Overview

Brought to you by YData

Dataset statistics

Number of variables5
Number of observations106
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory41.2 B

Variable types

Numeric3
Categorical1
Text1

Alerts

SHAPE__Area is highly overall correlated with SHAPE__LengthHigh correlation
SHAPE__Length is highly overall correlated with SHAPE__AreaHigh correlation
OBJECTID is uniformly distributed Uniform
OBJECTID has unique values Unique
GLOBALID has unique values Unique
SHAPE__Area has unique values Unique
SHAPE__Length has unique values Unique

Reproduction

Analysis started2024-11-11 11:55:03.960959
Analysis finished2024-11-11 11:55:05.544528
Duration1.58 second
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

OBJECTID
Real number (ℝ)

Uniform  Unique 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.5
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2024-11-11T12:55:05.662067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.25
Q127.25
median53.5
Q379.75
95-th percentile100.75
Maximum106
Range105
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation30.743563
Coefficient of variation (CV)0.57464604
Kurtosis-1.2
Mean53.5
Median Absolute Deviation (MAD)26.5
Skewness0
Sum5671
Variance945.16667
MonotonicityStrictly increasing
2024-11-11T12:55:05.810009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
80 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
73 1
 
0.9%
72 1
 
0.9%
71 1
 
0.9%
Other values (96) 96
90.6%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%
98 1
0.9%
97 1
0.9%

GRIDCODE
Categorical

Distinct5
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size976.0 B
2
40 
1
38 
3
13 
4
13 
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters106
Distinct characters5
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 row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 40
37.7%
1 38
35.8%
3 13
 
12.3%
4 13
 
12.3%
5 2
 
1.9%

Length

2024-11-11T12:55:05.926903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T12:55:06.024581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2 40
37.7%
1 38
35.8%
3 13
 
12.3%
4 13
 
12.3%
5 2
 
1.9%

Most occurring characters

ValueCountFrequency (%)
2 40
37.7%
1 38
35.8%
3 13
 
12.3%
4 13
 
12.3%
5 2
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 40
37.7%
1 38
35.8%
3 13
 
12.3%
4 13
 
12.3%
5 2
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 40
37.7%
1 38
35.8%
3 13
 
12.3%
4 13
 
12.3%
5 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 40
37.7%
1 38
35.8%
3 13
 
12.3%
4 13
 
12.3%
5 2
 
1.9%

GLOBALID
Text

Unique 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size976.0 B
2024-11-11T12:55:06.547995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters3816
Distinct characters17
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

Unique106 ?
Unique (%)100.0%

Sample

1st rowa1702dce-649f-440d-acc6-1550b81fc829
2nd row681bd8ce-8138-43df-ac60-782f15d2d3e2
3rd rowa6224c97-b4db-4119-8acc-b3a53901228a
4th row66bdef73-0f92-437f-8071-06bc2e45adf9
5th row560534f9-452a-4079-a8d6-35323e9d8e43
ValueCountFrequency (%)
a1702dce-649f-440d-acc6-1550b81fc829 1
 
0.9%
aa91fb04-c4e7-4f65-bd24-ae821bcd9eeb 1
 
0.9%
66bdef73-0f92-437f-8071-06bc2e45adf9 1
 
0.9%
560534f9-452a-4079-a8d6-35323e9d8e43 1
 
0.9%
a6a4e473-f507-49e5-b536-49424d7ec839 1
 
0.9%
b229d223-b56d-41f7-9cf7-2a29065b6fe3 1
 
0.9%
4b03827b-fe25-40aa-875c-99531b5f8910 1
 
0.9%
cbb815af-b98f-46b9-b193-88b582b5cb83 1
 
0.9%
88ce1c68-3127-42f1-8247-597a57009f8c 1
 
0.9%
7993fe28-d689-4ebe-9e3f-e56e4b468619 1
 
0.9%
Other values (96) 96
90.6%
2024-11-11T12:55:06.958753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 424
 
