Overview

Dataset statistics

Number of variables11
Number of observations26
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory103.1 B

Variable types

Text1
Numeric9
Categorical1

Dataset

Description행정중심복합도시건설청에서 제공하는 행복도시 내 야외체육 시설에 관한 사항으로 1,2,3 생 등의 면적 및 개소 현황 등에 데이터를 제공합니다.
Author행정중심복합도시건설청
URLhttps://www.data.go.kr/data/15106656/fileData.do

Alerts

1_2생활권_개소 is highly overall correlated with 1_2생활권_면적 and 6 other fieldsHigh correlation
1_2생활권_면적 is highly overall correlated with 1_2생활권_개소 and 4 other fieldsHigh correlation
3_4생활권_개소 is highly overall correlated with 1_2생활권_개소 and 7 other fieldsHigh correlation
3_4생활권_면적 is highly overall correlated with 1_2생활권_면적 and 4 other fieldsHigh correlation
5생활권_개소 is highly overall correlated with 1_2생활권_개소 and 5 other fieldsHigh correlation
5생활권_면적 is highly overall correlated with 1_2생활권_개소 and 5 other fieldsHigh correlation
6생활권_개소 is highly overall correlated with 1_2생활권_개소 and 7 other fieldsHigh correlation
6생활권_면적 is highly overall correlated with 1_2생활권_개소 and 6 other fieldsHigh correlation
S생활권_개소 is highly overall correlated with 1_2생활권_개소 and 3 other fieldsHigh correlation
5생활권_개소 has 1 (3.8%) missing valuesMissing
체육시설명 has unique valuesUnique
1_2생활권_개소 has 11 (42.3%) zerosZeros
1_2생활권_면적 has 11 (42.3%) zerosZeros
3_4생활권_개소 has 10 (38.5%) zerosZeros
3_4생활권_면적 has 10 (38.5%) zerosZeros
5생활권_개소 has 7 (26.9%) zerosZeros
5생활권_면적 has 8 (30.8%) zerosZeros
6생활권_개소 has 13 (50.0%) zerosZeros
6생활권_면적 has 13 (50.0%) zerosZeros
S생활권_면적 has 8 (30.8%) zerosZeros

Reproduction

Analysis started2023-12-12 13:55:26.169700
Analysis finished2023-12-12 13:55:33.890715
Duration7.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

체육시설명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T22:55:34.014259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1153846
Min length3

Characters and Unicode

Total characters107
Distinct characters65
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row체력단련장
2nd row배드민턴장
3rd row테니스장
4th row족구장
5th row배구장
ValueCountFrequency (%)
체력단련장 1
 
3.8%
배드민턴장 1
 
3.8%
익스트림장 1
 
3.8%
rc경기장 1
 
3.8%
인라인 1
 
3.8%
인공암벽 1
 
3.8%
멀티코트 1
 
3.8%
육상트랙 1
 
3.8%
다목적운동장 1
 
3.8%
크나이프 1
 
3.8%
Other values (16) 16
61.5%
2023-12-12T22:55:34.401152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 18
 
16.8%
구 6
 
5.6%
트 5
 
4.7%
이 4
 
3.7%
인 3
 
2.8%
야 3
 
2.8%
스 3
 
2.8%
레 2
 
1.9%
코 2
 
1.9%
프 2
 
1.9%
Other values (55) 59
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102
95.3%
Uppercase Letter 5
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 18
 
17.6%
구 6
 
5.9%
트 5
 
4.9%
이 4
 
3.9%
인 3
 
2.9%
야 3
 
2.9%
스 3
 
2.9%
레 2
 
2.0%
코 2
 
2.0%
프 2
 
2.0%
Other values (50) 54
52.9%
Uppercase Letter
ValueCountFrequency (%)
C 1
20.0%
R 1
20.0%
M 1
20.0%
T 1
20.0%
B 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102
95.3%
Latin 5
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 18
 
17.6%
구 6
 
5.9%
트 5
 
4.9%
이 4
 
3.9%
인 3
 
2.9%
야 3
 
2.9%
스 3
 
2.9%
레 2
 
2.0%
코 2
 
2.0%
프 2
 
2.0%
Other values (50) 54
52.9%
Latin
ValueCountFrequency (%)
C 1
20.0%
R 1
20.0%
M 1
20.0%
T 1
20.0%
B 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102
95.3%
ASCII 5
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 18
 
