Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells10000
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory742.2 KiB
Average record size in memory76.0 B

Variable types

Numeric3
Unsupported1
Categorical3
DateTime1

Dataset

Description경상북도 상주시 대금지급 테이블
Author경상북도 상주시
URLhttps://www.data.go.kr/data/15063670/fileData.do

Alerts

REG_DATE is highly overall correlated with SEQ and 2 other fieldsHigh correlation
MODIFY_DATE is highly overall correlated with SEQ and 2 other fieldsHigh correlation
SEQ is highly overall correlated with CONTRACT_MNG_NO and 2 other fieldsHigh correlation
CONTRACT_MNG_NO is highly overall correlated with SEQ and 2 other fieldsHigh correlation
PAYMENT_KIND is highly imbalanced (53.9%)Imbalance
MODIFY_DATE is highly imbalanced (59.5%)Imbalance
REG_DATE is highly imbalanced (59.5%)Imbalance
CONTRACT_SEQ has 10000 (100.0%) missing valuesMissing
SEQ has unique valuesUnique
CONTRACT_SEQ is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 23:10:26.869104
Analysis finished2023-12-11 23:10:28.704119
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SEQ
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33725.102
Minimum5
Maximum75289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:10:28.793346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile3118.9
Q116297
median33071.5
Q349874.25
95-th percentile71067.45
Maximum75289
Range75284
Interquartile range (IQR)33577.25

Descriptive statistics

Standard deviation20382.534
Coefficient of variation (CV)0.60437279
Kurtosis-0.94056154
Mean33725.102
Median Absolute Deviation (MAD)16789
Skewness0.19122796
Sum3.3725102 × 108
Variance4.154477 × 108
MonotonicityNot monotonic
2023-12-12T08:10:28.953382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4178 1
 
< 0.1%
19518 1
 
< 0.1%
38617 1
 
< 0.1%
72422 1
 
< 0.1%
27772 1
 
< 0.1%
23131 1
 
< 0.1%
74639 1
 
< 0.1%
4400 1
 
< 0.1%
1088 1
 
< 0.1%
20552 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
10 1
< 0.1%
28 1
< 0.1%
31 1
< 0.1%
40 1
< 0.1%
52 1
< 0.1%
66 1
< 0.1%
69 1
< 0.1%
70 1
< 0.1%
78 1
< 0.1%
ValueCountFrequency (%)
75289 1
< 0.1%
75283 1
< 0.1%
75277 1
< 0.1%
75273 1
< 0.1%
75269 1
< 0.1%
75266 1
< 0.1%
75253 1
< 0.1%
75245 1
< 0.1%
75226 1
< 0.1%
75205 1
< 0.1%

CONTRACT_SEQ
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

CONTRACT_MNG_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0129958 × 1011
Minimum2.004 × 1011
Maximum2.017 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:10:29.072617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.004 × 1011
5-th percentile2.00901 × 1011
Q12.011 × 1011
median2.013 × 1011
Q32.015 × 1011
95-th percentile2.017 × 1011
Maximum2.017 × 1011
Range1.3 × 109
Interquartile range (IQR)4 × 108

Descriptive statistics

Standard deviation2.4328872 × 108
Coefficient of variation (CV)0.0012085903
Kurtosis-0.78300088
Mean2.0129958 × 1011
Median Absolute Deviation (MAD)2 × 108
Skewness-0.19278069
Sum2.0129958 × 1015
Variance5.9189401 × 1016
MonotonicityNot monotonic
2023-12-12T08:10:29.179869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
201500000000 1502
15.0%
201300000000 1448
14.5%
201400000000 1343
13.4%
201200000000 1248
12.5%
201100000000 1123
11.2%
201700000000 844
8.4%
201000000000 790
7.9%
201600000000 757
7.6%
200901000000 548
 
