Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory507.8 KiB
Average record size in memory52.0 B

Variable types

Numeric3
Categorical1
Text1

Dataset

Description외국인국적별인구현황입니다. 행정동, 성별, 국적, 인원수 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=104

Alerts

인원수 has 8216 (82.2%) zerosZeros

Reproduction

Analysis started2024-01-09 21:16:04.210160
Analysis finished2024-01-09 21:16:05.481047
Duration1.27 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분기
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20167.984
Minimum20151
Maximum20191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:16:05.527953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20151
5-th percentile20151
Q120161
median20171
Q320181
95-th percentile20184
Maximum20191
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.564636
Coefficient of variation (CV)0.00057341558
Kurtosis-1.2392952
Mean20167.984
Median Absolute Deviation (MAD)10
Skewness0.037268001
Sum2.0167984 × 108
Variance133.74081
MonotonicityNot monotonic
2024-01-10T06:16:05.630257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20173 656
 
6.6%
20153 649
 
6.5%
20182 647
 
6.5%
20164 634
 
6.3%
20163 629
 
6.3%
20171 626
 
6.3%
20181 623
 
6.2%
20152 613
 
6.1%
20172 605
 
6.0%
20154 599
 
6.0%
Other values (7) 3719
37.2%
ValueCountFrequency (%)
20151 587
5.9%
20152 613
6.1%
20153 649
6.5%
20154 599
6.0%
20161 581
5.8%
20162 589
5.9%
20163 629
6.3%
20164 634
6.3%
20171 626
6.3%
20172 605
6.0%
ValueCountFrequency (%)
20191 195
 
1.9%
20184 585
5.9%
20183 595
5.9%
20182 647
6.5%
20181 623
6.2%
20174 587
5.9%
20173 656
6.6%
20172 605
6.0%
20171 626
6.3%
20164 634
6.3%

행정동코드
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4449827 × 109
Minimum4.4131 × 109
Maximum4.4825 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:16:05.729520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.4131 × 109
5-th percentile4.4131 × 109
Q14.418 × 109
median4.425 × 109
Q34.477 × 109
95-th percentile4.4825 × 109
Maximum4.4825 × 109
Range69400000
Interquartile range (IQR)59000000

Descriptive statistics

Standard deviation29396653
Coefficient of variation (CV)0.0066134459
Kurtosis-1.8657572
Mean4.4449827 × 109
Median Absolute Deviation (MAD)11900000
Skewness0.23127966
Sum4.4449827 × 1013
Variance8.6416318 × 1014
MonotonicityNot monotonic
2024-01-10T06:16:05.829273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4420000000 676
 
6.8%
4413100000 663
 
6.6%
4471000000 652
 
6.5%
4415000000 643
 
6.4%
4480000000 642
 
6.4%
4476000000 636
 
6.4%
4418000000 626
 
6.3%
4425000000 624
 
6.2%
4482500000 620
 
6.2%
4413300000 616
 
6.2%
Other values (6) 3602
36.0%
ValueCountFrequency (%)
4413100000 663
6.6%
4413300000 616
6.2%
4415000000 643
6.4%
4418000000 626
6.3%
4420000000 676
6.8%
4421000000 584
5.8%
4423000000 603
6.0%
4425000000 624
6.2%
4427000000 600
6.0%
4471000000 652
6.5%
ValueCountFrequency (%)
4482500000 620
6.2%
4481000000 615
6.2%
4480000000 642
6.4%
4479000000 615
6.2%
4477000000 585
5.9%
4476000000 636
6.4%
4471000000 652
6.5%
4427000000 600
6.0%
4425000000 624
6.2%
4423000000 603
6.0%

성별코드
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5046 
2
4954 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 5046
50.5%
2 4954
49.5%

Length

2024-01-10T06:16:05.937114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:16:06.013836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5046
50.5%
2 4954
49.5%

국적
Text

Distinct204
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T06:16:06.262691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.0183
Min length2

Characters and Unicode

Total characters40183
Distinct characters185
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소말리아
2nd row나우루
3rd row포르투갈
4th row토고
5th row스와질란드
ValueCountFrequency (%)
가나 68
 
0.7%
불가리아 68
 
0.7%
수리남 68
 
0.7%
뉴질랜드 67
 
0.7%
이집트 67
 
0.7%
브라질 66
 
0.7%
가봉 65
 
0.7%
캐나다 65
 
0.7%
코트디부아르 65
 
0.7%
타이완 64
 
0.6%
Other values (194) 9337
93.4%
2024-01-10T06:16:06.657491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2855
 
