Overview

Dataset statistics

Number of variables29
Number of observations304210
Missing cells3073999
Missing cells (%)34.8%
Total size in memory67.3 MiB
Average record size in memory232.0 B

Variable types

Unsupported1
Text5
Numeric23

Dataset

DescriptionA cleaned dataset from the WAMEX database.
URLhttps://www.dmp.wa.gov.au/WAMEX-Minerals-Exploration-1476.aspx

Alerts

Quality has 6553 (2.2%) missing valuesMissing
Moisture has 148917 (49.0%) missing valuesMissing
Fe_XRF_pct has 94816 (31.2%) missing valuesMissing
Al2O3_XRF_pct has 94816 (31.2%) missing valuesMissing
SiO2_XRF_pct has 94816 (31.2%) missing valuesMissing
TiO2_XRF_pct has 94818 (31.2%) missing valuesMissing
CaO_XRF_pct has 95273 (31.3%) missing valuesMissing
MgO_XRF_pct has 94885 (31.2%) missing valuesMissing
MnO_XRF_pct has 94971 (31.2%) missing valuesMissing
Na2O_XRF_pct has 96625 (31.8%) missing valuesMissing
K2O_XRF_pct has 94879 (31.2%) missing valuesMissing
P_XRF_pct has 94846 (31.2%) missing valuesMissing
S_XRF_pct has 94893 (31.2%) missing valuesMissing
LOI_1000S_pct has 217788 (71.6%) missing valuesMissing
LOI_1000_pct has 158862 (52.2%) missing valuesMissing
LOI_371_pct has 158863 (52.2%) missing valuesMissing
LOI_650_pct has 158863 (52.2%) missing valuesMissing
LOI_Total has 94819 (31.2%) missing valuesMissing
BaO_XRF_pct has 301134 (99.0%) missing valuesMissing
Mn_Calc_pct has 177845 (58.5%) missing valuesMissing
Weight_kg has 303772 (99.9%) missing valuesMissing
Cr_XRF_pct has 300889 (98.9%) missing valuesMissing
PRIORITY is highly skewed (γ1 = 551.5250044)Skewed
S_XRF_pct is highly skewed (γ1 = 47.91431409)Skewed
SAMPLEID is an unsupported type, check if it needs cleaning or further analysisUnsupported
SAMPFROM has 7555 (2.5%) zerosZeros

Reproduction

Analysis started2023-07-19 22:59:58.027877
Analysis finished2023-07-19 23:00:03.458049
Duration5.43 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

SAMPLEID
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.3 MiB

HOLEID
Text

Distinct7583
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2023-07-20T07:00:04.751034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.006452779
Min length6

Characters and Unicode

Total characters1827223
Distinct characters17
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

Unique23 ?
Unique (%)< 0.1%

Sample

1st rowCB0001
2nd rowCB0001
3rd rowCB0001
4th rowCB0001
5th rowCB0001
ValueCountFrequency (%)
cc1595 144
 
< 0.1%
cc1596 140
 
< 0.1%
cc2399 137
 
< 0.1%
cc2338 132
 
< 0.1%
cc3482 132
 
< 0.1%
cc1888 127
 
< 0.1%
cb1798 125
 
< 0.1%
cc1698 123
 
< 0.1%
cc1985 121
 
< 0.1%
cc1443 121
 
< 0.1%
Other values (7573) 302908
99.6%
2023-07-20T07:00:06.472991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 446137
24.4%
0 186140
10.2%
1 184991
10.1%
2 166604
 
9.1%
3 143931
 
7.9%
B 139306
 
7.6%
4 96047
 
5.3%
6 91718
 
5.0%
9 88965
 
4.9%
7 87540
 
4.8%
Other values (7) 195844
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1216840
66.6%
Uppercase Letter 610383
33.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 186140
15.3%
1 184991
15.2%
2 166604
13.7%
3 143931
11.8%
4 96047
7.9%
6 91718
7.5%
9 88965
7.3%
7 87540
7.2%
5 86592
7.1%
8 84312
6.9%
Uppercase Letter
ValueCountFrequency (%)
C 446137
73.1%
B 139306
 
22.8%
W 11542
 
1.9%
K 7969
 
1.3%
F 3573
 
0.6%
D 1749
 
0.3%
P 107
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1216840
66.6%
Latin 610383
33.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 186140
15.3%
1 184991
15.2%
2 166604
13.7%
3 143931
11.8%
4 96047
7.9%
6 91718
7.5%
9 88965
7.3%
7 87540
7.2%
5 86592
7.1%
8 84312
6.9%
Latin
ValueCountFrequency (%)
C 446137
73.1%
B 139306
 
22.8%
W 11542
 
1.9%
K 7969
 
1.3%
F 3573
 
0.6%
D 1749
 
0.3%
P 107
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1827223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 446137
24.4%
0 186140
10.2%
1 184991
10.1%
2 166604
 
9.1%
3 143931
 
7.9%
B 139306
 
7.6%
4 96047
 
5.3%
6 91718
 
5.0%
9 88965
 
4.9%
7 87540
 
4.8%
Other values (7) 195844
10.7%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2023-07-20T07:00:06.982031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters608420
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 rowCB
2nd rowCB
3rd rowCB
4th rowCB
5th rowCB
ValueCountFrequency (%)
cc 153362
50.4%
cb 139306
45.8%
wk 7969
 
2.6%
wf 3573
 
1.2%
2023-07-20T07:00:07.644320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 446030
73.3%
B 139306
 
22.9%
W 11542
 
1.9%
K 7969
 
1.3%
F 3573
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 608420
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 446030
73.3%
B 139306
 
22.9%
W 11542
 
1.9%
K 7969
 
1.3%
F 3573
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 608420
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 446030
73.3%
B 139306
 
22.9%
W 11542
 
1.9%
K 7969
 
1.3%
F 3573
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 608420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 446030
73.3%
B 139306
 
22.9%
W 11542
 
1.9%
K 7969
 
1.3%
F 3573
 
0.6%

SAMPFROM
Real number (ℝ)

