Overview

Dataset statistics

Number of variables33
Number of observations100
Missing cells740
Missing cells (%)22.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.3 KiB
Average record size in memory279.3 B

Variable types

Text6
Categorical15
Numeric8
Unsupported2
Boolean2

Alerts

faci_gb_cd has constant value ""Constant
faci_gb_nm has constant value ""Constant
nation_yn has constant value ""Constant
fcob_cd is highly imbalanced (66.6%)Imbalance
fcob_nm is highly imbalanced (66.6%)Imbalance
ftype_cd is highly imbalanced (66.6%)Imbalance
ftype_nm is highly imbalanced (66.6%)Imbalance
fmng_type_gb_cd is highly imbalanced (85.9%)Imbalance
fmng_type_gb_nm is highly imbalanced (85.9%)Imbalance
stand_cpt_psn_cnt is highly imbalanced (81.9%)Imbalance
stand_seat_cnt is highly imbalanced (80.9%)Imbalance
faci_stat is highly imbalanced (71.4%)Imbalance
fmng_dept_nm has 57 (57.0%) missing valuesMissing
fmng_user_tel has 54 (54.0%) missing valuesMissing
addr_cpb_cd has 12 (12.0%) missing valuesMissing
addr_emd_cd has 66 (66.0%) missing valuesMissing
addr_emd_nm has 66 (66.0%) missing valuesMissing
addr_amd_cd has 100 (100.0%) missing valuesMissing
addr_amd_nm has 100 (100.0%) missing valuesMissing
faci_road_addr1 has 54 (54.0%) missing valuesMissing
faci_point_x has 20 (20.0%) missing valuesMissing
faci_point_y has 20 (20.0%) missing valuesMissing
tot_faci_area has 93 (93.0%) missing valuesMissing
faci_homepage has 98 (98.0%) missing valuesMissing
addr_amd_cd is an unsupported type, check if it needs cleaning or further analysisUnsupported
addr_amd_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 09:41:24.676241
Analysis finished2023-12-10 09:41:25.563347
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

faci_nm
Text

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:41:25.903510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length8.31
Min length2

Characters and Unicode

Total characters831
Distinct characters169
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)72.0%

Sample

1st row(외립석입구)
2nd row용인시여성회관YMCA스포츠센터
3rd row신리 봉계초등학교 (강당뒤)
4th row신리 봉계초등학교 (강당뒤)
5th row성당뒤
ValueCountFrequency (%)
공터 20
 
12.0%
체육시설 9
 
5.4%
동네체육시설 5
 
3.0%
구 5
 
3.0%
야외운동기구 4
 
2.4%
공원 2
 
1.2%
운동기구 2
 
1.2%
산청읍 2
 
1.2%
산청군 2
 
1.2%
와~스타디움 2
 
1.2%
Other values (107) 114
68.3%
2023-12-10T18:41:26.571073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
10.2%
) 39
 
4.7%
공 36
 
4.3%
( 35
 
4.2%
구 34
 
4.1%
터 24
 
2.9%
동 23
 
2.8%
시 20
 
2.4%
육 19
 
2.3%
장 18
 
2.2%
Other values (159) 498
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 656
78.9%
Space Separator 85
 
10.2%
Close Punctuation 39
 
4.7%
Open Punctuation 35
 
4.2%
Uppercase Letter 8
 
1.0%
Math Symbol 4
 
0.5%
Other Punctuation 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 36
 
5.5%
구 34
 
5.2%
터 24
 
3.7%
동 23
 
3.5%
시 20
 
3.0%
육 19
 
2.9%
장 18
 
2.7%
체 16
 
2.4%
설 16
 
2.4%
원 16
 
2.4%
Other values (149) 434
66.2%
Uppercase Letter
ValueCountFrequency (%)
Y 2
25.0%
M 2
25.0%
A 2
25.0%
C 2
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
, 2
50.0%
Space Separator
ValueCountFrequency (%)
85
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 655
78.8%
Common 167
 
20.1%
Latin 8
 
1.0%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 36
 
5.5%
구 34
 
5.2%
터 24
 
3.7%
동 23
 
3.5%
시 20
 
3.1%
육 19
 
2.9%
장 18
 
2.7%
체 16
 
2.4%
설 16
 
2.4%
원 16
 
2.4%
Other values (148) 433
66.1%
Common
ValueCountFrequency (%)
85
50.9%
) 39
23.4%
( 35
21.0%
~ 4
 
2.4%
. 2
 
1.2%
, 2
 
1.2%
Latin
ValueCountFrequency (%)
Y 2
25.0%
M 2
25.0%
A 2
25.0%
C 2
25.0%
Han
ValueCountFrequency (%)
岐 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 655
78.8%
ASCII 175
 
21.1%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
48.6%
) 39
22.3%
( 35
20.0%
~ 4
 
2.3%
. 2
 
1.1%
, 2
 
1.1%
Y 2
 
1.1%
M 2
 
1.1%
A 2
 
1.1%
C 2
 
1.1%
Hangul
ValueCountFrequency (%)
공 36
 
5.5%
구 34
 
5.2%
터 24
 
3.7%
동 23
 
3.5%
시 20
 
3.1%
육 19
 
2.9%
장 18
 
2.7%
체 16
 
2.4%
설 16
 
2.4%
원 16
 
2.4%
Other values (148) 433
66.1%
CJK
ValueCountFrequency (%)
岐 1
100.0%

faci_gb_cd
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
P 100
100.0%

Length

2023-12-10T18:41:26.797825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:26.939354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
p 100
100.0%

faci_gb_nm
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공 100
100.0%

Length

2023-12-10T18:41:27.208523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:27.377984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공 100
100.0%

fcob_cd
Categorical

IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
P08
85 
P14
 
4
P09
 
3
P02
 
3
P01
 
2
Other values (3)
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st rowP08
2nd rowP09
3rd rowP08
4th rowP08
5th rowP08

Common Values

ValueCountFrequency (%)
P08 85
85.0%
P14 4
 
4.0%
P09 3
 
3.0%
P02 3
 
3.0%
P01 2
 
2.0%
P11 1
 
1.0%
P17 1
 
1.0%
P06 1
 
1.0%

Length

2023-12-10T18:41:27.551386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:27.740496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
p08 85
85.0%
p14 4
 
