Overview

Dataset statistics

Number of variables4
Number of observations480
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 KiB
Average record size in memory33.3 B

Variable types

Text2
Categorical1
Numeric1

Dataset

Description한국광해광업공단에서는 국가별(브라질, 캐나다,중국 등)로 보유하는 주요 광물종류 매장량의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3070586/fileData.do

Reproduction

Analysis started2023-12-12 04:54:01.626297
Analysis finished2023-12-12 04:54:02.194562
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국가
Text

Distinct107
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-12T13:54:02.448268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length31
Mean length9.6979167
Min length4

Characters and Unicode

Total characters4655
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)11.2%

Sample

1st rowAlgeria
2nd rowAlgeria
3rd rowArgentina
4th rowArgentina
5th rowArgentina
ValueCountFrequency (%)
united 40
 
5.8%
states 40
 
5.8%
china 36
 
5.2%
world 35
 
5.1%
total 35
 
5.1%
rounded 35
 
5.1%
russia 30
 
4.3%
other 27
 
3.9%
countries 26
 
3.8%
india 24
 
3.5%
Other values (117) 363
52.5%
2023-12-12T13:54:03.065965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 548
 
11.8%
i 363
 
7.8%
e 358
 
7.7%
t 345
 
7.4%
n 327
 
7.0%
r 297
 
6.4%
o 233
 
5.0%
d 231
 
5.0%
211
 
4.5%
s 209
 
4.5%
Other values (43) 1533
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3774
81.1%
Uppercase Letter 556
 
11.9%
Space Separator 211
 
4.5%
Close Punctuation 48
 
1.0%
Open Punctuation 48
 
1.0%
Other Punctuation 17
 
0.4%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 548
14.5%
i 363
9.6%
e 358
9.5%
t 345
9.1%
n 327
8.7%
r 297
7.9%
o 233
 
6.2%
d 231
 
6.1%
s 209
 
5.5%
u 198
 
5.2%
Other values (16) 665
17.6%
Uppercase Letter
ValueCountFrequency (%)
C 71
12.8%
S 71
12.8%
A 51
9.2%
U 49
8.8%
I 40
 
7.2%
R 35
 
6.3%
W 35
 
6.3%
B 34
 
6.1%
O 27
 
4.9%
T 26
 
4.7%
Other values (12) 117
21.0%
Space Separator
ValueCountFrequency (%)
211
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%
Decimal Number
ValueCountFrequency (%)
8 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4330
93.0%
Common 325
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 548
12.7%
i 363
 
8.4%
e 358
 
8.3%
t 345
 
8.0%
n 327
 
7.6%
r 297
 
6.9%
o 233
 
5.4%
d 231
 
5.3%
s 209
 
4.8%
u 198
 
4.6%
Other values (38) 1221
28.2%
Common
ValueCountFrequency (%)
211
64.9%
) 48
 
14.8%
( 48
 
14.8%
, 17
 
5.2%
8 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4655
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 548
 
11.8%
i 363
 
7.8%
e 358
 
7.7%
t 345
 
7.4%
n 327
 
7.0%
r 297
 
6.4%
o 233
 
5.0%
d 231
 
5.0%
211
 
4.5%
s 209
 
4.5%
Other values (43) 1533
32.9%

광종
Text

Distinct53
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-12T13:54:03.370687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length22
Mean length12.027083
Min length3

Characters and Unicode

Total characters5773
Distinct characters40
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st rowHELIUM
2nd rowPHOSPHATE ROCK
3rd rowLITHIUM
4th rowMOLYBDENUM
5th rowSILVER
ValueCountFrequency (%)
iron 33
 
4.4%
phosphate 25
 
3.3%
titanium 25
 
3.3%
mineral 25
 
3.3%
rock 25
 
3.3%
and 23
 
3.1%
gold 17
 
2.3%
ore(crude 16
 
2.1%
ore 16
 
2.1%
ore(iron 16
 
2.1%
Other values (64) 526
70.4%
2023-12-12T13:54:03.863090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 453
 
7.8%
A 432
 
7.5%
N 427
 
7.4%
I 414
 
7.2%
T 363
 
6.3%
O 361
 
6.3%
R 342
 
5.9%
M 268
 
4.6%
267
 
4.6%
L 238
 
4.1%
Other values (30) 2208
38.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4757
82.4%
Lowercase Letter 517
 
9.0%
Space Separator 267
 
4.6%
Open Punctuation 107
 
1.9%
Close Punctuation 107
 
1.9%
Decimal Number 12
 
0.2%
Dash Punctuation 6
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 453
 
9.5%
A 432
 
9.1%
N 427
 
9.0%
I 414
 
8.7%
T 363
 
7.6%
O 361
 
7.6%
R 342
 
7.2%
M 268
 
5.6%
L 238
 
5.0%
S 228
 
4.8%
Other values (13) 1231
25.9%
Lowercase Letter
ValueCountFrequency (%)
e 95
18.4%
r 65
12.6%
n 63
12.2%
t 57
11.0%
i 56
10.8%
o 55
10.6%
c 32
 
6.2%
u 26
 
5.0%
l 25
 
4.8%
d 16
 
3.1%
Other values (2) 27
 
5.2%
Space Separator
ValueCountFrequency (%)
267
100.0%
Open Punctuation
ValueCountFrequency (%)
( 107
100.0%
Close Punctuation
ValueCountFrequency (%)
) 107
100.0%
Decimal Number
ValueCountFrequency (%)
2 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5274
91.4%
Common 499
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 453
 
