Overview

Dataset statistics

Number of variables6
Number of observations530
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.1 KiB
Average record size in memory54.2 B

Variable types

Numeric6

Alerts

SPOT_LO is highly overall correlated with SPOT_ALHigh correlation
SPOT_AL is highly overall correlated with SPOT_LOHigh correlation
GRDNT_RT is highly overall correlated with GRDNT_VALHigh correlation
GRDNT_VAL is highly overall correlated with GRDNT_RTHigh correlation
SPOT_LO has unique valuesUnique
SPOT_LA has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:28:11.897709
Analysis finished2024-03-13 12:28:21.920577
Duration10.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SPOT_LO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct530
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.61401
Minimum126.6139
Maximum126.61421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-13T21:28:22.112368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.6139
5-th percentile126.61391
Q1126.61394
median126.61399
Q3126.61406
95-th percentile126.61416
Maximum126.61421
Range0.0003152744
Interquartile range (IQR)0.00011935942

Descriptive statistics

Standard deviation7.9467089 × 10-5
Coefficient of variation (CV)6.2763269 × 10-7
Kurtosis-0.5187771
Mean126.61401
Median Absolute Deviation (MAD)5.742125 × 10-5
Skewness0.64162518
Sum67105.423
Variance6.3150182 × 10-9
MonotonicityNot monotonic
2024-03-13T21:28:22.715831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6138966777 1
 
0.2%
126.6141466456 1
 
0.2%
126.6139169882 1
 
0.2%
126.6139147366 1
 
0.2%
126.613912485 1
 
0.2%
126.6139102333 1
 
0.2%
126.6139079817 1
 
0.2%
126.6139057301 1
 
0.2%
126.6139034785 1
 
0.2%
126.6139012268 1
 
0.2%
Other values (520) 520
98.1%
ValueCountFrequency (%)
126.6138966777 1
0.2%
126.6138966869 1
0.2%
126.6138966961 1
0.2%
126.6138967052 1
0.2%
126.6138989294 1
0.2%
126.6138989385 1
0.2%
126.6138989477 1
0.2%
126.6138989569 1
0.2%
126.613898966 1
0.2%
126.6138989752 1
0.2%
ValueCountFrequency (%)
126.6142119521 1
0.2%
126.6142097005 1
0.2%
126.6142074488 1
0.2%
126.6142051972 1
0.2%
126.6142029456 1
0.2%
126.6142006939 1
0.2%
126.6141984423 1
0.2%
126.6141961907 1
0.2%
126.614193939 1
0.2%
126.6141916874 1
0.2%

SPOT_LA
Real number (ℝ)

UNIQUE 

Distinct530
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.162047
Minimum37.162043
Maximum37.162053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-13T21:28:23.085626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.162043
5-th percentile37.162043
Q137.162045
median37.162047
Q337.16205
95-th percentile37.162052
Maximum37.162053
Range1.09739 × 10-5
Interquartile range (IQR)4.779225 × 10-6

Descriptive statistics

Standard deviation2.8019878 × 10-6
Coefficient of variation (CV)7.5399177 × 10-8
Kurtosis-0.65482344
Mean37.162047
Median Absolute Deviation (MAD)1.84605 × 10-6
Skewness0
Sum19695.885
Variance7.8511356 × 10-12
MonotonicityNot monotonic
2024-03-13T21:28:23.457124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.1620533233 1
 
0.2%
37.1620469291 1
 
0.2%
37.162044379 1
 
0.2%
37.1620443717 1
 
0.2%
37.1620443643 1
 
0.2%
37.162044357 1
 
0.2%
37.1620443497 1
 
0.2%
37.1620443423 1
 
0.2%
37.162044335 1
 
0.2%
37.1620443276 1
 
0.2%
Other values (520) 520
98.1%
ValueCountFrequency (%)
37.1620425182 1
0.2%
37.1620425256 1
0.2%
37.1620425329 1
0.2%
37.1620425402 1
0.2%
37.1620425476 1
0.2%
37.1620425549 1
0.2%
37.1620425622 1
0.2%
37.1620425696 1
0.2%
37.1620425769 1
0.2%
37.1620425843 1
0.2%
ValueCountFrequency (%)
37.1620534921 1
0.2%
37.1620534847 1
0.2%
37.1620534774 1
0.2%
37.1620534701 1
0.2%
37.1620534627 1
0.2%
37.1620534554 1
0.2%
37.162053448 1
0.2%
37.1620534407 1
0.2%
37.1620534334 1
0.2%
37.162053426 1
0.2%

SPOT_AL
Real number (ℝ)

