Overview

Dataset statistics

Number of variables12
Number of observations1560922
Missing cells0
Missing cells (%)0.0%
Duplicate rows65797
Duplicate rows (%)4.2%
Total size in memory142.9 MiB
Average record size in memory96.0 B

Variable types

Numeric11
Categorical1

Alerts

Dataset has 65797 (4.2%) duplicate rowsDuplicates
B2 is highly overall correlated with B3 and 7 other fieldsHigh correlation
B3 is highly overall correlated with B2 and 7 other fieldsHigh correlation
B4 is highly overall correlated with B2 and 7 other fieldsHigh correlation
B5 is highly overall correlated with B2 and 7 other fieldsHigh correlation
B6 is highly overall correlated with B2 and 7 other fieldsHigh correlation
B7 is highly overall correlated with B2 and 7 other fieldsHigh correlation
B8 is highly overall correlated with B2 and 8 other fieldsHigh correlation
B11 is highly overall correlated with B2 and 7 other fieldsHigh correlation
B12 is highly overall correlated with B2 and 7 other fieldsHigh correlation
DEM is highly overall correlated with SlopesHigh correlation
Slopes is highly overall correlated with B8 and 1 other fieldsHigh correlation
B2 has 280258 (18.0%) zerosZeros
B3 has 278145 (17.8%) zerosZeros
B5 has 278091 (17.8%) zerosZeros
B6 has 278091 (17.8%) zerosZeros

Reproduction

Analysis started2023-09-02 14:13:52.067730
Analysis finished2023-09-02 14:15:20.255593
Duration1 minute and 28.19 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

B2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1395
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.36021
Minimum0
Maximum3218
Zeros280258
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size11.9 MiB
2023-09-02T09:15:20.365658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q153
median129
Q3189
95-th percentile470
Maximum3218
Range3218
Interquartile range (IQR)136

Descriptive statistics

Standard deviation174.75879
Coefficient of variation (CV)1.1105653
Kurtosis10.225491
Mean157.36021
Median Absolute Deviation (MAD)66
Skewness2.8407089
Sum2.4562702 × 108
Variance30540.633
MonotonicityNot monotonic
2023-09-02T09:15:20.476900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 280258
 
18.0%
117 8324
 
0.5%
126 8321
 
0.5%
130 8157
 
0.5%
120 7936
 
0.5%
118 7914
 
0.5%
113 7883
 
0.5%
116 7861
 
0.5%
108 7824
 
0.5%
110 7805
 
0.5%
Other values (1385) 1208639
77.4%
ValueCountFrequency (%)
0 280258
18.0%
1 179
 
< 0.1%
2 191
 
< 0.1%
3 226
 
< 0.1%
4 214
 
< 0.1%
5 266
 
< 0.1%
6 227
 
< 0.1%
7 273
 
< 0.1%
8 271
 
< 0.1%
9 304
 
< 0.1%
ValueCountFrequency (%)
3218 1
 
< 0.1%
2626 1
 
< 0.1%
2186 1
 
< 0.1%
2184 1
 
< 0.1%
2126 1
 
< 0.1%
2096 3
< 0.1%
1968 2
< 0.1%
1908 2
< 0.1%
1848 1
 
< 0.1%
1838 2
< 0.1%

B3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1698
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.50104
Minimum0
Maximum3072
Zeros278145
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size11.9 MiB
2023-09-02T09:15:20.588846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1109
median214
Q3281
95-th percentile589
Maximum3072
Range3072
Interquartile range (IQR)172

Descriptive statistics

Standard deviation222.69406
Coefficient of variation (CV)0.95781964
Kurtosis9.3330298
Mean232.50104
Median Absolute Deviation (MAD)78
Skewness2.5985295
Sum3.6291598 × 108
Variance49592.644
MonotonicityNot monotonic
2023-09-02T09:15:20.689908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 278145
 
17.8%
212 8103
 
0.5%
208 7813
 
0.5%
201 7812
 
0.5%
217 7775
 
0.5%
206 7734
 
0.5%
215 7722
 
0.5%
210 7664
 
0.5%
203 7658
 
0.5%
205 7655
 
0.5%
Other values (1688) 1212841
77.7%
ValueCountFrequency (%)
0 278145
17.8%
1 15
 