4.0%
p09 3
 
3.0%
p02 3
 
3.0%
p01 2
 
2.0%
p11 1
 
1.0%
p17 1
 
1.0%
p06 1
 
1.0%

fcob_nm
Categorical

IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
간이운동장
85 
국궁장
 
4
체육관
 
3
축구장
 
3
육상경기장
 
2
Other values (3)
 
3

Length

Max length5
Median length5
Mean length4.77
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row간이운동장
2nd row체육관
3rd row간이운동장
4th row간이운동장
5th row간이운동장

Common Values

ValueCountFrequency (%)
간이운동장 85
85.0%
국궁장 4
 
4.0%
체육관 3
 
3.0%
축구장 3
 
3.0%
육상경기장 2
 
2.0%
수영장 1
 
1.0%
골프연습장 1
 
1.0%
테니스장 1
 
1.0%

Length

2023-12-10T18:41:28.020077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:28.227128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
간이운동장 85
85.0%
국궁장 4
 
4.0%
체육관 3
 
3.0%
축구장 3
 
3.0%
육상경기장 2
 
2.0%
수영장 1
 
1.0%
골프연습장 1
 
1.0%
테니스장 1
 
1.0%

ftype_cd
Categorical

IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
P0801
85 
P1401
 
4
P0903
 
3
P0201
 
3
P0101
 
2
Other values (3)
 
3

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st rowP0801
2nd rowP0903
3rd rowP0801
4th rowP0801
5th rowP0801

Common Values

ValueCountFrequency (%)
P0801 85
85.0%
P1401 4
 
4.0%
P0903 3
 
3.0%
P0201 3
 
3.0%
P0101 2
 
2.0%
P1101 1
 
1.0%
P1701 1
 
1.0%
P0601 1
 
1.0%

Length

2023-12-10T18:41:28.428740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:28.637821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
p0801 85
85.0%
p1401 4
 
4.0%
p0903 3
 
3.0%
p0201 3
 
3.0%
p0101 2
 
2.0%
p1101 1
 
1.0%
p1701 1
 
1.0%
p0601 1
 
1.0%

ftype_nm
Categorical

IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
간이운동장
85 
국궁장
 
4
생활체육관
 
3
축구장
 
3
육상경기장
 
2
Other values (3)
 
3

Length

Max length5
Median length5
Mean length4.83
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row간이운동장
2nd row생활체육관
3rd row간이운동장
4th row간이운동장
5th row간이운동장

Common Values

ValueCountFrequency (%)
간이운동장 85
85.0%
국궁장 4
 
4.0%
생활체육관 3
 
3.0%
축구장 3
 
3.0%
육상경기장 2
 
2.0%
수영장 1
 
1.0%
골프연습장 1
 
1.0%
테니스장 1
 
1.0%

Length

2023-12-10T18:41:28.859437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:29.071113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
간이운동장 85
85.0%
국궁장 4
 
4.0%
생활체육관 3
 
3.0%
축구장 3
 
3.0%
육상경기장 2
 
2.0%
수영장 1
 
1.0%
골프연습장 1
 
1.0%
테니스장 1
 
1.0%

fmng_type_gb_cd
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
98 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.06
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 98
98.0%
<NA> 2
 
2.0%

Length

2023-12-10T18:41:29.314381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:29.500228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 98
98.0%
na 2
 
2.0%

fmng_type_gb_nm
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
지방자치단체
98 
<NA>
 
2

Length

Max length6
Median length6
Mean length5.96
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방자치단체 98
98.0%
<NA> 2
 
2.0%

Length

2023-12-10T18:41:29.732793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:29.945617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방자치단체 98
98.0%
na 2
 
2.0%

fmng_cp_cd
Real number (ℝ)

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.922 × 109
Minimum1.1 × 109
Maximum4.8 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:30.088283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.1 × 109
Q14.1 × 109
median4.25 × 109
Q34.7 × 109
95-th percentile4.8 × 109
Maximum4.8 × 109
Range3.7 × 109
Interquartile range (IQR)6 × 108

Descriptive statistics

Standard deviation1.1259636 × 109
Coefficient of variation (CV)0.28708913
Kurtosis1.4451792
Mean3.922 × 109
Median Absolute Deviation (MAD)4.5 × 108
Skewness-1.6313711
Sum3.922 × 1011
Variance1.2677939 × 1018
MonotonicityNot monotonic
2023-12-10T18:41:30.326900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4700000000 26
26.0%
4100000000 24
24.0%
1100000000 10
 
10.0%
2600000000 9
 
9.0%
4800000000 8
 
8.0%
4600000000 8
 
8.0%
4200000000 5
 
5.0%
4300000000 4
 
4.0%
4400000000 2
 
2.0%
4500000000 2
 
2.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
1100000000 10
10.0%
2600000000 9
 
9.0%
2900000000 1
 
1.0%
3100000000 1
 
1.0%
4100000000 24
24.0%
4200000000 5
 
5.0%
4300000000 4
 
4.0%
4400000000 2
 
2.0%
4500000000 2
 
2.0%
4600000000 8
 
8.0%
ValueCountFrequency (%)
4800000000 8
 
8.0%
4700000000 26
26.0%
4600000000 8
 
8.0%
4500000000 2
 
2.0%
4400000000 2
 
2.0%
4300000000 4
 
4.0%
4200000000 5
 
5.0%
4100000000 24
24.0%
3100000000 1
 
1.0%
2900000000 1
 
1.0%

fmng_cp_nm
Categorical

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경상북도
26 
경기도
24 
서울특별시
10 
부산광역시
9 
경상남도
8 
Other values (7)
23 

Length

Max length5
Median length4.5
Mean length3.92
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row경상북도
2nd row경기도
3rd row경상북도
4th row경상북도
5th row경상북도

Common Values

ValueCountFrequency (%)
경상북도 26
26.0%
경기도 24
24.0%
서울특별시 10
 
10.0%
부산광역시 9
 
9.0%
경상남도 8
 
8.0%
전라남도 8
 
8.0%
강원도 5
 
5.0%
충청북도 4
 
4.0%
충청남도 2
 
2.0%
전라북도 2
 
2.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T18:41:30.600458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 26
26.0%
경기도 24
24.0%
서울특별시 10
 
