Overview

Dataset statistics

Number of variables7
Number of observations62
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory62.1 B

Variable types

Numeric4
Text1
Categorical2

Alerts

기본키 is highly overall correlated with 집계구 코드High correlation
2017년 총 인구수(명) is highly overall correlated with 2018년 총 인구수(명)High correlation
2018년 총 인구수(명) is highly overall correlated with 2017년 총 인구수(명) and 1 other fieldsHigh correlation
전년대비 증감율(%) is highly overall correlated with 집계구 코드High correlation
집계구 코드 is highly overall correlated with 기본키 and 2 other fieldsHigh correlation
기본키 has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:08:10.524033
Analysis finished2023-12-10 12:08:13.132303
Duration2.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.5
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2023-12-10T21:08:13.248858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.05
Q116.25
median31.5
Q346.75
95-th percentile58.95
Maximum62
Range61
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation18.041619
Coefficient of variation (CV)0.5727498
Kurtosis-1.2
Mean31.5
Median Absolute Deviation (MAD)15.5
Skewness0
Sum1953
Variance325.5
MonotonicityStrictly increasing
2023-12-10T21:08:13.407784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
48 1
 
1.6%
35 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
Other values (52) 52
83.9%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%
54 1
1.6%
53 1
1.6%

지점
Text

Distinct31
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size628.0 B
2023-12-10T21:08:13.632479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters868
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowA-0010-0391S-6
2nd rowA-0010-0391S-6
3rd rowA-0010-0728S-6
4th rowA-0010-0728S-6
5th rowA-0010-2583E-7
ValueCountFrequency (%)
a-0010-0391s-6 2
 
3.2%
a-0160-0058e-4 2
 
3.2%
a-0600-0636s-4 2
 
3.2%
a-0600-0547s-4 2
 
3.2%
a-0550-1837s-4 2
 
3.2%
a-0550-1490e-4 2
 
3.2%
a-0500-0701e-8 2
 
3.2%
a-0450-1129e-4 2
 
3.2%
a-0450-0703e-4 2
 
3.2%
a-0450-0557e-4 2
 
3.2%
Other values (21) 42
67.7%
2023-12-10T21:08:14.012218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 202
23.3%
- 186
21.4%
A 62
 
7.1%
1 58
 
6.7%
4 56
 
6.5%
6 50
 
5.8%
5 50
 
5.8%
2 48
 
5.5%
E 40
 
4.6%
7 30
 
3.5%
Other values (4) 86
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 558
64.3%
Dash Punctuation 186
 
21.4%
Uppercase Letter 124
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 202
36.2%
1 58
 
10.4%
4 56
 
10.0%
6 50
 
9.0%
5 50
 
9.0%
2 48
 
8.6%
7 30
 
5.4%
8 28
 
5.0%
3 18
 
3.2%
9 18
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
A 62
50.0%
E 40
32.3%
S 22
 
17.7%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 744
85.7%
Latin 124
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 202
27.2%
- 186
25.0%
1 58
 
7.8%
4 56
 
7.5%
6 50
 
6.7%
5 50
 
6.7%
2 48
 
6.5%
7 30
 
4.0%
8 28
 
3.8%
3 18
 
2.4%
Latin
ValueCountFrequency (%)
A 62
50.0%
E 40
32.3%
S 22
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 202
23.3%
- 186
21.4%
A 62
 
7.1%
1 58
 
6.7%
4 56
 
6.5%
6 50
 
5.8%
5 50
 
5.8%
2 48
 
5.5%
E 40
 
4.6%
7 30
 
3.5%
Other values (4) 86
9.9%

집계구 코드
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Memory size628.0 B
경상남도 진주시 정촌면
 
4
울산광역시 울주군 삼남면
 
2
충청북도 옥천군 옥천읍
 
2
충청북도 옥천군 군북면
 
2
경상남도 사천시 축동면
 
2
Other values (25)
50 

Length

Max length14
Median length12
Mean length12
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울산광역시 울주군 삼남면
2nd row울산광역시 울주군 삼남면
3rd row경상북도 경주시 건천읍
4th row경상북도 경주시 건천읍
5th row충청북도 옥천군 옥천읍

Common Values

ValueCountFrequency (%)
경상남도 진주시 정촌면 4
 
6.5%
울산광역시 울주군 삼남면 2
 
3.2%
충청북도 옥천군 옥천읍 2
 
3.2%
충청북도 옥천군 군북면 2
 
3.2%
경상남도 사천시 축동면 2
 
3.2%
전라남도 담양군 고서면 2
 
3.2%
전라남도 담양군 봉산면 2
 
3.2%
경상남도 함양군 함양읍 2
 
3.2%
경상남도 거창군 남상면 2
 
3.2%
경상북도 고령군 성산면 2
 
3.2%
Other values (20) 40
64.5%

Length

2023-12-10T21:08:14.169443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 14
 
