Overview

Dataset statistics

Number of variables7
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory64.9 B

Variable types

Text1
Numeric6

Dataset

Description성남시 개인서비스 요금 정보(품목, 수정구가격, 중원구가격, 분당구가격, 이번주가격, 지난주가격, 증감률)입니다.
URLhttps://www.data.go.kr/data/15031821/fileData.do

Alerts

수정구가격 is highly overall correlated with 중원구가격 and 3 other fieldsHigh correlation
중원구가격 is highly overall correlated with 수정구가격 and 3 other fieldsHigh correlation
분당구가격 is highly overall correlated with 수정구가격 and 3 other fieldsHigh correlation
이번주가격 is highly overall correlated with 수정구가격 and 3 other fieldsHigh correlation
지난주가격 is highly overall correlated with 수정구가격 and 3 other fieldsHigh correlation
품목 has unique valuesUnique
분당구가격 has unique valuesUnique
수정구가격 has 3 (6.7%) zerosZeros
중원구가격 has 3 (6.7%) zerosZeros
분당구가격 has 1 (2.2%) zerosZeros
이번주가격 has 1 (2.2%) zerosZeros
지난주가격 has 1 (2.2%) zerosZeros
증감률 has 37 (82.2%) zerosZeros

Reproduction

Analysis started2023-12-12 00:39:50.037270
Analysis finished2023-12-12 00:39:53.829924
Duration3.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-12T09:39:54.001456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.4666667
Min length2

Characters and Unicode

Total characters201
Distinct characters104
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

Unique45 ?
Unique (%)100.0%

Sample

1st row설렁탕
2nd row냉면
3rd row비빔밥
4th row갈비탕
5th row삼계탕
ValueCountFrequency (%)
설렁탕 1
 
2.2%
김밥 1
 
2.2%
의복수선료 1
 
2.2%
숙박료(여관 1
 
2.2%
숙박료(호텔 1
 
2.2%
콘도이용료 1
 
2.2%
미용료(파마 1
 
2.2%
이용료(남자커트 1
 
2.2%
목욕료 1
 
2.2%
찜질방이용료 1
 
2.2%
Other values (35) 35
77.8%
2023-12-12T09:39:54.358560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
료 20
 
10.0%
용 11
 
5.5%
이 10
 
5.0%
장 6
 
3.0%
) 5
 
2.5%
( 5
 
2.5%
수 4
 
2.0%
비 4
 
2.0%
관 4
 
2.0%
탕 4
 
2.0%
Other values (94) 128
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 189
94.0%
Close Punctuation 5
 
2.5%
Open Punctuation 5
 
2.5%
Uppercase Letter 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 20
 
10.6%
용 11
 
5.8%
이 10
 
5.3%
장 6
 
3.2%
수 4
 
2.1%
비 4
 
2.1%
관 4
 
2.1%
탕 4
 
2.1%
면 3
 
1.6%
밥 3
 
1.6%
Other values (90) 120
63.5%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 189
94.0%
Common 10
 
5.0%
Latin 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 20
 
10.6%
용 11
 
5.8%
이 10
 
5.3%
장 6
 
3.2%
수 4
 
2.1%
비 4
 
2.1%
관 4
 
2.1%
탕 4
 
2.1%
면 3
 
1.6%
밥 3
 
1.6%
Other values (90) 120
63.5%
Common
ValueCountFrequency (%)
) 5
50.0%
( 5
50.0%
Latin
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 189
94.0%
ASCII 12
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
료 20
 
10.6%
용 11
 
5.8%
이 10
 
5.3%
장 6
 
3.2%
수 4
 
2.1%
비 4
 
2.1%
관 4
 
2.1%
탕 4
 
2.1%
면 3
 
1.6%
밥 3
 
1.6%
Other values (90) 120
63.5%
ASCII
ValueCountFrequency (%)
) 5
41.7%
( 5
41.7%
P 1
 
