Overview

Dataset statistics

Number of variables16
Number of observations222
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.7 KiB
Average record size in memory141.6 B

Variable types

Numeric13
Text1
Categorical2

Dataset

Description인천광역시 중구 관내에 연령별 인구 현황에 대한 데이터 입니다.파일명 인천광역시_중구_연령별인구현황파일내용 연령,성별, 구, 동별 등
Author인천광역시 중구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15089337&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 중구 and 3 other fieldsHigh correlation
중구 is highly overall correlated with 연번 and 11 other fieldsHigh correlation
신포동 is highly overall correlated with 중구 and 9 other fieldsHigh correlation
연안동 is highly overall correlated with 중구 and 10 other fieldsHigh correlation
신흥동 is highly overall correlated with 중구 and 10 other fieldsHigh correlation
도원동 is highly overall correlated with 중구 and 9 other fieldsHigh correlation
율목동 is highly overall correlated with 중구 and 9 other fieldsHigh correlation
동인천동 is highly overall correlated with 중구 and 9 other fieldsHigh correlation
개항동 is highly overall correlated with 중구 and 9 other fieldsHigh correlation
영종동 is highly overall correlated with 연번 and 11 other fieldsHigh correlation
영종1동 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
운서동 is highly overall correlated with 연번 and 10 other fieldsHigh correlation
용유동 is highly overall correlated with 중구 and 8 other fieldsHigh correlation
연번 has unique valuesUnique
중구 has 11 (5.0%) zerosZeros
신포동 has 22 (9.9%) zerosZeros
연안동 has 24 (10.8%) zerosZeros
신흥동 has 24 (10.8%) zerosZeros
도원동 has 25 (11.3%) zerosZeros
율목동 has 33 (14.9%) zerosZeros
동인천동 has 23 (10.4%) zerosZeros
개항동 has 24 (10.8%) zerosZeros
영종동 has 21 (9.5%) zerosZeros
영종1동 has 25 (11.3%) zerosZeros
운서동 has 23 (10.4%) zerosZeros
용유동 has 33 (14.9%) zerosZeros

Reproduction

Analysis started2024-01-28 11:46:40.122790
Analysis finished2024-01-28 11:46:52.725020
Duration12.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct222
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.5
Minimum1
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:52.785480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.05
Q156.25
median111.5
Q3166.75
95-th percentile210.95
Maximum222
Range221
Interquartile range (IQR)110.5

Descriptive statistics

Standard deviation64.230055
Coefficient of variation (CV)0.57605431
Kurtosis-1.2
Mean111.5
Median Absolute Deviation (MAD)55.5
Skewness0
Sum24753
Variance4125.5
MonotonicityStrictly increasing
2024-01-28T20:46:52.889886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
154 1
 
0.5%
143 1
 
0.5%
144 1
 
0.5%
145 1
 
0.5%
146 1
 
0.5%
147 1
 
0.5%
148 1
 
0.5%
149 1
 
0.5%
150 1
 
0.5%
Other values (212) 212
95.5%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
222 1
0.5%
221 1
0.5%
220 1
0.5%
219 1
0.5%
218 1
0.5%
217 1
0.5%
216 1
0.5%
215 1
0.5%
214 1
0.5%
213 1
0.5%

연령
Text

Distinct111
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-01-28T20:46:53.109367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.981982
Min length7

Characters and Unicode

Total characters1994
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0세 - 0세
2nd row0세 - 0세
3rd row1세 - 1세
4th row1세 - 1세
5th row2세 - 2세
ValueCountFrequency (%)
220
33.1%
0세 4
 
0.6%
81세 4
 
0.6%
80세 4
 
0.6%
79세 4
 
0.6%
78세 4
 
0.6%
77세 4
 
0.6%
76세 4
 
0.6%
75세 4
 
0.6%
74세 4
 
0.6%
Other values (103) 408
61.4%
2024-01-28T20:46:53.447308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
442
22.2%
442
22.2%
- 220
11.0%
1 128
 
