Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory99.3 B

Variable types

Numeric7
Text1
Categorical3

Dataset

Description샘플 데이터
Author지디에스컨설팅그룹
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=9ef339f0-3071-11eb-a877-a5b67dc5814b

Alerts

수소이온농도 has constant value ""Constant
잔류염소 is highly overall correlated with 행정기관코드 and 1 other fieldsHigh correlation
탁도 is highly overall correlated with 행정기관코드 and 1 other fieldsHigh correlation
행정기관코드 is highly overall correlated with 총인구 and 4 other fieldsHigh correlation
총인구 is highly overall correlated with 행정기관코드 and 2 other fieldsHigh correlation
15세미만인구 is highly overall correlated with 행정기관코드 and 3 other fieldsHigh correlation
65세이상인구 is highly overall correlated with 행정기관코드 and 2 other fieldsHigh correlation
15세미만인구비 is highly overall correlated with 15세미만인구High correlation
행정기관코드 has unique valuesUnique
행정기관코드명 has unique valuesUnique
15세미만인구 has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:37:04.237510
Analysis finished2023-12-10 12:37:13.182612
Duration8.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정기관코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1103705.1
Minimum1101053
Maximum1107060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:37:13.305997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1101057.9
Q11102066.5
median1104054.5
Q31105062.2
95-th percentile1107052.1
Maximum1107060
Range6007
Interquartile range (IQR)2995.75

Descriptive statistics

Standard deviation1858.4336
Coefficient of variation (CV)0.0016838136
Kurtosis-1.1182606
Mean1103705.1
Median Absolute Deviation (MAD)1983
Skewness0.10907068
Sum1.1037051 × 108
Variance3453775.4
MonotonicityStrictly increasing
2023-12-10T21:37:13.572879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101053 1
 
1.0%
1104073 1
 
1.0%
1105062 1
 
1.0%
1105061 1
 
1.0%
1105060 1
 
1.0%
1105059 1
 
1.0%
1105058 1
 
1.0%
1105057 1
 
1.0%
1105056 1
 
1.0%
1105055 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1101053 1
1.0%
1101054 1
1.0%
1101055 1
1.0%
1101056 1
1.0%
1101057 1
1.0%
1101058 1
1.0%
1101060 1
1.0%
1101061 1
1.0%
1101063 1
1.0%
1101064 1
1.0%
ValueCountFrequency (%)
1107060 1
1.0%
1107059 1
1.0%
1107057 1
1.0%
1107055 1
1.0%
1107054 1
1.0%
1107052 1
1.0%
1106091 1
1.0%
1106090 1
1.0%
1106089 1
1.0%
1106088 1
1.0%

행정기관코드명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:37:14.063941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length3.89
Min length2

Characters and Unicode

Total characters389
Distinct characters102
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row사직동
2nd row삼청동
3rd row부암동
4th row평창동
5th row무악동
ValueCountFrequency (%)
사직동 1
 
1.0%
왕십리도선동 1
 
1.0%
구의2동 1
 
1.0%
구의1동 1
 
1.0%
능동 1
 
1.0%
중곡4동 1
 
1.0%
중곡3동 1
 
1.0%
중곡2동 1
 
1.0%
중곡1동 1
 
1.0%
군자동 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T21:37:14.701732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 101
26.0%
2 23
 
5.9%
1 21
 
5.4%
가 11
 
2.8%
이 7
 
1.8%
3 7
 
1.8%
자 6
 
1.5%
신 6
 
1.5%
수 6
 
1.5%
로 6
 
1.5%
Other values (92) 195
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 323
83.0%
Decimal Number 61
 
15.7%
Other Punctuation 5
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 101
31.3%
가 11
 
3.4%
이 7
 
2.2%
자 6
 
1.9%
신 6
 
1.9%
수 6
 
1.9%
로 6
 
1.9%
양 5
 
1.5%
청 5
 
1.5%
중 5
 
1.5%
Other values (84) 165
51.1%
Decimal Number
ValueCountFrequency (%)
2 23
37.7%
1 21
34.4%
3 7
 
