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 외국인직접투자통계로 국가별 對한국 투자금액 현황으로 연도별 외국인투자 도착금액을 제공합니다.
Author대한무역투자진흥공사
URLhttps://www.data.go.kr/data/15044496/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 63 (43.2%) zerosZeros
2014년 has 65 (44.5%) zerosZeros
2015년 has 73 (50.0%) zerosZeros
2016년 has 64 (43.8%) zerosZeros
2017년 has 56 (38.4%) zerosZeros
2018년 has 57 (39.0%) zerosZeros
2019년 has 66 (45.2%) zerosZeros
2020년 has 62 (42.5%) zerosZeros
2021년 has 62 (42.5%) zerosZeros

Reproduction

Analysis started2023-12-12 01:37:16.188728
Analysis finished2023-12-12 01:37:26.799477
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
2023-12-12T10:37:27.211917image/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%
2023-12-12T10:37:27.871422image/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 

Distinct83
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75273654
Minimum0
Maximum2.8890756 × 109
Zeros63
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T10:37:28.079527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median184782.5
Q33572592.8
95-th percentile3.0542749 × 108
Maximum2.8890756 × 109
Range2.8890756 × 109
Interquartile range (IQR)3572592.8

Descriptive statistics

Standard deviation3.1350083 × 108
Coefficient of variation (CV)4.1648148
Kurtosis49.603733
Mean75273654
Median Absolute Deviation (MAD)184782.5
Skewness6.4994658
Sum1.0989953 × 1010
Variance9.8282771 × 1016
MonotonicityNot monotonic
2023-12-12T10:37:28.683830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
43.2%
1100306719 2
 
1.4%
822342 1
 
0.7%
1141333 1
 
0.7%
475202 1
 
0.7%
74649767 1
 
0.7%
45311550 1
 
0.7%
1301377 1
 
0.7%
13710944 1
 
0.7%
178000 1
 
0.7%
Other values (73) 73
50.0%
ValueCountFrequency (%)
0 63
43.2%
90498 1
 
0.7%
90695 1
 
0.7%
90909 1
 
0.7%
91401 1
 
0.7%
94607 1
 
0.7%
95196 1
 
0.7%
96189 1
 
0.7%
178000 1
 
0.7%
182150 1
 
0.7%
ValueCountFrequency (%)
2889075598 1
0.7%
1602866649 1
0.7%
1100306719 2
1.4%
767694298 1
0.7%
722056735 1
0.7%
454307540 1
0.7%
307323729 1
0.7%
299738757 1
0.7%
248773456 1
0.7%
229100555 1
0.7%

2014년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87732591
Minimum0
Maximum2.2691489 × 109
Zeros65
Zeros (%)44.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T10:37:28.920174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median125000
Q31978808.8
95-th percentile4.3529099 × 108
Maximum2.2691489 × 109
Range2.2691489 × 109
Interquartile range (IQR)1978808.8

Descriptive statistics

Standard deviation3.2699857 × 108
Coefficient of variation (CV)3.7272189
Kurtosis27.6207
Mean87732591
Median Absolute Deviation (MAD)125000
Skewness5.1456756
Sum1.2808958 × 1010
Variance1.0692806 × 1017
MonotonicityNot monotonic
2023-12-12T10:37:29.120506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
44.5%
459860481 2
 
1.4%
254260 2
 
1.4%
12982610 1
 
0.7%
1432970 1
 
0.7%
1089000 1
 
0.7%
684186 1
 
0.7%
1556525 1
 
0.7%
1890000 1
 
0.7%
1296299 1
 
0.7%
Other values (70) 70
47.9%
ValueCountFrequency (%)
0 65
44.5%
19876 1
 
0.7%
80000 1
 
0.7%
94188 1
 
0.7%
95234 1
 
0.7%
98000 1
 
0.7%
98721 1
 
0.7%
99981 1
 
0.7%
100000 1
 
0.7%
150000 1
 
0.7%
ValueCountFrequency (%)
2269148895 1
0.7%
1922356951 1
0.7%
1809479796 1
0.7%
1646708731 1
0.7%
557272243 1
0.7%
459860481 2
1.4%
443714711 1
0.7%
410019823 1
0.7%
377064979 1
0.7%
369377492 1
0.7%