8.3%
C 1
 
8.3%

수정구가격
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18139.644
Minimum0
Maximum134466
Zeros3
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T09:39:54.495348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile98.8
Q14653
median8571
Q315080
95-th percentile80859.4
Maximum134466
Range134466
Interquartile range (IQR)10427

Descriptive statistics

Standard deviation29304.536
Coefficient of variation (CV)1.6154967
Kurtosis10.323368
Mean18139.644
Median Absolute Deviation (MAD)5429
Skewness3.2232025
Sum816284
Variance8.5875583 × 108
MonotonicityNot monotonic
2023-12-12T09:39:54.635211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 3
 
6.7%
10200 1
 
2.2%
7535 1
 
2.2%
8184 1
 
2.2%
4653 1
 
2.2%
37444 1
 
2.2%
91253 1
 
2.2%
39285 1
 
2.2%
11153 1
 
2.2%
8571 1
 
2.2%
Other values (33) 33
73.3%
ValueCountFrequency (%)
0 3
6.7%
494 1
 
2.2%
1220 1
 
2.2%
2564 1
 
2.2%
2928 1
 
2.2%
3666 1
 
2.2%
3885 1
 
2.2%
3892 1
 
2.2%
3900 1
 
2.2%
4653 1
 
2.2%
ValueCountFrequency (%)
134466 1
2.2%
132363 1
2.2%
91253 1
2.2%
39285 1
2.2%
37444 1
2.2%
33700 1
2.2%
26923 1
2.2%
22153 1
2.2%
17414 1
2.2%
17000 1
2.2%

중원구가격
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15675.111
Minimum0
Maximum138600
Zeros3
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T09:39:54.771441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101.4
Q14000
median8625
Q314428
95-th percentile43400
Maximum138600
Range138600
Interquartile range (IQR)10428

Descriptive statistics

Standard deviation25238.595
Coefficient of variation (CV)1.6101063
Kurtosis15.569403
Mean15675.111
Median Absolute Deviation (MAD)4625
Skewness3.7969429
Sum705380
Variance6.369867 × 108
MonotonicityNot monotonic
2023-12-12T09:39:54.887527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 3
 
6.7%
4000 3
 
6.7%
7687 2
 
4.4%
104658 1
 
2.2%
3100 1
 
2.2%
8250 1
 
2.2%
36000 1
 
2.2%
45250 1
 
2.2%
11857 1
 
2.2%
8750 1
 
2.2%
Other values (30) 30
66.7%
ValueCountFrequency (%)
0 3
6.7%
507 1
 
2.2%
1280 1
 
2.2%
3100 1
 
2.2%
3400 1
 
2.2%
3600 1
 
2.2%
3662 1
 
2.2%
4000 3
6.7%
4240 1
 
2.2%
5875 1
 
2.2%
ValueCountFrequency (%)
138600 1
2.2%
104658 1
2.2%
45250 1
2.2%
36000 1
2.2%
35333 1
2.2%
23571 1
2.2%
21714 1
2.2%
20750 1
2.2%
15750 1
2.2%
15100 1
2.2%

분당구가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23079.556
Minimum0
Maximum177416
Zeros1
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T09:39:55.010940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1662.4
Q16766
median10500
Q316913
95-th percentile121000
Maximum177416
Range177416
Interquartile range (IQR)10147

Descriptive statistics

Standard deviation37760.494
Coefficient of variation (CV)1.6361014
Kurtosis9.1300689
Mean23079.556
Median Absolute Deviation (MAD)6083
Skewness3.0730084
Sum1038580
Variance1.4258549 × 109
MonotonicityNot monotonic
2023-12-12T09:39:55.136250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
10250 1
 
