Overview

Dataset statistics

Number of variables17
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows23
Duplicate rows (%)2.3%
Total size in memory148.6 KiB
Average record size in memory152.1 B

Variable types

Categorical4
Numeric13

Dataset

Description한국주택금융공사 신탁자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072806/fileData.do

Alerts

BASIS_MM has constant value ""Constant
OPER_DY has constant value ""Constant
Dataset has 23 (2.3%) duplicate rowsDuplicates
T07 is highly overall correlated with T08 and 1 other fieldsHigh correlation
T08 is highly overall correlated with T07 and 1 other fieldsHigh correlation
T09 is highly overall correlated with T07 and 1 other fieldsHigh correlation
T10 is highly overall correlated with T11High correlation
T11 is highly overall correlated with T10High correlation
T14 is highly overall correlated with T15 and 1 other fieldsHigh correlation
T15 is highly overall correlated with T14 and 1 other fieldsHigh correlation
T16 is highly overall correlated with T14 and 1 other fieldsHigh correlation
T12 is highly imbalanced (98.3%)Imbalance
T05 is highly skewed (γ1 = 29.91068345)Skewed
T07 is highly skewed (γ1 = 24.02790843)Skewed
T08 is highly skewed (γ1 = 25.61828575)Skewed
T09 is highly skewed (γ1 = 21.9651479)Skewed
T14 is highly skewed (γ1 = 30.50916908)Skewed
T15 is highly skewed (γ1 = 30.82602665)Skewed
T16 is highly skewed (γ1 = 30.22875795)Skewed
T03 has 225 (22.5%) zerosZeros
T04 has 32 (3.2%) zerosZeros
T05 has 985 (98.5%) zerosZeros
T06 has 951 (95.1%) zerosZeros
T07 has 968 (96.8%) zerosZeros
T08 has 971 (97.1%) zerosZeros
T09 has 970 (97.0%) zerosZeros
T10 has 907 (90.7%) zerosZeros
T11 has 876 (87.6%) zerosZeros
T13 has 993 (99.3%) zerosZeros
T14 has 994 (99.4%) zerosZeros
T15 has 995 (99.5%) zerosZeros
T16 has 994 (99.4%) zerosZeros

Reproduction

Analysis started2023-12-11 23:44:37.273090
Analysis finished2023-12-11 23:44:57.227130
Duration19.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LIQD_PLAN_CD
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KHFCMB2018S-20
804 
KHFCMB2018S-19
196 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2018S-20
2nd rowKHFCMB2018S-20
3rd rowKHFCMB2018S-20
4th rowKHFCMB2018S-20
5th rowKHFCMB2018S-20

Common Values

ValueCountFrequency (%)
KHFCMB2018S-20 804
80.4%
KHFCMB2018S-19 196
 
19.6%

Length

2023-12-12T08:44:57.280688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:44:57.364021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
khfcmb2018s-20 804
80.4%
khfcmb2018s-19 196
 
19.6%

BASIS_MM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
201806
1000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201806
2nd row201806
3rd row201806
4th row201806
5th row201806

Common Values

ValueCountFrequency (%)
201806 1000
100.0%

Length

2023-12-12T08:44:57.452458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:44:57.532333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201806 1000
100.0%

T03
Real number (ℝ)

ZEROS 

Distinct657
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264842.97
Minimum0
Maximum10500000
Zeros225
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:44:57.647741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130123
median198587
Q3365902.5
95-th percentile780583.3
Maximum10500000
Range10500000
Interquartile range (IQR)335779.5

Descriptive statistics

Standard deviation419214
Coefficient of variation (CV)1.5828776
Kurtosis355.93236
Mean264842.97
Median Absolute Deviation (MAD)168427.5
Skewness14.995603
Sum2.6484297 × 108
Variance1.7574038 × 1011
MonotonicityNot monotonic
2023-12-12T08:44:57.779114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 225
 