10.0%
부산광역시 9
 
9.0%
경상남도 8
 
8.0%
전라남도 8
 
8.0%
강원도 5
 
5.0%
충청북도 4
 
4.0%
충청남도 2
 
2.0%
전라북도 2
 
2.0%
Other values (2) 2
 
2.0%

fmng_cpb_cd
Real number (ℝ)

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.95887 × 109
Minimum1.129 × 109
Maximum4.886 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:30.885966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.129 × 109
5-th percentile1.129 × 109
Q14.119 × 109
median4.2935 × 109
Q34.715 × 109
95-th percentile4.82415 × 109
Maximum4.886 × 109
Range3.757 × 109
Interquartile range (IQR)5.96 × 108

Descriptive statistics

Standard deviation1.1261257 × 109
Coefficient of variation (CV)0.28445636
Kurtosis1.4797259
Mean3.95887 × 109
Median Absolute Deviation (MAD)4.215 × 108
Skewness-1.6361978
Sum3.95887 × 1011
Variance1.2681592 × 1018
MonotonicityNot monotonic
2023-12-10T18:41:31.151314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4715000000 14
14.0%
4119000000 12
 
12.0%
1129000000 10
 
10.0%
4686000000 7
 
7.0%
2671000000 6
 
6.0%
4728000000 4
 
4.0%
4159000000 4
 
4.0%
4127000000 4
 
4.0%
4886000000 3
 
3.0%
4717000000 3
 
3.0%
Other values (23) 33
33.0%
ValueCountFrequency (%)
1129000000 10
10.0%
2617000000 1
 
1.0%
2650000000 2
 
2.0%
2671000000 6
6.0%
2920000000 1
 
1.0%
3111000000 1
 
1.0%
4119000000 12
12.0%
4125000000 1
 
1.0%
4127000000 4
 
4.0%
4146000000 2
 
2.0%
ValueCountFrequency (%)
4886000000 3
 
3.0%
4827000000 2
 
2.0%
4824000000 2
 
2.0%
4822000000 1
 
1.0%
4792000000 2
 
2.0%
4783000000 1
 
1.0%
4728000000 4
 
4.0%
4717000000 3
 
3.0%
4715000000 14
14.0%
4713000000 2
 
2.0%

fmng_cpb_nm
Categorical

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
김천시
14 
부천시
12 
성북구
10 
함평군
7 
기장군
6 
Other values (28)
51 

Length

Max length4
Median length3
Mean length2.99
Min length2

Unique

Unique14 ?
Unique (%)14.0%

Sample

1st row김천시
2nd row용인시
3rd row김천시
4th row김천시
5th row김천시

Common Values

ValueCountFrequency (%)
김천시 14
14.0%
부천시 12
 
12.0%
성북구 10
 
10.0%
함평군 7
 
7.0%
기장군 6
 
6.0%
문경시 4
 
4.0%
화성시 4
 
4.0%
안산시 4
 
4.0%
산청군 3
 
3.0%
안동시 3
 
3.0%
Other values (23) 33
33.0%

Length

2023-12-10T18:41:31.425890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김천시 14
14.0%
부천시 12
 
12.0%
성북구 10
 
10.0%
함평군 7
 
7.0%
기장군 6
 
6.0%
문경시 4
 
4.0%
화성시 4
 
4.0%
안산시 4
 
4.0%
산청군 3
 
3.0%
안동시 3
 
3.0%
Other values (23) 33
33.0%

fmng_dept_nm
Text

MISSING 

Distinct22
Distinct (%)51.2%
Missing57
Missing (%)57.0%
Memory size932.0 B
2023-12-10T18:41:31.717405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.4883721
Min length3

Characters and Unicode

Total characters236
Distinct characters52
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

Unique18 ?
Unique (%)41.9%

Sample

1st row스포츠산업과
2nd row용인시 여성회관팀
3rd row스포츠산업과
4th row스포츠산업과
5th row스포츠산업과
ValueCountFrequency (%)
스포츠산업과 14
31.8%
문화관광과 7
15.9%
체육지원과 2
 
4.5%
체육시설부 2
 
4.5%
서구동 1
 
2.3%
문화체육과 1
 
2.3%
문화체육관광과 1
 
2.3%
문화누리관 1
 
2.3%
경포동자치센터 1
 
2.3%
문화관광체육과 1
 
2.3%
Other values (13) 13
29.5%
2023-12-10T18:41:32.244471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 31
 
13.1%
업 15
 
6.4%
육 15
 
6.4%
포 15
 
6.4%
스 14
 
5.9%
츠 14
 
5.9%
산 14
 
5.9%
체 14
 
5.9%
문 12
 
5.1%
화 12
 
5.1%
Other values (42) 80
33.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 235
99.6%
Space Separator 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 31
 
13.2%
업 15
 
6.4%
육 15
 
6.4%
포 15
 
6.4%
스 14
 
6.0%
츠 14
 
6.0%
산 14
 
6.0%
체 14
 
6.0%
문 12
 
5.1%
화 12
 
5.1%
Other values (41) 79
33.6%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 235
99.6%
Common 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 31
 
13.2%
업 15
 
6.4%
육 15
 
6.4%
포 15
 
6.4%
스 14
 
6.0%
츠 14
 
6.0%
산 14
 
6.0%
체 14
 
6.0%
문 12
 
5.1%
화 12
 
5.1%
Other values (41) 79
33.6%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 235
99.6%
ASCII 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 31
 
13.2%
업 15
 
6.4%
육 15
 
6.4%
포 15
 
6.4%
스 14
 
6.0%
츠 14
 
6.0%
산 14
 
6.0%
체 14
 
6.0%
문 12
 
5.1%
화 12
 
5.1%
Other values (41) 79
33.6%
ASCII
ValueCountFrequency (%)
1
100.0%

fmng_user_tel
Text

MISSING 

Distinct35
Distinct (%)76.1%
Missing54
Missing (%)54.0%
Memory size932.0 B
2023-12-10T18:41:33.085372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.913043
Min length8

Characters and Unicode

Total characters548
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

Unique29 ?
Unique (%)63.0%

Sample

1st row054-420-8033
2nd row031-324-8881
3rd row054-420-8031
4th row054-420-7928
5th row054-420-8092
ValueCountFrequency (%)
051-709-4122 5
 