< 0.1%
2 23
 
< 0.1%
3 25
 
< 0.1%
4 20
 
< 0.1%
5 18
 
< 0.1%
6 23
 
< 0.1%
7 13
 
< 0.1%
8 27
 
< 0.1%
9 30
 
< 0.1%
ValueCountFrequency (%)
3072 1
 
< 0.1%
2762 1
 
< 0.1%
2744 3
< 0.1%
2403 1
 
< 0.1%
2060 2
< 0.1%
2007 1
 
< 0.1%
1961 2
< 0.1%
1910 1
 
< 0.1%
1849 4
< 0.1%
1845 2
< 0.1%

B4
Real number (ℝ)

Distinct2197
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.66698
Minimum-100
Maximum3038
Zeros0
Zeros (%)0.0%
Negative277344
Negative (%)17.8%
Memory size11.9 MiB
2023-09-02T09:15:20.801118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q177
median268
Q3390
95-th percentile681
Maximum3038
Range3138
Interquartile range (IQR)313

Descriptive statistics

Standard deviation316.41605
Coefficient of variation (CV)1.1519989
Kurtosis7.1332481
Mean274.66698
Median Absolute Deviation (MAD)144
Skewness2.0768811
Sum4.2873373 × 108
Variance100119.12
MonotonicityNot monotonic
2023-09-02T09:15:20.910324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 277344
 
17.8%
324 4225
 
0.3%
342 4018
 
0.3%
321 4005
 
0.3%
313 3949
 
0.3%
339 3936
 
0.3%
349 3930
 
0.3%
328 3922
 
0.3%
317 3914
 
0.3%
331 3897
 
0.2%
Other values (2187) 1247782
79.9%
ValueCountFrequency (%)
-100 277344
17.8%
1 35
 
< 0.1%
2 34
 
< 0.1%
3 36
 
< 0.1%
4 39
 
< 0.1%
5 47
 
< 0.1%
6 77
 
< 0.1%
7 59
 
< 0.1%
8 81
 
< 0.1%
9 72
 
< 0.1%
ValueCountFrequency (%)
3038 1
 
< 0.1%
2836 3
< 0.1%
2764 1
 
< 0.1%
2341 2
< 0.1%
2336 1
 
< 0.1%
2326 1
 
< 0.1%
2319 1
 
< 0.1%
2317 4
< 0.1%
2305 3
< 0.1%
2277 1
 
< 0.1%

B5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2322
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400.29949
Minimum-100
Maximum2625
Zeros278091
Zeros (%)17.8%
Negative4
Negative (%)< 0.1%
Memory size11.9 MiB
2023-09-02T09:15:21.022739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0
Q1207
median403
Q3503
95-th percentile801
Maximum2625
Range2725
Interquartile range (IQR)296

Descriptive statistics

Standard deviation341.97762
Coefficient of variation (CV)0.85430442
Kurtosis7.8265553
Mean400.29949
Median Absolute Deviation (MAD)120
Skewness2.2284627
Sum6.2483627 × 108
Variance116948.69
MonotonicityNot monotonic
2023-09-02T09:15:21.133100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 278091
 
17.8%
412 4487
 
0.3%
423 4370
 
0.3%
425 4367
 
0.3%
414 4313
 
0.3%
415 4298
 
0.3%
408 4285
 
0.3%
440 4280
 
0.3%
405 4244
 
0.3%
439 4244
 
0.3%
Other values (2312) 1243943
79.7%
ValueCountFrequency (%)
-100 4
 
< 0.1%
0 278091
17.8%
19 1
 
< 0.1%
21 6
 
< 0.1%
22 3
 
< 0.1%
23 3
 
< 0.1%
24 12
 
< 0.1%
25 4
 
< 0.1%
26 68
 
< 0.1%
27 192
 
< 0.1%
ValueCountFrequency (%)
2625 4
 
< 0.1%
2602 6
< 0.1%
2585 10
< 0.1%
2561 6
< 0.1%
2555 4
 
< 0.1%
2550 5
< 0.1%
2528 2
 
< 0.1%
2525 3
 
< 0.1%
2515 3
 
< 0.1%
2498 4
 
< 0.1%

B6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2616
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean556.52337
Minimum-100
Maximum4986
Zeros278091
Zeros (%)17.8%
Negative4
Negative (%)< 0.1%
Memory size11.9 MiB
2023-09-02T09:15:21.236429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0
Q1474
median580
Q3654
95-th percentile1426
Maximum4986
Range5086
Interquartile range (IQR)180