ZEROS 

Distinct454
Distinct (%)0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean25.9974794
Minimum0
Maximum143
Zeros7555
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-07-20T07:00:08.041414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median22
Q338
95-th percentile65
Maximum143
Range143
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.30480867
Coefficient of variation (CV)0.7810298975
Kurtosis1.124519927
Mean25.9974794
Median Absolute Deviation (MAD)13
Skewness1.072666668
Sum7908563.22
Variance412.2852551
MonotonicityNot monotonic
2023-07-20T07:00:08.474332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7555
 
2.5%
3 7290
 
2.4%
6 7281
 
2.4%
9 7223
 
2.4%
12 7064
 
2.3%
4 7021
 
2.3%
10 6998
 
2.3%
7 6993
 
2.3%
2 6950
 
2.3%
15 6933
 
2.3%
Other values (444) 232897
76.6%
ValueCountFrequency (%)
0 7555
2.5%
0.2 1
 
< 0.1%
0.25 2
 
< 0.1%
0.5 16
 
< 0.1%
0.6 1
 
< 0.1%
ValueCountFrequency (%)
143 1
< 0.1%
142 1
< 0.1%
141 1
< 0.1%
140 1
< 0.1%
139 2
< 0.1%

SAMPTO
Real number (ℝ)

Distinct476
Distinct (%)0.2%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean27.0609512
Minimum0.25
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-07-20T07:00:08.920491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile3
Q111
median23
Q339
95-th percentile66
Maximum144
Range143.75
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.29043811
Coefficient of variation (CV)0.7498050588
Kurtosis1.129522824
Mean27.0609512
Median Absolute Deviation (MAD)13
Skewness1.073872097
Sum8232076.66
Variance411.7018786
MonotonicityNot monotonic
2023-07-20T07:00:09.434080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 7304
 
2.4%
3 7293
 
2.4%
9 7244
 
2.4%
12 7166
 
2.4%
10 7052
 
2.3%
4 7022
 
2.3%
7 6998
 
2.3%
2 6950
 
2.3%
15 6923
 
2.3%
5 6919
 
2.3%
Other values (466) 233334
76.7%
ValueCountFrequency (%)
0.25 2
 
< 0.1%
0.5 15
< 0.1%
0.6 1
 
< 0.1%
0.75 2
 
< 0.1%
0.9 1
 
< 0.1%
ValueCountFrequency (%)
144 1
< 0.1%
143 1
< 0.1%
142 1
< 0.1%
141 1
< 0.1%
140 2
< 0.1%
Distinct21
Distinct (%)< 0.1%
Missing24
Missing (%)< 0.1%
Memory size2.3 MiB
2023-07-20T07:00:10.068085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length6.985798163
Min length3

Characters and Unicode

Total characters2124982
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRC Chip
2nd rowRC Chip
3rd rowRC Chip
4th rowRC Chip
5th rowRC Chip
ValueCountFrequency (%)
rc 302430
49.9%
chip 302430
49.9%
core 740
 
0.1%
frac 310
 
0.1%
lumps 206
 
< 0.1%
fines 206
 
< 0.1%
pet 97
 
< 0.1%
geot 27
 
< 0.1%
jig_conc 23
 
< 0.1%
jig_feed 23
 
< 0.1%
Other values (12) 124
 
< 0.1%
2023-07-20T07:00:10.926812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 605630
28.5%
i 302838
14.3%
p 302636
14.2%
R 302452
14.2%
h 302442
14.2%
302430
14.2%
e 1123
 
0.1%
r 1072
 
0.1%
o 790
 
< 0.1%
F 564
 
< 0.1%
Other values (26) 3005
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 913077
43.0%
Uppercase Letter 909286
42.8%
Space Separator 302430
 
14.2%
Connector Punctuation 147
 
< 0.1%
Decimal Number 42
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 302838
33.2%
p 302636
33.1%
h 302442
33.1%
e 1123
 
0.1%
r 1072
 
0.1%
o 790
 
0.1%
s 424
 
< 0.1%
a 386
 
< 0.1%
c 355
 
< 0.1%
n 229
 
< 0.1%
Other values (8) 782
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
C 605630
66.6%
R 302452
33.3%
F 564
 
0.1%
L 206
 
< 0.1%
J 113
 
< 0.1%
P 97
 
< 0.1%
N 48
 
< 0.1%
T 44
 
< 0.1%
M 33
 
< 0.1%
G 27
 
< 0.1%
Other values (4) 72
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 21
50.0%
2 21
50.0%
Space Separator
ValueCountFrequency (%)
302430
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1822363
85.8%
Common 302619
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 605630
33.2%
i 302838
16.6%
p 302636
16.6%
R 302452
16.6%
h 302442
16.6%
e 1123
 
0.1%
r 1072
 
0.1%
o 790
 
< 0.1%
F 564
 
< 0.1%
s 424
 
< 0.1%
Other values (22) 2392
 
0.1%
Common
ValueCountFrequency (%)
302430
99.9%
_ 147
 
< 0.1%
1 21
 
< 0.1%
2 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2124982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 605630
28.5%
i 302838
14.3%
p 302636
14.2%
R 302452
14.2%
h 302442
14.2%
302430
14.2%
e 1123
 
0.1%
r 1072
 
0.1%
o 790
 
< 0.1%
F 564
 
< 0.1%
Other values (26) 3005
 
0.1%

PRIORITY
Real number (ℝ)

SKEWED 

Distinct21
Distinct (%)< 0.1%
Missing22
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.744904125
Minimum-6.3
Maximum208083
Zeros0
Zeros (%)0.0%
Negative36
Negative (%)< 0.1%
Memory size2.3 MiB
2023-07-20T07:00:11.277497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.3
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum208083
Range208089.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation377.2813104
Coefficient of variation (CV)216.2189343
Kurtosis304182.5532
Mean1.744904125
Median Absolute Deviation (MAD)0
Skewness551.5250044
Sum530778.896
Variance142341.1872
MonotonicityNot monotonic
2023-07-20T07:00:11.649737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 302872
99.6%
20 894
 
0.3%
2 137
 
< 0.1%
0.106 26
 
< 0.1%
-0.038 26
 
< 0.1%
4 26
 
< 0.1%
0.212 26
 
< 0.1%
0.5 26
 
< 0.1%
0.053 26
 
< 0.1%
0.038 26
 
< 0.1%
Other values (11) 103
 
< 0.1%
ValueCountFrequency (%)
-6.3 10
 
< 0.1%
-0.038 26
< 0.1%
0.038 26
< 0.1%
0.053 26
< 0.1%
0.075 25
< 0.1%
ValueCountFrequency (%)
208083 1
 