8.6%
A 432
 
8.2%
N 427
 
8.1%
I 414
 
7.8%
T 363
 
6.9%
O 361
 
6.8%
R 342
 
6.5%
M 268
 
5.1%
L 238
 
4.5%
S 228
 
4.3%
Other values (25) 1748
33.1%
Common
ValueCountFrequency (%)
267
53.5%
( 107
21.4%
) 107
21.4%
2 12
 
2.4%
- 6
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 453
 
7.8%
A 432
 
7.5%
N 427
 
7.4%
I 414
 
7.2%
T 363
 
6.3%
O 361
 
6.3%
R 342
 
5.9%
M 268
 
4.6%
267
 
4.6%
L 238
 
4.1%
Other values (30) 2208
38.2%

단위
Categorical

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
thousand metric tons
232 
metric tons
166 
million metric tons
32 
thousand metric dry tons
 
14
metric tons of rare-earth-oxide (REO) equivalent
 
13
Other values (3)
 
23

Length

Max length48
Median length24
Mean length17.35625
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmillion cubic meters
2nd rowthousand metric tons
3rd rowmetric tons
4th rowthousand metric tons
5th rowmetric tons

Common Values

ValueCountFrequency (%)
thousand metric tons 232
48.3%
metric tons 166
34.6%
million metric tons 32
 
6.7%
thousand metric dry tons 14
 
2.9%
metric tons of rare-earth-oxide (REO) equivalent 13
 
2.7%
kilograms 11
 
2.3%
million carats 7
 
1.5%
million cubic meters 5
 
1.0%

Length

2023-12-12T13:54:04.018158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:54:04.168041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
metric 457
35.2%
tons 457
35.2%
thousand 246
19.0%
million 44
 
3.4%
dry 14
 
1.1%
of 13
 
1.0%
rare-earth-oxide 13
 
1.0%
reo 13
 
1.0%
equivalent 13
 
1.0%
kilograms 11
 
0.8%
Other values (3) 17
 
1.3%

매장량
Real number (ℝ)

Distinct259
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4105891.5
Minimum8
Maximum3.3 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-12T13:54:04.311831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile487
Q18375
median81000
Q3712500
95-th percentile21000000
Maximum3.3 × 108
Range3.2999999 × 108
Interquartile range (IQR)704125

Descriptive statistics

Standard deviation19736832
Coefficient of variation (CV)4.8069541
Kurtosis162.59159
Mean4105891.5
Median Absolute Deviation (MAD)80000
Skewness11.138618
Sum1.9708279 × 109
Variance3.8954253 × 1014
MonotonicityNot monotonic
2023-12-12T13:54:04.476257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 8
 
1.7%
1000000 6
 
1.2%
400000 6
 
1.2%
30000 6
 
1.2%
22000 6
 
1.2%
21000000 5
 
1.0%
12000 5
 
1.0%
310000 5
 
1.0%
37000 5
 
1.0%
150000 5
 
1.0%
Other values (249) 423
88.1%
ValueCountFrequency (%)
8 1
0.2%
11 1
0.2%
20 1
0.2%
21 1
0.2%
24 1
0.2%
38 1
0.2%
43 1
0.2%
45 1
0.2%
61 1
0.2%
72 1
0.2%
ValueCountFrequency (%)
330000000 1
0.2%
130000000 1
0.2%
110000000 1
0.2%
100000000 1
0.2%
90000000 1
0.2%
74000000 1
0.2%
72000000 1
0.2%
70000000 1
0.2%
63000000 1
0.2%
52000000 1
0.2%

Interactions

2023-12-12T13:54:01.799634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:54:04.582370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광종단위매장량
광종1.0001.0000.439
단위1.0001.0000.129
매장량0.4390.1291.000
2023-12-12T13:54:04.684939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
매장량단위
매장량1.0000.079
단위0.0791.000

Missing values

2023-12-12T13:54:01.961969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:54:02.105814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

국가광종단위매장량
0AlgeriaHELIUMmillion cubic meters1800
1AlgeriaPHOSPHATE ROCKthousand metric tons2200000
2ArgentinaLITHIUMmetric tons2700000
3ArgentinaMOLYBDENUMthousand metric tons100
4ArgentinaSILVERmetric tons6500
5ArmeniaMOLYBDENUMthousand metric tons150
6ArmeniaRHENIUMkilograms95000
7AustraliaANTIMONYmetric tons120000
8AustraliaBAUXITEthousand metric dry tons5100000
9AustraliaCOBALTmetric tons1500000
국가광종단위매장량
470World total (rounded)TINmetric tons4600000
471World total (rounded)TUNGSTENmetric tons3800000
472World total (rounded)VANADIUMmetric tons26000
473World total (rounded)ZINCthousand metric tons210000
474World total (rounded)ZIRCONIUM AND HAFNIUMmetric tons68000
475World total (rutile, rounded)TITANIUM MINERAL CONCENTRATES(rutile)thousand metric tons49000
476World total, natural (rounded)SODA ASHthousand metric tons25000000
477ZambiaCOPPERthousand metric tons19000
478ZimbabweLITHIUMmetric tons310000
479ZimbabwePLATINUM-GROUP METALSkilograms1200000