Overview

Dataset statistics

Number of variables6
Number of observations511
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.1 KiB
Average record size in memory54.3 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 12:31:09.643761
Analysis finished2024-03-13 12:31:17.264836
Duration7.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SPOT_LO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct511
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.32042
Minimum129.32037
Maximum129.32046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-03-13T21:31:17.556185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.32037
5-th percentile129.32038
Q1129.3204
median129.32042
Q3129.32044
95-th percentile129.32045
Maximum129.32046
Range9.01923 × 10-5
Interquartile range (IQR)3.60785 × 10-5

Descriptive statistics

Standard deviation2.2441493 × 10-5
Coefficient of variation (CV)1.7353403 × 10-7
Kurtosis-0.89868014
Mean129.32042
Median Absolute Deviation (MAD)1.80125 × 10-5
Skewness-0.31150307
Sum66082.735
Variance5.0362062 × 10-10
MonotonicityNot monotonic
2024-03-13T21:31:17.893296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.3204368467 1
 
0.2%
129.320439102 1
 
0.2%
129.3203803399 1
 
0.2%
129.3203780845 1
 
0.2%
129.3203758291 1
 
0.2%
129.3203735737 1
 
0.2%
129.3204570305 1
 
0.2%
129.3204547751 1
 
0.2%
129.3204525197 1
 
0.2%
129.3204502643 1
 
0.2%
Other values (501) 501
98.0%
ValueCountFrequency (%)
129.320371326 1
0.2%
129.3203735508 1
0.2%
129.3203735584 1
0.2%
129.3203735661 1
0.2%
129.3203735737 1
0.2%
129.3203735814 1
0.2%
129.320373589 1
0.2%
129.3203757985 1
0.2%
129.3203758062 1
0.2%
129.3203758138 1
0.2%
ValueCountFrequency (%)
129.3204615183 1
0.2%
129.3204615107 1
0.2%
129.3204592782 1
0.2%
129.3204592706 1
0.2%
129.3204592629 1
0.2%
129.3204592553 1
0.2%
129.3204570381 1
0.2%
129.3204570305 1
0.2%
129.3204570228 1
0.2%
129.3204570152 1
0.2%

SPOT_LA
Real number (ℝ)

UNIQUE 

Distinct511
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.287508
Minimum37.287494
Maximum37.28753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-03-13T21:31:18.319522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.287494
5-th percentile37.287496
Q137.287501
median37.287507
Q337.287514
95-th percentile37.287525
Maximum37.28753
Range3.59067 × 10-5
Interquartile range (IQR)1.27181 × 10-5

Descriptive statistics

Standard deviation8.8074581 × 10-6
Coefficient of variation (CV)2.3620399 × 10-7
Kurtosis-0.66592654
Mean37.287508
Median Absolute Deviation (MAD)7.141 × 10-6
Skewness0.48950494
Sum19053.917
Variance7.7571318 × 10-11
MonotonicityStrictly decreasing
2024-03-13T21:31:18.787865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2875302738 1
 
0.2%
37.2875302677 1
 
0.2%
37.2875015936 1
 
0.2%
37.2875015998 1
 
0.2%
37.2875016059 1
 
0.2%
37.287501612 1
 
0.2%
37.2875031881 1
 
0.2%
37.2875031942 1
 
0.2%
37.2875032003 1
 
0.2%
37.2875032064 1
 
0.2%
Other values (501) 501
98.0%
ValueCountFrequency (%)
37.2874943671 1
0.2%
37.2874943732 1
0.2%
37.2874943793 1
0.2%
37.2874943854 1
0.2%
37.2874943916 1
0.2%
37.2874943977 1
0.2%
37.2874959677 1
0.2%
37.2874959738 1
0.2%
37.2874959799 1
0.2%
37.287495986 1
0.2%
ValueCountFrequency (%)
37.2875302738 1
0.2%
37.2875302677 1
0.2%
37.2875302616 1
0.2%
37.287528484 1
0.2%
37.2875284779 1
0.2%
37.2875284718 1
0.2%
37.2875284656 1
0.2%
37.2875284595 1
0.2%
37.2875284534 1
0.2%
37.2875266941 1
0.2%

SPOT_AL
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1955519
Minimum-1.002
Maximum17.907
Zeros0
Zeros (%)0.0%
Negative143
Negative (%)28.0%
Memory size4.6 KiB
2024-03-13T21:31:19.194687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.002
5-th percentile-0.7545
Q1-0.049
median0.973
Q32.0345
95-th percentile4.367
Maximum17.907
Range18.909
Interquartile range (IQR)2.0835

