Overview

Dataset statistics

Number of variables11
Number of observations146
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory97.9 B

Variable types

Text1
Numeric9
Categorical1

Dataset

Description외국인투자촉진법에 의한 외국인직접투자 국가별 對한국 투자금액 현황 추이로 연도별 외국인투자신고금액을 제공합니다.
Author대한무역투자진흥공사
URLhttps://www.data.go.kr/data/15044497/fileData.do

Alerts

데이터기준일 has constant value ""Constant
2013년 is highly overall correlated with 2014년 and 7 other fieldsHigh correlation
2014년 is highly overall correlated with 2013년 and 7 other fieldsHigh correlation
2015년 is highly overall correlated with 2013년 and 7 other fieldsHigh correlation
2016년 is highly overall correlated with 2013년 and 7 other fieldsHigh correlation
2017년 is highly overall correlated with 2013년 and 7 other fieldsHigh correlation
2018년 is highly overall correlated with 2013년 and 7 other fieldsHigh correlation
2019년 is highly overall correlated with 2013년 and 7 other fieldsHigh correlation
2020년 is highly overall correlated with 2013년 and 7 other fieldsHigh correlation
2021년 is highly overall correlated with 2013년 and 7 other fieldsHigh correlation
국가명 has unique valuesUnique
2013년 has 56 (38.4%) zerosZeros
2014년 has 60 (41.1%) zerosZeros
2015년 has 63 (43.2%) zerosZeros
2016년 has 62 (42.5%) zerosZeros
2017년 has 50 (34.2%) zerosZeros
2018년 has 48 (32.9%) zerosZeros
2019년 has 57 (39.0%) zerosZeros
2020년 has 55 (37.7%) zerosZeros
2021년 has 53 (36.3%) zerosZeros

Reproduction

Analysis started2023-12-12 08:28:10.850462
Analysis finished2023-12-12 08:28:21.457718
Duration10.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국가명
Text

UNIQUE 

Distinct146
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T17:28:22.047160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8.5
Mean length3.890411
Min length2

Characters and Unicode

Total characters568
Distinct characters167
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)100.0%

Sample

1st row가나
2nd row그레나다
3rd row그루지아
4th row그리스
5th row기니
ValueCountFrequency (%)
가나 1
 
0.7%
일본 1
 
0.7%
영국 1
 
0.7%
우즈베키스탄 1
 
0.7%
예멘 1
 
0.7%
오만 1
 
0.7%
오스트리아 1
 
0.7%
요르단 1
 
0.7%
우간다 1
 
0.7%
우루과이 1
 
0.7%
Other values (136) 136
93.2%
2023-12-12T17:28:22.642230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 38
 
6.7%
스 24
 
4.2%
이 21
 
3.7%
리 20
 
3.5%
르 16
 
2.8%
타 13
 
2.3%
라 12
 
2.1%
니 12
 
2.1%
트 10
 
1.8%
나 10
 
1.8%
Other values (157) 392
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 552
97.2%
Open Punctuation 5
 
0.9%
Close Punctuation 5
 
0.9%
Uppercase Letter 5
 
0.9%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 38
 
6.9%
스 24
 
4.3%
이 21
 
3.8%
리 20
 
3.6%
르 16
 
2.9%
타 13
 
2.4%
라 12
 
2.2%
니 12
 
2.2%
트 10
 
1.8%
나 10
 
1.8%
Other values (150) 376
68.1%
Uppercase Letter
ValueCountFrequency (%)
A 2
40.0%
M 1
20.0%
L 1
20.0%
T 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 552
97.2%
Common 11
 
1.9%
Latin 5
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 38
 
6.9%
스 24
 
4.3%
이 21
 
3.8%
리 20
 
3.6%
르 16
 
2.9%
타 13
 
2.4%
라 12
 
2.2%
니 12
 
2.2%
트 10
 
1.8%
나 10
 
1.8%
Other values (150) 376
68.1%
Latin
ValueCountFrequency (%)
A 2
40.0%
M 1
20.0%
L 1
20.0%
T 1
20.0%
Common
ValueCountFrequency (%)
( 5
45.5%
) 5
45.5%
1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 552
97.2%
ASCII 16
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 38
 
