Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells54015
Missing cells (%)33.8%
Duplicate rows1156
Duplicate rows (%)11.6%
Total size in memory1.4 MiB
Average record size in memory145.0 B

Variable types

Numeric7
Categorical5
DateTime3
Text1

Dataset

Description전라남도 여수시 공영자전거 정회원정보(정회원 시작일, 종료일, 결제 일자, 결제방법, 결제구분, 등록일 등)등을 제공하고 있습니다.
Author전라남도 여수시
URLhttps://www.data.go.kr/data/15049716/fileData.do

Alerts

Dataset has 1156 (11.6%) duplicate rowsDuplicates
PAYMENT_KIND is highly overall correlated with PAY_DATE and 4 other fieldsHigh correlation
PAYMENT_NUM is highly overall correlated with START_DATE and 10 other fieldsHigh correlation
TMP_PAYMENT_NUM is highly overall correlated with START_DATE and 9 other fieldsHigh correlation
TMP_PAYMENT_KIND is highly overall correlated with PAY_DATE and 4 other fieldsHigh correlation
TERM_YN is highly overall correlated with START_DATE and 4 other fieldsHigh correlation
START_DATE is highly overall correlated with END_DATE and 7 other fieldsHigh correlation
END_DATE is highly overall correlated with START_DATE and 7 other fieldsHigh correlation
PAY_DATE is highly overall correlated with START_DATE and 7 other fieldsHigh correlation
PRICE is highly overall correlated with END_DATE and 3 other fieldsHigh correlation
TMP_END_DATE is highly overall correlated with START_DATE and 5 other fieldsHigh correlation
TMP_PAY_DATE is highly overall correlated with START_DATE and 6 other fieldsHigh correlation
TMP_PRICE is highly overall correlated with START_DATE and 6 other fieldsHigh correlation
PAYMENT_KIND is highly imbalanced (73.2%)Imbalance
PAYMENT_NUM is highly imbalanced (95.2%)Imbalance
TMP_PAYMENT_KIND is highly imbalanced (92.6%)Imbalance
TMP_PAYMENT_NUM is highly imbalanced (99.6%)Imbalance
PAY_DATE has 4333 (43.3%) missing valuesMissing
PRICE has 8425 (84.2%) missing valuesMissing
TMP_END_DATE has 4670 (46.7%) missing valuesMissing
TMP_PAY_DATE has 9058 (90.6%) missing valuesMissing
TMP_PRICE has 9701 (97.0%) missing valuesMissing
TMP_START_DT has 8914 (89.1%) missing valuesMissing
TMP_END_DT has 8914 (89.1%) missing valuesMissing
END_DATE is highly skewed (γ1 = 82.96777264)Skewed

Reproduction

Analysis started2023-12-12 08:38:17.821199
Analysis finished2023-12-12 08:38:27.403346
Duration9.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

START_DATE
Real number (ℝ)

HIGH CORRELATION 

Distinct1455
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20149099
Minimum20110627
Maximum20190930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:38:27.810235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110627
5-th percentile20110627
Q120110627
median20160730
Q320181221
95-th percentile20190816
Maximum20190930
Range80303
Interquartile range (IQR)70594

Descriptive statistics

Standard deviation35871.111
Coefficient of variation (CV)0.0017802836
Kurtosis-1.8713017
Mean20149099
Median Absolute Deviation (MAD)30087.5
Skewness-0.028380896
Sum2.0149099 × 1011
Variance1.2867366 × 109
MonotonicityNot monotonic
2023-12-12T17:38:28.052328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110627 4211
42.1%
20190504 35
 
0.4%
20190825 33
 
0.3%
20190819 27
 
0.3%
20181006 27
 
0.3%
20190823 25
 
0.2%
20190607 23
 
0.2%
20190505 21
 
0.2%
20190817 21
 
0.2%
20190914 20
 
0.2%
Other values (1445) 5557
55.6%
ValueCountFrequency (%)
20110627 4211
42.1%
20111001 15
 
