Overview

Dataset statistics

Number of variables9
Number of observations250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.9 KiB
Average record size in memory77.5 B

Variable types

Numeric3
Categorical5
Text1

Alerts

SETLE_PRICE has constant value ""Constant
FILE_NM has constant value ""Constant
BASE_DE has constant value ""Constant
SIGNGU_CD is highly overall correlated with CTPRVN_NMHigh correlation
MRHST_CO is highly overall correlated with OFFLN_MRHST_COHigh correlation
OFFLN_MRHST_CO is highly overall correlated with MRHST_COHigh correlation
CTPRVN_NM is highly overall correlated with SIGNGU_CDHigh correlation
SIGNGU_CD has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:50:27.733731
Analysis finished2023-12-10 09:50:30.812582
Duration3.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SIGNGU_CD
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29868.48
Minimum11010
Maximum39020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-10T18:50:30.968073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11134.5
Q124042.5
median32315
Q336327.5
95-th percentile38114.55
Maximum39020
Range28010
Interquartile range (IQR)12285

Descriptive statistics

Standard deviation8085.1571
Coefficient of variation (CV)0.27069195
Kurtosis0.32057893
Mean29868.48
Median Absolute Deviation (MAD)4140
Skewness-1.1477138
Sum7467120
Variance65369765
MonotonicityStrictly increasing
2023-12-10T18:50:31.280484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11010 1
 
0.4%
35310 1
 
0.4%
34330 1
 
0.4%
34340 1
 
0.4%
34350 1
 
0.4%
34360 1
 
0.4%
34370 1
 
0.4%
34380 1
 
0.4%
35011 1
 
0.4%
35012 1
 
0.4%
Other values (240) 240
96.0%
ValueCountFrequency (%)
11010 1
0.4%
11020 1
0.4%
11030 1
0.4%
11040 1
0.4%
11050 1
0.4%
11060 1
0.4%
11070 1
0.4%
11080 1
0.4%
11090 1
0.4%
11100 1
0.4%
ValueCountFrequency (%)
39020 1
0.4%
39010 1
0.4%
38400 1
0.4%
38390 1
0.4%
38380 1
0.4%
38370 1
0.4%
38360 1
0.4%
38350 1
0.4%
38340 1
0.4%
38330 1
0.4%

CTPRVN_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
경기도
42 
서울특별시
25 
경상북도
24 
전라남도
22 
경상남도
22 
Other values (12)
115 

Length

Max length7
Median length5
Mean length4.092
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row서울특별시
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
경기도 42
16.8%
서울특별시 25
10.0%
경상북도 24
9.6%
전라남도 22
8.8%
경상남도 22
8.8%
강원도 18
7.2%
부산광역시 16
 
6.4%
충청남도 16
 
6.4%
전라북도 15
 
6.0%
충청북도 14
 
5.6%
Other values (7) 36
14.4%

Length

2023-12-10T18:50:31.569332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 42
16.8%
서울특별시 25
10.0%
경상북도 24
9.6%
전라남도 22
8.8%
경상남도 22
8.8%
강원도 18
7.2%
부산광역시 16
 
6.4%
충청남도 16
 
6.4%
전라북도 15
 
6.0%
충청북도 14
 
5.6%
Other values (7) 36
14.4%

SIGNGU_NM
Text

Distinct228
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-10T18:50:32.172088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.484
Min length2

Characters and Unicode

Total characters871
Distinct characters147
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

Unique221 ?
Unique (%)88.4%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동구 6
 
2.1%
중구 6
 
2.1%
서구 5
 
1.8%
북구 5
 
1.8%
창원시 5
 
1.8%
남구 5
 
1.8%
수원시 4
 
1.4%
청주시 4
 
1.4%
용인시 3
 
1.1%
고양시 3
 
1.1%
Other values (227) 236
83.7%
2023-12-10T18:50:32.990766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 106
 
12.2%
시 100
 
11.5%
군 85
 
9.8%
32
 
3.7%
주 24
 
2.8%
천 23
 
2.6%
산 23
 
2.6%
양 22
 
2.5%
성 21
 
2.4%
동 20
 
2.3%
Other values (137) 415
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 839
96.3%
Space Separator 32
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 106
 
