Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells100
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.9 KiB
Average record size in memory142.3 B

Variable types

Categorical11
Text2
Numeric3
Unsupported1

Alerts

BASE_YMD has constant value ""Constant
signgn_engl_nm is highly overall correlated with city_gn_gu_cd and 11 other fieldsHigh correlation
ctprvn_jlang_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
city_do_cd is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
ctprvn_engl_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
ctprvn_chnlng_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
signgn_jlang_nm is highly overall correlated with city_gn_gu_cd and 11 other fieldsHigh correlation
signgn_chnlng_nm is highly overall correlated with city_gn_gu_cd and 11 other fieldsHigh correlation
ctprvn_klang_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
signgn_klang_nm is highly overall correlated with city_gn_gu_cd and 11 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with lo and 11 other fieldsHigh correlation
lo is highly overall correlated with city_gn_gu_cd and 9 other fieldsHigh correlation
la is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
se_nm is highly overall correlated with city_gn_gu_cd and 5 other fieldsHigh correlation
tel_no has 100 (100.0%) missing valuesMissing
entrp_nm has unique valuesUnique
tel_no is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 09:52:12.992084
Analysis finished2023-12-10 09:52:18.066988
Duration5.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

se_nm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
지하철 수유실
58 
지하철역 자전거보관소
37 
지하철 유실물센터
 
2
지하철 관광안내소
 
2
지하철역 물품보관함
 
1

Length

Max length11
Median length7
Mean length8.59
Min length7

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row지하철 수유실
2nd row지하철역 자전거보관소
3rd row지하철 유실물센터
4th row지하철 수유실
5th row지하철역 자전거보관소

Common Values

ValueCountFrequency (%)
지하철 수유실 58
58.0%
지하철역 자전거보관소 37
37.0%
지하철 유실물센터 2
 
2.0%
지하철 관광안내소 2
 
2.0%
지하철역 물품보관함 1
 
1.0%

Length

2023-12-10T18:52:18.232241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:18.471197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하철 62
31.0%
수유실 58
29.0%
지하철역 38
19.0%
자전거보관소 37
18.5%
유실물센터 2
 
1.0%
관광안내소 2
 
1.0%
물품보관함 1
 
0.5%

entrp_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:18.936945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length14.33
Min length3

Characters and Unicode

Total characters1433
Distinct characters115
Distinct categories5 ?
Distinct scripts2 ?
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 row경춘선 춘천역 수유방
2nd row경춘선 춘천역 자전거보관소
3rd row춘천역 경춘선 유실물센터
4th row경춘선 강촌역 수유방
5th row경춘선 강촌역 자전거보관소
ValueCountFrequency (%)
수유방 58
17.7%
자전거보관소 37
 
11.3%
경춘선 21
 
6.4%
신분당선 21
 
6.4%
경의중앙선 17
 
5.2%
출구 16
 
4.9%
경강선 12
 
3.7%
서해선 11
 
3.4%
김포골드라인 8
 
2.4%
정자역 7
 
2.1%
Other values (76) 119
36.4%
2023-12-10T18:52:19.903445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
227
 
15.8%
역 99
 
6.9%
선 91
 
6.4%
수 65
 
4.5%
유 62
 
4.3%
방 58
 
4.0%
경 53
 
3.7%
자 44
 
3.1%
소 41
 
2.9%
관 38
 
2.7%
Other values (105) 655
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1184
82.6%
Space Separator 227
 
15.8%
Decimal Number 16
 
1.1%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 99
 
8.4%
선 91
 
7.7%
수 65
 
5.5%
유 62
 
5.2%
방 58
 
4.9%
경 53
 
4.5%
자 44
 
3.7%
소 41
 
3.5%
관 38
 
3.2%
전 38
 
3.2%
Other values (96) 595
50.3%
Decimal Number
ValueCountFrequency (%)
2 4
25.0%
1 4
25.0%
4 3
18.8%
3 3
18.8%
5 1
 
6.2%
6 1
 
6.2%
Space Separator
ValueCountFrequency (%)
227
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1184
82.6%
Common 249
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 99
 
