Overview

Dataset statistics

Number of variables9
Number of observations27
Missing cells5
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory81.9 B

Variable types

Categorical3
Text2
Numeric4

Dataset

Description2021-10-28
Author빅데이터통합플랫폼
URLhttps://bigdata.gwangju.go.kr/usr/dataSet/getDataDetailView.rd?dataSetUncd=DS000087107

Alerts

구 has constant value ""Constant
동 is highly overall correlated with 데이터기준년도High correlation
데이터기준년도 is highly overall correlated with 이용회원수 and 4 other fieldsHigh correlation
이용회원수 is highly overall correlated with 데이터기준년도High correlation
시설설치일 is highly overall correlated with 데이터기준년도High correlation
위도 is highly overall correlated with 데이터기준년도High correlation
경도 is highly overall correlated with 데이터기준년도High correlation
데이터기준년도 is highly imbalanced (77.1%)Imbalance
시설설치일 has 1 (3.7%) missing valuesMissing
위도 has 2 (7.4%) missing valuesMissing
경도 has 2 (7.4%) missing valuesMissing
시설명 has unique valuesUnique
시설소재지 has unique valuesUnique

Reproduction

Analysis started2024-01-07 18:41:17.988143
Analysis finished2024-01-07 18:41:19.855722
Duration1.87 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
동구
27 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동구 27
100.0%

Length

2024-01-08T03:41:19.903126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-08T03:41:19.971745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 27
100.0%

동
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
계림동
13 
동명동
3 
산수동
3 
대인동
2 
충장로5가
 
1
Other values (5)
5 

Length

Max length5
Median length3
Mean length3.037037
Min length2

Unique

Unique6 ?
Unique (%)22.2%

Sample

1st row대인동
2nd row충장로5가
3rd row궁동
4th row불로동
5th row호남동

Common Values

ValueCountFrequency (%)
계림동 13
48.1%
동명동 3
 
11.1%
산수동 3
 
11.1%
대인동 2
 
7.4%
충장로5가 1
 
3.7%
궁동 1
 
3.7%
불로동 1
 
3.7%
호남동 1
 
3.7%
장동 1
 
3.7%
<NA> 1
 
3.7%

Length

2024-01-08T03:41:20.059027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-08T03:41:20.151254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계림동 13
48.1%
동명동 3
 
11.1%
산수동 3
 
11.1%
대인동 2
 
7.4%
충장로5가 1
 
3.7%
궁동 1
 
3.7%
불로동 1
 
3.7%
호남동 1
 
3.7%
장동 1
 
3.7%
na 1
 
3.7%

시설명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-01-08T03:41:20.309948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length3.1481481
Min length2

Characters and Unicode

Total characters85
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row상이군경회
2nd row충수
3rd row충의
4th row수영
5th row삼성
ValueCountFrequency (%)
상이군경회 1
 
3.7%
수성 1
 
3.7%
산수 1
 
3.7%
정암 1
 
3.7%
푸른길두산위브 1
 
3.7%
구구팔팔대명 1
 
3.7%
참판 1
 
3.7%
풍성 1
 
3.7%
두산위브 1
 
3.7%
금호주상복합 1
 
3.7%
Other values (17) 17
63.0%
2024-01-08T03:41:20.775635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 7
 
8.2%
녀 4
 
4.7%
부 4
 
4.7%
명 3
 
3.5%
산 3
 
3.5%
성 3
 
3.5%
동 2
 
2.4%
금 2
 
2.4%
이 2
 
2.4%
림 2
 
2.4%
Other values (44) 53
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 7
 
8.2%
녀 4
 
4.7%
부 4
 
4.7%
명 3
 
3.5%
산 3
 
3.5%
성 3
 
3.5%
동 2
 
2.4%
금 2
 
2.4%
이 2
 
2.4%
림 2
 
2.4%
Other values (44) 53
62.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 7
 
8.2%
녀 4
 
4.7%
부 4
 
4.7%
명 3
 
3.5%
산 3
 
3.5%
성 3
 
3.5%
동 2
 
2.4%
금 2
 
2.4%
이 2
 
2.4%
림 2
 
2.4%
Other values (44) 53
62.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 7
 
8.2%
녀 4
 
4.7%
부 4
 
4.7%
명 3
 
3.5%
산 3
 
3.5%
성 3
 
3.5%
동 2
 
2.4%
금 2
 
2.4%
이 2
 
2.4%
림 2
 
2.4%
Other values (44) 53
62.4%

이용회원수
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.333333
Minimum21
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-08T03:41:20.871785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median30
Q336.5
95-th percentile47.5
Maximum54
Range33
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation8.8230467
Coefficient of variation (CV)0.2815866
Kurtosis0.34836777
Mean31.333333
Median Absolute Deviation (MAD)7
Skewness0.88140078
Sum846
Variance77.846154
MonotonicityNot monotonic
2024-01-08T03:41:20.961934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
30 4
14.8%
28 3
11.1%
21 3
11.1%
40 2
 