2.2%
8580 1
 
2.2%
6266 1
 
2.2%
70000 1
 
2.2%
133750 1
 
2.2%
0 1
 
2.2%
51300 1
 
2.2%
12846 1
 
2.2%
10500 1
 
2.2%
25333 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
0 1
2.2%
481 1
2.2%
1218 1
2.2%
3440 1
2.2%
3620 1
2.2%
3700 1
2.2%
3900 1
2.2%
3978 1
2.2%
4133 1
2.2%
6266 1
2.2%
ValueCountFrequency (%)
177416 1
2.2%
147285 1
2.2%
133750 1
2.2%
70000 1
2.2%
51300 1
2.2%
46975 1
2.2%
25333 1
2.2%
22888 1
2.2%
22520 1
2.2%
20307 1
2.2%

이번주가격
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20012.889
Minimum0
Maximum150160
Zeros1
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T09:39:55.259670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1622.4
Q16123
median9418
Q315416
95-th percentile99563.6
Maximum150160
Range150160
Interquartile range (IQR)9293

Descriptive statistics

Standard deviation31760.449
Coefficient of variation (CV)1.5869997
Kurtosis9.5573894
Mean20012.889
Median Absolute Deviation (MAD)5410
Skewness3.1351931
Sum900580
Variance1.0087261 × 109
MonotonicityNot monotonic
2023-12-12T09:39:55.390672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4008 2
 
4.4%
9927 1
 
2.2%
8381 1
 
2.2%
47814 1
 
2.2%
112501 1
 
2.2%
0 1
 
2.2%
45278 1
 
2.2%
11952 1
 
2.2%
9273 1
 
2.2%
15416 1
 
2.2%
Other values (34) 34
75.6%
ValueCountFrequency (%)
0 1
2.2%
494 1
2.2%
1239 1
2.2%
3156 1
2.2%
3194 1
2.2%
3768 1
2.2%
3866 1
2.2%
4008 2
4.4%
4973 1
2.2%
5050 1
2.2%
ValueCountFrequency (%)
150160 1
2.2%
128102 1
2.2%
112501 1
2.2%
47814 1
2.2%
45278 1
2.2%
38669 1
2.2%
24460 1
2.2%
21807 1
2.2%
19673 1
2.2%
16275 1
2.2%

지난주가격
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20073.844
Minimum0
Maximum150160
Zeros1
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T09:39:55.536444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1622.4
Q16123
median9418
Q315185
95-th percentile101783.6
Maximum150160
Range150160
Interquartile range (IQR)9062

Descriptive statistics

Standard deviation31964.245
Coefficient of variation (CV)1.592333
Kurtosis9.4296588
Mean20073.844
Median Absolute Deviation (MAD)5324
Skewness3.122586
Sum903323
Variance1.021713 × 109
MonotonicityNot monotonic
2023-12-12T09:39:55.993203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4008 2
 
4.4%
9927 1
 
2.2%
8381 1
 
2.2%
47814 1
 
2.2%
115276 1
 
2.2%
0 1
 
2.2%
46389 1
 
2.2%
11926 1
 
2.2%
9207 1
 
2.2%
14388 1
 
2.2%
Other values (34) 34
75.6%
ValueCountFrequency (%)
0 1
2.2%
494 1
2.2%
1239 1
2.2%
3156 1
2.2%
3201 1
2.2%
3818 1
2.2%
3866 1
2.2%
4008 2
4.4%
4973 1
2.2%
5050 1
2.2%
ValueCountFrequency (%)
150160 1
2.2%
128022 1
2.2%
115276 1
2.2%
47814 1
2.2%
46389 1
2.2%
38669 1
2.2%
24460 1
2.2%
21807 1
2.2%
19673 1
2.2%
16275 1
2.2%

증감률
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.040222222
Minimum-2.41
Maximum7.14
Zeros37
Zeros (%)82.2%
Negative4
Negative (%)8.9%
Memory size537.0 B
2023-12-12T09:39:56.112236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.41
5-th percentile-1.092
Q10
median0
Q30
95-th percentile0.188
Maximum7.14
Range9.55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2132029
Coefficient of variation (CV)30.162503
Kurtosis28.231864
Mean0.040222222
Median Absolute Deviation (MAD)0
Skewness4.3508826
Sum1.81
Variance1.4718613
MonotonicityNot monotonic
2023-12-12T09:39:56.214118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 37
82.2%
-0.22 1
 