10.9%
031-369-6273 4
 
8.7%
055-831-2420 2
 
4.3%
031-481-4990 2
 
4.3%
055-970-6414 2
 
4.3%
031-324-8881 2
 
4.3%
054-420-8112 1
 
2.2%
031-860-2144 1
 
2.2%
033-330-2754 1
 
2.2%
054-420-7991 1
 
2.2%
Other values (25) 25
54.3%
2023-12-10T18:41:33.705407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 91
16.6%
- 91
16.6%
4 74
13.5%
3 50
9.1%
1 46
8.4%
5 41
7.5%
2 41
7.5%
8 32
 
5.8%
9 31
 
5.7%
7 29
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 457
83.4%
Dash Punctuation 91
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91
19.9%
4 74
16.2%
3 50
10.9%
1 46
10.1%
5 41
9.0%
2 41
9.0%
8 32
 
7.0%
9 31
 
6.8%
7 29
 
6.3%
6 22
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91
16.6%
- 91
16.6%
4 74
13.5%
3 50
9.1%
1 46
8.4%
5 41
7.5%
2 41
7.5%
8 32
 
5.8%
9 31
 
5.7%
7 29
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91
16.6%
- 91
16.6%
4 74
13.5%
3 50
9.1%
1 46
8.4%
5 41
7.5%
2 41
7.5%
8 32
 
5.8%
9 31
 
5.7%
7 29
 
5.3%

addr_cp_cd
Real number (ℝ)

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.922 × 109
Minimum1.1 × 109
Maximum4.8 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:33.915814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.1 × 109
Q14.1 × 109
median4.25 × 109
Q34.7 × 109
95-th percentile4.8 × 109
Maximum4.8 × 109
Range3.7 × 109
Interquartile range (IQR)6 × 108

Descriptive statistics

Standard deviation1.1259636 × 109
Coefficient of variation (CV)0.28708913
Kurtosis1.4451792
Mean3.922 × 109
Median Absolute Deviation (MAD)4.5 × 108
Skewness-1.6313711
Sum3.922 × 1011
Variance1.2677939 × 1018
MonotonicityNot monotonic
2023-12-10T18:41:34.112743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4700000000 26
26.0%
4100000000 24
24.0%
1100000000 10
 
10.0%
2600000000 9
 
9.0%
4800000000 8
 
8.0%
4600000000 8
 
8.0%
4200000000 5
 
5.0%
4300000000 4
 
4.0%
4400000000 2
 
2.0%
4500000000 2
 
2.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
1100000000 10
10.0%
2600000000 9
 
9.0%
2900000000 1
 
1.0%
3100000000 1
 
1.0%
4100000000 24
24.0%
4200000000 5
 
5.0%
4300000000 4
 
4.0%
4400000000 2
 
2.0%
4500000000 2
 
2.0%
4600000000 8
 
8.0%
ValueCountFrequency (%)
4800000000 8
 
8.0%
4700000000 26
26.0%
4600000000 8
 
8.0%
4500000000 2
 
2.0%
4400000000 2
 
2.0%
4300000000 4
 
4.0%
4200000000 5
 
5.0%
4100000000 24
24.0%
3100000000 1
 
1.0%
2900000000 1
 
1.0%

addr_cp_nm
Categorical

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경상북도
26 
경기도
24 
서울특별시
10 
부산광역시
9 
경상남도
8 
Other values (7)
23 

Length

Max length5
Median length4.5
Mean length3.92
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row경상북도
2nd row경기도
3rd row경상북도
4th row경상북도
5th row경상북도

Common Values

ValueCountFrequency (%)
경상북도 26
26.0%
경기도 24
24.0%
서울특별시 10
 
10.0%
부산광역시 9
 
9.0%
경상남도 8
 
8.0%
전라남도 8
 
8.0%
강원도 5
 
5.0%
충청북도 4
 
4.0%
충청남도 2
 
2.0%
전라북도 2
 
2.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T18:41:34.357713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 26
26.0%
경기도 24
24.0%
서울특별시 10
 
10.0%
부산광역시 9
 
9.0%
경상남도 8
 
8.0%
전라남도 8
 
8.0%
강원도 5
 
5.0%
충청북도 4
 
4.0%
충청남도 2
 
2.0%
전라북도 2
 
2.0%
Other values (2) 2
 
2.0%

addr_cpb_cd
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)37.5%
Missing12
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean3.9370477 × 109
Minimum1.129 × 109
Maximum4.886 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:34.589200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.129 × 109
5-th percentile1.129 × 109
Q14.1265 × 109
median4.467 × 109
Q34.715 × 109
95-th percentile4.82595 × 109
Maximum4.886 × 109
Range3.757 × 109
Interquartile range (IQR)5.885 × 108

Descriptive statistics

Standard deviation1.1996104 × 109
Coefficient of variation (CV)0.30469796
Kurtosis0.85300359
Mean3.9370477 × 109
Median Absolute Deviation (MAD)2.74 × 108
Skewness-1.4890359
Sum3.464602 × 1011
Variance1.4390652 × 1018
MonotonicityNot monotonic
2023-12-10T18:41:34.859020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4715000000 14
14.0%
1129000000 10
 
10.0%
4686000000 7
 
7.0%
2671000000 6
 
6.0%
4728000000 4
 
4.0%
4159000000 4
 
4.0%
4127000000 4
 
4.0%
4886000000 3
 
3.0%
4717000000 3
 
3.0%
4420000000 2
 
2.0%
Other values (23) 31
31.0%
(Missing) 12
 
12.0%
ValueCountFrequency (%)
1129000000 10
10.0%
2617000000 1
 
1.0%
2650000000 2
 
2.0%
2671000000 6
6.0%
2920000000 1
 
1.0%
3111000000 1
 
1.0%
4125000000 1
 
1.0%
4127000000 4
 
4.0%
4146500000 2
 
2.0%
4159000000 4
 
4.0%
ValueCountFrequency (%)
4886000000 3
 
3.0%
4827000000 2
 
2.0%
4824000000 2
 
2.0%
4822000000 1
 
1.0%
4792000000 2
 
2.0%
4783000000 1
 
1.0%
4728000000 4
 
4.0%
4717000000 3
 
3.0%
4715000000 14
14.0%
4713000000 2
 
2.0%

addr_cpb_nm
Categorical

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
김천시
14 
<NA>
12 
성북구
10 
함평군
7 
기장군
6 
Other values (29)
51 