2015년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2260904 × 108
Minimum0
Maximum2.3625327 × 109
Zeros73
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T10:37:29.374862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24250
Q31483948.5
95-th percentile1.07733 × 109
Maximum2.3625327 × 109
Range2.3625327 × 109
Interquartile range (IQR)1483948.5

Descriptive statistics

Standard deviation4.1666991 × 108
Coefficient of variation (CV)3.3983619
Kurtosis17.216576
Mean1.2260904 × 108
Median Absolute Deviation (MAD)24250
Skewness4.1135694
Sum1.790092 × 1010
Variance1.7361381 × 1017
MonotonicityNot monotonic
2023-12-12T10:37:29.583406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
50.0%
1221088617 2
 
1.4%
550301 1
 
0.7%
88013 1
 
0.7%
1773501709 1
 
0.7%
1226148422 1
 
0.7%
660365 1
 
0.7%
1059618 1
 
0.7%
59648008 1
 
0.7%
584634 1
 
0.7%
Other values (63) 63
43.2%
ValueCountFrequency (%)
0 73
50.0%
48500 1
 
0.7%
50000 1
 
0.7%
87183 1
 
0.7%
88013 1
 
0.7%
88565 1
 
0.7%
90293 1
 
0.7%
91140 1
 
0.7%
92626 1
 
0.7%
93171 1
 
0.7%
ValueCountFrequency (%)
2362532724 1
0.7%
2333793564 1
0.7%
2297964109 1
0.7%
1773501709 1
0.7%
1226148422 1
0.7%
1221088617 2
1.4%
1135747113 1
0.7%
902078655 1
0.7%
855095635 1
0.7%
383806133 1
0.7%

2016년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84838763
Minimum0
Maximum2.112779 × 109
Zeros64
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T10:37:29.764891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median126219
Q33043892.8
95-th percentile4.4432746 × 108
Maximum2.112779 × 109
Range2.112779 × 109
Interquartile range (IQR)3043892.8

Descriptive statistics

Standard deviation3.0915387 × 108
Coefficient of variation (CV)3.6440166
Kurtosis22.077444
Mean84838763
Median Absolute Deviation (MAD)126219
Skewness4.607727
Sum1.2386459 × 1010
Variance9.5576112 × 1016
MonotonicityNot monotonic
2023-12-12T10:37:29.968225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
43.8%
1540730247 2
 
1.4%
1848990 1
 
0.7%
807809730 1
 
0.7%
3475890 1
 
0.7%
1919372 1
 
0.7%
35223899 1
 
0.7%
1391217 1
 
0.7%
4501510 1
 
0.7%
310255 1
 
0.7%
Other values (72) 72
49.3%
ValueCountFrequency (%)
0 64
43.8%
85727 1
 
0.7%
86472 1
 
0.7%
86938 1
 
0.7%
87200 1
 
0.7%
88418 1
 
0.7%
90490 1
 
0.7%
90834 1
 
0.7%
91437 1
 
0.7%
99938 1
 
0.7%
ValueCountFrequency (%)
2112778960 1
0.7%
1540730247 2
1.4%
1517913392 1
0.7%
1083009390 1
0.7%
938201925 1
0.7%
807809730 1
0.7%
479461568 1
0.7%
338925141 1
0.7%
293258239 1
0.7%
229510956 1
0.7%

2017년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0808027 × 108
Minimum0
Maximum1.9626361 × 109
Zeros56
Zeros (%)38.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T10:37:30.175135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median145907
Q34067646
95-th percentile1.0491152 × 109
Maximum1.9626361 × 109
Range1.9626361 × 109
Interquartile range (IQR)4067646

Descriptive statistics

Standard deviation3.6427416 × 108
Coefficient of variation (CV)3.3704037
Kurtosis15.346881
Mean1.0808027 × 108
Median Absolute Deviation (MAD)145907
Skewness3.9589848
Sum1.577972 × 1010
Variance1.3269566 × 1017
MonotonicityNot monotonic
2023-12-12T10:37:30.377937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
38.4%
1962636074 2
 
