(일반)
3rd row기타
4th row기타
5th row기타
ValueCountFrequency (%)
기타 3360
33.4%
새송이(일반 465
 
4.6%
콩나물(일반 246
 
2.4%
숙주나물(일반 240
 
2.4%
적상추 172
 
1.7%
청상추 169
 
1.7%
깐마늘 165
 
1.6%
대파(일반 124
 
1.2%
금싸라기 122
 
1.2%
후지 121
 
1.2%
Other values (265) 4861
48.4%
2023-12-11T12:34:39.851098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3708
 
8.8%
( 3562
 
8.5%
) 3562
 
8.5%
3546
 
8.5%
3527
 
8.4%
3434
 
8.2%
899
 
2.1%
774
 
1.8%
701
 
1.7%
646
 
1.5%
Other values (244) 17549
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34663
82.7%
Open Punctuation 3562
 
8.5%
Close Punctuation 3562
 
8.5%
Connector Punctuation 76
 
0.2%
Space Separator 45
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3708
 
10.7%
3546
 
10.2%
3527
 
10.2%
3434
 
9.9%
899
 
2.6%
774
 
2.2%
701
 
2.0%
646
 
1.9%
642
 
1.9%
513
 
1.5%
Other values (240) 16273
46.9%
Open Punctuation
ValueCountFrequency (%)
( 3562
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3562
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 76
100.0%
Space Separator
ValueCountFrequency (%)
45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34663
82.7%
Common 7245
 
17.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3708
 
10.7%
3546
 
10.2%
3527
 
10.2%
3434
 
9.9%
899
 
2.6%
774
 
2.2%
701
 
2.0%
646
 
1.9%
642
 
1.9%
513
 
1.5%
Other values (240) 16273
46.9%
Common
ValueCountFrequency (%)
( 3562
49.2%
) 3562
49.2%
_ 76
 
1.0%
45
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34663
82.7%
ASCII 7245
 
17.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3708
 
10.7%
3546
 
10.2%
3527
 
10.2%
3434
 
9.9%
899
 
2.6%
774
 
2.2%
701
 
2.0%
646
 
1.9%
642
 
1.9%
513
 
1.5%
Other values (240) 16273
46.9%
ASCII
ValueCountFrequency (%)
( 3562
49.2%
) 3562
49.2%
_ 76
 
1.0%
45
 
0.6%

SPCIES_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct372
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128488.02
Minimum10302
Maximum911099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:40.058102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10302
5-th percentile60199
Q1100501
median110301
Q3132501
95-th percentile171101
Maximum911099
Range900797
Interquartile range (IQR)32000

Descriptive statistics

Standard deviation112216.32
Coefficient of variation (CV)0.87336016
Kurtosis40.685143
Mean128488.02
Median Absolute Deviation (MAD)20101
Skewness6.2297691
Sum1.2848802 × 109
Variance1.2592502 × 1010
MonotonicityNot monotonic
2023-12-11T12:34:40.250016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171101 465
 
4.7%
101601 239
 
2.4%
101701 215
 
2.1%
139900 209
 
2.1%
179900 201
 
2.0%
911099 184
 
1.8%
60199 181
 
1.8%
100502 172
 
1.7%
100501 169
 
1.7%
120906 165
 
1.7%
Other values (362) 7800
78.0%
ValueCountFrequency (%)
10302 1
 
< 0.1%
10306 1
 
< 0.1%
10399 1
 
< 0.1%
30112 1
 
< 0.1%
30118 1
 
< 0.1%
30402 11
0.1%
30501 5
0.1%
40199 1
 
< 0.1%
40302 2
 
< 0.1%
40701 1
 
< 0.1%
ValueCountFrequency (%)
911099 184
 
1.8%
760904 1
 
< 0.1%
190700 5
 
0.1%
179999 48
 
0.5%
179900 201
2.0%
171901 4
 
< 0.1%
171801 2
 
< 0.1%
171101 465
4.7%
170901 2
 
< 0.1%
170799 17
 
0.2%

GRAD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
없음
7639 
특(1등)
1262 
 
357
상(2등)
 
246
보통(3등)
 
187
Other values (4)
 
309

Length

Max length6
Median length2
Mean length2.4794
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row특(1등)
2nd row상(2등)
3rd row없음
4th row보통(3등)
5th row없음