Length

Max length7
Median length3
Mean length3.23
Min length2

Unique

Unique15 ?
Unique (%)15.0%

Sample

1st row김천시
2nd row용인시 수지구
3rd row김천시
4th row김천시
5th row김천시

Common Values

ValueCountFrequency (%)
김천시 14
14.0%
<NA> 12
 
12.0%
성북구 10
 
10.0%
함평군 7
 
7.0%
기장군 6
 
6.0%
문경시 4
 
4.0%
화성시 4
 
4.0%
안산시 4
 
4.0%
산청군 3
 
3.0%
안동시 3
 
3.0%
Other values (24) 33
33.0%

Length

2023-12-10T18:41:35.312050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김천시 14
 
13.6%
na 12
 
11.7%
성북구 10
 
9.7%
함평군 7
 
6.8%
기장군 6
 
5.8%
문경시 4
 
3.9%
화성시 4
 
3.9%
안산시 4
 
3.9%
산청군 3
 
2.9%
안동시 3
 
2.9%
Other values (25) 36
35.0%

addr_emd_cd
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)82.4%
Missing66
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean4.1676248 × 109
Minimum2.6170103 × 109
Maximum4.886034 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:35.531185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6170103 × 109
5-th percentile2.6710252 × 109
Q14.1465101 × 109
median4.3965181 × 109
Q34.7897775 × 109
95-th percentile4.886025 × 109
Maximum4.886034 × 109
Range2.2690237 × 109
Interquartile range (IQR)6.4326743 × 108

Descriptive statistics

Standard deviation8.1501011 × 108
Coefficient of variation (CV)0.19555746
Kurtosis-0.13793586
Mean4.1676248 × 109
Median Absolute Deviation (MAD)3.5900496 × 108
Skewness-1.1935962
Sum1.4169924 × 1011
Variance6.6424147 × 1017
MonotonicityNot monotonic
2023-12-10T18:41:35.746983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4728011000 4
 
4.0%
4420011100 2
 
2.0%
4146510100 2
 
2.0%
4824025024 2
 
2.0%
4311210100 1
 
1.0%
4792025024 1
 
1.0%
4792033027 1
 
1.0%
2671025033 1
 
1.0%
2671033029 1
 
1.0%
2671025331 1
 
1.0%
Other values (18) 18
 
18.0%
(Missing) 66
66.0%
ValueCountFrequency (%)
2617010300 1
1.0%
2671025033 1
1.0%
2671025331 1
1.0%
2671025333 1
1.0%
2671025628 1
1.0%
2671033027 1
1.0%
2671033029 1
1.0%
4125010300 1
1.0%
4146510100 2
2.0%
4180025021 1
1.0%
ValueCountFrequency (%)
4886034024 1
 
1.0%
4886025032 1
 
1.0%
4886025021 1
 
1.0%
4827038039 1
 
1.0%
4827038035 1
 
1.0%
4824025024 2
2.0%
4792033027 1
 
1.0%
4792025024 1
 
1.0%
4783035035 1
 
1.0%
4728011000 4
4.0%

addr_emd_nm
Text

MISSING 

Distinct28
Distinct (%)82.4%
Missing66
Missing (%)66.0%
Memory size932.0 B
2023-12-10T18:41:36.119956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.1176471
Min length2

Characters and Unicode

Total characters106
Distinct characters52
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)70.6%

Sample

1st row풍덕천동
2nd row모전동
3rd row풍덕천동
4th row범학리
5th row산청리
ValueCountFrequency (%)
모전동 4
 
11.8%
풍덕천동 2
 
5.9%
수석리 2
 
5.9%
풍기동 2
 
5.9%
하송리 1
 
2.9%
좌천동 1
 
2.9%
소지리 1
 
2.9%
시랑리 1
 
2.9%
고촌리 1
 
2.9%
반룡리 1
 
2.9%
Other values (18) 18
52.9%
2023-12-10T18:41:36.706954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 21
19.8%
동 13
 
12.3%
모 5
 
4.7%
풍 4
 
3.8%
수 4
 
3.8%
전 4
 
3.8%
기 3
 
2.8%
천 3
 
2.8%
산 2
 
1.9%
촌 2
 
1.9%
Other values (42) 45
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
98.1%
Open Punctuation 1
 
0.9%
Close Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 21
20.2%
동 13
 
12.5%
모 5
 
4.8%
풍 4
 
3.8%
수 4
 
3.8%
전 4
 
3.8%
기 3
 
2.9%
천 3
 
2.9%
산 2
 
1.9%
촌 2
 
1.9%
Other values (40) 43
41.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102
96.2%
Common 2
 
1.9%
Han 2
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 21
20.6%
동 13
 
12.7%
모 5
 
4.9%
풍 4
 
3.9%
수 4
 
3.9%
전 4
 
3.9%
기 3
 
2.9%
천 3
 
2.9%
산 2
 
2.0%
촌 2
 
2.0%
Other values (38) 41
40.2%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%
Han
ValueCountFrequency (%)
岐 1
50.0%
岩 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102
96.2%
ASCII 2
 
1.9%
CJK 2
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 21
20.6%
동 13
 
12.7%
모 5
 
4.9%
풍 4
 
3.9%
수 4
 
3.9%
전 4
 
3.9%
기 3
 
2.9%
천 3
 
2.9%
산 2
 
2.0%
촌 2
 
2.0%
Other values (38) 41
40.2%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%
CJK
ValueCountFrequency (%)
岐 1
50.0%
岩 1
50.0%

addr_amd_cd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

addr_amd_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

faci_road_addr1
Text

MISSING 

Distinct42
Distinct (%)91.3%
Missing54
Missing (%)54.0%
Memory size932.0 B
2023-12-10T18:41:37.202484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23
Mean length19.652174
Min length6

Characters and Unicode

Total characters904
Distinct characters123
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)82.6%

Sample

1st row경기도 용인시 수지구 문정로7번길 15 (풍덕천동)
2nd row경상북도 문경시 시청2길 21 (모전동)
3rd row경기도 용인시 수지구 문정로7번길 15 (풍덕천동)
4th row경상남도 산청군 산청읍 범학길 100
5th row경상남도 산청군 산청읍 친환경로 2678
ValueCountFrequency (%)
경상남도 7
 