11.1%
4 318
 
8.3%
9 243
 
6.4%
8 239
 
6.3%
a 231
 
6.1%
b 206
 
5.4%
6 205
 
5.4%
5 205
 
5.4%
1 204
 
5.3%
2 200
 
5.2%
Other values (7) 1341
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 424
 
11.1%
4 318
 
8.3%
9 243
 
6.4%
8 239
 
6.3%
a 231
 
6.1%
b 206
 
5.4%
6 205
 
5.4%
5 205
 
5.4%
1 204
 
5.3%
2 200
 
5.2%
Other values (7) 1341
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 424
 
11.1%
4 318
 
8.3%
9 243
 
6.4%
8 239
 
6.3%
a 231
 
6.1%
b 206
 
5.4%
6 205
 
5.4%
5 205
 
5.4%
1 204
 
5.3%
2 200
 
5.2%
Other values (7) 1341
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 424
 
11.1%
4 318
 
8.3%
9 243
 
6.4%
8 239
 
6.3%
a 231
 
6.1%
b 206
 
5.4%
6 205
 
5.4%
5 205
 
5.4%
1 204
 
5.3%
2 200
 
5.2%
Other values (7) 1341
35.1%

SHAPE__Area
Real number (ℝ)

High correlation  Unique 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11398288
Minimum1034.543
Maximum4.9007966 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2024-11-11T12:55:07.213348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1034.543
5-th percentile24134.528
Q1347697.2
median1179840.9
Q33897266.8
95-th percentile44440046
Maximum4.9007966 × 108
Range4.9007863 × 108
Interquartile range (IQR)3549569.6

Descriptive statistics

Standard deviation50592438
Coefficient of variation (CV)4.4385995
Kurtosis78.185915
Mean11398288
Median Absolute Deviation (MAD)1099447.7
Skewness8.4313924
Sum1.2082186 × 109
Variance2.5595948 × 1015
MonotonicityNot monotonic
2024-11-11T12:55:07.673318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24351.63672 1
 
0.9%
419313.6504 1
 
0.9%
1034.577148 1
 
0.9%
62808822.66 1
 
0.9%
2797686.531 1
 
0.9%
4118692.05 1
 
0.9%
2362448.598 1
 
0.9%
523533.5869 1
 
0.9%
415876.7266 1
 
0.9%
706938.5029 1
 
0.9%
Other values (96) 96
90.6%
ValueCountFrequency (%)
1034.542969 1
0.9%
1034.577148 1
0.9%
1513.461914 1
0.9%
2050.356445 1
0.9%
24128.7041 1
0.9%
24130.74805 1
0.9%
24145.86914 1
0.9%
24174.85938 1
0.9%
24189.40918 1
0.9%
24237.01855 1
0.9%
ValueCountFrequency (%)
490079664.5 1
0.9%
127169826.9 1
0.9%
113012705.1 1
0.9%
62808822.66 1
0.9%
55766934.86 1
0.9%
46821419.54 1
0.9%
37295926.08 1
0.9%
24839718.35 1
0.9%
21183210.83 1
0.9%
18834960.17 1
0.9%

SHAPE__Length
Real number (ℝ)

High correlation  Unique 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18670.01
Minimum146.65999
Maximum502515.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2024-11-11T12:55:07.818925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum146.65999
5-th percentile621.41231
Q12510.2885
median4521.3448
Q310724.127
95-th percentile94943.802
Maximum502515.46
Range502368.8
Interquartile range (IQR)8213.8389

Descriptive statistics

Standard deviation56649.503
Coefficient of variation (CV)3.0342514
Kurtosis52.030624
Mean18670.01
Median Absolute Deviation (MAD)3081.1958
Skewness6.6186322
Sum1979021
Variance3.2091662 × 109
MonotonicityNot monotonic
2024-11-11T12:55:07.957575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
624.201079 1
 