Common Values

ValueCountFrequency (%)
없음 7639
76.4%
특(1등) 1262
 
12.6%
357
 
3.6%
상(2등) 246
 
2.5%
보통(3등) 187
 
1.9%
121
 
1.2%
4등 83
 
0.8%
5등 78
 
0.8%
보통 27
 
0.3%

Length

2023-12-11T12:34:40.429633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:34:40.595231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 7639
76.4%
특(1등 1262
 
12.6%
357
 
3.6%
상(2등 246
 
2.5%
보통(3등 187
 
1.9%
121
 
1.2%
4등 83
 
0.8%
5등 78
 
0.8%
보통 27
 
0.3%

GRAD_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.3717
Minimum10
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:40.719046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median10
Q310
95-th percentile12
Maximum15
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83126904
Coefficient of variation (CV)0.08014781
Kurtosis10.278975
Mean10.3717
Median Absolute Deviation (MAD)0
Skewness2.9912147
Sum103717
Variance0.69100821
MonotonicityNot monotonic
2023-12-11T12:34:40.852347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10 7639
76.4%
11 1619
 
16.2%
12 367
 
3.7%
13 214
 
2.1%
14 83
 
0.8%
15 78
 
0.8%
ValueCountFrequency (%)
10 7639
76.4%
11 1619
 
16.2%
12 367
 
3.7%
13 214
 
2.1%
14 83
 
0.8%
15 78
 
0.8%
ValueCountFrequency (%)
15 78
 
0.8%
14 83
 
0.8%
13 214
 
2.1%
12 367
 
3.7%
11 1619
 
16.2%
10 7639
76.4%

DELNGBUNDLE_QY
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.753
Minimum0.1
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:40.994586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1
Q12
median4
Q350
95-th percentile331
Maximum900
Range899.9
Interquartile range (IQR)48

Descriptive statistics

Standard deviation127.87228
Coefficient of variation (CV)2.0057452
Kurtosis7.4107079
Mean63.753
Median Absolute Deviation (MAD)3
Skewness2.6162893
Sum637530
Variance16351.319
MonotonicityNot monotonic
2023-12-11T12:34:41.468949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 2144
21.4%
2.0 1851
18.5%
4.0 1101
11.0%
10.0 610
 
6.1%
100.0 553
 
5.5%
150.0 407
 
4.1%
5.0 399
 
4.0%
300.0 367
 
3.7%
200.0 341
 
3.4%
15.0 294
 
2.9%
Other values (59) 1933
19.3%
ValueCountFrequency (%)
0.1 9
 
0.1%
0.2 8
 
0.1%
0.4 4
 
< 0.1%
0.5 2
 
< 0.1%
0.7 32
 
0.3%
1.0 2144
21.4%
1.2 34
 
0.3%
1.3 11
 
0.1%
1.4 1
 
< 0.1%
1.5 86
 
0.9%
ValueCountFrequency (%)
900.0 7
 
0.1%
800.0 14
 
0.1%
750.0 7
 
0.1%
700.0 25
 
0.2%
650.0 1
 
< 0.1%
600.0 10
 
0.1%
500.0 284
2.8%
450.0 2
 
< 0.1%
400.0 121
1.2%
380.0 1
 
< 0.1%

STNDRD
Text

Distinct76
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:34:41.710565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.329
Min length1

Characters and Unicode

Total characters63290
Distinct characters46
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st rowkg 상자
2nd rowkg 상자
3rd rowg PP대
4th rowkg 상자
5th rowkg 상자
ValueCountFrequency (%)
kg 7315
38.8%
상자 4236
22.5%
비닐봉지 2676
 
14.2%
g 2602
 
13.8%
봉지 376
 
2.0%
201
 
1.1%
그물망 195
 
1.0%
p-box 135
 
0.7%
60내 105
 
0.6%
50내 85
 
0.5%
Other values (36) 914
 
4.9%
2023-12-11T12:34:42.102387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21844
34.5%
g 9917
15.7%
k 7315
 
11.6%
4236
 
6.7%
4236
 
6.7%
3052
 
4.8%
3052
 
4.8%
2676
 
4.2%
2676
 
4.2%
497
 
0.8%
Other values (36) 3789
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22199
35.1%
Space Separator 21844
34.5%
Lowercase Letter 17232
27.2%
Decimal Number 1204
 