< 0.1%
60 16
 
< 0.1%
25 10
 
< 0.1%
20 894
0.3%
15 10
 
< 0.1%

Quality
Text

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing6553
Missing (%)2.2%
Memory size2.3 MiB
2023-07-20T07:00:11.908059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters297657
Distinct characters7
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 rowG
2nd rowM
3rd rowG
4th rowG
5th rowG
ValueCountFrequency (%)
g 216455
72.7%
n 50251
 
16.9%
m 16471
 
5.5%
c 7668
 
2.6%
p 6724
 
2.3%
w 67
 
< 0.1%
d 21
 
< 0.1%
2023-07-20T07:00:12.451359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 216455
72.7%
N 50251
 
16.9%
M 16471
 
5.5%
C 7668
 
2.6%
P 6724
 
2.3%
W 67
 
< 0.1%
D 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 297657
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 216455
72.7%
N 50251
 
16.9%
M 16471
 
5.5%
C 7668
 
2.6%
P 6724
 
2.3%
W 67
 
< 0.1%
D 21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 297657
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 216455
72.7%
N 50251
 
16.9%
M 16471
 
5.5%
C 7668
 
2.6%
P 6724
 
2.3%
W 67
 
< 0.1%
D 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 297657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 216455
72.7%
N 50251
 
16.9%
M 16471
 
5.5%
C 7668
 
2.6%
P 6724
 
2.3%
W 67
 
< 0.1%
D 21
 
< 0.1%

Moisture
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing148917
Missing (%)49.0%
Memory size2.3 MiB
2023-07-20T07:00:12.680155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters155293
Distinct characters3
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 rowD
2nd rowD
3rd rowD
4th rowD
5th rowW
ValueCountFrequency (%)
m 71009
45.7%
w 55861
36.0%
d 28423
18.3%
2023-07-20T07:00:13.396199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 71009
45.7%
W 55861
36.0%
D 28423
18.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 155293
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 71009
45.7%
W 55861
36.0%
D 28423
18.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 155293
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 71009
45.7%
W 55861
36.0%
D 28423
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 71009
45.7%
W 55861
36.0%
D 28423
18.3%

Fe_XRF_pct
Real number (ℝ)

MISSING 

Distinct6626
Distinct (%)3.2%
Missing94816
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean41.92806489
Minimum0.19
Maximum68.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-07-20T07:00:13.764090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile14.78
Q131
median45.06
Q354.17
95-th percentile60.7
Maximum68.04
Range67.85
Interquartile range (IQR)23.17

Descriptive statistics

Standard deviation14.69775379
Coefficient of variation (CV)0.3505469148
Kurtosis-0.5714113622
Mean41.92806489
Median Absolute Deviation (MAD)10.8
Skewness-0.5907143095
Sum8779485.22
Variance216.0239665
MonotonicityNot monotonic
2023-07-20T07:00:14.117742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.8 169
 
0.1%
53.4 167
 
0.1%
51.6 155
 
0.1%
54.6 151
 
< 0.1%
51 148
 
< 0.1%
54.5 146
 
< 0.1%
54.4 146
 
< 0.1%
52.3 146
 
< 0.1%
52.7 142
 
< 0.1%
55.4 142
 
< 0.1%
Other values (6616) 207882
68.3%
(Missing) 94816
31.2%
ValueCountFrequency (%)
0.19 1
< 0.1%
0.26 1
< 0.1%
0.42 1
< 0.1%
0.43 1
< 0.1%
0.44 1
< 0.1%
ValueCountFrequency (%)
68.04 1
< 0.1%
67.71 1
< 0.1%
67.66 1
< 0.1%
67.58 1
< 0.1%
67.55 1
< 0.1%

Al2O3_XRF_pct
Real number (ℝ)

MISSING 

Distinct3528
Distinct (%)1.7%
Missing94816
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean6.622290944
Minimum0.03
Maximum60.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-07-20T07:00:14.633003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.75
Q12.01
median4.75
Q39.95
95-th percentile17.5
Maximum60.74
Range60.71
Interquartile range (IQR)7.94

Descriptive statistics

Standard deviation5.829797282
Coefficient of variation (CV)0.8803293802
Kurtosis2.978003388
Mean6.622290944
Median Absolute Deviation (MAD)3.27
Skewness1.464120483
Sum1386667.99
Variance33.98653635
MonotonicityNot monotonic
2023-07-20T07:00:15.006608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6 410
 
0.1%
1.37 394
 
0.1%
1.38 393
 
0.1%
1.35 385
 
0.1%
1.51 378
 
0.1%
1.39 377
 
0.1%
1.27 375
 
0.1%
1.2 374
 
0.1%
1.13 374
 
0.1%
1.75 373
 
0.1%
Other values (3518) 205561
67.6%
(Missing) 94816
31.2%
ValueCountFrequency (%)
0.03 1
< 0.1%
0.04 1
< 0.1%
0.07 1
< 0.1%
0.08 1
< 0.1%
0.09 2
< 0.1%
ValueCountFrequency (%)
60.74 1
< 0.1%
57.92 1
< 0.1%
55.71 1
< 0.1%
53.6 1
< 0.1%
53.29 1
< 0.1%

SiO2_XRF_pct
Real number (ℝ)

MISSING 

Distinct8948
Distinct (%)4.3%
Missing94816
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean22.54825559
Minimum0.45
Maximum97.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-07-20T07:00:15.373307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile2.48
Q16.5
median13.83
Q335.21
95-th percentile63.91
Maximum97.65
Range97.2
Interquartile range (IQR)28.71

Descriptive statistics

Standard deviation20.72322228
Coefficient of variation (CV)0.9190609977
Kurtosis0.1738277545
Mean22.54825559
Median Absolute Deviation (MAD)9.43
Skewness1.101539324
Sum4721469.43
Variance429.4519415
MonotonicityNot monotonic
2023-07-20T07:00:15.836570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.5 150
 
