Overview

Dataset statistics

Number of variables6
Number of observations400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.4 KiB
Average record size in memory52.3 B

Variable types

Numeric4
Text1
DateTime1

Alerts

126.724655 is highly overall correlated with 6338High correlation
6338 is highly overall correlated with 126.724655High correlation
1 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:41:17.538802
Analysis finished2023-12-10 06:41:20.363010
Duration2.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

1
Real number (ℝ)

UNIQUE 

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.5
Minimum2
Maximum401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:41:20.470573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile21.95
Q1101.75
median201.5
Q3301.25
95-th percentile381.05
Maximum401
Range399
Interquartile range (IQR)199.5

Descriptive statistics

Standard deviation115.6143
Coefficient of variation (CV)0.57376824
Kurtosis-1.2
Mean201.5
Median Absolute Deviation (MAD)100
Skewness0
Sum80600
Variance13366.667
MonotonicityStrictly increasing
2023-12-10T15:41:20.679486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
0.2%
266 1
 
0.2%
276 1
 
0.2%
275 1
 
0.2%
274 1
 
0.2%
273 1
 
0.2%
272 1
 
0.2%
271 1
 
0.2%
270 1
 
0.2%
269 1
 
0.2%
Other values (390) 390
97.5%
ValueCountFrequency (%)
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
11 1
0.2%
ValueCountFrequency (%)
401 1
0.2%
400 1
0.2%
399 1
0.2%
398 1
0.2%
397 1
0.2%
396 1
0.2%
395 1
0.2%
394 1
0.2%
393 1
0.2%
392 1
0.2%

T_96289981
Text

Distinct155
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-10T15:41:21.128670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4000
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st rowT_73322493
2nd rowT_45081461
3rd rowT_44934973
4th rowT_47791477
5th rowT_43689831
ValueCountFrequency (%)
t_48230939 4
 
1.0%
t_42224958 3
 
0.8%
t_72443569 3
 
0.8%
t_42298201 3
 
0.8%
t_48743645 3
 
0.8%
t_94312402 3
 
0.8%
t_92408066 3
 
0.8%
t_42151714 3
 
0.8%
t_43250369 3
 
0.8%
t_45081461 3
 
0.8%
Other values (145) 369
92.2%
2023-12-10T15:41:21.709802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 436
10.9%
T 400
10.0%
_ 400
10.0%
9 361
9.0%
8 340
8.5%
7 338
8.5%
3 328
8.2%
6 326
8.2%
2 292
7.3%
1 291
7.3%
Other values (2) 488
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3200
80.0%
Uppercase Letter 400
 
10.0%
Connector Punctuation 400
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 436
13.6%
9 361
11.3%
8 340
10.6%
7 338
10.6%
3 328
10.2%
6 326
10.2%
2 292
9.1%
1 291
9.1%
0 247
7.7%
5 241
7.5%
Uppercase Letter
ValueCountFrequency (%)
T 400
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
90.0%
Latin 400
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 436
12.1%
_ 400
11.1%
9 361
10.0%
8 340
9.4%
7 338
9.4%
3 328
9.1%
6 326
9.1%
2 292
8.1%
1 291
8.1%
0 247
6.9%
Latin
ValueCountFrequency (%)
T 400
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 436
10.9%
T 400
10.0%
_ 400
10.0%
9 361
9.0%
8 340
8.5%
7 338
8.5%
3 328
8.2%
6 326
8.2%
2 292
7.3%
1 291
7.3%
Other values (2) 488
12.2%

37.610664
Real number (ℝ)

Distinct320
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.495463
Minimum36.942387
Maximum37.778496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:41:21.957537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.942387
5-th percentile37.408438
Q137.460609
median37.491552
Q337.527853
95-th percentile37.602383
Maximum37.778496
Range0.836109
Interquartile range (IQR)0.067243

Descriptive statistics

Standard deviation0.077751495
Coefficient of variation (CV)0.0020736241
Kurtosis18.83539
Mean37.495463
Median Absolute Deviation (MAD)0.0333745
Skewness-2.264883
Sum14998.185
Variance0.006045295
MonotonicityNot monotonic
2023-12-10T15:41:22.227815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.41697 3
 
0.8%
37.52941 3
 
0.8%
37.48688 3
 
0.8%
37.444748 3
 
0.8%
37.465614 3
 
0.8%
37.53863 3
 
0.8%
37.516132 3
 
0.8%
37.43951 3
 
0.8%
37.416527 3
 
0.8%
37.573513 3
 
0.8%
Other values (310) 370
92.5%
ValueCountFrequency (%)
36.942387 1
0.2%
36.9425 1
0.2%
36.94261 1
0.2%
37.309208 1
0.2%
37.30923 1
0.2%
37.30925 1
0.2%
37.379356 1
0.2%
37.37936 1
0.2%
37.3861 1
0.2%
37.386143 1
0.2%
ValueCountFrequency (%)
37.778496 1
0.2%
37.778492 2
0.5%
37.676044 1
0.2%
37.67604 1
0.2%
37.622265 1
0.2%
37.622257 1
0.2%
37.622242 1
0.2%
37.621788 1
0.2%
37.621784 1
0.2%
37.62178 1
0.2%

126.724655
Real number (ℝ)

HIGH CORRELATION 

Distinct271
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.81692
Minimum126.49017
Maximum127.46164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:41:22.449571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.49017
5-th percentile126.63904
Q1126.69006
median126.72668
Q3126.94211
95-th percentile127.11883
Maximum127.46164
Range0.971474
Interquartile range (IQR)0.2520535

