Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells32
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory87.3 B

Variable types

Categorical2
Numeric5
Text3

Alerts

stats_year has constant value ""Constant
sn is highly overall correlated with area_cd and 3 other fieldsHigh correlation
area_cd is highly overall correlated with sn and 2 other fieldsHigh correlation
cnslt_tel_no is highly overall correlated with sn and 1 other fieldsHigh correlation
cnter_la is highly overall correlated with sn and 1 other fieldsHigh correlation
area_cd_nm is highly overall correlated with sn and 2 other fieldsHigh correlation
reprsnt_tel_no has 3 (3.0%) missing valuesMissing
cnslt_tel_no has 29 (29.0%) missing valuesMissing
sn has unique valuesUnique
cnter_nm has unique valuesUnique
cnter_addr has unique valuesUnique
cnter_la has unique valuesUnique
cnter_lo has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:00:13.581306
Analysis finished2023-12-10 10:00:20.650200
Duration7.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

stats_year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 100
100.0%

Length

2023-12-10T19:00:20.808699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:00:20.976460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 100
100.0%

sn
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.47
Minimum8
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:21.267658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile13.95
Q133.75
median58.5
Q383.25
95-th percentile123.2
Maximum200
Range192
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation35.526759
Coefficient of variation (CV)0.57795281
Kurtosis1.4302964
Mean61.47
Median Absolute Deviation (MAD)25
Skewness0.90358712
Sum6147
Variance1262.1506
MonotonicityNot monotonic
2023-12-10T19:00:21.687751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1
 
1.0%
72 1
 
1.0%
93 1
 
1.0%
91 1
 
1.0%
87 1
 
1.0%
85 1
 
1.0%
84 1
 
1.0%
81 1
 
1.0%
80 1
 
1.0%
75 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
8 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
13 1
1.0%
14 1
1.0%
15 1
1.0%
16 1
1.0%
17 1
1.0%
18 1
1.0%
ValueCountFrequency (%)
200 1
1.0%
146 1
1.0%
145 1
1.0%
144 1
1.0%
127 1
1.0%
123 1
1.0%
120 1
1.0%
111 1
1.0%
107 1
1.0%
103 1
1.0%

area_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.56
Minimum11
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:21.956629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q123
median31
Q332
95-th percentile35
Maximum37
Range26
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.3718142
Coefficient of variation (CV)0.30376684
Kurtosis0.027726763
Mean27.56
Median Absolute Deviation (MAD)1.5
Skewness-1.2626029
Sum2756
Variance70.087273
MonotonicityNot monotonic
2023-12-10T19:00:22.182082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
31 32
32.0%
11 18
18.0%
32 18
18.0%
34 12
 
12.0%
23 9
 
9.0%
35 4
 
4.0%
33 3
 
3.0%
37 3
 
3.0%
25 1
 
1.0%
ValueCountFrequency (%)
11 18
18.0%
23 9
 
9.0%
25 1
 
1.0%
31 32
32.0%
32 18
18.0%
33 3
 
3.0%
34 12
 
12.0%
35 4
 
4.0%
37 3
 
3.0%
ValueCountFrequency (%)
37 3
 
3.0%
35 4
 
4.0%
34 12
 
12.0%
33 3
 
3.0%
32 18
18.0%
31 32
32.0%
25 1
 
1.0%
23 9
 
9.0%
11 18
18.0%

area_cd_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
32 
서울특별시
18 
강원도
18 
충청남도
12 
인천광역시
9 
Other values (5)
11 

Length

Max length5
Median length4.5
Mean length3.78
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 32
32.0%
서울특별시 18
18.0%
강원도 18
18.0%
충청남도 12
 
12.0%
인천광역시 9
 
9.0%
충청북도 3
 
3.0%
경상북도 3
 
3.0%
전라북도 3
 
3.0%
전라남도 1
 
1.0%
대전광역시 1
 
1.0%

Length

2023-12-10T19:00:22.462476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:00:22.759379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 32
32.0%
서울특별시 18
18.0%
강원도 18
18.0%
충청남도 12
 