6.9%
스 24
 
4.3%
이 21
 
3.8%
리 20
 
3.6%
르 16
 
2.9%
타 13
 
2.4%
라 12
 
2.2%
니 12
 
2.2%
트 10
 
1.8%
나 10
 
1.8%
Other values (150) 376
68.1%
ASCII
ValueCountFrequency (%)
( 5
31.2%
) 5
31.2%
A 2
 
12.5%
1
 
6.2%
M 1
 
6.2%
L 1
 
6.2%
T 1
 
6.2%

2013년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1185208 × 108
Minimum0
Maximum3.5254548 × 109
Zeros56
Zeros (%)38.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T17:28:22.850104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median362289
Q37264479.2
95-th percentile5.1774258 × 108
Maximum3.5254548 × 109
Range3.5254548 × 109
Interquartile range (IQR)7264479.2

Descriptive statistics

Standard deviation4.3447205 × 108
Coefficient of variation (CV)3.8843449
Kurtosis36.571083
Mean1.1185208 × 108
Median Absolute Deviation (MAD)362289
Skewness5.7414907
Sum1.6330403 × 1010
Variance1.8876597 × 1017
MonotonicityNot monotonic
2023-12-12T17:28:23.026059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
38.4%
95000 3
 
2.1%
1785059030 2
 
1.4%
100000 2
 
1.4%
91000 2
 
1.4%
450000 1
 
0.7%
98718534 1
 
0.7%
116757845 1
 
0.7%
1797650 1
 
0.7%
13994187 1
 
0.7%
Other values (76) 76
52.1%
ValueCountFrequency (%)
0 56
38.4%
91000 2
 
1.4%
95000 3
 
2.1%
95129 1
 
0.7%
100000 2
 
1.4%
120000 1
 
0.7%
187900 1
 
0.7%
188000 1
 
0.7%
191166 1
 
0.7%
192129 1
 
0.7%
ValueCountFrequency (%)
3525454804 1
0.7%
2689750499 1
0.7%
1785059030 2
1.4%
976484325 1
0.7%
711570008 1
0.7%
618007903 1
0.7%
529928051 1
0.7%
481186149 1
0.7%
450041014 1
0.7%
431044224 1
0.7%

2014년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3304012 × 108
Minimum0
Maximum3.6062307 × 109
Zeros60
Zeros (%)41.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T17:28:23.212394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median257010
Q37618367.5
95-th percentile5.4097339 × 108
Maximum3.6062307 × 109
Range3.6062307 × 109
Interquartile range (IQR)7618367.5

Descriptive statistics

Standard deviation4.7671834 × 108
Coefficient of variation (CV)3.5832677
Kurtosis27.510455
Mean1.3304012 × 108
Median Absolute Deviation (MAD)257010
Skewness5.0068691
Sum1.9423857 × 1010
Variance2.2726038 × 1017
MonotonicityNot monotonic
2023-12-12T17:28:23.441947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
41.1%
95000 3
 
2.1%
425110142 2
 
1.4%
300000 2
 
1.4%
259020 2
 
1.4%
13945126 1
 
0.7%
1479953 1
 
0.7%
1116308 1
 
0.7%
1784250 1
 
0.7%
1771335 1
 
0.7%
Other values (72) 72
49.3%
ValueCountFrequency (%)
0 60
41.1%
50000 1
 
0.7%
88000 1
 
0.7%
95000 3
 
2.1%
98000 1
 
0.7%
99970 1
 
0.7%
100000 1
 
0.7%
150000 1
 
0.7%
195000 1
 
0.7%
196646 1
 
0.7%
ValueCountFrequency (%)
3606230748 1
0.7%
2487648074 1
0.7%
2380447593 1
0.7%
1920125059 1
0.7%
1672654319 1
0.7%
1189362352 1
0.7%
1061041800 1
0.7%
572071328 1
0.7%
447679586 1
0.7%
432423520 1
0.7%