6.4%
0 86
 
4.3%
2 84
 
4.2%
7 84
 
4.2%
3 84
 
4.2%
4 84
 
4.2%
5 84
 
4.2%
Other values (5) 256
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 886
44.4%
Other Letter 446
22.4%
Space Separator 442
22.2%
Dash Punctuation 220
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 128
14.4%
0 86
9.7%
2 84
9.5%
7 84
9.5%
3 84
9.5%
4 84
9.5%
5 84
9.5%
6 84
9.5%
8 84
9.5%
9 84
9.5%
Other Letter
ValueCountFrequency (%)
442
99.1%
2
 
0.4%
2
 
0.4%
Space Separator
ValueCountFrequency (%)
442
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548
77.6%
Hangul 446
 
22.4%

Most frequent character per script

Common
ValueCountFrequency (%)
442
28.6%
- 220
14.2%
1 128
 
8.3%
0 86
 
5.6%
2 84
 
5.4%
7 84
 
5.4%
3 84
 
5.4%
4 84
 
5.4%
5 84
 
5.4%
6 84
 
5.4%
Other values (2) 168
 
10.9%
Hangul
ValueCountFrequency (%)
442
99.1%
2
 
0.4%
2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548
77.6%
Hangul 446
 
22.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
442
99.1%
2
 
0.4%
2
 
0.4%
ASCII
ValueCountFrequency (%)
442
28.6%
- 220
14.2%
1 128
 
8.3%
0 86
 
5.6%
2 84
 
5.4%
7 84
 
5.4%
3 84
 
5.4%
4 84
 
5.4%
5 84
 
5.4%
6 84
 
5.4%
Other values (2) 168
 
10.9%

성별
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
111 
111 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
111
50.0%
111
50.0%

Length

2024-01-28T20:46:53.550925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:46:53.622322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
111
50.0%
111
50.0%

중구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct189
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean706.78378
Minimum0
Maximum1596
Zeros11
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:53.707976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1307.75
median729
Q31124.5
95-th percentile1461.1
Maximum1596
Range1596
Interquartile range (IQR)816.75

Descriptive statistics

Standard deviation479.97566
Coefficient of variation (CV)0.67909829
Kurtosis-1.1650349
Mean706.78378
Median Absolute Deviation (MAD)403.5
Skewness-0.046577614
Sum156906
Variance230376.63
MonotonicityNot monotonic
2024-01-28T20:46:53.817394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
5.0%
2 5
 
2.3%
1151 4
 
1.8%
1 3
 
1.4%
3 3
 
1.4%
14 2
 
0.9%
1276 2
 
0.9%
957 2
 
0.9%
778 2
 
0.9%
698 2
 
0.9%
Other values (179) 186
83.8%
ValueCountFrequency (%)
0 11
5.0%
1 3
 
1.4%
2 5
2.3%
3 3
 
1.4%
4 2
 
0.9%
8 1
 
0.5%
11 1
 
0.5%
12 1
 
0.5%
13 1
 
0.5%
14 2
 
0.9%
ValueCountFrequency (%)
1596 1
0.5%
1581 1
0.5%
1551 1
0.5%
1536 1
0.5%
1530 1
0.5%
1516 1
0.5%
1504 1
0.5%
1496 1
0.5%
1494 1
0.5%
1492 1
0.5%

신포동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.004505
Minimum0
Maximum84
Zeros22
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:53.939411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median20
Q335
95-th percentile69.9
Maximum84
Range84
Interquartile range (IQR)27

Descriptive statistics

Standard deviation21.16911
Coefficient of variation (CV)0.84661184
Kurtosis-0.065848777
Mean25.004505
Median Absolute Deviation (MAD)13.5
Skewness0.84806264
Sum5551
Variance448.1312
MonotonicityNot monotonic
2024-01-28T20:46:54.041148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
9.9%
14 11
 
