Overview

Dataset statistics

Number of variables7
Number of observations43
Missing cells6
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory63.1 B

Variable types

Text2
Numeric4
DateTime1

Dataset

Description인천광역시 서구 물가정보 데이터로 품 목 명, 조사규격(통계청 기준), 전월금액(평균값), 전월값(평균), 금액(평균값), 전월대비상승율 등으로 구성 되어 있습니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15091381&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
전월금액(평균값) is highly overall correlated with 전월값(평균) and 1 other fieldsHigh correlation
전월값(평균) is highly overall correlated with 전월금액(평균값) and 1 other fieldsHigh correlation
금액(평균값) is highly overall correlated with 전월금액(평균값) and 1 other fieldsHigh correlation
전월금액(평균값) has 2 (4.7%) missing valuesMissing
전월값(평균) has 2 (4.7%) missing valuesMissing
금액(평균값) has 2 (4.7%) missing valuesMissing
품목명 has unique valuesUnique
전월대비상승율 has 5 (11.6%) zerosZeros

Reproduction

Analysis started2024-03-18 04:55:31.111178
Analysis finished2024-03-18 04:55:32.836497
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-03-18T13:55:32.995118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.6511628
Min length2

Characters and Unicode

Total characters200
Distinct characters102
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row쓰레기봉투료
2nd row정화조청소료
3rd row설렁탕
4th row냉면
5th row비빔밥
ValueCountFrequency (%)
쓰레기봉투료 1
 
2.3%
라면(외식 1
 
2.3%
커피(외식 1
 
2.3%
국산차(외식 1
 
2.3%
세탁료 1
 
2.3%
의복수선료 1
 
2.3%
공동주택관리비 1
 
2.3%
택배이용료 1
 
2.3%
수영장이용료 1
 
2.3%
볼링장이용료 1
 
2.3%
Other values (33) 33
76.7%
2024-03-18T13:55:33.408622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
료 18
 
9.0%
용 10
 
5.0%
이 9
 
4.5%
) 8
 
4.0%
( 7
 
3.5%
장 6
 
3.0%
외 6
 
3.0%
식 6
 
3.0%
수 4
 
2.0%
비 4
 
2.0%
Other values (92) 122
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 183
91.5%
Close Punctuation 8
 
4.0%
Open Punctuation 7
 
3.5%
Uppercase Letter 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 18
 
9.8%
용 10
 
5.5%
이 9
 
4.9%
장 6
 
3.3%
외 6
 
3.3%
식 6
 
3.3%
수 4
 
2.2%
비 4
 
2.2%
탕 4
 
2.2%
밥 3
 
1.6%
Other values (88) 113
61.7%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 183
91.5%
Common 15
 
7.5%
Latin 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 18
 
9.8%
용 10
 
5.5%
이 9
 
4.9%
장 6
 
3.3%
외 6
 
3.3%
식 6
 
3.3%
수 4
 
2.2%
비 4
 
2.2%
탕 4
 
2.2%
밥 3
 
1.6%
Other values (88) 113
61.7%
Common
ValueCountFrequency (%)
) 8
53.3%
( 7
46.7%
Latin
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 183
91.5%
ASCII 17
 
8.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
료 18
 
9.8%
용 10
 
5.5%
이 9
 
4.9%
장 6
 
3.3%
외 6
 
3.3%
식 6
 
3.3%
수 4
 
2.2%
비 4
 
2.2%
탕 4
 
2.2%
밥 3
 
1.6%
Other values (88) 113
61.7%
ASCII
ValueCountFrequency (%)
) 8
47.1%
( 7
41.2%
P 1
 
5.9%
C 1
 
5.9%

조사규격(통계청 기준)
Text

Distinct35
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-03-18T13:55:33.672716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.209302
Min length2

Characters and Unicode

Total characters439
Distinct characters142
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)79.1%

Sample

1st row20(리터)
2nd row기본 750(리터)
3rd row1인분(보통)
4th row물냉면, 1인분(보통)
5th row1인분(보통)
ValueCountFrequency (%)
1인분(보통 11
 