11.5%
4 5
 
8.2%
5 3
 
4.9%
6 1
 
1.6%
7 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
· 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 323
83.0%
Common 66
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 101
31.3%
가 11
 
3.4%
이 7
 
2.2%
자 6
 
1.9%
신 6
 
1.9%
수 6
 
1.9%
로 6
 
1.9%
양 5
 
1.5%
청 5
 
1.5%
중 5
 
1.5%
Other values (84) 165
51.1%
Common
ValueCountFrequency (%)
2 23
34.8%
1 21
31.8%
3 7
 
10.6%
4 5
 
7.6%
· 5
 
7.6%
5 3
 
4.5%
6 1
 
1.5%
7 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 323
83.0%
ASCII 61
 
15.7%
None 5
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 101
31.3%
가 11
 
3.4%
이 7
 
2.2%
자 6
 
1.9%
신 6
 
1.9%
수 6
 
1.9%
로 6
 
1.9%
양 5
 
1.5%
청 5
 
1.5%
중 5
 
1.5%
Other values (84) 165
51.1%
ASCII
ValueCountFrequency (%)
2 23
37.7%
1 21
34.4%
3 7
 
11.5%
4 5
 
8.2%
5 3
 
4.9%
6 1
 
1.6%
7 1
 
1.6%
None
ValueCountFrequency (%)
· 5
100.0%

총인구
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16710.43
Minimum1543
Maximum35902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:37:14.923215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1543
5-th percentile4902.15
Q110041.75
median15552
Q323004.25
95-th percentile30628.85
Maximum35902
Range34359
Interquartile range (IQR)12962.5

Descriptive statistics

Standard deviation8360.0987
Coefficient of variation (CV)0.50029226
Kurtosis-0.76475923
Mean16710.43
Median Absolute Deviation (MAD)6549.5
Skewness0.2792826
Sum1671043
Variance69891251
MonotonicityNot monotonic
2023-12-10T21:37:15.138471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12267 2
 
2.0%
9379 1
 
1.0%
28159 1
 
1.0%
28500 1
 
1.0%
24846 1
 
1.0%
22663 1
 
1.0%
11438 1
 
1.0%
30603 1
 
1.0%
17309 1
 
1.0%
21713 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
1543 1
1.0%
1939 1
1.0%
2808 1
1.0%
3328 1
1.0%
4620 1
1.0%
4917 1
1.0%
5501 1
1.0%
5506 1
1.0%
5594 1
1.0%
5792 1
1.0%
ValueCountFrequency (%)
35902 1
1.0%
35634 1
1.0%
33531 1
1.0%
31711 1
1.0%
31120 1
1.0%
30603 1
1.0%
30107 1
1.0%
29665 1
1.0%
28500 1
1.0%
28383 1
1.0%

15세미만인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1760.94
Minimum67
Maximum6679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:37:15.402811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile327.2
Q11012.5
median1615.5
Q32285
95-th percentile3914.7
Maximum6679
Range6612
Interquartile range (IQR)1272.5

Descriptive statistics

Standard deviation1162.0732
Coefficient of variation (CV)0.6599164
Kurtosis2.4450117
Mean1760.94
Median Absolute Deviation (MAD)645
Skewness1.2151764
Sum176094
Variance1350414.1
MonotonicityNot monotonic
2023-12-10T21:37:15.628962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1062 1
 
1.0%
3021 1
 
1.0%
3395 1
 
1.0%
3083 1
 
1.0%
1876 1
 
1.0%
1015 1
 
1.0%
3217 1
 
1.0%
1638 1
 
1.0%
2274 1
 
1.0%
1328 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
67 1
1.0%
185 1
1.0%
199 1
1.0%
266 1
1.0%
293 1
1.0%
329 1
1.0%
334 1
1.0%
393 1
1.0%
412 1
1.0%
416 1
1.0%
ValueCountFrequency (%)
6679 1
1.0%
4665 1
1.0%
4481 1
1.0%
4359 1
1.0%
4270 1
1.0%
3896 1
1.0%
3395 1
1.0%
3344 1
1.0%
3313 1
1.0%
3249 1
1.0%