0.9%
3025.784683 1
 
0.9%
146.6601102 1
 
0.9%
144429.6719 1
 
0.9%
8748.165314 1
 
0.9%
10718.74064 1
 
0.9%
8022.399393 1
 
0.9%
2933.125125 1
 
0.9%
2459.353794 1
 
0.9%
3375.644626 1
 
0.9%
Other values (96) 96
90.6%
ValueCountFrequency (%)
146.6599918 1
0.9%
146.6601102 1
0.9%
155.6130485 1
0.9%
220.9965803 1
0.9%
621.337319 1
0.9%
621.3636591 1
0.9%
621.5582709 1
0.9%
621.9313204 1
0.9%
622.1184356 1
0.9%
622.7303509 1
0.9%
ValueCountFrequency (%)
502515.4603 1
0.9%
168901.7806 1
0.9%
165257.8253 1
0.9%
144429.6719 1
0.9%
135857.4925 1
0.9%
110347.7102 1
0.9%
48732.07931 1
0.9%
45603.66279 1
0.9%
38674.25941 1
0.9%
37128.97665 1
0.9%

Interactions

2024-11-11T12:55:04.685132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:55:04.097651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:55:04.426269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:55:04.794967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:55:04.233369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:55:04.507749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:55:04.917954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:55:04.323208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:55:04.593344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-11T12:55:08.044774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
GRIDCODEOBJECTIDSHAPE__AreaSHAPE__Length
GRIDCODE1.0000.1530.0780.107
OBJECTID0.1531.0000.0530.087
SHAPE__Area0.0780.0531.0000.990
SHAPE__Length0.1070.0870.9901.000

Missing values

2024-11-11T12:55:05.376026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-11T12:55:05.491960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OBJECTIDGRIDCODEGLOBALIDSHAPE__AreaSHAPE__Length
011a1702dce-649f-440d-acc6-1550b81fc8292.435164e+04624.201079
121681bd8ce-8138-43df-ac60-782f15d2d3e24.869767e+04935.832708
231a6224c97-b4db-4119-8acc-b3a53901228a2.131651e+052269.895184
34166bdef73-0f92-437f-8071-06bc2e45adf92.118321e+0727216.880295
452560534f9-452a-4079-a8d6-35323e9d8e431.018227e+064239.589988
561a6a4e473-f507-49e5-b536-49424d7ec8391.295123e+0725955.882644
673b229d223-b56d-41f7-9cf7-2a29065b6fe33.933924e+068598.479481
7824b03827b-fe25-40aa-875c-99531b5f89101.195353e+051463.629114
892cbb815af-b98f-46b9-b193-88b582b5cb835.528547e+052862.655248
910488ce1c68-3127-42f1-8247-597a57009f8c6.178946e+052959.164805
OBJECTIDGRIDCODEGLOBALIDSHAPE__AreaSHAPE__Length
969714a54adee-a53a-44a0-9c41-37ff660017f72.483972e+0745603.662792
9798325e93784-f54f-4ce4-bac2-4742b8abef9e4.900797e+08502515.460257
9899167621624-f01a-45b4-97c8-5f4daa52f2752.413075e+04621.363659
9910010649d748-b637-462d-a2a6-674548dbe96d2.327919e+069154.918293
1001011c003bf11-ebe6-4073-9ae4-071434c2bb501.055416e+0714607.504199
10110229118fe08-6bdb-4afe-99b8-4b314352b8914.018207e+0613806.195408
10210324e96532c-7b1a-4584-9fe6-3ed4904d95524.682142e+07110347.710221
10310415b72e8f5-6b35-45f3-a630-2e858f3bc6a22.412870e+04621.337319
1041051217a0815-9a9d-4b45-b124-55171692b5d71.863743e+052157.898048
105106112ed320e-044b-4ea1-a658-ecb1314d1f182.212154e+0611071.965063