3.3%
기장군 6
 
2.9%
부산광역시 6
 
2.9%
경상북도 5
 
2.4%
화성시 4
 
1.9%
모전동 4
 
1.9%
문경시 4
 
1.9%
경기도 4
 
1.9%
경북 4
 
1.9%
15 3
 
1.4%
Other values (130) 163
77.6%
2023-12-10T18:41:38.079691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164
 
18.1%
시 30
 
3.3%
2 30
 
3.3%
1 28
 
3.1%
경 28
 
3.1%
도 26
 
2.9%
동 23
 
2.5%
산 21
 
2.3%
길 20
 
2.2%
- 19
 
2.1%
Other values (113) 515
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 551
61.0%
Space Separator 164
 
18.1%
Decimal Number 150
 
16.6%
Dash Punctuation 19
 
2.1%
Close Punctuation 10
 
1.1%
Open Punctuation 10
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
 
5.4%
경 28
 
5.1%
도 26
 
4.7%
동 23
 
4.2%
산 21
 
3.8%
길 20
 
3.6%
로 17
 
3.1%
군 16
 
2.9%
남 15
 
2.7%
읍 14
 
2.5%
Other values (99) 341
61.9%
Decimal Number
ValueCountFrequency (%)
2 30
20.0%
1 28
18.7%
3 16
10.7%
4 15
10.0%
0 13
8.7%
5 12
 
8.0%
7 11
 
7.3%
6 10
 
6.7%
8 8
 
5.3%
9 7
 
4.7%
Space Separator
ValueCountFrequency (%)
164
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 551
61.0%
Common 353
39.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
 
5.4%
경 28
 
5.1%
도 26
 
4.7%
동 23
 
4.2%
산 21
 
3.8%
길 20
 
3.6%
로 17
 
3.1%
군 16
 
2.9%
남 15
 
2.7%
읍 14
 
2.5%
Other values (99) 341
61.9%
Common
ValueCountFrequency (%)
164
46.5%
2 30
 
8.5%
1 28
 
7.9%
- 19
 
5.4%
3 16
 
4.5%
4 15
 
4.2%
0 13
 
3.7%
5 12
 
3.4%
7 11
 
3.1%
6 10
 
2.8%
Other values (4) 35
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 551
61.0%
ASCII 353
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164
46.5%
2 30
 
8.5%
1 28
 
7.9%
- 19
 
5.4%
3 16
 
4.5%
4 15
 
4.2%
0 13
 
3.7%
5 12
 
3.4%
7 11
 
3.1%
6 10
 
2.8%
Other values (4) 35
 
9.9%
Hangul
ValueCountFrequency (%)
시 30
 
5.4%
경 28
 
5.1%
도 26
 
4.7%
동 23
 
4.2%
산 21
 
3.8%
길 20
 
3.6%
로 17
 
3.1%
군 16
 
2.9%
남 15
 
2.7%
읍 14
 
2.5%
Other values (99) 341
61.9%

faci_point_x
Real number (ℝ)

MISSING 

Distinct73
Distinct (%)91.2%
Missing20
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean127.58962
Minimum126.50911
Maximum129.49839
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:38.348830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.50911
5-th percentile126.54104
Q1126.81649
median127.0574
Q3128.32755
95-th percentile129.21551
Maximum129.49839
Range2.98928
Interquartile range (IQR)1.5110554

Descriptive statistics

Standard deviation0.92721466
Coefficient of variation (CV)0.0072671639
Kurtosis-1.0213032
Mean127.58962
Median Absolute Deviation (MAD)0.42360556
Skewness0.68904615
Sum10207.169
Variance0.85972702
MonotonicityNot monotonic
2023-12-10T18:41:38.613208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.816493894 4
 
4.0%
127.02237446156266 2
 
2.0%
128.086391019 2
 
2.0%
129.1043658849 2
 
2.0%
127.0954999861 2
 
2.0%
129.210528481299 1
 
1.0%
127.031753 1
 
1.0%
126.749832 1
 
1.0%
126.750045 1
 
1.0%
126.509105 1
 
1.0%
Other values (63) 63
63.0%
(Missing) 20
 
20.0%
ValueCountFrequency (%)
126.509105 1
1.0%
126.513155 1
1.0%
126.516048 1
1.0%
126.519969 1
1.0%
126.542147 1
1.0%
126.609596 1
1.0%
126.622406 1
1.0%
126.749832 1
1.0%
126.750045 1
1.0%
126.764654 1
1.0%
ValueCountFrequency (%)
129.498385 1
1.0%
129.278180622645 1
1.0%
129.275085 1
1.0%
129.26374100748 1
1.0%
129.2129733371 1
1.0%
129.210528481299 1
1.0%
129.1827805406 1
1.0%
129.170966540087 1
1.0%
129.161396721137 1
1.0%
129.1043658849 2
2.0%

faci_point_y
Real number (ℝ)

MISSING 

Distinct73
Distinct (%)91.2%
Missing20
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean36.47817
Minimum34.847189
Maximum38.099404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:38.865760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.847189
5-th percentile35.06602
Q135.352296
median36.660983
Q337.483505
95-th percentile37.610259
Maximum38.099404
Range3.2522152
Interquartile range (IQR)2.1312091

Descriptive statistics

Standard deviation1.0294947
Coefficient of variation (CV)0.028222213
Kurtosis-1.6292373
Mean36.47817
Median Absolute Deviation (MAD)0.89931856
Skewness-0.23092473
Sum2918.2536
Variance1.0598593
MonotonicityNot monotonic
2023-12-10T18:41:39.145162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3194760178 4
 
4.0%
36.76887516415933 2
 
2.0%
35.0876568676 2
 
2.0%
35.15287128 2
 
2.0%
37.320524052 2
 
2.0%
35.8270459600073 1
 
1.0%
37.599942 1
 
1.0%
37.487183 1
 
1.0%
37.487751 1
 
1.0%
35.103057 1
 
1.0%
Other values (63) 63
63.0%
(Missing) 20
 
20.0%
ValueCountFrequency (%)
34.847189 1
1.0%
34.9648373195 1
1.0%
35.042516 1
1.0%
35.063719 1
1.0%
35.066141 1
1.0%
35.0876568676 2
2.0%
35.103057 1
1.0%
35.111323 1
1.0%
35.1269799608 1
1.0%
35.135601 1
1.0%
ValueCountFrequency (%)
38.0994042341 1
1.0%
37.905857385 1
1.0%
37.616625 1
1.0%
37.614111 1
1.0%
37.610056 1
1.0%
37.607571 1
1.0%
37.60591 1
1.0%
37.605577 1
1.0%
37.605125 1
1.0%
37.600157 1
1.0%

tot_faci_area
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing93
Missing (%)93.0%
Infinite0
Infinite (%)0.0%
Mean13231.429
Minimum897
Maximum53692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:39.367795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum897
5-th percentile1227.9
Q12487.5
median3471
Q314792.5
95-th percentile44251.3
Maximum53692
Range52795
Interquartile range (IQR)12305