1.9%
Uppercase Letter 676
 
1.1%
Dash Punctuation 135
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4236
19.1%
4236
19.1%
3052
13.7%
3052
13.7%
2676
12.1%
2676
12.1%
497
 
2.2%
203
 
0.9%
201
 
0.9%
195
 
0.9%
Other values (17) 1175
 
5.3%
Decimal Number
ValueCountFrequency (%)
0 417
34.6%
5 215
17.9%
1 150
 
12.5%
4 114
 
9.5%
6 110
 
9.1%
7 64
 
5.3%
3 59
 
4.9%
2 58
 
4.8%
8 12
 
1.0%
9 5
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
P 251
37.1%
B 135
20.0%
X 135
20.0%
O 135
20.0%
E 20
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
g 9917
57.5%
k 7315
42.5%
Space Separator
ValueCountFrequency (%)
21844
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23183
36.6%
Hangul 22199
35.1%
Latin 17908
28.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4236
19.1%
4236
19.1%
3052
13.7%
3052
13.7%
2676
12.1%
2676
12.1%
497
 
2.2%
203
 
0.9%
201
 
0.9%
195
 
0.9%
Other values (17) 1175
 
5.3%
Common
ValueCountFrequency (%)
21844
94.2%
0 417
 
1.8%
5 215
 
0.9%
1 150
 
0.6%
- 135
 
0.6%
4 114
 
0.5%
6 110
 
0.5%
7 64
 
0.3%
3 59
 
0.3%
2 58
 
0.3%
Other values (2) 17
 
0.1%
Latin
ValueCountFrequency (%)
g 9917
55.4%
k 7315
40.8%
P 251
 
1.4%
B 135
 
0.8%
X 135
 
0.8%
O 135
 
0.8%
E 20
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41091
64.9%
Hangul 22199
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21844
53.2%
g 9917
24.1%
k 7315
 
17.8%
0 417
 
1.0%
P 251
 
0.6%
5 215
 
0.5%
1 150
 
0.4%
B 135
 
0.3%
- 135
 
0.3%
X 135
 
0.3%
Other values (9) 577
 
1.4%
Hangul
ValueCountFrequency (%)
4236
19.1%
4236
19.1%
3052
13.7%
3052
13.7%
2676
12.1%
2676
12.1%
497
 
2.2%
203
 
0.9%
201
 
0.9%
195
 
0.9%
Other values (17) 1175
 
5.3%
Distinct144
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:34:42.468701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length2.8512
Min length1

Characters and Unicode

Total characters28512
Distinct characters185
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row쑥갓
2nd row피망(단고추)
3rd row기타
4th row기타
5th row고구마
ValueCountFrequency (%)
기타 693
 
6.9%
상추 469
 
4.7%
새송이 465
 
4.7%
사과 352
 
3.5%
치커리 300
 
3.0%
풋고추 296
 
3.0%
대파 292
 
2.9%
콩나물 246
 
2.5%
숙주나물 240
 
2.4%
마늘 218
 
2.2%
Other values (134) 6429
64.3%
2023-12-11T12:34:42.975687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1659
 
5.8%
1152
 
4.0%
1139
 
4.0%
1021
 
3.6%
910
 
3.2%
908
 
3.2%
782
 
2.7%
711
 
2.5%
674
 
2.4%
654
 
2.3%
Other values (175) 18902
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27808
97.5%
Open Punctuation 352
 
1.2%
Close Punctuation 352
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1659
 
6.0%
1152
 
4.1%
1139
 
4.1%
1021
 
3.7%
910
 
3.3%
908
 
3.3%
782
 
2.8%
711
 
2.6%
674
 
2.4%
654
 
2.4%
Other values (173) 18198
65.4%
Open Punctuation
ValueCountFrequency (%)
( 352
100.0%
Close Punctuation
ValueCountFrequency (%)
) 352
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27808
97.5%
Common 704
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1659
 
6.0%
1152
 
4.1%
1139
 
4.1%
1021
 
3.7%
910
 
3.3%
908
 
3.3%
782
 
2.8%
711
 
2.6%
674
 
2.4%
654
 
2.4%
Other values (173) 18198
65.4%
Common
ValueCountFrequency (%)
( 352
50.0%
) 352
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27808
97.5%
ASCII 704
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1659
 