7.5%
경상남도 10
 
5.4%
충청북도 8
 
4.3%
전라북도 6
 
3.2%
전라남도 6
 
3.2%
남원시 6
 
3.2%
충청남도 4
 
2.2%
울산광역시 4
 
2.2%
정촌면 4
 
2.2%
옥천군 4
 
2.2%
Other values (53) 120
64.5%

성별
Categorical

Distinct2
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size628.0 B
남자
31 
여자
31 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남자
2nd row여자
3rd row남자
4th row여자
5th row남자

Common Values

ValueCountFrequency (%)
남자 31
50.0%
여자 31
50.0%

Length

2023-12-10T21:08:14.287444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:14.399373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남자 31
50.0%
여자 31
50.0%

2017년 총 인구수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6880.4516
Minimum982
Maximum28430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2023-12-10T21:08:14.534325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum982
5-th percentile1456.8
Q12179
median2878
Q38646
95-th percentile26832.8
Maximum28430
Range27448
Interquartile range (IQR)6467

Descriptive statistics

Standard deviation7759.3887
Coefficient of variation (CV)1.1277441
Kurtosis1.6020018
Mean6880.4516
Median Absolute Deviation (MAD)1148
Skewness1.65093
Sum426588
Variance60208113
MonotonicityNot monotonic
2023-12-10T21:08:14.685928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3050 2
 
3.2%
2876 2
 
3.2%
17794 1
 
1.6%
6726 1
 
1.6%
1472 1
 
1.6%
1618 1
 
1.6%
1456 1
 
1.6%
1490 1
 
1.6%
15728 1
 
1.6%
13302 1
 
1.6%
Other values (50) 50
80.6%
ValueCountFrequency (%)
982 1
1.6%
1044 1
1.6%
1410 1
1.6%
1456 1
1.6%
1472 1
1.6%
1490 1
1.6%
1596 1
1.6%
1618 1
1.6%
1724 1
1.6%
1736 1
1.6%
ValueCountFrequency (%)
28430 1
1.6%
27392 1
1.6%
27022 1
1.6%
26872 1
1.6%
26088 1
1.6%
19650 1
1.6%
17794 1
1.6%
17682 1
1.6%
16746 1
1.6%
16236 1
1.6%

2018년 총 인구수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6864
Minimum950
Maximum27802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2023-12-10T21:08:14.911262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum950
5-th percentile1431.2
Q12147
median3056
Q38495
95-th percentile25942
Maximum27802
Range26852
Interquartile range (IQR)6348

Descriptive statistics

Standard deviation7670.5044
Coefficient of variation (CV)1.1174977
Kurtosis1.5364304
Mean6864
Median Absolute Deviation (MAD)1378
Skewness1.6352518
Sum425568
Variance58836638
MonotonicityNot monotonic
2023-12-10T21:08:15.127681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3910 2
 
3.2%
3802 2
 
3.2%
18532 1
 
1.6%
6544 1
 
1.6%
1454 1
 
1.6%
1594 1
 
1.6%
1430 1
 
1.6%
1484 1
 
1.6%
15180 1
 
1.6%
12742 1
 
1.6%
Other values (50) 50
80.6%
ValueCountFrequency (%)
950 1
1.6%
1032 1
1.6%
1428 1
1.6%
1430 1
1.6%
1454 1
1.6%
1484 1
1.6%
1592 1
1.6%
1594 1
1.6%
1660 1
1.6%
1696 1
1.6%
ValueCountFrequency (%)
27802 1
1.6%
27426 1
1.6%
27252 1
1.6%
25964 1
1.6%
25524 1
1.6%
18538 1
1.6%
18532 1
1.6%
17854 1
1.6%
17544 1
1.6%
16372 1
1.6%

전년대비 증감율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11564516
Minimum-3.42
Maximum13.87
Zeros0
Zeros (%)0.0%
Negative41
Negative (%)66.1%
Memory size690.0 B
2023-12-10T21:08:15.302932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.42
5-th percentile-2.909
Q1-1.7175
median-0.86
Q30.435
95-th percentile11.895
Maximum13.87
Range17.29
Interquartile range (IQR)2.1525

Descriptive statistics

Standard deviation3.7511872
Coefficient of variation (CV)32.437044
Kurtosis7.5949384
Mean0.11564516
Median Absolute Deviation (MAD)1.14
Skewness2.7556381
Sum7.17
Variance14.071405
MonotonicityNot monotonic
2023-12-10T21:08:15.485489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.36 2
 