65세이상인구
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2166.52
Minimum126
Maximum4910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:37:15.835284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile727
Q11392.75
median2032
Q32949.5
95-th percentile4143.7
Maximum4910
Range4784
Interquartile range (IQR)1556.75

Descriptive statistics

Standard deviation1037.7058
Coefficient of variation (CV)0.47897356
Kurtosis-0.20613539
Mean2166.52
Median Absolute Deviation (MAD)729
Skewness0.47622406
Sum216652
Variance1076833.3
MonotonicityNot monotonic
2023-12-10T21:37:16.056511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2412 2
 
2.0%
1503 1
 
1.0%
2108 1
 
1.0%
3000 1
 
1.0%
3064 1
 
1.0%
2501 1
 
1.0%
1302 1
 
1.0%
3744 1
 
1.0%
2068 1
 
1.0%
2397 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
126 1
1.0%
303 1
1.0%
505 1
1.0%
572 1
1.0%
708 1
1.0%
728 1
1.0%
768 1
1.0%
812 1
1.0%
890 1
1.0%
962 1
1.0%
ValueCountFrequency (%)
4910 1
1.0%
4606 1
1.0%
4475 1
1.0%
4422 1
1.0%
4347 1
1.0%
4133 1
1.0%
4079 1
1.0%
3837 1
1.0%
3744 1
1.0%
3439 1
1.0%

15세미만인구비
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.0078
Minimum3.88
Maximum18.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:37:16.272371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.88
5-th percentile5.7175
Q18.05
median9.955
Q311.92
95-th percentile14.441
Maximum18.74
Range14.86
Interquartile range (IQR)3.87

Descriptive statistics

Standard deviation2.8621033
Coefficient of variation (CV)0.28598726
Kurtosis0.41105998
Mean10.0078
Median Absolute Deviation (MAD)1.975
Skewness0.39523717
Sum1000.78
Variance8.1916355
MonotonicityNot monotonic
2023-12-10T21:37:16.514428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.47 2
 
2.0%
12.16 2
 
2.0%
11.91 2
 
2.0%
8.37 2
 
2.0%
7.6 2
 
2.0%
7.14 2
 
2.0%
11.32 1
 
1.0%
13.87 1
 
1.0%
8.87 1
 
1.0%
10.51 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
3.88 1
1.0%
4.34 1
1.0%
5.06 1
1.0%
5.44 1
1.0%
5.48 1
1.0%
5.73 1
1.0%
5.88 1
1.0%
5.98 1
1.0%
6.01 1
1.0%
6.07 1
1.0%
ValueCountFrequency (%)
18.74 1
1.0%
18.36 1
1.0%
15.6 1
1.0%
15.52 1
1.0%
15.22 1
1.0%
14.4 1
1.0%
14.39 1
1.0%
13.87 1
1.0%
13.72 1
1.0%
13.54 1
1.0%

65세이상인구비
Real number (ℝ)

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.4468
Minimum6.5
Maximum20.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:37:16.752830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile10.0185
Q112.2125
median13.345
Q314.57
95-th percentile16.9055
Maximum20.37
Range13.87
Interquartile range (IQR)2.3575

Descriptive statistics

Standard deviation2.427573
Coefficient of variation (CV)0.18053165
Kurtosis0.92304467
Mean13.4468
Median Absolute Deviation (MAD)1.19
Skewness0.16251738
Sum1344.68
Variance5.8931109
MonotonicityNot monotonic
2023-12-10T21:37:17.235621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.33 2
 