5.5%
200801000000 396
 
4.0%
ValueCountFrequency (%)
200400000000 1
 
< 0.1%
200801000000 396
 
4.0%
200901000000 548
 
5.5%
201000000000 790
7.9%
201100000000 1123
11.2%
201200000000 1248
12.5%
201300000000 1448
14.5%
201400000000 1343
13.4%
201500000000 1502
15.0%
201600000000 757
7.6%
ValueCountFrequency (%)
201700000000 844
8.4%
201600000000 757
7.6%
201500000000 1502
15.0%
201400000000 1343
13.4%
201300000000 1448
14.5%
201200000000 1248
12.5%
201100000000 1123
11.2%
201000000000 790
7.9%
200901000000 548
 
5.5%
200801000000 396
 
4.0%

PAYMENT_KIND
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
준공금
8138 
기성금
1254 
선금
 
364
노무비지급금
 
244

Length

Max length6
Median length3
Mean length3.0368
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row준공금
2nd row준공금
3rd row준공금
4th row준공금
5th row기성금

Common Values

ValueCountFrequency (%)
준공금 8138
81.4%
기성금 1254
 
12.5%
선금 364
 
3.6%
노무비지급금 244
 
2.4%

Length

2023-12-12T08:10:29.341843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:10:29.474004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
준공금 8138
81.4%
기성금 1254
 
12.5%
선금 364
 
3.6%
노무비지급금 244
 
2.4%

PAYMENT_PRICE
Real number (ℝ)

Distinct7645
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19895799
Minimum10
Maximum3.6 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:10:29.597408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile410000
Q12114350
median6187750
Q313916670
95-th percentile55190357
Maximum3.6 × 109
Range3.6 × 109
Interquartile range (IQR)11802320

Descriptive statistics

Standard deviation92797720
Coefficient of variation (CV)4.6641867
Kurtosis496.848
Mean19895799
Median Absolute Deviation (MAD)4710855
Skewness18.758346
Sum1.9895799 × 1011
Variance8.6114169 × 1015
MonotonicityNot monotonic
2023-12-12T08:10:29.727369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18000000 20
 
0.2%
4500000 17
 
0.2%
9500000 16
 
0.2%
19000000 15
 
0.1%
2700000 15
 
0.1%
385000 14
 
0.1%
900000 14
 
0.1%
3000000 13
 
0.1%
440000 13
 
0.1%
1000000 13
 
0.1%
Other values (7635) 9850
98.5%
ValueCountFrequency (%)
10 2
< 0.1%
5650 1
< 0.1%
16140 1
< 0.1%
16160 1
< 0.1%
17310 1
< 0.1%
18200 1
< 0.1%
20000 1
< 0.1%
20800 1
< 0.1%
27030 1
< 0.1%
27900 1
< 0.1%
ValueCountFrequency (%)
3600000000 1
< 0.1%
3017033440 1
< 0.1%
2527000000 1
< 0.1%
2063000000 1
< 0.1%
1899600000 1
< 0.1%
1800000000 1
< 0.1%
1575000000 1
< 0.1%
1540725000 1
< 0.1%
1532900000 1
< 0.1%
1490307000 1
< 0.1%

PAYMENT_DATE
Date

Distinct2160
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2008-02-26 00:00:00
Maximum2020-01-23 00:00:00
2023-12-12T08:10:29.872372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:10:30.027238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MODIFY_DATE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-08-21 3:31
9191 
2020-08-21 3:32
 
809

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-08-21 3:31
2nd row2020-08-21 3:31
3rd row2020-08-21 3:31
4th row2020-08-21 3:31
5th row2020-08-21 3:31

Common Values

ValueCountFrequency (%)
2020-08-21 3:31 9191
91.9%
2020-08-21 3:32 809
 
8.1%

Length

2023-12-12T08:10:30.186088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:10:30.277128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-08-21 10000
50.0%
3:31 9191
46.0%
3:32 809
 
4.0%

REG_DATE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-08-21 3:31
9191 
2020-08-21 3:32
 
809

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-08-21 3:31
2nd row2020-08-21 3:31
3rd row2020-08-21 3:31
4th row2020-08-21 3:31
5th row2020-08-21 3:31

Common Values

ValueCountFrequency (%)
2020-08-21 3:31 9191
91.9%
2020-08-21 3:32 809
 
8.1%

Length

2023-12-12T08:10:30.399608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:10:30.516048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-08-21 10000
50.0%
3:31 9191
46.0%
3:32 809
 