6.0%
1152
 
4.1%
1139
 
4.1%
1021
 
3.7%
910
 
3.3%
908
 
3.3%
782
 
2.8%
711
 
2.6%
674
 
2.4%
654
 
2.4%
Other values (173) 18198
65.4%
ASCII
ValueCountFrequency (%)
( 352
50.0%
) 352
50.0%

STNDRD_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117582.5
Minimum100000
Maximum121981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:43.202485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile110000
Q1110900
median120100
Q3120100
95-th percentile120800
Maximum121981
Range21981
Interquartile range (IQR)9200

Descriptive statistics

Standard deviation4518.3182
Coefficient of variation (CV)0.038426792
Kurtosis0.1226334
Mean117582.5
Median Absolute Deviation (MAD)100
Skewness-1.2251741
Sum1.175825 × 109
Variance20415199
MonotonicityNot monotonic
2023-12-11T12:34:43.417450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120100 3620
36.2%
110800 1393
 
13.9%
120800 1254
 
12.5%
120000 1159
 
11.6%
110000 658
 
6.6%
110900 271
 
2.7%
120500 172
 
1.7%
120200 104
 
1.0%
120900 102
 
1.0%
120126 86
 
0.9%
Other values (66) 1181
 
11.8%
ValueCountFrequency (%)
100000 4
 
< 0.1%
101100 79
 
0.8%
110000 658
6.6%
110100 80
 
0.8%
110200 30
 
0.3%
110300 6
 
0.1%
110400 45
 
0.4%
110500 21
 
0.2%
110800 1393
13.9%
110802 19
 
0.2%
ValueCountFrequency (%)
121981 36
0.4%
121900 8
 
0.1%
121400 3
 
< 0.1%
121206 1
 
< 0.1%
121202 1
 
< 0.1%
121200 26
0.3%
121126 1
 
< 0.1%
121101 57
0.6%
121100 47
0.5%
121000 1
 
< 0.1%

DELNG_QY
Real number (ℝ)

SKEWED 

Distinct1057
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean432.875
Minimum1
Maximum481206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:43.620813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median40
Q3166.25
95-th percentile1172.8
Maximum481206
Range481205
Interquartile range (IQR)156.25

Descriptive statistics

Standard deviation5808.7394
Coefficient of variation (CV)13.418976
Kurtosis4910.7849
Mean432.875
Median Absolute Deviation (MAD)37
Skewness63.988038
Sum4328750
Variance33741454
MonotonicityNot monotonic
2023-12-11T12:34:43.838099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 573
 
5.7%
10 557
 
5.6%
1 485
 
4.9%
20 436
 
4.4%
3 322
 
3.2%
5 317
 
3.2%
100 218
 
2.2%
30 210
 
2.1%
4 210
 
2.1%
40 210
 
2.1%
Other values (1047) 6462
64.6%
ValueCountFrequency (%)
1 485
4.9%
2 573
5.7%
3 322
3.2%
4 210
 
2.1%
5 317
3.2%
6 141
 
1.4%
7 108
 
1.1%
8 127
 
1.3%
9 74
 
0.7%
10 557
5.6%
ValueCountFrequency (%)
481206 1
< 0.1%
206275 1
< 0.1%
154095 1
< 0.1%
81277 1
< 0.1%
77030 1
< 0.1%
68733 1
< 0.1%
59129 1
< 0.1%
47117 1
< 0.1%
45324 1
< 0.1%
31032 1
< 0.1%

MUMM_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct1288
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12606.618
Minimum1
Maximum293000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:44.022413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile950
Q12213.75
median6000
Q313000
95-th percentile50000
Maximum293000
Range292999
Interquartile range (IQR)10786.25

Descriptive statistics

Standard deviation20100.052
Coefficient of variation (CV)1.5944047
Kurtosis26.738186
Mean12606.618
Median Absolute Deviation (MAD)4196.5
Skewness4.2234507
Sum1.2606618 × 108
Variance4.0401208 × 108
MonotonicityNot monotonic
2023-12-11T12:34:44.276768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 375
 
3.8%
7000 375
 
3.8%
4000 319
 
3.2%
9000 296
 
3.0%
4500 223
 
2.2%
10000 171
 
1.7%
8500 168
 
1.7%
6000 163
 
1.6%
1000 154
 
1.5%
12000 153
 
1.5%
Other values (1278) 7603
76.0%
ValueCountFrequency (%)
1 8
0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
26 1
 