0.1%
20111003 5
 
0.1%
20111004 5
 
0.1%
20111005 2
 
< 0.1%
20111006 6
 
0.1%
20111007 3
 
< 0.1%
20111008 1
 
< 0.1%
20111009 3
 
< 0.1%
20111010 1
 
< 0.1%
ValueCountFrequency (%)
20190930 2
 
< 0.1%
20190929 2
 
< 0.1%
20190928 13
0.1%
20190927 5
 
0.1%
20190926 4
 
< 0.1%
20190925 9
0.1%
20190924 15
0.1%
20190923 7
0.1%
20190922 7
0.1%
20190921 3
 
< 0.1%

END_DATE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1606
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20150766
Minimum20110930
Maximum30000410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:38:28.268464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110930
5-th percentile20110930
Q120110930
median20161218
Q320190107
95-th percentile20190827
Maximum30000410
Range9889480
Interquartile range (IQR)79177

Descriptive statistics

Standard deviation104830
Coefficient of variation (CV)0.0052022838
Kurtosis7796.1034
Mean20150766
Median Absolute Deviation (MAD)29612.5
Skewness82.967773
Sum2.0150766 × 1011
Variance1.0989329 × 1010
MonotonicityNot monotonic
2023-12-12T17:38:28.505620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110930 4212
42.1%
20190504 33
 
0.3%
20190825 31
 
0.3%
20190607 29
 
0.3%
20190823 27
 
0.3%
20181006 25
 
0.2%
20181103 23
 
0.2%
20190819 22
 
0.2%
20190807 21
 
0.2%
20190505 21
 
0.2%
Other values (1596) 5556
55.6%
ValueCountFrequency (%)
20110930 4212
42.1%
20111018 1
 
< 0.1%
20111031 1
 
< 0.1%
20111101 3
 
< 0.1%
20111102 2
 
< 0.1%
20111103 2
 
< 0.1%
20111104 2
 
< 0.1%
20111105 2
 
< 0.1%
20111106 1
 
< 0.1%
20111107 1
 
< 0.1%
ValueCountFrequency (%)
30000410 1
< 0.1%
20200928 1
< 0.1%
20200927 1
< 0.1%
20200925 1
< 0.1%
20200924 1
< 0.1%
20200919 1
< 0.1%
20200918 2
< 0.1%
20200915 1
< 0.1%
20200913 1
< 0.1%
20200907 1
< 0.1%

PAYMENT_KIND
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8434 
1
 
527
2
 
248
05
 
156
02
 
152
Other values (12)
 
483

Length

Max length4
Median length4
Mean length3.6052
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8434
84.3%
1 527
 
5.3%
2 248
 
2.5%
05 156
 
1.6%
02 152
 
1.5%
15 134
 
1.3%
01 112
 
1.1%
08 72
 
0.7%
04 47
 
0.5%
40
 
0.4%
Other values (7) 78
 
0.8%

Length

2023-12-12T17:38:28.702573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8434
84.7%
1 527
 
5.3%
2 248
 
2.5%
05 156
 
1.6%
02 152
 
1.5%
15 134
 
1.3%
01 112
 
1.1%
08 72
 
0.7%
04 47
 
0.5%
09 31
 
0.3%
Other values (6) 47
 
0.5%

PAYMENT_NUM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9946 
 
54

Length

Max length4
Median length4
Mean length3.9838
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9946
99.5%
54
 
0.5%

Length

2023-12-12T17:38:28.857595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:38:28.975078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9946
100.0%

PAY_DATE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2561
Distinct (%)45.2%
Missing4333
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean5.4326441 × 1012
Minimum2011705
Maximum2.019093 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:38:29.129158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011705
5-th percentile20110707
Q120110732
median20120606
Q32.0120115 × 1013
95-th percentile2.0190508 × 1013
Maximum2.019093 × 1013
Range2.0190928 × 1013
Interquartile range (IQR)2.0120095 × 1013