< 0.1%
11 150
 
< 0.1%
3.14 149
 
< 0.1%
12.7 149
 
< 0.1%
11.3 147
 
< 0.1%
10.2 144
 
< 0.1%
2.74 144
 
< 0.1%
3.6 144
 
< 0.1%
11.2 141
 
< 0.1%
10.7 138
 
< 0.1%
Other values (8938) 207938
68.4%
(Missing) 94816
31.2%
ValueCountFrequency (%)
0.45 1
< 0.1%
0.47 1
< 0.1%
0.53 1
< 0.1%
0.55 1
< 0.1%
0.62 1
< 0.1%
ValueCountFrequency (%)
97.65 1
< 0.1%
97.02 1
< 0.1%
96.9 1
< 0.1%
96.45 1
< 0.1%
96.3 1
< 0.1%

TiO2_XRF_pct
Real number (ℝ)

MISSING 

Distinct2644
Distinct (%)1.3%
Missing94818
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean0.3741135717
Minimum-0.01
Maximum9.051
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)< 0.1%
Memory size2.3 MiB
2023-07-20T07:00:16.192643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0.028
Q10.072
median0.2
Q30.564
95-th percentile1.181
Maximum9.051
Range9.061
Interquartile range (IQR)0.492

Descriptive statistics

Standard deviation0.4221180005
Coefficient of variation (CV)1.128315123
Kurtosis21.13803496
Mean0.3741135717
Median Absolute Deviation (MAD)0.155
Skewness2.715554211
Sum78336.389
Variance0.1781836064
MonotonicityNot monotonic
2023-07-20T07:00:16.556682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 2086
 
0.7%
0.04 2042
 
0.7%
0.05 1933
 
0.6%
0.06 1646
 
0.5%
0.07 1515
 
0.5%
0.02 1415
 
0.5%
0.08 1294
 
0.4%
0.09 1188
 
0.4%
0.1 1069
 
0.4%
0.032 1006
 
0.3%
Other values (2634) 194198
63.8%
(Missing) 94818
31.2%
ValueCountFrequency (%)
-0.01 4
< 0.1%
-0.001 8
< 0.1%
0.001 1
 
< 0.1%
0.002 1
 
< 0.1%
0.003 1
 
< 0.1%
ValueCountFrequency (%)
9.051 1
< 0.1%
8.865 1
< 0.1%
8.858 1
< 0.1%
8.788 1
< 0.1%
8.648 1
< 0.1%

CaO_XRF_pct
Real number (ℝ)

MISSING 

Distinct1723
Distinct (%)0.8%
Missing95273
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean0.2732557661
Minimum-0.01
Maximum40.71
Zeros1
Zeros (%)< 0.1%
Negative2233
Negative (%)0.7%
Memory size2.3 MiB
2023-07-20T07:00:17.064886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0.02
Q10.03
median0.07
Q30.12
95-th percentile0.51
Maximum40.71
Range40.72
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation1.608485282
Coefficient of variation (CV)5.886372701
Kurtosis175.859307
Mean0.2732557661
Median Absolute Deviation (MAD)0.04
Skewness12.54946623
Sum57093.24
Variance2.587224902
MonotonicityNot monotonic
2023-07-20T07:00:17.529460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 23454
 
7.7%
0.02 21605
 
7.1%
0.04 20206
 
6.6%
0.05 16923
 
5.6%
0.06 14415
 
4.7%
0.07 12407
 
4.1%
0.08 10416
 
3.4%
0.09 9205
 
3.0%
0.1 8419
 
2.8%
0.11 7627
 
2.5%
Other values (1713) 64260
21.1%
(Missing) 95273
31.3%
ValueCountFrequency (%)
-0.01 2083
 
0.7%
-0.001 150
 
< 0.1%
0 1
 
< 0.1%
0.01 5140
 
1.7%
0.02 21605
7.1%
ValueCountFrequency (%)
40.71 1
< 0.1%
37.3 1
< 0.1%
36.64 1
< 0.1%
36.5 1
< 0.1%
36.17 1
< 0.1%

MgO_XRF_pct
Real number (ℝ)

MISSING 

Distinct1142
Distinct (%)0.5%
Missing94885
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean0.3160286731
Minimum-0.01
Maximum29.2
Zeros0
Zeros (%)0.0%
Negative562
Negative (%)0.2%
Memory size2.3 MiB
2023-07-20T07:00:18.083890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0.03
Q10.07
median0.11
Q30.22
95-th percentile0.71
Maximum29.2
Range29.21
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation1.264189827
Coefficient of variation (CV)4.000237745
Kurtosis137.546569
Mean0.3160286731
Median Absolute Deviation (MAD)0.06
Skewness11.00911831
Sum66152.702
Variance1.598175918
MonotonicityNot monotonic
2023-07-20T07:00:18.450611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06 14266
 
4.7%
0.05 14109
 
4.6%
0.07 13351
 
4.4%
0.04 12048
 
4.0%
0.08 11917
 
3.9%
0.09 10693
 
3.5%
0.1 9890
 
3.3%
0.11 8522
 
2.8%
0.12 7844
 
2.6%
0.03 7304
 
2.4%
Other values (1132) 99381
32.7%
(Missing) 94885
31.2%
ValueCountFrequency (%)
-0.01 502
 
0.2%
-0.001 60
 
< 0.1%
0.01 563
 
0.2%
0.02 3045
1.0%
0.028 2
 
< 0.1%
ValueCountFrequency (%)
29.2 1
< 0.1%
28.1 1
< 0.1%
26.7 1
< 0.1%
25.8 1
< 0.1%
25.7 1
< 0.1%

MnO_XRF_pct
Real number (ℝ)

MISSING 

Distinct2131
Distinct (%)1.0%
Missing94971
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean1.34856268
Minimum-0.01
Maximum66.9
Zeros0
Zeros (%)0.0%
Negative1276
Negative (%)0.4%
Memory size2.3 MiB
2023-07-20T07:00:18.851043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0.03
Q10.1
median0.24
Q30.64
95-th percentile6.78
Maximum66.9
Range66.91
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation4.09698402
Coefficient of variation (CV)3.03803752
Kurtosis38.31012117
Mean1.34856268
Median Absolute Deviation (MAD)0.177
Skewness5.644702698
Sum282171.9066
Variance16.78527806
MonotonicityNot monotonic
2023-07-20T07:00:19.281145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 6001
 