22.5%
555555 8
 
0.8%
833333 6
 
0.6%
150615 6
 
0.6%
500000 6
 
0.6%
458333 5
 
0.5%
305555 4
 
0.4%
75307 4
 
0.4%
135554 4
 
0.4%
105430 4
 
0.4%
Other values (647) 728
72.8%
ValueCountFrequency (%)
0 225
22.5%
1897 1
 
0.1%
1954 1
 
0.1%
2060 1
 
0.1%
6492 1
 
0.1%
11210 1
 
0.1%
13592 1
 
0.1%
13981 1
 
0.1%
14988 1
 
0.1%
15036 1
 
0.1%
ValueCountFrequency (%)
10500000 1
0.1%
1888888 1
0.1%
1658333 1
0.1%
1468212 1
0.1%
1454768 1
0.1%
1450000 1
0.1%
1376224 1
0.1%
1322621 1
0.1%
1250000 1
0.1%
1216666 1
0.1%

T04
Real number (ℝ)

ZEROS 

Distinct879
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297189.9
Minimum0
Maximum1369526
Zeros32
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:44:57.917079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19926.65
Q1144245.25
median278119.5
Q3411613.75
95-th percentile652213.95
Maximum1369526
Range1369526
Interquartile range (IQR)267368.5

Descriptive statistics

Standard deviation204938.19
Coefficient of variation (CV)0.68958667
Kurtosis3.632059
Mean297189.9
Median Absolute Deviation (MAD)133800.5
Skewness1.3227131
Sum2.971899 × 108
Variance4.1999662 × 1010
MonotonicityNot monotonic
2023-12-12T08:44:58.048206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
3.2%
467123 10
 
1.0%
433150 7
 
0.7%
373698 6
 
0.6%
416164 6
 
0.6%
192500 4
 
0.4%
450136 4
 
0.4%
129452 3
 
0.3%
249698 3
 
0.3%
64166 3
 
0.3%
Other values (869) 922
92.2%
ValueCountFrequency (%)
0 32
3.2%
4054 1
 
0.1%
5725 1
 
0.1%
6164 1
 
0.1%
9870 1
 
0.1%
10144 1
 
0.1%
11866 1
 
0.1%
12194 1
 
0.1%
14219 1
 
0.1%
14230 1
 
0.1%
ValueCountFrequency (%)
1369526 1
0.1%
1328116 1
0.1%
1281434 1
0.1%
1259977 1
0.1%
1239480 1
0.1%
1209849 1
0.1%
1181824 1
0.1%
1144452 1
0.1%
1122916 1
0.1%
1068438 1
0.1%

T05
Real number (ℝ)

SKEWED  ZEROS 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.829
Minimum0
Maximum1775
Zeros985
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:44:58.179799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1775
Range1775
Interquartile range (IQR)0

Descriptive statistics

Standard deviation57.21228
Coefficient of variation (CV)20.2235
Kurtosis924.38353
Mean2.829
Median Absolute Deviation (MAD)0
Skewness29.910683
Sum2829
Variance3273.245
MonotonicityNot monotonic
2023-12-12T08:44:58.307609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 985
98.5%
64 2
 
0.2%
37 1
 
0.1%
20 1
 
0.1%
120 1
 
0.1%
76 1
 
0.1%
113 1
 
0.1%
75 1
 
0.1%
108 1
 
0.1%
2 1
 
0.1%
Other values (5) 5
 
0.5%
ValueCountFrequency (%)
0 985
98.5%
2 1
 
0.1%
15 1
 
0.1%
17 1
 
0.1%
20 1
 
0.1%
37 1
 
0.1%
64 2
 
0.2%
75 1
 
0.1%
76 1
 
0.1%
108 1
 
0.1%
ValueCountFrequency (%)
1775 1
0.1%
234 1
0.1%
120 1
0.1%
113 1
0.1%
109 1
0.1%
108 1
0.1%
76 1
0.1%
75 1
0.1%
64 2
0.2%
37 1
0.1%

T06
Real number (ℝ)

ZEROS 

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.453
Minimum0
Maximum1107
Zeros951
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:44:58.436381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1107
Range1107
Interquartile range (IQR)0

Descriptive statistics

Standard deviation50.836543
Coefficient of variation (CV)6.8209504
Kurtosis244.16934
Mean7.453
Median Absolute Deviation (MAD)0
Skewness13.453674
Sum7453
Variance2584.3541
MonotonicityNot monotonic
2023-12-12T08:44:58.554020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 951
95.1%
115 2
 