5.0%
1 10
 
4.5%
13 8
 
3.6%
35 7
 
3.2%
26 7
 
3.2%
32 6
 
2.7%
33 6
 
2.7%
8 6
 
2.7%
17 5
 
2.3%
Other values (59) 134
60.4%
ValueCountFrequency (%)
0 22
9.9%
1 10
4.5%
2 2
 
0.9%
3 5
 
2.3%
4 3
 
1.4%
5 5
 
2.3%
6 2
 
0.9%
7 2
 
0.9%
8 6
 
2.7%
9 4
 
1.8%
ValueCountFrequency (%)
84 1
 
0.5%
79 1
 
0.5%
76 3
1.4%
75 2
0.9%
73 1
 
0.5%
72 2
0.9%
71 1
 
0.5%
70 1
 
0.5%
68 2
0.9%
66 1
 
0.5%

연안동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.468468
Minimum0
Maximum59
Zeros24
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:54.139468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median20
Q333
95-th percentile46
Maximum59
Range59
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.756436
Coefficient of variation (CV)0.68735391
Kurtosis-0.80049302
Mean21.468468
Median Absolute Deviation (MAD)12
Skewness0.21490962
Sum4766
Variance217.75239
MonotonicityNot monotonic
2024-01-28T20:46:54.242699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
10.8%
16 10
 
4.5%
33 9
 
4.1%
17 8
 
3.6%
39 7
 
3.2%
37 7
 
3.2%
20 7
 
3.2%
22 7
 
3.2%
18 7
 
3.2%
9 7
 
3.2%
Other values (44) 129
58.1%
ValueCountFrequency (%)
0 24
10.8%
1 7
 
3.2%
2 4
 
1.8%
3 3
 
1.4%
4 2
 
0.9%
5 3
 
1.4%
6 1
 
0.5%
7 2
 
0.9%
8 4
 
1.8%
9 7
 
3.2%
ValueCountFrequency (%)
59 1
 
0.5%
58 1
 
0.5%
54 1
 
0.5%
53 1
 
0.5%
52 1
 
0.5%
49 2
0.9%
48 2
0.9%
47 1
 
0.5%
46 4
1.8%
45 2
0.9%

신흥동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.815315
Minimum0
Maximum141
Zeros24
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:54.346072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132
median64
Q395.75
95-th percentile123.95
Maximum141
Range141
Interquartile range (IQR)63.75

Descriptive statistics

Standard deviation40.838022
Coefficient of variation (CV)0.66064569
Kurtosis-1.1786368
Mean61.815315
Median Absolute Deviation (MAD)32
Skewness-0.086756644
Sum13723
Variance1667.744
MonotonicityNot monotonic
2024-01-28T20:46:54.457753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
10.8%
95 5
 
2.3%
87 5
 
2.3%
52 4
 
1.8%
1 4
 
1.8%
39 4
 
1.8%
4 4
 
1.8%
63 4
 
1.8%
110 4
 
1.8%
37 3
 
1.4%
Other values (94) 161
72.5%
ValueCountFrequency (%)
0 24
10.8%
1 4
 
1.8%
2 2
 
0.9%
3 3
 
1.4%
4 4
 
1.8%
7 3
 
1.4%
9 1
 
0.5%
11 1
 
0.5%
13 3
 
1.4%
18 1
 
0.5%
ValueCountFrequency (%)
141 1
 
0.5%
132 1
 
0.5%
131 2
0.9%
129 1
 
0.5%
127 1
 
0.5%
126 2
0.9%
125 3
1.4%
124 1
 
0.5%
123 1
 
0.5%
122 1
 
0.5%

도원동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.702703
Minimum0
Maximum51
Zeros25
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:54.568909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median14
Q326
95-th percentile42.95
Maximum51
Range51
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.439049
Coefficient of variation (CV)0.80460328
Kurtosis-0.62473082
Mean16.702703
Median Absolute Deviation (MAD)11.5
Skewness0.54710075
Sum3708
Variance180.60805
MonotonicityNot monotonic
2024-01-28T20:46:54.672151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 25
 
11.3%
2 18
 
8.1%
26 9
 
4.1%
8 9
 
4.1%
18 8
 
3.6%
12 8
 
3.6%
14 7
 
3.2%
16 7
 
3.2%
24 6
 
2.7%
23 6
 
2.7%
Other values (37) 119
53.6%
ValueCountFrequency (%)
0 25
11.3%
1 6
 
2.7%
2 18
8.1%
3 2
 
0.9%
4 3
 
1.4%
5 4
 
1.8%
6 6
 
2.7%
7 5
 
2.3%
8 9
 
4.1%
9 4
 
1.8%
ValueCountFrequency (%)
51 1
 
0.5%
50 1
 
0.5%
48 1
 
0.5%
47 1
 
0.5%
46 2
0.9%
45 1
 
0.5%
44 3
1.4%
43 2
0.9%
42 3
1.4%
41 1
 
0.5%

율목동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.509009
Minimum0
Maximum45
Zeros33
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:54.772228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q321
95-th percentile34
Maximum45
Range45
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.220029
Coefficient of variation (CV)0.83055903
Kurtosis-0.66268012
Mean13.509009
Median Absolute Deviation (MAD)9
Skewness0.53835439
Sum2999
Variance125.88906
MonotonicityNot monotonic
2024-01-28T20:46:54.876371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 33
 