12.0%
인천광역시 9
 
9.0%
충청북도 3
 
3.0%
경상북도 3
 
3.0%
전라북도 3
 
3.0%
전라남도 1
 
1.0%
대전광역시 1
 
1.0%

cnter_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:00:23.438152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length18.88
Min length12

Characters and Unicode

Total characters1888
Distinct characters100
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 row금천구 건강가정 · 다문화가족지원센터
2nd row은평구 건강가정 · 다문화가족지원센터
3rd row서울시 건강가정지원센터
4th row강화군 건강가정 · 다문화가족지원센터
5th row계양구 건강가정 · 다문화가족지원센터
ValueCountFrequency (%)
· 84
22.8%
다문화가족지원센터 84
22.8%
건강가정 84
22.8%
건강가정지원센터 15
 
4.1%
금천구 1
 
0.3%
은평구 1
 
0.3%
금산군 1
 
0.3%
횡성군 1
 
0.3%
화천군 1
 
0.3%
태백시 1
 
0.3%
Other values (95) 95
25.8%
2023-12-10T19:00:24.219378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
272
14.4%
가 185
 
9.8%
정 103
 
5.5%
원 103
 
5.5%
강 102
 
5.4%
터 101
 
5.3%
센 101
 
5.3%
건 100
 
5.3%
지 99
 
5.2%
화 88
 
4.7%
Other values (90) 634
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1532
81.1%
Space Separator 272
 
14.4%
Other Punctuation 84
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 185
 
12.1%
정 103
 
6.7%
원 103
 
6.7%
강 102
 
6.7%
터 101
 
6.6%
센 101
 
6.6%
건 100
 
6.5%
지 99
 
6.5%
화 88
 
5.7%
문 86
 
5.6%
Other values (88) 464
30.3%
Space Separator
ValueCountFrequency (%)
272
100.0%
Other Punctuation
ValueCountFrequency (%)
· 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1532
81.1%
Common 356
 
18.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 185
 
12.1%
정 103
 
6.7%
원 103
 
6.7%
강 102
 
6.7%
터 101
 
6.6%
센 101
 
6.6%
건 100
 
6.5%
지 99
 
6.5%
화 88
 
5.7%
문 86
 
5.6%
Other values (88) 464
30.3%
Common
ValueCountFrequency (%)
272
76.4%
· 84
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1532
81.1%
ASCII 272
 
14.4%
None 84
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
272
100.0%
Hangul
ValueCountFrequency (%)
가 185
 
12.1%
정 103
 
6.7%
원 103
 
6.7%
강 102
 
6.7%
터 101
 
6.6%
센 101
 
6.6%
건 100
 
6.5%
지 99
 
6.5%
화 88
 
5.7%
문 86
 
5.6%
Other values (88) 464
30.3%
None
ValueCountFrequency (%)
· 84
100.0%

cnter_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:00:24.932713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length36
Mean length27.01
Min length13

Characters and Unicode

Total characters2701
Distinct characters243
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
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 row서울특별시 금천구 금하로11길 40 1층,3층 (시흥동)
2nd row서울 은평구 통일로 1050 은평구가족통합지원센터 2센터
3rd row서울특별시 중구 소파로4길 6
4th row인천광역시 강화군 북문길67번길 11-1, 2층(관청리 523번지)
5th row인천광역시 계양구 계양산로102번길 5, 3층
ValueCountFrequency (%)
경기도 29
 
4.8%
2층 19
 
3.2%
강원도 15
 
2.5%
3층 14
 
2.3%
서울특별시 14
 
2.3%
충청남도 10
 
1.7%
1층 10
 
1.7%
4층 10
 
1.7%
인천광역시 8
 
1.3%
서울 4
 
0.7%
Other values (427) 466
77.8%
2023-12-10T19:00:25.921712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
499
 