0.2%
62 2
 
0.2%
131 2
 
0.2%
53 2
 
0.2%
16 1
 
0.1%
96 1
 
0.1%
92 1
 
0.1%
85 1
 
0.1%
225 1
 
0.1%
Other values (36) 36
 
3.6%
ValueCountFrequency (%)
0 951
95.1%
16 1
 
0.1%
27 1
 
0.1%
29 1
 
0.1%
35 1
 
0.1%
38 1
 
0.1%
42 1
 
0.1%
51 1
 
0.1%
52 1
 
0.1%
53 2
 
0.2%
ValueCountFrequency (%)
1107 1
0.1%
563 1
0.1%
418 1
0.1%
391 1
0.1%
358 1
0.1%
271 1
0.1%
235 1
0.1%
225 1
0.1%
197 1
0.1%
187 1
0.1%

T07
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184356.03
Minimum0
Maximum68000000
Zeros968
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:44:58.660459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum68000000
Range68000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2386555.5
Coefficient of variation (CV)12.945362
Kurtosis660.10528
Mean184356.03
Median Absolute Deviation (MAD)0
Skewness24.027908
Sum1.8435603 × 108
Variance5.695647 × 1012
MonotonicityNot monotonic
2023-12-12T08:44:58.788544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 968
96.8%
10000000 6
 
0.6%
3000000 4
 
0.4%
1000000 2
 
0.2%
5000000 1
 
0.1%
1480000 1
 
0.1%
170392 1
 
0.1%
500000 1
 
0.1%
920000 1
 
0.1%
600000 1
 
0.1%
Other values (14) 14
 
1.4%
ValueCountFrequency (%)
0 968
96.8%
6400 1
 
0.1%
100000 1
 
0.1%
170392 1
 
0.1%
200000 1
 
0.1%
399238 1
 
0.1%
440000 1
 
0.1%
500000 1
 
0.1%
600000 1
 
0.1%
700000 1
 
0.1%
ValueCountFrequency (%)
68000000 1
 
0.1%
20000000 1
 
0.1%
10000000 6
0.6%
5000000 1
 
0.1%
4500000 1
 
0.1%
3000000 4
0.4%
2140000 1
 
0.1%
2000000 1
 
0.1%
1480000 1
 
0.1%
1300000 1
 
0.1%

T08
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct30
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean185.156
Minimum0
Maximum81972
Zeros971
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:44:58.911292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum81972
Range81972
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2817.2726
Coefficient of variation (CV)15.21567
Kurtosis721.61976
Mean185.156
Median Absolute Deviation (MAD)0
Skewness25.618286
Sum185156
Variance7937025.1
MonotonicityNot monotonic
2023-12-12T08:44:59.029482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 971
97.1%
19 1
 
0.1%
4430 1
 
0.1%
27424 1
 
0.1%
2653 1
 
0.1%
7134 1
 
0.1%
12216 1
 
0.1%
43 1
 
0.1%
128 1
 
0.1%
5657 1
 
0.1%
Other values (20) 20
 
2.0%
ValueCountFrequency (%)
0 971
97.1%
5 1
 
0.1%
19 1
 
0.1%
43 1
 
0.1%
128 1
 
0.1%
196 1
 
0.1%
203 1
 
0.1%
316 1
 
0.1%
492 1
 
0.1%
522 1
 
0.1%
ValueCountFrequency (%)
81972 1
0.1%
27424 1
0.1%
12216 1
0.1%
8321 1
0.1%
8219 1
0.1%
7191 1
0.1%
7134 1
0.1%
5657 1
0.1%
4430 1
0.1%
4109 1
0.1%

T09
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct30
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2202.32
Minimum0
Maximum714744
Zeros970
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:44:59.151820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum714744
Range714744
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26034.672
Coefficient of variation (CV)11.821475
Kurtosis571.5115
Mean2202.32
Median Absolute Deviation (MAD)0
Skewness21.965148
Sum2202320
Variance6.7780414 × 108
MonotonicityNot monotonic
2023-12-12T08:44:59.267533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 970
97.0%
119124 2
 