Descriptive statistics

Standard deviation401.91046
Coefficient of variation (CV)0.72218075
Kurtosis4.0455995
Mean556.52337
Median Absolute Deviation (MAD)85
Skewness1.3223239
Sum8.6868957 × 108
Variance161532.02
MonotonicityNot monotonic
2023-09-02T09:15:21.347765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 278091
 
17.8%
590 6165
 
0.4%
588 6126
 
0.4%
603 6071
 
0.4%
580 6013
 
0.4%
591 5965
 
0.4%
596 5964
 
0.4%
583 5954
 
0.4%
604 5929
 
0.4%
610 5889
 
0.4%
Other values (2606) 1228755
78.7%
ValueCountFrequency (%)
-100 4
 
< 0.1%
0 278091
17.8%
8 12
 
< 0.1%
9 11
 
< 0.1%
10 31
 
< 0.1%
11 68
 
< 0.1%
12 199
 
< 0.1%
13 214
 
< 0.1%
14 302
 
< 0.1%
15 303
 
< 0.1%
ValueCountFrequency (%)
4986 2
 
< 0.1%
4203 6
< 0.1%
4181 9
< 0.1%
4059 4
< 0.1%
4021 3
 
< 0.1%
3892 1
 
< 0.1%
3868 6
< 0.1%
3855 6
< 0.1%
3793 6
< 0.1%
3787 6
< 0.1%

B7
Real number (ℝ)

Distinct2978
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean610.96628
Minimum-100
Maximum5679
Zeros0
Zeros (%)0.0%
Negative277277
Negative (%)17.8%
Memory size11.9 MiB
2023-09-02T09:15:21.459056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1543
median653
Q3731
95-th percentile1667
Maximum5679
Range5779
Interquartile range (IQR)188

Descriptive statistics

Standard deviation475.71243
Coefficient of variation (CV)0.77862305
Kurtosis3.059298
Mean610.96628
Median Absolute Deviation (MAD)89
Skewness0.94529658
Sum9.536707 × 108
Variance226302.31
MonotonicityNot monotonic
2023-09-02T09:15:21.560143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 277277
 
17.8%
677 5864
 
0.4%
652 5834
 
0.4%
667 5700
 
0.4%
661 5686
 
0.4%
666 5654
 
0.4%
660 5648
 
0.4%
658 5624
 
0.4%
674 5615
 
0.4%
662 5587
 
0.4%
Other values (2968) 1232433
79.0%
ValueCountFrequency (%)
-100 277277
17.8%
1 6
 
< 0.1%
2 6
 
< 0.1%
11 8
 
< 0.1%
13 35
 
< 0.1%
14 47
 
< 0.1%
15 123
 
< 0.1%
16 268
 
< 0.1%
17 257
 
< 0.1%
18 257
 
< 0.1%
ValueCountFrequency (%)
5679 2
 
< 0.1%
5147 6
< 0.1%
4942 9
< 0.1%
4799 6
< 0.1%
4738 3
 
< 0.1%
4730 6
< 0.1%
4696 4
< 0.1%
4619 6
< 0.1%
4614 4
< 0.1%
4544 1
 
< 0.1%

B8
Real number (ℝ)

Distinct3623
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean640.15381
Minimum-100
Maximum6742
Zeros0
Zeros (%)0.0%
Negative277160
Negative (%)17.8%
Memory size11.9 MiB
2023-09-02T09:15:21.671392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1559
median686
Q3773
95-th percentile1717
Maximum6742
Range6842
Interquartile range (IQR)214

Descriptive statistics

Standard deviation495.74526
Coefficient of variation (CV)0.77441585
Kurtosis3.5241627
Mean640.15381
Median Absolute Deviation (MAD)100
Skewness0.96548136
Sum9.9923017 × 108
Variance245763.36
MonotonicityNot monotonic
2023-09-02T09:15:21.780652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 277160
 
17.8%
684 6762
 
0.4%
735 6155
 
0.4%
697 6103
 
0.4%
703 6095
 
0.4%
725 6084
 
0.4%
709 6067
 
0.4%
719 6049
 
0.4%
722 6044
 
0.4%
711 6032
 
0.4%
Other values (3613) 1228371
78.7%
ValueCountFrequency (%)
-100 277160
17.8%
8 3
 