2.0%
0.04 5968
 
2.0%
0.06 5868
 
1.9%
0.07 5845
 
1.9%
0.03 5834
 
1.9%
0.08 5550
 
1.8%
0.09 5541
 
1.8%
0.1 5298
 
1.7%
0.02 5274
 
1.7%
0.11 5025
 
1.7%
Other values (2121) 153035
50.3%
(Missing) 94971
31.2%
ValueCountFrequency (%)
-0.01 748
0.2%
-0.001 528
0.2%
0.001 6
 
< 0.1%
0.002 4
 
< 0.1%
0.003 2
 
< 0.1%
ValueCountFrequency (%)
66.9 1
< 0.1%
60.9 1
< 0.1%
60.7 1
< 0.1%
59.58 1
< 0.1%
58.9 1
< 0.1%

Na2O_XRF_pct
Real number (ℝ)

MISSING 

Distinct2001
Distinct (%)1.0%
Missing96625
Missing (%)31.8%
Infinite0
Infinite (%)0.0%
Mean0.08262307007
Minimum-0.01
Maximum9.505
Zeros14
Zeros (%)< 0.1%
Negative4632
Negative (%)1.5%
Memory size2.3 MiB
2023-07-20T07:00:19.783739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0.006
Q10.019
median0.035
Q30.066
95-th percentile0.29
Maximum9.505
Range9.515
Interquartile range (IQR)0.047

Descriptive statistics

Standard deviation0.2014154395
Coefficient of variation (CV)2.437762714
Kurtosis134.8352176
Mean0.08262307007
Median Absolute Deviation (MAD)0.02
Skewness8.592829585
Sum17151.31
Variance0.04056817927
MonotonicityNot monotonic
2023-07-20T07:00:20.239742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 7314
 
2.4%
0.03 5812
 
1.9%
-0.001 4599
 
1.5%
0.01 4527
 
1.5%
0.04 4201
 
1.4%
0.017 3510
 
1.2%
0.018 3499
 
1.2%
0.015 3431
 
1.1%
0.016 3422
 
1.1%
0.019 3390
 
1.1%
Other values (1991) 163880
53.9%
(Missing) 96625
31.8%
ValueCountFrequency (%)
-0.01 33
 
< 0.1%
-0.001 4599
1.5%
0 14
 
< 0.1%
0.001 346
 
0.1%
0.002 865
 
0.3%
ValueCountFrequency (%)
9.505 1
< 0.1%
8.866 1
< 0.1%
7.506 1
< 0.1%
6.118 1
< 0.1%
5.61 1
< 0.1%

K2O_XRF_pct
Real number (ℝ)

MISSING 

Distinct1606
Distinct (%)0.8%
Missing94879
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean0.1871515829
Minimum-0.001
Maximum10.2
Zeros10
Zeros (%)< 0.1%
Negative1677
Negative (%)0.6%
Memory size2.3 MiB
2023-07-20T07:00:20.625888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.001
5-th percentile0.007
Q10.02
median0.05
Q30.166
95-th percentile0.774
Maximum10.2
Range10.201
Interquartile range (IQR)0.146

Descriptive statistics

Standard deviation0.4385702488
Coefficient of variation (CV)2.343395883
Kurtosis81.65946671
Mean0.1871515829
Median Absolute Deviation (MAD)0.037
Skewness7.374588484
Sum39176.628
Variance0.1923438631
MonotonicityNot monotonic
2023-07-20T07:00:21.034644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 5907
 
1.9%
0.01 4665
 
1.5%
0.03 3931
 
1.3%
0.016 3109
 
1.0%
0.014 3094
 
1.0%
0.015 3089
 
1.0%
0.018 3048
 
1.0%
0.013 3033
 
1.0%
0.017 3006
 
1.0%
0.012 2947
 
1.0%
Other values (1596) 173502
57.0%
(Missing) 94879
31.2%
ValueCountFrequency (%)
-0.001 1677
0.6%
0 10
 
< 0.1%
0.001 44
 
< 0.1%
0.002 327
 
0.1%
0.003 725
0.2%
ValueCountFrequency (%)
10.2 1
< 0.1%
9.5 1
< 0.1%
9.21 1
< 0.1%
9.01 1
< 0.1%
8.9 1
< 0.1%

P_XRF_pct
Real number (ℝ)

MISSING 

Distinct615
Distinct (%)0.3%
Missing94846
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean0.04080798275
Minimum-0.001
Maximum1.27
Zeros2
Zeros (%)< 0.1%
Negative1356
Negative (%)0.4%
Memory size2.3 MiB
2023-07-20T07:00:21.505965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.001
5-th percentile0.01
Q10.024
median0.034
Q30.048
95-th percentile0.094
Maximum1.27
Range1.271
Interquartile range (IQR)0.024

Descriptive statistics

Standard deviation0.03070065558
Coefficient of variation (CV)0.7523198528
Kurtosis38.74649532
Mean0.04080798275
Median Absolute Deviation (MAD)0.012
Skewness3.767838772
Sum8543.7225
Variance0.0009425302528
MonotonicityNot monotonic
2023-07-20T07:00:21.880269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 10342
 
3.4%
0.02 7603
 
2.5%
0.04 7451
 
2.4%
0.031 4891
 
1.6%
0.032 4778
 
1.6%
0.033 4748
 
1.6%
0.029 4726
 
1.6%
0.034 4597
 
1.5%
0.028 4510
 
1.5%
0.027 4486
 
1.5%
Other values (605) 151232
49.7%
(Missing) 94846
31.2%
ValueCountFrequency (%)
-0.001 1304
0.4%
-0.0004 52
 
< 0.1%
0 2
 
< 0.1%
0.0004 3
 
< 0.1%
0.0009 3
 
< 0.1%
ValueCountFrequency (%)
1.27 1
< 0.1%
1.06 1
< 0.1%
0.591 1
< 0.1%
0.574 1
< 0.1%
0.57 1
< 0.1%

S_XRF_pct
Real number (ℝ)