13.3%
성인 3
 
3.6%
1인분 3
 
3.6%
일반인 2
 
2.4%
기본 2
 
2.4%
신사복 2
 
2.4%
고기 2
 
2.4%
200g정도 2
 
2.4%
일반 2
 
2.4%
무게:20kg이하 1
 
1.2%
Other values (53) 53
63.9%
2024-03-18T13:55:34.081631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
9.1%
인 23
 
5.2%
( 22
 
5.0%
) 22
 
5.0%
1 18
 
4.1%
, 18
 
4.1%
분 14
 
3.2%
통 14
 
3.2%
보 13
 
3.0%
기 12
 
2.7%
Other values (132) 243
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 287
65.4%
Space Separator 40
 
9.1%
Decimal Number 39
 
8.9%
Open Punctuation 22
 
5.0%
Close Punctuation 22
 
5.0%
Other Punctuation 20
 
4.6%
Lowercase Letter 8
 
1.8%
Math Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 23
 
8.0%
분 14
 
4.9%
통 14
 
4.9%
보 13
 
4.5%
기 12
 
4.2%
반 11
 
3.8%
일 10
 
3.5%
성 6
 
2.1%
정 4
 
1.4%
도 4
 
1.4%
Other values (115) 176
61.3%
Decimal Number
ValueCountFrequency (%)
1 18
46.2%
0 11
28.2%
2 6
 
15.4%
4 1
 
2.6%
3 1
 
2.6%
5 1
 
2.6%
7 1
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
g 5
62.5%
k 1
 
12.5%
m 1
 
12.5%
c 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
, 18
90.0%
: 2
 
10.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Math Symbol
ValueCountFrequency (%)
× 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 287
65.4%
Common 144
32.8%
Latin 8
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 23
 
8.0%
분 14
 
4.9%
통 14
 
4.9%
보 13
 
4.5%
기 12
 
4.2%
반 11
 
3.8%
일 10
 
3.5%
성 6
 
2.1%
정 4
 
1.4%
도 4
 
1.4%
Other values (115) 176
61.3%
Common
ValueCountFrequency (%)
40
27.8%
( 22
15.3%
) 22
15.3%
1 18
12.5%
, 18
12.5%
0 11
 
7.6%
2 6
 
4.2%
: 2
 
1.4%
4 1
 
0.7%
× 1
 
0.7%
Other values (3) 3
 
2.1%
Latin
ValueCountFrequency (%)
g 5
62.5%
k 1
 
12.5%
m 1
 
12.5%
c 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 287
65.4%
ASCII 151
34.4%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
26.5%
( 22
14.6%
) 22
14.6%
1 18
11.9%
, 18
11.9%
0 11
 
7.3%
2 6
 
4.0%
g 5
 
3.3%
: 2
 
1.3%
k 1
 
0.7%
Other values (6) 6
 
4.0%
Hangul
ValueCountFrequency (%)
인 23
 
8.0%
분 14
 
4.9%
통 14
 
4.9%
보 13
 
4.5%
기 12
 
4.2%
반 11
 
3.8%
일 10
 
3.5%
성 6
 
2.1%
정 4
 
1.4%
도 4
 
1.4%
Other values (115) 176
61.3%
None
ValueCountFrequency (%)
× 1
100.0%

전월금액(평균값)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)90.2%
Missing2
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean13868.098
Minimum620
Maximum93000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-03-18T13:55:34.220456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum620
5-th percentile1500
Q16080
median9500
Q315980
95-th percentile34000
Maximum93000
Range92380
Interquartile range (IQR)9900

Descriptive statistics

Standard deviation16023.871
Coefficient of variation (CV)1.1554484
Kurtosis15.115669
Mean13868.098
Median Absolute Deviation (MAD)5233
Skewness3.5005076
Sum568592
Variance2.5676444 × 108
MonotonicityNot monotonic
2024-03-18T13:55:34.398534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
7100 3
 