< 0.1%
9 26
 
< 0.1%
10 54
 
< 0.1%
11 141
 
< 0.1%
12 166
 
< 0.1%
13 185
 
< 0.1%
14 188
 
< 0.1%
15 151
 
< 0.1%
16 152
 
< 0.1%
ValueCountFrequency (%)
6742 2
< 0.1%
6472 1
 
< 0.1%
6412 2
< 0.1%
6332 1
 
< 0.1%
6276 2
< 0.1%
6174 3
< 0.1%
6038 1
 
< 0.1%
5846 2
< 0.1%
5772 2
< 0.1%
5748 2
< 0.1%

B11
Real number (ℝ)

Distinct3133
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean682.84681
Minimum-100
Maximum3496
Zeros0
Zeros (%)0.0%
Negative277277
Negative (%)17.8%
Memory size11.9 MiB
2023-09-02T09:15:21.902973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1470
median738
Q3879
95-th percentile1321
Maximum3496
Range3596
Interquartile range (IQR)409

Descriptive statistics

Standard deviation554.81284
Coefficient of variation (CV)0.81249973
Kurtosis4.0444371
Mean682.84681
Median Absolute Deviation (MAD)169
Skewness1.2779133
Sum1.0658706 × 109
Variance307817.29
MonotonicityNot monotonic
2023-09-02T09:15:22.006157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 277277
 
17.8%
782 3184
 
0.2%
746 3184
 
0.2%
751 3160
 
0.2%
770 3100
 
0.2%
777 3092
 
0.2%
775 3086
 
0.2%
767 3085
 
0.2%
792 3062
 
0.2%
833 3025
 
0.2%
Other values (3123) 1255667
80.4%
ValueCountFrequency (%)
-100 277277
17.8%
15 18
 
< 0.1%
16 53
 
< 0.1%
17 200
 
< 0.1%
18 440
 
< 0.1%
19 755
 
< 0.1%
20 1052
 
0.1%
21 1372
 
0.1%
22 1429
 
0.1%
23 1628
 
0.1%
ValueCountFrequency (%)
3496 4
 
< 0.1%
3447 6
< 0.1%
3436 4
 
< 0.1%
3409 6
< 0.1%
3408 10
< 0.1%
3400 2
 
< 0.1%
3375 6
< 0.1%
3350 3
 
< 0.1%
3339 7
< 0.1%
3336 6
< 0.1%

B12
Real number (ℝ)

Distinct2995
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean486.27803
Minimum-100
Maximum3769
Zeros0
Zeros (%)0.0%
Negative277277
Negative (%)17.8%
Memory size11.9 MiB
2023-09-02T09:15:22.238746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1231
median493.5
Q3639
95-th percentile1059
Maximum3769
Range3869
Interquartile range (IQR)408

Descriptive statistics

Standard deviation487.88313
Coefficient of variation (CV)1.0033008
Kurtosis6.8130229
Mean486.27803
Median Absolute Deviation (MAD)177.5
Skewness2.0656009
Sum7.5904208 × 108
Variance238029.95
MonotonicityNot monotonic
2023-09-02T09:15:22.349871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 277277
 
17.8%
584 3233
 
0.2%
566 3087
 
0.2%
590 3056
 
0.2%
558 3028
 
0.2%
559 3007
 
0.2%
552 3001
 
0.2%
553 2979
 
0.2%
561 2965
 
0.2%
580 2957
 
0.2%
Other values (2985) 1256332
80.5%
ValueCountFrequency (%)
-100 277277
17.8%
10 49
 
< 0.1%
11 183
 
< 0.1%
12 498
 
< 0.1%
13 845
 
0.1%
14 1451
 
0.1%
15 1665
 
0.1%
16 1822
 
0.1%
17 2328
 
0.1%
18 2412
 
0.2%
ValueCountFrequency (%)
3769 10
< 0.1%
3373 4
 
< 0.1%
3325 6
< 0.1%
3285 4
 
< 0.1%
3276 10
< 0.1%
3264 6
< 0.1%
3232 5
< 0.1%
3218 2
 
< 0.1%
3197 3
 
< 0.1%
3194 3
 
< 0.1%

DEM
Real number (ℝ)

