Overview

Dataset statistics

Number of variables4
Number of observations100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.3 MiB
Average record size in memory171.0 B

Variable types

Text1
DateTime1
Numeric2

Alerts

entries is highly overall correlated with exitsHigh correlation
exits is highly overall correlated with entriesHigh correlation
entries has 1803 (1.8%) zerosZeros
exits has 4660 (4.7%) zerosZeros

Reproduction

Analysis started2024-05-06 20:36:31.358184
Analysis finished2024-05-06 20:36:35.071492
Duration3.71 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

station_name
Text

Distinct320
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
2024-05-06T16:36:35.529935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length15
Median length13
Mean length10.95356
Min length4

Characters and Unicode

Total characters1095356
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVERNON-JACKSON
2nd rowVERNON-JACKSON
3rd rowVERNON-JACKSON
4th rowVERNON-JACKSON
5th rowVERNON-JACKSON
ValueCountFrequency (%)
st 32318
 
15.2%
av 15286
 
7.2%
sutphin-archer 6128
 
2.9%
atl 3746
 
1.8%
av-barclay 3746
 
1.8%
sq 3340
 
1.6%
34 2848
 
1.3%
blvd 2130
 
1.0%
avenue 2118
 
1.0%
fulton 1886
 
0.9%
Other values (371) 138742
65.4%
2024-05-06T16:36:35.976382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112288
 
10.3%
A 97800
 
8.9%
T 94447
 
8.6%
S 85749
 
7.8%
R 63457
 
5.8%
E 63147
 
5.8%
N 50051
 
4.6%
L 39376
 
3.6%
H 38836
 
3.5%
C 38811
 
3.5%
Other values (31) 411394
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1095356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
112288
 
10.3%
A 97800
 
8.9%
T 94447
 
8.6%
S 85749
 
7.8%
R 63457
 
5.8%
E 63147
 
5.8%
N 50051
 
4.6%
L 39376
 
3.6%
H 38836
 
3.5%
C 38811
 
3.5%
Other values (31) 411394
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1095356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
112288
 
10.3%
A 97800
 
8.9%
T 94447
 
8.6%
S 85749
 
7.8%
R 63457
 
5.8%
E 63147
 
5.8%
N 50051
 
4.6%
L 39376
 
3.6%
H 38836
 
3.5%
C 38811
 
3.5%
Other values (31) 411394
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1095356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
112288
 
10.3%
A 97800
 
8.9%
T 94447
 
8.6%
S 85749
 
7.8%
R 63457
 
5.8%
E 63147
 
5.8%
N 50051
 
4.6%
L 39376
 
3.6%
H 38836
 
3.5%
C 38811
 
3.5%
Other values (31) 411394
37.6%

created_dt
Date

Distinct5765
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2023-06-05 00:00:00+00:00
Maximum2023-07-28 21:00:00+00:00
2024-05-06T16:36:36.135429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-06T16:36:36.310222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

entries
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86709
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36912697
Minimum0
Maximum2.1471706 × 109
Zeros1803
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-05-06T16:36:36.474006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6614.7
Q1271262
median1358986.5
Q36155866.5
95-th percentile70120227
Maximum2.1471706 × 109
Range2.1471706 × 109
Interquartile range (IQR)5884604.5

Descriptive statistics

Standard deviation2.0853999 × 108
Coefficient of variation (CV)5.6495463
Kurtosis57.945429
Mean36912697
Median Absolute Deviation (MAD)1325942
Skewness7.4637764
Sum3.6912697 × 1012
Variance4.3488929 × 1016
MonotonicityNot monotonic
2024-05-06T16:36:36.634352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1803
 
