Overview

Dataset statistics

Number of variables17
Number of observations725
Missing cells6
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory104.2 KiB
Average record size in memory147.2 B

Variable types

Categorical4
Text2
Numeric11

Dataset

Description한국광해광업공단에서는 대륙별 국가별 주요 광물자원(연,아연,동,금,니켈,은,주석,알루미늄,코발트,몰리브덴,안티모니,카드뮴)의 2011~2022년 생산량 자료 등 국가별 개발동향 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3070609/fileData.do

Alerts

단위 is highly overall correlated with 광종High correlation
광종 is highly overall correlated with 품목 and 1 other fieldsHigh correlation
2011 생산량 is highly overall correlated with 2012 생산량 and 9 other fieldsHigh correlation
2012 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
2013 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
2014 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
2015 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
2016 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
2017 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
2018 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
2019 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
2020 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
2021 생산량 is highly overall correlated with 2011 생산량 and 9 other fieldsHigh correlation
품목 is highly overall correlated with 광종High correlation
2011 생산량 has 91 (12.6%) zerosZeros
2012 생산량 has 82 (11.3%) zerosZeros
2013 생산량 has 75 (10.3%) zerosZeros
2014 생산량 has 77 (10.6%) zerosZeros
2015 생산량 has 72 (9.9%) zerosZeros
2016 생산량 has 79 (10.9%) zerosZeros
2017 생산량 has 80 (11.0%) zerosZeros
2018 생산량 has 88 (12.1%) zerosZeros
2019 생산량 has 92 (12.7%) zerosZeros
2020 생산량 has 93 (12.8%) zerosZeros
2021 생산량 has 95 (13.1%) zerosZeros

Reproduction

Analysis started2023-12-11 23:41:15.439655
Analysis finished2023-12-11 23:41:31.658682
Duration16.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대륙
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
ASIA
223 
EUROPE
193 
AMERICA
156 
AFRICA
119 
OCEANIA
34 

Length

Max length7
Median length6
Mean length5.6468966
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFRICA
2nd rowAFRICA
3rd rowAFRICA
4th rowAFRICA
5th rowAFRICA

Common Values

ValueCountFrequency (%)
ASIA 223
30.8%
EUROPE 193
26.6%
AMERICA 156
21.5%
AFRICA 119
16.4%
OCEANIA 34
 
4.7%

Length

2023-12-12T08:41:31.757119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:41:31.917042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
asia 223
30.8%
europe 193
26.6%
america 156
21.5%
africa 119
16.4%
oceania 34
 
4.7%

국가
Text

Distinct141
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2023-12-12T08:41:32.250759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length7.8786207
Min length4

Characters and Unicode

Total characters5712
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)4.1%

Sample

1st rowAlgeria
2nd rowAlgeria
3rd rowAlgeria
4th rowBotswana
5th rowBotswana
ValueCountFrequency (%)
south 23
 
2.7%
korea 19
 
2.3%
russia 18
 
2.1%
australia 18
 
2.1%
china 18
 
2.1%
canada 15
 
1.8%
brazil 15
 
1.8%
kazakhstan 15
 
1.8%
africa 15
 
1.8%
republic 15
 
1.8%
Other values (141) 671
79.7%
2023-12-12T08:41:32.770337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 901
15.8%
i 518
 
9.1%
n 445
 
7.8%
e 371
 
6.5%
r 328
 
5.7%
o 300
 
5.3%
u 201
 
3.5%
l 197
 
3.4%
t 193
 
3.4%
s 176
 
3.1%
Other values (43) 2082
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4672
81.8%
Uppercase Letter 869
 
15.2%
Space Separator 122
 
2.1%
Other Punctuation 48
 
0.8%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 901
19.3%
i 518
11.1%
n 445
9.5%
e 371
 
7.9%
r 328
 
7.0%
o 300
 
6.4%
u 201
 
4.3%
l 197
 
4.2%
t 193
 
4.1%
s 176
 
3.8%
Other values (16) 1042
22.3%
Uppercase Letter
ValueCountFrequency (%)
A 92
 
10.6%
S 88
 
10.1%
C 73
 
8.4%
M 62
 
7.1%
B 55
 
6.3%
I 52
 
6.0%
P 51
 
5.9%
K 50
 
5.8%
N 49
 
5.6%
R 45
 
5.2%
Other values (13) 252
29.0%
Other Punctuation
ValueCountFrequency (%)
. 46
95.8%
& 2
 
4.2%
Space Separator
ValueCountFrequency (%)
122
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5541
97.0%
Common 171
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 901
16.3%
i 518
 
