Overview

Dataset statistics

Number of variables56
Number of observations5585
Missing cells285215
Missing cells (%)91.2%
Total size in memory2.4 MiB
Average record size in memory448.0 B

Variable types

Text21
Numeric35

Dataset

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

Alerts

Sample_number has 4883 (87.4%) missing valuesMissing
Bulk_Hole_No has 4886 (87.5%) missing valuesMissing
From (m) has 4903 (87.8%) missing valuesMissing
To (m) has 4903 (87.8%) missing valuesMissing
Sample_Thickness (m) has 4887 (87.5%) missing valuesMissing
Lump (%) has 4883 (87.4%) missing valuesMissing
Fines (%) has 4883 (87.4%) missing valuesMissing
L/F Ratio has 4883 (87.4%) missing valuesMissing
Dry Weight Lump (kg) has 5421 (97.1%) missing valuesMissing
Dry Weight Fines (kg) has 5402 (96.7%) missing valuesMissing
Moisture (%) has 5363 (96.0%) missing valuesMissing
Fe has 5210 (93.3%) missing valuesMissing
SiO2 has 5211 (93.3%) missing valuesMissing
Al2O3 has 5211 (93.3%) missing valuesMissing
TiO2 has 5211 (93.3%) missing valuesMissing
MnO has 5211 (93.3%) missing valuesMissing
CaO has 5211 (93.3%) missing valuesMissing
P has 5211 (93.3%) missing valuesMissing
S has 5211 (93.3%) missing valuesMissing
MgO has 5211 (93.3%) missing valuesMissing
K2O has 5211 (93.3%) missing valuesMissing
Na2O has 5211 (93.3%) missing valuesMissing
LOI371 has 5211 (93.3%) missing valuesMissing
LOI650 has 5211 (93.3%) missing valuesMissing
LOI1000 has 5211 (93.3%) missing valuesMissing
LOITotal has 4889 (87.5%) missing valuesMissing
Fe_Lump has 5282 (94.6%) missing valuesMissing
SiO2_Lump has 5282 (94.6%) missing valuesMissing
Al2O3_Lump has 5282 (94.6%) missing valuesMissing
TiO2_Lump has 5282 (94.6%) missing valuesMissing
MnO_Lump has 5282 (94.6%) missing valuesMissing
CaO_Lump has 5282 (94.6%) missing valuesMissing
P_Lump has 5282 (94.6%) missing valuesMissing
S _Lump has 5282 (94.6%) missing valuesMissing
MgO_Lump has 5282 (94.6%) missing valuesMissing
K2O_Lump has 5282 (94.6%) missing valuesMissing
Na2O_Lump has 5282 (94.6%) missing valuesMissing
LOI371_Lump has 5282 (94.6%) missing valuesMissing
LOI650_Lump has 5282 (94.6%) missing valuesMissing
LOI1000_Lump has 5282 (94.6%) missing valuesMissing
LOITotal_Lump has 4883 (87.4%) missing valuesMissing
Fe_Head has 4883 (87.4%) missing valuesMissing
SiO2_Head has 4883 (87.4%) missing valuesMissing
Al2O3_Head has 4883 (87.4%) missing valuesMissing
TiO2_Head has 4883 (87.4%) missing valuesMissing
MnO_Head has 4883 (87.4%) missing valuesMissing
CaO_Head has 4883 (87.4%) missing valuesMissing
P_Head has 4883 (87.4%) missing valuesMissing
S _Head has 4883 (87.4%) missing valuesMissing
MgO_Head has 4883 (87.4%) missing valuesMissing
K2O_Head has 4883 (87.4%) missing valuesMissing
Na2O_Head has 4883 (87.4%) missing valuesMissing
LOI371_Head has 4883 (87.4%) missing valuesMissing
LOI650_Head has 4883 (87.4%) missing valuesMissing
LOI1000_Head has 4883 (87.4%) missing valuesMissing
LOITotal_Head has 4883 (87.4%) missing valuesMissing
Lump (%) has 196 (3.5%) zerosZeros
LOITotal has 322 (5.8%) zerosZeros
K2O_Lump has 90 (1.6%) zerosZeros
Na2O_Lump has 80 (1.4%) zerosZeros
LOITotal_Lump has 399 (7.1%) zerosZeros

Reproduction

Analysis started2023-07-19 23:02:48.416469
Analysis finished2023-07-19 23:02:50.958707
Duration2.54 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Sample_number
Text

MISSING 

Distinct702
Distinct (%)100.0%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:02:52.276559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.028490028
Min length5

Characters and Unicode

Total characters3530
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique702 ?
Unique (%)100.0%

Sample

1st row30125
2nd row30126
3rd row30127
4th row30128
5th row30129
ValueCountFrequency (%)
30985 1
 
0.1%
30136 1
 
0.1%
30127 1
 
0.1%
30128 1
 
0.1%
30129 1
 
0.1%
30130 1
 
0.1%
30131 1
 
0.1%
30132 1
 
0.1%
30133 1
 
0.1%
30134 1
 
0.1%
Other values (692) 692
98.6%
2023-07-20T07:02:54.308852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 972
27.5%
0 678
19.2%
1 482
13.7%
4 251
 
7.1%
2 229
 
6.5%
8 205
 
5.8%
9 195
 
5.5%
6 187
 
5.3%
5 185
 
5.2%
7 135
 
3.8%
Other values (3) 11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3519
99.7%
Lowercase Letter 8
 
0.2%
Other Punctuation 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 972
27.6%
0 678
19.3%
1 482
13.7%
4 251
 
7.1%
2 229
 
6.5%
8 205
 
5.8%
9 195
 
5.5%
6 187
 
5.3%
5 185
 
5.3%
7 135
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
a 4
50.0%
v 4
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3522
99.8%
Latin 8
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 972
27.6%
0 678
19.3%
1 482
13.7%
4 251
 
7.1%
2 229
 
6.5%
8 205
 
5.8%
9 195
 
5.5%
6 187
 
5.3%
5 185
 
5.3%
7 135
 
3.8%
Latin
ValueCountFrequency (%)
a 4
50.0%
v 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 972
27.5%
0 678
19.2%
1 482
13.7%
4 251
 
7.1%
2 229
 
6.5%
8 205
 
5.8%
9 195
 
5.5%
6 187
 
5.3%
5 185
 
5.2%
7 135
 
3.8%
Other values (3) 11
 
0.3%

Bulk_Hole_No
Text

MISSING 

Distinct18
Distinct (%)2.6%
Missing4886
Missing (%)87.5%
Memory size43.8 KiB
2023-07-20T07:02:55.076144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3495
Distinct characters13
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 rowCBS02
2nd rowCBS02
3rd rowCBS02
4th rowCBS02
5th rowCBS02
ValueCountFrequency (%)
cbs13 64
 
9.2%
cbs10 55
 
7.9%
cbs09 47
 
6.7%
cbs18 44
 
6.3%
cbs07 43
 
6.2%
cbs19 41
 
5.9%
cbs16 40
 
5.7%
cbs14 40
 
5.7%
cbs02 39
 
5.6%
cbs15 39
 
5.6%
Other values (8) 247
35.3%
2023-07-20T07:02:56.302051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 699
20.0%
B 699
20.0%
S 699
20.0%
1 410
11.7%
0 368
10.5%
2 112
 
3.2%
3 95
 
2.7%
9 88
 
2.5%
4 76
 
2.2%
6 70
 
2.0%
Other values (3) 179
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2097
60.0%
Decimal Number 1398
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 410
29.3%
0 368
26.3%
2 112
 
8.0%
3 95
 
6.8%
9 88
 
6.3%
4 76
 
5.4%
6 70
 
5.0%
8 68
 
4.9%
5 68
 
4.9%
7 43
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
C 699
33.3%
B 699
33.3%
S 699
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2097
60.0%
Common 1398
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 410
29.3%
0 368
26.3%
2 112
 
8.0%
3 95
 
6.8%
9 88
 
6.3%
4 76
 
5.4%
6 70
 
5.0%
8 68
 
4.9%
5 68
 
4.9%
7 43
 
3.1%
Latin
ValueCountFrequency (%)
C 699
33.3%
B 699
33.3%
S 699
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 699
20.0%
B 699
20.0%
S 699
20.0%
1 410
11.7%
0 368
10.5%
2 112
 
3.2%
3 95
 
2.7%
9 88
 
2.5%
4 76
 
2.2%
6 70
 
2.0%
Other values (3) 179
 
5.1%

From (m)
Real number (ℝ)

MISSING 

Distinct432
Distinct (%)63.3%
Missing4903
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean25.13969208
Minimum2.2
Maximum49.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:02:56.901495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile8.2
Q116.8
median25.8
Q332.925
95-th percentile42.785
Maximum49.6
Range47.4
Interquartile range (IQR)16.125

Descriptive statistics

Standard deviation10.42719986
Coefficient of variation (CV)0.4147703889
Kurtosis-0.7225035539
Mean25.13969208
Median Absolute Deviation (MAD)8
Skewness0.01704696456
Sum17145.27
Variance108.726497
MonotonicityNot monotonic
2023-07-20T07:02:57.456968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.7 6
 
0.1%
14.4 5
 
0.1%
21.5 5
 
0.1%
17.3 5
 
0.1%
16 4
 
0.1%
29.5 4
 
0.1%
30.5 4
 
0.1%
30.7 4
 
0.1%
32.2 4
 
0.1%
35.8 4
 
0.1%
Other values (422) 637
 
11.4%
(Missing) 4903
87.8%
ValueCountFrequency (%)
2.2 1
< 0.1%
2.8 1
< 0.1%
3.1 1
< 0.1%
3.7 1
< 0.1%
4.3 1
< 0.1%
ValueCountFrequency (%)
49.6 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%
48.6 2
< 0.1%
48.3 1
< 0.1%

To (m)
Real number (ℝ)

MISSING 

Distinct438
Distinct (%)64.2%
Missing4903
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean25.54240469
Minimum2.8
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:02:57.962637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile8.6075
Q117.3
median26.175
Q333.2
95-th percentile43.085
Maximum50
Range47.2
Interquartile range (IQR)15.9

Descriptive statistics

Standard deviation10.39040907
Coefficient of variation (CV)0.4067905585
Kurtosis-0.7146387261
Mean25.54240469
Median Absolute Deviation (MAD)7.975
Skewness0.01772523571
Sum17419.92
Variance107.9606007
MonotonicityNot monotonic
2023-07-20T07:02:58.598722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.5 5
 
0.1%
17.3 5
 
0.1%
27.7 5
 
0.1%
30.7 4
 
0.1%
30.5 4
 
0.1%
17.8 4
 
0.1%
25.1 4
 
0.1%
29.5 4
 
0.1%
35.8 4
 
0.1%
14.8 4
 
0.1%
Other values (428) 639
 
11.4%
(Missing) 4903
87.8%
ValueCountFrequency (%)
2.8 1
< 0.1%
3.1 1
< 0.1%
3.7 1
< 0.1%
4.3 1
< 0.1%
4.8 1
< 0.1%
ValueCountFrequency (%)
50 1
< 0.1%
49.6 1
< 0.1%
49.1 2
< 0.1%
48.6 2
< 0.1%
48.3 1
< 0.1%

Sample_Thickness (m)
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)3.3%
Missing4887
Missing (%)87.5%
Infinite0
Infinite (%)0.0%
Mean0.393982808
Minimum-0.3
Maximum1
Zeros22
Zeros (%)0.4%
Negative2
Negative (%)< 0.1%
Memory size43.8 KiB
2023-07-20T07:02:59.118198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.3
5-th percentile0.1425
Q10.3
median0.4
Q30.5
95-th percentile0.6
Maximum1
Range1.3
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1541558575
Coefficient of variation (CV)0.3912755946
Kurtosis1.263167201
Mean0.393982808
Median Absolute Deviation (MAD)0.1
Skewness-0.4342415747
Sum275
Variance0.0237640284
MonotonicityNot monotonic
2023-07-20T07:02:59.675749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.4 156
 
2.8%
0.5 119
 
2.1%
0.3 110
 
2.0%
0.35 55
 
1.0%
0.6 53
 
0.9%
0.2 40
 
0.7%
0.55 33
 
0.6%
0.25 28
 
0.5%
0.45 27
 
0.5%
0 22
 
0.4%
Other values (13) 55
 
1.0%
(Missing) 4887
87.5%
ValueCountFrequency (%)
-0.3 1
 
< 0.1%
-0.25 1
 
< 0.1%
0 22
0.4%
0.05 2
 
< 0.1%
0.1 9
0.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.85 1
 
< 0.1%
0.8 1
 
< 0.1%
0.75 1
 
< 0.1%
0.7 14
0.3%

Lump (%)
Real number (ℝ)