14.9%
1 11
 
5.0%
19 9
 
4.1%
5 9
 
4.1%
9 9
 
4.1%
8 9
 
4.1%
20 9
 
4.1%
2 8
 
3.6%
29 8
 
3.6%
14 8
 
3.6%
Other values (31) 109
49.1%
ValueCountFrequency (%)
0 33
14.9%
1 11
 
5.0%
2 8
 
3.6%
3 6
 
2.7%
4 4
 
1.8%
5 9
 
4.1%
6 6
 
2.7%
7 6
 
2.7%
8 9
 
4.1%
9 9
 
4.1%
ValueCountFrequency (%)
45 1
 
0.5%
42 1
 
0.5%
39 1
 
0.5%
38 1
 
0.5%
37 1
 
0.5%
36 2
0.9%
35 4
1.8%
34 3
1.4%
33 2
0.9%
32 1
 
0.5%

동인천동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.864865
Minimum0
Maximum79
Zeros23
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:54.974670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median23
Q336
95-th percentile63
Maximum79
Range79
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.47663
Coefficient of variation (CV)0.78329926
Kurtosis-0.51489105
Mean24.864865
Median Absolute Deviation (MAD)15
Skewness0.55651275
Sum5520
Variance379.33912
MonotonicityNot monotonic
2024-01-28T20:46:55.082197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
10.4%
22 7
 
3.2%
26 6
 
2.7%
1 6
 
2.7%
28 6
 
2.7%
8 6
 
2.7%
5 5
 
2.3%
27 5
 
2.3%
11 5
 
2.3%
13 5
 
2.3%
Other values (55) 148
66.7%
ValueCountFrequency (%)
0 23
10.4%
1 6
 
2.7%
2 5
 
2.3%
3 4
 
1.8%
4 4
 
1.8%
5 5
 
2.3%
6 4
 
1.8%
7 4
 
1.8%
8 6
 
2.7%
9 4
 
1.8%
ValueCountFrequency (%)
79 1
 
0.5%
71 1
 
0.5%
70 1
 
0.5%
68 2
0.9%
67 1
 
0.5%
65 2
0.9%
64 3
1.4%
63 2
0.9%
61 1
 
0.5%
60 1
 
0.5%

개항동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.454955
Minimum0
Maximum102
Zeros24
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:55.206684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median30
Q350
95-th percentile70.95
Maximum102
Range102
Interquartile range (IQR)39

Descriptive statistics

Standard deviation24.002927
Coefficient of variation (CV)0.76308889
Kurtosis-0.7342823
Mean31.454955
Median Absolute Deviation (MAD)20
Skewness0.39407022
Sum6983
Variance576.1405
MonotonicityNot monotonic
2024-01-28T20:46:55.315871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
10.8%
15 7
 
3.2%
55 7
 
3.2%
3 6
 
2.7%
30 6
 
2.7%
2 5
 
2.3%
37 5
 
2.3%
34 5
 
2.3%
16 5
 
2.3%
44 5
 
2.3%
Other values (67) 147
66.2%
ValueCountFrequency (%)
0 24
10.8%
1 4
 
1.8%
2 5
 
2.3%
3 6
 
2.7%
4 3
 
1.4%
5 1
 
0.5%
6 3
 
1.4%
7 3
 
1.4%
8 4
 
1.8%
9 2
 
0.9%
ValueCountFrequency (%)
102 1
0.5%
88 1
0.5%
87 1
0.5%
85 1
0.5%
83 1
0.5%
80 2
0.9%
78 1
0.5%
75 1
0.5%
73 1
0.5%
72 1
0.5%