9.3%
n 445
 
8.0%
e 371
 
6.7%
r 328
 
5.9%
o 300
 
5.4%
u 201
 
3.6%
l 197
 
3.6%
t 193
 
3.5%
s 176
 
3.2%
Other values (39) 1911
34.5%
Common
ValueCountFrequency (%)
122
71.3%
. 46
 
26.9%
& 2
 
1.2%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 901
15.8%
i 518
 
9.1%
n 445
 
7.8%
e 371
 
6.5%
r 328
 
5.7%
o 300
 
5.3%
u 201
 
3.5%
l 197
 
3.4%
t 193
 
3.4%
s 176
 
3.1%
Other values (43) 2082
36.4%

광종
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
121 
108 
아연
90 
알루미늄
83 
79 
Other values (7)
244 

Length

Max length4
Median length1
Mean length1.8744828
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row아연
3rd row아연
4th row
5th row

Common Values

ValueCountFrequency (%)
121
16.7%
108
14.9%
아연 90
12.4%
알루미늄 83
11.4%
79
10.9%
니켈 63
8.7%
61
8.4%
주석 44
 
6.1%
코발트 21
 
2.9%
안티모니 20
 
2.8%
Other values (2) 35
 
4.8%

Length

2023-12-12T08:41:32.935605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
121
16.7%
108
14.9%
아연 90
12.4%
알루미늄 83
11.4%
79
10.9%
니켈 63
8.7%
61
8.4%
주석 44
 
6.1%
코발트 21
 
2.9%
안티모니 20
 
2.8%
Other values (2) 35
 
4.8%

품목
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
mine
424 
refined
235 
bauxite
 
34
slab
 
32

Length

Max length7
Median length4
Mean length5.1131034
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrefined
2nd rowmine
3rd rowslab
4th rowmine
5th rowmine

Common Values

ValueCountFrequency (%)
mine 424
58.5%
refined 235
32.4%
bauxite 34
 
4.7%
slab 32
 
4.4%

Length

2023-12-12T08:41:33.066838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:41:33.199076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mine 424
58.5%
refined 235
32.4%
bauxite 34
 
4.7%
slab 32
 
4.4%

단위
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
천톤
604 
kg
61 
 
60

Length

Max length2
Median length2
Mean length1.9172414
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row천톤
2nd row천톤
3rd row천톤
4th row천톤
5th row천톤

Common Values

ValueCountFrequency (%)
천톤 604
83.3%
kg 61
 
8.4%
60
 
8.3%

Length

2023-12-12T08:41:33.332390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:41:33.446740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
천톤 604
83.3%
kg 61
 
8.4%
60
 
8.3%

2011 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct589
Distinct (%)81.8%
Missing5
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean919.98706
Minimum0
Maximum123900
Zeros91
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:33.570859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.287
median33.3305
Q3248.1
95-th percentile2988.8158
Maximum123900
Range123900
Interquartile range (IQR)244.813

Descriptive statistics

Standard deviation6110.11
Coefficient of variation (CV)6.6415173
Kurtosis257.04363
Mean919.98706
Median Absolute Deviation (MAD)33.3305
Skewness14.616469
Sum662390.68
Variance37333444
MonotonicityNot monotonic
2023-12-12T08:41:33.748500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 91
 
12.6%
7.0 5
 
0.7%
36.0 4
 
0.6%
4.0 3
 
0.4%
7.5 3
 
0.4%
12.0 3
 
0.4%
18.0 3
 
0.4%
10.0 3
 
0.4%
13.0 2
 
0.3%
0.216 2
 
0.3%
Other values (579) 601
82.9%
(Missing) 5
 
0.7%
ValueCountFrequency (%)
0.0 91
12.6%
0.005 1
 
0.1%
0.024 1
 
0.1%
0.04 1
 
0.1%
0.05 1
 
0.1%
0.075 1
 
0.1%
0.091 1
 
0.1%
0.098 1
 
0.1%
0.166 1
 
0.1%
0.21 1
 
0.1%
ValueCountFrequency (%)
123900.0 1
0.1%
69977.0 1
0.1%
40643.852 1
0.1%
37173.844 1
0.1%
34969.0 1
0.1%
33624.6 1
0.1%
20072.0 1
0.1%
17695.39 1
0.1%
13000.0 1
0.1%
10441.0 1
0.1%

2012 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct604
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean956.89446
Minimum0
Maximum135600
Zeros82
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:33.936867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.814
median31.231
Q3249.183
95-th percentile2896.2686
Maximum135600
Range135600
Interquartile range (IQR)245.369