< 0.1%
35 1
 
< 0.1%
40 1
 
< 0.1%
52 1
 
< 0.1%
58 1
 
< 0.1%
ValueCountFrequency (%)
293000 1
 
< 0.1%
250000 1
 
< 0.1%
247480 1
 
< 0.1%
237900 1
 
< 0.1%
210700 1
 
< 0.1%
190000 3
< 0.1%
189200 1
 
< 0.1%
183482 1
 
< 0.1%
175000 2
< 0.1%
170000 1
 
< 0.1%

MXMM_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct1154
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14624.702
Minimum1
Maximum1276232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:44.485978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1000
Q12700
median7000
Q315000
95-th percentile55000
Maximum1276232
Range1276231
Interquartile range (IQR)12300

Descriptive statistics

Standard deviation29467.246
Coefficient of variation (CV)2.0148955
Kurtosis495.46368
Mean14624.702
Median Absolute Deviation (MAD)5000
Skewness15.721599
Sum1.4624702 × 108
Variance8.6831858 × 108
MonotonicityNot monotonic
2023-12-11T12:34:44.708955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 384
 
3.8%
7000 328
 
3.3%
9000 312
 
3.1%
4000 274
 
2.7%
4500 218
 
2.2%
10000 202
 
2.0%
12000 192
 
1.9%
8500 173
 
1.7%
11000 168
 
1.7%
6000 167
 
1.7%
Other values (1144) 7582
75.8%
ValueCountFrequency (%)
1 8
 
0.1%
330 1
 
< 0.1%
340 1
 
< 0.1%
350 1
 
< 0.1%
400 1
 
< 0.1%
420 1
 
< 0.1%
450 26
0.3%
483 1
 
< 0.1%
500 15
0.1%
540 1
 
< 0.1%
ValueCountFrequency (%)
1276232 1
< 0.1%
922853 1
< 0.1%
745832 1
< 0.1%
470900 1
< 0.1%
450900 1
< 0.1%
445150 1
< 0.1%
434100 1
< 0.1%
364500 1
< 0.1%
360856 1
< 0.1%
360400 1
< 0.1%

AVRG_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct2091
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13539.067
Minimum1
Maximum433411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:44.947764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1000
Q12575
median7000
Q314000
95-th percentile52000
Maximum433411
Range433410
Interquartile range (IQR)11425

Descriptive statistics

Standard deviation21836.173
Coefficient of variation (CV)1.6128271
Kurtosis43.780178
Mean13539.067
Median Absolute Deviation (MAD)4928
Skewness5.036287
Sum1.3539067 × 108
Variance4.7681847 × 108
MonotonicityNot monotonic
2023-12-11T12:34:45.168914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 312
 
3.1%
9000 277
 
2.8%
4000 273
 
2.7%
7000 263
 
2.6%
4500 192
 
1.9%
8500 181
 
1.8%
6000 145
 
1.5%
1000 140
 
1.4%
10000 139
 
1.4%
12000 130
 
1.3%
Other values (2081) 7948
79.5%
ValueCountFrequency (%)
1 8
 
0.1%
330 1
 
< 0.1%
340 1
 
< 0.1%
350 1
 
< 0.1%
400 1
 
< 0.1%
420 1
 
< 0.1%
445 1
 
< 0.1%
450 26
0.3%
455 1
 
< 0.1%
483 1
 
< 0.1%
ValueCountFrequency (%)
433411 1
< 0.1%
309825 1
< 0.1%
299150 1
< 0.1%
293000 1
< 0.1%
272169 1
< 0.1%
250000 1
< 0.1%
247480 1
< 0.1%
242233 1
< 0.1%
240624 1
< 0.1%
223677 1
< 0.1%

AUC_CO
Real number (ℝ)

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.953
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:34:45.386788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum42
Range41
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.5726354
Coefficient of variation (CV)1.3172736
Kurtosis55.113098
Mean1.953
Median Absolute Deviation (MAD)0
Skewness6.3127798
Sum19530
Variance6.6184528
MonotonicityNot monotonic
2023-12-11T12:34:45.582420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 6505
65.0%
2 1861
 