18.5%
시 84
 
3.1%
1 83
 
3.1%
로 81
 
3.0%
2 79
 
2.9%
층 69
 
2.6%
도 68
 
2.5%
3 59
 
2.2%
구 50
 
1.9%
길 43
 
1.6%
Other values (233) 1586
58.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1688
62.5%
Space Separator 499
 
18.5%
Decimal Number 416
 
15.4%
Other Punctuation 31
 
1.1%
Dash Punctuation 22
 
0.8%
Open Punctuation 21
 
0.8%
Close Punctuation 21
 
0.8%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 84
 
5.0%
로 81
 
4.8%
층 69
 
4.1%
도 68
 
4.0%
구 50
 
3.0%
길 43
 
2.5%
동 43
 
2.5%
경 39
 
2.3%
천 38
 
2.3%
기 34
 
2.0%
Other values (214) 1139
67.5%
Decimal Number
ValueCountFrequency (%)
1 83
20.0%
2 79
19.0%
3 59
14.2%
4 42
10.1%
5 38
9.1%
0 31
 
7.5%
6 25
 
6.0%
9 25
 
6.0%
7 19
 
4.6%
8 15
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
H 1
33.3%
L 1
33.3%
B 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 30
96.8%
? 1
 
3.2%
Space Separator
ValueCountFrequency (%)
499
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1688
62.5%
Common 1010
37.4%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 84
 
5.0%
로 81
 
4.8%
층 69
 
4.1%
도 68
 
4.0%
구 50
 
3.0%
길 43
 
2.5%
동 43
 
2.5%
경 39
 
2.3%
천 38
 
2.3%
기 34
 
2.0%
Other values (214) 1139
67.5%
Common
ValueCountFrequency (%)
499
49.4%
1 83
 
8.2%
2 79
 
7.8%
3 59
 
5.8%
4 42
 
4.2%
5 38
 
3.8%
0 31
 
3.1%
, 30
 
3.0%
6 25
 
2.5%
9 25
 
2.5%
Other values (6) 99
 
9.8%
Latin
ValueCountFrequency (%)
H 1
33.3%
L 1
33.3%
B 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1688
62.5%
ASCII 1013
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
499
49.3%
1 83
 
8.2%
2 79
 
7.8%
3 59
 
5.8%
4 42
 
4.1%
5 38
 
3.8%
0 31
 
3.1%
, 30
 
3.0%
6 25
 
2.5%
9 25
 
2.5%
Other values (9) 102
 
10.1%
Hangul
ValueCountFrequency (%)
시 84
 
5.0%
로 81
 
4.8%
층 69
 
4.1%
도 68
 
4.0%
구 50
 
3.0%
길 43
 
2.5%
동 43
 
2.5%
경 39
 
2.3%
천 38
 
2.3%
기 34
 
2.0%
Other values (214) 1139
67.5%

reprsnt_tel_no
Text

MISSING 

Distinct97
Distinct (%)100.0%
Missing3
Missing (%)3.0%
Memory size932.0 B
2023-12-10T19:00:26.646793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.9072165
Min length9

Characters and Unicode

Total characters961
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)100.0%

Sample

1st row028037747
2nd row023763761
3rd row023180227
4th row0329321005
5th row0325471017
ValueCountFrequency (%)
0325080121 1
 
1.0%
0313789761 1
 
1.0%
0335358377 1
 
1.0%
0417503990 1
 
1.0%
0333443458 1
 
1.0%
0335544003 1
 
1.0%
0332518014 1
 
1.0%
0333758400 1
 
1.0%
0336730020 1
 
1.0%
0334802727 1
 
1.0%
Other values (87) 87
89.7%
2023-12-10T19:00:27.450385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 176
18.3%
3 169
17.6%
1 104
10.8%
2 99
10.3%
5 82
8.5%
7 76
7.9%
4 68
 