1.4%
189622 1
 
0.7%
863397 1
 
0.7%
4129107 1
 
0.7%
558151 1
 
0.7%
59630475 1
 
0.7%
1094820 1
 
0.7%
1941352 1
 
0.7%
91552 1
 
0.7%
Other values (80) 80
54.8%
ValueCountFrequency (%)
0 56
38.4%
87935 1
 
0.7%
88506 1
 
0.7%
88829 1
 
0.7%
88983 1
 
0.7%
89274 1
 
0.7%
89488 1
 
0.7%
89985 1
 
0.7%
90024 1
 
0.7%
90098 1
 
0.7%
ValueCountFrequency (%)
1962636074 2
1.4%
1939245311 1
0.7%
1461753009 1
0.7%
1281933841 1
0.7%
1278423581 1
0.7%
1248687141 1
0.7%
1149530941 1
0.7%
747867981 1
0.7%
411133107 1
0.7%
284082785 1
0.7%

2018년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3593259 × 108
Minimum0
Maximum3.8402176 × 109
Zeros57
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T10:37:30.576666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median189564
Q32336643
95-th percentile9.751428 × 108
Maximum3.8402176 × 109
Range3.8402176 × 109
Interquartile range (IQR)2336643

Descriptive statistics

Standard deviation4.8810526 × 108
Coefficient of variation (CV)3.590789
Kurtosis29.108504
Mean1.3593259 × 108
Median Absolute Deviation (MAD)189564
Skewness5.039968
Sum1.9846159 × 1010
Variance2.3824674 × 1017
MonotonicityNot monotonic
2023-12-12T10:37:30.771704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
39.0%
2355755372 2
 
1.4%
592875 1
 
0.7%
795000 1
 
0.7%
88300 1
 
0.7%
93633 1
 
0.7%
89172 1
 
0.7%
790745777 1
 
0.7%
1036608480 1
 
0.7%
483587 1
 
0.7%
Other values (79) 79
54.1%
ValueCountFrequency (%)
0 57
39.0%
31487 1
 
0.7%
88300 1
 
0.7%
88582 1
 
0.7%
89087 1
 
0.7%
89172 1
 
0.7%
93633 1
 
0.7%
94100 1
 
0.7%
94402 1
 
0.7%
94857 1
 
0.7%
ValueCountFrequency (%)
3840217582 1
0.7%
2355755372 2
1.4%
2004188016 1
0.7%
1389412758 1
0.7%
1085345575 1
0.7%
1065817568 1
0.7%
1036608480 1
0.7%
790745777 1
0.7%
638586604 1
0.7%
545928144 1
0.7%

2019년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0266062 × 108
Minimum0
Maximum2.3229385 × 109
Zeros66
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T10:37:30.967230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median176748.5
Q33669495.2
95-th percentile7.7385439 × 108
Maximum2.3229385 × 109
Range2.3229385 × 109
Interquartile range (IQR)3669495.2

Descriptive statistics

Standard deviation3.6267842 × 108
Coefficient of variation (CV)3.53279
Kurtosis18.791656
Mean1.0266062 × 108
Median Absolute Deviation (MAD)176748.5
Skewness4.2778819
Sum1.4988451 × 1010
Variance1.3153564 × 1017
MonotonicityNot monotonic
2023-12-12T10:37:31.134993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66
45.2%
1540699846 2
 
1.4%
179970 1
 
0.7%
173857898 1
 
0.7%
188774703 1
 
0.7%
1191655845 1
 
0.7%
271633 1
 
0.7%
44997592 1
 
0.7%
52911000 1
 
0.7%
886308 1
 
0.7%
Other values (70) 70
47.9%
ValueCountFrequency (%)
0 66
45.2%
83033 1
 
0.7%
83042 1
 
0.7%
85462 1
 
0.7%
93924 1
 
0.7%
148093 1
 
0.7%
168252 1
 
0.7%
173527 1
 
0.7%
179970 1
 
0.7%
198733 1
 
0.7%
ValueCountFrequency (%)
2322938470 1
0.7%
2034320410 1
0.7%
1540699846 2
1.4%
1408882278 1
0.7%
1191655845 1
0.7%
1131747982 1
0.7%
801728892 1
0.7%
690230885 1
0.7%
452416381 1
0.7%
311805296 1
0.7%