HIGH CORRELATION 

Distinct177
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.1550057
Minimum-2.264
Maximum-1.984
Zeros0
Zeros (%)0.0%
Negative530
Negative (%)100.0%
Memory size4.8 KiB
2024-03-13T21:28:23.733037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.264
5-th percentile-2.233
Q1-2.19475
median-2.153
Q3-2.125
95-th percentile-2.075
Maximum-1.984
Range0.28
Interquartile range (IQR)0.06975

Descriptive statistics

Standard deviation0.053049839
Coefficient of variation (CV)-0.02461703
Kurtosis0.19024039
Mean-2.1550057
Median Absolute Deviation (MAD)0.0335
Skewness0.37930972
Sum-1142.153
Variance0.0028142854
MonotonicityNot monotonic
2024-03-13T21:28:24.014659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.23 12
 
2.3%
-2.153 11
 
2.1%
-2.155 10
 
1.9%
-2.152 10
 
1.9%
-2.124 8
 
1.5%
-2.079 8
 
1.5%
-2.133 8
 
1.5%
-2.154 7
 
1.3%
-2.174 7
 
1.3%
-2.126 7
 
1.3%
Other values (167) 442
83.4%
ValueCountFrequency (%)
-2.264 1
 
0.2%
-2.263 1
 
0.2%
-2.259 2
0.4%
-2.254 2
0.4%
-2.251 3
0.6%
-2.25 1
 
0.2%
-2.246 2
0.4%
-2.243 1
 
0.2%
-2.24 2
0.4%
-2.238 2
0.4%
ValueCountFrequency (%)
-1.984 1
0.2%
-1.985 1
0.2%
-1.986 1
0.2%
-1.99 1
0.2%
-1.992 1
0.2%
-1.997 1
0.2%
-2.003 1
0.2%
-2.005 1
0.2%
-2.009 1
0.2%
-2.014 1
0.2%

GRDNT_RT
Real number (ℝ)

HIGH CORRELATION 

Distinct423
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5643774
Minimum0.09
Maximum23.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-13T21:28:24.301963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.828
Q12.0525
median3.77
Q35.875
95-th percentile11.8555
Maximum23.1
Range23.01
Interquartile range (IQR)3.8225

Descriptive statistics

Standard deviation3.6758264
Coefficient of variation (CV)0.8053292
Kurtosis5.3035806
Mean4.5643774
Median Absolute Deviation (MAD)1.795
Skewness2.0283625
Sum2419.12
Variance13.5117
MonotonicityNot monotonic
2024-03-13T21:28:25.165258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.02 4
 
0.8%
5.02 4
 
0.8%
0.98 4
 
0.8%
2.36 3
 
0.6%
4.18 3
 
0.6%
3.97 3
 
0.6%
3.83 3
 
0.6%
2.38 3
 
0.6%
1.98 3
 
0.6%
4.44 3
 
0.6%
Other values (413) 497
93.8%
ValueCountFrequency (%)
0.09 1
0.2%
0.17 1
0.2%
0.21 1
0.2%
0.26 1
0.2%
0.28 1
0.2%
0.35 1
0.2%
0.45 1
0.2%
0.47 2
0.4%
0.53 1
0.2%
0.58 1
0.2%
ValueCountFrequency (%)
23.1 1
0.2%
22.32 1
0.2%
20.69 1
0.2%
20.02 1
0.2%
19.8 1
0.2%
18.23 1
0.2%
18.04 1
0.2%
17.81 1
0.2%
17.47 1
0.2%
17.1 1
0.2%

GRDNT_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct353
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6080755
Minimum0.05
Maximum13.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-13T21:28:25.482278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.4745
Q11.18
median2.16
Q33.3575
95-th percentile6.76
Maximum13.01
Range12.96
Interquartile range (IQR)2.1775

Descriptive statistics

Standard deviation2.0872224
Coefficient of variation (CV)0.80029217
Kurtosis5.1238973
Mean2.6080755
Median Absolute Deviation (MAD)1.03
Skewness1.9966343
Sum1382.28
Variance4.3564972
MonotonicityNot monotonic
2024-03-13T21:28:25.809870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 5
 
0.9%
1.16 5
 
0.9%
1.14 5
 
0.9%
2.27 4
 
0.8%
1.73 4
 
0.8%
1.51 4
 
0.8%
1.36 4
 
0.8%
0.56 4
 
0.8%
3.56 4
 
0.8%
3.6 3
 
0.6%
Other values (343) 488
92.1%
ValueCountFrequency (%)
0.05 1
0.2%
0.1 1
0.2%
0.12 1
0.2%
0.15 1
0.2%
0.16 1
0.2%
0.2 1
0.2%
0.26 1
0.2%
0.27 2
0.4%
0.3 1
0.2%
0.33 1
0.2%
ValueCountFrequency (%)
13.01 1
0.2%
12.58 1
0.2%
11.69 1
0.2%
11.32 1
0.2%
11.2 1
0.2%
10.33 1
0.2%
10.23 1
0.2%
10.1 1
0.2%
9.91 1
0.2%
9.71 1
0.2%