8.4%
선 91
 
7.7%
수 65
 
5.5%
유 62
 
5.2%
방 58
 
4.9%
경 53
 
4.5%
자 44
 
3.7%
소 41
 
3.5%
관 38
 
3.2%
전 38
 
3.2%
Other values (96) 595
50.3%
Common
ValueCountFrequency (%)
227
91.2%
2 4
 
1.6%
1 4
 
1.6%
4 3
 
1.2%
) 3
 
1.2%
( 3
 
1.2%
3 3
 
1.2%
5 1
 
0.4%
6 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1184
82.6%
ASCII 249
 
17.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
227
91.2%
2 4
 
1.6%
1 4
 
1.6%
4 3
 
1.2%
) 3
 
1.2%
( 3
 
1.2%
3 3
 
1.2%
5 1
 
0.4%
6 1
 
0.4%
Hangul
ValueCountFrequency (%)
역 99
 
8.4%
선 91
 
7.7%
수 65
 
5.5%
유 62
 
5.2%
방 58
 
4.9%
경 53
 
4.5%
자 44
 
3.7%
소 41
 
3.5%
관 38
 
3.2%
전 38
 
3.2%
Other values (96) 595
50.3%

rn_adres
Text

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:20.369448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length19.42
Min length10

Characters and Unicode

Total characters1942
Distinct characters109
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)46.0%

Sample

1st row강원 춘천시 공지로 591
2nd row강원 춘천시 공지로 591
3rd row강원 춘천시 근화동
4th row강원 춘천시 남산면 강촌로 150
5th row강원 춘천시 남산면 강촌로 150
ValueCountFrequency (%)
경기도 83
 
17.4%
분당구 16
 
3.3%
성남시 16
 
3.3%
춘천시 15
 
3.1%
강원도 12
 
2.5%
수원시 12
 
2.5%
영통구 10
 
2.1%
김포시 8
 
1.7%
광주시 8
 
1.7%
고양시 8
 
1.7%
Other values (145) 290
60.7%
2023-12-10T18:52:21.104327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
378
19.5%
도 105
 
5.4%
시 103
 
5.3%
경 98
 
5.0%
기 85
 
4.4%
로 74
 
3.8%
0 50
 
2.6%
1 46
 
2.4%
구 43
 
2.2%
원 43
 
2.2%
Other values (99) 917
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1231
63.4%
Space Separator 378
 
19.5%
Decimal Number 316
 
16.3%
Dash Punctuation 17
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 105
 
8.5%
시 103
 
8.4%
경 98
 
8.0%
기 85
 
6.9%
로 74
 
6.0%
구 43
 
3.5%
원 43
 
3.5%
남 32
 
2.6%
양 27
 
2.2%
강 27
 
2.2%
Other values (87) 594
48.3%
Decimal Number
ValueCountFrequency (%)
0 50
15.8%
1 46
14.6%
3 37
11.7%
2 34
10.8%
5 33
10.4%
4 33
10.4%
6 26
8.2%
7 23
7.3%
9 19
 
6.0%
8 15
 
4.7%
Space Separator
ValueCountFrequency (%)
378
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1231
63.4%
Common 711
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 105
 
8.5%
시 103
 
8.4%
경 98
 
8.0%
기 85
 
6.9%
로 74
 
6.0%
구 43
 
3.5%
원 43
 
3.5%
남 32
 
2.6%
양 27
 
2.2%
강 27
 
2.2%
Other values (87) 594
48.3%
Common
ValueCountFrequency (%)
378
53.2%
0 50
 
7.0%
1 46
 
6.5%
3 37
 
5.2%
2 34
 
4.8%
5 33
 
4.6%
4 33
 
4.6%
6 26
 
3.7%
7 23
 
3.2%
9 19
 
2.7%
Other values (2) 32
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1231
63.4%
ASCII 711
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
378
53.2%
0 50
 
7.0%
1 46
 
6.5%
3 37
 
5.2%
2 34
 
4.8%
5 33
 
4.6%
4 33
 
4.6%
6 26
 
3.7%
7 23
 
3.2%
9 19
 
2.7%
Other values (2) 32
 
4.5%
Hangul
ValueCountFrequency (%)
도 105
 
8.5%
시 103
 
8.4%
경 98
 
8.0%
기 85
 
6.9%
로 74
 
6.0%
구 43
 
3.5%
원 43
 
3.5%
남 32
 
2.6%
양 27
 
2.2%
강 27
 
2.2%
Other values (87) 594
48.3%

ctprvn_klang_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
83 
강원도
17 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 83
83.0%
강원도 17
 