12.6%
시 100
 
11.9%
군 85
 
10.1%
주 24
 
2.9%
천 23
 
2.7%
산 23
 
2.7%
양 22
 
2.6%
성 21
 
2.5%
동 20
 
2.4%
원 18
 
2.1%
Other values (136) 397
47.3%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 839
96.3%
Common 32
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 106
 
12.6%
시 100
 
11.9%
군 85
 
10.1%
주 24
 
2.9%
천 23
 
2.7%
산 23
 
2.7%
양 22
 
2.6%
성 21
 
2.5%
동 20
 
2.4%
원 18
 
2.1%
Other values (136) 397
47.3%
Common
ValueCountFrequency (%)
32
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 839
96.3%
ASCII 32
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 106
 
12.6%
시 100
 
11.9%
군 85
 
10.1%
주 24
 
2.9%
천 23
 
2.7%
산 23
 
2.7%
양 22
 
2.6%
성 21
 
2.5%
동 20
 
2.4%
원 18
 
2.1%
Other values (136) 397
47.3%
ASCII
ValueCountFrequency (%)
32
100.0%

MRHST_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-10T18:50:33.213208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q130
median55
Q380
95-th percentile100
Maximum100
Range90
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.780432
Coefficient of variation (CV)0.52328058
Kurtosis-1.2246952
Mean55
Median Absolute Deviation (MAD)25
Skewness0
Sum13750
Variance828.31325
MonotonicityNot monotonic
2023-12-10T18:50:33.400903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 25
10.0%
20 25
10.0%
30 25
10.0%
40 25
10.0%
50 25
10.0%
60 25
10.0%
70 25
10.0%
80 25
10.0%
90 25
10.0%
100 25
10.0%
ValueCountFrequency (%)
10 25
10.0%
20 25
10.0%
30 25
10.0%
40 25
10.0%
50 25
10.0%
60 25
10.0%
70 25
10.0%
80 25
10.0%
90 25
10.0%
100 25
10.0%
ValueCountFrequency (%)
100 25
10.0%
90 25
10.0%
80 25
10.0%
70 25
10.0%
60 25
10.0%
50 25
10.0%
40 25
10.0%
30 25
10.0%
20 25
10.0%
10 25
10.0%

ONLINE_MRHST_CO
Categorical

Distinct5
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
153 
1
60 
2
28 
3
 
7
6
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
0 153
61.2%
1 60
 
24.0%
2 28
 
11.2%
3 7
 
2.8%
6 2
 
0.8%

Length

2023-12-10T18:50:33.612842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:33.854494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 153
61.2%
1 60
 
24.0%
2 28
 
11.2%
3 7
 
2.8%
6 2
 
0.8%

OFFLN_MRHST_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.404
Minimum7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-10T18:50:34.087354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10
Q130
median54
Q380
95-th percentile99
Maximum100
Range93
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.746098
Coefficient of variation (CV)0.52838206
Kurtosis-1.229975
Mean54.404
Median Absolute Deviation (MAD)25
Skewness-0.0068166876
Sum13601
Variance826.33814
MonotonicityNot monotonic
2023-12-10T18:50:34.331757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
50 21
 
8.4%
20 17
 
6.8%
90 16
 
6.4%
10 16
 
6.4%
70 16
 
6.4%
80 16
 
6.4%
30 15
 
6.0%
60 15
 
6.0%
40 11
 
4.4%
100 10
 
4.0%
Other values (25) 97
38.8%
ValueCountFrequency (%)
7 2
 
0.8%
8 2
 
0.8%
9 5
 
2.0%
10 16
6.4%
17 2
 
0.8%
18 1
 
0.4%
19 5
 
2.0%
20 17
6.8%
28 1
 
0.4%
29 9
3.6%
ValueCountFrequency (%)
100 10
4.0%
99 8
3.2%
98 6
 
2.4%
94 1
 
0.4%
90 16
6.4%
89 8
3.2%
88 1
 
0.4%
80 16
6.4%
79 4
 
1.6%
78 5
 
2.0%

SETLE_PRICE
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
100000
250 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100000
2nd row100000
3rd row100000
4th row100000
5th row100000