Descriptive statistics

Standard deviation6520.4006
Coefficient of variation (CV)6.8141273
Kurtosis280.25684
Mean956.89446
Median Absolute Deviation (MAD)31.231
Skewness15.312328
Sum693748.48
Variance42515625
MonotonicityNot monotonic
2023-12-12T08:41:34.127422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 82
 
11.3%
0.8 4
 
0.6%
6.0 3
 
0.4%
90.0 3
 
0.4%
3.0 3
 
0.4%
4.0 3
 
0.4%
1.8 2
 
0.3%
27.0 2
 
0.3%
36.0 2
 
0.3%
32.0 2
 
0.3%
Other values (594) 619
85.4%
ValueCountFrequency (%)
0.0 82
11.3%
0.001 1
 
0.1%
0.005 1
 
0.1%
0.006 1
 
0.1%
0.019 1
 
0.1%
0.024 1
 
0.1%
0.026 1
 
0.1%
0.042 1
 
0.1%
0.087 1
 
0.1%
0.125 1
 
0.1%
ValueCountFrequency (%)
135600.0 1
0.1%
76281.247 1
0.1%
44052.271 1
0.1%
34987.8 1
0.1%
31443.325 1
0.1%
29784.0 1
0.1%
23534.0 1
0.1%
19614.0 1
0.1%
16507.96 1
0.1%
10547.0 1
0.1%

2013 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct606
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1061.9841
Minimum0
Maximum152100
Zeros75
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:34.335109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.158
median35.84
Q3251.854
95-th percentile2924.8712
Maximum152100
Range152100
Interquartile range (IQR)247.696

Descriptive statistics

Standard deviation7406.8155
Coefficient of variation (CV)6.9745068
Kurtosis265.64399
Mean1061.9841
Median Absolute Deviation (MAD)35.84
Skewness14.881118
Sum769938.49
Variance54860915
MonotonicityNot monotonic
2023-12-12T08:41:34.540584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 75
 
10.3%
9.0 4
 
0.6%
0.6 4
 
0.6%
42.0 4
 
0.6%
7.0 3
 
0.4%
12.0 3
 
0.4%
100.0 3
 
0.4%
0.5 3
 
0.4%
75.0 3
 
0.4%
4.8 3
 
0.4%
Other values (596) 620
85.5%
ValueCountFrequency (%)
0.0 75
10.3%
0.005 1
 
0.1%
0.072 1
 
0.1%
0.082 1
 
0.1%
0.084 1
 
0.1%
0.087 1
 
0.1%
0.097 1
 
0.1%
0.1 1
 
0.1%
0.119 1
 
0.1%
0.16 1
 
0.1%
ValueCountFrequency (%)
152100.0 1
0.1%
81119.334 1
0.1%
57023.8 1
0.1%
50339.383 1
0.1%
36062.0 1
0.1%
33896.0 1
0.1%
26534.0 1
0.1%
20498.0 1
0.1%
20421.0 1
0.1%
10010.0 1
0.1%

2014 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct614
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean971.89274
Minimum0
Maximum123193
Zeros77
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:34.690852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.2
median35.8
Q3251.332
95-th percentile2903.6476
Maximum123193
Range123193
Interquartile range (IQR)247.132

Descriptive statistics

Standard deviation6412.2418
Coefficient of variation (CV)6.5976846
Kurtosis224.04298
Mean971.89274
Median Absolute Deviation (MAD)35.8
Skewness13.822041
Sum704622.24
Variance41116845
MonotonicityNot monotonic
2023-12-12T08:41:35.141391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 77
 
10.6%
2.0 5
 
0.7%
9.0 3
 
0.4%
8.0 3
 
0.4%
18.0 3
 
0.4%
3.324 2
 
0.3%
27.0 2
 
0.3%
72.0 2
 
0.3%
44.0 2
 
0.3%
37.054 2
 
0.3%
Other values (604) 624
86.1%
ValueCountFrequency (%)
0.0 77
10.6%
0.003 1
 
0.1%
0.012 1
 
0.1%
0.014 1
 
0.1%
0.043 1
 
0.1%
0.072 1
 
0.1%
0.075 1
 
0.1%
0.093 1
 
0.1%
0.1 1
 
0.1%
0.118 1
 
0.1%
ValueCountFrequency (%)
123193.0 1
0.1%
78631.998 1
0.1%
59212.401 1
0.1%
39292.0 1
0.1%
36313.2 1
0.1%
28316.7 1
0.1%
20688.0 1
0.1%
20287.8 1
0.1%
11452.0 1
0.1%
9676.697 1
0.1%