Distinct2465
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.6675
Minimum-100
Maximum2714
Zeros0
Zeros (%)0.0%
Negative277991
Negative (%)17.8%
Memory size11.9 MiB
2023-09-02T09:15:22.461693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q157
median77
Q395
95-th percentile861
Maximum2714
Range2814
Interquartile range (IQR)38

Descriptive statistics

Standard deviation335.3016
Coefficient of variation (CV)2.5660673
Kurtosis16.558229
Mean130.6675
Median Absolute Deviation (MAD)20
Skewness4.0054483
Sum2.0396177 × 108
Variance112427.16
MonotonicityNot monotonic
2023-09-02T09:15:22.570862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 277991
 
17.8%
75 35167
 
2.3%
76 34697
 
2.2%
77 32529
 
2.1%
74 32055
 
2.1%
57 30437
 
1.9%
73 28437
 
1.8%
78 28201
 
1.8%
89 27748
 
1.8%
88 27528
 
1.8%
Other values (2455) 1006132
64.5%
ValueCountFrequency (%)
-100 277991
17.8%
33 10
 
< 0.1%
34 10
 
< 0.1%
35 18
 
< 0.1%
36 17
 
< 0.1%
37 45
 
< 0.1%
38 76
 
< 0.1%
39 110
 
< 0.1%
40 192
 
< 0.1%
41 406
 
< 0.1%
ValueCountFrequency (%)
2714 2
< 0.1%
2710 2
< 0.1%
2708 2
< 0.1%
2705 2
< 0.1%
2686 1
< 0.1%
2675 1
< 0.1%
2667 1
< 0.1%
2666 1
< 0.1%
2633 1
< 0.1%
2630 1
< 0.1%

Slopes
Real number (ℝ)

Distinct697433
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-12.196625
Minimum-100
Maximum65.50516
Zeros6810
Zeros (%)0.4%
Negative269351
Negative (%)17.3%
Memory size11.9 MiB
2023-09-02T09:15:22.692211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q11.754809
median3.4848507
Q35.6482186
95-th percentile19.606129
Maximum65.50516
Range165.50516
Interquartile range (IQR)3.8934096

Descriptive statistics

Standard deviation40.633337
Coefficient of variation (CV)-3.331523
Kurtosis0.86927927
Mean-12.196625
Median Absolute Deviation (MAD)1.9535821
Skewness-1.6142603
Sum-19037981
Variance1651.0681
MonotonicityNot monotonic
2023-09-02T09:15:22.795360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 269351
 
17.3%
0 6810
 
0.4%
4.429936 × 10-7105
 
< 0.1%
0.0013261533 64
 
< 0.1%
2.29061 48
 
< 0.1%
0.00466951 48
 
< 0.1%
2.4890287 × 10-646
 
< 0.1%
0.00016105852 43
 
< 0.1%
0.25575498 42
 
< 0.1%
0.0007456413 39
 
< 0.1%
Other values (697423) 1284326
82.3%
ValueCountFrequency (%)
-100 269351
17.3%
0 6810
 
0.4%
1.8005126 × 10-736
 
< 0.1%
2.0843184 × 10-74
 
< 0.1%
4.429936 × 10-7105
 
< 0.1%
2.4890287 × 10-646
 
< 0.1%
2.5279196 × 10-61
 
< 0.1%
3.5439489 × 10-64
 
< 0.1%
1.1523281 × 10-58
 
< 0.1%
1.9737226 × 10-52
 
< 0.1%
ValueCountFrequency (%)
65.50516 4
< 0.1%
65.26338 1
 
< 0.1%
64.96299 2
< 0.1%
64.58259 1
 
< 0.1%
64.306786 2
< 0.1%
63.899036 1
 
< 0.1%
63.854317 1
 
< 0.1%
63.809032 1
 
< 0.1%
63.58361 2
< 0.1%
63.365345 1
 
< 0.1%

clase
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
suelo_desnudo
1072666 
ciudad
288214 
vegetacion
 
69816
agua
 
46035
palma
 
22205
Other values (3)
 
61986

Length

Max length15
Median length13
Mean length11.192556
Min length4

Characters and Unicode

Total characters17470707
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
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 rowpalma
2nd rowpalma
3rd rowpalma
4th rowpalma
5th rowpalma