Descriptive statistics

Standard deviation1.6706577
Coefficient of variation (CV)1.3973946
Kurtosis18.773899
Mean1.1955519
Median Absolute Deviation (MAD)1.025
Skewness2.5064321
Sum610.927
Variance2.7910971
MonotonicityNot monotonic
2024-03-13T21:31:19.484740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.008 4
 
0.8%
-0.049 4
 
0.8%
-0.053 3
 
0.6%
1.385 3
 
0.6%
-0.711 3
 
0.6%
-0.757 3
 
0.6%
-0.16 2
 
0.4%
-0.032 2
 
0.4%
-0.056 2
 
0.4%
-0.052 2
 
0.4%
Other values (457) 483
94.5%
ValueCountFrequency (%)
-1.002 1
0.2%
-1.0 1
0.2%
-0.994 1
0.2%
-0.992 1
0.2%
-0.98 1
0.2%
-0.972 1
0.2%
-0.957 1
0.2%
-0.946 1
0.2%
-0.931 1
0.2%
-0.929 1
0.2%
ValueCountFrequency (%)
17.907 1
0.2%
5.522 1
0.2%
5.472 1
0.2%
5.466 1
0.2%
5.459 1
0.2%
5.438 1
0.2%
4.751 1
0.2%
4.735 1
0.2%
4.689 1
0.2%
4.648 1
0.2%

GRDNT_RT
Real number (ℝ)

HIGH CORRELATION 

Distinct505
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.89935
Minimum2.29
Maximum1062.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-03-13T21:31:19.840281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.29
5-th percentile21.12
Q150.23
median78.65
Q3144.39
95-th percentile346.65
Maximum1062.05
Range1059.76
Interquartile range (IQR)94.16

Descriptive statistics

Standard deviation120.67888
Coefficient of variation (CV)1.0065015
Kurtosis12.457994
Mean119.89935
Median Absolute Deviation (MAD)38.42
Skewness2.9242301
Sum61268.57
Variance14563.391
MonotonicityNot monotonic
2024-03-13T21:31:20.666137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.67 2
 
0.4%
50.23 2
 
0.4%
21.54 2
 
0.4%
88.96 2
 
0.4%
91.46 2
 
0.4%
21.33 2
 
0.4%
174.05 1
 
0.2%
358.24 1
 
0.2%
309.56 1
 
0.2%
43.57 1
 
0.2%
Other values (495) 495
96.9%
ValueCountFrequency (%)
2.29 1
0.2%
4.29 1
0.2%
6.54 1
0.2%
7.37 1
0.2%
7.42 1
0.2%
7.97 1
0.2%
8.69 1
0.2%
10.45 1
0.2%
11.67 1
0.2%
11.83 1
0.2%
ValueCountFrequency (%)
1062.05 1
0.2%
729.46 1
0.2%
710.84 1
0.2%
708.44 1
0.2%
649.92 1
0.2%
637.47 1
0.2%
634.44 1
0.2%
559.89 1
0.2%
556.94 1
0.2%
540.26 1
0.2%

GRDNT_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct490
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.834795
Minimum1.31
Maximum84.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-03-13T21:31:20.897556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.31
5-th percentile11.925
Q126.67
median38.19
Q355.295
95-th percentile73.91
Maximum84.62
Range83.31
Interquartile range (IQR)28.625

Descriptive statistics

Standard deviation19.09474
Coefficient of variation (CV)0.46760956
Kurtosis-0.76436348
Mean40.834795
Median Absolute Deviation (MAD)13.99
Skewness0.24852181
Sum20866.58
Variance364.6091
MonotonicityNot monotonic
2024-03-13T21:31:21.123595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.66 3
 
0.6%
32.61 2
 
0.4%
12.15 2
 
0.4%
15.9 2
 
0.4%
29.1 2
 
0.4%
32.89 2
 
0.4%
30.87 2
 
0.4%
35.44 2
 
0.4%
41.18 2
 
0.4%
26.67 2
 
0.4%
Other values (480) 490
95.9%
ValueCountFrequency (%)
1.31 1
0.2%
2.45 1
0.2%
3.74 1
0.2%
4.22 1
0.2%
4.24 1
0.2%
4.56 1
0.2%
4.96 1
0.2%
5.97 1
0.2%
6.66 1
0.2%
6.75 1
0.2%
ValueCountFrequency (%)
84.62 1
0.2%
82.19 1
0.2%
81.99 1
0.2%
81.97 1
0.2%
81.25 1
0.2%
81.08 1
0.2%
81.04 1
0.2%
79.87 1
0.2%
79.82 1
0.2%
79.51 1
0.2%