Descriptive statistics

Standard deviation19283.412
Coefficient of variation (CV)1.4573945
Kurtosis3.8706725
Mean13231.429
Median Absolute Deviation (MAD)2574
Skewness1.9999098
Sum92620
Variance3.7184997 × 108
MonotonicityNot monotonic
2023-12-10T18:41:39.553848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
897 1
 
1.0%
2975 1
 
1.0%
7362 1
 
1.0%
3471 1
 
1.0%
53692 1
 
1.0%
22223 1
 
1.0%
2000 1
 
1.0%
(Missing) 93
93.0%
ValueCountFrequency (%)
897 1
1.0%
2000 1
1.0%
2975 1
1.0%
3471 1
1.0%
7362 1
1.0%
22223 1
1.0%
53692 1
1.0%
ValueCountFrequency (%)
53692 1
1.0%
22223 1
1.0%
7362 1
1.0%
3471 1
1.0%
2975 1
1.0%
2000 1
1.0%
897 1
1.0%

stand_cpt_psn_cnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
95 
35008
 
2
430
 
2
50
 
1

Length

Max length5
Median length4
Mean length3.98
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 95
95.0%
35008 2
 
2.0%
430 2
 
2.0%
50 1
 
1.0%

Length

2023-12-10T18:41:39.768878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:39.950948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 95
95.0%
35008 2
 
2.0%
430 2
 
2.0%
50 1
 
1.0%

stand_seat_cnt
Categorical

IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
94 
35008
 
2
430
 
2
505
 
1
112
 
1

Length

Max length5
Median length4
Mean length3.98
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 94
94.0%
35008 2
 
2.0%
430 2
 
2.0%
505 1
 
1.0%
112 1
 
1.0%

Length

2023-12-10T18:41:40.154930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:40.340260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 94
94.0%
35008 2
 
2.0%
430 2
 
2.0%
505 1
 
1.0%
112 1
 
1.0%

faci_homepage
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing98
Missing (%)98.0%
Memory size932.0 B
2023-12-10T18:41:40.651904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length52.5
Mean length52.5
Min length27

Characters and Unicode

Total characters105
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowhttp://www.yonginymca.or.kr
2nd rowhttp://www.yonginsports.com/icarusx/erp.php?STADIUM=Y#stMode=LIST=1=0=STADIUM=
ValueCountFrequency (%)
http://www.yonginymca.or.kr 1
50.0%
http://www.yonginsports.com/icarusx/erp.php?stadium=y#stmode=list=1=0=stadium 1
50.0%
2023-12-10T18:41:41.368095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 6
 
5.7%
o 6
 
5.7%
p 6
 
5.7%
t 6
 
5.7%
/ 6
 
5.7%
w 6
 
5.7%
. 6
 
5.7%
r 5
 
4.8%
s 4
 
3.8%
n 4
 
3.8%
Other values (26) 50
47.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61
58.1%
Uppercase Letter 20
 
19.0%
Other Punctuation 16
 
15.2%
Math Symbol 6
 
5.7%
Decimal Number 2
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 6
 
9.8%
p 6
 
9.8%
t 6
 
9.8%
w 6
 
9.8%
r 5
 
8.2%
s 4
 
6.6%
n 4
 
6.6%
y 3
 
4.9%
h 3
 
4.9%
c 3
 
4.9%
Other values (9) 15
24.6%
Uppercase Letter
ValueCountFrequency (%)
M 3
15.0%
I 3
15.0%
T 3
15.0%
S 3
15.0%
A 2
10.0%
D 2
10.0%
U 2
10.0%
L 1
 
5.0%
Y 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
/ 6
37.5%
. 6
37.5%
: 2
 
12.5%
# 1
 
6.2%
? 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Math Symbol
ValueCountFrequency (%)
= 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
77.1%
Common 24
 
22.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 6
 
7.4%
p 6
 
7.4%
t 6
 
7.4%
w 6
 
7.4%
r 5
 
6.2%
s 4
 
4.9%
n 4
 
4.9%
y 3
 
3.7%
M 3
 
3.7%
I 3
 
3.7%
Other values (18) 35
43.2%
Common
ValueCountFrequency (%)
= 6
25.0%
/ 6
25.0%
. 6
25.0%
: 2
 
8.3%
1 1
 
4.2%
# 1
 
4.2%
? 1
 
4.2%
0 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 6
 
5.7%
o 6
 
5.7%
p 6
 
5.7%
t 6
 
5.7%
/ 6
 
5.7%
w 6
 
5.7%
. 6
 
5.7%
r 5
 
4.8%
s 4
 
3.8%
n 4
 
3.8%
Other values (26) 50
47.6%

nation_yn
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
100 
ValueCountFrequency (%)
False 100
100.0%
2023-12-10T18:41:41.549049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

faci_stat
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
95 
99
 
5

Length

Max length2
Median length1
Mean length1.05
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 95
95.0%
99 5
 
5.0%

Length

2023-12-10T18:41:41.739787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:42.025614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 95
95.0%
99 5
 