Descriptive statistics

Standard deviation8.9444849 × 1012
Coefficient of variation (CV)1.6464331
Kurtosis-0.91972679
Mean5.4326441 × 1012
Median Absolute Deviation (MAD)9902
Skewness1.0394766
Sum3.0786794 × 1016
Variance8.0003811 × 1025
MonotonicityNot monotonic
2023-12-12T17:38:29.318554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110723 70
 
0.7%
20110725 65
 
0.7%
20110715 63
 
0.6%
20110721 58
 
0.6%
20110705 57
 
0.6%
20110724 57
 
0.6%
20110718 57
 
0.6%
20110704 53
 
0.5%
20110707 52
 
0.5%
20110727 52
 
0.5%
Other values (2551) 5083
50.8%
(Missing) 4333
43.3%
ValueCountFrequency (%)
2011705 7
0.1%
20110628 1
 
< 0.1%
20110629 1
 
< 0.1%
20110630 15
0.1%
20110634 1
 
< 0.1%
20110637 1
 
< 0.1%
20110639 1
 
< 0.1%
20110641 1
 
< 0.1%
20110645 1
 
< 0.1%
20110648 1
 
< 0.1%
ValueCountFrequency (%)
20190930101725 1
< 0.1%
20190930101446 1
< 0.1%
20190929215457 1
< 0.1%
20190929112452 1
< 0.1%
20190928212951 1
< 0.1%
20190928204701 1
< 0.1%
20190928133428 1
< 0.1%
20190928133042 1
< 0.1%
20190926175640 1
< 0.1%
20190926144911 1
< 0.1%

PRICE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.4%
Missing8425
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean10011.429
Minimum0
Maximum30000
Zeros53
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:38:29.489725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3000
Q13000
median5000
Q320000
95-th percentile30000
Maximum30000
Range30000
Interquartile range (IQR)17000

Descriptive statistics

Standard deviation9609.3914
Coefficient of variation (CV)0.95984218
Kurtosis-0.43908399
Mean10011.429
Median Absolute Deviation (MAD)2000
Skewness1.0439152
Sum15768000
Variance92340403
MonotonicityNot monotonic
2023-12-12T17:38:29.614012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3000 512
 
5.1%
5000 508
 
5.1%
20000 212
 
2.1%
30000 186
 
1.9%
18000 104
 
1.0%
0 53
 
0.5%
(Missing) 8425
84.2%
ValueCountFrequency (%)
0 53
 
0.5%
3000 512
5.1%
5000 508
5.1%
18000 104
 
1.0%
20000 212
2.1%
30000 186
 
1.9%
ValueCountFrequency (%)
30000 186
 
1.9%
20000 212
2.1%
18000 104
 
1.0%
5000 508
5.1%
3000 512
5.1%
0 53
 
0.5%

START_DT
Date

Distinct1456
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2001-01-15 00:00:00
Maximum2031-12-18 00:00:00
2023-12-12T17:38:29.790105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:29.971459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

END_DT
Text

Distinct1607
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:38:30.325533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique684 ?
Unique (%)6.8%

Sample

1st row17/04/03
2nd row11/09/30
3rd row18/02/08
4th row19/09/16
5th row19/06/26
ValueCountFrequency (%)
11/09/30 4212
42.1%
19/05/04 33
 
0.3%
19/08/25 31
 
0.3%
19/06/07 29
 
0.3%
19/08/23 27
 
0.3%
18/10/06 25
 
0.2%
18/11/03 23
 
0.2%
19/08/19 22
 
0.2%
19/07/04 21
 
0.2%
19/08/07 21
 
0.2%
Other values (1597) 5556
55.6%
2023-12-12T17:38:30.830456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 20000
25.0%
1 18187
22.7%
0 16021
20.0%
9 7784
 
9.7%
3 5698
 
7.1%
2 3376
 
4.2%
8 3178
 
4.0%
7 2023
 
2.5%
5 1291
 
1.6%
6 1253
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
75.0%
Other Punctuation 20000
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18187
30.3%
0 16021
26.7%
9 7784
13.0%
3 5698
 