2.0%
13.7 2
 
2.0%
15.91 2
 
2.0%
11.04 2
 
2.0%
12.66 2
 
2.0%
11.38 1
 
1.0%
12.23 1
 
1.0%
11.95 1
 
1.0%
12.26 1
 
1.0%
10.06 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
6.5 1
1.0%
7.49 1
1.0%
8.23 1
1.0%
9.16 1
1.0%
9.23 1
1.0%
10.06 1
1.0%
10.18 1
1.0%
10.51 1
1.0%
10.53 1
1.0%
10.54 1
1.0%
ValueCountFrequency (%)
20.37 1
1.0%
19.83 1
1.0%
19.64 1
1.0%
17.86 1
1.0%
17.58 1
1.0%
16.87 1
1.0%
16.68 1
1.0%
16.62 1
1.0%
16.61 1
1.0%
16.59 1
1.0%

미세먼지량
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.0265
Minimum18.85
Maximum546.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:37:17.522803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.85
5-th percentile19.4
Q120.56
median21.37
Q356.0575
95-th percentile197.594
Maximum546.68
Range527.83
Interquartile range (IQR)35.4975

Descriptive statistics

Standard deviation81.425664
Coefficient of variation (CV)1.4278566
Kurtosis16.753863
Mean57.0265
Median Absolute Deviation (MAD)1.97
Skewness3.673385
Sum5702.65
Variance6630.1388
MonotonicityNot monotonic
2023-12-10T21:37:17.767378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20.56 22
22.0%
21.37 12
12.0%
19.4 12
12.0%
19.67 9
 
9.0%
25.61 7
 
7.0%
21.52 4
 
4.0%
102.42 4
 
4.0%
226.21 2
 
2.0%
188.51 2
 
2.0%
128.03 2
 
2.0%
Other values (20) 24
24.0%
ValueCountFrequency (%)
18.85 1
 
1.0%
19.4 12
12.0%
19.67 9
9.0%
20.56 22
22.0%
21.37 12
12.0%
21.52 4
 
4.0%
25.61 7
 
7.0%
36.46 1
 
1.0%
39.33 1
 
1.0%
39.96 1
 
1.0%
ValueCountFrequency (%)
546.68 1
1.0%
433.58 1
1.0%
226.21 2
2.0%
207.36 1
1.0%
197.08 1
1.0%
191.94 1
1.0%
188.51 2
2.0%
131.96 1
1.0%
128.03 2
2.0%
113.11 2
2.0%

수소이온농도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
7.1
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7.1
2nd row7.1
3rd row7.1
4th row7.1
5th row7.1

Common Values

ValueCountFrequency (%)
7.1 100
100.0%

Length

2023-12-10T21:37:18.000820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:37:18.128149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7.1 100
100.0%

잔류염소
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.5
48 
0.44
34 
0.48
9 
0.52
9 

Length

Max length4
Median length4
Mean length3.52
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.48
4th row0.48
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 48
48.0%
0.44 34
34.0%
0.48 9
 
9.0%
0.52 9
 
9.0%

Length

2023-12-10T21:37:18.324441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:37:18.571607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.5 48
48.0%
0.44 34
34.0%
0.48 9
 
9.0%
0.52 9
 
9.0%

탁도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.06
66 
0.05
34 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.06
2nd row0.06
3rd row0.06
4th row0.06
5th row0.06

Common Values

ValueCountFrequency (%)
0.06 66
66.0%
0.05 34
34.0%

Length

2023-12-10T21:37:18.773984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:37:18.926373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.06 66
66.0%
0.05 34
34.0%