18.6%
3 670
 
6.7%
4 315
 
3.1%
5 164
 
1.6%
6 127
 
1.3%
7 72
 
0.7%
8 59
 
0.6%
9 36
 
0.4%
10 27
 
0.3%
Other values (27) 164
 
1.6%
ValueCountFrequency (%)
1 6505
65.0%
2 1861
 
18.6%
3 670
 
6.7%
4 315
 
3.1%
5 164
 
1.6%
6 127
 
1.3%
7 72
 
0.7%
8 59
 
0.6%
9 36
 
0.4%
10 27
 
0.3%
ValueCountFrequency (%)
42 1
< 0.1%
41 1
< 0.1%
35 2
< 0.1%
34 1
< 0.1%
33 1
< 0.1%
32 1
< 0.1%
31 1
< 0.1%
30 2
< 0.1%
29 2
< 0.1%
28 1
< 0.1%

Interactions

2023-12-11T12:34:35.135681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:14.413479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:16.680273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:18.253939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:19.775011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:21.451944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:23.057594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:24.598257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:26.550873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:28.065029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:29.640778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:31.302733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:33.306923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:35.257438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:14.554704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:16.839649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:18.364324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:19.910932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:21.631434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:23.197235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:24.735115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:26.707997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:28.185923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:29.808727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:31.473247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:33.516034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:35.367983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:14.683885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:16.997487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:18.497869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:20.045264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:21.743697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:23.328873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:25.201859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:26.869811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:28.288411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:29.968092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:31.645446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:33.651431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:35.479998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:14.832480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:17.152645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:18.625648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:20.173725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:21.830334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:23.456040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:25.294912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.016794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:28.380072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:30.101850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:31.780030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:33.756504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:35.630574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:14.981356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:17.265508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:18.726036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:20.279215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:21.948865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:23.559172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:25.383343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.155295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:28.489396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:30.214359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:31.931828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:34.189274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:35.767876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:15.160717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:17.380620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:18.838606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:20.377536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:22.056098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:23.663299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:25.465245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.268511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:28.635105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:30.327367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:32.105552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:34.312441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:35.910482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:15.290984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:17.504505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:18.945379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:20.499141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:22.170776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:23.788825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:25.588690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.367226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:28.738568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:30.437640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:32.295852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:34.419385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:36.008519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:15.419342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:17.627816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:19.071315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:20.651672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:22.278033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:23.931415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:25.702993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.465031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:28.844566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:30.567593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:32.458198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:34.515982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:36.104643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:15.547718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:17.731238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:19.199540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:20.824771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:22.381624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:24.054386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:25.830