Overview

Dataset statistics

Number of variables3
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory704.0 B
Average record size in memory32.0 B

Variable types

Text1
Numeric2

Dataset

Description인천광역시 부평구 동별 여성 인구 현황 데이터입니다.(동별,인구수 여자,19세이상 여자)ex) 부평1동,19409,16934
Author인천광역시 부평구
URLhttps://www.data.go.kr/data/15089247/fileData.do

Alerts

인구수 여자 is highly overall correlated with 19세이상 여자 High correlation
19세이상 여자 is highly overall correlated with 인구수 여자High correlation
동별 has unique valuesUnique
인구수 여자 has unique valuesUnique
19세이상 여자 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:18:57.524781
Analysis finished2023-12-12 05:18:58.177217
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동별
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T14:18:58.304893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9545455
Min length3

Characters and Unicode

Total characters87
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row부평1동
2nd row부평2동
3rd row부평3동
4th row부평4동
5th row부평5동
ValueCountFrequency (%)
부평1동 1
 
4.5%
부평2동 1
 
4.5%
십정1동 1
 
4.5%
일신동 1
 
4.5%
부개3동 1
 
4.5%
부개2동 1
 
4.5%
부개1동 1
 
4.5%
삼산2동 1
 
4.5%
삼산1동 1
 
4.5%
갈산2동 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T14:18:58.862915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 22
25.3%
부 9
10.3%
산 8
 
9.2%
1 7
 
8.0%
2 7
 
8.0%
평 6
 
6.9%
곡 4
 
4.6%
3 3
 
3.4%
개 3
 
3.4%
4 2
 
2.3%
Other values (10) 16
18.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
75.9%
Decimal Number 21
 
24.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 22
33.3%
부 9
13.6%
산 8
 
12.1%
평 6
 
9.1%
곡 4
 
6.1%
개 3
 
4.5%
청 2
 
3.0%
천 2
 
3.0%
갈 2
 
3.0%
삼 2
 
3.0%
Other values (4) 6
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
75.9%
Common 21
 
24.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
33.3%
부 9
13.6%
산 8
 
12.1%
평 6
 
9.1%
곡 4
 
6.1%
개 3
 
4.5%
청 2
 
3.0%
천 2
 
3.0%
갈 2
 
3.0%
삼 2
 
3.0%
Other values (4) 6
 
9.1%
Common
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
75.9%
ASCII 21
 
24.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 22
33.3%
부 9
13.6%
산 8
 
12.1%
평 6
 
9.1%
곡 4
 
6.1%
개 3
 
4.5%
청 2
 
3.0%
천 2
 
3.0%
갈 2
 
3.0%
삼 2
 
3.0%
Other values (4) 6
 
9.1%
ASCII
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%

인구수 여자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11278.818
Minimum3075
Maximum18336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T14:18:59.041031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3075
5-th percentile5916.55
Q17618
median10170.5
Q315244.25
95-th percentile17994.65
Maximum18336
Range15261
Interquartile range (IQR)7626.25

Descriptive statistics

Standard deviation4516.7582
Coefficient of variation (CV)0.40046379
Kurtosis-1.1939815
Mean11278.818
Median Absolute Deviation (MAD)2893.5
Skewness0.19027213
Sum248134
Variance20401105
MonotonicityNot monotonic
2023-12-12T14:18:59.169560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
18336 1
 
4.5%
7816 1
 
4.5%
10425 1
 
4.5%
12209 1
 
4.5%
5857 1
 
4.5%
15176 1
 
4.5%
9916 1
 
4.5%
8088 1
 
4.5%
14472 1
 
4.5%
16958 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
3075 1
4.5%
5857 1
4.5%
7048 1
4.5%
7506 1
4.5%
7535 1
4.5%
7552 1
4.5%
7816 1
4.5%
8088 1
4.5%
8791 1
4.5%
9200 1
4.5%
ValueCountFrequency (%)
18336 1
4.5%
18036 1
4.5%
17209 1
4.5%
16958 1
4.5%
16860 1
4.5%
15267 1
4.5%
15176 1
4.5%
14472 1
4.5%
12209 1
4.5%
10802 1
4.5%

19세이상 여자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9869.4545
Minimum2718
Maximum16268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T14:18:59.298960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2718
5-th percentile5137.55
Q16833
median9015.5
Q313165
95-th percentile16015.95
Maximum16268
Range13550
Interquartile range (IQR)6332

Descriptive statistics

Standard deviation3881.7519
Coefficient of variation (CV)0.39330967
Kurtosis-1.0235412
Mean9869.4545
Median Absolute Deviation (MAD)2599
Skewness0.21278342
Sum217128
Variance15067998
MonotonicityNot monotonic
2023-12-12T14:18:59.444387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
16268 1
 
4.5%
7040 1
 
4.5%
9437 1
 
4.5%
10716 1
 
4.5%
5076 1
 
4.5%
13307 1
 
4.5%
8594 1
 
4.5%
7302 1
 
4.5%
11923 1
 
4.5%
14636 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
2718 1
4.5%
5076 1
4.5%
6307 1
4.5%
6526 1
4.5%
6690 1
4.5%
6764 1
4.5%
7040 1
4.5%
7302 1
4.5%
7669 1
4.5%
8237 1
4.5%
ValueCountFrequency (%)
16268 1
4.5%
16075 1
4.5%
14894 1
4.5%
14636 1
4.5%
14592 1
4.5%
13307 1
4.5%
12739 1
4.5%
11923 1
4.5%
10716 1
4.5%
9618 1
4.5%

Interactions

2023-12-12T14:18:57.811441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:57.613717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:57.917706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:57.709550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:18:59.539219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동별인구수 여자19세이상 여자
동별1.0001.0001.000
인구수 여자1.0001.0001.000
19세이상 여자1.0001.0001.000
2023-12-12T14:18:59.632477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구수 여자19세이상 여자
인구수 여자1.0000.995
19세이상 여자0.9951.000

Missing values

2023-12-12T14:18:58.067321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:18:58.147110image/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

동별인구수 여자19세이상 여자
0부평1동1833616268
1부평2동75066690
2부평3동70486307
3부평4동1803616075
4부평5동1686014592
5부평6동75356764
6산곡1동75526526
7산곡2동1526712739
8산곡3동108029618
9산곡4동87917669
동별인구수 여자19세이상 여자
12갈산1동78167040
13갈산2동92008237
14삼산1동1695814636
15삼산2동1447211923
16부개1동80887302
17부개2동99168594
18부개3동1517613307
19일신동58575076
20십정1동1220910716
21십정2동104259437