9.5%
2 3376
 
5.6%
8 3178
 
5.3%
7 2023
 
3.4%
5 1291
 
2.2%
6 1253
 
2.1%
4 1189
 
2.0%
Other Punctuation
ValueCountFrequency (%)
/ 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 20000
25.0%
1 18187
22.7%
0 16021
20.0%
9 7784
 
9.7%
3 5698
 
7.1%
2 3376
 
4.2%
8 3178
 
4.0%
7 2023
 
2.5%
5 1291
 
1.6%
6 1253
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 20000
25.0%
1 18187
22.7%
0 16021
20.0%
9 7784
 
9.7%
3 5698
 
7.1%
2 3376
 
4.2%
8 3178
 
4.0%
7 2023
 
2.5%
5 1291
 
1.6%
6 1253
 
1.6%

TMP_END_DATE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct389
Distinct (%)7.3%
Missing4670
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean20114652
Minimum20110627
Maximum20191023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:38:31.024100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110627
5-th percentile20110627
Q120110627
median20110930
Q320110930
95-th percentile20150710
Maximum20191023
Range80396
Interquartile range (IQR)303

Descriptive statistics

Standard deviation14449.803
Coefficient of variation (CV)0.00071837199
Kurtosis14.676035
Mean20114652
Median Absolute Deviation (MAD)0
Skewness3.9440727
Sum1.072111 × 1011
Variance2.087968 × 108
MonotonicityNot monotonic
2023-12-12T17:38:31.227289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110930 3320
33.2%
20110627 1577
 
15.8%
20130112 3
 
< 0.1%
20180514 3
 
< 0.1%
20141116 3
 
< 0.1%
20150826 3
 
< 0.1%
20130907 3
 
< 0.1%
20190508 2
 
< 0.1%
20131208 2
 
< 0.1%
20181003 2
 
< 0.1%
Other values (379) 412
 
4.1%
(Missing) 4670
46.7%
ValueCountFrequency (%)
20110627 1577
15.8%
20110930 3320
33.2%
20111012 1
 
< 0.1%
20111102 1
 
< 0.1%
20111105 1
 
< 0.1%
20111106 2
 
< 0.1%
20111109 2
 
< 0.1%
20111114 1
 
< 0.1%
20111121 2
 
< 0.1%
20111126 1
 
< 0.1%
ValueCountFrequency (%)
20191023 1
< 0.1%
20191009 1
< 0.1%
20190928 1
< 0.1%
20190927 1
< 0.1%
20190923 1
< 0.1%
20190919 1
< 0.1%
20190911 1
< 0.1%
20190903 1
< 0.1%
20190814 1
< 0.1%
20190811 1
< 0.1%

TERM_YN
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4670 
0
4251 
1
1016 
2
 
63

Length

Max length4
Median length1
Mean length2.401
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 4670
46.7%
0 4251
42.5%
1 1016
 
10.2%
2 63
 
0.6%

Length

2023-12-12T17:38:31.400249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:38:31.553245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4670
46.7%
0 4251
42.5%
1 1016
 
10.2%
2 63
 
0.6%

TMP_PAYMENT_KIND
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9703 
1
 
91
2
 
64
15
 
30
05
 
30
Other values (9)
 
82

Length

Max length4
Median length4
Mean length3.9245
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9703
97.0%
1 91
 
0.9%
2 64
 
0.6%
15 30
 
0.3%
05 30
 
0.3%
01 23
 
0.2%
02 21
 
0.2%
08 12
 
0.1%
04 6
 
0.1%
6
 
0.1%
Other values (4) 14
 
0.1%

Length

2023-12-12T17:38:31.734273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9703
97.1%
1 91
 
0.9%
2 64
 
0.6%
15 30
 
0.3%
05 30
 
0.3%
01 23
 
0.2%
02 21
 
0.2%
08 12
 
0.1%
04 6
 
0.1%
24 4
 
< 0.1%
Other values (3) 10
 
0.1%

TMP_PAYMENT_NUM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9997 
0
 
3

Length

Max length4
Median length4
Mean length3.9991
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9997
> 99.9%
0 3
 