SLANT_DRC
Real number (ℝ)

Distinct528
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean185.35664
Minimum0.1
Maximum359.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-13T21:28:26.162828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile15.134
Q164.22
median197.12
Q3298.2175
95-th percentile346.72
Maximum359.61
Range359.51
Interquartile range (IQR)233.9975

Descriptive statistics

Standard deviation118.53903
Coefficient of variation (CV)0.63951863
Kurtosis-1.5143254
Mean185.35664
Median Absolute Deviation (MAD)114.48
Skewness-0.12117027
Sum98239.02
Variance14051.501
MonotonicityNot monotonic
2024-03-13T21:28:26.460429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
338.81 2
 
0.4%
292.06 2
 
0.4%
215.23 1
 
0.2%
163.3 1
 
0.2%
213.24 1
 
0.2%
215.22 1
 
0.2%
201.44 1
 
0.2%
181.67 1
 
0.2%
166.92 1
 
0.2%
171.56 1
 
0.2%
Other values (518) 518
97.7%
ValueCountFrequency (%)
0.1 1
0.2%
0.25 1
0.2%
1.37 1
0.2%
1.64 1
0.2%
1.89 1
0.2%
3.2 1
0.2%
3.46 1
0.2%
3.73 1
0.2%
3.9 1
0.2%
5.3 1
0.2%
ValueCountFrequency (%)
359.61 1
0.2%
358.15 1
0.2%
357.95 1
0.2%
357.92 1
0.2%
357.29 1
0.2%
357.05 1
0.2%
356.83 1
0.2%
356.53 1
0.2%
355.9 1
0.2%
355.74 1
0.2%

Interactions

2024-03-13T21:28:20.295149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:12.423902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:14.356060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:16.045064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:17.925048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:19.052495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:20.481545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:12.663117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:14.611367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:16.554841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:18.098220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:19.287602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:20.688160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:13.034917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:14.824811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:16.839847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:18.317976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:19.519499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:20.922031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:13.595390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:15.084471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:17.086039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:18.527644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:19.707686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:21.112060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:13.863419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:15.370950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:17.355464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:18.683604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:19.861615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:21.322050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:14.099020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:15.638353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:17.708721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:18.865904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:28:20.107909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:28:26.672850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
SPOT_LO1.0000.7370.9100.4370.4350.615
SPOT_LA0.7371.0000.6860.2510.2460.424
SPOT_AL0.9100.6861.0000.3110.3280.420
GRDNT_RT0.4370.2510.3111.0000.9990.307
GRDNT_VAL0.4350.2460.3280.9991.0000.281
SLANT_DRC0.6150.4240.4200.3070.2811.000
2024-03-13T21:28:26.921941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
SPOT_LO1.000-0.1330.774-0.270-0.2700.035
SPOT_LA-0.1331.000-0.252-0.018-0.0180.030
SPOT_AL0.774-0.2521.000-0.249-0.249-0.027
GRDNT_RT-0.270-0.018-0.2491.0001.0000.077
GRDNT_VAL-0.270-0.018-0.2491.0001.0000.077
SLANT_DRC0.0350.030-0.0270.0770.0771.000

Missing values

2024-03-13T21:28:21.593647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:28:21.824209image/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

SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
0126.61389737.162053-2.2093.782.17215.23
1126.61389937.162053-2.2013.692.12183.74
2126.61390137.162053-2.1985.032.88136.89
3126.61390337.162053-2.1986.583.77111.39
4126.61390637.162053-2.2125.843.3484.65
5126.61390837.162053-2.236.233.5654.81
6126.6139137.162053-2.255.963.4137.67
7126.61391237.162053-2.2594.632.6513.05
8126.61391537.162053-2.2644.482.57348.06
9126.61391737.162053-2.2635.263.01326.55
SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
520126.61395537.162043-2.074.32.46356.83
521126.61395837.162043-2.0775.343.0647.57
522126.6139637.162043-2.0937.574.3369.85
523126.61396237.162043-2.1124.282.4568.17
524126.61396437.162043-2.1211.140.6525.74
525126.61396737.162043-2.1230.960.55349.28
526126.61396937.162043-2.1241.911.1309.23
527126.61397137.162043-2.1253.832.19293.39
528126.61397337.162043-2.1275.092.91286.09
529126.61397637.162043-2.135.082.91284.51