4.0%

Interactions

2023-12-12T08:10:28.104143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:10:27.469510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:10:27.771224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:10:28.214415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:10:27.558066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:10:27.864778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:10:28.309426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:10:27.666613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:10:27.961777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:10:30.580378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQCONTRACT_MNG_NOPAYMENT_KINDPAYMENT_PRICEMODIFY_DATEREG_DATE
SEQ1.0000.9220.1670.0390.9380.938
CONTRACT_MNG_NO0.9221.0000.1550.0350.6300.630
PAYMENT_KIND0.1670.1551.0000.2010.0970.097
PAYMENT_PRICE0.0390.0350.2011.0000.0000.000
MODIFY_DATE0.9380.6300.0970.0001.0001.000
REG_DATE0.9380.6300.0970.0001.0001.000
2023-12-12T08:10:30.672530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
REG_DATEMODIFY_DATEPAYMENT_KIND
REG_DATE1.0000.9990.064
MODIFY_DATE0.9991.0000.064
PAYMENT_KIND0.0640.0641.000
2023-12-12T08:10:30.761590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQCONTRACT_MNG_NOPAYMENT_PRICEPAYMENT_KINDMODIFY_DATEREG_DATE
SEQ1.0000.9930.0060.1080.9770.977
CONTRACT_MNG_NO0.9931.0000.0280.1070.6790.679
PAYMENT_PRICE0.0060.0281.0000.1290.0000.000
PAYMENT_KIND0.1080.1070.1291.0000.0640.064
MODIFY_DATE0.9770.6790.0000.0641.0000.999
REG_DATE0.9770.6790.0000.0640.9991.000

Missing values

2023-12-12T08:10:28.466066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:10:28.640881image/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

SEQCONTRACT_SEQCONTRACT_MNG_NOPAYMENT_KINDPAYMENT_PRICEPAYMENT_DATEMODIFY_DATEREG_DATE
42994178<NA>200901000000준공금59466002009-05-292020-08-21 3:312020-08-21 3:31
44575190<NA>200901000000준공금130494902009-12-232020-08-21 3:312020-08-21 3:31
32612890<NA>200901000000준공금29120002009-03-182020-08-21 3:312020-08-21 3:31
3405742995<NA>201400000000준공금99148002014-03-272020-08-21 3:312020-08-21 3:31
2768114891<NA>201100000000기성금110506802011-08-312020-08-21 3:312020-08-21 3:31
5185053291<NA>201500000000준공금16500002015-07-062020-08-21 3:312020-08-21 3:31
4607059340<NA>201600000000선금352570002016-03-312020-08-21 3:312020-08-21 3:31
2824520763<NA>201200000000준공금51390002012-12-142020-08-21 3:312020-08-21 3:31
1607528984<NA>201300000000준공금20680002013-06-192020-08-21 3:312020-08-21 3:31
3982832239<NA>201300000000준공금169968002013-04-302020-08-21 3:312020-08-21 3:31
SEQCONTRACT_SEQCONTRACT_MNG_NOPAYMENT_KINDPAYMENT_PRICEPAYMENT_DATEMODIFY_DATEREG_DATE
1958024306<NA>201200000000준공금26289002012-03-212020-08-21 3:312020-08-21 3:31
3450041273<NA>201400000000준공금296395002014-07-082020-08-21 3:312020-08-21 3:31
6208372820<NA>201700000000준공금86518702017-12-202020-08-21 3:322020-08-21 3:32
1159011469<NA>201100000000준공금178600002011-03-312020-08-21 3:312020-08-21 3:31
2194815446<NA>201100000000준공금3514802011-04-142020-08-21 3:312020-08-21 3:31
1807129179<NA>201300000000준공금23700002013-08-062020-08-21 3:312020-08-21 3:31
4741457788<NA>201600000000준공금876000002016-12-272020-08-21 3:312020-08-21 3:31
2539620091<NA>201200000000노무비지급금39600002012-11-122020-08-21 3:312020-08-21 3:31
3293440795<NA>201400000000준공금120175002015-02-042020-08-21 3:312020-08-21 3:31
5734146101<NA>201500000000준공금66200002015-05-072020-08-21 3:312020-08-21 3:31