< 0.1%

Length

2023-12-12T17:38:31.897892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:38:32.043034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9997
> 99.9%
0 3
 
< 0.1%

TMP_PAY_DATE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct768
Distinct (%)81.5%
Missing9058
Missing (%)90.6%
Infinite0
Infinite (%)0.0%
Mean6.2657292 × 1012
Minimum20110630
Maximum2.0190923 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:38:32.208937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110630
5-th percentile20110714
Q120120505
median20145670
Q32.0121124 × 1013
95-th percentile2.01704 × 1013
Maximum2.0190923 × 1013
Range2.0190903 × 1013
Interquartile range (IQR)2.0121104 × 1013

Descriptive statistics

Standard deviation9.330174 × 1012
Coefficient of variation (CV)1.4890803
Kurtosis-1.3342122
Mean6.2657292 × 1012
Median Absolute Deviation (MAD)34453
Skewness0.81769049
Sum5.9023169 × 1015
Variance8.7052146 × 1025
MonotonicityNot monotonic
2023-12-12T17:38:32.446760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110715 9
 
0.1%
20110723 7
 
0.1%
20110705 7
 
0.1%
20110706 6
 
0.1%
20110711 6
 
0.1%
20110716 5
 
0.1%
20110725 5
 
0.1%
20110707 5
 
0.1%
20110718 5
 
0.1%
20110713 4
 
< 0.1%
Other values (758) 883
 
8.8%
(Missing) 9058
90.6%
ValueCountFrequency (%)
20110630 4
< 0.1%
20110635 1
 
< 0.1%
20110651 1
 
< 0.1%
20110702 2
 
< 0.1%
20110703 3
< 0.1%
20110704 3
< 0.1%
20110705 7
0.1%
20110706 6
0.1%
20110707 5
0.1%
20110709 1
 
< 0.1%
ValueCountFrequency (%)
20190923160451 1
< 0.1%
20190909224122 1
< 0.1%
20190715220142 1
< 0.1%
20190625163517 1
< 0.1%
20190611161859 1
< 0.1%
20190531083648 1
< 0.1%
20190514143556 1
< 0.1%
20190415161149 1
< 0.1%
20190405134207 1
< 0.1%
20181129141747 1
< 0.1%

TMP_PRICE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)2.0%
Missing9701
Missing (%)97.0%
Infinite0
Infinite (%)0.0%
Mean8080.2676
Minimum0
Maximum30000
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:38:32.599193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3000
Q13000
median3000
Q318000
95-th percentile21000
Maximum30000
Range30000
Interquartile range (IQR)15000

Descriptive statistics

Standard deviation8355.7788
Coefficient of variation (CV)1.0340968
Kurtosis0.29813182
Mean8080.2676
Median Absolute Deviation (MAD)0
Skewness1.3149472
Sum2416000
Variance69819039
MonotonicityNot monotonic
2023-12-12T17:38:32.724496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3000 163
 
1.6%
20000 49
 
0.5%
5000 49
 
0.5%
30000 15
 
0.1%
18000 14
 
0.1%
0 9
 
0.1%
(Missing) 9701
97.0%
ValueCountFrequency (%)
0 9
 
0.1%
3000 163
1.6%
5000 49
 
0.5%
18000 14
 
0.1%
20000 49
 
0.5%
30000 15
 
0.1%
ValueCountFrequency (%)
30000 15
 
0.1%
20000 49
 
0.5%
18000 14
 
0.1%
5000 49
 
0.5%
3000 163
1.6%
0 9
 
0.1%

TMP_START_DT
Date

MISSING 

Distinct383
Distinct (%)35.3%
Missing8914
Missing (%)89.1%
Memory size156.2 KiB
Minimum2001-01-17 00:00:00
Maximum2031-12-18 00:00:00
2023-12-12T17:38:32.910305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:33.110550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TMP_END_DT
Date

