Overview

Dataset statistics

Number of variables3
Number of observations66
Missing cells14
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory27.0 B

Variable types

Numeric1
Categorical1
Text1

Alerts

RESD_CD(대도시거주지코드) is highly overall correlated with RESD_DO_NM(시도)High correlation
RESD_DO_NM(시도) is highly overall correlated with RESD_CD(대도시거주지코드)High correlation
RESD_CT_NM(시군구) has 14 (21.2%) missing valuesMissing
RESD_CD(대도시거주지코드) has unique valuesUnique

Reproduction

Analysis started2024-04-17 17:35:08.534587
Analysis finished2024-04-17 17:35:08.794060
Duration0.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RESD_CD(대도시거주지코드)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38858.97
Minimum26000
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-04-18T02:35:08.852166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26000
5-th percentile28147.5
Q141111.5
median41283
Q341565
95-th percentile45750
Maximum50000
Range24000
Interquartile range (IQR)453.5

Descriptive statistics

Standard deviation6014.16
Coefficient of variation (CV)0.1547689
Kurtosis-0.24225957
Mean38858.97
Median Absolute Deviation (MAD)277
Skewness-0.98816869
Sum2564692
Variance36170120
MonotonicityStrictly increasing
2024-04-18T02:35:08.976155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26000 1
 
1.5%
41590 1
 
1.5%
41310 1
 
1.5%
41360 1
 
1.5%
41370 1
 
1.5%
41390 1
 
1.5%
41410 1
 
1.5%
41430 1
 
1.5%
41450 1
 
1.5%
41461 1
 
1.5%
Other values (56) 56
84.8%
ValueCountFrequency (%)
26000 1
1.5%
27000 1
1.5%
28110 1
1.5%
28140 1
1.5%
28170 1
1.5%
28185 1
1.5%
28200 1
1.5%
28237 1
1.5%
28245 1
1.5%
28260 1
1.5%
ValueCountFrequency (%)
50000 1
1.5%
48000 1
1.5%
47000 1
1.5%
46000 1
1.5%
45000 1
1.5%
44000 1
1.5%
43000 1
1.5%
42000 1
1.5%
41830 1
1.5%
41820 1
1.5%

RESD_DO_NM(시도)
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size660.0 B
경기
42 
인천
10 
부산
 
1
대구
 
1
광주
 
1
Other values (11)
11 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique14 ?
Unique (%)21.2%

Sample

1st row부산
2nd row대구
3rd row인천
4th row인천
5th row인천

Common Values

ValueCountFrequency (%)
경기 42
63.6%
인천 10
 
15.2%
부산 1
 
1.5%
대구 1
 
1.5%
광주 1
 
1.5%
대전 1
 
1.5%
울산 1
 
1.5%
세종 1
 
1.5%
강원 1
 
1.5%
충북 1
 
1.5%
Other values (6) 6
 
9.1%

Length

2024-04-18T02:35:09.081260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 42
63.6%
인천 10
 
15.2%
부산 1
 
1.5%
대구 1
 
1.5%
광주 1
 
1.5%
대전 1
 
1.5%
울산 1
 
1.5%
세종 1
 
1.5%
강원 1
 
1.5%
충북 1
 
1.5%
Other values (6) 6
 
9.1%

RESD_CT_NM(시군구)
Text

MISSING 

Distinct52
Distinct (%)100.0%
Missing14
Missing (%)21.2%
Memory size660.0 B
2024-04-18T02:35:09.285790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.3269231
Min length2

Characters and Unicode

Total characters225
Distinct characters63
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

Unique52 ?
Unique (%)100.0%

Sample

1st row중구
2nd row동구
3rd row남구
4th row연수구
5th row남동구
ValueCountFrequency (%)
수원시 4
 
5.8%
성남시 3
 
4.3%
용인시 3
 
4.3%
고양시 3
 
4.3%
안산시 2
 
2.9%
안양시 2
 
2.9%
여주시 1
 
1.4%
가평군 1
 
1.4%
일산서구 1
 
1.4%
과천시 1
 
1.4%
Other values (48) 48
69.6%
2024-04-18T02:35:09.573348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 40
17.8%
구 26
 
11.6%
17
 
7.6%
양 10
 
4.4%
안 8
 
3.6%
남 7
 
3.1%
수 7
 
3.1%
원 6
 
2.7%
천 6
 
2.7%
군 6
 
2.7%
Other values (53) 92
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 208
92.4%
Space Separator 17
 
7.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 40
19.2%
구 26
 
12.5%
양 10
 
4.8%
안 8
 
3.8%
남 7
 
3.4%
수 7
 
3.4%
원 6
 
2.9%
천 6
 
2.9%
군 6
 
2.9%
성 5
 
2.4%
Other values (52) 87
41.8%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 208
92.4%
Common 17
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 40
19.2%
구 26
 
12.5%
양 10
 
4.8%
안 8
 
3.8%
남 7
 
3.4%
수 7
 
3.4%
원 6
 
2.9%
천 6
 
2.9%
군 6
 
2.9%
성 5
 
2.4%
Other values (52) 87
41.8%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 208
92.4%
ASCII 17
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 40
19.2%
구 26
 
12.5%
양 10
 
4.8%
안 8
 
3.8%
남 7
 
3.4%
수 7
 
3.4%
원 6
 
2.9%
천 6
 
2.9%
군 6
 
2.9%
성 5
 
2.4%
Other values (52) 87
41.8%
ASCII
ValueCountFrequency (%)
17
100.0%

Interactions

2024-04-18T02:35:08.623986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T02:35:09.647239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RESD_CD(대도시거주지코드)RESD_DO_NM(시도)RESD_CT_NM(시군구)
RESD_CD(대도시거주지코드)1.0000.9871.000
RESD_DO_NM(시도)0.9871.0001.000
RESD_CT_NM(시군구)1.0001.0001.000
2024-04-18T02:35:09.716811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RESD_CD(대도시거주지코드)RESD_DO_NM(시도)
RESD_CD(대도시거주지코드)1.0000.879
RESD_DO_NM(시도)0.8791.000

Missing values

2024-04-18T02:35:08.711943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T02:35:08.767506image/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

RESD_CD(대도시거주지코드)RESD_DO_NM(시도)RESD_CT_NM(시군구)
026000부산<NA>
127000대구<NA>
228110인천중구
328140인천동구
428170인천남구
528185인천연수구
628200인천남동구
728237인천부평구
828245인천계양구
928260인천서구
RESD_CD(대도시거주지코드)RESD_DO_NM(시도)RESD_CT_NM(시군구)
5641820경기가평군
5741830경기양평군
5842000강원<NA>
5943000충북<NA>
6044000충남<NA>
6145000전북<NA>
6246000전남<NA>
6347000경북<NA>
6448000경남<NA>
6550000제주<NA>