MISSING  ZEROS 

Distinct197
Distinct (%)28.1%
Missing4883
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean13.41481481
Minimum0
Maximum59.3
Zeros196
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:00.241395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.8
Q320.2
95-th percentile26.68
Maximum59.3
Range59.3
Interquartile range (IQR)20.2

Descriptive statistics

Standard deviation9.793366261
Coefficient of variation (CV)0.7300411072
Kurtosis0.4428919255
Mean13.41481481
Median Absolute Deviation (MAD)5.7
Skewness0.1232108284
Sum9417.2
Variance95.91002272
MonotonicityNot monotonic
2023-07-20T07:03:00.796806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 196
 
3.5%
16.4 9
 
0.2%
18.7 8
 
0.1%
13.9 7
 
0.1%
19.9 6
 
0.1%
17.8 6
 
0.1%
22.2 6
 
0.1%
17.4 6
 
0.1%
18.5 6
 
0.1%
20.2 6
 
0.1%
Other values (187) 446
 
8.0%
(Missing) 4883
87.4%
ValueCountFrequency (%)
0 196
3.5%
5 1
 
< 0.1%
5.3 1
 
< 0.1%
5.9 1
 
< 0.1%
6.2 1
 
< 0.1%
ValueCountFrequency (%)
59.3 1
< 0.1%
55.7 1
< 0.1%
50.9 1
< 0.1%
50.4 1
< 0.1%
47.5 1
< 0.1%

Fines (%)
Text

MISSING 

Distinct197
Distinct (%)28.1%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:03:02.209515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.162393162
Min length1

Characters and Unicode

Total characters2220
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)10.1%

Sample

1st row85.5
2nd row86.5
3rd row88.3
4th row81.3
5th row78.7
ValueCountFrequency (%)
196
27.9%
83.6 9
 
1.3%
81.3 8
 
1.1%
86.1 7
 
1.0%
81.5 6
 
0.9%
77.9 6
 
0.9%
83.0 6
 
0.9%
79.8 6
 
0.9%
85.6 6
 
0.9%
82.6 6
 
0.9%
Other values (187) 446
63.5%
2023-07-20T07:03:04.314101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 506
22.8%
8 396
17.8%
7 272
12.3%
- 196
 
8.8%
1 124
 
5.6%
9 116
 
5.2%
6 112
 
5.0%
3 104
 
4.7%
2 104
 
4.7%
5 101
 
4.5%
Other values (2) 189
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1518
68.4%
Other Punctuation 506
 
22.8%
Dash Punctuation 196
 
8.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 396
26.1%
7 272
17.9%
1 124
 
8.2%
9 116
 
7.6%
6 112
 
7.4%
3 104
 
6.9%
2 104
 
6.9%
5 101
 
6.7%
0 95
 
6.3%
4 94
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 506
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2220
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 506
22.8%
8 396
17.8%
7 272
12.3%
- 196
 
8.8%
1 124
 
5.6%
9 116
 
5.2%
6 112
 
5.0%
3 104
 
4.7%
2 104
 
4.7%
5 101
 
4.5%
Other values (2) 189
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 506
22.8%
8 396
17.8%
7 272
12.3%
- 196
 
8.8%
1 124
 
5.6%
9 116
 
5.2%
6 112
 
5.0%
3 104
 
4.7%
2 104
 
4.7%
5 101
 
4.5%
Other values (2) 189
 
8.5%

L/F Ratio
Text

MISSING 

Distinct259
Distinct (%)36.9%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:03:05.757151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length3.883190883
Min length1

Characters and Unicode

Total characters2726
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)17.9%

Sample

1st row0.170
2nd row0.156
3rd row0.133
4th row0.230
5th row0.270
ValueCountFrequency (%)
196
27.9%
0.216 6
 
0.9%
0.161 6
 
0.9%
0.230 6
 
0.9%
0.196 6
 
0.9%
0.178 5
 
0.7%
0.227 5
 
0.7%
0.228 5
 
0.7%
0.284 5
 
0.7%
0.173 5
 
0.7%
Other values (249) 457
65.1%
2023-07-20T07:03:07.602070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 618
22.7%
. 506
18.6%
2 333
12.2%
1 278
10.2%
- 196
 
7.2%
3 177
 
6.5%
4 112
 
4.1%
8 110
 
4.0%
7 104
 
3.8%
6 103
 
3.8%
Other values (2) 189
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2024
74.2%
Other Punctuation 506
 
18.6%
Dash Punctuation 196
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 618
30.5%
2 333
16.5%
1 278
13.7%
3 177
 
8.7%
4 112
 
5.5%
8 110
 
5.4%
7 104
 
5.1%
6 103
 
5.1%
9 95
 
4.7%
5 94
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 506
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 618
22.7%
. 506
18.6%
2 333
12.2%
1 278
10.2%
- 196
 
7.2%
3 177
 
6.5%
4 112
 
4.1%
8 110
 
4.0%
7 104
 
3.8%
6 103
 
3.8%
Other values (2) 189
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 618
22.7%
. 506
18.6%
2 333
12.2%
1 278
10.2%
- 196
 
7.2%
3 177
 
6.5%
4 112
 
4.1%
8 110
 
4.0%
7 104
 
3.8%
6 103
 
3.8%
Other values (2) 189
 
6.9%

Dry Weight Lump (kg)
Text

MISSING 

Distinct126
Distinct (%)76.8%
Missing5421
Missing (%)97.1%
Memory size43.8 KiB
2023-07-20T07:03:09.025363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.067073171
Min length4

Characters and Unicode

Total characters667
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)61.6%

Sample

1st row0.31
2nd row0.52
3rd row0.41
4th row0.32
5th row0.31
ValueCountFrequency (%)
0.41 9
 
5.5%
0.32 5
 
3.0%
0.31 3
 
1.8%
1.55 3
 
1.8%
0.52 3
 
1.8%
3.76 2
 
1.2%
4.33 2
 
1.2%
4.72 2
 
1.2%
0.40 2
 
1.2%
1.84 2
 
1.2%
Other values (116) 131
79.9%
2023-07-20T07:03:10.783885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 165
24.7%
1 99
14.8%
3 67
10.0%
0 59
 
8.8%
2 58
 
8.7%
4 53
 
7.9%
5 39
 
5.8%
7 38
 
5.7%
6 32
 
4.8%
9 31
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 502
75.3%
Other Punctuation 165
 
24.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 99
19.7%
3 67
13.3%
0 59
11.8%
2 58
11.6%
4 53
10.6%
5 39
 
7.8%
7 38
 
7.6%
6 32
 
6.4%
9 31
 
6.2%
8 26
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 667
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 165
24.7%
1 99
14.8%
3 67
10.0%
0 59
 
8.8%
2 58
 
8.7%
4 53
 
7.9%
5 39
 
5.8%
7 38
 
5.7%
6 32
 
4.8%
9 31
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 165
24.7%
1 99
14.8%
3 67
10.0%
0 59
 
8.8%
2 58
 
8.7%
4 53
 
7.9%
5 39
 
5.8%
7 38
 
5.7%
6 32
 
4.8%
9 31
 
4.6%

Dry Weight Fines (kg)
Real number (ℝ)

MISSING 

Distinct47
Distinct (%)25.7%
Missing5402
Missing (%)96.7%
Infinite0
Infinite (%)0.0%
Mean13.12377049
Minimum0
Maximum19.67
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:11.341767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6
Q115.05
median15.8
Q315.81
95-th percentile16.009
Maximum19.67
Range19.67
Interquartile range (IQR)0.76

Descriptive statistics

Standard deviation5.753506384
Coefficient of variation (CV)0.4384034594
Kurtosis0.3214347348
Mean13.12377049
Median Absolute Deviation (MAD)0.1
Skewness-1.448258968
Sum2401.65
Variance33.10283571
MonotonicityNot monotonic
2023-07-20T07:03:11.926409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
15.8 50
 
0.9%
1.6 21
 
0.4%
15.85 14
 
0.3%
1.65 11
 
0.2%
15.75 10
 
0.2%
15.9 8
 
0.1%
15.65 6
 
0.1%
15.45 5
 
0.1%
15.81 4
 
0.1%
15.6 3
 
0.1%
Other values (37) 51
 
0.9%
(Missing) 5402
96.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.6 21
0.4%
1.65 11
0.2%
1.66 1
 
< 0.1%
1.7 1
 
< 0.1%
ValueCountFrequency (%)
19.67 1
< 0.1%
19.61 2
< 0.1%
19.6 1
< 0.1%
19.58 2
< 0.1%
19.57 1
< 0.1%

Moisture (%)
Real number (ℝ)

MISSING 

Distinct172
Distinct (%)77.5%
Missing5363
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean2.323333333
Minimum0.23
Maximum7.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:12.472203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.33
Q10.78
median1.655
Q33.8175
95-th percentile5.196
Maximum7.27
Range7.04
Interquartile range (IQR)3.0375

Descriptive statistics

Standard deviation1.739931519
Coefficient of variation (CV)0.7488944843
Kurtosis-0.9404014114
Mean2.323333333
Median Absolute Deviation (MAD)1.265
Skewness0.5285928651
Sum515.78
Variance3.027361689
MonotonicityNot monotonic
2023-07-20T07:03:13.055910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62 6
 
0.1%
0.33 5
 
0.1%
3.37 3
 
0.1%
0.95 3
 
0.1%
0.32 3
 
0.1%
1.05 3
 
0.1%
0.29 3
 
0.1%
0.23 3
 
0.1%
0.49 2
 
< 0.1%
4.65 2
 
< 0.1%
Other values (162) 189
 
3.4%
(Missing) 5363
96.0%
ValueCountFrequency (%)
0.23 3
0.1%
0.26 1
 
< 0.1%
0.29 3
0.1%
0.31 1
 
< 0.1%
0.32 3
0.1%
ValueCountFrequency (%)
7.27 1
< 0.1%
6.63 1
< 0.1%
6.28 1
< 0.1%
6.11 1
< 0.1%
6.06 1
< 0.1%

Fe
Text

MISSING 

Distinct311
Distinct (%)82.9%
Missing5210
Missing (%)93.3%
Memory size43.8 KiB
2023-07-20T07:03:14.580158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.005333333
Min length5

Characters and Unicode

Total characters1877
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique258 ?
Unique (%)68.8%

Sample

1st row47.66
2nd row49.04
3rd row62.40
4th row64.04
5th row64.30
ValueCountFrequency (%)
60.93 4
 
1.1%
60.24 4
 
1.1%
59.57 3
 
0.8%
59.91 3
 
0.8%
60.47 3
 
0.8%
59.40 3
 
0.8%
60.91 3
 
0.8%
57.28 3
 
0.8%
59.21 3
 
0.8%
56.42 2
 
0.5%
Other values (301) 344
91.7%
2023-07-20T07:03:16.746244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 374
19.9%
5 297
15.8%
6 266
14.2%
0 168
9.0%
9 143
 
7.6%
4 140
 
7.5%
1 120
 
6.4%
3 97
 
5.2%
8 96
 
5.1%
7 93
 
5.0%
Other values (6) 83
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1496
79.7%
Other Punctuation 374
 
19.9%
Uppercase Letter 7
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 297
19.9%
6 266
17.8%
0 168
11.2%
9 143
9.6%
4 140
9.4%
1 120
8.0%
3 97
 
6.5%
8 96
 
6.4%
7 93
 
6.2%
2 76
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
I 2
28.6%
S 2
28.6%
M 1
14.3%
N 1
14.3%
G 1
14.3%
Other Punctuation
ValueCountFrequency (%)
. 374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1870
99.6%
Latin 7
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
. 374
20.0%
5 297
15.9%
6 266
14.2%
0 168
9.0%
9 143
 
7.6%
4 140
 
7.5%
1 120
 
6.4%
3 97
 
5.2%
8 96
 
5.1%
7 93
 
5.0%
Latin
ValueCountFrequency (%)
I 2
28.6%
S 2
28.6%
M 1
14.3%
N 1
14.3%
G 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 374
19.9%
5 297
15.8%
6 266
14.2%
0 168
9.0%
9 143
 