17.0%

Length

2023-12-10T18:52:21.318882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:21.471605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 83
83.0%
강원도 17
 
17.0%

signgn_klang_nm
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
성남시 분당구
16 
춘천시
15 
수원시 영통구
10 
광주시
8 
김포시
8 
Other values (13)
43 

Length

Max length8
Median length3
Mean length4.76
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
성남시 분당구 16
16.0%
춘천시 15
15.0%
수원시 영통구 10
10.0%
광주시 8
8.0%
김포시 8
8.0%
남양주시 7
7.0%
시흥시 7
7.0%
안산시 단원구 5
 
5.0%
양평군 4
 
4.0%
가평군 4
 
4.0%
Other values (8) 16
16.0%

Length

2023-12-10T18:52:21.772001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 16
11.3%
분당구 16
11.3%
춘천시 15
10.6%
수원시 12
 
8.5%
영통구 10
 
7.1%
고양시 8
 
5.7%
광주시 8
 
5.7%
김포시 8
 
5.7%
남양주시 7
 
5.0%
시흥시 7
 
5.0%
Other values (12) 34
24.1%

ctprvn_engl_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Gyeonggi-do
83 
Gangwon-do
17 

Length

Max length11
Median length11
Mean length10.83
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGangwon-do
2nd rowGangwon-do
3rd rowGangwon-do
4th rowGangwon-do
5th rowGangwon-do

Common Values

ValueCountFrequency (%)
Gyeonggi-do 83
83.0%
Gangwon-do 17
 
17.0%

Length

2023-12-10T18:52:21.996832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:22.170589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gyeonggi-do 83
83.0%
gangwon-do 17
 
17.0%

signgn_engl_nm
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Seongnam-si Bundang-gu
16 
Chuncheon-si
15 
Suwon-si Yeongtong-gu
10 
Hwaseong-si
8 
Gimpo-si
8 
Other values (13)
43 

Length

Max length22
Median length18
Mean length15.05
Min length7

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st rowChuncheon-si
2nd rowChuncheon-si
3rd rowChuncheon-si
4th rowChuncheon-si
5th rowChuncheon-si

Common Values

ValueCountFrequency (%)
Seongnam-si Bundang-gu 16
16.0%
Chuncheon-si 15
15.0%
Suwon-si Yeongtong-gu 10
10.0%
Hwaseong-si 8
8.0%
Gimpo-si 8
8.0%
Namyangju-si 7
7.0%
Siheung-si 7
7.0%
Ansan-si Danwon-gu 5
 
5.0%
Yangpyeong-gun 4
 
4.0%
Gapyeong-gun 4
 
4.0%
Other values (8) 16
16.0%

Length

2023-12-10T18:52:22.383844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seongnam-si 16
11.3%
bundang-gu 16
11.3%
chuncheon-si 15
10.6%
suwon-si 12
 
8.5%
yeongtong-gu 10
 
7.1%
goyang-si 8
 
5.7%
hwaseong-si 8
 
5.7%
gimpo-si 8
 
5.7%
namyangju-si 7
 
5.0%
siheung-si 7
 
5.0%
Other values (12) 34
24.1%

ctprvn_chnlng_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
京畿道
83 
江原道
17 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江原道
2nd row江原道
3rd row江原道
4th row江原道
5th row江原道

Common Values

ValueCountFrequency (%)
京畿道 83
83.0%
江原道 17
 
17.0%

Length

2023-12-10T18:52:22.668891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:22.880484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
京畿道 83
83.0%
江原道 17
 
17.0%

signgn_chnlng_nm
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
城南市 盆唐區
16 
春川市
15 
水原市 靈通區
10 
華城市
8 
金浦市
8 
Other values (13)
43 

Length

Max length8
Median length3
Mean length4.76
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row春川市
2nd row春川市
3rd row春川市
4th row春川市
5th row春川市

Common Values

ValueCountFrequency (%)
城南市 盆唐區 16
16.0%
春川市 15
15.0%
水原市 靈通區 10
10.0%
華城市 8
8.0%
金浦市 8
8.0%
南楊州市 7
7.0%
始興市 7
7.0%
安山市 檀園區 5
 