2015 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct612
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean987.88436
Minimum0
Maximum120700
Zeros72
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:35.305363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.4
median36.54
Q3257.148
95-th percentile2853.4296
Maximum120700
Range120700
Interquartile range (IQR)252.748

Descriptive statistics

Standard deviation6441.6294
Coefficient of variation (CV)6.5206311
Kurtosis210.24136
Mean987.88436
Median Absolute Deviation (MAD)36.453
Skewness13.403936
Sum716216.16
Variance41494590
MonotonicityNot monotonic
2023-12-12T08:41:35.475692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 72
 
9.9%
15.0 4
 
0.6%
18.0 4
 
0.6%
8.0 4
 
0.6%
0.9 3
 
0.4%
12.0 3
 
0.4%
42.0 2
 
0.3%
43.0 2
 
0.3%
22.902 2
 
0.3%
200.0 2
 
0.3%
Other values (602) 627
86.5%
ValueCountFrequency (%)
0.0 72
9.9%
0.005 1
 
0.1%
0.022 1
 
0.1%
0.04 1
 
0.1%
0.042 1
 
0.1%
0.045 1
 
0.1%
0.054 1
 
0.1%
0.087 1
 
0.1%
0.1 2
 
0.3%
0.104 1
 
0.1%
ValueCountFrequency (%)
120700.0 1
0.1%
80909.262 1
0.1%
60787.599 1
0.1%
37057.0 1
0.1%
36062.0 1
0.1%
31518.0 1
0.1%
26383.0 1
0.1%
20904.708 1
0.1%
10010.0 1
0.1%
10000.0 1
0.1%

2016 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct597
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1031.5622
Minimum0
Maximum108000
Zeros79
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:35.645097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.287
median35.186
Q3241.271
95-th percentile2796
Maximum108000
Range108000
Interquartile range (IQR)236.984

Descriptive statistics

Standard deviation6482.3111
Coefficient of variation (CV)6.2839753
Kurtosis157.8524
Mean1031.5622
Median Absolute Deviation (MAD)35.186
Skewness11.705224
Sum747882.57
Variance42020357
MonotonicityNot monotonic
2023-12-12T08:41:35.830374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 79
 
10.9%
12.0 5
 
0.7%
8.0 4
 
0.6%
100.0 4
 
0.6%
22.6 3
 
0.4%
28.0 3
 
0.4%
400.0 3
 
0.4%
60.0 3
 
0.4%
135.0 3
 
0.4%
5.0 3
 
0.4%
Other values (587) 615
84.8%
ValueCountFrequency (%)
0.0 79
10.9%
0.004 1
 
0.1%
0.007 1
 
0.1%
0.036 1
 
0.1%
0.041 1
 
0.1%
0.051 1
 
0.1%
0.054 1
 
0.1%
0.058 1
 
0.1%
0.071 1
 
0.1%
0.073 1
 
0.1%
ValueCountFrequency (%)
108000.0 1
0.1%
83517.148 1
0.1%
66157.689 1
0.1%
45046.0 1
0.1%
39244.2 1
0.1%
32697.996 1
0.1%
32423.932 1
0.1%
28280.0 1
0.1%
24219.0 1
0.1%
11888.0 1
0.1%

2017 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct605
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1097.8266
Minimum0
Maximum97683
Zeros80
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:36.023281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median35.671
Q3245.2
95-th percentile2896.2
Maximum97683
Range97683
Interquartile range (IQR)240.7

Descriptive statistics

Standard deviation6865.8551
Coefficient of variation (CV)6.2540432
Kurtosis122.99732
Mean1097.8266
Median Absolute Deviation (MAD)35.671
Skewness10.584129
Sum795924.29
Variance47139966
MonotonicityNot monotonic
2023-12-12T08:41:36.217840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 80
 
11.0%
12.0 5
 
0.7%
9.0 3
 
0.4%
10.0 3
 
0.4%
25.0 3
 
0.4%
43.0 3
 
0.4%
4.5 3
 
0.4%
15.0 3
 
0.4%
70.0 2
 
0.3%
4.0 2
 
0.3%
Other values (595) 618
85.2%
ValueCountFrequency (%)
0.0 80
11.0%
0.004 1
 
0.1%
0.013 1
 
0.1%
0.021 1
 
0.1%
0.023 1
 
0.1%
0.05 1
 
0.1%
0.066 1
 
0.1%
0.068 1
 
0.1%
0.079 1
 
0.1%
0.092 1
 
0.1%
ValueCountFrequency (%)
97683.0 1
0.1%
89420.586 1
0.1%
69600.0 1
0.1%
68392.954 1
0.1%
51701.564 1
0.1%
38242.1 1
0.1%
35189.054 1
0.1%
28100.0 1
0.1%
22775.993 1
0.1%
16533.0 1
0.1%