7.0%
8800 2
 
4.7%
7000 2
 
4.7%
6080 1
 
2.3%
12500 1
 
2.3%
4267 1
 
2.3%
93000 1
 
2.3%
11200 1
 
2.3%
34000 1
 
2.3%
1500 1
 
2.3%
Other values (27) 27
62.8%
(Missing) 2
 
4.7%
ValueCountFrequency (%)
620 1
2.3%
625 1
2.3%
1500 1
2.3%
3160 1
2.3%
3720 1
2.3%
3800 1
2.3%
4100 1
2.3%
4267 1
2.3%
4400 1
2.3%
4520 1
2.3%
ValueCountFrequency (%)
93000 1
2.3%
51250 1
2.3%
34000 1
2.3%
33600 1
2.3%
21200 1
2.3%
19400 1
2.3%
19200 1
2.3%
16960 1
2.3%
16780 1
2.3%
16000 1
2.3%

전월값(평균)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)90.2%
Missing2
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean13868.098
Minimum620
Maximum93000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-03-18T13:55:34.528935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum620
5-th percentile1500
Q16080
median9500
Q315980
95-th percentile34000
Maximum93000
Range92380
Interquartile range (IQR)9900

Descriptive statistics

Standard deviation16023.871
Coefficient of variation (CV)1.1554484
Kurtosis15.115669
Mean13868.098
Median Absolute Deviation (MAD)5233
Skewness3.5005076
Sum568592
Variance2.5676444 × 108
MonotonicityNot monotonic
2024-03-18T13:55:34.728309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
7100 3
 
7.0%
8800 2
 
4.7%
7000 2
 
4.7%
6080 1
 
2.3%
12500 1
 
2.3%
4267 1
 
2.3%
93000 1
 
2.3%
11200 1
 
2.3%
34000 1
 
2.3%
1500 1
 
2.3%
Other values (27) 27
62.8%
(Missing) 2
 
4.7%
ValueCountFrequency (%)
620 1
2.3%
625 1
2.3%
1500 1
2.3%
3160 1
2.3%
3720 1
2.3%
3800 1
2.3%
4100 1
2.3%
4267 1
2.3%
4400 1
2.3%
4520 1
2.3%
ValueCountFrequency (%)
93000 1
2.3%
51250 1
2.3%
34000 1
2.3%
33600 1
2.3%
21200 1
2.3%
19400 1
2.3%
19200 1
2.3%
16960 1
2.3%
16780 1
2.3%
16000 1
2.3%

금액(평균값)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)85.4%
Missing2
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean14593.976
Minimum575
Maximum121000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-03-18T13:55:34.842196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum575
5-th percentile1300
Q16700
median9100
Q316300
95-th percentile31000
Maximum121000
Range120425
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation19714.859
Coefficient of variation (CV)1.3508902
Kurtosis22.120546
Mean14593.976
Median Absolute Deviation (MAD)5100
Skewness4.3558285
Sum598353
Variance3.8867566 × 108
MonotonicityNot monotonic
2024-03-18T13:55:34.952207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
9000 3
 
7.0%
3520 2
 
4.7%
7300 2
 
4.7%
4500 2
 
4.7%
22250 2
 
4.7%
15333 1
 
2.3%
15250 1
 
2.3%
11000 1
 
2.3%
9250 1
 
2.3%
16400 1
 
2.3%
Other values (25) 25
58.1%
(Missing) 2
 
4.7%
ValueCountFrequency (%)
575 1
2.3%
620 1
2.3%
1300 1
2.3%
3520 2
4.7%
3900 1
2.3%
4100 1
2.3%
4500 2
4.7%
4540 1
2.3%
6700 1
2.3%
7000 1
2.3%
ValueCountFrequency (%)
121000 1
2.3%
57500 1
2.3%
31000 1
2.3%
22250 2
4.7%
20600 1
2.3%
20200 1
2.3%
19250 1
2.3%
17140 1
2.3%
16400 1
2.3%
16300 1
2.3%

전월대비상승율
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95348837
Minimum-29.1
Maximum29.1
Zeros5
Zeros (%)11.6%
Negative17
Negative (%)39.5%
Memory size519.0 B
2024-03-18T13:55:35.068925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-29.1
5-th percentile-13.39
Q1-4.75
median0
Q35.3
95-th percentile21.44
Maximum29.1
Range58.2
Interquartile range (IQR)10.05

Descriptive statistics

Standard deviation10.713151
Coefficient of variation (CV)11.235743
Kurtosis1.4851274
Mean0.95348837
Median Absolute Deviation (MAD)5.2
Skewness0.15278894
Sum41
Variance114.77159
MonotonicityNot monotonic
2024-03-18T13:55:35.172526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 5
 