영종동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct127
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.78378
Minimum0
Maximum267
Zeros21
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:55.420304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median88
Q3168.5
95-th percentile224.85
Maximum267
Range267
Interquartile range (IQR)137.5

Descriptive statistics

Standard deviation75.72281
Coefficient of variation (CV)0.74395751
Kurtosis-1.1157656
Mean101.78378
Median Absolute Deviation (MAD)71
Skewness0.22347388
Sum22596
Variance5733.944
MonotonicityNot monotonic
2024-01-28T20:46:55.781349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
9.5%
1 5
 
2.3%
83 5
 
2.3%
56 4
 
1.8%
2 4
 
1.8%
177 3
 
1.4%
5 3
 
1.4%
139 3
 
1.4%
84 3
 
1.4%
162 3
 
1.4%
Other values (117) 168
75.7%
ValueCountFrequency (%)
0 21
9.5%
1 5
 
2.3%
2 4
 
1.8%
3 3
 
1.4%
4 1
 
0.5%
5 3
 
1.4%
6 1
 
0.5%
9 1
 
0.5%
10 2
 
0.9%
15 2
 
0.9%
ValueCountFrequency (%)
267 1
0.5%
264 1
0.5%
262 1
0.5%
252 1
0.5%
251 1
0.5%
241 1
0.5%
236 1
0.5%
234 1
0.5%
231 1
0.5%
226 2
0.9%

영종1동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct164
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.3018
Minimum0
Maximum754
Zeros25
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:55.883814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q136.25
median207
Q3367.5
95-th percentile577.15
Maximum754
Range754
Interquartile range (IQR)331.25

Descriptive statistics

Standard deviation193.59945
Coefficient of variation (CV)0.84429972
Kurtosis-0.66000763
Mean229.3018
Median Absolute Deviation (MAD)170
Skewness0.53056738
Sum50905
Variance37480.746
MonotonicityNot monotonic
2024-01-28T20:46:55.988362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
11.3%
209 4
 
1.8%
123 3
 
1.4%
1 3
 
1.4%
2 3
 
1.4%
436 3
 
1.4%
12 3
 
1.4%
507 3
 
1.4%
205 2
 
0.9%
523 2
 
0.9%
Other values (154) 171
77.0%
ValueCountFrequency (%)
0 25
11.3%
1 3
 
1.4%
2 3
 
1.4%
3 2
 
0.9%
4 1
 
0.5%
5 2
 
0.9%
7 1
 
0.5%
8 2
 
0.9%
9 1
 
0.5%
10 2
 
0.9%
ValueCountFrequency (%)
754 1
0.5%
693 1
0.5%
691 1
0.5%
687 1
0.5%
679 1
0.5%
642 1
0.5%
639 1
0.5%
625 1
0.5%
614 1
0.5%
584 1
0.5%

운서동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct156
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.31982
Minimum0
Maximum639
Zeros23
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:56.091625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median125.5
Q3265.75
95-th percentile511.25
Maximum639
Range639
Interquartile range (IQR)225.75

Descriptive statistics

Standard deviation151.61941
Coefficient of variation (CV)0.92270919
Kurtosis0.51919678
Mean164.31982
Median Absolute Deviation (MAD)108
Skewness1.0146336
Sum36479
Variance22988.445
MonotonicityNot monotonic
2024-01-28T20:46:56.190748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
10.4%
1 6
 
2.7%
2 4
 
1.8%
164 3
 
1.4%
40 3
 
1.4%
82 3
 
1.4%
5 3
 
1.4%
118 3
 
1.4%
57 2
 
0.9%
212 2
 
0.9%
Other values (146) 170
76.6%
ValueCountFrequency (%)
0 23
10.4%
1 6
 
2.7%
2 4
 
1.8%
3 2
 
0.9%
4 2
 
0.9%
5 3
 
1.4%
8 1
 
0.5%
12 1
 
0.5%
14 1
 
0.5%
16 2
 
0.9%
ValueCountFrequency (%)
639 1
0.5%
598 1
0.5%
575 2
0.9%
572 1
0.5%
569 1
0.5%
565 1
0.5%
541 1
0.5%
535 1
0.5%
526 1
0.5%
518 1
0.5%