Common Values

ValueCountFrequency (%)
suelo_desnudo 1072666
68.7%
ciudad 288214
 
18.5%
vegetacion 69816
 
4.5%
agua 46035
 
2.9%
palma 22205
 
1.4%
cafe_sombra 21879
 
1.4%
cafe_semisombra 20690
 
1.3%
cafe_expuesto 19417
 
1.2%

Length

2023-09-02T09:15:22.906574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T09:15:23.030905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
suelo_desnudo 1072666
68.7%
ciudad 288214
 
18.5%
vegetacion 69816
 
4.5%
agua 46035
 
2.9%
palma 22205
 
1.4%
cafe_sombra 21879
 
1.4%
cafe_semisombra 20690
 
1.3%
cafe_expuesto 19417
 
1.2%

Most occurring characters

ValueCountFrequency (%)
d 2721760
15.6%
u 2498998
14.3%
e 2406474
13.8%
o 2277134
13.0%
s 2228008
12.8%
n 1142482
6.5%
_ 1134652
6.5%
l 1094871
6.3%
a 599065
 
3.4%
c 420016
 
2.4%
Other values (10) 947247
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16336055
93.5%
Connector Punctuation 1134652
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 2721760
16.7%
u 2498998
15.3%
e 2406474
14.7%
o 2277134
13.9%
s 2228008
13.6%
n 1142482
7.0%
l 1094871
6.7%
a 599065
 
3.7%
c 420016
 
2.6%
i 378720
 
2.3%
Other values (9) 568527
 
3.5%
Connector Punctuation
ValueCountFrequency (%)
_ 1134652
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16336055
93.5%
Common 1134652
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 2721760
16.7%
u 2498998
15.3%
e 2406474
14.7%
o 2277134
13.9%
s 2228008
13.6%
n 1142482
7.0%
l 1094871
6.7%
a 599065
 
3.7%
c 420016
 
2.6%
i 378720
 
2.3%
Other values (9) 568527
 
3.5%
Common
ValueCountFrequency (%)
_ 1134652
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17470707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 2721760
15.6%
u 2498998
14.3%
e 2406474
13.8%
o 2277134
13.0%
s 2228008
12.8%
n 1142482
6.5%
_ 1134652
6.5%
l 1094871
6.3%
a 599065
 
3.4%
c 420016
 
2.4%
Other values (10) 947247
 
5.4%

Interactions

2023-09-02T09:15:11.728361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:15.605213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:21.154364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:26.964875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:32.744147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:38.530970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:44.163954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:49.663678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:55.266685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:00.808783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:06.310684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:12.202013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:16.140618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:21.637167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:27.513408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:33.277133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:39.067501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:44.666339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:50.293215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:55.758673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:01.305929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:06.772922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:12.670941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:16.626697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:22.158772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:28.092858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:33.789988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:39.567085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:45.185911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:50.785754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:56.280402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:01.791240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:07.278805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:13.147033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:17.126257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:22.668444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:28.638398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:34.318670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:40.086027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:45.699779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:51.297045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:56.776881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:02.287839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:07.758296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:13.621148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:17.597278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:23.192957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:29.154058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:34.814178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:40.576539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:46.201585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:51.778673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:57.301731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:02.753704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:08.281076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:14.107722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:18.112611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:23.701631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:29.672666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:35.469147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:41.106081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:46.699404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:52.282923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:57.777603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:03.259555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:08.769727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:14.575668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:18.582860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:24.194314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:30.187293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:35.979155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:41.613734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:47.198291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:52.761900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:58.274311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:03.735871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:09.286782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:15.081902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:19.088936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:24.692947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:30.699921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:36.500306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:42.133472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:47.699601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:53.269258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:58.786151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:04.369701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:09.776885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:15.578318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:19.584499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:25.337224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:31.214544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:37.013148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:42.652835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:48.199674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:53.757653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:59.308367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:04.849792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:10.295951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:16.084140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:20.088969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:25.864815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:31.707195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:37.523287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:43.163907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:48.693969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:54.264199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:59.819827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:05.334188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:10.783444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:16.568815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:20.663719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:26.376480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:32.219216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:38.034098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:43.654954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:49.183229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:14:54.750335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:00.324108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:05.801172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-02T09:15:11.261357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-02T09:15:23.130097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
B2B3B4B5B6B7B8B11B12DEMSlopesclase
B21.0000.9850.9740.9170.6800.6060.6010.8140.9260.3850.3450.125
B30.9851.0000.9590.9280.7400.6680.6670.8170.9110.4030.3620.119
B40.9740.9591.0000.9310.6630.5920.5950.8400.9350.3740.3520.188
B50.9170.9280.9311.0000.7770.7020.6690.8950.9530.3860.3810.164
B60.6800.7400.6630.7771.0000.9790.9030.7110.6850.4660.4750.205
B70.6060.6680.5920.7020.9791.0000.9200.6680.6130.4620.4960.200
B80.6010.6670.5950.6690.9030.9201.0000.6760.5910.4350.5010.185
B110.8140.8170.8400.8950.7110.6680.6761.0000.9010.3030.4250.269
B120.9260.9110.9350.9530.6850.6130.5910.9011.0000.3840.3580.179
DEM0.3850.4030.3740.3860.4660.4620.4350.3030.3841.0000.5310.406
Slopes0.3450.3620.3520.3810.4750.4960.5010.4250.3580.5311.0000.437
clase0.1250.1190.1880.1640.2050.2000.1850.2690.1790.4060.4371.000