7.4%
23 2
 
7.4%
22 2
 
7.4%
25 2
 
7.4%
36 1
 
3.7%
34 1
 
3.7%
35 1
 
3.7%
Other values (6) 6
22.2%
ValueCountFrequency (%)
21 3
11.1%
22 2
7.4%
23 2
7.4%
25 2
7.4%
28 3
11.1%
30 4
14.8%
31 1
 
3.7%
34 1
 
3.7%
35 1
 
3.7%
36 1
 
3.7%
ValueCountFrequency (%)
54 1
3.7%
49 1
3.7%
44 1
3.7%
40 2
7.4%
39 1
3.7%
37 1
3.7%
36 1
3.7%
35 1
3.7%
34 1
3.7%
31 1
3.7%

시설소재지
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-01-08T03:41:21.139148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length26
Mean length22.518519
Min length13

Characters and Unicode

Total characters608
Distinct characters72
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

Unique27 ?
Unique (%)100.0%

Sample

1st row동구 구성로204번길 6-1(대인동69-1)
2nd row동구 충장로 14-3(충장로5가93-2)
3rd row동구 예술길 19-8 (궁동35-11)
4th row동구 서석로7번길 12-14(불로동11-2)
5th row동구 중앙로148번길 11-23(호남동39-10)
ValueCountFrequency (%)
동구 27
27.0%
무등로 3
 
3.0%
백서로224번길 2
 
2.0%
산수1동 2
 
2.0%
중앙로 2
 
2.0%
계림1동 2
 
2.0%
8 2
 
2.0%
경양로 2
 
2.0%
서석로7번길 1
 
1.0%
2층(계림동1624 1
 
1.0%
Other values (56) 56
56.0%
2024-01-08T03:41:21.431423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78
 
12.8%
동 52
 
8.6%
1 46
 
7.6%
구 28
 
4.6%
- 27
 
4.4%
로 26
 
4.3%
2 25
 
4.1%
( 23
 
3.8%
) 22
 
3.6%
4 21
 
3.5%
Other values (62) 260
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 265
43.6%
Decimal Number 180
29.6%
Space Separator 78
 
12.8%
Dash Punctuation 27
 
4.4%
Open Punctuation 23
 
3.8%
Close Punctuation 22
 
3.6%
Other Punctuation 11
 
1.8%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 52
19.6%
구 28
 
10.6%
로 26
 
9.8%
계 19
 
7.2%
림 18
 
6.8%
길 17
 
6.4%
번 13
 
4.9%
산 7
 
2.6%
층 6
 
2.3%
수 5
 
1.9%
Other values (45) 74
27.9%
Decimal Number
ValueCountFrequency (%)
1 46
25.6%
2 25
13.9%
4 21
11.7%
8 15
 
8.3%
3 15
 
8.3%
5 15
 
8.3%
9 14
 
7.8%
6 12
 
6.7%
0 11
 
6.1%
7 6
 
3.3%
Other Punctuation
ValueCountFrequency (%)
, 9
81.8%
. 2
 
18.2%
Space Separator
ValueCountFrequency (%)
78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 341
56.1%
Hangul 265
43.6%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 52
19.6%
구 28
 
10.6%
로 26
 
9.8%
계 19
 
7.2%
림 18
 
6.8%
길 17
 
6.4%
번 13
 
4.9%
산 7
 
2.6%
층 6
 
2.3%
수 5
 
1.9%
Other values (45) 74
27.9%
Common
ValueCountFrequency (%)
78
22.9%
1 46
13.5%
- 27
 
7.9%
2 25
 
7.3%
( 23
 
6.7%
) 22
 
6.5%
4 21
 
6.2%
8 15
 
4.4%
3 15
 
4.4%
5 15
 
4.4%
Other values (6) 54
15.8%
Latin
ValueCountFrequency (%)
A 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 343
56.4%
Hangul 265
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
78
22.7%
1 46
13.4%
- 27
 
7.9%
2 25
 
7.3%
( 23
 
6.7%
) 22
 
6.4%
4 21
 
6.1%
8 15
 
4.4%
3 15
 
4.4%
5 15
 
4.4%
Other values (7) 56
16.3%
Hangul
ValueCountFrequency (%)
동 52
19.6%
구 28
 
10.6%
로 26
 
9.8%
계 19
 
7.2%
림 18
 
6.8%
길 17
 
6.4%
번 13
 
4.9%
산 7
 
2.6%
층 6
 
2.3%
수 5
 
1.9%
Other values (45) 74
27.9%

시설설치일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)96.2%
Missing1
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean18552431
Minimum2019221
Maximum20140625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-08T03:41:21.543617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2019221
5-th percentile6389991.8
Q119795984
median19970569
Q319998464
95-th percentile20080535
Maximum20140625
Range18121404
Interquartile range (IQR)202480.75