MISSING 

Distinct388
Distinct (%)35.7%
Missing8914
Missing (%)89.1%
Memory size156.2 KiB
Minimum2001-01-17 00:00:00
Maximum2031-12-18 00:00:00
2023-12-12T17:38:33.324395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:33.519319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T17:38:25.695616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:20.395947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:21.269674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:22.403876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.248355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.847691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:24.667158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:25.816257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:20.519251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:21.677188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:22.558503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.333790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.954216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:24.780691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:25.933424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:20.623714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:21.771737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:22.701124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.411779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:24.063922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:24.895484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:26.069816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:20.744089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:21.876404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:22.818264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.500201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:24.180871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:25.054150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:26.188721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:20.868084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:22.002054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:22.930109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.578725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:24.309944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:25.192168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:26.318475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:21.021915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:22.146350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.045103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.662896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:24.469070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:25.326282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:26.440093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:21.153695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:22.295994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.154695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:23.760560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:24.577426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:38:25.515932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:38:33.670389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
START_DATEEND_DATEPAYMENT_KINDPAY_DATEPRICETMP_END_DATETERM_YNTMP_PAYMENT_KINDTMP_PAY_DATETMP_PRICE
START_DATE1.0000.0950.6050.2140.8320.8890.2990.4890.4900.743
END_DATE0.0951.0000.0000.0000.0000.0000.0920.0000.0000.000
PAYMENT_KIND0.6050.0001.0000.9970.4830.3410.6980.9480.1740.304
PAY_DATE0.2140.0000.9971.0000.1790.6990.763NaN0.000NaN
PRICE0.8320.0000.4830.1791.0000.6970.1290.3060.2300.902
TMP_END_DATE0.8890.0000.3410.6990.6971.0000.6360.5830.9980.852
TERM_YN0.2990.0920.6980.7630.1290.6361.0000.4550.2430.055
TMP_PAYMENT_KIND0.4890.0000.948NaN0.3060.5830.4551.0001.0000.313
TMP_PAY_DATE0.4900.0000.1740.0000.2300.9980.2431.0001.0000.034
TMP_PRICE0.7430.0000.304NaN0.9020.8520.0550.3130.0341.000
2023-12-12T17:38:33.830609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PAYMENT_KINDPAYMENT_NUMTMP_PAYMENT_NUMTMP_PAYMENT_KINDTERM_YN
PAYMENT_KIND1.0001.0001.0000.5800.505
PAYMENT_NUM1.0001.0001.0001.0001.000
TMP_PAYMENT_NUM1.0001.0001.0001.0001.000
TMP_PAYMENT_KIND0.5801.0001.0001.0000.285
TERM_YN0.5051.0001.0000.2851.000
2023-12-12T17:38:34.315589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
START_DATEEND_DATEPAY_DATEPRICETMP_END_DATETMP_PAY_DATETMP_PRICEPAYMENT_KINDPAYMENT_NUMTERM_YNTMP_PAYMENT_KINDTMP_PAYMENT_NUM
START_DATE1.0000.9950.7810.4350.5190.6630.5090.3161.0000.6560.2251.000
END_DATE0.9951.0000.7800.5800.5190.6430.5930.0001.0000.0000.0001.000
PAY_DATE0.7810.7801.0000.433-0.0930.6630.5010.9491.0000.9791.000NaN
PRICE0.4350.5800.4331.0000.2880.1470.7520.2701.0000.0970.1681.000
TMP_END_DATE0.5190.519-0.0930.2881.0000.8200.5850.1391.0000.4950.2851.000
TMP_PAY_DATE0.6630.6430.6630.1470.8201.0000.4380.1361.0000.3960.9811.000
TMP_PRICE0.5090.5930.5010.7520.5850.4381.0000.1671.0000.0400.1721.000
PAYMENT_KIND0.3160.0000.9490.2700.1390.1360.1671.0001.0000.5050.5801.000
PAYMENT_NUM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
TERM_YN0.6560.0000.9790.0970.4950.3960.0400.5051.0001.0000.2851.000
TMP_PAYMENT_KIND0.2250.0001.0000.1680.2850.9810.1720.5801.0000.2851.0001.000
TMP_PAYMENT_NUM1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T17:38:26.626090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:38:26.929485image/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.
2023-12-12T17:38:27.189937image/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

