Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory44.3 B

Variable types

Numeric3
Categorical2

Alerts

seq_no is highly overall correlated with colct_dt and 1 other fieldsHigh correlation
colct_dt is highly overall correlated with seq_no and 1 other fieldsHigh correlation
before_macadrs_nm is highly overall correlated with seq_no and 1 other fieldsHigh correlation
seq_no has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:06:17.788354
Analysis finished2023-12-10 10:06:20.387121
Duration2.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1325755.2
Minimum476340
Maximum2298557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:20.520783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum476340
5-th percentile476344.95
Q1905521.5
median1572861.5
Q32097146.2
95-th percentile2097166
Maximum2298557
Range1822217
Interquartile range (IQR)1191624.8

Descriptive statistics

Standard deviation623214.14
Coefficient of variation (CV)0.47008237
Kurtosis-1.3552248
Mean1325755.2
Median Absolute Deviation (MAD)524286
Skewness-0.070462622
Sum1.3257552 × 108
Variance3.8839587 × 1011
MonotonicityNot monotonic
2023-12-10T19:06:20.790632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1572860 1
 
1.0%
1572876 1
 
1.0%
476358 1
 
1.0%
2097162 1
 
1.0%
1572878 1
 
1.0%
1048591 1
 
1.0%
476357 1
 
1.0%
2097161 1
 
1.0%
1572877 1
 
1.0%
1048590 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
476340 1
1.0%
476341 1
1.0%
476342 1
1.0%
476343 1
1.0%
476344 1
1.0%
476345 1
1.0%
476346 1
1.0%
476347 1
1.0%
476348 1
1.0%
476349 1
1.0%
ValueCountFrequency (%)
2298557 1
1.0%
2298556 1
1.0%
2298555 1
1.0%
2097168 1
1.0%
2097167 1
1.0%
2097166 1
1.0%
2097165 1
1.0%
2097164 1
1.0%
2097163 1
1.0%
2097162 1
1.0%

before_macadrs_nm
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
78:A3:51:63:16:5C
22 
78:A3:51:63:24:B8
11 
78:A3:51:63:18:E0
10 
78:A3:51:63:19:64
8 
78:A3:51:63:17:E4
7 
Other values (9)
42 

Length

Max length17
Median length17
Mean length17
Min length17

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row78:A3:51:63:1A:6C
2nd row78:A3:51:63:22:4C
3rd row78:A3:51:63:24:20
4th row78:A3:51:63:18:E0
5th row78:A3:51:63:1A:6C

Common Values

ValueCountFrequency (%)
78:A3:51:63:16:5C 22
22.0%
78:A3:51:63:24:B8 11
11.0%
78:A3:51:63:18:E0 10
10.0%
78:A3:51:63:19:64 8
 
8.0%
78:A3:51:63:17:E4 7
 
7.0%
78:A3:51:63:1A:6C 6
 
6.0%
78:A3:51:63:1B:54 6
 
6.0%
78:A3:51:63:1B:58 5
 
5.0%
78:A3:51:63:1D:C4 5
 
5.0%
78:A3:51:63:19:F4 5
 
5.0%
Other values (4) 15
15.0%

Length

2023-12-10T19:06:21.050600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78:a3:51:63:16:5c 22
22.0%
78:a3:51:63:24:b8 11
11.0%
78:a3:51:63:18:e0 10
10.0%
78:a3:51:63:19:64 8
 
8.0%
78:a3:51:63:17:e4 7
 
7.0%
78:a3:51:63:1a:6c 6
 
6.0%
78:a3:51:63:1b:54 6
 
6.0%
78:a3:51:63:1b:58 5
 
5.0%
78:a3:51:63:1d:c4 5
 
5.0%
78:a3:51:63:19:f4 5
 
5.0%
Other values (4) 15
15.0%

macadrs_nm
Categorical

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
78:A3:51:63:1A:2C
7 
78:A3:51:63:1A:6C
 