667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.558109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:28.985654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:30.683150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:32.630364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:34.609608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:36.222107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:15.693696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:17.822064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:19.327334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:20.966165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:22.534348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:24.151903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:25.973794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.671389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:29.108829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:30.820628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:32.774903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:34.695209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:36.347795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:15.891533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:17.921613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:19.445722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:21.103826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:22.692699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:24.251127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:26.128963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.781728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:29.249949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:30.953367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:32.925322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:34.808933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:36.467150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:16.061167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:18.040421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:19.560231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:21.226671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:22.836209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:24.353233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:26.283196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.887334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:29.377977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:31.082860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:33.078141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:34.907698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:36.577593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:16.550056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:18.152813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:19.673649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:21.331236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:22.954546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:24.485384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:26.412263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:27.976738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:29.512710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:31.199156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:33.203223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:35.021877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:34:45.761808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AUCNG_DENACF_DISTB_CNTERNACF_DISTB_CNTER_CDCATGORY_NMCATGORY_CDPRDLST_CDSPCIES_CDGRADGRAD_CDDELNGBUNDLE_QYSTNDRDSTNDRD_CDDELNG_QYMUMM_AMTMXMM_AMTAVRG_AMTAUC_CO
AUCNG_DE1.0000.6880.7840.3990.1870.1720.1710.4820.3770.1650.4810.1160.0000.1330.0660.1260.067
NACF_DISTB_CNTER0.6881.0000.9990.6080.3760.3710.3710.4980.4260.2620.6280.2090.0000.1950.1110.1590.070
NACF_DISTB_CNTER_CD0.7840.9991.0000.7050.4150.4090.4090.5330.6370.4110.7540.4890.0000.2300.0810.1650.000
CATGORY_NM0.3990.6080.7051.0001.0000.9940.9940.5140.4990.4000.8410.4670.0000.4040.1970.3650.133
CATGORY_CD0.1870.3760.4151.0001.0001.0001.0000.2860.2060.3820.6360.2180.0000.3240.1330.2170.082
PRDLST_CD0.1720.3710.4090.9941.0001.0001.0000.2620.1880.3840.6150.2220.0240.2930.1330.1990.079
SPCIES_CD0.1710.3710.4090.9941.0001.0001.0000.2630.1880.3840.6150.2220.0240.2930.1330.1990.079
GRAD0.4820.4980.5330.5140.2860.2620.2631.0001.0000.2150.7170.2330.0000.1790.0000.2420.102
GRAD_CD0.3770.4260.6370.4990.2060.1880.1881.0001.0000.2290.6590.2320.0000.1650.0000.1670.098
DELNGBUNDLE_QY0.1650.2620.4110.4000.3820.3840.3840.2150.2291.0000.7780.7460.0000.1750.0000.1020.052
STNDRD0.4810.6280.7540.8410.6360.6150.6150.7170.6590.7781.0001.0000.0000.5670.1870.4970.000
STNDRD_CD0.1160.2090.4890.4670.2180.2220.2220.2330.2320.7461.0001.0000.0000.1600.0320.1130.150
DELNG_QY0.0000.0000.0000.0000.0000.0240.0240.0000.0000.0000.0000.0001.0000.0000.0000.0000.692
MUMM_AMT0.1330.1950.2300.4040.3240.2930.2930.1790.1650.1750.5670.1600.0001.0000.6610.8780.000
MXMM_AMT0.0660.1110.0810.1970.1330.1330.1330.0000.0000.0000.1870.0320.0000.6611.0000.8980.129
AVRG_AMT0.1260.1590.1650.3650.2170.1990.1990.2420.1670.1020.4970.1130.0000.8780.8981.0000.031
AUC_CO0.0670.0700.0000.1330.0820.0790.0790.1020.0980.0520.0000.1500.6920.0000.1290.0311.000
2023-12-11T12:34:46.039629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CATGORY_NMNACF_DISTB_CNTERGRAD
CATGORY_NM1.0000.3230.229
NACF_DISTB_CNTER0.3231.0000.291
GRAD0.2290.2911.000
2023-12-11T12:34:46.235233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AUCNG_DENACF_DISTB_CNTER_CDCATGORY_CDPRDLST_CDSPCIES_CDGRAD_CDDELNGBUNDLE_QYSTNDRD_CDDELNG_QYMUMM_AMTMXMM_AMTAVRG_AMTAUC_CONACF_DISTB_CNTERCATGORY_NMGRAD
AUCNG_DE1.0000.792-0.091-0.090-0.0890.131-0.0160.2090.0500.1220.0600.081-0.1620.4990.1970.263
NACF_DISTB_CNTER_CD0.7921.000-0.080-0.081-0.0800.146-0.0430.2390.0360.1850.1130.138-0.2000.9710.3150.288
CATGORY_CD-0.091-0.0801.0000.9850.985-0.039-0.070-0.067-0.196-0.216-0.195-0.203-0.0500.1750.9990.128
PRDLST_CD-0.090-0.0810.9851.0001.000-0.039-0.066-0.078-0.209-0.225-0.203-0.211-0.0610.2480.9820.155
SPCIES_CD-0.089-0.0800.9851.0001.000-0.039-0.065-0.078-0.208-0.225-0.204-0.211-0.0610.2480.9820.155
GRAD_CD0.1310.146-0.039-0.039-0.0391.000-0.0780.3520.0660.1390.1560.154-0.0100.2700.2581.000
DELNGBUNDLE_QY-0.016-0.043-0.070-0.066-0.065-0.0781.000-0.5670.135-0.153-0.150-0.154-0.0030.1360.1610.099
STNDRD_CD0.2090.239-0.067-0.078-0.0780.352-0.5671.000-0.0770.4200.4180.425-0.0190.1430.2720.151
DELNG_QY0.0500.036-0.196-0.209-0.2080.0660.135-0.0771.000-0.349-0.293-0.3200.4240.0000.0000.000
MUMM_AMT0.1220.185-0.216-0.225-0.2250.139-0.1530.420-0.3491.0000.9250.959-0.1080.1000.1630.082
MXMM_AMT0.0600.113-0.195-0.203-0.2040.156-0.1500.418-0.2930.9251.0000.9920.0660.0390.0880.000
AVRG_AMT0.0810.138-0.203-0.211-0.2110.154-0.1540.425-0.3200.9590.9921.0000.0050.0840.1510.079
AUC_CO-0.162-0.200-0.050-0.061-0.061-0.010-0.003-0.0190.424-0.1080.0660.0051.0000.0350.0500.046
NACF_DISTB_CNTER0.4990.9710.1750.2480.2480.2700.1360.1430.0000.1000.0390.0840.0351.0000.3230.291
CATGORY_NM0.1970.3150.9990.9820.9820.2580.1610.2720.0000.1630.0880.1510.0500.3231.0000.229
GRAD0.2630.2880.1280.1550.1551.0000.0990.1510.0000.0820.0000.0790.0460.2910.2291.000