5.0%
楊平郡 4
 
4.0%
加平郡 4
 
4.0%
Other values (8) 16
16.0%

Length

2023-12-10T18:52:23.072796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
城南市 16
11.3%
盆唐區 16
11.3%
春川市 15
10.6%
水原市 12
 
8.5%
靈通區 10
 
7.1%
高陽市 8
 
5.7%
華城市 8
 
5.7%
金浦市 8
 
5.7%
南楊州市 7
 
5.0%
始興市 7
 
5.0%
Other values (12) 34
24.1%

ctprvn_jlang_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
京畿道
83 
江原道
17 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江原道
2nd row江原道
3rd row江原道
4th row江原道
5th row江原道

Common Values

ValueCountFrequency (%)
京畿道 83
83.0%
江原道 17
 
17.0%

Length

2023-12-10T18:52:23.303309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:23.483232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
京畿道 83
83.0%
江原道 17
 
17.0%

signgn_jlang_nm
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
城南市（ソンナムシ）盆塘区（プンダング）
16 
春川市（チュンチョンシ）
15 
水原市（スウォンシ）霊通区（ヨントング）
10 
光州市（クァンジュシ）
8 
金浦市（キンポシ）
8 
Other values (13)
43 

Length

Max length21
Median length19
Mean length14.44
Min length8

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row春川市（チュンチョンシ）
2nd row春川市（チュンチョンシ）
3rd row春川市（チュンチョンシ）
4th row春川市（チュンチョンシ）
5th row春川市（チュンチョンシ）

Common Values

ValueCountFrequency (%)
城南市（ソンナムシ）盆塘区（プンダング） 16
16.0%
春川市（チュンチョンシ） 15
15.0%
水原市（スウォンシ）霊通区（ヨントング） 10
10.0%
光州市（クァンジュシ） 8
8.0%
金浦市（キンポシ） 8
8.0%
南揚州市（ナムヤンジュシ） 7
7.0%
始興市（シフンシ） 7
7.0%
安山市（アンサンシ）檀園区（ダンウォンク） 5
 
5.0%
楊平郡（ヤンピョングン） 4
 
4.0%
加平郡（カピョングン） 4
 
4.0%
Other values (8) 16
16.0%

Length

2023-12-10T18:52:23.704635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
城南市（ソンナムシ）盆塘区（プンダング） 16
16.0%
春川市（チュンチョンシ） 15
15.0%
水原市（スウォンシ）霊通区（ヨントング） 10
10.0%
光州市（クァンジュシ） 8
8.0%
金浦市（キンポシ） 8
8.0%
南揚州市（ナムヤンジュシ） 7
7.0%
始興市（シフンシ） 7
7.0%
安山市（アンサンシ）檀園区（ダンウォンク） 5
 
5.0%
加平郡（カピョングン） 4
 
4.0%
楊平郡（ヤンピョングン） 4
 
4.0%
Other values (8) 16
16.0%

city_do_cd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
31
83 
32
17 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row32
2nd row32
3rd row32
4th row32
5th row32

Common Values

ValueCountFrequency (%)
31 83
83.0%
32 17
 
17.0%

Length

2023-12-10T18:52:23.936434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:24.128433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31 83
83.0%
32 17
 
17.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31285.04
Minimum31013
Maximum32360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:24.311858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31013
5-th percentile31014
Q131023
median31130
Q331280
95-th percentile32010
Maximum32360
Range1347
Interquartile range (IQR)257

Descriptive statistics

Standard deviation356.79641
Coefficient of variation (CV)0.011404697
Kurtosis0.86317811
Mean31285.04
Median Absolute Deviation (MAD)107
Skewness1.5207658
Sum3128504
Variance127303.68
MonotonicityNot monotonic
2023-12-10T18:52:24.557422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
31023 16
16.0%
32010 15
15.0%
31014 10
10.0%
31250 8
8.0%
31230 8
8.0%
31130 7
7.0%
31150 7
7.0%
31092 5
 