2020년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92870731
Minimum0
Maximum2.0868556 × 109
Zeros62
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T10:37:31.346131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median86235
Q32319699.5
95-th percentile5.7200003 × 108
Maximum2.0868556 × 109
Range2.0868556 × 109
Interquartile range (IQR)2319699.5

Descriptive statistics

Standard deviation3.3389346 × 108
Coefficient of variation (CV)3.5952496
Kurtosis22.475777
Mean92870731
Median Absolute Deviation (MAD)86235
Skewness4.6111506
Sum1.3559127 × 1010
Variance1.1148484 × 1017
MonotonicityNot monotonic
2023-12-12T10:37:31.548210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62
42.5%
2086855622 2
 
1.4%
848657392 1
 
0.7%
227912764 1
 
0.7%
733145 1
 
0.7%
729369 1
 
0.7%
51479102 1
 
0.7%
34000 1
 
0.7%
24686915 1
 
0.7%
147142 1
 
0.7%
Other values (74) 74
50.7%
ValueCountFrequency (%)
0 62
42.5%
23200 1
 
0.7%
34000 1
 
0.7%
80000 1
 
0.7%
80141 1
 
0.7%
81733 1
 
0.7%
81774 1
 
0.7%
82213 1
 
0.7%
83700 1
 
0.7%
84810 1
 
0.7%
ValueCountFrequency (%)
2086855622 2
1.4%
1854584878 1
0.7%
1065011735 1
0.7%
1041315644 1
0.7%
1019888929 1
0.7%
848657392 1
0.7%
583888779 1
0.7%
536333769 1
0.7%
456534602 1
0.7%
450515017 1
0.7%

2021년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4078476 × 108
Minimum0
Maximum3.5599205 × 109
Zeros62
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T10:37:31.745392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median90786
Q36071824.5
95-th percentile8.8259285 × 108
Maximum3.5599205 × 109
Range3.5599205 × 109
Interquartile range (IQR)6071824.5

Descriptive statistics

Standard deviation4.8939368 × 108
Coefficient of variation (CV)3.4761836
Kurtosis24.101844
Mean1.4078476 × 108
Median Absolute Deviation (MAD)90786
Skewness4.6857732
Sum2.0554575 × 1010
Variance2.3950618 × 1017
MonotonicityNot monotonic
2023-12-12T10:37:31.927622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62
42.5%
2005744995 2
 
1.4%
680987090 1
 
0.7%
915891 1
 
0.7%
360177521 1
 
0.7%
690446482 1
 
0.7%
8930741 1
 
0.7%
909657 1
 
0.7%
45364090 1
 
0.7%
2116171 1
 
0.7%
Other values (74) 74
50.7%
ValueCountFrequency (%)
0 62
42.5%
84617 1
 
0.7%
84721 1
 
0.7%
85404 1
 
0.7%
87650 1
 
0.7%
88610 1
 
0.7%
89087 1
 
0.7%
89318 1
 
0.7%
89717 1
 
0.7%
89993 1
 
0.7%
ValueCountFrequency (%)
3559920486 1
0.7%
2519170450 1
0.7%
2314887094 1
0.7%
2005744995 2
1.4%
940294674 1
0.7%
914135905 1
0.7%
912515315 1
0.7%
792825460 1
0.7%
690446482 1
0.7%
680987090 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

2023-12-12T10:37:32.111579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:37:32.223813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-31 146
100.0%