7.6%
4 140
 
7.5%
1 120
 
6.4%
3 97
 
5.2%
8 96
 
5.1%
7 93
 
5.0%
Other values (6) 83
 
4.4%

SiO2
Real number (ℝ)

MISSING 

Distinct272
Distinct (%)72.7%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean4.443208556
Minimum1.51
Maximum23.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:17.373736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.51
5-th percentile1.9765
Q12.4525
median3.39
Q35.6575
95-th percentile9.7485
Maximum23.51
Range22
Interquartile range (IQR)3.205

Descriptive statistics

Standard deviation2.86946322
Coefficient of variation (CV)0.6458088076
Kurtosis9.972041894
Mean4.443208556
Median Absolute Deviation (MAD)1.17
Skewness2.517399963
Sum1661.76
Variance8.233819168
MonotonicityNot monotonic
2023-07-20T07:03:17.954953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.97 7
 
0.1%
2.25 6
 
0.1%
2.85 5
 
0.1%
2.22 5
 
0.1%
2 4
 
0.1%
2.2 4
 
0.1%
2.51 4
 
0.1%
2.19 3
 
0.1%
2.93 3
 
0.1%
3.26 3
 
0.1%
Other values (262) 330
 
5.9%
(Missing) 5211
93.3%
ValueCountFrequency (%)
1.51 1
< 0.1%
1.61 1
< 0.1%
1.62 1
< 0.1%
1.67 1
< 0.1%
1.69 1
< 0.1%
ValueCountFrequency (%)
23.51 1
< 0.1%
21.23 1
< 0.1%
19.04 1
< 0.1%
14.93 1
< 0.1%
13.69 1
< 0.1%

Al2O3
Real number (ℝ)

MISSING 

Distinct236
Distinct (%)63.1%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean2.804438503
Minimum0.75
Maximum18.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:18.561778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile1.0165
Q11.51
median1.975
Q33.255
95-th percentile6.48
Maximum18.23
Range17.48
Interquartile range (IQR)1.745

Descriptive statistics

Standard deviation2.182845765
Coefficient of variation (CV)0.7783539426
Kurtosis11.37253254
Mean2.804438503
Median Absolute Deviation (MAD)0.565
Skewness2.784331759
Sum1048.86
Variance4.764815636
MonotonicityNot monotonic
2023-07-20T07:03:19.490612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.68 7
 
0.1%
1.41 6
 
0.1%
1.76 5
 
0.1%
1.57 5
 
0.1%
1.61 4
 
0.1%
1.43 4
 
0.1%
1.54 4
 
0.1%
1.73 4
 
0.1%
1.45 4
 
0.1%
2.43 3
 
0.1%
Other values (226) 328
 
5.9%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0.75 1
 
< 0.1%
0.83 1
 
< 0.1%
0.84 2
< 0.1%
0.87 3
0.1%
0.88 2
< 0.1%
ValueCountFrequency (%)
18.23 1
< 0.1%
15.52 1
< 0.1%
13.04 1
< 0.1%
10.97 1
< 0.1%
10.38 1
< 0.1%

TiO2
Real number (ℝ)

MISSING 

Distinct165
Distinct (%)44.1%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.1319438503
Minimum0.03
Maximum0.957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:20.028079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.03765
Q10.05125
median0.072
Q30.13925
95-th percentile0.4687
Maximum0.957
Range0.927
Interquartile range (IQR)0.088

Descriptive statistics

Standard deviation0.1416423187
Coefficient of variation (CV)1.073504513
Kurtosis6.142187065
Mean0.1319438503
Median Absolute Deviation (MAD)0.024
Skewness2.369720996
Sum49.347
Variance0.02006254644
MonotonicityNot monotonic
2023-07-20T07:03:20.752263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 12
 
0.2%
0.051 9
 
0.2%
0.04 9
 
0.2%
0.06 8
 
0.1%
0.052 8
 
0.1%
0.072 8
 
0.1%
0.048 7
 
0.1%
0.042 7
 
0.1%
0.073 7
 
0.1%
0.057 7
 
0.1%
Other values (155) 292
 
5.2%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0.03 2
< 0.1%
0.032 3
0.1%
0.034 3
0.1%
0.035 2
< 0.1%
0.036 3
0.1%
ValueCountFrequency (%)
0.957 1
< 0.1%
0.751 1
< 0.1%
0.735 1
< 0.1%
0.711 1
< 0.1%
0.641 1
< 0.1%

MnO
Real number (ℝ)

MISSING 

Distinct76
Distinct (%)20.3%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.441631016
Minimum0
Maximum18.6
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:21.529919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.15
Q30.25
95-th percentile1.2
Maximum18.6
Range18.6
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation1.523889219
Coefficient of variation (CV)3.450593739
Kurtosis73.61782818
Mean0.441631016
Median Absolute Deviation (MAD)0.07
Skewness7.879155067
Sum165.17
Variance2.322238351
MonotonicityNot monotonic
2023-07-20T07:03:22.254230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
0.7%
0.14 22
 
0.4%
0.17 18
 
0.3%
0.12 17
 
0.3%
0.1 16
 
0.3%
0.13 15
 
0.3%
0.15 14
 
0.3%
0.11 14
 
0.3%
0.18 13
 
0.2%
0.09 12
 
0.2%
Other values (66) 195
 
3.5%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0 38
0.7%
0.03 5
 
0.1%
0.04 9
 
0.2%
0.05 6
 
0.1%
0.06 7
 
0.1%
ValueCountFrequency (%)
18.6 1
< 0.1%
13.1 1
< 0.1%
9.14 1
< 0.1%
8.89 1
< 0.1%
7.75 1
< 0.1%

CaO
Real number (ℝ)

MISSING 

Distinct48
Distinct (%)12.8%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.1339572193
Minimum0
Maximum3
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:22.971833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.03
median0.05
Q30.08
95-th percentile0.5175
Maximum3
Range3
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.3208515117
Coefficient of variation (CV)2.395178949
Kurtosis32.29265313
Mean0.1339572193
Median Absolute Deviation (MAD)0.02
Skewness5.317399634
Sum50.1
Variance0.1029456925
MonotonicityNot monotonic
2023-07-20T07:03:23.828281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.03 55
 
1.0%
0.05 46
 
0.8%
0.04 45
 
0.8%
0.02 42
 
0.8%
0.07 42
 
0.8%
0.06 27
 
0.5%
0.08 23
 
0.4%
0.1 11
 
0.2%
0.09 11
 
0.2%
0.11 6
 
0.1%
Other values (38) 66
 
1.2%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0 4
 
0.1%
0.01 4
 
0.1%
0.02 42
0.8%
0.03 55
1.0%
0.04 45
0.8%
ValueCountFrequency (%)
3 1
< 0.1%
2.29 1
< 0.1%
2.05 1
< 0.1%
1.88 1
< 0.1%
1.73 1
< 0.1%

P
Real number (ℝ)

MISSING 

Distinct79
Distinct (%)21.1%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.04451069519
Minimum0.011
Maximum0.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:24.451966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.011
5-th percentile0.019
Q10.03
median0.041
Q30.055
95-th percentile0.084
Maximum0.125
Range0.114
Interquartile range (IQR)0.025

Descriptive statistics

Standard deviation0.0198147897
Coefficient of variation (CV)0.4451691804
Kurtosis1.099387152
Mean0.04451069519
Median Absolute Deviation (MAD)0.013
Skewness0.9692243838
Sum16.647
Variance0.0003926258907
MonotonicityNot monotonic
2023-07-20T07:03:25.186091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.035 16
 
0.3%
0.038 14
 
0.3%
0.043 12
 
0.2%
0.039 11
 
0.2%
0.021 10
 
0.2%
0.054 10
 
0.2%
0.028 10
 
0.2%
0.041 10
 
0.2%
0.022 10
 
0.2%
0.033 9
 
0.2%
Other values (69) 262
 
4.7%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0.011 1
 
< 0.1%
0.012 1
 
< 0.1%
0.014 3
0.1%
0.016 2
< 0.1%
0.017 4
0.1%
ValueCountFrequency (%)
0.125 1
< 0.1%
0.112 1
< 0.1%
0.11 2
< 0.1%
0.103 1
< 0.1%
0.102 1
< 0.1%

S
Real number (ℝ)

MISSING 

Distinct58
Distinct (%)15.5%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.0266657754
Minimum0.008
Maximum0.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:25.752040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.012
Q10.017
median0.023
Q30.033
95-th percentile0.053
Maximum0.15
Range0.142
Interquartile range (IQR)0.016

Descriptive statistics

Standard deviation0.01563381414
Coefficient of variation (CV)0.5862876254
Kurtosis18.48835539
Mean0.0266657754
Median Absolute Deviation (MAD)0.007
Skewness3.308394423
Sum9.973
Variance0.0002444161446
MonotonicityNot monotonic
2023-07-20T07:03:26.352621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.017 23
 
0.4%
0.016 22
 
0.4%
0.019 21
 
0.4%
0.021 17
 
0.3%
0.034 16
 
0.3%
0.033 15
 
0.3%
0.012 15
 
0.3%
0.015 15
 
0.3%
0.025 14
 
0.3%
0.018 14
 
0.3%
Other values (48) 202
 
3.6%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0.008 1
 
< 0.1%
0.01 2
 
< 0.1%
0.011 11
0.2%
0.012 15
0.3%
0.013 12
0.2%
ValueCountFrequency (%)
0.15 1
< 0.1%
0.137 1
< 0.1%
0.101 1
< 0.1%
0.096 1
< 0.1%
0.078 1
< 0.1%

MgO
Real number (ℝ)

MISSING 

Distinct25
Distinct (%)6.7%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.08157754011
Minimum0.03
Maximum0.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:27.029248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.04
Q10.06
median0.07
Q30.1
95-th percentile0.16
Maximum0.29
Range0.26
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.04185924491
Coefficient of variation (CV)0.5131221762
Kurtosis5.711852617
Mean0.08157754011
Median Absolute Deviation (MAD)0.02
Skewness2.128665782
Sum30.51
Variance0.001752196384
MonotonicityNot monotonic
2023-07-20T07:03:27.526372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.06 84
 
1.5%
0.07 61
 
1.1%
0.05 50
 
0.9%
0.08 38
 
0.7%
0.04 26
 
0.5%
0.1 23
 
0.4%
0.11 22
 
0.4%
0.12 13
 
0.2%
0.09 9
 
0.2%
0.13 7
 
0.1%
Other values (15) 41
 
0.7%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0.03 6
 
0.1%
0.04 26
 
0.5%
0.05 50
0.9%
0.06 84
1.5%
0.07 61
1.1%
ValueCountFrequency (%)
0.29 1
 
< 0.1%
0.27 1
 
< 0.1%
0.26 3
0.1%
0.25 1
 
< 0.1%
0.24 1
 
< 0.1%

K2O
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)7.2%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.04088235294
Minimum0.01
Maximum0.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:28.038372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.01
median0.02
Q30.03
95-th percentile0.11
Maximum0.88
Range0.87
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.0911235914
Coefficient of variation (CV)2.22892238
Kurtosis42.03566025
Mean0.04088235294
Median Absolute Deviation (MAD)0.01
Skewness6.124811296
Sum15.29
Variance0.00830350891
MonotonicityNot monotonic
2023-07-20T07:03:28.484272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.01 141
 
2.5%
0.02 99
 
1.8%
0.03 56
 
1.0%
0.04 20
 
0.4%
0.05 11
 
0.2%
0.06 7
 
0.1%
0.09 6
 
0.1%
0.08 5
 
0.1%
0.07 5
 
0.1%
0.11 4
 
0.1%
Other values (17) 20
 
0.4%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0.01 141
2.5%
0.02 99
1.8%
0.03 56
 
1.0%
0.04 20
 
0.4%
0.05 11
 
0.2%
ValueCountFrequency (%)
0.88 1
< 0.1%
0.7 1
< 0.1%
0.67 1
< 0.1%
0.66 1
< 0.1%
0.56 1
< 0.1%

Na2O
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)4.8%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.02537433155
Minimum0
Maximum0.32
Zeros28
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:28.962204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.03
95-th percentile0.0735
Maximum0.32
Range0.32
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.02940416312
Coefficient of variation (CV)1.15881528
Kurtosis33.62758139
Mean0.02537433155
Median Absolute Deviation (MAD)0.01
Skewness4.64451125
Sum9.49
Variance0.0008646048085
MonotonicityNot monotonic
2023-07-20T07:03:29.474762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.02 119
 