5.0%
31370 4
 
4.0%
31380 4
 
4.0%
Other values (8) 16
16.0%
ValueCountFrequency (%)
31013 2
 
2.0%
31014 10
10.0%
31023 16
16.0%
31050 2
 
2.0%
31092 5
 
5.0%
31101 3
 
3.0%
31103 3
 
3.0%
31104 2
 
2.0%
31120 2
 
2.0%
31130 7
7.0%
ValueCountFrequency (%)
32360 1
 
1.0%
32040 1
 
1.0%
32010 15
15.0%
31380 4
 
4.0%
31370 4
 
4.0%
31250 8
8.0%
31230 8
8.0%
31150 7
7.0%
31130 7
7.0%
31120 2
 
2.0%

lo
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.1501
Minimum126.60395
Maximum129.11889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:24.813874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.60395
5-th percentile126.69976
Q1126.80905
median127.10986
Q3127.33866
95-th percentile127.72381
Maximum129.11889
Range2.514942
Interquartile range (IQR)0.52961403

Descriptive statistics

Standard deviation0.37136879
Coefficient of variation (CV)0.0029207117
Kurtosis6.5202262
Mean127.1501
Median Absolute Deviation (MAD)0.28990995
Skewness1.6432537
Sum12715.01
Variance0.13791478
MonotonicityNot monotonic
2023-12-10T18:52:25.108278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1091911 7
 
7.0%
127.1098561 7
 
7.0%
127.0480197 6
 
6.0%
127.6341122 3
 
3.0%
127.0442598 3
 
3.0%
127.7238103 3
 
3.0%
127.1282293 2
 
2.0%
127.1441203 2
 
2.0%
126.7008625 2
 
2.0%
127.1146013 2
 
2.0%
Other values (53) 63
63.0%
ValueCountFrequency (%)
126.6039524 1
1.0%
126.6605169 1
1.0%
126.6669522 1
1.0%
126.6781365 1
1.0%
126.6787744 1
1.0%
126.7008625 2
2.0%
126.7384011 1
1.0%
126.741902 1
1.0%
126.7436867 1
1.0%
126.759723 1
1.0%
ValueCountFrequency (%)
129.1188944 1
 
1.0%
127.7258346 2
2.0%
127.7238103 3
3.0%
127.7165689 2
2.0%
127.7124994 1
 
1.0%
127.6341122 3
3.0%
127.5892957 2
2.0%
127.5577397 2
2.0%
127.5471562 1
 
1.0%
127.5120901 2
2.0%

la
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.533206
Minimum37.25251
Maximum38.238902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:25.519674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.25251
5-th percentile37.288887
Q137.368861
median37.478183
Q337.650663
95-th percentile37.864003
Maximum38.238902
Range0.9863921
Interquartile range (IQR)0.28180148

Descriptive statistics

Standard deviation0.20322179
Coefficient of variation (CV)0.0054144534
Kurtosis-0.072648575
Mean37.533206
Median Absolute Deviation (MAD)0.1556753
Skewness0.68295922
Sum3753.3206
Variance0.041299097
MonotonicityNot monotonic
2023-12-10T18:52:25.765406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3688613 7
 
7.0%
37.3897865 7
 
7.0%
37.2888873 6
 
6.0%
37.8057785 3
 
3.0%
37.3019804 3
 
3.0%
37.8640033 3
 
3.0%
37.3956759 2
 
2.0%
37.6480708 2
 
2.0%
37.6338585 2
 
2.0%
37.6338653 2
 
2.0%
Other values (53) 63
63.0%
ValueCountFrequency (%)
37.2525098 1
 
1.0%
37.2639869 1
 
1.0%
37.2656777 1
 
1.0%
37.2888873 6
6.0%
37.3019804 3
3.0%
37.3099559 1
 
1.0%
37.3132293 1
 
1.0%
37.3198128 1
 
1.0%
37.3350971 1
 
1.0%
37.3491793 1
 
1.0%
ValueCountFrequency (%)
38.2389019 1
 
1.0%
37.880565 1
 
1.0%
37.8764761 2
2.0%
37.8640033 3
3.0%
37.8320708 2
2.0%
37.8307704 2
2.0%
37.8196285 2
2.0%
37.8146909 2
2.0%
37.8057785 3
3.0%
37.735423 2
2.0%

tel_no
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

BASE_YMD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-12-09
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-09
2nd row2020-12-09
3rd row2020-12-09
4th row2020-12-09
5th row2020-12-09