2018 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct585
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1150.4995
Minimum0
Maximum95947.593
Zeros88
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:36.387249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.23
median34.9
Q3249.884
95-th percentile2931.792
Maximum95947.593
Range95947.593
Interquartile range (IQR)245.654

Descriptive statistics

Standard deviation7115.4116
Coefficient of variation (CV)6.184628
Kurtosis114.55021
Mean1150.4995
Median Absolute Deviation (MAD)34.9
Skewness10.259437
Sum834112.17
Variance50629083
MonotonicityNot monotonic
2023-12-12T08:41:36.570196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 88
 
12.1%
9.0 6
 
0.8%
12.0 5
 
0.7%
8.0 4
 
0.6%
10.0 4
 
0.6%
0.7 3
 
0.4%
5.0 3
 
0.4%
30.0 3
 
0.4%
15.0 3
 
0.4%
6.0 3
 
0.4%
Other values (575) 603
83.2%
ValueCountFrequency (%)
0.0 88
12.1%
0.002 1
 
0.1%
0.006 1
 
0.1%
0.01 1
 
0.1%
0.032 1
 
0.1%
0.053 1
 
0.1%
0.06 1
 
0.1%
0.062 1
 
0.1%
0.073 1
 
0.1%
0.077 1
 
0.1%
ValueCountFrequency (%)
95947.593 1
0.1%
89584.0 1
0.1%
78360.0 1
0.1%
70751.214 1
0.1%
59573.707 1
0.1%
36447.29 1
0.1%
32377.0 1
0.1%
29700.0 1
0.1%
25233.0 1
0.1%
23228.82 1
0.1%

2019 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct588
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1144.6537
Minimum0
Maximum105543.79
Zeros92
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:36.752742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.478
median34.4
Q3233.832
95-th percentile2853.0168
Maximum105543.79
Range105543.79
Interquartile range (IQR)229.354

Descriptive statistics

Standard deviation7110.5949
Coefficient of variation (CV)6.2120055
Kurtosis124.20302
Mean1144.6537
Median Absolute Deviation (MAD)34.4
Skewness10.595301
Sum829873.91
Variance50560560
MonotonicityNot monotonic
2023-12-12T08:41:36.963142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 92
 
12.7%
12.0 6
 
0.8%
16.0 4
 
0.6%
8.0 4
 
0.6%
9.0 3
 
0.4%
30.0 3
 
0.4%
50.0 3
 
0.4%
10.0 3
 
0.4%
5.0 3
 
0.4%
200.0 2
 
0.3%
Other values (578) 602
83.0%
ValueCountFrequency (%)
0.0 92
12.7%
0.003 1
 
0.1%
0.009 1
 
0.1%
0.018 1
 
0.1%
0.03 1
 
0.1%
0.05 2
 
0.3%
0.055 1
 
0.1%
0.076 1
 
0.1%
0.105 1
 
0.1%
0.12 1
 
0.1%
ValueCountFrequency (%)
105543.789 1
0.1%
85000.0 1
0.1%
73323.663 1
0.1%
70173.327 1
0.1%
60229.0 1
0.1%
35974.528 1
0.1%
31937.9 1
0.1%
27950.0 1
0.1%
22307.024 1
0.1%
16592.7 1
0.1%

2020 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct565
Distinct (%)78.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1150.0788
Minimum0
Maximum103626.81
Zeros93
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:37.146083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.9985
median30.218
Q3237.375
95-th percentile2612.65
Maximum103626.81
Range103626.81
Interquartile range (IQR)233.3765

Descriptive statistics

Standard deviation7324.0556
Coefficient of variation (CV)6.3683076
Kurtosis119.04133
Mean1150.0788
Median Absolute Deviation (MAD)30.218
Skewness10.461674
Sum832657.05
Variance53641790
MonotonicityNot monotonic
2023-12-12T08:41:37.300826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 93
 