7.1%
8 63
 
6.6%
6 62
 
6.5%
9 61
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 960
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
18.3%
3 169
17.6%
1 104
10.8%
2 99
10.3%
5 82
8.5%
7 76
7.9%
4 68
 
7.1%
8 63
 
6.6%
6 62
 
6.5%
9 61
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
18.3%
3 169
17.6%
1 104
10.8%
2 99
10.3%
5 82
8.5%
7 76
7.9%
4 68
 
7.1%
8 63
 
6.6%
6 62
 
6.5%
9 61
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
18.3%
3 169
17.6%
1 104
10.8%
2 99
10.3%
5 82
8.5%
7 76
7.9%
4 68
 
7.1%
8 63
 
6.6%
6 62
 
6.5%
9 61
 
6.3%

cnslt_tel_no
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)100.0%
Missing29
Missing (%)29.0%
Infinite0
Infinite (%)0.0%
Mean1.030304 × 109
Minimum23227594
Maximum7.0779101 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:27.745920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23227594
5-th percentile25878106
Q13.1350845 × 108
median3.1996592 × 108
Q33.3677468 × 108
95-th percentile7.074302 × 109
Maximum7.0779101 × 109
Range7.0546825 × 109
Interquartile range (IQR)23266226

Descriptive statistics

Standard deviation2.0725124 × 109
Coefficient of variation (CV)2.0115542
Kurtosis4.7845287
Mean1.030304 × 109
Median Absolute Deviation (MAD)16357024
Skewness2.5328813
Sum7.3151585 × 1010
Variance4.2953075 × 1018
MonotonicityNot monotonic
2023-12-10T19:00:28.012261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29570266 1
 
1.0%
336819332 1
 
1.0%
336483098 1
 
1.0%
3180088049 1
 
1.0%
312678788 1
 
1.0%
317902966 1
 
1.0%
7074552070 1
 
1.0%
316153957 1
 
1.0%
319499162 1
 
1.0%
316375516 1
 
1.0%
Other values (61) 61
61.0%
(Missing) 29
29.0%
ValueCountFrequency (%)
23227594 1
1.0%
24310085 1
1.0%
24354143 1
1.0%
25762852 1
1.0%
25993360 1
1.0%
27441090 1
1.0%
27949184 1
1.0%
28465432 1
1.0%
29195141 1
1.0%
29570266 1
1.0%
ValueCountFrequency (%)
7077910070 1
1.0%
7077762982 1
1.0%
7074674040 1
1.0%
7074552070 1
1.0%
7074051983 1
1.0%
7071190426 1
1.0%
7044575469 1
1.0%
3180455475 1
1.0%
3180088049 1
1.0%
616924173 1
1.0%

cnter_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.279798
Minimum34.759605
Maximum38.38012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:28.289396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.759605
5-th percentile36.074544
Q137.156974
median37.470631
Q337.586266
95-th percentile38.071617
Maximum38.38012
Range3.620515
Interquartile range (IQR)0.42929125

Descriptive statistics

Standard deviation0.63090168
Coefficient of variation (CV)0.01692342
Kurtosis2.6322986
Mean37.279798
Median Absolute Deviation (MAD)0.1844145
Skewness-1.4764625
Sum3727.9798
Variance0.39803693
MonotonicityNot monotonic
2023-12-10T19:00:28.591823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.455468 1
 