6
78:A3:51:63:1D:CC
 
6
78:A3:51:63:18:E0
 
6
78:A3:51:63:1B:58
 
6
Other values (23)
69 

Length

Max length17
Median length17
Mean length17
Min length17

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row78:A3:51:63:1B:58
2nd row78:A3:51:63:1A:2C
3rd row78:A3:51:63:1A:6C
4th row78:A3:51:63:19:F4
5th row78:A3:51:63:1D:CC

Common Values

ValueCountFrequency (%)
78:A3:51:63:1A:2C 7
 
7.0%
78:A3:51:63:1A:6C 6
 
6.0%
78:A3:51:63:1D:CC 6
 
6.0%
78:A3:51:63:18:E0 6
 
6.0%
78:A3:51:63:1B:58 6
 
6.0%
78:A3:51:63:18:30 5
 
5.0%
78:A3:51:63:22:B0 5
 
5.0%
78:A3:51:63:1D:C4 5
 
5.0%
78:A3:51:63:22:A8 5
 
5.0%
78:A3:51:63:1E:E0 5
 
5.0%
Other values (18) 44
44.0%

Length

2023-12-10T19:06:21.262298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78:a3:51:63:1a:2c 7
 
7.0%
78:a3:51:63:1d:cc 6
 
6.0%
78:a3:51:63:18:e0 6
 
6.0%
78:a3:51:63:1b:58 6
 
6.0%
78:a3:51:63:1a:6c 6
 
6.0%
78:a3:51:63:18:30 5
 
5.0%
78:a3:51:63:22:b0 5
 
5.0%
78:a3:51:63:1d:c4 5
 
5.0%
78:a3:51:63:22:a8 5
 
5.0%
78:a3:51:63:1e:e0 5
 
5.0%
Other values (18) 44
44.0%

colct_dt
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.021044 × 1013
Minimum2.0210303 × 1013
Maximum2.0210606 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:21.471318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210303 × 1013
5-th percentile2.0210303 × 1013
Q12.021038 × 1013
median2.0210503 × 1013
Q32.0210528 × 1013
95-th percentile2.0210528 × 1013
Maximum2.0210606 × 1013
Range3.0301 × 108
Interquartile range (IQR)1.4771925 × 108

Descriptive statistics

Standard deviation93388321
Coefficient of variation (CV)4.6207961 × 10-6
Kurtosis-1.2696747
Mean2.021044 × 1013
Median Absolute Deviation (MAD)96937000
Skewness-0.35805746
Sum2.021044 × 1015
Variance8.7213786 × 1015
MonotonicityNot monotonic
2023-12-10T19:06:21.698503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20210503091000 25
25.0%
20210406154000 23
23.0%
20210528120000 21
21.0%
20210303121000 13
13.0%
20210303120000 12
12.0%
20210606130000 3
 
3.0%
20210528115000 3
 
3.0%
ValueCountFrequency (%)
20210303120000 12
12.0%
20210303121000 13
13.0%
20210406154000 23
23.0%
20210503091000 25
25.0%
20210528115000 3
 
3.0%
20210528120000 21
21.0%
20210606130000 3
 
3.0%
ValueCountFrequency (%)
20210606130000 3
 
3.0%
20210528120000 21
21.0%
20210528115000 3
 
3.0%
20210503091000 25
25.0%
20210406154000 23
23.0%
20210303121000 13
13.0%
20210303120000 12
12.0%

mvmn_popltn_co
Real number (ℝ)

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.84
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:21.976600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile13
Maximum68
Range67
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.1917919
Coefficient of variation (CV)2.1332791
Kurtosis41.577297
Mean3.84
Median Absolute Deviation (MAD)0
Skewness5.9489961
Sum384
Variance67.105455
MonotonicityNot monotonic
2023-12-10T19:06:22.193093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 55
55.0%
2 15
 