12.8%
8.0 5
 
0.7%
28.0 4
 
0.6%
200.0 4
 
0.6%
12.0 4
 
0.6%
100.0 4
 
0.6%
0.1 4
 
0.6%
0.12 3
 
0.4%
25.0 3
 
0.4%
10.0 3
 
0.4%
Other values (555) 597
82.3%
ValueCountFrequency (%)
0.0 93
12.8%
0.002 1
 
0.1%
0.026 1
 
0.1%
0.094 1
 
0.1%
0.1 4
 
0.6%
0.101 1
 
0.1%
0.106 1
 
0.1%
0.12 3
 
0.4%
0.124 1
 
0.1%
0.142 1
 
0.1%
ValueCountFrequency (%)
103626.811 1
0.1%
87766.199 1
0.1%
80000.0 1
0.1%
73200.0 1
0.1%
64530.0 1
0.1%
37080.401 1
0.1%
32897.8 1
0.1%
25859.0 1
0.1%
22543.0 1
0.1%
20315.303 1
0.1%

2021 생산량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct571
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1149.0399
Minimum0
Maximum103266.4
Zeros95
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T08:41:37.491548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median31.224
Q3215.069
95-th percentile3020.1746
Maximum103266.4
Range103266.4
Interquartile range (IQR)210.569

Descriptive statistics

Standard deviation7421.6047
Coefficient of variation (CV)6.4589616
Kurtosis128.84707
Mean1149.0399
Median Absolute Deviation (MAD)31.224
Skewness10.879547
Sum833053.94
Variance55080217
MonotonicityNot monotonic
2023-12-12T08:41:37.666138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 95
 
13.1%
10.0 7
 
1.0%
12.0 6
 
0.8%
0.72 4
 
0.6%
200.0 4
 
0.6%
0.7 4
 
0.6%
6.0 4
 
0.6%
9.0 3
 
0.4%
15.0 3
 
0.4%
20.0 3
 
0.4%
Other values (561) 592
81.7%
ValueCountFrequency (%)
0.0 95
13.1%
0.06 1
 
0.1%
0.1 2
 
0.3%
0.108 2
 
0.3%
0.12 2
 
0.3%
0.127 1
 
0.1%
0.144 1
 
0.1%
0.169 1
 
0.1%
0.18 1
 
0.1%
0.228 1
 
0.1%
ValueCountFrequency (%)
103266.404 1
0.1%
95000.0 1
0.1%
90621.577 1
0.1%
75000.0 1
0.1%
42622.0 1
0.1%
38502.609 1
0.1%
35949.6 1
0.1%
25781.0 1
0.1%
22152.195 1
0.1%
16800.0 1
0.1%
Distinct560
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2023-12-12T08:41:38.051747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4.3475862
Min length1

Characters and Unicode

Total characters3152
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

Unique522 ?
Unique (%)72.0%

Sample

1st row9.6
2nd row1.2
3rd row0
4th row0
5th row0.572
ValueCountFrequency (%)
0 93
 
12.8%
21
 
2.9%
12 8
 
1.1%
0.72 5
 
0.7%
15 4
 
0.6%
3 3
 
0.4%
9.6 3
 
0.4%
120 3
 
0.4%
1.8 3
 
0.4%
6 3
 
0.4%
Other values (550) 579
79.9%
2023-12-12T08:41:38.670969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 501
15.9%
1 374
11.9%
0 319
10.1%
2 311
9.9%
3 271
8.6%
6 246
7.8%
4 240
7.6%
5 238
7.6%
7 219
6.9%
8 212
6.7%
Other values (2) 221
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2630
83.4%
Other Punctuation 501
 
15.9%
Dash Punctuation 21
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 374
14.2%
0 319
12.1%
2 311
11.8%
3 271
10.3%
6 246
9.4%
4 240
9.1%
5 238
9.0%
7 219
8.3%
8 212
8.1%
9 200
7.6%
Other Punctuation
ValueCountFrequency (%)
. 501
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 501
15.9%
1 374
11.9%
0 319
10.1%
2 311
9.9%
3 271
8.6%
6 246
7.8%
4 240
7.6%
5 238
7.6%
7 219
6.9%
8 212
6.7%
Other values (2) 221
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 501
15.9%
1 374
11.9%
0 319
10.1%
2 311
9.9%
3 271
8.6%
6 246
7.8%
4 240
7.6%
5 238
7.6%
7 219
6.9%
8 212
6.7%
Other values (2) 221
7.0%