SLANT_DRC
Real number (ℝ)

Distinct505
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.32577
Minimum0.5
Maximum357.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-03-13T21:31:21.379786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile16.005
Q163.185
median88.59
Q3112.33
95-th percentile271.62
Maximum357.79
Range357.29
Interquartile range (IQR)49.145

Descriptive statistics

Standard deviation74.40862
Coefficient of variation (CV)0.72013611
Kurtosis2.7513347
Mean103.32577
Median Absolute Deviation (MAD)24.63
Skewness1.6636638
Sum52799.47
Variance5536.6427
MonotonicityNot monotonic
2024-03-13T21:31:21.610310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.73 2
 
0.4%
97.27 2
 
0.4%
91.46 2
 
0.4%
105.23 2
 
0.4%
72.2 2
 
0.4%
85.53 2
 
0.4%
240.59 1
 
0.2%
196.15 1
 
0.2%
247.35 1
 
0.2%
208.65 1
 
0.2%
Other values (495) 495
96.9%
ValueCountFrequency (%)
0.5 1
0.2%
1.23 1
0.2%
1.58 1
0.2%
3.63 1
0.2%
3.68 1
0.2%
4.61 1
0.2%
4.64 1
0.2%
4.65 1
0.2%
4.9 1
0.2%
5.73 1
0.2%
ValueCountFrequency (%)
357.79 1
0.2%
356.38 1
0.2%
356.08 1
0.2%
355.82 1
0.2%
354.34 1
0.2%
352.56 1
0.2%
352.08 1
0.2%
350.19 1
0.2%
347.75 1
0.2%
347.47 1
0.2%

Interactions

2024-03-13T21:31:14.725282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:09.943628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:10.777718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:11.649617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:12.512373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:13.365373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:15.017691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:10.053279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:10.916295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:11.792111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:12.660157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:13.491504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:15.294698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:10.244786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:11.090884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:11.938563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:12.804067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:13.686605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:15.519042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:10.382810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:11.248866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:12.064769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:12.964327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:13.865443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:15.809027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:10.523608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:11.397013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:12.205716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:13.087129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:14.059808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:16.101212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:10.628868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:11.524122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:12.330457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:13.222517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:31:14.337416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:31:21.781238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
SPOT_LO1.0000.3270.9120.2920.5120.555
SPOT_LA0.3271.0000.3250.4590.6740.783
SPOT_AL0.9120.3251.0000.2370.3670.434
GRDNT_RT0.2920.4590.2371.0000.8150.420
GRDNT_VAL0.5120.6740.3670.8151.0000.649
SLANT_DRC0.5550.7830.4340.4200.6491.000
2024-03-13T21:31:21.985963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
SPOT_LO1.0000.197-0.979-0.221-0.2210.292
SPOT_LA0.1971.000-0.186-0.488-0.4880.262
SPOT_AL-0.979-0.1861.0000.2100.210-0.290
GRDNT_RT-0.221-0.4880.2101.0001.000-0.436
GRDNT_VAL-0.221-0.4880.2101.0001.000-0.436
SLANT_DRC0.2920.262-0.290-0.436-0.4361.000

Missing values

2024-03-13T21:31:16.691234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:31:17.107141image/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
0129.32043737.28753-0.167.424.248.58
1129.32043937.28753-0.15615.348.72326.81
2129.32044137.28753-0.17713.447.65331.12
3129.32043237.287528-0.19415.158.61347.75
4129.32043537.287528-0.16921.2912.02352.08
5129.32043737.287528-0.155222.2865.7843.2
6129.32043937.287528-0.14791.4642.4432.13
7129.32044137.287528-0.1629.0216.18355.82
8129.32044437.287528-0.16422.7112.79347.47
9129.32042837.287527-0.06120.5811.63344.53
SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
501129.32045537.287496-0.895347.0773.9338.12
502129.32045737.287496-0.282128.4752.113.75
503129.32045937.287496-0.087164.0158.6310.94
504129.32046237.287496-0.007216.5165.2120.97
505129.32037637.2874944.751263.9469.2519.49
506129.32037837.2874944.581340.2273.6226.82
507129.3203837.2874944.443416.4276.529.32
508129.32038337.2874944.362380.0475.2623.47
509129.32038537.2874944.144267.6669.5119.03
510129.32038737.2874944.0188.2762.037.26