Common Values

ValueCountFrequency (%)
100000 250
100.0%

Length

2023-12-10T18:50:34.602903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:34.775832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100000 250
100.0%

FILE_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
KC_616_CLT_NURI_SOC_FOCUS_2019
250 

Length

Max length30
Median length30
Mean length30
Min length30

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_616_CLT_NURI_SOC_FOCUS_2019
2nd rowKC_616_CLT_NURI_SOC_FOCUS_2019
3rd rowKC_616_CLT_NURI_SOC_FOCUS_2019
4th rowKC_616_CLT_NURI_SOC_FOCUS_2019
5th rowKC_616_CLT_NURI_SOC_FOCUS_2019

Common Values

ValueCountFrequency (%)
KC_616_CLT_NURI_SOC_FOCUS_2019 250
100.0%

Length

2023-12-10T18:50:34.951519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:35.103258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_616_clt_nuri_soc_focus_2019 250
100.0%

BASE_DE
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
sample
250 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsample
2nd rowsample
3rd rowsample
4th rowsample
5th rowsample

Common Values

ValueCountFrequency (%)
sample 250
100.0%

Length

2023-12-10T18:50:35.301602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:35.464880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sample 250
100.0%

Interactions

2023-12-10T18:50:29.436839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.256700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.788195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:29.622974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.430836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.973136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:29.783092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.608459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:29.265346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:50:35.583154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIGNGU_CDCTPRVN_NMMRHST_COONLINE_MRHST_COOFFLN_MRHST_CO
SIGNGU_CD1.0000.9930.0000.2800.000
CTPRVN_NM0.9931.0000.0000.3840.000
MRHST_CO0.0000.0001.0000.2311.000
ONLINE_MRHST_CO0.2800.3840.2311.0000.229
OFFLN_MRHST_CO0.0000.0001.0000.2291.000
2023-12-10T18:50:35.810505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CTPRVN_NMONLINE_MRHST_CO
CTPRVN_NM1.0000.203
ONLINE_MRHST_CO0.2031.000
2023-12-10T18:50:35.975927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIGNGU_CDMRHST_COOFFLN_MRHST_COCTPRVN_NMONLINE_MRHST_CO
SIGNGU_CD1.0000.0400.0710.9510.174
MRHST_CO0.0401.0000.9960.0000.096
OFFLN_MRHST_CO0.0710.9961.0000.0000.095
CTPRVN_NM0.9510.0000.0001.0000.203
ONLINE_MRHST_CO0.1740.0960.0950.2031.000

Missing values

2023-12-10T18:50:30.007746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:50:30.305724image/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

SIGNGU_CDCTPRVN_NMSIGNGU_NMMRHST_COONLINE_MRHST_COOFFLN_MRHST_COSETLE_PRICEFILE_NMBASE_DE
011010서울특별시종로구1019100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
111020서울특별시중구20317100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
211030서울특별시용산구30129100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
311040서울특별시성동구40337100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
411050서울특별시광진구50149100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
511060서울특별시동대문구60159100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
611070서울특별시중랑구70070100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
711080서울특별시성북구80080100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
811090서울특별시강북구90189100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
911100서울특별시도봉구1000100100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
SIGNGU_CDCTPRVN_NMSIGNGU_NMMRHST_COONLINE_MRHST_COOFFLN_MRHST_COSETLE_PRICEFILE_NMBASE_DE
24038330경상남도창녕군10010100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
24138340경상남도고성군20020100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
24238350경상남도남해군30030100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
24338360경상남도하동군40040100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
24438370경상남도산청군50050100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
24538380경상남도함양군60060100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
24638390경상남도거창군70070100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
24738400경상남도합천군80080100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
24839010제주특별자치도제주시90288100000KC_616_CLT_NURI_SOC_FOCUS_2019sample
24939020제주특별자치도서귀포시100199100000KC_616_CLT_NURI_SOC_FOCUS_2019sample