2015년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4832199 × 108
Minimum0
Maximum5.4788856 × 109
Zeros63
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T17:28:23.651431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median165917.5
Q34198385.2
95-th percentile1.0321078 × 109
Maximum5.4788856 × 109
Range5.4788856 × 109
Interquartile range (IQR)4198385.2

Descriptive statistics

Standard deviation5.7945418 × 108
Coefficient of variation (CV)3.9067315
Kurtosis51.99262
Mean1.4832199 × 108
Median Absolute Deviation (MAD)165917.5
Skewness6.5015161
Sum2.165501 × 1010
Variance3.3576714 × 1017
MonotonicityNot monotonic
2023-12-12T17:28:23.831749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
43.2%
100000 2
 
1.4%
746441020 2
 
1.4%
95000 2
 
1.4%
1141274 1
 
0.7%
2000000 1
 
0.7%
1977273346 1
 
0.7%
1663270411 1
 
0.7%
882214 1
 
0.7%
2537357 1
 
0.7%
Other values (71) 71
48.6%
ValueCountFrequency (%)
0 63
43.2%
60000 1
 
0.7%
85000 1
 
0.7%
86000 1
 
0.7%
90153 1
 
0.7%
93000 1
 
0.7%
95000 2
 
1.4%
100000 2
 
1.4%
152004 1
 
0.7%
179831 1
 
0.7%
ValueCountFrequency (%)
5478885605 1
0.7%
2521119140 1
0.7%
1977273346 1
0.7%
1663270411 1
0.7%
1515992565 1
0.7%
1277296966 1
0.7%
1268362115 1
0.7%
1127330000 1
0.7%
746441020 2
1.4%
535804320 1
0.7%

2016년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.736565 × 108
Minimum0
Maximum4.0586709 × 109
Zeros62
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T17:28:24.005475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median257694.5
Q37000114
95-th percentile1.0373963 × 109
Maximum4.0586709 × 109
Range4.0586709 × 109
Interquartile range (IQR)7000114

Descriptive statistics

Standard deviation6.5754729 × 108
Coefficient of variation (CV)3.7864825
Kurtosis23.731293
Mean1.736565 × 108
Median Absolute Deviation (MAD)257694.5
Skewness4.8050156
Sum2.5353849 × 1010
Variance4.3236844 × 1017
MonotonicityNot monotonic
2023-12-12T17:28:24.188191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62
42.5%
100000 2
 
1.4%
4058670888 2
 
1.4%
681200 1
 
0.7%
2049057973 1
 
0.7%
1244955181 1
 
0.7%
3701929 1
 
0.7%
1410750 1
 
0.7%
41324640 1
 
0.7%
2021339 1
 
0.7%
Other values (73) 73
50.0%
ValueCountFrequency (%)
0 62
42.5%
83056 1
 
0.7%
87000 1
 
0.7%
89245 1
 
0.7%
90000 1
 
0.7%
92000 1
 
0.7%
100000 2
 
1.4%
190000 1
 
0.7%
191000 1
 
0.7%
194000 1
 
0.7%
ValueCountFrequency (%)
4058670888 2
1.4%
3872870926 1
0.7%
2347377910 1
0.7%
2118379762 1
0.7%
2049057973 1
0.7%
1547397877 1
0.7%
1244955181 1
0.7%
414719689 1
0.7%
360787828 1
0.7%
359932957 1
0.7%

2017년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6437818 × 108
Minimum0
Maximum4.7102991 × 109
Zeros50
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T17:28:24.365568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median225000
Q315542077
95-th percentile1.05106 × 109
Maximum4.7102991 × 109
Range4.7102991 × 109
Interquartile range (IQR)15542077

Descriptive statistics

Standard deviation5.4373989 × 108
Coefficient of variation (CV)3.3078593
Kurtosis36.033957
Mean1.6437818 × 108
Median Absolute Deviation (MAD)225000
Skewness5.3307499
Sum2.3999215 × 1010
Variance2.9565307 × 1017
MonotonicityNot monotonic
2023-12-12T17:28:24.531067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
34.2%
200000 4
 