MISSING  SKEWED 

Distinct1212
Distinct (%)0.6%
Missing94893
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean0.05669833315
Minimum-0.001
Maximum30.5
Zeros0
Zeros (%)0.0%
Negative1639
Negative (%)0.5%
Memory size2.3 MiB
2023-07-20T07:00:22.290414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.001
5-th percentile0.008
Q10.02
median0.031
Q30.05
95-th percentile0.13
Maximum30.5
Range30.501
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.2415888524
Coefficient of variation (CV)4.260951583
Kurtosis4180.686797
Mean0.05669833315
Median Absolute Deviation (MAD)0.014
Skewness47.91431409
Sum11867.925
Variance0.05836517359
MonotonicityNot monotonic
2023-07-20T07:00:22.632141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 9560
 
3.1%
0.03 7978
 
2.6%
0.04 5852
 
1.9%
0.01 4977
 
1.6%
0.025 4067
 
1.3%
0.05 4034
 
1.3%
0.022 4003
 
1.3%
0.024 3925
 
1.3%
0.021 3915
 
1.3%
0.023 3804
 
1.3%
Other values (1202) 157202
51.7%
(Missing) 94893
31.2%
ValueCountFrequency (%)
-0.001 1639
0.5%
0.001 105
 
< 0.1%
0.002 404
 
0.1%
0.003 798
0.3%
0.004 1257
0.4%
ValueCountFrequency (%)
30.5 1
< 0.1%
28.5 1
< 0.1%
25.1 1
< 0.1%
24.2 1
< 0.1%
23.3 1
< 0.1%

LOI_1000S_pct
Real number (ℝ)

MISSING 

Distinct2359
Distinct (%)2.7%
Missing217788
Missing (%)71.6%
Infinite0
Infinite (%)0.0%
Mean8.014274143
Minimum-0.132
Maximum46.2
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size2.3 MiB
2023-07-20T07:00:23.134961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.132
5-th percentile2.2
Q15.67
median8.14
Q310.22
95-th percentile12.45
Maximum46.2
Range46.332
Interquartile range (IQR)4.55

Descriptive statistics

Standard deviation3.846429695
Coefficient of variation (CV)0.4799473573
Kurtosis16.36094209
Mean8.014274143
Median Absolute Deviation (MAD)2.26
Skewness2.248024287
Sum692609.6
Variance14.7950214
MonotonicityNot monotonic
2023-07-20T07:00:23.480322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.7 415
 
0.1%
10.3 405
 
0.1%
10.2 401
 
0.1%
10.4 394
 
0.1%
10.6 388
 
0.1%
10.1 387
 
0.1%
10.8 387
 
0.1%
10.9 370
 
0.1%
10.5 365
 
0.1%
11 359
 
0.1%
Other values (2349) 82551
 
27.1%
(Missing) 217788
71.6%
ValueCountFrequency (%)
-0.132 1
< 0.1%
0.2 1
< 0.1%
0.21 1
< 0.1%
0.26 2
< 0.1%
0.32 1
< 0.1%
ValueCountFrequency (%)
46.2 1
< 0.1%
45.6 1
< 0.1%
45.5 2
< 0.1%
45.44 1
< 0.1%
45.4 1
< 0.1%

LOI_1000_pct
Real number (ℝ)

MISSING 

Distinct1905
Distinct (%)1.3%
Missing158862
Missing (%)52.2%
Infinite0
Infinite (%)0.0%
Mean0.5048176308
Minimum-8.54
Maximum40.5
Zeros190
Zeros (%)0.1%
Negative240
Negative (%)0.1%
Memory size2.3 MiB
2023-07-20T07:00:23.986276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.54
5-th percentile0.09
Q10.23
median0.33
Q30.45
95-th percentile1.16
Maximum40.5
Range49.04
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation1.375314208
Coefficient of variation (CV)2.724378318
Kurtosis306.2627109
Mean0.5048176308
Median Absolute Deviation (MAD)0.11
Skewness15.90545135
Sum73374.233
Variance1.891489171
MonotonicityNot monotonic
2023-07-20T07:00:24.509922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.34 3641
 
1.2%
0.35 3569
 
1.2%
0.31 3519
 
1.2%
0.33 3511
 
1.2%
0.32 3474
 
1.1%
0.3 3391
 
1.1%
0.28 3374
 
1.1%
0.29 3360
 
1.1%
0.36 3320
 
1.1%
0.37 3308
 
1.1%
Other values (1895) 110881
36.4%
(Missing) 158862
52.2%
ValueCountFrequency (%)
-8.54 1
< 0.1%
-7.11 1
< 0.1%
-4.91 1
< 0.1%
-4.78 1
< 0.1%
-3.61 1
< 0.1%
ValueCountFrequency (%)
40.5 1
< 0.1%
40.49 1
< 0.1%
40.06 1
< 0.1%
39.99 1
< 0.1%
39.83 1
< 0.1%

LOI_371_pct
Real number (ℝ)

MISSING 

Distinct1336
Distinct (%)0.9%
Missing158863
Missing (%)52.2%
Infinite0
Infinite (%)0.0%
Mean5.53621622
Minimum-0.36
Maximum23.81
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size2.3 MiB
2023-07-20T07:00:24.953283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.36
5-th percentile1.29
Q13.23
median5.81
Q37.68
95-th percentile9.44
Maximum23.81
Range24.17
Interquartile range (IQR)4.45

Descriptive statistics

Standard deviation2.649643521
Coefficient of variation (CV)0.4786018855
Kurtosis-0.7830394414
Mean5.53621622
Median Absolute Deviation (MAD)2.14
Skewness-0.07264013234
Sum804672.419
Variance7.02061079
MonotonicityNot monotonic
2023-07-20T07:00:25.332934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6 232
 
0.1%
6.89 227
 
0.1%
8.07 226
 
0.1%
6.97 224
 
0.1%
7.52 224
 
0.1%
7.69 223
 
0.1%
7.65 223
 
0.1%
7.49 223
 
0.1%
7.45 223
 
0.1%
7.55 222
 
0.1%
Other values (1326) 143100
47.0%
(Missing) 158863
52.2%
ValueCountFrequency (%)
-0.36 1
< 0.1%
-0.001 1
< 0.1%
0.04 1
< 0.1%
0.06 2
< 0.1%
0.07 2
< 0.1%
ValueCountFrequency (%)
23.81 1
< 0.1%
22.45 1
< 0.1%
21.5 1
< 0.1%
21.4 1
< 0.1%
21.35 1
< 0.1%

