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

1 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:42:19.229794
Analysis finished2023-12-10 06:42:22.712076
Duration3.48 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
2023-12-10T15:42:22.830760image/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
2023-12-10T15:42:23.058309image/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

Distinct147
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-10T15:42:23.515554image/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_72443569 3
 
0.8%
t_42224958 3
 
0.8%
t_48597157 3
 
0.8%
t_24091552 3
 
0.8%
t_97828098 3
 
0.8%
t_96363225 3
 
0.8%
t_92408066 3
 
0.8%
t_43323613 3
 
0.8%
t_43396856 3
 
0.8%
Other values (137) 369
92.2%
2023-12-10T15:42:24.204128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 427
10.7%
T 400
10.0%
_ 400
10.0%
9 364
9.1%
7 346
8.6%
8 338
8.5%
3 332
8.3%
6 325
8.1%
2 300
7.5%
1 292
7.3%
Other values (2) 476
11.9%

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 (%)
4 427
13.3%
9 364
11.4%
7 346
10.8%
8 338
10.6%
3 332
10.4%
6 325
10.2%
2 300
9.4%
1 292
9.1%
0 239
7.5%
5 237
7.4%
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 (%)
4 427
11.9%
_ 400
11.1%
9 364
10.1%
7 346
9.6%
8 338
9.4%
3 332
9.2%
6 325
9.0%
2 300
8.3%
1 292
8.1%
0 239
6.6%
Latin
ValueCountFrequency (%)
T 400
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 427
10.7%
T 400
10.0%
_ 400
10.0%
9 364
9.1%
7 346
8.6%
8 338
8.5%
3 332
8.3%
6 325
8.1%
2 300
7.5%
1 292
7.3%
Other values (2) 476
11.9%

37.610664
Real number (ℝ)

Distinct320
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.49702
Minimum36.942387
Maximum37.778496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:42:24.450529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.942387
5-th percentile37.408438
Q137.458736
median37.497562
Q337.52941
95-th percentile37.603048
Maximum37.778496
Range0.836109
Interquartile range (IQR)0.070674

Descriptive statistics

Standard deviation0.078940472
Coefficient of variation (CV)0.0021052466
Kurtosis17.818791
Mean37.49702
Median Absolute Deviation (MAD)0.0354325
Skewness-2.1733569
Sum14998.808
Variance0.006231598
MonotonicityNot monotonic
2023-12-10T15:42:24.653973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.52941 3
 
0.8%
37.51823 3
 
0.8%
37.41697 3
 
0.8%
37.465614 3
 
0.8%
37.53863 3
 
0.8%
37.43951 3
 
0.8%
37.443947 3
 
0.8%
37.470848 3
 
0.8%
37.416527 3
 
0.8%
37.602383 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 2
0.5%
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)

Distinct268
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.82548
Minimum126.49017
Maximum127.46164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:42:24.843600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.49017
5-th percentile126.63904
Q1126.6923
median126.7345
Q3126.97359
95-th percentile127.12786
Maximum127.46164
Range0.971474
Interquartile range (IQR)0.28129

Descriptive statistics

Standard deviation0.17522246
Coefficient of variation (CV)0.0013816029
Kurtosis-0.099133724
Mean126.82548
Median Absolute Deviation (MAD)0.0852275
Skewness0.78711379
Sum50730.193
Variance0.03070291
MonotonicityNot monotonic
2023-12-10T15:42:25.082369image/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%
126.982376 3
 
0.8%
126.83376 3
 
0.8%
126.70156 3
 
0.8%
126.65866 3
 
0.8%
126.74964 3
 
0.8%
126.8875 3
 
0.8%
126.892075 3
 
0.8%
126.97362 3
 
0.8%
Other values (258) 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%
127.1333 1
0.2%

8
Real number (ℝ)

Distinct40
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.6175
Minimum1
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:42:25.294282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median10
Q317
95-th percentile33
Maximum116
Range115
Interquartile range (IQR)11

Descriptive statistics

Standard deviation13.625724
Coefficient of variation (CV)1.0006039
Kurtosis24.905837
Mean13.6175
Median Absolute Deviation (MAD)5
Skewness4.0825274
Sum5447
Variance185.66034
MonotonicityNot monotonic
2023-12-10T15:42:25.481404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5 38
 
9.5%
6 32
 
8.0%
7 28
 
7.0%
13 20
 
5.0%
10 20
 
5.0%
4 20
 
5.0%
9 18
 
4.5%
2 16
 
4.0%
14 16
 
4.0%
3 15
 
3.8%
Other values (30) 177
44.2%
ValueCountFrequency (%)
1 7
 
1.8%
2 16
4.0%
3 15
 
3.8%
4 20
5.0%
5 38
9.5%
6 32
8.0%
7 28
7.0%
8 14
 
3.5%
9 18
4.5%
10 20
5.0%
ValueCountFrequency (%)
116 1
 
0.2%
115 1
 
0.2%
114 1
 
0.2%
93 1
 
0.2%
60 3
0.8%
50 1
 
0.2%
40 3
0.8%
38 1
 
0.2%
34 5
1.2%
33 4
1.0%

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
2023-12-10T15:42:25.660209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:25.870830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

Interactions

2023-12-10T15:42:21.397289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:19.529321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:20.182301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:20.804048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:21.572633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:19.704566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:20.370109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:20.975206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:22.056060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:19.882327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:20.512864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:21.099649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:22.275981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:20.027643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:20.652982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:21.245600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:42:26.099563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
137.610664126.72465582020-09-10 22:00:00
11.0000.0000.1320.0000.946
37.6106640.0001.0000.6760.1070.000
126.7246550.1320.6761.0000.2320.000
80.0000.1070.2321.0000.000
2020-09-10 22:00:000.9460.0000.0000.0001.000
2023-12-10T15:42:26.265440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
137.610664126.7246558
11.0000.0200.0370.012
37.6106640.0201.0000.231-0.021
126.7246550.0370.2311.000-0.121
80.012-0.021-0.1211.000

Missing values

2023-12-10T15:42:22.482456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:42:22.649874image/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_9628998137.610664126.72465582020-09-10 22:00:00
02T_7332249337.621788127.08755132020-09-10 22:00:00
13T_4508146137.554714126.67306522020-09-10 22:00:00
24T_4493497337.52825126.67643102020-09-10 22:00:00
35T_4779147737.502796127.04183252020-09-10 22:00:00
46T_4368983137.52204126.7964232020-09-10 22:00:00
57T_9738863637.527405126.90563102020-09-10 22:00:00
68T_9270104137.462093126.6375562020-09-10 22:00:00
79T_1837854637.50068126.730644602020-09-10 22:00:00
810T_7310276337.55852126.859764182020-09-10 22:00:00
911T_6944057837.778492127.1154872020-09-10 22:00:00
1T_9628998137.610664126.72465582020-09-10 22:00:00
390392T_4618011637.45826127.0732722020-09-10 22:00:02
391393T_7295627537.47288126.9030622020-09-10 22:00:02
392394T_4852391437.51511127.06652102020-09-10 22:00:02
393395T_9636322537.49011127.06872262020-09-10 22:00:02
394396T_9782809837.473686127.06097222020-09-10 22:00:02
395397T_2409155237.52459127.0236532020-09-10 22:00:02
396398T_4859715737.53764126.892075342020-09-10 22:00:02
397399T_4383631837.439304126.70612122020-09-10 22:00:02
398400T_7383519937.60681126.97359262020-09-10 22:00:02
399401T_9687593137.510433127.03252202020-09-10 22:00:02