Missing values

2023-12-11T12:34:36.780650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:34:37.147070image/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

AUCNG_DENACF_DISTB_CNTERNACF_DISTB_CNTER_CDCATGORY_NMCATGORY_CDPRDLST_CDSPCIES_NMSPCIES_CDGRADGRAD_CDDELNGBUNDLE_QYSTNDRDPRDLST_NMSTNDRD_CDDELNG_QYMUMM_AMTMXMM_AMTAVRG_AMTAUC_CO
1989820130222양재 유통센터99000101엽경채류101012기타101299특(1등)114.0kg 상자쑥갓12010021400014000140001
7110720130125양재 유통센터99000101양채류131302피망(단고추)(일반)130201상(2등)1210.0kg 상자피망(단고추)120100136000085000725002
6097320130706양재 유통센터99000101산채류141499기타149900없음10100.0g PP대기타11040034032403240321
5396520141103청주 유통센터99000301버섯류171799기타179900보통(3등)132.0kg 상자기타1201001001000100010001
3220920130418양재 유통센터99000101서류5502기타50299없음102.0kg 상자고구마1201006205940850072202
4741820130614양재 유통센터99000101엽경채류101012쑥갓(일반)101201없음102.0kg 상자쑥갓120100303500350035001
5327120141017대전 유통센터99000801버섯류171711새송이(일반)1711015등152.0kg 비닐봉지새송이120800102000200020001
7301020161209안성 물류센터99001501과실류6601기타601991115.0kg 상자 55내사과120155364800048000480001
2101520130219청주 유통센터99000301엽경채류101017숙주나물(일반)101701없음104.0kg숙주나물120000344000400040001
3104620130417양재 유통센터99000101엽경채류101002얼갈이배추100201없음10400.0g 비닐봉지얼갈이배추1108002371000100010002
AUCNG_DENACF_DISTB_CNTERNACF_DISTB_CNTER_CDCATGORY_NMCATGORY_CDPRDLST_CDSPCIES_NMSPCIES_CDGRADGRAD_CDDELNGBUNDLE_QYSTNDRDPRDLST_NMSTNDRD_CDDELNG_QYMUMM_AMTMXMM_AMTAVRG_AMTAUC_CO
2674920130318양재 유통센터99000101과채류9901가시오이90103특(1등)111.0kg 비닐봉지오이120800802500250025001
5991220130715양재 유통센터99000101수실류7701생율70120없음105.0kg 비닐봉지12080039500095000950002
6548020130528양재 유통센터99000101엽경채류101001기타100199없음1010.0kg배추12000021500015000150001
5890120160824안성 물류센터99001501과일과채류8802금싸라기802011110.0kg 상자 50내참외120125161800018000180001
1658420130206양재 유통센터99000101엽경채류101008섬초100802없음104.0kg 상자시금치1201001601600016000160001
5401120140516목포 유통센터99001001농림가공919110기타911099없음10300.0g기타식품110000124500450045001
7347420160717안성 물류센터99001501산채류141408기타140899없음103.0kg 상자12010016678667866781
5419420141106청주 유통센터99000301엽경채류101017숙주나물(일반)101701없음104.0kg숙주나물12000074000400040001
3725320161008안성 물류센터99001501조미채소류121209깐마늘120906없음1020.0kg 비닐봉지마늘120800101450001450001450001
3288820130426양재 유통센터99000101과일과채류8806방울토마토80601없음105.0kg 대방울토마토1200827411750024750203854