17.6%
구 6
 
5.9%
트 5
 
4.9%
이 4
 
3.9%
인 3
 
2.9%
야 3
 
2.9%
스 3
 
2.9%
레 2
 
2.0%
코 2
 
2.0%
프 2
 
2.0%
Other values (50) 54
52.9%
ASCII
ValueCountFrequency (%)
C 1
20.0%
R 1
20.0%
M 1
20.0%
T 1
20.0%
B 1
20.0%

1_2생활권_개소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3846154
Minimum0
Maximum86
Zeros11
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T22:55:34.516846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q37.5
95-th percentile29.25
Maximum86
Range86
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation17.638768
Coefficient of variation (CV)2.3885832
Kurtosis17.002774
Mean7.3846154
Median Absolute Deviation (MAD)1.5
Skewness3.9607085
Sum192
Variance311.12615
MonotonicityNot monotonic
2023-12-12T22:55:34.636761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
42.3%
2 3
 
11.5%
8 2
 
7.7%
1 2
 
7.7%
4 2
 
7.7%
86 1
 
3.8%
35 1
 
3.8%
6 1
 
3.8%
12 1
 
3.8%
11 1
 
3.8%
ValueCountFrequency (%)
0 11
42.3%
1 2
 
7.7%
2 3
 
11.5%
4 2
 
7.7%
6 1
 
3.8%
8 2
 
7.7%
10 1
 
3.8%
11 1
 
3.8%
12 1
 
3.8%
35 1
 
3.8%
ValueCountFrequency (%)
86 1
 
3.8%
35 1
 
3.8%
12 1
 
3.8%
11 1
 
3.8%
10 1
 
3.8%
8 2
7.7%
6 1
 
3.8%
4 2
7.7%
2 3
11.5%
1 2
7.7%

1_2생활권_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3850.7308
Minimum0
Maximum25000
Zeros11
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T22:55:34.749670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2224
Q35421.25
95-th percentile15303
Maximum25000
Range25000
Interquartile range (IQR)5421.25

Descriptive statistics

Standard deviation5868.429
Coefficient of variation (CV)1.523978
Kurtosis6.995439
Mean3850.7308
Median Absolute Deviation (MAD)2224
Skewness2.5036
Sum100119
Variance34438459
MonotonicityNot monotonic
2023-12-12T22:55:34.904222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 11
42.3%
4300 1
 
3.8%
17940 1
 
3.8%
2760 1
 
3.8%
620 1
 
3.8%
1848 1
 
3.8%
2600 1
 
3.8%
3696 1
 
3.8%
25000 1
 
3.8%
4600 1
 
3.8%
Other values (6) 6
23.1%
ValueCountFrequency (%)
0 11
42.3%
620 1
 
3.8%
1848 1
 
3.8%
2600 1
 
3.8%
2760 1
 
3.8%
3696 1
 
3.8%
4300 1
 
3.8%
4600 1
 
3.8%
5215 1
 
3.8%
5490 1
 
3.8%
ValueCountFrequency (%)
25000 1
3.8%
17940 1
3.8%
7392 1
3.8%
6528 1
3.8%
6080 1
3.8%
6050 1
3.8%
5490 1
3.8%
5215 1
3.8%
4600 1
3.8%
4300 1
3.8%

3_4생활권_개소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4615385
Minimum0
Maximum53
Zeros10
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T22:55:35.013560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34.75
95-th percentile10.5
Maximum53
Range53
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation10.304293
Coefficient of variation (CV)2.309583
Kurtosis21.682857
Mean4.4615385
Median Absolute Deviation (MAD)2
Skewness4.5065977
Sum116
Variance106.17846
MonotonicityNot monotonic
2023-12-12T22:55:35.118902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 10
38.5%
5 4
 
15.4%
4 4
 
15.4%
2 2
 
7.7%
1 2
 
7.7%
53 1
 
3.8%
12 1
 
3.8%
3 1
 
3.8%
6 1
 
3.8%
ValueCountFrequency (%)
0 10
38.5%
1 2
 
7.7%
2 2
 
7.7%
3 1
 
3.8%
4 4
 
15.4%
5 4
 
15.4%
6 1
 
3.8%
12 1
 
3.8%
53 1
 
3.8%
ValueCountFrequency (%)
53 1
 
3.8%
12 1
 
3.8%
6 1
 
3.8%
5 4
 
15.4%
4 4
 
15.4%
3 1
 
3.8%
2 2
 
7.7%
1 2
 
7.7%
0 10
38.5%

3_4생활권_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2722.8462
Minimum0
Maximum15700
Zeros10
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T22:55:35.255790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1990
Q33696
95-th percentile8359.5
Maximum15700
Range15700
Interquartile range (IQR)3696