2.1%
0.01 115
 
2.1%
0.03 55
 
1.0%
0 28
 
0.5%
0.05 14
 
0.3%
0.04 11
 
0.2%
0.06 8
 
0.1%
0.07 5
 
0.1%
0.08 4
 
0.1%
0.11 4
 
0.1%
Other values (8) 11
 
0.2%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0 28
 
0.5%
0.01 115
2.1%
0.02 119
2.1%
0.03 55
1.0%
0.04 11
 
0.2%
ValueCountFrequency (%)
0.32 1
< 0.1%
0.22 1
< 0.1%
0.15 1
< 0.1%
0.14 1
< 0.1%
0.13 1
< 0.1%

LOI371
Real number (ℝ)

MISSING 

Distinct271
Distinct (%)72.5%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean6.449331551
Minimum0.33
Maximum9.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:30.053269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile2.0465
Q15.595
median7.04
Q37.775
95-th percentile8.7905
Maximum9.52
Range9.19
Interquartile range (IQR)2.18

Descriptive statistics

Standard deviation1.994259142
Coefficient of variation (CV)0.30921951
Kurtosis0.8037255105
Mean6.449331551
Median Absolute Deviation (MAD)0.98
Skewness-1.190062549
Sum2412.05
Variance3.977069525
MonotonicityNot monotonic
2023-07-20T07:03:30.647284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 5
 
0.1%
7.41 4
 
0.1%
8.17 4
 
0.1%
7.55 4
 
0.1%
7.04 4
 
0.1%
7.88 3
 
0.1%
7.53 3
 
0.1%
6.77 3
 
0.1%
7.23 3
 
0.1%
8.04 3
 
0.1%
Other values (261) 338
 
6.1%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0.33 1
< 0.1%
0.43 1
< 0.1%
0.44 1
< 0.1%
0.6 1
< 0.1%
0.61 1
< 0.1%
ValueCountFrequency (%)
9.52 1
< 0.1%
9.18 1
< 0.1%
9.16 1
< 0.1%
9.12 1
< 0.1%
8.99 1
< 0.1%

LOI650
Real number (ℝ)

MISSING 

Distinct139
Distinct (%)37.2%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean1.002513369
Minimum0.3
Maximum4.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:31.255717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.39
Q10.71
median0.85
Q31.15
95-th percentile1.8605
Maximum4.95
Range4.65
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.5454747727
Coefficient of variation (CV)0.5441072305
Kurtosis13.33527216
Mean1.002513369
Median Absolute Deviation (MAD)0.17
Skewness2.914525776
Sum374.94
Variance0.2975427277
MonotonicityNot monotonic
2023-07-20T07:03:31.904550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.86 14
 
0.3%
0.79 13
 
0.2%
0.8 10
 
0.2%
0.75 10
 
0.2%
0.77 10
 
0.2%
0.71 10
 
0.2%
0.81 9
 
0.2%
0.7 8
 
0.1%
0.9 8
 
0.1%
0.83 7
 
0.1%
Other values (129) 275
 
4.9%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0.3 2
 
< 0.1%
0.31 1
 
< 0.1%
0.33 1
 
< 0.1%
0.34 5
0.1%
0.35 1
 
< 0.1%
ValueCountFrequency (%)
4.95 1
< 0.1%
4.03 1
< 0.1%
3.95 1
< 0.1%
3.3 1
< 0.1%
3.29 1
< 0.1%

LOI1000
Real number (ℝ)

MISSING 

Distinct62
Distinct (%)16.6%
Missing5211
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.3029411765
Minimum0.07
Maximum2.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:32.702904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.1465
Q10.2
median0.25
Q30.31
95-th percentile0.7335
Maximum2.38
Range2.31
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.2393947053
Coefficient of variation (CV)0.7902349497
Kurtosis25.3972254
Mean0.3029411765
Median Absolute Deviation (MAD)0.06
Skewness4.446097163
Sum113.3
Variance0.05730982495
MonotonicityNot monotonic
2023-07-20T07:03:33.303473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 26
 
0.5%
0.2 19
 
0.3%
0.23 19
 
0.3%
0.18 18
 
0.3%
0.22 18
 
0.3%
0.19 17
 
0.3%
0.31 17
 
0.3%
0.27 17
 
0.3%
0.28 15
 
0.3%
0.17 14
 
0.3%
Other values (52) 194
 
3.5%
(Missing) 5211
93.3%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.09 2
 
< 0.1%
0.11 3
0.1%
0.12 5
0.1%
0.13 3
0.1%
ValueCountFrequency (%)
2.38 1
 
< 0.1%
1.74 1
 
< 0.1%
1.73 1
 
< 0.1%
1.44 1
 
< 0.1%
1.31 3
0.1%

LOITotal
Real number (ℝ)

MISSING  ZEROS 

Distinct260
Distinct (%)37.4%
Missing4889
Missing (%)87.5%
Infinite0
Infinite (%)0.0%
Mean4.166882184
Minimum0
Maximum11.25
Zeros322
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:33.986012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.085
Q38.3725
95-th percentile9.7625
Maximum11.25
Range11.25
Interquartile range (IQR)8.3725

Descriptive statistics

Standard deviation4.166287054
Coefficient of variation (CV)0.9998571763
Kurtosis-1.804292141
Mean4.166882184
Median Absolute Deviation (MAD)3.085
Skewness0.1397972533
Sum2900.15
Variance17.35794782
MonotonicityNot monotonic
2023-07-20T07:03:34.763511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 322
 
5.8%
8.96 6
 
0.1%
8.34 5
 
0.1%
8.5 4
 
0.1%
7.57 4
 
0.1%
7.86 4
 
0.1%
8.06 4
 
0.1%
9.03 4
 
0.1%
9.14 4
 
0.1%
8.8 4
 
0.1%
Other values (250) 335
 
6.0%
(Missing) 4889
87.5%
ValueCountFrequency (%)
0 322
5.8%
1.03 1
 
< 0.1%
1.05 1
 
< 0.1%
1.1 1
 
< 0.1%
1.2 1
 
< 0.1%
ValueCountFrequency (%)
11.25 1
< 0.1%
11.13 2
< 0.1%
11.09 2
< 0.1%
11 1
< 0.1%
10.81 1
< 0.1%

Fe_Lump
Real number (ℝ)

MISSING 

Distinct259
Distinct (%)85.5%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean61.03973597
Minimum39.07
Maximum67.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:35.546185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39.07
5-th percentile56.4
Q159.67
median61.32
Q362.645
95-th percentile65.374
Maximum67.13
Range28.06
Interquartile range (IQR)2.975

Descriptive statistics

Standard deviation2.890676368
Coefficient of variation (CV)0.0473572882
Kurtosis10.96850681
Mean61.03973597
Median Absolute Deviation (MAD)1.42
Skewness-1.806464077
Sum18495.04
Variance8.356009864
MonotonicityNot monotonic
2023-07-20T07:03:36.462175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.26 3
 
0.1%
60.46 3
 
0.1%
62.38 3
 
0.1%
60.86 3
 
0.1%
61.78 3
 
0.1%
60.56 2
 
< 0.1%
60.73 2
 
< 0.1%
63.62 2
 
< 0.1%
63.13 2
 
< 0.1%
62.76 2
 
< 0.1%
Other values (249) 278
 
5.0%
(Missing) 5282
94.6%
ValueCountFrequency (%)
39.07 1
< 0.1%
51.43 1
< 0.1%
52.71 1
< 0.1%
52.72 1
< 0.1%
53.21 1
< 0.1%
ValueCountFrequency (%)
67.13 1
< 0.1%
66.85 1
< 0.1%
66.82 1
< 0.1%
66.79 1
< 0.1%
66.78 1
< 0.1%

SiO2_Lump
Real number (ℝ)

MISSING 

Distinct205
Distinct (%)67.7%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean3.234488449
Minimum1.02
Maximum32.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:37.062793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile1.32
Q11.685
median2.2
Q33.73
95-th percentile8.005
Maximum32.45
Range31.43
Interquartile range (IQR)2.045

Descriptive statistics

Standard deviation2.995812472
Coefficient of variation (CV)0.9262090497
Kurtosis32.91636952
Mean3.234488449
Median Absolute Deviation (MAD)0.66
Skewness4.546983052
Sum980.05
Variance8.97489237
MonotonicityNot monotonic
2023-07-20T07:03:37.598269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.58 4
 
0.1%
1.55 4
 
0.1%
1.75 4
 
0.1%
1.81 4
 
0.1%
1.93 4
 
0.1%
2.35 4
 
0.1%
1.78 4
 
0.1%
1.67 4
 
0.1%
1.41 4
 
0.1%
1.36 3
 
0.1%
Other values (195) 264
 
4.7%
(Missing) 5282
94.6%
ValueCountFrequency (%)
1.02 1
< 0.1%
1.08 1
< 0.1%
1.12 2
< 0.1%
1.15 1
< 0.1%
1.17 1
< 0.1%
ValueCountFrequency (%)
32.45 1
< 0.1%
17.33 1
< 0.1%
15.08 1
< 0.1%
14.9 1
< 0.1%
14.82 1
< 0.1%

Al2O3_Lump
Real number (ℝ)

MISSING 

Distinct153
Distinct (%)50.5%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean1.223762376
Minimum0.38
Maximum7.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:38.207508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.38
5-th percentile0.53
Q10.72
median0.98
Q31.425
95-th percentile2.649
Maximum7.9
Range7.52
Interquartile range (IQR)0.705

Descriptive statistics

Standard deviation0.8388941611
Coefficient of variation (CV)0.6855041284
Kurtosis16.65684519
Mean1.223762376
Median Absolute Deviation (MAD)0.31
Skewness3.266410556
Sum370.8
Variance0.7037434135
MonotonicityNot monotonic
2023-07-20T07:03:39.122169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65 7
 
0.1%
0.69 6
 
0.1%
0.62 5
 
0.1%
0.72 5
 
0.1%
0.89 5
 
0.1%
0.61 5
 
0.1%
0.74 5
 
0.1%
0.6 4
 
0.1%
0.96 4
 
0.1%
1.16 4
 
0.1%
Other values (143) 253
 
4.5%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0.38 1
< 0.1%
0.44 1
< 0.1%
0.45 1
< 0.1%
0.47 2
< 0.1%
0.48 1
< 0.1%
ValueCountFrequency (%)
7.9 1
< 0.1%
5.04 1
< 0.1%
4.91 1
< 0.1%
4.48 1
< 0.1%
4.36 1
< 0.1%

TiO2_Lump
Real number (ℝ)

MISSING 

Distinct82
Distinct (%)27.1%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.0402310231
Minimum0.01
Maximum0.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:39.774390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.015
Q10.019
median0.025
Q30.04
95-th percentile0.1096
Maximum0.52
Range0.51
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.04748530021
Coefficient of variation (CV)1.180315502
Kurtosis40.94311468
Mean0.0402310231
Median Absolute Deviation (MAD)0.007
Skewness5.380780675
Sum12.19
Variance0.002254853736
MonotonicityNot monotonic
2023-07-20T07:03:40.497071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.019 24
 
0.4%
0.018 18
 
0.3%
0.021 18
 
0.3%
0.029 16
 
0.3%
0.025 15
 
0.3%
0.022 12
 
0.2%
0.024 10
 
0.2%
0.017 10
 
0.2%
0.02 9
 
0.2%
0.015 9
 
0.2%
Other values (72) 162
 
2.9%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.011 3
0.1%
0.012 2
< 0.1%
0.013 4
0.1%
0.014 1
 
< 0.1%
ValueCountFrequency (%)
0.52 1
< 0.1%
0.305 1
< 0.1%
0.292 1
< 0.1%
0.221 1
< 0.1%
0.206 1
< 0.1%

MnO_Lump
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)13.2%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.1676567657
Minimum0.02
Maximum4.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:41.093383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.05
Q10.08
median0.11
Q30.16
95-th percentile0.309
Maximum4.25
Range4.23
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.3619220046
Coefficient of variation (CV)2.15870802
Kurtosis87.71525657
Mean0.1676567657
Median Absolute Deviation (MAD)0.03
Skewness9.024044343
Sum50.8
Variance0.1309875374
MonotonicityNot monotonic
2023-07-20T07:03:41.619259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.11 29
 