Interactions

2023-12-12T10:37:25.438612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:16.626700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:17.784990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:18.833684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:20.124781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:21.066699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:22.181436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:23.336308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:24.423058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:25.541017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:16.761403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:17.917645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:18.938250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:20.203316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:21.184591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:22.303409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:23.459689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:24.557017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:25.638705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:16.881882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:18.068717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:19.054110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:20.292185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:21.308650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:22.434895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:23.570404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:24.653433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:25.741307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:17.001508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:18.180391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:19.154262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:20.385423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:21.413001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:22.541868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:23.679139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:24.749664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:25.845715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:17.120180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:18.291082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:19.238467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:20.472614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:21.535635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:22.678746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:23.796487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:24.855756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:25.966860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:17.260059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:18.409455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:19.365904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:20.593107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:21.678672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:22.824917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:23.934653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:24.976453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:26.090826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:17.405394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:18.526001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:19.478205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:20.708318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:21.799981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:22.945001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:24.061223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:25.110085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:26.191185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:17.521598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:18.637783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:19.560955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:20.819597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:21.922479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:23.074840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:24.177223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:25.226134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:26.318608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:17.644960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:18.728281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:19.659705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:20.922362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:22.054951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:23.192610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:24.303560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:37:25.324414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:37:32.318540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2013년2014년2015년2016년2017년2018년2019년2020년2021년
2013년1.0000.9760.7500.8790.8430.8320.9140.8410.854
2014년0.9761.0000.7620.8710.8420.7380.9060.8090.809
2015년0.7500.7621.0000.8880.7740.8700.8480.9380.936
2016년0.8790.8710.8881.0000.9630.7780.9590.8650.867
2017년0.8430.8420.7740.9631.0000.7370.9070.8780.911
2018년0.8320.7380.8700.7780.7371.0000.7720.9000.947
2019년0.9140.9060.8480.9590.9070.7721.0000.8770.883
2020년0.8410.8090.9380.8650.8780.9000.8771.0000.969
2021년0.8540.8090.9360.8670.9110.9470.8830.9691.000
2023-12-12T10:37:32.468691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2013년2014년2015년2016년2017년2018년2019년2020년2021년
2013년1.0000.8520.7930.7550.7220.7320.7310.7160.758
2014년0.8521.0000.8390.8060.7620.7740.7790.7670.775
2015년0.7930.8391.0000.8300.7960.8110.8310.7640.845
2016년0.7550.8060.8301.0000.8220.8110.8010.7780.763
2017년0.7220.7620.7960.8221.0000.7830.7960.7000.800
2018년0.7320.7740.8110.8110.7831.0000.7710.7550.778
2019년0.7310.7790.8310.8010.7960.7711.0000.7630.805
2020년0.7160.7670.7640.7780.7000.7550.7631.0000.728
2021년0.7580.7750.8450.7630.8000.7780.8050.7281.000

Missing values

2023-12-12T10:37:26.490803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:37:26.721337image/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
0가나71123104447215161591896229498929038708799522021-12-31
1그레나다00008998500002021-12-31
2그루지아000000012992302021-12-31
3그리스019343500000002021-12-31
4기니0000000002021-12-31
5기타1915802849977902930027171008370015685802021-12-31
6기타(구주)47764600000084810854042021-12-31
7기타(미주)080000037191140000000002021-12-31
8기타(아주)188326930921102449604731139440208221302021-12-31
9기타(아프리카)01000000000329740002021-12-31
국가명2013년2014년2015년2016년2017년2018년2019년2020년2021년데이터기준일
136팔레스타인0000879358858252312325119810413342021-12-31
137페루0000141870258764001737772021-12-31
138포르투칼61840000170000399995000910782021-12-31
139폴란드387491214603301481706005301420020159242021-12-31
140프랑스299738757228550568119138315136187917278684012638586604917067281260052371551917732021-12-31
141핀란드257598372803456525574848784831259074015882944777584445935423897432021-12-31
142필리핀477084530310013635586561592232502313586021005616800870300918392021-12-31
143헝가리002649940003118052960266000002021-12-31
144호주275389411238375192704624935839827356654132004188016542956944751997532790182021-12-31
145홍콩130989589410019823855095635938201925128193384110853455758017288928486573925980959782021-12-31