3.2%
0.42 2
 
3.2%
13.87 2
 
3.2%
-1.12 2
 
3.2%
2.03 1
 
1.6%
-1.37 1
 
1.6%
-2.28 1
 
1.6%
-0.34 1
 
1.6%
-1.71 1
 
1.6%
-3.28 1
 
1.6%
Other values (48) 48
77.4%
ValueCountFrequency (%)
-3.42 1
1.6%
-3.28 1
1.6%
-2.96 1
1.6%
-2.91 1
1.6%
-2.89 1
1.6%
-2.83 1
1.6%
-2.28 1
1.6%
-2.27 1
1.6%
-2.26 1
1.6%
-2.25 1
1.6%
ValueCountFrequency (%)
13.87 2
3.2%
12.36 2
3.2%
3.06 1
1.6%
2.33 1
1.6%
2.32 1
1.6%
2.28 1
1.6%
2.03 1
1.6%
1.57 1
1.6%
1.26 1
1.6%
1.24 1
1.6%

Interactions

2023-12-10T21:08:12.467961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:10.841185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:11.251257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:12.026783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:12.586272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:10.925642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:11.364454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:12.137987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:12.695165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:11.023337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:11.449224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:12.245123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:12.798968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:11.141349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:11.550851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:12.344558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:08:15.574706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점집계구 코드성별2017년 총 인구수(명)2018년 총 인구수(명)전년대비 증감율(%)
기본키1.0000.9730.9870.0000.4940.5250.768
지점0.9731.0001.0000.0000.8930.9260.942
집계구 코드0.9871.0001.0000.0000.8980.9280.969
성별0.0000.0000.0001.0000.0000.0000.000
2017년 총 인구수(명)0.4940.8930.8980.0001.0000.9910.000
2018년 총 인구수(명)0.5250.9260.9280.0000.9911.0000.589
전년대비 증감율(%)0.7680.9420.9690.0000.0000.5891.000
2023-12-10T21:08:15.685107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계구 코드성별
집계구 코드1.0000.000
성별0.0001.000
2023-12-10T21:08:15.770000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키2017년 총 인구수(명)2018년 총 인구수(명)전년대비 증감율(%)집계구 코드성별
기본키1.000-0.227-0.260-0.0110.6420.000
2017년 총 인구수(명)-0.2271.0000.9920.0090.4870.000
2018년 총 인구수(명)-0.2600.9921.0000.0790.5420.000
전년대비 증감율(%)-0.0110.0090.0791.0000.5900.000
집계구 코드0.6420.4870.5420.5901.0000.000
성별0.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T21:08:12.921398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:08:13.070089image/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

기본키지점집계구 코드성별2017년 총 인구수(명)2018년 총 인구수(명)전년대비 증감율(%)
01A-0010-0391S-6울산광역시 울주군 삼남면남자17794185322.03
12A-0010-0391S-6울산광역시 울주군 삼남면여자16746175442.33
23A-0010-0728S-6경상북도 경주시 건천읍남자91068844-1.46
34A-0010-0728S-6경상북도 경주시 건천읍여자93028892-2.25
45A-0010-2583E-7충청북도 옥천군 옥천읍남자27022272520.42
56A-0010-2583E-7충청북도 옥천군 옥천읍여자27392274260.06
67A-0010-2626S-6충청북도 옥천군 군북면남자28382712-2.27
78A-0010-2626S-6충청북도 옥천군 군북면여자28462686-2.89
89A-0100-0668E-6경상남도 사천시 축동면남자15961592-0.13
910A-0100-0668E-6경상남도 사천시 축동면여자17361660-2.24
기본키지점집계구 코드성별2017년 총 인구수(명)2018년 총 인구수(명)전년대비 증감율(%)
5253A-0550-1490E-4경상북도 군위군 군위읍남자726674481.24
5354A-0550-1490E-4경상북도 군위군 군위읍여자713472040.49
5455A-0550-1837S-4경상북도 안동시 남후면남자17241696-0.82
5556A-0550-1837S-4경상북도 안동시 남후면여자202821563.06
5657A-0600-0547S-4강원도 춘천시 남산면남자34103366-0.65
5758A-0600-0547S-4강원도 춘천시 남산면여자34263356-1.03
5859A-0600-0636S-4강원도 춘천시 동산면남자203820440.15
5960A-0600-0636S-4강원도 춘천시 동산면여자141014280.63
6061A-6000-0396E-4부산광역시 금정구 선두구동남자235424662.32
6162A-6000-0396E-4부산광역시 금정구 선두구동여자203020821.26