1.0%
38.191337 1
 
1.0%
36.783235 1
 
1.0%
36.78405 1
 
1.0%
36.109456 1
 
1.0%
37.487043 1
 
1.0%
38.113434 1
 
1.0%
37.15957 1
 
1.0%
37.867417 1
 
1.0%
37.184632 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
34.759605 1
1.0%
35.438476 1
1.0%
35.567554 1
1.0%
35.805959 1
1.0%
36.047836 1
1.0%
36.07595 1
1.0%
36.109456 1
1.0%
36.17635 1
1.0%
36.202627 1
1.0%
36.275675 1
1.0%
ValueCountFrequency (%)
38.38012 1
1.0%
38.191337 1
1.0%
38.145376 1
1.0%
38.113434 1
1.0%
38.103358 1
1.0%
38.069946 1
1.0%
38.061226 1
1.0%
38.030209 1
1.0%
37.903999 1
1.0%
37.870211 1
1.0%

cnter_lo
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.32
Minimum126.30928
Maximum130.90881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:28.874815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.30928
5-th percentile126.62825
Q1126.88992
median127.0388
Q3127.50579
95-th percentile128.90436
Maximum130.90881
Range4.599532
Interquartile range (IQR)0.61586725

Descriptive statistics

Standard deviation0.75943914
Coefficient of variation (CV)0.0059648064
Kurtosis4.7497913
Mean127.32
Median Absolute Deviation (MAD)0.2431435
Skewness1.9473516
Sum12732
Variance0.57674781
MonotonicityNot monotonic
2023-12-10T19:00:29.258103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.904102 1
 
1.0%
128.553436 1
 
1.0%
127.004463 1
 
1.0%
126.46157 1
 
1.0%
127.487656 1
 
1.0%
127.98461 1
 
1.0%
127.701338 1
 
1.0%
128.984057 1
 
1.0%
127.731719 1
 
1.0%
128.468512 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.309279 1
1.0%
126.46157 1
1.0%
126.483656 1
1.0%
126.608451 1
1.0%
126.622129 1
1.0%
126.628567 1
1.0%
126.642211 1
1.0%
126.64343 1
1.0%
126.650396 1
1.0%
126.676661 1
1.0%
ValueCountFrequency (%)
130.908811 1
1.0%
129.366205 1
1.0%
129.152817 1
1.0%
129.11384 1
1.0%
128.984057 1
1.0%
128.900161 1
1.0%
128.742055 1
1.0%
128.664443 1
1.0%
128.625289 1
1.0%
128.553436 1
1.0%

Interactions

2023-12-10T19:00:18.872399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:14.509620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:15.355086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:16.238800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:17.824748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:19.140220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:14.693228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:15.521111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:16.431686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:18.033468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:19.325411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:14.835622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:15.696249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:16.989054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:18.220764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:19.512420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:15.011661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:15.906212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:17.325145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:18.440285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:19.691343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:15.185306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:16.087463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:17.602068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:18.667302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:00:29.444315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
snarea_cdarea_cd_nmcnter_nmcnter_addrreprsnt_tel_nocnslt_tel_nocnter_lacnter_lo
sn1.0000.9330.9551.0001.0001.0000.0000.8650.658
area_cd0.9331.0001.0001.0001.0001.0000.0000.7060.676
area_cd_nm0.9551.0001.0001.0001.0001.0000.0000.9400.767
cnter_nm1.0001.0001.0001.0001.0001.0001.0001.0001.000
cnter_addr1.0001.0001.0001.0001.0001.0001.0001.0001.000
reprsnt_tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.000
cnslt_tel_no0.0000.0000.0001.0001.0001.0001.0000.0000.000
cnter_la0.8650.7060.9401.0001.0001.0000.0001.0000.266
cnter_lo0.6580.6760.7671.0001.0001.0000.0000.2661.000
2023-12-10T19:00:30.079819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
snarea_cdcnslt_tel_nocnter_lacnter_loarea_cd_nm
sn1.0000.9460.588-0.5190.3700.836
area_cd0.9461.0000.643-0.4740.3480.978
cnslt_tel_no0.5880.6431.000-0.1330.0650.000
cnter_la-0.519-0.474-0.1331.0000.1210.592
cnter_lo0.3700.3480.0650.1211.0000.500
area_cd_nm0.8360.9780.0000.5920.5001.000