Interactions

2023-12-12T08:41:29.804134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:16.522388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:17.834550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:19.006808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:20.517604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:21.696674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:22.971307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:24.470863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:25.836516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:27.091796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:28.589596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:29.907918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:16.638442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:17.940546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:19.124153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:20.647508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:21.812819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:23.121701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:24.593325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:25.957460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:27.213147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:28.706592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:30.022428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:16.766899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:18.042542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:19.227031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:20.743653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:21.935217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:23.295497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:24.744971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:26.079516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:27.317017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:28.835174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:30.134005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:16.891484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:18.167840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:19.326439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:20.860157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:22.034309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:23.429404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:24.880944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:26.191442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:27.444405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:28.957580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:30.232467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:16.986822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:18.295763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:19.419044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:20.975244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:22.177527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:23.559261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:24.979924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:26.311786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:27.548869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:29.076454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:30.357653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:17.078730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:18.396804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:19.543682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:21.072430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:22.280267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:23.684831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:25.084274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:26.447860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:27.658269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:29.198979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:30.480119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:17.212587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:18.491145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:19.658622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:21.168910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:22.377635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:23.820043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:25.211416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:26.565449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:28.029240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:29.293885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:30.614301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:17.362541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:18.591868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:20.067181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:21.269798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:22.486864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:23.951340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:25.346494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:26.682514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:28.125678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:29.398074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:30.749241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:17.493207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:18.696274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:20.169405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:21.358288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:22.602167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:24.074807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:25.479049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:26.795553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:28.236862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:29.502725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:30.899180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:17.622218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:18.812190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:20.282663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:21.452285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:22.732453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:24.210402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:25.603093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:26.904212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:28.345996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:29.600193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:31.014886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:17.723014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:18.900032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:20.383788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:21.559871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:22.845611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:24.348226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:25.705595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:26.997290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:28.461346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:41:29.694240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:41:38.823631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대륙광종품목단위2011 생산량2012 생산량2013 생산량2014 생산량2015 생산량2016 생산량2017 생산량2018 생산량2019 생산량2020 생산량2021 생산량
대륙1.0000.1170.0710.0000.0970.0920.0980.0820.1180.1010.0820.0000.0920.1130.037
광종0.1171.0000.8831.0000.2840.2620.2830.2180.1430.2980.2240.2480.2820.2430.268
품목0.0710.8831.0000.1980.3040.2930.2990.2680.2590.3560.2040.3720.3820.3500.415
단위0.0001.0000.1981.0000.1660.1380.1670.1330.0630.2440.1940.1800.2150.1910.211
2011 생산량0.0970.2840.3040.1661.0000.9981.0000.9510.9440.9450.8390.8910.9390.9360.925
2012 생산량0.0920.2620.2930.1380.9981.0000.9980.9620.9570.9600.8470.9070.9360.9360.939
2013 생산량0.0980.2830.2990.1671.0000.9981.0000.9510.9440.9450.8390.8910.9390.9360.925
2014 생산량0.0820.2180.2680.1330.9510.9620.9511.0000.9940.9670.9820.9430.9640.9530.956
2015 생산량0.1180.1430.2590.0630.9440.9570.9440.9941.0000.9380.9770.9230.9330.9450.947
2016 생산량0.1010.2980.3560.2440.9450.9600.9450.9670.9381.0000.9640.9860.9980.9920.990
2017 생산량0.0820.2240.2040.1940.8390.8470.8390.9820.9770.9641.0000.9450.9620.9670.942
2018 생산량0.0000.2480.3720.1800.8910.9070.8910.9430.9230.9860.9451.0000.9860.9880.986
2019 생산량0.0920.2820.3820.2150.9390.9360.9390.9640.9330.9980.9620.9861.0000.9940.990
2020 생산량0.1130.2430.3500.1910.9360.9360.9360.9530.9450.9920.9670.9880.9941.0000.992
2021 생산량0.0370.2680.4150.2110.9250.9390.9250.9560.9470.9900.9420.9860.9900.9921.000
2023-12-12T08:41:39.007392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대륙품목단위광종
대륙1.0000.0580.0000.064
품목0.0581.0000.1870.591
단위0.0000.1871.0000.994
광종0.0640.5910.9941.000
2023-12-12T08:41:39.132416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2011 생산량2012 생산량2013 생산량2014 생산량2015 생산량2016 생산량2017 생산량2018 생산량2019 생산량2020 생산량2021 생산량대륙광종품목단위
2011 생산량1.0000.9720.9290.9010.8790.8650.8520.8360.8120.7930.7880.0660.1140.2000.069
2012 생산량0.9721.0000.9570.9310.9060.8890.8700.8530.8300.8100.8130.0620.1050.1920.057
2013 생산량0.9290.9571.0000.9690.9300.9090.8870.8780.8560.8390.8360.0660.1140.1960.069
2014 생산량0.9010.9310.9691.0000.9610.9320.9130.8900.8590.8470.8490.0520.1070.1870.089
2015 생산량0.8790.9060.9300.9611.0000.9650.9480.9240.8920.8780.8560.0750.0690.1800.042
2016 생산량0.8650.8890.9090.9320.9651.0000.9830.9600.9300.9150.8960.0610.1300.1650.158
2017 생산량0.8520.8700.8870.9130.9480.9831.0000.9780.9470.9320.9080.0520.1100.1410.131
2018 생산량0.8360.8530.8780.8900.9240.9600.9781.0000.9720.9530.9200.0000.1070.1730.115
2019 생산량0.8120.8300.8560.8590.8920.9300.9470.9721.0000.9750.9410.0560.1220.1780.138
2020 생산량0.7930.8100.8390.8470.8780.9150.9320.9530.9751.0000.9630.0690.1050.1620.122
2021 생산량0.7880.8130.8360.8490.8560.8960.9080.9200.9410.9631.0000.0220.1160.1950.136
대륙0.0660.0620.0660.0520.0750.0610.0520.0000.0560.0690.0221.0000.0640.0580.000
광종0.1140.1050.1140.1070.0690.1300.1100.1070.1220.1050.1160.0641.0000.5910.994
품목0.2000.1920.1960.1870.1800.1650.1410.1730.1780.1620.1950.0580.5911.0000.187
단위0.0690.0570.0690.0890.0420.1580.1310.1150.1380.1220.1360.0000.9940.1871.000