Descriptive statistics

Standard deviation4869300.8
Coefficient of variation (CV)0.2624616
Kurtosis10.132891
Mean18552431
Median Absolute Deviation (MAD)64755.5
Skewness-3.3680143
Sum4.8236322 × 108
Variance2.371009 × 1013
MonotonicityNot monotonic
2024-01-08T03:41:21.650356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
19720210 2
 
7.4%
19981014 1
 
3.7%
19950901 1
 
3.7%
19780910 1
 
3.7%
2019822 1
 
3.7%
2019221 1
 
3.7%
20140625 1
 
3.7%
19990801 1
 
3.7%
20070823 1
 
3.7%
20001019 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
2019221 1
3.7%
2019822 1
3.7%
19500501 1
3.7%
19680410 1
3.7%
19720210 2
7.4%
19780910 1
3.7%
19841205 1
3.7%
19920714 1
3.7%
19940622 1
3.7%
19950901 1
3.7%
ValueCountFrequency (%)
20140625 1
3.7%
20080605 1
3.7%
20080325 1
3.7%
20070823 1
3.7%
20050225 1
3.7%
20020122 1
3.7%
20001019 1
3.7%
19990801 1
3.7%
19990130 1
3.7%
19981014 1
3.7%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)100.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean35.155941
Minimum35.1462
Maximum35.1643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-08T03:41:21.748322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.1462
5-th percentile35.147508
Q135.152798
median35.155881
Q335.159574
95-th percentile35.163921
Maximum35.1643
Range0.01809937
Interquartile range (IQR)0.00677612

Descriptive statistics

Standard deviation0.0055011111
Coefficient of variation (CV)0.00015647743
Kurtosis-0.96487741
Mean35.155941
Median Absolute Deviation (MAD)0.00369314
Skewness-0.17321587
Sum878.89852
Variance3.0262223 × 10-5
MonotonicityNot monotonic
2024-01-08T03:41:21.846741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
35.15184155 1
 
3.7%
35.154581 1
 
3.7%
35.1545823 1
 
3.7%
35.15706311 1
 
3.7%
35.16095624 1
 
3.7%
35.16161156 1
 
3.7%
35.15878304 1
 
3.7%
35.16359189 1
 
3.7%
35.16400383 1
 
3.7%
35.15907156 1
 
3.7%
Other values (15) 15
55.6%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
35.14620036 1
3.7%
35.14750296 1
3.7%
35.14752724 1
3.7%
35.14783291 1
3.7%
35.15030263 1
3.7%
35.15184155 1
3.7%
35.15279816 1
3.7%
35.1531344 1
3.7%
35.15353293 1
3.7%
35.15413716 1
3.7%
ValueCountFrequency (%)
35.16429973 1
3.7%
35.16400383 1
3.7%
35.16359189 1
3.7%
35.16292289 1
3.7%
35.16161156 1
3.7%
35.16095624 1
3.7%
35.15957428 1
3.7%
35.15907156 1
3.7%
35.1589999 1
3.7%
35.15878304 1
3.7%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)100.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean126.92098
Minimum126.9105
Maximum126.92929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-08T03:41:21.949042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.9105
5-th percentile126.91353
Q1126.91716
median126.92023
Q3126.92537
95-th percentile126.92875
Maximum126.92929
Range0.0187954
Interquartile range (IQR)0.0082145

Descriptive statistics

Standard deviation0.0051792449
Coefficient of variation (CV)4.0806848 × 10-5
Kurtosis-0.80524093
Mean126.92098
Median Absolute Deviation (MAD)0.0037673
Skewness-0.031456301
Sum3173.0244
Variance2.6824578 × 10-5
MonotonicityNot monotonic
2024-01-08T03:41:22.053997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
126.9104988 1
 