Interactions

2023-12-10T21:37:11.213172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:04.734032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:05.626120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:06.598315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:07.630016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:08.641009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:10.029167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:11.391035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:04.881315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:05.746862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:06.744556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:07.757710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:08.825262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:10.182773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:11.530088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:05.041571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:05.865801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:06.913615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:07.893791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:09.035075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:10.349367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:11.666091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:05.180695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:06.086032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:07.096221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:08.033170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:09.195367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:10.498695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:11.855219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:05.285656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:06.200079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:07.229017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:08.190722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:09.408312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:10.717157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:12.106579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:05.405456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:06.333052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:07.388163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:08.323245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:09.622914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:10.899364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:12.300831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:05.520547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:06.477790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:07.515786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:08.467383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:09.756974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:37:11.050209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:37:19.048908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관코드행정기관코드명총인구15세미만인구65세이상인구15세미만인구비65세이상인구비미세먼지량잔류염소탁도
행정기관코드1.0001.0000.5130.6240.5640.0000.4230.3760.9380.947
행정기관코드명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
총인구0.5131.0001.0000.7600.9240.4160.4810.4100.5670.634
15세미만인구0.6241.0000.7601.0000.7090.6550.4030.1420.6590.449
65세이상인구0.5641.0000.9240.7091.0000.4260.5850.4440.5610.488
15세미만인구비0.0001.0000.4160.6550.4261.0000.3640.0000.1740.098
65세이상인구비0.4231.0000.4810.4030.5850.3641.0000.7400.1560.384
미세먼지량0.3761.0000.4100.1420.4440.0000.7401.0000.4180.439
잔류염소0.9381.0000.5670.6590.5610.1740.1560.4181.0001.000
탁도0.9471.0000.6340.4490.4880.0980.3840.4391.0001.000
2023-12-10T21:37:19.240068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
잔류염소탁도
잔류염소1.0000.990
탁도0.9901.000
2023-12-10T21:37:19.395119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관코드총인구15세미만인구65세이상인구15세미만인구비65세이상인구비미세먼지량잔류염소탁도
행정기관코드1.0000.7670.6880.7100.226-0.410-0.4500.6620.776
총인구0.7671.0000.9130.9460.356-0.443-0.4700.3670.475
15세미만인구0.6880.9131.0000.9080.669-0.353-0.4370.3360.326
65세이상인구0.7100.9460.9081.0000.391-0.188-0.3670.3580.359
15세미만인구비0.2260.3560.6690.3911.000-0.129-0.2480.1050.090
65세이상인구비-0.410-0.443-0.353-0.188-0.1291.0000.4450.0860.281
미세먼지량-0.450-0.470-0.437-0.367-0.2480.4451.0000.2770.309
잔류염소0.6620.3670.3360.3580.1050.0860.2771.0000.990
탁도0.7760.4750.3260.3590.0900.2810.3090.9901.000

Missing values

2023-12-10T21:37:12.811159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:37:13.068796image/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

행정기관코드행정기관코드명총인구15세미만인구65세이상인구15세미만인구비65세이상인구비미세먼지량수소이온농도잔류염소탁도
01101053사직동93791062150311.3216.03226.217.10.50.06
11101054삼청동28082665729.4720.37131.967.10.50.06
21101055부암동11012109615739.9514.2855.327.10.480.06
31101056평창동185942038303210.9616.3136.467.10.480.06
41101057무악동81061234115215.2214.2118.857.10.50.06
51101058교남동462047676810.316.62113.117.10.50.06
61101060가회동49174468129.0716.5175.47.10.50.06
71101061종로1·2·3·4가동759841613575.4817.86546.687.10.50.06
81101063종로5·6가동57922939625.0616.61101.117.10.50.06
91101064이화동951969910977.3411.5259.987.10.50.06
행정기관코드행정기관코드명총인구15세미만인구65세이상인구15세미만인구비65세이상인구비미세먼지량수소이온농도잔류염소탁도
901106088장안2동311204481383714.412.3321.377.10.480.06
911106089이문1동33531254740797.612.1621.527.10.480.06
921106090이문2동223762881294612.8813.1721.527.10.480.06
931106091답십리1동253783133313412.3512.3520.567.10.480.06
941107052면목2동264542828334810.6912.6621.377.10.440.05
951107054면목4동213822255296110.5513.8521.377.10.440.05
961107055면목5동113751148157110.0913.8121.377.10.440.05
971107057면목7동23191226632549.7714.0321.377.10.440.05
981107059상봉1동254443344283213.1411.1321.527.10.480.06
991107060상봉2동17924132624387.413.621.527.10.440.05