용유동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.558559
Minimum0
Maximum77
Zeros33
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-01-28T20:46:56.294488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q325
95-th percentile53.95
Maximum77
Range77
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.334184
Coefficient of variation (CV)1.0468414
Kurtosis0.66436142
Mean16.558559
Median Absolute Deviation (MAD)9
Skewness1.2222829
Sum3676
Variance300.47393
MonotonicityNot monotonic
2024-01-28T20:46:56.403976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
14.9%
7 12
 
5.4%
2 10
 
4.5%
8 9
 
4.1%
4 9
 
4.1%
1 8
 
3.6%
9 8
 
3.6%
5 8
 
3.6%
3 7
 
3.2%
16 6
 
2.7%
Other values (46) 112
50.5%
ValueCountFrequency (%)
0 33
14.9%
1 8
 
3.6%
2 10
 
4.5%
3 7
 
3.2%
4 9
 
4.1%
5 8
 
3.6%
6 4
 
1.8%
7 12
 
5.4%
8 9
 
4.1%
9 8
 
3.6%
ValueCountFrequency (%)
77 1
 
0.5%
69 1
 
0.5%
61 1
 
0.5%
60 2
0.9%
58 2
0.9%
57 1
 
0.5%
56 3
1.4%
54 1
 
0.5%
53 2
0.9%
51 2
0.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-07-31
222 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-31
2nd row2023-07-31
3rd row2023-07-31
4th row2023-07-31
5th row2023-07-31

Common Values

ValueCountFrequency (%)
2023-07-31 222
100.0%

Length

2024-01-28T20:46:56.508037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:46:56.580731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-31 222
100.0%