2.7%
90000 4
 
2.7%
100000 4
 
2.7%
93000 2
 
1.4%
1051074774 2
 
1.4%
18510600 1
 
0.7%
279780168 1
 
0.7%
1725454 1
 
0.7%
54338954 1
 
0.7%
Other values (76) 76
52.1%
ValueCountFrequency (%)
0 50
34.2%
87000 1
 
0.7%
88000 1
 
0.7%
88888 1
 
0.7%
90000 4
 
2.7%
92000 1
 
0.7%
93000 2
 
1.4%
97940 1
 
0.7%
100000 4
 
2.7%
142000 1
 
0.7%
ValueCountFrequency (%)
4710299091 1
0.7%
2218061702 1
0.7%
1842430396 1
0.7%
1793130129 1
0.7%
1792110615 1
0.7%
1715144793 1
0.7%
1051074774 2
1.4%
1051015730 1
0.7%
1014476000 1
0.7%
809192169 1
0.7%

2018년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0182072 × 108
Minimum0
Maximum5.8789673 × 109
Zeros48
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T17:28:24.734405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median343146.5
Q310891251
95-th percentile1.4057392 × 109
Maximum5.8789673 × 109
Range5.8789673 × 109
Interquartile range (IQR)10891251

Descriptive statistics

Standard deviation6.8048139 × 108
Coefficient of variation (CV)3.3717122
Kurtosis36.011176
Mean2.0182072 × 108
Median Absolute Deviation (MAD)343146.5
Skewness5.3472764
Sum2.9465825 × 1010
Variance4.6305493 × 1017
MonotonicityNot monotonic
2023-12-12T17:28:24.883122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
32.9%
100000 6
 
4.1%
270000 2
 
1.4%
95000 2
 
1.4%
2564088052 2
 
1.4%
4802898 1
 
0.7%
777572 1
 
0.7%
150000 1
 
0.7%
94993 1
 
0.7%
58100000 1
 
0.7%
Other values (81) 81
55.5%
ValueCountFrequency (%)
0 48
32.9%
88912 1
 
0.7%
90000 1
 
0.7%
93528 1
 
0.7%
94993 1
 
0.7%
95000 2
 
1.4%
96000 1
 
0.7%
100000 6
 
4.1%
101857 1
 
0.7%
116764 1
 
0.7%
ValueCountFrequency (%)
5878967267 1
0.7%
2742637515 1
0.7%
2564088052 2
1.4%
2007356783 1
0.7%
1503439890 1
0.7%
1475084644 1
0.7%
1419528306 1
0.7%
1364371766 1
0.7%
1302072914 1
0.7%
1171853390 1
0.7%

2019년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7002027 × 108
Minimum0
Maximum6.8492238 × 109
Zeros57
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T17:28:25.054789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median314880
Q323167218
95-th percentile1.2239728 × 109
Maximum6.8492238 × 109
Range6.8492238 × 109
Interquartile range (IQR)23167218

Descriptive statistics

Standard deviation6.6795663 × 108
Coefficient of variation (CV)3.9286882
Kurtosis70.19997
Mean1.7002027 × 108
Median Absolute Deviation (MAD)314880
Skewness7.557078
Sum2.4822959 × 1010
Variance4.4616605 × 1017
MonotonicityNot monotonic
2023-12-12T17:28:25.209749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
39.0%
90000 3
 
2.1%
1494756855 2
 
1.4%
100000 2
 
1.4%
14463206 1
 
0.7%
268459 1
 
0.7%
280045 1
 
0.7%
974842255 1
 
0.7%
1429900586 1
 
0.7%
451094503 1
 
0.7%
Other values (76) 76
52.1%
ValueCountFrequency (%)
0 57
39.0%
82433 1
 
0.7%
86000 1
 
0.7%
88347 1
 
0.7%
90000 3
 
2.1%
100000 2
 
1.4%
139200 1
 
0.7%
173227 1
 
0.7%
190000 1
 
0.7%
249646 1
 
0.7%
ValueCountFrequency (%)
6849223822 1
0.7%
2087513766 1
0.7%
1912079052 1
0.7%
1665574760 1
0.7%
1494756855 2
1.4%
1429900586 1
0.7%
1307016294 1
0.7%
974842255 1
0.7%
731186717 1
0.7%
634518521 1
0.7%