0.5%
0.09 24
 
0.4%
0.1 24
 
0.4%
0.08 23
 
0.4%
0.07 23
 
0.4%
0.12 18
 
0.3%
0.13 18
 
0.3%
0.14 16
 
0.3%
0.06 15
 
0.3%
0.17 11
 
0.2%
Other values (30) 102
 
1.8%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0.02 4
 
0.1%
0.03 5
 
0.1%
0.04 5
 
0.1%
0.05 10
0.2%
0.06 15
0.3%
ValueCountFrequency (%)
4.25 1
< 0.1%
3.54 1
< 0.1%
2.81 1
< 0.1%
1.66 1
< 0.1%
0.78 1
< 0.1%

CaO_Lump
Real number (ℝ)

MISSING 

Distinct44
Distinct (%)14.5%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.09537953795
Minimum-0.01
Maximum2.34
Zeros34
Zeros (%)0.6%
Negative6
Negative (%)0.1%
Memory size43.8 KiB
2023-07-20T07:03:42.204806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0
Q10.02
median0.03
Q30.05
95-th percentile0.469
Maximum2.34
Range2.35
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.2672172725
Coefficient of variation (CV)2.801620538
Kurtosis40.42478033
Mean0.09537953795
Median Absolute Deviation (MAD)0.01
Skewness5.84768879
Sum28.9
Variance0.07140507071
MonotonicityNot monotonic
2023-07-20T07:03:42.842408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.02 92
 
1.6%
0.03 50
 
0.9%
0 34
 
0.6%
0.04 29
 
0.5%
0.01 12
 
0.2%
0.05 12
 
0.2%
0.06 12
 
0.2%
0.07 11
 
0.2%
-0.01 6
 
0.1%
0.11 5
 
0.1%
Other values (34) 40
 
0.7%
(Missing) 5282
94.6%
ValueCountFrequency (%)
-0.01 6
 
0.1%
0 34
 
0.6%
0.01 12
 
0.2%
0.02 92
1.6%
0.03 50
0.9%
ValueCountFrequency (%)
2.34 1
< 0.1%
2.28 1
< 0.1%
2.02 1
< 0.1%
1.2 1
< 0.1%
0.93 1
< 0.1%

P_Lump
Real number (ℝ)

MISSING 

Distinct67
Distinct (%)22.1%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.03822772277
Minimum0.009
Maximum0.115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:43.512511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.0151
Q10.027
median0.035
Q30.047
95-th percentile0.0758
Maximum0.115
Range0.106
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.01730915685
Coefficient of variation (CV)0.452790687
Kurtosis1.681601628
Mean0.03822772277
Median Absolute Deviation (MAD)0.01
Skewness1.090958207
Sum11.583
Variance0.000299606911
MonotonicityNot monotonic
2023-07-20T07:03:44.109022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.032 15
 
0.3%
0.028 13
 
0.2%
0.031 13
 
0.2%
0.034 12
 
0.2%
0.037 11
 
0.2%
0.025 11
 
0.2%
0.051 10
 
0.2%
0.039 10
 
0.2%
0.035 9
 
0.2%
0.045 8
 
0.1%
Other values (57) 191
 
3.4%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0.009 2
< 0.1%
0.01 1
 
< 0.1%
0.011 3
0.1%
0.012 4
0.1%
0.014 3
0.1%
ValueCountFrequency (%)
0.115 1
< 0.1%
0.095 1
< 0.1%
0.094 1
< 0.1%
0.088 1
< 0.1%
0.084 2
< 0.1%

S _Lump
Real number (ℝ)

MISSING 

Distinct51
Distinct (%)16.8%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.01791089109
Minimum0.002
Maximum0.103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:44.733238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.006
Q10.01
median0.014
Q30.023
95-th percentile0.0379
Maximum0.103
Range0.101
Interquartile range (IQR)0.013

Descriptive statistics

Standard deviation0.01243986782
Coefficient of variation (CV)0.6945420952
Kurtosis8.621604539
Mean0.01791089109
Median Absolute Deviation (MAD)0.005
Skewness2.270624685
Sum5.427
Variance0.0001547503115
MonotonicityNot monotonic
2023-07-20T07:03:45.383783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.012 29
 
0.5%
0.011 26
 
0.5%
0.009 20
 
0.4%
0.014 15
 
0.3%
0.008 15
 
0.3%
0.013 14
 
0.3%
0.007 12
 
0.2%
0.01 12
 
0.2%
0.019 12
 
0.2%
0.018 10
 
0.2%
Other values (41) 138
 
2.5%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0.002 1
 
< 0.1%
0.003 2
 
< 0.1%
0.004 4
0.1%
0.005 7
0.1%
0.006 9
0.2%
ValueCountFrequency (%)
0.103 1
< 0.1%
0.065 1
< 0.1%
0.064 1
< 0.1%
0.062 1
< 0.1%
0.057 1
< 0.1%

MgO_Lump
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)6.6%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.04320132013
Minimum-0.01
Maximum0.18
Zeros17
Zeros (%)0.3%
Negative7
Negative (%)0.1%
Memory size43.8 KiB
2023-07-20T07:03:45.863010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0
Q10.03
median0.04
Q30.06
95-th percentile0.09
Maximum0.18
Range0.19
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.03062239147
Coefficient of variation (CV)0.7088299935
Kurtosis3.291199217
Mean0.04320132013
Median Absolute Deviation (MAD)0.01
Skewness1.327621205
Sum13.09
Variance0.0009377308592
MonotonicityNot monotonic
2023-07-20T07:03:46.427898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.03 56
 
1.0%
0.04 55
 
1.0%
0.05 41
 
0.7%
0.06 30
 
0.5%
0.02 28
 
0.5%
0.01 20
 
0.4%
0 17
 
0.3%
0.07 13
 
0.2%
0.08 11
 
0.2%
0.09 10
 
0.2%
Other values (10) 22
 
0.4%
(Missing) 5282
94.6%
ValueCountFrequency (%)
-0.01 7
 
0.1%
0 17
 
0.3%
0.01 20
 
0.4%
0.02 28
0.5%
0.03 56
1.0%
ValueCountFrequency (%)
0.18 1
< 0.1%
0.17 1
< 0.1%
0.16 1
< 0.1%
0.15 2
< 0.1%
0.14 1
< 0.1%

K2O_Lump
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)4.6%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.01148514851
Minimum0
Maximum0.17
Zeros90
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:46.884760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.01
95-th percentile0.03
Maximum0.17
Range0.17
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.01830858402
Coefficient of variation (CV)1.594109471
Kurtosis29.75353776
Mean0.01148514851
Median Absolute Deviation (MAD)0
Skewness4.823536562
Sum3.48
Variance0.0003352042489
MonotonicityNot monotonic
2023-07-20T07:03:47.354714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.01 172
 
3.1%
0 90
 
1.6%
0.02 18
 
0.3%
0.03 9
 
0.2%
0.06 3
 
0.1%
0.04 2
 
< 0.1%
0.07 2
 
< 0.1%
0.1 1
 
< 0.1%
0.08 1
 
< 0.1%
0.17 1
 
< 0.1%
Other values (4) 4
 
0.1%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0 90
1.6%
0.01 172
3.1%
0.02 18
 
0.3%
0.03 9
 
0.2%
0.04 2
 
< 0.1%
ValueCountFrequency (%)
0.17 1
< 0.1%
0.13 1
< 0.1%
0.11 1
< 0.1%
0.1 1
< 0.1%
0.09 1
< 0.1%

Na2O_Lump
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)2.0%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.01161716172
Minimum0
Maximum0.07
Zeros80
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:47.838623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.02
95-th percentile0.03
Maximum0.07
Range0.07
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.01062727821
Coefficient of variation (CV)0.9147912781
Kurtosis8.590008041
Mean0.01161716172
Median Absolute Deviation (MAD)0.01
Skewness2.005802786
Sum3.52
Variance0.0001129390422
MonotonicityNot monotonic
2023-07-20T07:03:48.341113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.01 128
 
2.3%
0 80
 
1.4%
0.02 76
 
1.4%
0.03 15
 
0.3%
0.07 3
 
0.1%
0.06 1
 
< 0.1%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0 80
1.4%
0.01 128
2.3%
0.02 76
1.4%
0.03 15
 
0.3%
0.06 1
 
< 0.1%
ValueCountFrequency (%)
0.07 3
 
0.1%
0.06 1
 
< 0.1%
0.03 15
 
0.3%
0.02 76
1.4%
0.01 128
2.3%

LOI371_Lump
Real number (ℝ)

MISSING 

Distinct229
Distinct (%)75.6%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean6.784752475
Minimum0.69
Maximum9.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:49.280287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.69
5-th percentile2.637
Q16.115
median7.13
Q38
95-th percentile8.906
Maximum9.42
Range8.73
Interquartile range (IQR)1.885

Descriptive statistics

Standard deviation1.747306116
Coefficient of variation (CV)0.2575342465
Kurtosis1.626626063
Mean6.784752475
Median Absolute Deviation (MAD)0.9
Skewness-1.321027799
Sum2055.78
Variance3.053078664
MonotonicityNot monotonic
2023-07-20T07:03:49.857988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.76 4
 
0.1%
7.06 4
 
0.1%
6.91 4
 
0.1%
7.34 4
 
0.1%
8.02 3
 
0.1%
6.33 3
 
0.1%
7.03 3
 
0.1%
7.44 3
 
0.1%
7.13 3
 
0.1%
7.07 3
 
0.1%
Other values (219) 269
 
4.8%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0.69 1
< 0.1%
1.45 1
< 0.1%
1.49 1
< 0.1%
1.63 1
< 0.1%
1.64 1
< 0.1%
ValueCountFrequency (%)
9.42 1
< 0.1%
9.4 1
< 0.1%
9.36 1
< 0.1%
9.24 1
< 0.1%
9.19 1
< 0.1%

LOI650_Lump
Real number (ℝ)

MISSING 

Distinct84
Distinct (%)27.7%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.683960396
Minimum0.21
Maximum1.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:50.486550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.371
Q10.57
median0.66
Q30.79
95-th percentile0.999
Maximum1.73
Range1.52
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.1975885041
Coefficient of variation (CV)0.2888888089
Kurtosis3.196962783
Mean0.683960396
Median Absolute Deviation (MAD)0.11
Skewness0.9172317114
Sum207.24
Variance0.03904121697
MonotonicityNot monotonic
2023-07-20T07:03:51.080464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61 11
 
0.2%
0.58 10
 
0.2%
0.74 9
 
0.2%
0.63 9
 
0.2%
0.54 9
 
0.2%
0.81 8
 
0.1%
0.76 8
 
0.1%
0.79 8
 
0.1%
0.6 8
 
0.1%
0.62 8
 
0.1%
Other values (74) 215
 
3.8%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0.21 1
< 0.1%
0.25 1
< 0.1%
0.26 1
< 0.1%
0.28 1
< 0.1%
0.3 1
< 0.1%
ValueCountFrequency (%)
1.73 1
< 0.1%
1.43 1
< 0.1%
1.32 1
< 0.1%
1.3 1
< 0.1%
1.25 1
< 0.1%

LOI1000_Lump
Real number (ℝ)

MISSING 

Distinct48
Distinct (%)15.8%
Missing5282
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean0.2110891089
Minimum0.05
Maximum1.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:51.716955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.09
Q10.14
median0.17
Q30.245
95-th percentile0.42
Maximum1.35
Range1.3
Interquartile range (IQR)0.105

Descriptive statistics

Standard deviation0.1459059458
Coefficient of variation (CV)0.6912054655
Kurtosis27.03786004
Mean0.2110891089
Median Absolute Deviation (MAD)0.05
Skewness4.3162629
Sum63.96
Variance0.02128854501
MonotonicityNot monotonic
2023-07-20T07:03:52.386147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.16 28
 
0.5%
0.11 19
 
0.3%
0.12 18
 
0.3%
0.17 18
 
0.3%
0.15 17
 
0.3%
0.18 15
 
0.3%
0.19 15
 
0.3%
0.14 15
 
0.3%
0.21 14
 
0.3%
0.2 13
 
0.2%
Other values (38) 131
 
2.3%
(Missing) 5282
94.6%
ValueCountFrequency (%)
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 6
0.1%
0.09 8
0.1%
ValueCountFrequency (%)
1.35 1
< 0.1%
1.27 1
< 0.1%
1.19 1
< 0.1%
0.62 2
< 0.1%
0.6 1
< 0.1%

LOITotal_Lump
Real number (ℝ)