7.1%
리 1631
 
4.1%
스 1525
 
3.8%
이 1188
 
3.0%
니 1144
 
2.8%
르 1136
 
2.8%
라 979
 
2.4%
국 878
 
2.2%
나 764
 
1.9%
비 730
 
1.8%
Other values (175) 27353
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40020
99.6%
Dash Punctuation 59
 
0.1%
Close Punctuation 52
 
0.1%
Open Punctuation 52
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2855
 
7.1%
리 1631
 
4.1%
스 1525
 
3.8%
이 1188
 
3.0%
니 1144
 
2.9%
르 1136
 
2.8%
라 979
 
2.4%
국 878
 
2.2%
나 764
 
1.9%
비 730
 
1.8%
Other values (172) 27190
67.9%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40020
99.6%
Common 163
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2855
 
7.1%
리 1631
 
4.1%
스 1525
 
3.8%
이 1188
 
3.0%
니 1144
 
2.9%
르 1136
 
2.8%
라 979
 
2.4%
국 878
 
2.2%
나 764
 
1.9%
비 730
 
1.8%
Other values (172) 27190
67.9%
Common
ValueCountFrequency (%)
- 59
36.2%
) 52
31.9%
( 52
31.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40020
99.6%
ASCII 163
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2855
 
7.1%
리 1631
 
4.1%
스 1525
 
3.8%
이 1188
 
3.0%
니 1144
 
2.9%
르 1136
 
2.8%
라 979
 
2.4%
국 878
 
2.2%
나 764
 
1.9%
비 730
 
1.8%
Other values (172) 27190
67.9%
ASCII
ValueCountFrequency (%)
- 59
36.2%
) 52
31.9%
( 52
31.9%

인원수
Real number (ℝ)

ZEROS 

Distinct263
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8547
Minimum0
Maximum2366
Zeros8216
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:16:06.779787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23
Maximum2366
Range2366
Interquartile range (IQR)0

Descriptive statistics

Standard deviation71.748331
Coefficient of variation (CV)7.2806205
Kurtosis296.70901
Mean9.8547
Median Absolute Deviation (MAD)0
Skewness14.733136
Sum98547
Variance5147.823
MonotonicityNot monotonic
2024-01-10T06:16:06.891827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8216
82.2%
1 506
 
5.1%
2 195
 
1.9%
3 127
 
1.3%
4 71
 
0.7%
5 58
 
0.6%
6 46
 
0.5%
7 37
 
0.4%
8 36
 
0.4%
10 25
 
0.2%
Other values (253) 683
 
6.8%
ValueCountFrequency (%)
0 8216
82.2%
1 506
 
5.1%
2 195
 
1.9%
3 127
 
1.3%
4 71
 
0.7%
5 58
 
0.6%
6 46
 
0.5%
7 37
 
0.4%
8 36
 
0.4%
9 22
 
0.2%
ValueCountFrequency (%)
2366 1
< 0.1%
1623 1
< 0.1%
1612 1
< 0.1%
1594 1
< 0.1%
1528 1
< 0.1%
1358 1
< 0.1%
1296 1
< 0.1%
1279 1
< 0.1%
1216 1
< 0.1%
1152 1
< 0.1%

Interactions

2024-01-10T06:16:05.090612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:16:04.573923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:16:04.827054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:16:05.178602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:16:04.662987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:16:04.919654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:16:05.267223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:16:04.749226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:16:05.005236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:16:06.962830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분기행정동코드성별코드인원수
분기1.0000.1740.0060.000
행정동코드0.1741.0000.0000.069
성별코드0.0060.0001.0000.054
인원수0.0000.0690.0541.000
2024-01-10T06:16:07.042630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분기행정동코드인원수성별코드
분기1.000-0.0440.0430.014
행정동코드-0.0441.000-0.1760.000
인원수0.043-0.1761.0000.040
성별코드0.0140.0000.0401.000

Missing values

2024-01-10T06:16:05.362337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:16:05.444656image/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

분기행정동코드성별코드국적인원수
656412017344760000001소말리아0
292782016144770000002나우루0
437032016444131000002포르투갈0
899512018344760000001토고0
251742016144131000002스와질란드0
395582016344230000001에리트레아0
500782017144133000001한국계러시아인2
429992016344825000001바베이도스0
732102017444810000001덴마크0
760472018144210000002마케도니아0
분기행정동코드성별코드국적인원수
162232015344710000001팔레스타인0
841822018244770000001스웨덴0
472672016444760000001세르비아0
247512015444825000002모리셔스0
835912018244710000002베트남417
450002016444200000001이스라엘1
855572018244810000002체코0
481102016444790000001케냐1
705502017444250000001리투아니아2
768132018144250000002바베이도스0