2020년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5589193 × 108
Minimum0
Maximum5.3022487 × 109
Zeros55
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T17:28:25.417503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median183650
Q36037598.5
95-th percentile1.036047 × 109
Maximum5.3022487 × 109
Range5.3022487 × 109
Interquartile range (IQR)6037598.5

Descriptive statistics

Standard deviation5.9370733 × 108
Coefficient of variation (CV)3.8084546
Kurtosis41.494504
Mean1.5589193 × 108
Median Absolute Deviation (MAD)183650
Skewness5.7967333
Sum2.2760222 × 1010
Variance3.524884 × 1017
MonotonicityNot monotonic
2023-12-12T17:28:25.589637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
37.7%
85000 3
 
2.1%
100000 2
 
1.4%
2013612441 2
 
1.4%
90000 2
 
1.4%
475000 1
 
0.7%
164744 1
 
0.7%
1991303577 1
 
0.7%
792409422 1
 
0.7%
196916659 1
 
0.7%
Other values (77) 77
52.7%
ValueCountFrequency (%)
0 55
37.7%
81307 1
 
0.7%
83000 1
 
0.7%
85000 3
 
2.1%
85433 1
 
0.7%
86000 1
 
0.7%
87000 1
 
0.7%
90000 2
 
1.4%
92000 1
 
0.7%
95634 1
 
0.7%
ValueCountFrequency (%)
5302248736 1
0.7%
2280678504 1
0.7%
2013612441 2
1.4%
1991303577 1
0.7%
1846852794 1
0.7%
1548818421 1
0.7%
1117259490 1
0.7%
792409422 1
0.7%
727157088 1
0.7%
587902485 1
0.7%

2021년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.356172 × 108
Minimum0
Maximum5.2580594 × 109
Zeros53
Zeros (%)36.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T17:28:25.741616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median262937.5
Q38591876.5
95-th percentile1.1517806 × 109
Maximum5.2580594 × 109
Range5.2580594 × 109
Interquartile range (IQR)8591876.5

Descriptive statistics

Standard deviation8.5180108 × 108
Coefficient of variation (CV)3.6151906
Kurtosis22.581219
Mean2.356172 × 108
Median Absolute Deviation (MAD)262937.5
Skewness4.7074629
Sum3.4400111 × 1010
Variance7.2556508 × 1017
MonotonicityNot monotonic
2023-12-12T17:28:25.895941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53
36.3%
90000 4
 
2.7%
4887515790 2
 
1.4%
91000 2
 
1.4%
300000 1
 
0.7%
1888238735 1
 
0.7%
1210904835 1
 
0.7%
8612098 1
 
0.7%
2022217 1
 
0.7%
45204106 1
 
0.7%
Other values (79) 79
54.1%
ValueCountFrequency (%)
0 53
36.3%
85000 1
 
0.7%
86940 1
 
0.7%
88000 1
 
0.7%
88621 1
 
0.7%
89716 1
 
0.7%
90000 4
 
2.7%
90653 1
 
0.7%
91000 2
 
1.4%
95000 1
 
0.7%
ValueCountFrequency (%)
5258059385 1
0.7%
4887515790 2
1.4%
4189051220 1
0.7%
2836081014 1
0.7%
1888238735 1
0.7%
1800543510 1
0.7%
1210904835 1
0.7%
974407947 1
0.7%
828845521 1
0.7%
814933367 1
0.7%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2021-12-31
146 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-31
2nd row2021-12-31
3rd row2021-12-31
4th row2021-12-31
5th row2021-12-31

Common Values

ValueCountFrequency (%)
2021-12-31 146
100.0%

Length

2023-12-12T17:28:26.077589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:28:26.235878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-31 146
100.0%