MISSING  ZEROS 

Distinct232
Distinct (%)33.0%
Missing4883
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean3.314643875
Minimum0
Maximum10.9
Zeros399
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size43.8 KiB
2023-07-20T07:03:52.986831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.76
95-th percentile9.52
Maximum10.9
Range10.9
Interquartile range (IQR)7.76

Descriptive statistics

Standard deviation3.993589739
Coefficient of variation (CV)1.20483222
Kurtosis-1.599165984
Mean3.314643875
Median Absolute Deviation (MAD)0
Skewness0.4888683948
Sum2326.88
Variance15.948759
MonotonicityNot monotonic
2023-07-20T07:03:53.599112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 399
 
7.1%
8.3 4
 
0.1%
7.5 3
 
0.1%
9.49 3
 
0.1%
6.69 3
 
0.1%
7.78 3
 
0.1%
9.75 3
 
0.1%
8.92 3
 
0.1%
9.16 3
 
0.1%
8.93 3
 
0.1%
Other values (222) 275
 
4.9%
(Missing) 4883
87.4%
ValueCountFrequency (%)
0 399
7.1%
1.21 1
 
< 0.1%
2.1 1
 
< 0.1%
2.42 1
 
< 0.1%
2.43 1
 
< 0.1%
ValueCountFrequency (%)
10.9 1
< 0.1%
10.52 1
< 0.1%
10.42 1
< 0.1%
10.38 1
< 0.1%
10.27 1
< 0.1%

Fe_Head
Text

MISSING 

Distinct217
Distinct (%)30.9%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:03:54.948242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.675213675
Min length1

Characters and Unicode

Total characters1878
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)25.1%

Sample

1st row-
2nd row-
3rd row50.29
4th row-
5th row62.94
ValueCountFrequency (%)
418
59.5%
value 20
 
2.8%
60.58 4
 
0.6%
56.40 3
 
0.4%
60.76 3
 
0.4%
60.44 3
 
0.4%
60.12 3
 
0.4%
60.63 3
 
0.4%
60.27 3
 
0.4%
60.02 3
 
0.4%
Other values (207) 239
34.0%
2023-07-20T07:03:57.138794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 418
22.3%
. 264
14.1%
6 230
12.2%
5 165
 
8.8%
0 146
 
7.8%
9 90
 
4.8%
1 86
 
4.6%
8 75
 
4.0%
7 73
 
3.9%
2 72
 
3.8%
Other values (9) 259
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1056
56.2%
Dash Punctuation 418
 
22.3%
Other Punctuation 304
 
16.2%
Uppercase Letter 100
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 230
21.8%
5 165
15.6%
0 146
13.8%
9 90
 
8.5%
1 86
 
8.1%
8 75
 
7.1%
7 73
 
6.9%
2 72
 
6.8%
4 68
 
6.4%
3 51
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
U 20
20.0%
E 20
20.0%
L 20
20.0%
A 20
20.0%
V 20
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
86.8%
! 20
 
6.6%
# 20
 
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1778
94.7%
Latin 100
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 418
23.5%
. 264
14.8%
6 230
12.9%
5 165
 
9.3%
0 146
 
8.2%
9 90
 
5.1%
1 86
 
4.8%
8 75
 
4.2%
7 73
 
4.1%
2 72
 
4.0%
Other values (4) 159
 
8.9%
Latin
ValueCountFrequency (%)
U 20
20.0%
E 20
20.0%
L 20
20.0%
A 20
20.0%
V 20
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 418
22.3%
. 264
14.1%
6 230
12.2%
5 165
 
8.8%
0 146
 
7.8%
9 90
 
4.8%
1 86
 
4.6%
8 75
 
4.0%
7 73
 
3.9%
2 72
 
3.8%
Other values (9) 259
13.8%

SiO2_Head
Text

MISSING 

Distinct198
Distinct (%)28.2%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:03:58.804742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.300569801
Min length1

Characters and Unicode

Total characters1615
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)21.1%

Sample

1st row-
2nd row-
3rd row12.94
4th row-
5th row3.71
ValueCountFrequency (%)
419
59.7%
value 19
 
2.7%
2.02 4
 
0.6%
2.14 4
 
0.6%
2.05 4
 
0.6%
1.93 4
 
0.6%
2.76 4
 
0.6%
2.03 3
 
0.4%
2.18 3
 
0.4%
2.80 3
 
0.4%
Other values (188) 235
33.5%
2023-07-20T07:04:01.824272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 419
25.9%
. 264
16.3%
2 171
10.6%
3 102
 
6.3%
1 87
 
5.4%
4 74
 
4.6%
5 66
 
4.1%
8 61
 
3.8%
9 61
 
3.8%
7 60
 
3.7%
Other values (9) 250
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 799
49.5%
Dash Punctuation 419
25.9%
Other Punctuation 302
 
18.7%
Uppercase Letter 95
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 171
21.4%
3 102
12.8%
1 87
10.9%
4 74
9.3%
5 66
 
8.3%
8 61
 
7.6%
9 61
 
7.6%
7 60
 
7.5%
6 59
 
7.4%
0 58
 
7.3%
Uppercase Letter
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
87.4%
# 19
 
6.3%
! 19
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1520
94.1%
Latin 95
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 419
27.6%
. 264
17.4%
2 171
11.2%
3 102
 
6.7%
1 87
 
5.7%
4 74
 
4.9%
5 66
 
4.3%
8 61
 
4.0%
9 61
 
4.0%
7 60
 
3.9%
Other values (4) 155
 
10.2%
Latin
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 419
25.9%
. 264
16.3%
2 171
10.6%
3 102
 
6.3%
1 87
 
5.4%
4 74
 
4.6%
5 66
 
4.1%
8 61
 
3.8%
9 61
 
3.8%
7 60
 
3.7%
Other values (9) 250
15.5%

Al2O3_Head
Text

MISSING 

Distinct164
Distinct (%)23.4%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:03.485526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.290598291
Min length1

Characters and Unicode

Total characters1608
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)14.1%

Sample

1st row-
2nd row-
3rd row6.95
4th row-
5th row1.51
ValueCountFrequency (%)
419
59.7%
value 19
 
2.7%
1.58 6
 
0.9%
1.98 5
 
0.7%
1.50 5
 
0.7%
1.28 5
 
0.7%
1.33 5
 
0.7%
1.45 4
 
0.6%
1.92 4
 
0.6%
1.43 4
 
0.6%
Other values (154) 226
32.2%
2023-07-20T07:04:05.505339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 419
26.1%
. 264
16.4%
1 223
13.9%
2 90
 
5.6%
3 75
 
4.7%
5 68
 
4.2%
8 63
 
3.9%
0 61
 
3.8%
9 61
 
3.8%
4 56
 
3.5%
Other values (9) 228
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
49.3%
Dash Punctuation 419
26.1%
Other Punctuation 302
 
18.8%
Uppercase Letter 95
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 223
28.2%
2 90
11.4%
3 75
 
9.5%
5 68
 
8.6%
8 63
 
8.0%
0 61
 
7.7%
9 61
 
7.7%
4 56
 
7.1%
7 50
 
6.3%
6 45
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
87.4%
# 19
 
6.3%
! 19
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1513
94.1%
Latin 95
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 419
27.7%
. 264
17.4%
1 223
14.7%
2 90
 
5.9%
3 75
 
5.0%
5 68
 
4.5%
8 63
 
4.2%
0 61
 
4.0%
9 61
 
4.0%
4 56
 
3.7%
Other values (4) 133
 
8.8%
Latin
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 419
26.1%
. 264
16.4%
1 223
13.9%
2 90
 
5.6%
3 75
 
4.7%
5 68
 
4.2%
8 63
 
3.9%
0 61
 
3.8%
9 61
 
3.8%
4 56
 
3.5%
Other values (9) 228
14.2%

TiO2_Head
Text

MISSING 

Distinct33
Distinct (%)4.7%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:06.043177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.290598291
Min length1

Characters and Unicode

Total characters1608
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.9%

Sample

1st row-
2nd row-
3rd row0.27
4th row-
5th row0.06
ValueCountFrequency (%)
419
59.7%
0.05 67
 
9.5%
0.04 56
 
8.0%
0.06 31
 
4.4%
0.08 19
 
2.7%
value 19
 
2.7%
0.07 19
 
2.7%
0.03 18
 
2.6%
0.10 6
 
0.9%
0.11 6
 
0.9%
Other values (23) 42
 
6.0%
2023-07-20T07:04:07.209123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 487
30.3%
- 419
26.1%
. 264
16.4%
5 71
 
4.4%
4 60
 
3.7%
1 37
 
2.3%
6 34
 
2.1%
3 29
 
1.8%
7 25
 
1.6%
8 24
 
1.5%
Other values (9) 158
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
49.3%
Dash Punctuation 419
26.1%
Other Punctuation 302
 
18.8%
Uppercase Letter 95
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 487
61.5%
5 71
 
9.0%
4 60
 
7.6%
1 37
 
4.7%
6 34
 
4.3%
3 29
 
3.7%
7 25
 
3.2%
8 24
 
3.0%
2 16
 
2.0%
9 9
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
E 19
20.0%
A 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
87.4%
! 19
 
6.3%
# 19
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1513
94.1%
Latin 95
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 487
32.2%
- 419
27.7%
. 264
17.4%
5 71
 
4.7%
4 60
 
4.0%
1 37
 
2.4%
6 34
 
2.2%
3 29
 
1.9%
7 25
 
1.7%
8 24
 
1.6%
Other values (4) 63
 
4.2%
Latin
ValueCountFrequency (%)
E 19
20.0%
A 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 487
30.3%
- 419
26.1%
. 264
16.4%
5 71
 
4.4%
4 60
 
3.7%
1 37
 
2.3%
6 34
 
2.1%
3 29
 
1.8%
7 25
 
1.6%
8 24
 
1.5%
Other values (9) 158
 
9.8%

MnO_Head
Text

MISSING 

Distinct43
Distinct (%)6.1%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:08.002137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.132478632
Min length1

Characters and Unicode

Total characters1497
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)1.3%

Sample

1st row-
2nd row-
3rd row0.59
4th row-
5th row-
ValueCountFrequency (%)
454
64.7%
0.13 19
 
2.7%
0.12 17
 
2.4%
value 17
 
2.4%
0.09 14
 
2.0%
0.14 14
 
2.0%
0.16 13
 
1.9%
0.08 13
 
1.9%
0.10 12
 
1.7%
0.17 12
 
1.7%
Other values (33) 117
 
16.7%
2023-07-20T07:04:09.155054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 454
30.3%
0 298
19.9%
. 231
15.4%
1 147
 
9.8%
2 63
 
4.2%
3 40
 
2.7%
4 29
 
1.9%
8 25
 
1.7%
6 25
 
1.7%
9 23
 
1.5%
Other values (9) 162
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 693
46.3%
Dash Punctuation 454
30.3%
Other Punctuation 265
 
17.7%
Uppercase Letter 85
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 298
43.0%
1 147
21.2%
2 63
 
9.1%
3 40
 
5.8%
4 29
 
4.2%
8 25
 
3.6%
6 25
 
3.6%
9 23
 
3.3%
5 23
 
3.3%
7 20
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
E 17
20.0%
U 17
20.0%
A 17
20.0%
V 17
20.0%
L 17
20.0%
Other Punctuation
ValueCountFrequency (%)
. 231
87.2%
! 17
 
6.4%
# 17
 
6.4%
Dash Punctuation
ValueCountFrequency (%)
- 454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1412
94.3%
Latin 85
 
5.7%

Most frequent character per script

Common
ValueCountFrequency (%)
- 454
32.2%
0 298
21.1%
. 231
16.4%
1 147
 
10.4%
2 63
 
4.5%
3 40
 
2.8%
4 29
 
2.1%
8 25
 
1.8%
6 25
 
1.8%
9 23
 
1.6%
Other values (4) 77
 
5.5%
Latin
ValueCountFrequency (%)
E 17
20.0%
U 17
20.0%
A 17
20.0%
V 17
20.0%
L 17
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 454
30.3%
0 298
19.9%
. 231
15.4%
1 147
 
9.8%
2 63
 
4.2%
3 40
 
2.7%
4 29
 
1.9%
8 25
 
1.7%
6 25
 
1.7%
9 23
 
1.5%
Other values (9) 162
 
10.8%

CaO_Head
Text

MISSING 

Distinct45
Distinct (%)6.4%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:09.689212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.145299145
Min length1