Descriptive statistics

Standard deviation3597.0896
Coefficient of variation (CV)1.321077
Kurtosis5.9519029
Mean2722.8462
Median Absolute Deviation (MAD)1990
Skewness2.1434849
Sum70794
Variance12939053
MonotonicityNot monotonic
2023-12-12T22:55:35.407299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 10
38.5%
3696 2
 
7.7%
2650 1
 
3.8%
8970 1
 
3.8%
1380 1
 
3.8%
2600 1
 
3.8%
5544 1
 
3.8%
3600 1
 
3.8%
720 1
 
3.8%
15700 1
 
3.8%
Other values (6) 6
23.1%
ValueCountFrequency (%)
0 10
38.5%
720 1
 
3.8%
745 1
 
3.8%
1380 1
 
3.8%
2600 1
 
3.8%
2650 1
 
3.8%
2750 1
 
3.8%
3040 1
 
3.8%
3600 1
 
3.8%
3696 2
 
7.7%
ValueCountFrequency (%)
15700 1
3.8%
8970 1
3.8%
6528 1
3.8%
5544 1
3.8%
4600 1
3.8%
4575 1
3.8%
3696 2
7.7%
3600 1
3.8%
3040 1
3.8%
2750 1
3.8%

5생활권_개소
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)28.0%
Missing1
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean2.04
Minimum0
Maximum13
Zeros7
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T22:55:35.849094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile4.8
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7
Coefficient of variation (CV)1.3235294
Kurtosis11.356205
Mean2.04
Median Absolute Deviation (MAD)1
Skewness2.9774535
Sum51
Variance7.29
MonotonicityNot monotonic
2023-12-12T22:55:35.957939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 7
26.9%
1 6
23.1%
2 5
19.2%
3 3
11.5%
4 2
 
7.7%
13 1
 
3.8%
5 1
 
3.8%
(Missing) 1
 
3.8%
ValueCountFrequency (%)
0 7
26.9%
1 6
23.1%
2 5
19.2%
3 3
11.5%
4 2
 
7.7%
5 1
 
3.8%
13 1
 
3.8%
ValueCountFrequency (%)
13 1
 
3.8%
5 1
 
3.8%
4 2
 
7.7%
3 3
11.5%
2 5
19.2%
1 6
23.1%
0 7
26.9%

5생활권_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1256.2692
Minimum0
Maximum8970
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T22:55:36.071048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median505
Q32119.5
95-th percentile2973
Maximum8970
Range8970
Interquartile range (IQR)2119.5

Descriptive statistics

Standard deviation1889.2215
Coefficient of variation (CV)1.5038349
Kurtosis10.933501
Mean1256.2692
Median Absolute Deviation (MAD)505
Skewness2.9203069
Sum32663
Variance3569157.9
MonotonicityNot monotonic
2023-12-12T22:55:36.173194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 8
30.8%
360 3
 
11.5%
650 1
 
3.8%
720 1
 
3.8%
690 1
 
3.8%
924 1
 
3.8%
1950 1
 
3.8%
2772 1
 
3.8%
200 1
 
3.8%
2400 1
 
3.8%
Other values (7) 7
26.9%
ValueCountFrequency (%)
0 8
30.8%
200 1
 
3.8%
298 1
 
3.8%
360 3
 
11.5%
650 1
 
3.8%
690 1
 
3.8%
720 1
 
3.8%
924 1
 
3.8%
1848 1
 
3.8%
1950 1
 
3.8%
ValueCountFrequency (%)
8970 1
3.8%
3040 1
3.8%
2772 1
3.8%
2745 1
3.8%
2400 1
3.8%
2200 1
3.8%
2176 1
3.8%
1950 1
3.8%
1848 1
3.8%
924 1
3.8%

6생활권_개소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6153846
Minimum0
Maximum10
Zeros13
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T22:55:36.281463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q33
95-th percentile4.75
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3336996
Coefficient of variation (CV)1.4446712
Kurtosis5.5934764
Mean1.6153846
Median Absolute Deviation (MAD)0.5
Skewness2.0817555
Sum42
Variance5.4461538
MonotonicityNot monotonic
2023-12-12T22:55:36.387592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 13
50.0%
3 4
 