Interactions

2023-12-12T17:28:20.114974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:11.300955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:12.359808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:13.478018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:14.823723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:15.892968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:16.981532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:18.020024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:19.052613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:20.221499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:11.422440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:12.487548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:13.605374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:14.926694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:16.011478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:17.106735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:18.141481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:19.176374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:20.336078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:11.543220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:12.611975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:13.717439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:15.045285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:16.136510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:17.217941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:18.291723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:19.295347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:20.445323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:11.652076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:12.743334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:13.839175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:15.170569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:16.259649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:17.324000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:18.406211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:19.420732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:20.566006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:11.785836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:12.871689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:13.970023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:15.309733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:16.412661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:17.442571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:18.522898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:19.542105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:20.662625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:11.882507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:12.980678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:14.066095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:15.428367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:16.535875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:17.560051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:18.609708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:19.659277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:20.768317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:12.004709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:13.106069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:14.170110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:15.543322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:16.645721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:17.715400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:18.711384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:19.798655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:20.898190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:12.125957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:13.246950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:14.292783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:15.671837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:16.759176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:17.823617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:18.823378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:19.904255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:21.028034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:12.237234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:13.379040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:14.728632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:15.771279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:16.865118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:17.932720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:18.941125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:20.018670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:28:26.328456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2013년2014년2015년2016년2017년2018년2019년2020년2021년
2013년1.0000.9110.9710.8120.9380.9440.8230.9560.848
2014년0.9111.0000.9220.8820.8630.8450.8840.9510.870
2015년0.9710.9221.0000.7790.9320.9400.8090.9740.866
2016년0.8120.8820.7791.0000.7380.7750.9440.8540.858
2017년0.9380.8630.9320.7381.0000.9550.8930.9490.785
2018년0.9440.8450.9400.7750.9551.0000.8210.9580.752
2019년0.8230.8840.8090.9440.8930.8211.0000.8620.785
2020년0.9560.9510.9740.8540.9490.9580.8621.0000.878
2021년0.8480.8700.8660.8580.7850.7520.7850.8781.000
2023-12-12T17:28:26.502720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2013년2014년2015년2016년2017년2018년2019년2020년2021년
2013년1.0000.7990.7620.7330.7420.6920.6740.6850.732
2014년0.7991.0000.7970.7680.7390.7600.7380.7570.763
2015년0.7620.7971.0000.8210.7480.7890.8050.7730.759
2016년0.7330.7680.8211.0000.7500.7720.7980.7270.729
2017년0.7420.7390.7480.7501.0000.7800.7490.7470.749
2018년0.6920.7600.7890.7720.7801.0000.7820.7520.738
2019년0.6740.7380.8050.7980.7490.7821.0000.7880.788
2020년0.6850.7570.7730.7270.7470.7520.7881.0000.759
2021년0.7320.7630.7590.7290.7490.7380.7880.7591.000

Missing values

2023-12-12T17:28:21.167341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:28:21.377808image/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

국가명2013년2014년2015년2016년2017년2018년2019년2020년2021년데이터기준일
0가나7475690882531261826520000010000029100009956732021-12-31
1그레나다00009000000002021-12-31
2그루지아000000017000002021-12-31
3그리스019664600000002021-12-31
4기니00000090000002021-12-31
5기타2144000849993378777020000027000008500015636402021-12-31
6기타(구주)48100000000085000900002021-12-31
7기타(미주)240000000040000000002021-12-31
8기타(아주)190000030000003499704734189500008130702021-12-31
9기타(아프리카)95000999700000329760002021-12-31
국가명2013년2014년2015년2016년2017년2018년2019년2020년2021년데이터기준일
136팔레스타인00008800028800052206755173413950012021-12-31
137페루00001420002600000854331710842021-12-31
138포르투칼1496200000025356339320088912883470906532021-12-31
139폴란드3879970150000015854700500000002005295002600000001000000001020120642021-12-31
140프랑스5299280512063375171029518361935404722797801686865336811468755732008577622219996202021-12-31
141핀란드257655004504356540476766689621851060011676482896319521708421490592021-12-31
142필리핀1019954095871820187002552376922951099000087872394623194209521300950002021-12-31
143헝가리0064655185000005000003096249110266000002021-12-31
144호주504905191398325878519326779156689142330126200735678310757267263222911252418082021-12-31
145홍콩97648432510610418001515992565211837976217921106151503439890191207905211172594906359902252021-12-31