15.0%
3 8
 
8.0%
4 4
 
4.0%
9 3
 
3.0%
5 2
 
2.0%
6 2
 
2.0%
13 2
 
2.0%
7 2
 
2.0%
10 1
 
1.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
1 55
55.0%
2 15
 
15.0%
3 8
 
8.0%
4 4
 
4.0%
5 2
 
2.0%
6 2
 
2.0%
7 2
 
2.0%
8 1
 
1.0%
9 3
 
3.0%
10 1
 
1.0%
ValueCountFrequency (%)
68 1
 
1.0%
40 1
 
1.0%
19 1
 
1.0%
14 1
 
1.0%
13 2
2.0%
11 1
 
1.0%
10 1
 
1.0%
9 3
3.0%
8 1
 
1.0%
7 2
2.0%

Interactions

2023-12-10T19:06:19.618522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:18.124366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:18.608525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:19.794465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:18.292468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:19.222911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:19.949429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:18.444013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:19.433939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:06:22.366572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nobefore_macadrs_nmmacadrs_nmcolct_dtmvmn_popltn_co
seq_no1.0000.9610.0001.0000.418
before_macadrs_nm0.9611.0000.0000.9480.399
macadrs_nm0.0000.0001.0000.0000.000
colct_dt1.0000.9480.0001.0000.361
mvmn_popltn_co0.4180.3990.0000.3611.000
2023-12-10T19:06:22.535159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
before_macadrs_nmmacadrs_nm
before_macadrs_nm1.0000.000
macadrs_nm0.0001.000
2023-12-10T19:06:22.732162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nocolct_dtmvmn_popltn_cobefore_macadrs_nmmacadrs_nm
seq_no1.0000.9790.0620.8470.000
colct_dt0.9791.0000.1040.8470.000
mvmn_popltn_co0.0620.1041.0000.2070.000
before_macadrs_nm0.8470.8470.2071.0000.000
macadrs_nm0.0000.0000.0000.0001.000

Missing values

2023-12-10T19:06:20.142203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:06:20.323101image/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

seq_nobefore_macadrs_nmmacadrs_nmcolct_dtmvmn_popltn_co
0157286078:A3:51:63:1A:6C78:A3:51:63:1B:58202105030910001
1229855578:A3:51:63:22:4C78:A3:51:63:1A:2C202106061300001
247634078:A3:51:63:24:2078:A3:51:63:1A:6C202103031200002
3104857478:A3:51:63:18:E078:A3:51:63:19:F4202104061540009
4157286178:A3:51:63:1A:6C78:A3:51:63:1D:CC202105030910005
5209714578:A3:51:63:24:B878:A3:51:63:1A:6C202105281150001
647634178:A3:51:63:24:2078:A3:51:63:1B:54202103031200001
7229855678:A3:51:63:22:4C78:A3:51:63:22:A82021060613000010
8157286278:A3:51:63:1A:6C78:A3:51:63:1D:D0202105030910005
9209714678:A3:51:63:24:B878:A3:51:63:1B:58202105281150001
seq_nobefore_macadrs_nmmacadrs_nmcolct_dtmvmn_popltn_co
9047636278:A3:51:63:18:3078:A3:51:63:18:E0202103031210008
91104859678:A3:51:63:19:F478:A3:51:63:1A:2C2021040615400068
92157288378:A3:51:63:1D:C478:A3:51:63:22:B0202105030910001
93209716778:A3:51:63:18:3078:A3:51:63:18:E02021052812000011
9447636378:A3:51:63:18:3078:A3:51:63:19:F4202103031210001
95104859778:A3:51:63:19:F478:A3:51:63:1A:4C202104061540002
96157288478:A3:51:63:1D:C478:A3:51:63:24:20202105030910001
97209716878:A3:51:63:18:3078:A3:51:63:1A:4C202105281200001
9847636478:A3:51:63:18:3078:A3:51:63:1A:4C202103031210001
99104859878:A3:51:63:19:F478:A3:51:63:1A:6C202104061540002