Interactions

2024-01-28T20:46:51.356319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:40.561856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.382077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.278824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.112890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.203002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.089614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.970458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.791173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.897662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.751003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.611792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.508976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.432020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:40.615590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.443645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.339390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.401727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.263551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.145507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.024096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.854981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.954931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.820342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.673427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.567989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.503302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:40.684048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.514183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.409645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.475953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.336468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.217521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.088298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.932277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.025109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.892157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.745032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.635996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.565946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:40.738914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.574346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.465155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.538749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.395668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.282929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.156966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.999267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.091572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.959000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.805943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.695815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.630831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:40.800183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.642032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.528799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.605502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.463859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.359563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.236378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.064173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.162025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.021830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.870676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.758180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.705608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:40.877076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.710238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.596562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.677982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.533832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.434062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.305171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.135082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.226797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.088115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.942817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.824046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.768689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:40.936009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.774257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.652977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.740762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.601787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.492491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.364694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.196821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.285671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.150697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.008067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.886113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.830814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:40.992097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.841218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.710590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.811474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.670766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.551170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.420391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.258612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.348097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.210154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.076034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.945223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.901257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.059160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.914552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.774836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.879444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.740124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.634022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.488293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.332864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.417027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.276756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.156492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.013056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:52.184738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.122284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.982928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.834379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.942701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.806992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.698604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.546315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.403884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.481979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.340258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.229278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.074971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:52.248067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.184093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.049452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.899251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.003772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.880953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.760528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.604482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.473660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.545341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.399686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.303472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.134956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:52.322491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.255493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.131775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.979917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.071376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.952058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.827293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.666559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.546395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.611710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.464233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.372128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.200838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:52.388649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:41.317118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:42.200392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:43.048458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:44.135026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.018298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:45.895761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:46.727618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:47.827294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:48.676111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:49.531394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:50.437768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:46:51.268384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T20:46:56.637815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번성별중구신포동연안동신흥동도원동율목동동인천동개항동영종동영종1동운서동용유동
연번1.0000.0000.9170.8550.8710.8960.8460.8290.8370.8480.9170.9130.9220.818
성별0.0001.0000.3620.3430.1220.0000.2650.1920.2010.0000.5080.0000.0000.000
중구0.9170.3621.0000.8310.7860.8960.7610.7420.7820.8010.9460.8540.8600.758
신포동0.8550.3430.8311.0000.8710.8420.8760.8510.9010.8980.8010.6970.7360.882
연안동0.8710.1220.7860.8711.0000.8320.8370.8420.8460.8640.7740.6680.6940.798
신흥동0.8960.0000.8960.8420.8321.0000.7920.7910.8180.8550.8570.7620.7850.738
도원동0.8460.2650.7610.8760.8370.7921.0000.8460.8710.8970.7120.6810.6860.836
율목동0.8290.1920.7420.8510.8420.7910.8461.0000.8720.8580.7230.6130.6850.866
동인천동0.8370.2010.7820.9010.8460.8180.8710.8721.0000.8940.7730.6500.6580.915
개항동0.8480.0000.8010.8980.8640.8550.8970.8580.8941.0000.7570.6910.7130.847
영종동0.9170.5080.9460.8010.7740.8570.7120.7230.7730.7571.0000.8300.8710.727
영종1동0.9130.0000.8540.6970.6680.7620.6810.6130.6500.6910.8301.0000.8330.672
운서동0.9220.0000.8600.7360.6940.7850.6860.6850.6580.7130.8710.8331.0000.591
용유동0.8180.0000.7580.8820.7980.7380.8360.8660.9150.8470.7270.6720.5911.000
2024-01-28T20:46:56.750201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번중구신포동연안동신흥동도원동율목동동인천동개항동영종동영종1동운서동용유동성별
연번1.000-0.591-0.211-0.242-0.398-0.164-0.146-0.146-0.224-0.563-0.787-0.676-0.0570.000
중구-0.5911.0000.7490.7750.8960.6580.6780.7100.7410.9710.8510.9400.6080.273
신포동-0.2110.7491.0000.9320.9110.9330.9260.9530.9500.7880.4450.6550.9290.258
연안동-0.2420.7750.9321.0000.9040.9080.9120.9210.9180.7900.5080.6750.8940.091
신흥동-0.3980.8960.9110.9041.0000.8480.8630.8840.9050.9030.6400.8190.8210.000
도원동-0.1640.6580.9330.9080.8481.0000.9170.9350.9320.6960.3640.5820.9270.199
율목동-0.1460.6780.9260.9120.8630.9171.0000.9240.9250.7010.3850.5930.9200.128
동인천동-0.1460.7100.9530.9210.8840.9350.9241.0000.9450.7460.3980.6060.9380.151
개항동-0.2240.7410.9500.9180.9050.9320.9250.9451.0000.7760.4190.6730.9110.000
영종동-0.5630.9710.7880.7900.9030.6960.7010.7460.7761.0000.8000.9010.6480.384
영종1동-0.7870.8510.4450.5080.6400.3640.3850.3980.4190.8001.0000.7800.3170.000
운서동-0.6760.9400.6550.6750.8190.5820.5930.6060.6730.9010.7801.0000.4860.000
용유동-0.0570.6080.9290.8940.8210.9270.9200.9380.9110.6480.3170.4861.0000.000
성별0.0000.2730.2580.0910.0000.1990.1280.1510.0000.3840.0000.0000.0001.000

Missing values

2024-01-28T20:46:52.498002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T20:46:52.662870image/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

연번연령성별중구신포동연안동신흥동도원동율목동동인천동개항동영종동영종1동운서동용유동데이터기준일자
010세 - 0세46053371258612548312023-07-31
120세 - 0세44854302026832288802023-07-31
231세 - 1세49669322067892677802023-07-31
341세 - 1세474212362017682756382023-07-31
452세 - 2세5601010392296733198822023-07-31
562세 - 2세44599304056662565912023-07-31
673세 - 3세542553922712793068232023-07-31
783세 - 3세52039372247763126622023-07-31
894세 - 4세6757849216810638010622023-07-31
9104세 - 4세597854465947434110012023-07-31
연번연령성별중구신포동연안동신흥동도원동율목동동인천동개항동영종동영종1동운서동용유동데이터기준일자
212213106세 - 106세0000000000002023-07-31
213214106세 - 106세0000000000002023-07-31
214215107세 - 107세0000000000002023-07-31
215216107세 - 107세0000000000002023-07-31
216217108세 - 108세0000000000002023-07-31
217218108세 - 108세0000000000002023-07-31
218219109세 - 109세0000000000002023-07-31
219220109세 - 109세0000000000002023-07-31
220221110세 이상1000000010002023-07-31
221222110세 이상1000100000002023-07-31