0.2%
2189 1
 
0.1%
35540 1
 
0.1%
117153 1
 
0.1%
16982 1
 
0.1%
108503 1
 
0.1%
1923 1
 
0.1%
5644 1
 
0.1%
115839 1
 
0.1%
Other values (20) 20
 
2.0%
ValueCountFrequency (%)
0 970
97.0%
75 1
 
0.1%
1167 1
 
0.1%
1923 1
 
0.1%
2189 1
 
0.1%
5246 1
 
0.1%
5644 1
 
0.1%
6917 1
 
0.1%
9477 1
 
0.1%
10526 1
 
0.1%
ValueCountFrequency (%)
714744 1
0.1%
234963 1
0.1%
136488 1
0.1%
119890 1
0.1%
119124 2
0.2%
117153 1
0.1%
115839 1
0.1%
108503 1
0.1%
59507 1
0.1%
59287 1
0.1%

T10
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31624.387
Minimum0
Maximum1658333
Zeros907
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:44:59.388169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile261527.65
Maximum1658333
Range1658333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation130739.07
Coefficient of variation (CV)4.134122
Kurtosis49.077395
Mean31624.387
Median Absolute Deviation (MAD)0
Skewness6.0541045
Sum31624387
Variance1.7092706 × 1010
MonotonicityNot monotonic
2023-12-12T08:44:59.710447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 907
90.7%
416666 2
 
0.2%
848455 1
 
0.1%
295050 1
 
0.1%
293061 1
 
0.1%
555555 1
 
0.1%
490008 1
 
0.1%
1658333 1
 
0.1%
383266 1
 
0.1%
13981 1
 
0.1%
Other values (83) 83
 
8.3%
ValueCountFrequency (%)
0 907
90.7%
13981 1
 
0.1%
14988 1
 
0.1%
17716 1
 
0.1%
22196 1
 
0.1%
22948 1
 
0.1%
44678 1
 
0.1%
47639 1
 
0.1%
52715 1
 
0.1%
59190 1
 
0.1%
ValueCountFrequency (%)
1658333 1
0.1%
1468212 1
0.1%
900000 1
0.1%
848455 1
0.1%
791666 1
0.1%
777777 1
0.1%
750000 1
0.1%
716666 1
0.1%
703941 1
0.1%
666666 1
0.1%

T11
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct118
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38684.336
Minimum0
Maximum1239480
Zeros876
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:45:00.143077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile341745
Maximum1239480
Range1239480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation123278.66
Coefficient of variation (CV)3.1867849
Kurtosis19.868632
Mean38684.336
Median Absolute Deviation (MAD)0
Skewness3.9961908
Sum38684336
Variance1.5197628 × 1010
MonotonicityNot monotonic
2023-12-12T08:45:00.533909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 876
87.6%
249698 3
 
0.3%
467123 3
 
0.3%
238232 2
 
0.2%
50342 2
 
0.2%
450136 2
 
0.2%
501435 1
 
0.1%
413123 1
 
0.1%
582689 1
 
0.1%
677574 1
 
0.1%
Other values (108) 108
 
10.8%
ValueCountFrequency (%)
0 876
87.6%
31372 1
 
0.1%
31892 1
 
0.1%
33226 1
 
0.1%
41205 1
 
0.1%
46519 1
 
0.1%
48125 1
 
0.1%
49315 1
 
0.1%
50342 2
 
0.2%
76438 1
 
0.1%
ValueCountFrequency (%)
1239480 1
0.1%
909095 1
0.1%
861814 1
0.1%
735342 1
0.1%
732534 1
0.1%
677574 1
0.1%
653581 1
0.1%
633610 1
0.1%
582689 1
0.1%
563315 1
0.1%

T12
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
997 
20
 
1
120
 
1
109
 
1

Length

Max length3
Median length1
Mean length1.005
Min length1

Unique

Unique3 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 997
99.7%
20 1
 
0.1%
120 1
 
0.1%
109 1
 
0.1%

Length

2023-12-12T08:45:00.860480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:45:01.013331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 997
99.7%
20 1
 
0.1%
120 1
 
0.1%
109 1
 
0.1%

T13
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.647
Minimum0
Maximum187
Zeros993
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:45:01.130799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum187
Range187
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.6457585
Coefficient of variation (CV)13.362842
Kurtosis279.7853
Mean0.647
Median Absolute Deviation (MAD)0
Skewness15.785321
Sum647
Variance74.74914
MonotonicityNot monotonic
2023-12-12T08:45:01.260982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 993
99.3%
115 1
 