Missing values

2023-09-02T09:15:16.894147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-02T09:15:17.936883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

B2B3B4B5B6B7B8B11B12DEMSlopesclase
0173.0237.0290.0374.0561.0631.0655.0776.0525.0132.01.947793palma
1173.0237.0290.0347.0585.0653.0655.0750.0491.0133.01.947793palma
2145.0216.0269.0347.0585.0653.0641.0750.0491.0134.02.198595palma
3124.0198.0222.0341.0579.0663.0696.0752.0503.0133.00.932456palma
4124.0198.0222.0343.0600.0663.0696.0746.0478.0134.00.932456palma
5121.0194.0224.0343.0600.0663.0691.0746.0478.0135.00.841870palma
698.0176.0183.0331.0616.0679.0744.0725.0443.0135.00.841870palma
752.0123.0111.0214.0553.0697.0643.0424.0233.098.04.343401palma
848.0123.097.0214.0553.0697.0684.0424.0233.098.02.660475palma
948.0123.097.0197.0554.0686.0684.0420.0222.099.01.031712palma
B2B3B4B5B6B7B8B11B12DEMSlopesclase
156091284.0178.0116.0285.01024.01272.01358.0580.0259.01843.016.707354cafe_sombra
156091384.0178.0116.0285.01024.01272.01358.0580.0259.01848.013.366072cafe_sombra
156091496.0168.083.0234.0743.0927.01344.0464.0206.01848.013.366072cafe_sombra
1560915116.0205.0116.0413.01242.01603.01622.0755.0322.01857.013.088222cafe_sombra
1560916114.0199.096.0420.01395.01772.01673.0726.0328.01867.013.088222cafe_sombra
1560917114.0199.096.0420.01395.01772.01673.0726.0328.01867.015.050369cafe_sombra
1560918107.0194.0111.0348.01130.01384.01668.0621.0286.01871.015.050369cafe_sombra
1560919140.0252.0129.0653.02021.02471.01804.01206.0555.01871.015.530117cafe_sombra
1560920140.0252.0129.0653.02021.02471.01804.01206.0555.01871.016.935495cafe_sombra
1560921133.0236.0111.0565.01675.02101.01888.01011.0464.01876.016.935495cafe_sombra

Duplicate rows

Most frequently occurring

B2B3B4B5B6B7B8B11B12DEMSlopesclase# duplicates
10.00.0-100.00.00.0-100.0-100.0-100.0-100.0-100.0-100.000000suelo_desnudo153423
00.00.0-100.00.00.0-100.0-100.0-100.0-100.0-100.0-100.000000ciudad115928
424737.054.038.051.033.038.031.023.019.057.00.000000agua6
448038.053.039.050.033.033.030.024.020.057.00.000000agua6
510940.057.040.053.025.029.023.018.016.057.00.000000agua6
535841.053.038.050.034.038.030.028.020.057.00.000000agua6
5460.00.0-100.00.00.0-100.0-100.0-100.0-100.0-100.02.062771ciudad5
578142.057.040.059.027.033.024.022.014.057.00.000000agua5
30.00.0-100.00.00.0-100.0-100.0-100.0-100.0-100.00.000068ciudad4
80.00.0-100.00.00.0-100.0-100.0-100.0-100.0-100.00.010785ciudad4