15.4%
1 3
 
11.5%
4 2
 
7.7%
2 2
 
7.7%
10 1
 
3.8%
5 1
 
3.8%
ValueCountFrequency (%)
0 13
50.0%
1 3
 
11.5%
2 2
 
7.7%
3 4
 
15.4%
4 2
 
7.7%
5 1
 
3.8%
10 1
 
3.8%
ValueCountFrequency (%)
10 1
 
3.8%
5 1
 
3.8%
4 2
 
7.7%
3 4
 
15.4%
2 2
 
7.7%
1 3
 
11.5%
0 13
50.0%

6생활권_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1929.9615
Minimum0
Maximum17940
Zeros13
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T22:55:36.506092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median74.5
Q32194
95-th percentile10187.5
Maximum17940
Range17940
Interquartile range (IQR)2194

Descriptive statistics

Standard deviation4137.4697
Coefficient of variation (CV)2.1438094
Kurtosis10.2098
Mean1929.9615
Median Absolute Deviation (MAD)74.5
Skewness3.1720014
Sum50179
Variance17118655
MonotonicityNot monotonic
2023-12-12T22:55:36.629508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 13
50.0%
2772 2
 
7.7%
500 1
 
3.8%
149 1
 
3.8%
2745 1
 
3.8%
2176 1
 
3.8%
2200 1
 
3.8%
1216 1
 
3.8%
17940 1
 
3.8%
12500 1
 
3.8%
Other values (3) 3
 
11.5%
ValueCountFrequency (%)
0 13
50.0%
149 1
 
3.8%
500 1
 
3.8%
924 1
 
3.8%
1035 1
 
3.8%
1216 1
 
3.8%
2176 1
 
3.8%
2200 1
 
3.8%
2745 1
 
3.8%
2772 2
 
7.7%
ValueCountFrequency (%)
17940 1
3.8%
12500 1
3.8%
3250 1
3.8%
2772 2
7.7%
2745 1
3.8%
2200 1
3.8%
2176 1
3.8%
1216 1
3.8%
1035 1
3.8%
924 1
3.8%

S생활권_개소
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size340.0 B
1
9 
2
7 
0
7 
7
1 
10
1 

Length

Max length4
Median length1
Mean length1.1538462
Min length1

Unique

Unique3 ?
Unique (%)11.5%

Sample

1st row7
2nd row2
3rd row10
4th row2
5th row0

Common Values

ValueCountFrequency (%)
1 9
34.6%
2 7
26.9%
0 7
26.9%
7 1
 
3.8%
10 1
 
3.8%
<NA> 1
 
3.8%

Length

2023-12-12T22:55:36.769683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:55:36.918293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9
34.6%
2 7
26.9%
0 7
26.9%
7 1
 
3.8%
10 1
 
3.8%
na 1
 
3.8%

S생활권_면적
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2854
Minimum0
Maximum17940
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T22:55:37.024204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median787
Q32262
95-th percentile11875
Maximum17940
Range17940
Interquartile range (IQR)2262

Descriptive statistics

Standard deviation4691.0703
Coefficient of variation (CV)1.6436827
Kurtosis3.5386844
Mean2854
Median Absolute Deviation (MAD)787
Skewness2.0103246
Sum74204
Variance22006141
MonotonicityNot monotonic
2023-12-12T22:55:37.169249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 8
30.8%
345 2
 
7.7%
350 1
 
3.8%
17940 1
 
3.8%
1600 1
 
3.8%
650 1
 
3.8%
924 1
 
3.8%
2400 1
 
3.8%
10000 1
 
3.8%
12500 1
 
3.8%
Other values (8) 8
30.8%
ValueCountFrequency (%)
0 8
30.8%
298 1
 
3.8%
345 2
 
7.7%
350 1
 
3.8%
650 1
 
3.8%
924 1
 
3.8%
1088 1
 
3.8%
1100 1
 
3.8%
1216 1
 
3.8%
1600 1
 
3.8%
ValueCountFrequency (%)
17940 1
3.8%
12500 1
3.8%
10000 1
3.8%
9150 1
3.8%
7850 1
3.8%
4600 1
3.8%
2400 1
3.8%
1848 1
3.8%
1600 1
3.8%
1216 1
3.8%