Characters and Unicode

Total characters1506
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)3.6%

Sample

1st row-
2nd row-
3rd row0.09
4th row-
5th row0.13
ValueCountFrequency (%)
453
64.5%
0.04 50
 
7.1%
0.03 38
 
5.4%
0.05 23
 
3.3%
0.02 23
 
3.3%
value 19
 
2.7%
0.07 17
 
2.4%
0.06 14
 
2.0%
0.08 6
 
0.9%
0.01 6
 
0.9%
Other values (35) 53
 
7.5%
2023-07-20T07:04:10.706036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 453
30.1%
0 411
27.3%
. 230
15.3%
4 59
 
3.9%
3 49
 
3.3%
1 39
 
2.6%
2 36
 
2.4%
5 30
 
2.0%
7 25
 
1.7%
6 21
 
1.4%
Other values (9) 153
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 690
45.8%
Dash Punctuation 453
30.1%
Other Punctuation 268
 
17.8%
Uppercase Letter 95
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 411
59.6%
4 59
 
8.6%
3 49
 
7.1%
1 39
 
5.7%
2 36
 
5.2%
5 30
 
4.3%
7 25
 
3.6%
6 21
 
3.0%
8 11
 
1.6%
9 9
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
E 19
20.0%
A 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 230
85.8%
! 19
 
7.1%
# 19
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1411
93.7%
Latin 95
 
6.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 453
32.1%
0 411
29.1%
. 230
16.3%
4 59
 
4.2%
3 49
 
3.5%
1 39
 
2.8%
2 36
 
2.6%
5 30
 
2.1%
7 25
 
1.8%
6 21
 
1.5%
Other values (4) 58
 
4.1%
Latin
ValueCountFrequency (%)
E 19
20.0%
A 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 453
30.1%
0 411
27.3%
. 230
15.3%
4 59
 
3.9%
3 49
 
3.3%
1 39
 
2.6%
2 36
 
2.4%
5 30
 
2.0%
7 25
 
1.7%
6 21
 
1.4%
Other values (9) 153
 
10.2%

P_Head
Text

MISSING 

Distinct11
Distinct (%)1.6%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:11.143547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.290598291
Min length1

Characters and Unicode

Total characters1608
Distinct characters19
Distinct categories4 ?
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 row-
2nd row-
3rd row0.06
4th row-
5th row0.02
ValueCountFrequency (%)
419
59.7%
0.04 62
 
8.8%
0.03 56
 
8.0%
0.05 48
 
6.8%
0.02 36
 
5.1%
0.06 32
 
4.6%
value 19
 
2.7%
0.07 15
 
2.1%
0.08 8
 
1.1%
0.09 4
 
0.6%
2023-07-20T07:04:12.053985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 528
32.8%
- 419
26.1%
. 264
16.4%
4 62
 
3.9%
3 56
 
3.5%
5 48
 
3.0%
2 36
 
2.2%
6 32
 
2.0%
U 19
 
1.2%
! 19
 
1.2%
Other values (9) 125
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
49.3%
Dash Punctuation 419
26.1%
Other Punctuation 302
 
18.8%
Uppercase Letter 95
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 528
66.7%
4 62
 
7.8%
3 56
 
7.1%
5 48
 
6.1%
2 36
 
4.5%
6 32
 
4.0%
7 15
 
1.9%
8 8
 
1.0%
9 4
 
0.5%
1 3
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
U 19
20.0%
E 19
20.0%
V 19
20.0%
L 19
20.0%
A 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
87.4%
! 19
 
6.3%
# 19
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1513
94.1%
Latin 95
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 528
34.9%
- 419
27.7%
. 264
17.4%
4 62
 
4.1%
3 56
 
3.7%
5 48
 
3.2%
2 36
 
2.4%
6 32
 
2.1%
! 19
 
1.3%
# 19
 
1.3%
Other values (4) 30
 
2.0%
Latin
ValueCountFrequency (%)
U 19
20.0%
E 19
20.0%
V 19
20.0%
L 19
20.0%
A 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 528
32.8%
- 419
26.1%
. 264
16.4%
4 62
 
3.9%
3 56
 
3.5%
5 48
 
3.0%
2 36
 
2.2%
6 32
 
2.0%
U 19
 
1.2%
! 19
 
1.2%
Other values (9) 125
 
7.8%

S _Head
Text

MISSING 

Distinct12
Distinct (%)1.7%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:12.432930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.290598291
Min length1

Characters and Unicode

Total characters1608
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row-
2nd row-
3rd row0.03
4th row-
5th row0.02
ValueCountFrequency (%)
419
59.7%
0.02 108
 
15.4%
0.01 59
 
8.4%
0.03 56
 
8.0%
0.04 23
 
3.3%
value 19
 
2.7%
0.05 7
 
1.0%
0.07 5
 
0.7%
0.08 2
 
0.3%
0.06 2
 
0.3%
Other values (2) 2
 
0.3%
2023-07-20T07:04:13.282459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 526
32.7%
- 419
26.1%
. 264
16.4%
2 109
 
6.8%
1 61
 
3.8%
3 56
 
3.5%
4 24
 
1.5%
U 19
 
1.2%
! 19
 
1.2%
E 19
 
1.2%
Other values (8) 92
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
49.3%
Dash Punctuation 419
26.1%
Other Punctuation 302
 
18.8%
Uppercase Letter 95
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 526
66.4%
2 109
 
13.8%
1 61
 
7.7%
3 56
 
7.1%
4 24
 
3.0%
5 7
 
0.9%
7 5
 
0.6%
8 2
 
0.3%
6 2
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U 19
20.0%
E 19
20.0%
A 19
20.0%
L 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
87.4%
! 19
 
6.3%
# 19
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1513
94.1%
Latin 95
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 526
34.8%
- 419
27.7%
. 264
17.4%
2 109
 
7.2%
1 61
 
4.0%
3 56
 
3.7%
4 24
 
1.6%
! 19
 
1.3%
# 19
 
1.3%
5 7
 
0.5%
Other values (3) 9
 
0.6%
Latin
ValueCountFrequency (%)
U 19
20.0%
E 19
20.0%
A 19
20.0%
L 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 526
32.7%
- 419
26.1%
. 264
16.4%
2 109
 
6.8%
1 61
 
3.8%
3 56
 
3.5%
4 24
 
1.5%
U 19
 
1.2%
! 19
 
1.2%
E 19
 
1.2%
Other values (8) 92
 
5.7%

MgO_Head
Text

MISSING 

Distinct22
Distinct (%)3.1%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:13.746408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.222222222
Min length1

Characters and Unicode

Total characters1560
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.7%

Sample

1st row-
2nd row-
3rd row0.23
4th row-
5th row0.07
ValueCountFrequency (%)
435
62.0%
0.05 54
 
7.7%
0.06 45
 
6.4%
0.04 37
 
5.3%
0.07 27
 
3.8%
0.10 19
 
2.7%
value 19
 
2.7%
0.09 14
 
2.0%
0.08 12
 
1.7%
0.12 8
 
1.1%
Other values (12) 32
 
4.6%
2023-07-20T07:04:14.856745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 463
29.7%
- 435
27.9%
. 248
15.9%
5 58
 
3.7%
1 55
 
3.5%
6 47
 
3.0%
4 42
 
2.7%
7 29
 
1.9%
U 19
 
1.2%
! 19
 
1.2%
Other values (9) 145
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 744
47.7%
Dash Punctuation 435
27.9%
Other Punctuation 286
 
18.3%
Uppercase Letter 95
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 463
62.2%
5 58
 
7.8%
1 55
 
7.4%
6 47
 
6.3%
4 42
 
5.6%
7 29
 
3.9%
9 15
 
2.0%
2 13
 
1.7%
8 12
 
1.6%
3 10
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
U 19
20.0%
E 19
20.0%
V 19
20.0%
L 19
20.0%
A 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 248
86.7%
! 19
 
6.6%
# 19
 
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 435
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1465
93.9%
Latin 95
 
6.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 463
31.6%
- 435
29.7%
. 248
16.9%
5 58
 
4.0%
1 55
 
3.8%
6 47
 
3.2%
4 42
 
2.9%
7 29
 
2.0%
! 19
 
1.3%
# 19
 
1.3%
Other values (4) 50
 
3.4%
Latin
ValueCountFrequency (%)
U 19
20.0%
E 19
20.0%
V 19
20.0%
L 19
20.0%
A 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 463
29.7%
- 435
27.9%
. 248
15.9%
5 58
 
3.7%
1 55
 
3.5%
6 47
 
3.0%
4 42
 
2.7%
7 29
 
1.9%
U 19
 
1.2%
! 19
 
1.2%
Other values (9) 145
 
9.3%

K2O_Head
Text

MISSING 

Distinct16
Distinct (%)2.3%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:15.236264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.192307692
Min length1

Characters and Unicode

Total characters1539
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.7%

Sample

1st row-
2nd row-
3rd row0.04
4th row-
5th row0.02
ValueCountFrequency (%)
442
63.0%
0.01 122
 
17.4%
0.02 64
 
9.1%
0.03 29
 
4.1%
value 19
 
2.7%
0.04 7
 
1.0%
0.07 5
 
0.7%
0.08 3
 
0.4%
0.05 2
 
0.3%
0.06 2
 
0.3%
Other values (6) 7
 
1.0%
2023-07-20T07:04:16.191100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 481
31.3%
- 442
28.7%
. 241
15.7%
1 127
 
8.3%
2 65
 
4.2%
3 29
 
1.9%
! 19
 
1.2%
E 19
 
1.2%
U 19
 
1.2%
L 19
 
1.2%
Other values (9) 78
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 723
47.0%
Dash Punctuation 442
28.7%
Other Punctuation 279
 
18.1%
Uppercase Letter 95
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 481
66.5%
1 127
 
17.6%
2 65
 
9.0%
3 29
 
4.0%
4 8
 
1.1%
7 5
 
0.7%
8 3
 
0.4%
5 2
 
0.3%
6 2
 
0.3%
9 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
E 19
20.0%
U 19
20.0%
L 19
20.0%
A 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 241
86.4%
! 19
 
6.8%
# 19
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
- 442
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1444
93.8%
Latin 95
 
6.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 481
33.3%
- 442
30.6%
. 241
16.7%
1 127
 
8.8%
2 65
 
4.5%
3 29
 
2.0%
! 19
 
1.3%
# 19
 
1.3%
4 8
 
0.6%
7 5
 
0.3%
Other values (4) 8
 
0.6%
Latin
ValueCountFrequency (%)
E 19
20.0%
U 19
20.0%
L 19
20.0%
A 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 481
31.3%
- 442
28.7%
. 241
15.7%
1 127
 
8.3%
2 65
 
4.2%
3 29
 
1.9%
! 19
 
1.2%
E 19
 
1.2%
U 19
 
1.2%
L 19
 
1.2%
Other values (9) 78
 
5.1%

Na2O_Head
Text

MISSING 

Distinct11
Distinct (%)1.6%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:16.525567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.102564103
Min length1

Characters and Unicode

Total characters1476
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row-
2nd row-
3rd row0.03
4th row-
5th row0.02
ValueCountFrequency (%)
460
65.5%
0.01 96
 
13.7%
0.02 79
 
11.3%
0.03 31
 
4.4%
value 16
 
2.3%
0.00 9
 
1.3%
0.05 4
 
0.6%
0.04 3
 
0.4%
0.06 2
 
0.3%
0.08 1
 
0.1%
2023-07-20T07:04:17.406852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 461
31.2%
- 460
31.2%
. 226
15.3%
1 96
 
6.5%
2 79
 
5.4%
3 31
 
2.1%
! 16
 
1.1%
E 16
 
1.1%
U 16
 
1.1%
L 16
 
1.1%
Other values (8) 59
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 678
45.9%
Dash Punctuation 460
31.2%
Other Punctuation 258
 
17.5%
Uppercase Letter 80
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 461
68.0%
1 96
 
14.2%
2 79
 
11.7%
3 31
 
4.6%
5 4
 
0.6%
4 3
 
0.4%
6 2
 
0.3%
8 1
 
0.1%
9 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
E 16
20.0%
U 16
20.0%
L 16
20.0%
A 16
20.0%
V 16
20.0%
Other Punctuation
ValueCountFrequency (%)
. 226
87.6%
! 16
 
6.2%
# 16
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
- 460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1396
94.6%
Latin 80
 
5.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 461
33.0%
- 460
33.0%
. 226
16.2%
1 96
 