START_DATEEND_DATEPAYMENT_KINDPAYMENT_NUMPAY_DATEPRICESTART_DTEND_DTTMP_END_DATETERM_YNTMP_PAYMENT_KINDTMP_PAYMENT_NUMTMP_PAY_DATETMP_PRICETMP_START_DTTMP_END_DT
206752017040320170403<NA><NA><NA><NA>17/04/0317/04/03<NA><NA><NA><NA><NA><NA><NA><NA>
59602011062720110930<NA><NA>20110803<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>
185112018020820180208<NA><NA><NA><NA>18/02/0818/02/08<NA><NA><NA><NA><NA><NA><NA><NA>
279952019091620190916<NA><NA><NA><NA>19/09/1619/09/16<NA><NA><NA><NA><NA><NA><NA><NA>
268862019062620190626<NA><NA><NA><NA>19/06/2619/06/26<NA><NA><NA><NA><NA><NA><NA><NA>
756720111105201302122<NA>201111051331372000011/11/0513/02/12201109301<NA><NA>20110814<NA>11/06/2711/09/30
83822011062720110930<NA><NA>20110821<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>
233182019021720190217<NA><NA><NA><NA>19/02/1719/02/17<NA><NA><NA><NA><NA><NA><NA><NA>
195702017060520170605<NA><NA><NA><NA>17/06/0517/06/05<NA><NA><NA><NA><NA><NA><NA><NA>
917520170417201804171<NA>201704171259003000017/04/1718/04/172016091611<NA>201509171220272000015/09/1716/09/16
START_DATEEND_DATEPAYMENT_KINDPAYMENT_NUMPAY_DATEPRICESTART_DTEND_DTTMP_END_DATETERM_YNTMP_PAYMENT_KINDTMP_PAYMENT_NUMTMP_PAY_DATETMP_PRICETMP_START_DTTMP_END_DT
212172019050520190505<NA><NA><NA><NA>19/05/0519/05/05<NA><NA><NA><NA><NA><NA><NA><NA>
252412019031720190317<NA><NA><NA><NA>19/03/1719/03/17<NA><NA><NA><NA><NA><NA><NA><NA>
42792011062720110930<NA><NA>20110725<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>
157982017082220170822<NA><NA><NA><NA>17/08/2217/08/22<NA><NA><NA><NA><NA><NA><NA><NA>
252392019031720190317<NA><NA><NA><NA>19/03/1719/03/17<NA><NA><NA><NA><NA><NA><NA><NA>
152662011062720110930<NA><NA>20160220<NA>11/06/2711/09/30201106270<NA><NA><NA><NA><NA><NA>
76142011062720110930<NA><NA>20110827<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>
261832019060720190607<NA><NA><NA><NA>19/06/0719/06/07<NA><NA><NA><NA><NA><NA><NA><NA>
289032019080720190807<NA><NA><NA><NA>19/08/0719/08/07<NA><NA><NA><NA><NA><NA><NA><NA>
284712019072420190724<NA><NA><NA><NA>19/07/2419/07/24<NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

START_DATEEND_DATEPAYMENT_KINDPAYMENT_NUMPAY_DATEPRICESTART_DTEND_DTTMP_END_DATETERM_YNTMP_PAYMENT_KINDTMP_PAYMENT_NUMTMP_PAY_DATETMP_PRICETMP_START_DTTMP_END_DT# duplicates
242011062720110930<NA><NA>20110723<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>70
262011062720110930<NA><NA>20110725<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>65
162011062720110930<NA><NA>20110715<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>63
222011062720110930<NA><NA>20110721<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>58
62011062720110930<NA><NA>20110705<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>57
192011062720110930<NA><NA>20110718<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>57
252011062720110930<NA><NA>20110724<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>57
4782011062720110930<NA><NA><NA><NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>54
52011062720110930<NA><NA>20110704<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>53
82011062720110930<NA><NA>20110707<NA>11/06/2711/09/30201109300<NA><NA><NA><NA><NA><NA>52