Interactions

2023-12-12T22:55:32.835655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:26.513425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.195129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.953661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.829172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:29.712276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.745651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.414666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.032611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.912378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:26.580467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.261525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.038709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.922528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:29.792520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.823939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.480067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.100289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:33.000263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:26.655885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.337770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.114172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:29.006728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.171232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.902977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.551235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.169808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:33.095209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:26.738634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.414304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.198979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:29.093070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.245025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.977650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.620115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.239467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:33.188384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:26.826905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.498114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.316950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:29.187025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.334898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.067827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.693936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.321848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:33.272442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:26.907357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.581110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.435284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:29.302110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.414307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.139781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.758043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.393870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:33.341881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:26.981235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.660150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.519603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:29.398329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.489301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.202386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.821122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.463842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:33.420283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.052425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.742771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.630324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:29.501248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.571605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.263961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.887873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.648704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:33.508708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.122556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:27.823099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:28.719614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:29.609317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:30.661512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.341057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:31.962508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:55:32.755008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:55:37.288343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체육시설명1_2생활권_개소1_2생활권_면적3_4생활권_개소3_4생활권_면적5생활권_개소5생활권_면적6생활권_개소6생활권_면적S생활권_개소S생활권_면적
체육시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1_2생활권_개소1.0001.0000.4100.9090.4310.8420.0000.8560.0000.6960.000
1_2생활권_면적1.0000.4101.0000.0990.6720.7350.8650.7270.8870.7540.787
3_4생활권_개소1.0000.9090.0991.0000.8740.7280.1320.7730.0000.5980.000
3_4생활권_면적1.0000.4310.6720.8741.0000.5560.7650.8020.7310.0680.895
5생활권_개소1.0000.8420.7350.7280.5561.0000.7030.8370.3710.9290.000
5생활권_면적1.0000.0000.8650.1320.7650.7031.0000.7010.7490.6750.598
6생활권_개소1.0000.8560.7270.7730.8020.8370.7011.0000.7490.6820.000
6생활권_면적1.0000.0000.8870.0000.7310.3710.7490.7491.0000.0790.852
S생활권_개소1.0000.6960.7540.5980.0680.9290.6750.6820.0791.0000.309
S생활권_면적1.0000.0000.7870.0000.8950.0000.5980.0000.8520.3091.000
2023-12-12T22:55:37.428297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1_2생활권_개소1_2생활권_면적3_4생활권_개소3_4생활권_면적5생활권_개소5생활권_면적6생활권_개소6생활권_면적S생활권_면적S생활권_개소
1_2생활권_개소1.0000.8390.8390.4910.7470.5150.8990.7440.1500.613
1_2생활권_면적0.8391.0000.5880.5350.4560.4790.7540.8560.4590.367
3_4생활권_개소0.8390.5881.0000.7200.8570.6900.8210.5930.1470.507
3_4생활권_면적0.4910.5350.7201.0000.4030.6040.5170.5090.4710.000
5생활권_개소0.7470.4560.8570.4031.0000.7980.7780.514-0.1250.625
5생활권_면적0.5150.4790.6900.6040.7981.0000.6480.6400.1260.299
6생활권_개소0.8990.7540.8210.5170.7780.6481.0000.8630.2000.532
6생활권_면적0.7440.8560.5930.5090.5140.6400.8631.0000.3980.000
S생활권_면적0.1500.4590.1470.471-0.1250.1260.2000.3981.0000.161
S생활권_개소0.6130.3670.5070.0000.6250.2990.5320.0000.1611.000

Missing values

2023-12-12T22:55:33.644154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:55:33.820879image/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

체육시설명1_2생활권_개소1_2생활권_면적3_4생활권_개소3_4생활권_면적5생활권_개소5생활권_면적6생활권_개소6생활권_면적S생활권_개소S생활권_면적
0체력단련장86430053265013650105007350
1배드민턴장3552155745229811492298
2테니스장65490545753274532745109150
3족구장126528126528421764217621088
4배구장000013600000
5게이트볼장11605052750422004220021100
6농구장10608053040530402121621216
7풋살장8739243696218483277221848
8야구장00215700000017850
9리틀야구장1460014600000014600
체육시설명1_2생활권_개소1_2생활권_면적3_4생활권_개소3_4생활권_면적5생활권_개소5생활권_면적6생활권_개소6생활권_면적S생활권_개소S생활권_면적
16트레킹코스0033600224000022400
17크나이프0000120000<NA>0
18다목적운동장436966554432772327721924
19육상트랙426004260031950532501650
20멀티코트21848436961924192400
21인공암벽162000<NA>00000
22인라인827604138026903103500
23RC경기장000000001345
24익스트림장000000001345
25론볼장0000000011600