0.1%
63 1
 
0.1%
62 1
 
0.1%
187 1
 
0.1%
97 1
 
0.1%
29 1
 
0.1%
94 1
 
0.1%
ValueCountFrequency (%)
0 993
99.3%
29 1
 
0.1%
62 1
 
0.1%
63 1
 
0.1%
94 1
 
0.1%
97 1
 
0.1%
115 1
 
0.1%
187 1
 
0.1%
ValueCountFrequency (%)
187 1
 
0.1%
115 1
 
0.1%
97 1
 
0.1%
94 1
 
0.1%
63 1
 
0.1%
62 1
 
0.1%
29 1
 
0.1%
0 993
99.3%

T14
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85040
Minimum0
Maximum68000000
Zeros994
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:45:01.401186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum68000000
Range68000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2178110.6
Coefficient of variation (CV)25.612778
Kurtosis949.32645
Mean85040
Median Absolute Deviation (MAD)0
Skewness30.509169
Sum85040000
Variance4.744166 × 1012
MonotonicityNot monotonic
2023-12-12T08:45:01.551206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 994
99.4%
4500000 1
 
0.1%
10000000 1
 
0.1%
440000 1
 
0.1%
68000000 1
 
0.1%
1100000 1
 
0.1%
1000000 1
 
0.1%
ValueCountFrequency (%)
0 994
99.4%
440000 1
 
0.1%
1000000 1
 
0.1%
1100000 1
 
0.1%
4500000 1
 
0.1%
10000000 1
 
0.1%
68000000 1
 
0.1%
ValueCountFrequency (%)
68000000 1
 
0.1%
10000000 1
 
0.1%
4500000 1
 
0.1%
1100000 1
 
0.1%
1000000 1
 
0.1%
440000 1
 
0.1%
0 994
99.4%

T15
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.104
Minimum0
Maximum81972
Zeros995
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:45:01.676702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum81972
Range81972
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2615.2295
Coefficient of variation (CV)26.388738
Kurtosis964.45078
Mean99.104
Median Absolute Deviation (MAD)0
Skewness30.826027
Sum99104
Variance6839425.2
MonotonicityNot monotonic
2023-12-12T08:45:01.803383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 995
99.5%
8321 1
 
0.1%
7191 1
 
0.1%
81972 1
 
0.1%
203 1
 
0.1%
1417 1
 
0.1%
ValueCountFrequency (%)
0 995
99.5%
203 1
 
0.1%
1417 1
 
0.1%
7191 1
 
0.1%
8321 1
 
0.1%
81972 1
 
0.1%
ValueCountFrequency (%)
81972 1
 
0.1%
8321 1
 
0.1%
7191 1
 
0.1%
1417 1
 
0.1%
203 1
 
0.1%
0 995
99.5%

T16
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean916.436
Minimum0
Maximum714744
Zeros994
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:45:01.925225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum714744
Range714744
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22975.64
Coefficient of variation (CV)25.070643
Kurtosis936.03208
Mean916.436
Median Absolute Deviation (MAD)0
Skewness30.228758
Sum916436
Variance5.2788003 × 108
MonotonicityNot monotonic
2023-12-12T08:45:02.075065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 994
99.4%
53113 1
 
0.1%
119124 1
 
0.1%
5246 1
 
0.1%
714744 1
 
0.1%
12790 1
 
0.1%
11419 1
 
0.1%
ValueCountFrequency (%)
0 994
99.4%
5246 1
 
0.1%
11419 1
 
0.1%
12790 1
 
0.1%
53113 1
 
0.1%
119124 1
 
0.1%
714744 1
 
0.1%
ValueCountFrequency (%)
714744 1
 
0.1%
119124 1
 
0.1%
53113 1
 
0.1%
12790 1
 
0.1%
11419 1
 
0.1%
5246 1
 
0.1%
0 994
99.4%

OPER_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20180928
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20180928
2nd row20180928
3rd row20180928
4th row20180928
5th row20180928

Common Values

ValueCountFrequency (%)
20180928 1000
100.0%

Length

2023-12-12T08:45:02.214218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:45:02.313306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20180928 1000
100.0%