3.7%
126.9280048 1
 
3.7%
126.9234924 1
 
3.7%
126.9253721 1
 
3.7%
126.9289378 1
 
3.7%
126.9292942 1
 
3.7%
126.9239979 1
 
3.7%
126.9202183 1
 
3.7%
126.9210861 1
 
3.7%
126.926171 1
 
3.7%
Other values (15) 15
55.6%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
126.9104988 1
3.7%
126.9131753 1
3.7%
126.9149689 1
3.7%
126.915051 1
3.7%
126.9163937 1
3.7%
126.916879 1
3.7%
126.9171576 1
3.7%
126.9184415 1
3.7%
126.918716 1
3.7%
126.9187774 1
3.7%
ValueCountFrequency (%)
126.9292942 1
3.7%
126.9289378 1
3.7%
126.9280048 1
3.7%
126.9274469 1
3.7%
126.9272174 1
3.7%
126.926171 1
3.7%
126.9253721 1
3.7%
126.9239979 1
3.7%
126.9234924 1
3.7%
126.9230425 1
3.7%

데이터기준년도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
2020
26 
<NA>
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
2020 26
96.3%
<NA> 1
 
3.7%

Length

2024-01-08T03:41:22.165989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-08T03:41:22.240807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 26
96.3%
na 1
 
3.7%

Interactions

2024-01-08T03:41:19.252892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:18.288130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:18.615517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:18.921966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:19.331019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:18.358072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:18.686561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:19.002893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:19.409089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:18.434980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:18.760871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:19.086819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:19.501451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:18.528314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:18.838080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-08T03:41:19.164077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-08T03:41:22.291698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동시설명이용회원수시설소재지시설설치일위도경도
동1.0001.0000.5531.0000.0000.8120.708
시설명1.0001.0001.0001.0001.0001.0001.000
이용회원수0.5531.0001.0001.0000.0000.0000.518
시설소재지1.0001.0001.0001.0001.0001.0001.000
시설설치일0.0001.0000.0001.0001.0000.0000.559
위도0.8121.0000.0001.0000.0001.0000.514
경도0.7081.0000.5181.0000.5590.5141.000
2024-01-08T03:41:22.376363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동데이터기준년도
동1.0001.000
데이터기준년도1.0001.000
2024-01-08T03:41:22.446406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이용회원수시설설치일위도경도동데이터기준년도
이용회원수1.000-0.028-0.258-0.0620.2751.000
시설설치일-0.0281.0000.419-0.0430.0001.000
위도-0.2580.4191.0000.2240.3641.000
경도-0.062-0.0430.2241.0000.3841.000
동0.2750.0000.3640.3841.0001.000
데이터기준년도1.0001.0001.0001.0001.0001.000

Missing values

2024-01-08T03:41:19.610309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-08T03:41:19.714645image/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.
2024-01-08T03:41:19.804099image/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

구동시설명이용회원수시설소재지시설설치일위도경도데이터기준년도
0동구대인동상이군경회25동구 구성로204번길 6-1(대인동69-1)1998101435.153134126.9150512020
1동구충장로5가충수34동구 충장로 14-3(충장로5가93-2)1995090135.151842126.9104992020
2동구궁동충의39동구 예술길 19-8 (궁동35-11)1998033135.150303126.9187772020
3동구불로동수영54동구 서석로7번길 12-14(불로동11-2)1950050135.1462126.9149692020
4동구호남동삼성30동구 중앙로148번길 11-23(호남동39-10)1997072935.147527126.9131752020
5동구장동파월30동구 동계천로 74(장동1-8)2008032535.152798126.9192092020
6동구대인동통일36동구 제봉로194번길 41992071435.153533126.9163942020
7동구동명동동명40동구 백서로224번길 81984120535.147833126.9274472020
8동구동명동동명부녀30동구 백서로224번길 6-81997040935.147503126.9272172020
9동구동명동무공수훈자21동구 중앙로254, 5층1999013035.154137126.9206272020
구동시설명이용회원수시설소재지시설설치일위도경도데이터기준년도
17동구계림동금호타운25동구 필문대로12-7,관리사무소1층(계림동,금호A.)1998020335.164004126.9210862020
18동구계림동금호주상복합23동구 중앙로 358, 4층별관(계림동,계림주상)2000101935.163592126.9202182020
19동구계림동두산위브23동구 무등로 374, 104동 1층(계림동,두산위브1차A.)2007082335.158783126.9239982020
20동구계림동풍성37동구 필문대로 96, 2층(계림동1624)1999080135.161612126.9292942020
21동구계림동참판44동구 참판로26번길 16-1(계림동)2014062535.160956126.9289382020
22동구<NA>구구팔팔대명49동구 계림로 51-26(계림동)2019221<NA><NA>2020
23동구계림동푸른길두산위브28동구 계림로30번길 15, 208동 1층(계림동푸른길두산위브)201982235.157063126.9253722020
24동구산수동정암30동구 경양로 309-6 (산수1동 489-14)1978091035.154582126.9234922020
25동구산수동산수31동구 산수길 8 (산수1동 401-8)1972021035.154581126.9280052020
26동구산수동덕수부녀21동구 무등로 417-10(산수1동<NA><NA><NA><NA>