Descriptive statistics

Standard deviation0.17197472
Coefficient of variation (CV)0.0013560866
Kurtosis0.1785975
Mean126.81692
Median Absolute Deviation (MAD)0.068935
Skewness0.89720361
Sum50726.768
Variance0.029575306
MonotonicityNot monotonic
2023-12-10T15:41:22.684875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.69009 5
 
1.2%
126.710175 4
 
1.0%
126.68081 3
 
0.8%
126.76966 3
 
0.8%
127.03322 3
 
0.8%
126.66569 3
 
0.8%
126.711914 3
 
0.8%
126.64881 3
 
0.8%
126.734505 3
 
0.8%
126.83376 3
 
0.8%
Other values (261) 367
91.8%
ValueCountFrequency (%)
126.490166 1
 
0.2%
126.49028 1
 
0.2%
126.52404 2
0.5%
126.55558 1
 
0.2%
126.55563 1
 
0.2%
126.55568 1
 
0.2%
126.62454 1
 
0.2%
126.62456 1
 
0.2%
126.63534 3
0.8%
126.63667 2
0.5%
ValueCountFrequency (%)
127.46164 1
0.2%
127.461266 1
0.2%
127.46091 1
0.2%
127.15615 1
0.2%
127.15592 1
0.2%
127.15569 1
0.2%
127.136215 2
0.5%
127.133415 1
0.2%
127.13337 1
0.2%
127.1311 1
0.2%

6338
Real number (ℝ)

HIGH CORRELATION 

Distinct104
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15873.115
Minimum1489
Maximum22826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:41:22.893895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1489
5-th percentile5268
Q17207
median21360.5
Q322121.5
95-th percentile22787.2
Maximum22826
Range21337
Interquartile range (IQR)14914.5

Descriptive statistics

Standard deviation7589.7889
Coefficient of variation (CV)0.47815371
Kurtosis-1.5051048
Mean15873.115
Median Absolute Deviation (MAD)1347.5
Skewness-0.53812829
Sum6349246
Variance57604895
MonotonicityNot monotonic
2023-12-10T15:41:23.111448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6792 46
 
11.5%
7207 33
 
8.2%
1489 19
 
4.8%
6369 13
 
3.2%
22810 8
 
2.0%
22826 6
 
1.5%
21976 6
 
1.5%
21561 6
 
1.5%
21004 6
 
1.5%
22119 6
 
1.5%
Other values (94) 251
62.7%
ValueCountFrequency (%)
1489 19
4.8%
5268 2
 
0.5%
5281 2
 
0.5%
5386 2
 
0.5%
5750 2
 
0.5%
5791 2
 
0.5%
6338 4
 
1.0%
6369 13
 
3.2%
6790 2
 
0.5%
6792 46
11.5%
ValueCountFrequency (%)
22826 6
1.5%
22810 8
2.0%
22809 3
 
0.8%
22791 3
 
0.8%
22787 3
 
0.8%
22783 3
 
0.8%
22765 2
 
0.5%
22738 3
 
0.8%
22734 2
 
0.5%
22732 1
 
0.2%

2020-09-10 22:00:00
Date

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Minimum2020-09-10 22:00:00
Maximum2020-09-10 22:00:02
2023-12-10T15:41:23.573681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:23.727038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

Interactions

2023-12-10T15:41:19.505407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:17.830574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:18.448744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:18.990504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:19.657388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:18.004610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:18.612515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:19.130948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:19.792619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:18.153454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:18.720247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:19.246177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:19.956305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:18.310911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:18.875198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:19.372016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:41:23.853789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
137.610664126.72465563382020-09-10 22:00:00
11.0000.0000.1370.1240.945
37.6106640.0001.0000.6760.1740.000
126.7246550.1370.6761.0000.6300.000
63380.1240.1740.6301.0000.000
2020-09-10 22:00:000.9450.0000.0000.0001.000
2023-12-10T15:41:23.979646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
137.610664126.7246556338
11.000-0.015-0.0220.036
37.610664-0.0151.0000.221-0.248
126.724655-0.0220.2211.000-0.747
63380.036-0.248-0.7471.000

Missing values

2023-12-10T15:41:20.130780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:41:20.298268image/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

1T_9628998137.610664126.72465563382020-09-10 22:00:00
02T_7332249337.621788127.0875514892020-09-10 22:00:00
13T_4508146137.554714126.673065226432020-09-10 22:00:00
24T_4493497337.52825126.67643221652020-09-10 22:00:00
35T_4779147737.502796127.0418367922020-09-10 22:00:00
46T_4368983137.52204126.79642227912020-09-10 22:00:00
57T_9738863637.527405126.9056380412020-09-10 22:00:00
68T_9270104137.462093126.63755223512020-09-10 22:00:00
79T_1837854637.50068126.730644145062020-09-10 22:00:00
810T_7310276337.55852126.85976414892020-09-10 22:00:00
911T_6944057837.778492127.11548214582020-09-10 22:00:00
1T_9628998137.610664126.72465563382020-09-10 22:00:00
390392T_7273654437.59514127.0705667922020-09-10 22:00:02
391393T_1706015937.487595126.76966210402020-09-10 22:00:02
392394T_2343235937.4979126.9219672072020-09-10 22:00:02
393395T_6819543637.47441126.70931219312020-09-10 22:00:02
394396T_7061247737.458736126.64881221192020-09-10 22:00:02
395397T_9760836737.5649126.8337667922020-09-10 22:00:02
396398T_2379857837.524403126.82821105462020-09-10 22:00:02
397399T_1764610937.440838126.710175221292020-09-10 22:00:02
398400T_7346898137.540554126.9736272072020-09-10 22:00:02
399401T_1867152137.416527126.72744214222020-09-10 22:00:02