Interactions

2023-12-12T08:44:55.428069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:38.094717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.443996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.640611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.682678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:43.242330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:44.831682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:46.520847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:47.934117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:49.271556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.865937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:52.454327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.002266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:55.516232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:38.180430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.531162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.716518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.762163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:43.360278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:44.956149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:46.642951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.031537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:49.363215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.968915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:52.558608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.119100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:55.606106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:38.272149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.626422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.809644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.919541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:43.465997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:45.073376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:46.755276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.148181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:49.453241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:51.102096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:52.674973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.220441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:55.722014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:38.371918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.720883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.890801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:42.000824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:43.592851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:45.201449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:46.859963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.268236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:49.794509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:51.223743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:52.778288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.321072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:55.803845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:38.485039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.807906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.964844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:42.075049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:43.695839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:45.303717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:46.952153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.372823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:49.919522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:51.316785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:52.896692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.416234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:55.896507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:38.587987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.905638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.046789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:42.182636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:43.810455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:45.431406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:47.063309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.474787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.036614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:51.443362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:53.032348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.512404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:55.983247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:38.675895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.985431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.116579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:42.260786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:43.914968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:45.569415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:47.158794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.577152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.127597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:51.566057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:53.141484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.613688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:56.077006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:38.801715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.079469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.197233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:42.346400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:44.059367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:45.716991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:47.270005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.682133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.225875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:51.711898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:53.270225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.722349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:56.194519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:38.926504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.197165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.277060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:42.657435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:44.192125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:45.863953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:47.385292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.765992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.334090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:51.836856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:53.405785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.819621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:56.512816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.046628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.301880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.353636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:42.749072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:44.336353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:45.975140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:47.495668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.854361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.440632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:51.969078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:53.530491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:54.949541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:56.619288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.153864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.404302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.432640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:42.864859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:44.478090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:46.105223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:47.601066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:48.949192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.568623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:52.093668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:53.636014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:55.087532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:56.713580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.259039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.486578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.516941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:42.979681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:44.607484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:46.249709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:47.714530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:49.043922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.670802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:52.227301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:53.742036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:55.225624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:56.793233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:39.346939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:40.559049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:41.596788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:43.121073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:44.719517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:46.394672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:47.803862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:49.128744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:50.769710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:52.333982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:53.856562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:55.335742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:45:02.406735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LIQD_PLAN_CDT03T04T05T06T07T08T09T10T11T12T13T14T15T16
LIQD_PLAN_CD1.0000.0000.1770.0290.0950.0000.0690.0000.0000.0410.0000.0000.0000.0000.000
T030.0001.0000.2720.0000.0000.0000.0000.0000.3580.0000.0000.0000.0000.0000.000
T040.1770.2721.0000.4540.0000.0000.0000.0510.1540.5890.0000.0000.0000.0000.000
T050.0290.0000.4541.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
T060.0950.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.3680.0000.0000.000
T070.0000.0000.0000.0000.0001.0000.9561.0000.0000.0000.0000.0000.7210.6750.721
T080.0690.0000.0000.0000.0000.9561.0000.9540.0000.0000.0000.0000.6750.7630.675
T090.0000.0000.0510.0000.0001.0000.9541.0000.0000.0000.0000.0000.7150.6750.715
T100.0000.3580.1540.0000.0000.0000.0000.0001.0000.6320.2860.5070.0000.0000.000
T110.0410.0000.5890.0000.0000.0000.0000.0000.6321.0000.1750.4180.0000.0000.000
T120.0000.0000.0000.0000.0000.0000.0000.0000.2860.1751.0000.0000.0000.0000.000
T130.0000.0000.0000.0000.3680.0000.0000.0000.5070.4180.0001.0000.0000.0000.000
T140.0000.0000.0000.0000.0000.7210.6750.7150.0000.0000.0000.0001.0000.9431.000
T150.0000.0000.0000.0000.0000.6750.7630.6750.0000.0000.0000.0000.9431.0000.943
T160.0000.0000.0000.0000.0000.7210.6750.7150.0000.0000.0000.0001.0000.9431.000
2023-12-12T08:45:02.923590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
T12LIQD_PLAN_CD
T121.0000.000
LIQD_PLAN_CD0.0001.000
2023-12-12T08:45:03.050398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
T03T04T05T06T07T08T09T10T11T13T14T15T16LIQD_PLAN_CDT12
T031.0000.3430.004-0.048-0.245-0.233-0.2370.147-0.001-0.046-0.105-0.096-0.1050.0000.000
T040.3431.000-0.0580.048-0.305-0.290-0.2950.0310.0600.004-0.130-0.119-0.1300.1350.000
T050.004-0.0581.0000.008-0.022-0.021-0.0220.0430.027-0.010-0.010-0.009-0.0100.0480.000
T06-0.0480.0480.0081.000-0.041-0.039-0.040-0.0100.0120.366-0.018-0.016-0.0180.0680.000
T07-0.245-0.305-0.022-0.0411.0000.9520.967-0.058-0.068-0.0150.4300.3940.4300.0000.000
T08-0.233-0.290-0.021-0.0390.9521.0000.914-0.055-0.065-0.0150.3760.4140.3760.0460.000
T09-0.237-0.295-0.022-0.0400.9670.9141.000-0.056-0.066-0.0150.4430.4060.4430.0000.000
T100.1470.0310.043-0.010-0.058-0.055-0.0561.0000.8480.135-0.025-0.023-0.0250.0000.131
T11-0.0010.0600.0270.012-0.068-0.065-0.0660.8481.0000.221-0.029-0.027-0.0290.0400.112
T13-0.0460.004-0.0100.366-0.015-0.015-0.0150.1350.2211.000-0.007-0.006-0.0070.0000.000
T14-0.105-0.130-0.010-0.0180.4300.3760.443-0.025-0.029-0.0071.0000.9131.0000.0000.000
T15-0.096-0.119-0.009-0.0160.3940.4140.406-0.023-0.027-0.0060.9131.0000.9130.0000.000
T16-0.105-0.130-0.010-0.0180.4300.3760.443-0.025-0.029-0.0071.0000.9131.0000.0000.000
LIQD_PLAN_CD0.0000.1350.0480.0680.0000.0460.0000.0000.0400.0000.0000.0000.0001.0000.000
T120.0000.0000.0000.0000.0000.0000.0000.1310.1120.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T08:44:56.939484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:44:57.154252image/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