2.2%
-1.31 1
 
2.2%
-2.41 1
 
2.2%
-2.39 1
 
2.2%
0.22 1
 
2.2%
0.72 1
 
2.2%
7.14 1
 
2.2%
0.06 1
 
2.2%
ValueCountFrequency (%)
-2.41 1
 
2.2%
-2.39 1
 
2.2%
-1.31 1
 
2.2%
-0.22 1
 
2.2%
0.0 37
82.2%
0.06 1
 
2.2%
0.22 1
 
2.2%
0.72 1
 
2.2%
7.14 1
 
2.2%
ValueCountFrequency (%)
7.14 1
 
2.2%
0.72 1
 
2.2%
0.22 1
 
2.2%
0.06 1
 
2.2%
0.0 37
82.2%
-0.22 1
 
2.2%
-1.31 1
 
2.2%
-2.39 1
 
2.2%
-2.41 1
 
2.2%

Interactions

2023-12-12T09:39:53.067403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:50.261235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:50.830492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.360532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.858545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.420025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:53.174269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:50.358129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:50.921422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.445191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.957730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.525828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:53.261228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:50.456521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.029314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.535382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.048507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.628247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:53.355301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:50.570609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.117285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.614066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.142707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.734711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:53.440357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:50.662603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.200625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.697754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.229950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.866017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:53.535464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:50.748339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.283635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:51.784697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.320136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:39:52.966356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:39:56.301149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목수정구가격중원구가격분당구가격이번주가격지난주가격증감률
품목1.0001.0001.0001.0001.0001.0001.000
수정구가격1.0001.0000.8280.8890.9100.9190.634
중원구가격1.0000.8281.0000.8990.9370.9450.216
분당구가격1.0000.8890.8991.0000.9910.9890.486
이번주가격1.0000.9100.9370.9911.0001.0000.447
지난주가격1.0000.9190.9450.9891.0001.0000.384
증감률1.0000.6340.2160.4860.4470.3841.000
2023-12-12T09:39:56.406617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수정구가격중원구가격분당구가격이번주가격지난주가격증감률
수정구가격1.0000.8770.9360.9450.9490.065
중원구가격0.8771.0000.8200.8320.8350.252
분당구가격0.9360.8201.0000.9930.9880.126
이번주가격0.9450.8320.9931.0000.9990.104
지난주가격0.9490.8350.9880.9991.0000.086
증감률0.0650.2520.1260.1040.0861.000

Missing values

2023-12-12T09:39:53.647819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:39:53.777330image/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

품목수정구가격중원구가격분당구가격이번주가격지난주가격증감률
0설렁탕10200933310250992799270.0
1냉면7535768710000840784070.0
2비빔밥657675007666724772470.0
3갈비탕12500125711338412818128180.0
4삼계탕14200144281530014642146420.0
5김치찌개백반725078128166774277420.0
6된장찌개백반710776877866755375530.0
7불고기13750100001880414184141840.0
8쇠고기33700353334697538669386690.0
9돼지갈비15080138921658315185151850.0
품목수정구가격중원구가격분당구가격이번주가격지난주가격증감률
35수영장이용료036006500505050500.0
36볼링장이용료390040003700386638660.0
37골프연습장이용료1344661386001774161501601501600.0
38당구장이용료8783877110700941894180.0
39노래방이용료26923235712288824460244600.0
40PC방이용료122012801218123912390.0
41사진촬영료17000217142030719673196730.0
42사진인화료4945074814944940.0
43영화관람료14000130001400013666136660.0
44운동경기관람료001200012000120000.0