5.0%

del_yn
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
54 
True
46 
ValueCountFrequency (%)
False 54
54.0%
True 46
46.0%
2023-12-10T18:41:42.145826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

faci_nmfaci_gb_cdfaci_gb_nmfcob_cdfcob_nmftype_cdftype_nmfmng_type_gb_cdfmng_type_gb_nmfmng_cp_cdfmng_cp_nmfmng_cpb_cdfmng_cpb_nmfmng_dept_nmfmng_user_teladdr_cp_cdaddr_cp_nmaddr_cpb_cdaddr_cpb_nmaddr_emd_cdaddr_emd_nmaddr_amd_cdaddr_amd_nmfaci_road_addr1faci_point_xfaci_point_ytot_faci_areastand_cpt_psn_cntstand_seat_cntfaci_homepagenation_ynfaci_statdel_yn
0(외립석입구)P공공P08간이운동장P0801간이운동장1지방자치단체4700000000경상북도4715000000김천시스포츠산업과054-420-80334700000000경상북도4715000000김천시<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0N
1용인시여성회관YMCA스포츠센터P공공P09체육관P0903생활체육관1지방자치단체4100000000경기도4146000000용인시용인시 여성회관팀031-324-88814100000000경기도4146500000용인시 수지구4146510100풍덕천동<NA><NA>경기도 용인시 수지구 문정로7번길 15 (풍덕천동)127.095537.320524<NA><NA><NA>http://www.yonginymca.or.krN0Y
2신리 봉계초등학교 (강당뒤)P공공P08간이운동장P0801간이운동장1지방자치단체4700000000경상북도4715000000김천시스포츠산업과054-420-80314700000000경상북도4715000000김천시<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0N
3신리 봉계초등학교 (강당뒤)P공공P08간이운동장P0801간이운동장1지방자치단체4700000000경상북도4715000000김천시스포츠산업과054-420-79284700000000경상북도4715000000김천시<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0Y
4성당뒤P공공P08간이운동장P0801간이운동장1지방자치단체4700000000경상북도4715000000김천시스포츠산업과054-420-80924700000000경상북도4715000000김천시<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0Y
5개나리 공원 야외운동기구P공공P08간이운동장P0801간이운동장1지방자치단체4700000000경상북도4728000000문경시<NA><NA>4700000000경상북도4728000000문경시4728011000모전동<NA><NA>경상북도 문경시 시청2길 21 (모전동)128.18796336.58697<NA><NA><NA><NA>N0N
6남산리 족구장P공공P08간이운동장P0801간이운동장1지방자치단체4700000000경상북도4715000000김천시스포츠산업과054-420-80674700000000경상북도4715000000김천시<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0N
7용인시여성회관YMCA스포츠센터수영장P공공P11수영장P1101수영장1지방자치단체4100000000경기도4146000000용인시평생교육과031-324-88814100000000경기도4146500000용인시 수지구4146510100풍덕천동<NA><NA>경기도 용인시 수지구 문정로7번길 15 (풍덕천동)127.095537.320524<NA><NA><NA>http://www.yonginsports.com/icarusx/erp.php?STADIUM=Y#stMode=LIST=1=0=STADIUM=N0N
8산청군 산청읍 범학리 (범학마을) 운동기구P공공P08간이운동장P0801간이운동장1지방자치단체4800000000경상남도4886000000산청군문화관광과055-970-64144800000000경상남도4886000000산청군4886025032범학리<NA><NA>경상남도 산청군 산청읍 범학길 100127.91392935.38914<NA><NA><NA><NA>N99Y
9산청군 산청읍 산청리 (청호마을)운동기구P공공P08간이운동장P0801간이운동장1지방자치단체4800000000경상남도4886000000산청군<NA><NA>4800000000경상남도4886000000산청군4886025021산청리<NA><NA>경상남도 산청군 산청읍 친환경로 2678127.87775535.416847<NA><NA><NA><NA>N99Y
faci_nmfaci_gb_cdfaci_gb_nmfcob_cdfcob_nmftype_cdftype_nmfmng_type_gb_cdfmng_type_gb_nmfmng_cp_cdfmng_cp_nmfmng_cpb_cdfmng_cpb_nmfmng_dept_nmfmng_user_teladdr_cp_cdaddr_cp_nmaddr_cpb_cdaddr_cpb_nmaddr_emd_cdaddr_emd_nmaddr_amd_cdaddr_amd_nmfaci_road_addr1faci_point_xfaci_point_ytot_faci_areastand_cpt_psn_cntstand_seat_cntfaci_homepagenation_ynfaci_statdel_yn
90공터P공공P08간이운동장P0801간이운동장1지방자치단체4100000000경기도4119000000부천시<NA><NA>4100000000경기도<NA><NA><NA><NA><NA><NA><NA>126.81755837.488604<NA><NA><NA><NA>N0Y
91공터P공공P08간이운동장P0801간이운동장1지방자치단체4100000000경기도4119000000부천시<NA><NA>4100000000경기도<NA><NA><NA><NA><NA><NA><NA>126.81967937.481237<NA><NA><NA><NA>N0Y
92공터P공공P08간이운동장P0801간이운동장1지방자치단체4600000000전라남도4686000000함평군<NA><NA>4600000000전라남도4686000000함평군<NA><NA><NA><NA><NA>126.62240635.135601<NA><NA><NA><NA>N0Y
93공터P공공P08간이운동장P0801간이운동장1지방자치단체4100000000경기도4119000000부천시<NA><NA>4100000000경기도<NA><NA><NA><NA><NA><NA><NA>126.76862937.485825<NA><NA><NA><NA>N0Y
94공터P공공P08간이운동장P0801간이운동장1지방자치단체4100000000경기도4119000000부천시<NA><NA>4100000000경기도<NA><NA><NA><NA><NA><NA><NA>126.81632337.480331<NA><NA><NA><NA>N0Y
95공터P공공P08간이운동장P0801간이운동장1지방자치단체4100000000경기도4119000000부천시<NA><NA>4100000000경기도<NA><NA><NA><NA><NA><NA><NA>126.76465437.516676<NA><NA><NA><NA>N0Y
96공터P공공P08간이운동장P0801간이운동장1지방자치단체4100000000경기도4119000000부천시<NA><NA>4100000000경기도<NA><NA><NA><NA><NA><NA><NA>126.77450937.485431<NA><NA><NA><NA>N0Y
97공터P공공P08간이운동장P0801간이운동장1지방자치단체4100000000경기도4119000000부천시<NA><NA>4100000000경기도<NA><NA><NA><NA><NA><NA><NA>126.79941437.482899<NA><NA><NA><NA>N0Y
98공터P공공P08간이운동장P0801간이운동장1지방자치단체4100000000경기도4119000000부천시<NA><NA>4100000000경기도<NA><NA><NA><NA><NA><NA><NA>126.8055337.483071<NA><NA><NA><NA>N0Y
99공터P공공P08간이운동장P0801간이운동장1지방자치단체4100000000경기도4119000000부천시<NA><NA>4100000000경기도<NA><NA><NA><NA><NA><NA><NA>126.8099837.484808<NA><NA><NA><NA>N0Y