LIQD_PLAN_CDBASIS_MMT03T04T05T06T07T08T09T10T11T12T13T14T15T16OPER_DY
0KHFCMB2018S-20201806420000163493000004200001634930000020180928
1KHFCMB2018S-20201806980000114445200000000000020180928
2KHFCMB2018S-2020180609625000000000000020180928
3KHFCMB2018S-202018067667812608800000000000020180928
4KHFCMB2018S-20201806736913677000000000000020180928
5KHFCMB2018S-20201806275968405400000000000020180928
6KHFCMB2018S-2020180612757221948600000000000020180928
7KHFCMB2018S-202018066889114544900000000000020180928
8KHFCMB2018S-2020180612049282191370000000000020180928
9KHFCMB2018S-202018061968393974700000000000020180928
LIQD_PLAN_CDBASIS_MMT03T04T05T06T07T08T09T10T11T12T13T14T15T16OPER_DY
990KHFCMB2018S-1920180630555535968400000000000020180928
991KHFCMB2018S-1920180658333365954600000000000020180928
992KHFCMB2018S-1920180619236741708300000000000020180928
993KHFCMB2018S-1920180612873827912500000000000020180928
994KHFCMB2018S-19201806517912112291600000000000020180928
995KHFCMB2018S-1920180630501562121600000000000020180928
996KHFCMB2018S-1920180610828022053200000000000020180928
997KHFCMB2018S-1920180622492248766600000000000020180928
998KHFCMB2018S-19201806554556416600000000000020180928
999KHFCMB2018S-1920180650497493187800000000000020180928

Duplicate rows

Most frequently occurring

LIQD_PLAN_CDBASIS_MMT03T04T05T06T07T08T09T10T11T12T13T14T15T16OPER_DY# duplicates
10KHFCMB2018S-202018060433150000000000000201809286
12KHFCMB2018S-202018060467123000000000000201809286
9KHFCMB2018S-202018060416164000000000000201809285
6KHFCMB2018S-202018060373698000000000000201809284
16KHFCMB2018S-20201806150615102739000000000000201809283
0KHFCMB2018S-192018062959564166000000000000201809282
1KHFCMB2018S-19201806133177288750000000000000201809282
2KHFCMB2018S-19201806342252160416000000000000201809282
3KHFCMB2018S-202018060239506000000000000201809282
4KHFCMB2018S-202018060303205000000000000201809282