LOI_650_pct
Real number (ℝ)

MISSING 

Distinct1461
Distinct (%)1.0%
Missing158863
Missing (%)52.2%
Infinite0
Infinite (%)0.0%
Mean1.764440264
Minimum-12.1
Maximum29.09
Zeros2
Zeros (%)< 0.1%
Negative8
Negative (%)< 0.1%
Memory size2.3 MiB
2023-07-20T07:00:25.713174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12.1
5-th percentile0.38
Q10.76
median1.21
Q32.22
95-th percentile4.69
Maximum29.09
Range41.19
Interquartile range (IQR)1.46

Descriptive statistics

Standard deviation1.659908663
Coefficient of variation (CV)0.9407565089
Kurtosis22.32440473
Mean1.764440264
Median Absolute Deviation (MAD)0.56
Skewness3.447683913
Sum256456.099
Variance2.755296768
MonotonicityNot monotonic
2023-07-20T07:00:26.160143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.71 1188
 
0.4%
0.74 1173
 
0.4%
0.69 1166
 
0.4%
0.73 1149
 
0.4%
0.72 1112
 
0.4%
0.76 1109
 
0.4%
0.78 1095
 
0.4%
0.7 1092
 
0.4%
0.75 1090
 
0.4%
0.68 1069
 
0.4%
Other values (1451) 134104
44.1%
(Missing) 158863
52.2%
ValueCountFrequency (%)
-12.1 1
< 0.1%
-0.34 1
< 0.1%
-0.25 1
< 0.1%
-0.14 1
< 0.1%
-0.1 1
< 0.1%
ValueCountFrequency (%)
29.09 1
< 0.1%
27.45 1
< 0.1%
25.49 1
< 0.1%
25.42 1
< 0.1%
25.2 1
< 0.1%

LOI_Total
Real number (ℝ)

MISSING 

Distinct6842
Distinct (%)3.3%
Missing94819
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean7.826390346
Minimum-0.132
Maximum46.35
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size2.3 MiB
2023-07-20T07:00:26.592405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.132
5-th percentile2.2
Q15.56
median7.97
Q310
95-th percentile12.18
Maximum46.35
Range46.482
Interquartile range (IQR)4.44

Descriptive statistics

Standard deviation3.660142067
Coefficient of variation (CV)0.4676666899
Kurtosis16.38463246
Mean7.826390346
Median Absolute Deviation (MAD)2.191
Skewness2.113954947
Sum1638775.701
Variance13.39663995
MonotonicityNot monotonic
2023-07-20T07:00:27.000436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.3 455
 
0.1%
10.2 453
 
0.1%
10.6 449
 
0.1%
10.8 435
 
0.1%
10.7 433
 
0.1%
10.4 432
 
0.1%
10.1 428
 
0.1%
10.5 420
 
0.1%
11 391
 
0.1%
10.9 389
 
0.1%
Other values (6832) 205106
67.4%
(Missing) 94819
31.2%
ValueCountFrequency (%)
-0.132 1
< 0.1%
-0.002 1
< 0.1%
0.08 1
< 0.1%
0.2 1
< 0.1%
0.21 1
< 0.1%
ValueCountFrequency (%)
46.35 1
< 0.1%
46.23 1
< 0.1%
46.2 1
< 0.1%
45.88 1
< 0.1%
45.86 1
< 0.1%

BaO_XRF_pct
Real number (ℝ)

MISSING 

Distinct373
Distinct (%)12.1%
Missing301134
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean0.06682477828
Minimum-0.01
Maximum0.49
Zeros0
Zeros (%)0.0%
Negative665
Negative (%)0.2%
Memory size2.3 MiB
2023-07-20T07:00:27.429669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile-0.01
Q10.01
median0.07
Q30.114
95-th percentile0.15
Maximum0.49
Range0.5
Interquartile range (IQR)0.104

Descriptive statistics

Standard deviation0.0635389062
Coefficient of variation (CV)0.9508285374
Kurtosis1.681749737
Mean0.06682477828
Median Absolute Deviation (MAD)0.05
Skewness0.7838491811
Sum205.553018
Variance0.004037192601
MonotonicityNot monotonic
2023-07-20T07:00:27.904232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.01 665
 
0.2%
0.01 195
 
0.1%
0.02 172
 
0.1%
0.03 76
 
< 0.1%
0.04 44
 
< 0.1%
0.12 39
 
< 0.1%
0.13 32
 
< 0.1%
0.09 28
 
< 0.1%
0.119519 27
 
< 0.1%
0.05 27
 
< 0.1%
Other values (363) 1771
 
0.6%
(Missing) 301134
99.0%
ValueCountFrequency (%)
-0.01 665
0.2%
0.001 2
 
< 0.1%
0.001117 1
 
< 0.1%
0.002 1
 
< 0.1%
0.003351 1
 
< 0.1%
ValueCountFrequency (%)
0.49 1
< 0.1%
0.42 2
< 0.1%
0.4 1
< 0.1%
0.334 1
< 0.1%
0.332 1
< 0.1%

Mn_Calc_pct
Real number (ℝ)

MISSING 

Distinct1153
Distinct (%)0.9%
Missing177845
Missing (%)58.5%
Infinite0
Infinite (%)0.0%
Mean0.2043089463
Minimum-0.01
Maximum28.4
Zeros85
Zeros (%)< 0.1%
Negative1571
Negative (%)0.5%
Memory size2.3 MiB
2023-07-20T07:00:28.246475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0.02
Q10.03
median0.05
Q30.09
95-th percentile0.36
Maximum28.4
Range28.41
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation1.208430291
Coefficient of variation (CV)5.914720392
Kurtosis178.1799059
Mean0.2043089463
Median Absolute Deviation (MAD)0.03
Skewness12.65378262
Sum25817.5
Variance1.460303768
MonotonicityNot monotonic
2023-07-20T07:00:28.600517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 25932
 