Missing values

2023-12-10T19:00:19.896712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:00:20.359319image/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.
2023-12-10T19:00:20.558181image/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

stats_yearsnarea_cdarea_cd_nmcnter_nmcnter_addrreprsnt_tel_nocnslt_tel_nocnter_lacnter_lo
02020811서울특별시금천구 건강가정 · 다문화가족지원센터서울특별시 금천구 금하로11길 40 1층,3층 (시흥동)028037747<NA>37.455468126.904102
120202111서울특별시은평구 건강가정 · 다문화가족지원센터서울 은평구 통일로 1050 은평구가족통합지원센터 2센터023763761<NA>37.637687126.918503
220202611서울특별시서울시 건강가정지원센터서울특별시 중구 소파로4길 6023180227<NA>37.559446126.988315
320202723인천광역시강화군 건강가정 · 다문화가족지원센터인천광역시 강화군 북문길67번길 11-1, 2층(관청리 523번지)0329321005<NA>37.748954126.483656
420202823인천광역시계양구 건강가정 · 다문화가족지원센터인천광역시 계양구 계양산로102번길 5, 3층0325471017<NA>37.547312126.727149
520202923인천광역시남동구 건강가정 · 다문화가족지원센터인천광역시 남동구 호구포로 203-31 남동구건강가정다문화가족지원센터 1층 (남동 하모니센터)0324673904<NA>37.401872126.707012
620203631경기도가평군 건강가정 · 다문화가족지원센터경기도 가평군 가평읍 석봉로191번길 10 2층0315829902<NA>37.832017127.508729
720201011서울특별시도봉구 건강가정 · 다문화가족지원센터서울특별시 도봉구 도봉로 552 (창5동 303 도봉구민회관 2층)0299568002995680037.654222127.038653
820201111서울특별시동대문구 건강가정 · 다문화가족지원센터서울 동대문구 청계천로 521 다사랑행복센터 6,7층0295707602957026637.571787127.034044
920201211서울특별시동작구 건강가정 · 다문화가족지원센터서울특별시 동작구 동작대로29길 63-26 2,3층0259933012599336037.488324126.977549
stats_yearsnarea_cdarea_cd_nmcnter_nmcnter_addrreprsnt_tel_nocnslt_tel_nocnter_lacnter_lo
9020208934충청남도당진시 건강가정지원센터충청남도 당진시 시청1로 38, 당진시종합복지타운 4층041360320041360320536.891708126.64343
9120209034충청남도보령시 건강가정 · 다문화가족지원센터충청남도 보령시 한내로 45 문화빌딩 2층041936850641934003336.346273126.608451
9220209234충청남도서천군 건강가정 · 다문화가족지원센터충청남도 서천군 서천로 38, 2층041953380841952191036.07595126.69548
9320209434충청남도예산군 건강가정 · 다문화가족지원센터충청남도 예산군 산성공원2길 15, 2층041339838141339838336.69226126.833955
94202012037경상북도울릉군 건강가정지원센터경상북도 울릉군 울릉읍 봉래2길 310547910205<NA>37.493632130.908811
95202012337경상북도포항시 건강가정 · 다문화가족지원센터경상북도 포항시 북구 선착로 18-100542449702<NA>36.047836129.366205
96202012737경상북도봉화군 건강가정지원센터경상북도 봉화군 봉화읍 거촌로 12-30546739023<NA>36.890931128.742055
97202014435전라북도정읍시 건강가정 · 다문화가족지원센터전라북도 정읍시 수성동 중앙2길 220635310309<NA>35.567554126.852993
98202014535전라북도고창군 건강가정지원센터전라북도 고창군 고창읍 성산로 57 1층0635611366<NA>35.438476126.699013
99202014635전라북도김제시 건강가정 · 다문화가족지원센터전라북도 김제시 요촌길 45 지평선어울림센터 3층0635458506<NA>35.805959126.896172