6.9%
2 79
 
5.7%
3 31
 
2.2%
! 16
 
1.1%
# 16
 
1.1%
5 4
 
0.3%
4 3
 
0.2%
Other values (3) 4
 
0.3%
Latin
ValueCountFrequency (%)
E 16
20.0%
U 16
20.0%
L 16
20.0%
A 16
20.0%
V 16
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 461
31.2%
- 460
31.2%
. 226
15.3%
1 96
 
6.5%
2 79
 
5.4%
3 31
 
2.1%
! 16
 
1.1%
E 16
 
1.1%
U 16
 
1.1%
L 16
 
1.1%
Other values (8) 59
 
4.0%

LOI371_Head
Text

MISSING 

Distinct193
Distinct (%)27.5%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:18.728988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.290598291
Min length1

Characters and Unicode

Total characters1608
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)20.2%

Sample

1st row-
2nd row-
3rd row3.98
4th row-
5th row3.18
ValueCountFrequency (%)
419
59.7%
value 19
 
2.7%
7.28 6
 
0.9%
7.06 5
 
0.7%
7.45 5
 
0.7%
7.87 4
 
0.6%
7.49 4
 
0.6%
6.89 4
 
0.6%
7.10 3
 
0.4%
7.61 3
 
0.4%
Other values (183) 230
32.8%
2023-07-20T07:04:20.546386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 419
26.1%
. 264
16.4%
7 155
 
9.6%
8 111
 
6.9%
6 100
 
6.2%
2 70
 
4.4%
9 69
 
4.3%
5 66
 
4.1%
4 65
 
4.0%
3 53
 
3.3%
Other values (9) 236
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
49.3%
Dash Punctuation 419
26.1%
Other Punctuation 302
 
18.8%
Uppercase Letter 95
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 155
19.6%
8 111
14.0%
6 100
12.6%
2 70
8.8%
9 69
8.7%
5 66
8.3%
4 65
8.2%
3 53
 
6.7%
1 52
 
6.6%
0 51
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
87.4%
# 19
 
6.3%
! 19
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1513
94.1%
Latin 95
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 419
27.7%
. 264
17.4%
7 155
 
10.2%
8 111
 
7.3%
6 100
 
6.6%
2 70
 
4.6%
9 69
 
4.6%
5 66
 
4.4%
4 65
 
4.3%
3 53
 
3.5%
Other values (4) 141
 
9.3%
Latin
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 419
26.1%
. 264
16.4%
7 155
 
9.6%
8 111
 
6.9%
6 100
 
6.2%
2 70
 
4.4%
9 69
 
4.3%
5 66
 
4.1%
4 65
 
4.0%
3 53
 
3.3%
Other values (9) 236
14.7%

LOI650_Head
Text

MISSING 

Distinct96
Distinct (%)13.7%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:21.489640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.290598291
Min length1

Characters and Unicode

Total characters1608
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)5.3%

Sample

1st row-
2nd row-
3rd row2.06
4th row-
5th row0.65
ValueCountFrequency (%)
419
59.7%
value 19
 
2.7%
0.78 13
 
1.9%
0.73 11
 
1.6%
0.72 9
 
1.3%
0.75 8
 
1.1%
0.86 7
 
1.0%
0.69 7
 
1.0%
0.79 7
 
1.0%
0.76 7
 
1.0%
Other values (86) 195
27.8%
2023-07-20T07:04:22.983968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 419
26.1%
. 264
16.4%
0 249
15.5%
1 89
 
5.5%
7 86
 
5.3%
8 75
 
4.7%
6 72
 
4.5%
3 56
 
3.5%
9 49
 
3.0%
5 44
 
2.7%
Other values (9) 205
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
49.3%
Dash Punctuation 419
26.1%
Other Punctuation 302
 
18.8%
Uppercase Letter 95
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249
31.4%
1 89
 
11.2%
7 86
 
10.9%
8 75
 
9.5%
6 72
 
9.1%
3 56
 
7.1%
9 49
 
6.2%
5 44
 
5.6%
2 43
 
5.4%
4 29
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
87.4%
# 19
 
6.3%
! 19
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1513
94.1%
Latin 95
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 419
27.7%
. 264
17.4%
0 249
16.5%
1 89
 
5.9%
7 86
 
5.7%
8 75
 
5.0%
6 72
 
4.8%
3 56
 
3.7%
9 49
 
3.2%
5 44
 
2.9%
Other values (4) 110
 
7.3%
Latin
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 419
26.1%
. 264
16.4%
0 249
15.5%
1 89
 
5.5%
7 86
 
5.3%
8 75
 
4.7%
6 72
 
4.5%
3 56
 
3.5%
9 49
 
3.0%
5 44
 
2.7%
Other values (9) 205
12.7%

LOI1000_Head
Text

MISSING 

Distinct49
Distinct (%)7.0%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:23.734265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.290598291
Min length1

Characters and Unicode

Total characters1608
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)2.8%

Sample

1st row-
2nd row-
3rd row0.41
4th row-
5th row0.29
ValueCountFrequency (%)
419
59.7%
0.18 22
 
3.1%
0.24 19
 
2.7%
0.23 19
 
2.7%
value 19
 
2.7%
0.19 17
 
2.4%
0.16 14
 
2.0%
0.20 13
 
1.9%
0.21 12
 
1.7%
0.22 11
 
1.6%
Other values (39) 137
 
19.5%
2023-07-20T07:04:25.260912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 419
26.1%
0 283
17.6%
. 264
16.4%
2 137
 
8.5%
1 128
 
8.0%
3 66
 
4.1%
4 40
 
2.5%
6 35
 
2.2%
8 29
 
1.8%
9 25
 
1.6%
Other values (9) 182
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
49.3%
Dash Punctuation 419
26.1%
Other Punctuation 302
 
18.8%
Uppercase Letter 95
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 283
35.7%
2 137
17.3%
1 128
16.2%
3 66
 
8.3%
4 40
 
5.1%
6 35
 
4.4%
8 29
 
3.7%
9 25
 
3.2%
5 25
 
3.2%
7 24
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
L 19
20.0%
U 19
20.0%
E 19
20.0%
A 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
87.4%
! 19
 
6.3%
# 19
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1513
94.1%
Latin 95
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 419
27.7%
0 283
18.7%
. 264
17.4%
2 137
 
9.1%
1 128
 
8.5%
3 66
 
4.4%
4 40
 
2.6%
6 35
 
2.3%
8 29
 
1.9%
9 25
 
1.7%
Other values (4) 87
 
5.8%
Latin
ValueCountFrequency (%)
L 19
20.0%
U 19
20.0%
E 19
20.0%
A 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 419
26.1%
0 283
17.6%
. 264
16.4%
2 137
 
8.5%
1 128
 
8.0%
3 66
 
4.1%
4 40
 
2.5%
6 35
 
2.2%
8 29
 
1.8%
9 25
 
1.6%
Other values (9) 182
11.3%

LOITotal_Head
Text

MISSING 

Distinct202
Distinct (%)28.8%
Missing4883
Missing (%)87.4%
Memory size43.8 KiB
2023-07-20T07:04:26.829166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.309116809
Min length1

Characters and Unicode

Total characters1621
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150 ?
Unique (%)21.4%

Sample

1st row-
2nd row-
3rd row6.45
4th row-
5th row4.12
ValueCountFrequency (%)
419
59.7%
value 19
 
2.7%
8.18 4
 
0.6%
8.41 4
 
0.6%
8.66 3
 
0.4%
7.90 3
 
0.4%
8.48 3
 
0.4%
8.06 3
 
0.4%
8.34 3
 
0.4%
8.21 3
 
0.4%
Other values (192) 238
33.9%
2023-07-20T07:04:28.878365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 419
25.8%
. 264
16.3%
8 148
 
9.1%
9 111
 
6.8%
7 84
 
5.2%
0 76
 
4.7%
6 69
 
4.3%
1 68
 
4.2%
2 64
 
3.9%
5 63
 
3.9%
Other values (9) 255
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 805
49.7%
Dash Punctuation 419
25.8%
Other Punctuation 302
 
18.6%
Uppercase Letter 95
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 148
18.4%
9 111
13.8%
7 84
10.4%
0 76
9.4%
6 69
8.6%
1 68
8.4%
2 64
8.0%
5 63
7.8%
4 62
7.7%
3 60
7.5%
Uppercase Letter
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%
Other Punctuation
ValueCountFrequency (%)
. 264
87.4%
! 19
 
6.3%
# 19
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1526
94.1%
Latin 95
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 419
27.5%
. 264
17.3%
8 148
 
9.7%
9 111
 
7.3%
7 84
 
5.5%
0 76
 
5.0%
6 69
 
4.5%
1 68
 
4.5%
2 64
 
4.2%
5 63
 
4.1%
Other values (4) 160
 
10.5%
Latin
ValueCountFrequency (%)
A 19
20.0%
E 19
20.0%
U 19
20.0%
L 19
20.0%
V 19
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1621
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 419
25.8%
. 264
16.3%
8 148
 
9.1%
9 111
 
6.8%
7 84
 
5.2%
0 76
 
4.7%
6 69
 
4.3%
1 68
 
4.2%
2 64
 
3.9%
5 63
 
3.9%
Other values (9) 255
15.7%

Sample

Sample_numberBulk_Hole_NoFrom (m)To (m)Sample_Thickness (m)Lump (%)Fines (%)L/F RatioDry Weight Lump (kg)Dry Weight Fines (kg)Moisture (%)FeSiO2Al2O3TiO2MnOCaOPSMgOK2ONa2OLOI371LOI650LOI1000LOITotalFe_LumpSiO2_LumpAl2O3_LumpTiO2_LumpMnO_LumpCaO_LumpP_LumpS _LumpMgO_LumpK2O_LumpNa2O_LumpLOI371_LumpLOI650_LumpLOI1000_LumpLOITotal_LumpFe_HeadSiO2_HeadAl2O3_HeadTiO2_HeadMnO_HeadCaO_HeadP_HeadS _HeadMgO_HeadK2O_HeadNa2O_HeadLOI371_HeadLOI650_HeadLOI1000_HeadLOITotal_Head
030125CBS0224.6024.900.3014.585.50.170NaNNaNNaN47.6621.232.740.0920.470.120.0390.0440.270.080.054.970.920.436.32NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00---------------
130126CBS0224.9025.300.4013.586.50.156NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00---------------
230127CBS0225.3025.700.4011.788.30.133NaNNaNNaN49.0413.647.560.2990.640.100.0590.0360.240.040.034.162.230.416.8059.657.692.400.0810.240.050.0280.0220.120.030.032.630.770.383.7850.2912.946.950.270.590.090.060.030.230.040.033.982.060.416.45
330128CBS0225.7026.000.3018.781.30.230NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00---------------
430129CBS02NaNNaN0.0021.378.70.2700.311.600.4062.404.101.680.0670.000.160.0220.0220.080.020.033.200.700.304.2064.952.290.870.0370.120.040.0140.0090.040.010.013.120.460.253.8362.943.711.510.06-0.130.020.020.070.020.023.180.650.294.12
530130CBS0226.4026.600.2020.479.60.256NaN1.600.3764.043.161.410.0620.000.040.0200.0210.070.020.022.410.530.333.27NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00---------------
630131CBS0226.6026.850.2522.477.60.2880.521.600.3564.303.231.480.0800.000.040.0190.0210.070.030.012.040.530.332.9066.481.670.650.0330.080.020.0090.0110.040.010.011.810.360.292.4664.792.881.290.07-0.040.020.020.060.020.011.990.490.322.80
730132CBS0226.8527.100.2516.683.40.1990.411.650.2364.912.901.280.0820.000.040.0170.0220.060.030.011.800.470.332.6066.781.340.470.0240.07-0.010.0090.0090.040.010.012.070.340.262.6765.222.641.150.07-0.030.020.020.060.020.011.840.450.322.61
830133CBS02NaNNaN0.0019.680.40.2430.321.600.2664.293.161.300.0820.000.050.0170.0190.070.030.012.270.490.303.0666.231.560.620.0340.090.030.0110.0100.050.010.022.120.360.262.7464.672.851.170.07-0.050.020.020.070.020.012.240.460.293.00
930134CBS0227.7028.000.3013.986.10.1610.311.600.2365.092.391.010.0590.000.030.0160.0170.060.020.012.280.390.272.9466.531.410.540.0270.080.020.0110.0100.040.010.012.350.330.232.9165.292.250.940.05-0.030.020.020.060.020.012.290.380.262.93