1.8%
1 423
 
0.4%
458752 390
 
0.4%
327680 330
 
0.3%
262144 257
 
0.3%
2 223
 
0.2%
524288 166
 
0.2%
3 128
 
0.1%
117440512 114
 
0.1%
100663296 78
 
0.1%
Other values (86699) 96088
96.1%
ValueCountFrequency (%)
0 1803
1.8%
1 423
 
0.4%
2 223
 
0.2%
3 128
 
0.1%
4 24
 
< 0.1%
5 39
 
< 0.1%
6 48
 
< 0.1%
7 71
 
0.1%
8 17
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
2147170571 1
< 0.1%
2147170566 1
< 0.1%
2147170492 1
< 0.1%
2147170440 1
< 0.1%
2147170376 1
< 0.1%
2147170330 1
< 0.1%
2147170309 1
< 0.1%
2147170302 1
< 0.1%
2147170216 1
< 0.1%
2147170160 1
< 0.1%

exits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86004
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29545131
Minimum0
Maximum2.1214762 × 109
Zeros4660
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-05-06T16:36:36.790277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q1195375.25
median1004706
Q34450152
95-th percentile18502234
Maximum2.1214762 × 109
Range2.1214762 × 109
Interquartile range (IQR)4254776.8

Descriptive statistics

Standard deviation1.8283964 × 108
Coefficient of variation (CV)6.1884864
Kurtosis71.630864
Mean29545131
Median Absolute Deviation (MAD)985346
Skewness8.1829201
Sum2.9545131 × 1012
Variance3.3430335 × 1016
MonotonicityNot monotonic
2024-05-06T16:36:36.969033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4660
 
4.7%
16777216 143
 
0.1%
65536 72
 
0.1%
131645 61
 
0.1%
3 53
 
0.1%
131653 50
 
0.1%
2 50
 
0.1%
131643 49
 
< 0.1%
450 48
 
< 0.1%
1 48
 
< 0.1%
Other values (85994) 94766
94.8%
ValueCountFrequency (%)
0 4660
4.7%
1 48
 
< 0.1%
2 50
 
0.1%
3 53
 
0.1%
4 6
 
< 0.1%
5 1
 
< 0.1%
6 30
 
< 0.1%
7 36
 
< 0.1%
8 1
 
< 0.1%
9 26
 
< 0.1%
ValueCountFrequency (%)
2121476226 1
< 0.1%
2121476148 1
< 0.1%
2121475863 1
< 0.1%
2121475599 1
< 0.1%
2121475492 1
< 0.1%
2121474632 1
< 0.1%
2121473752 1
< 0.1%
2050217643 1
< 0.1%
2050217079 1
< 0.1%
2050216561 1
< 0.1%

Interactions

2024-05-06T16:36:34.423325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-06T16:36:34.090434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-06T16:36:34.557689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-06T16:36:34.260604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-05-06T16:36:37.104986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
entriesexits
entries1.0000.822
exits0.8221.000

Missing values

2024-05-06T16:36:34.781708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-06T16:36:34.942851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

station_namecreated_dtentriesexits
0VERNON-JACKSON2023-07-28 16:00:00.000000 UTC60221704947323
1VERNON-JACKSON2023-07-28 12:00:00.000000 UTC60220684947077
2VERNON-JACKSON2023-07-28 04:00:00.000000 UTC60219184946510
3VERNON-JACKSON2023-07-28 08:00:00.000000 UTC60219544946739
4VERNON-JACKSON2023-07-28 00:00:00.000000 UTC60219184946498
5VERNON-JACKSON2023-07-28 20:00:00.000000 UTC60222704947721
6233 ST2023-07-28 17:00:00.000000 UTC47878274271382
7233 ST2023-07-28 05:00:00.000000 UTC47875194270983
8233 ST2023-07-28 09:00:00.000000 UTC47876614271090
9233 ST2023-07-28 21:00:00.000000 UTC47878774271623
station_namecreated_dtentriesexits
9999014 ST-UNION SQ2023-06-26 01:00:00.000000 UTC735242651772
9999114 ST-UNION SQ2023-06-26 17:00:00.000000 UTC735652652646
9999214 ST-UNION SQ2023-06-26 09:00:00.000000 UTC735300651978
9999314 ST-UNION SQ2023-06-26 13:00:00.000000 UTC735429652249
99994ATL AV-BARCLAY2023-06-26 09:45:00.000000 UTC0131642
99995ATL AV-BARCLAY2023-06-26 19:15:00.000000 UTC0131642
99996ATL AV-BARCLAY2023-06-26 01:30:00.000000 UTC0131642
99997ATL AV-BARCLAY2023-06-26 03:45:00.000000 UTC0131642
99998ATL AV-BARCLAY2023-06-26 04:30:00.000000 UTC0131642
99999ATL AV-BARCLAY2023-06-26 21:15:00.000000 UTC0131642