8.5%
0.05 16750
 
5.5%
0.03 11905
 
3.9%
0.04 10431
 
3.4%
0.09 8798
 
2.9%
0.06 6541
 
2.2%
0.07 5737
 
1.9%
0.08 5170
 
1.7%
0.12 4268
 
1.4%
0.1 3589
 
1.2%
Other values (1143) 27244
 
9.0%
(Missing) 177845
58.5%
ValueCountFrequency (%)
-0.01 1571
 
0.5%
0 85
 
< 0.1%
0.01 2760
 
0.9%
0.02 25932
8.5%
0.03 11905
3.9%
ValueCountFrequency (%)
28.4 1
< 0.1%
27.05 1
< 0.1%
25.08 1
< 0.1%
24.68 1
< 0.1%
24.11 1
< 0.1%

Weight_kg
Real number (ℝ)

MISSING 

Distinct421
Distinct (%)96.1%
Missing303772
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean54.32442922
Minimum5.35
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-07-20T07:00:29.037666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.35
5-th percentile18.227
Q131.235
median47.685
Q367.6125
95-th percentile117.3285
Maximum200
Range194.65
Interquartile range (IQR)36.3775

Descriptive statistics

Standard deviation32.35585728
Coefficient of variation (CV)0.5956041829
Kurtosis3.220768877
Mean54.32442922
Median Absolute Deviation (MAD)17.685
Skewness1.537487331
Sum23794.1
Variance1046.9015
MonotonicityNot monotonic
2023-07-20T07:00:29.436581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 3
 
< 0.1%
151 3
 
< 0.1%
49 3
 
< 0.1%
37.86 2
 
< 0.1%
54.36 2
 
< 0.1%
33.27 2
 
< 0.1%
20.57 2
 
< 0.1%
57.84 2
 
< 0.1%
44.38 2
 
< 0.1%
43.85 2
 
< 0.1%
Other values (411) 415
 
0.1%
(Missing) 303772
99.9%
ValueCountFrequency (%)
5.35 1
< 0.1%
6.85 1
< 0.1%
8.27 1
< 0.1%
8.8 1
< 0.1%
10.88 1
< 0.1%
ValueCountFrequency (%)
200 3
< 0.1%
186.01 1
 
< 0.1%
171.72 1
 
< 0.1%
151 3
< 0.1%
143.06 1
 
< 0.1%

Cr_XRF_pct
Real number (ℝ)

MISSING 

Distinct54
Distinct (%)1.6%
Missing300889
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean0.006420656429
Minimum-0.001
Maximum0.08
Zeros0
Zeros (%)0.0%
Negative818
Negative (%)0.3%
Memory size2.3 MiB
2023-07-20T07:00:30.062566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.001
5-th percentile-0.001
Q10.001
median0.004
Q30.01
95-th percentile0.023
Maximum0.08
Range0.081
Interquartile range (IQR)0.009

Descriptive statistics

Standard deviation0.00852846656
Coefficient of variation (CV)1.328285769
Kurtosis7.422359641
Mean0.006420656429
Median Absolute Deviation (MAD)0.005
Skewness2.116881175
Sum21.323
Variance7.273474186 × 10-5
MonotonicityNot monotonic
2023-07-20T07:00:30.525617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.001 818
 
0.3%
0.003 302
 
0.1%
0.001 297
 
0.1%
0.002 225
 
0.1%
0.005 219
 
0.1%
0.008 155
 
0.1%
0.004 145
 
< 0.1%
0.01 115
 
< 0.1%
0.007 111
 
< 0.1%
0.006 108
 
< 0.1%
Other values (44) 826
 
0.3%
(Missing) 300889
98.9%
ValueCountFrequency (%)
-0.001 818
0.3%
0.001 297
 
0.1%
0.002 225
 
0.1%
0.003 302
 
0.1%
0.004 145
 
< 0.1%
ValueCountFrequency (%)
0.08 1
< 0.1%
0.076 1
< 0.1%
0.072 1
< 0.1%
0.064 1
< 0.1%
0.058 1
< 0.1%

Sample

SAMPLEIDHOLEIDPROJECTCODESAMPFROMSAMPTOSAMPLETYPEPRIORITYQualityMoistureFe_XRF_pctAl2O3_XRF_pctSiO2_XRF_pctTiO2_XRF_pctCaO_XRF_pctMgO_XRF_pctMnO_XRF_pctNa2O_XRF_pctK2O_XRF_pctP_XRF_pctS_XRF_pctLOI_1000S_pctLOI_1000_pctLOI_371_pctLOI_650_pctLOI_TotalBaO_XRF_pctMn_Calc_pctWeight_kgCr_XRF_pct
090001CB0001CB0.01.0RC Chip1.0GNaN12.6815.2556.590.6880.170.480.080.0791.4200.0350.431NaN1.421.124.146.68NaN0.13NaNNaN
190002CB0001CB1.02.0RC Chip1.0MNaN11.3512.4361.760.6180.300.590.080.1011.4900.0290.466NaN1.411.113.275.79NaN0.23NaNNaN
290003CB0001CB2.03.0RC Chip1.0GNaN22.398.2248.180.4552.230.500.100.0641.2400.0380.551NaN1.501.362.445.30NaN1.73NaNNaN
390004CB0001CB3.04.0RC Chip1.0GNaN15.3110.4657.200.5441.410.530.050.0691.0100.0350.246NaN1.021.683.055.75NaN1.09NaNNaN
490005CB0001CB4.05.0RC Chip1.0GNaN18.369.0656.440.5690.170.350.040.0661.0000.0400.377NaN1.142.022.385.54NaN0.13NaNNaN
590006CB0001CB5.06.0RC Chip1.0GNaN19.088.2956.990.5180.140.330.080.0570.8310.0350.280NaN0.861.952.144.95NaN0.11NaNNaN
690007CB0001CB6.07.0RC Chip1.0GNaN15.0611.8259.100.7360.140.330.050.0620.7720.0340.051NaN0.441.613.135.18NaN0.11NaNNaN
790008CB0001CB7.08.0RC Chip1.0GNaN12.4616.1456.461.0660.130.370.040.0740.9210.0350.029NaN0.501.364.536.39NaN0.10NaNNaN
890009CB0001CB8.09.0RC Chip1.0GNaN15.1914.1855.860.9570.110.310.030.0720.7880.0320.036NaN0.431.693.515.63NaN0.09NaNNaN
990010CB0001CB9.010.0RC Chip1.0GNaN12.1316.9356.191.1050.120.340.040.0850.8890.0300.020NaN0.481.364.626.46NaN0.09NaNNaN