11.6%
2.7 2
 
4.7%
2.2 2
 
4.7%
-9.7 1
 
2.3%
-13.4 1
 
2.3%
13.1 1
 
2.3%
-4.2 1
 
2.3%
-5.2 1
 
2.3%
23.1 1
 
2.3%
2.6 1
 
2.3%
Other values (27) 27
62.8%
ValueCountFrequency (%)
-29.1 1
2.3%
-18.3 1
2.3%
-13.4 1
2.3%
-13.3 1
2.3%
-9.7 1
2.3%
-8.7 1
2.3%
-8.5 1
2.3%
-5.7 1
2.3%
-5.6 1
2.3%
-5.4 1
2.3%
ValueCountFrequency (%)
29.1 1
2.3%
23.1 1
2.3%
22.0 1
2.3%
16.4 1
2.3%
13.1 1
2.3%
12.8 1
2.3%
10.9 1
2.3%
10.3 1
2.3%
8.2 1
2.3%
5.7 1
2.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
Minimum2023-10-19 00:00:00
Maximum2023-10-19 00:00:00
2024-03-18T13:55:35.262367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:35.337198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T13:55:32.217976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.355063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.616744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.913031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:32.288935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.411969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.683826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.981083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:32.368736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.469939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.757035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:32.048152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:32.483950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.531551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:31.835804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:32.132822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T13:55:35.399995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목명조사규격(통계청 기준)전월금액(평균값)전월값(평균)금액(평균값)전월대비상승율
품목명1.0001.0001.0001.0001.0001.000
조사규격(통계청 기준)1.0001.0000.9760.9760.9760.808
전월금액(평균값)1.0000.9761.0001.0000.9130.671
전월값(평균)1.0000.9761.0001.0000.9130.671
금액(평균값)1.0000.9760.9130.9131.0000.791
전월대비상승율1.0000.8080.6710.6710.7911.000
2024-03-18T13:55:35.491232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전월금액(평균값)전월값(평균)금액(평균값)전월대비상승율
전월금액(평균값)1.0001.0000.9820.010
전월값(평균)1.0001.0000.9820.010
금액(평균값)0.9820.9821.0000.129
전월대비상승율0.0100.0100.1291.000

Missing values

2024-03-18T13:55:32.591523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:55:32.687030image/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.
2024-03-18T13:55:32.783053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

품목명조사규격(통계청 기준)전월금액(평균값)전월값(평균)금액(평균값)전월대비상승율데이터기준일자
0쓰레기봉투료20(리터)6206206200.02023-10-19
1정화조청소료기본 750(리터)<NA><NA><NA>0.02023-10-19
2설렁탕1인분(보통)10200102009000-13.32023-10-19
3냉면물냉면, 1인분(보통)71007100910022.02023-10-19
4비빔밥1인분(보통)70007000780010.32023-10-19
5갈비탕1인분(보통)1400014000152508.22023-10-19
6삼계탕1인분(보통)160001600015333-4.32023-10-19
7김치찌개백반1인분(보통)7100710073002.72023-10-19
8된장찌개백반1인분(보통)7100710073002.72023-10-19
9불고기쇠고기200g정도15780157802225029.12023-10-19
품목명조사규격(통계청 기준)전월금액(평균값)전월값(평균)금액(평균값)전월대비상승율데이터기준일자
33당구장이용료일반인, 저녁시간1120011200115002.62023-10-19
34노래방이용료성인, 저녁시간대, 일반실340003400031000-9.72023-10-19
35PC방이용료기본 1시간150015001300-5.42023-10-19
36사진촬영료반명함판 칼라 (3×4cm)212002120020600-2.92023-10-19
37사진인화료인화요금625625575-8.72023-10-19
38숙박료(여관)독방, 1박, 욕탕부설51250512505750010.92023-10-19
39이용료성인154001540014200-8.52023-10-19
40미용료성인여자 파마194001940016400-18.32023-10-19
41목욕료성인기준950095009250-2.72023-10-19
42찜질방이용료성인, 찜질복대여료 포함112501125011000-2.32023-10-19