Missing values

2023-12-12T08:41:31.187486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:41:31.436170image/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.
2023-12-12T08:41:31.591600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

대륙국가광종품목단위2011 생산량2012 생산량2013 생산량2014 생산량2015 생산량2016 생산량2017 생산량2018 생산량2019 생산량2020 생산량2021 생산량2022 생산량
0AFRICAAlgeriarefined천톤10.06.09.09.09.08.09.09.09.08.09.09.6
1AFRICAAlgeria아연mine천톤0.00.00.00.00.10.30.50.50.4981.60.931.2
2AFRICAAlgeria아연slab천톤33.46121.97714.68713.1717.5573.11.4720.00.00.00.00
3AFRICABotswanamine천톤22.25332.10348.4947.65513.88812.4151.2391.4620.00.00.00
4AFRICABotswanamine천톤1.5621.5221.2060.9580.7560.8330.9131.1050.9430.8520.6590.572
5AFRICABotswana니켈mine천톤15.67517.94822.84814.95216.78816.8780.00.00.00.00.00
6AFRICABotswanaminekg0.07.56422.59122.2282.80.00.00.00.00.010.40.04
7AFRICABurkina Fasomine천톤31.76628.933.71337.236.5438.5246.43653.23451.40658.37558.92357.6
8AFRICABurkina Fasomine천톤0.00.02.21.9472.02.20.00.00.00.00.00
9AFRICABurkina Fasominekg0.00.00.00.05.55.87.08.08.08.08.98.6
대륙국가광종품목단위2011 생산량2012 생산량2013 생산량2014 생산량2015 생산량2016 생산량2017 생산량2018 생산량2019 생산량2020 생산량2021 생산량2022 생산량
715OCEANIANew Zealandmine천톤11.7610.04912.46811.98912.6879.86610.28810.0458.2175.866.0035.625
716OCEANIANew Zealandrefined천톤13.03.00.00.00.00.00.00.00.00.00.00
717OCEANIANew Zealandminekg14.3245.6311.22315.81212.4987.968.026.3324.061.3931.3931392
718OCEANIAOther Oceaniamine천톤1.5891.8861.6820.4880.60.60.60.60.60.60.60.6
719OCEANIAOther Oceaniaminekg0.5920.9020.90.90.90.90.90.90.90.90.90.9
720OCEANIAPapua New Guineamine천톤130.456125.325105.48375.90245.280.0104.97896.399.482.863.262.7
721OCEANIAPapua New Guineamine천톤62.27157.61663.47956.27957.6461.95265.24667.56274.38652.544.940.4
722OCEANIAPapua New Guinea니켈mine천톤0.05.28311.3720.98725.58122.26934.66635.35532.72233.65931.59434.304
723OCEANIAPapua New Guineaminekg93.3181.390.19284.371.6100.066.093.0147.0130.090.290.1
724OCEANIASolomon Islands알루미늄bauxite천톤0.00.00.00.0291.889240.01520.01650.01234.0843.00.0155.4