Common Values

ValueCountFrequency (%)
2020-12-09 100
100.0%

Length

2023-12-10T18:52:26.006446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:26.168981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-09 100
100.0%

Interactions

2023-12-10T18:52:16.670104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:14.988420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:15.626157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:17.053941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:15.224809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:15.854478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:17.239872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:15.436185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:16.435294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:52:26.310014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
se_nmentrp_nmrn_adresctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cdcity_gn_gu_cdlola
se_nm1.0001.0000.8980.3950.8450.3950.8450.3950.8450.3950.8450.3950.8820.5980.721
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
rn_adres0.8981.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ctprvn_klang_nm0.3951.0001.0001.0001.0000.9981.0000.9981.0000.9981.0000.9981.0000.9830.987
signgn_klang_nm0.8451.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9970.956
ctprvn_engl_nm0.3951.0001.0000.9981.0001.0001.0000.9981.0000.9981.0000.9981.0000.9830.987
signgn_engl_nm0.8451.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9970.956
ctprvn_chnlng_nm0.3951.0001.0000.9981.0000.9981.0001.0001.0000.9981.0000.9981.0000.9830.987
signgn_chnlng_nm0.8451.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9970.956
ctprvn_jlang_nm0.3951.0001.0000.9981.0000.9981.0000.9981.0001.0001.0000.9981.0000.9830.987
signgn_jlang_nm0.8451.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9970.956
city_do_cd0.3951.0001.0000.9981.0000.9981.0000.9981.0000.9981.0001.0001.0000.9830.987
city_gn_gu_cd0.8821.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7310.841
lo0.5981.0001.0000.9830.9970.9830.9970.9830.9970.9830.9970.9830.7311.0000.787
la0.7211.0001.0000.9870.9560.9870.9560.9870.9560.9870.9560.9870.8410.7871.000
2023-12-10T18:52:26.632559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
signgn_engl_nmctprvn_jlang_nmcity_do_cdctprvn_engl_nmctprvn_chnlng_nmsigngn_jlang_nmsigngn_chnlng_nmctprvn_klang_nmse_nmsigngn_klang_nm
signgn_engl_nm1.0000.9150.9150.9150.9151.0001.0000.9150.5901.000
ctprvn_jlang_nm0.9151.0000.9640.9640.9640.9150.9150.9640.4740.915
city_do_cd0.9150.9641.0000.9640.9640.9150.9150.9640.4740.915
ctprvn_engl_nm0.9150.9640.9641.0000.9640.9150.9150.9640.4740.915
ctprvn_chnlng_nm0.9150.9640.9640.9641.0000.9150.9150.9640.4740.915
signgn_jlang_nm1.0000.9150.9150.9150.9151.0001.0000.9150.5901.000
signgn_chnlng_nm1.0000.9150.9150.9150.9151.0001.0000.9150.5901.000
ctprvn_klang_nm0.9150.9640.9640.9640.9640.9150.9151.0000.4740.915
se_nm0.5900.4740.4740.4740.4740.5900.5900.4741.0000.590
signgn_klang_nm1.0000.9150.9150.9150.9151.0001.0000.9150.5901.000
2023-12-10T18:52:26.942715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_gn_gu_cdlolase_nmctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cd
city_gn_gu_cd1.0000.5590.7720.5380.9850.9290.9850.9290.9850.9290.9850.9290.985
lo0.5591.0000.4040.4550.8660.8550.8660.8550.8660.8550.8660.8550.866
la0.7720.4041.0000.5440.8710.7750.8710.7750.8710.7750.8710.7750.871
se_nm0.5380.4550.5441.0000.4740.5900.4740.5900.4740.5900.4740.5900.474
ctprvn_klang_nm0.9850.8660.8710.4741.0000.9150.9640.9150.9640.9150.9640.9150.964
signgn_klang_nm0.9290.8550.7750.5900.9151.0000.9151.0000.9151.0000.9151.0000.915
ctprvn_engl_nm0.9850.8660.8710.4740.9640.9151.0000.9150.9640.9150.9640.9150.964
signgn_engl_nm0.9290.8550.7750.5900.9151.0000.9151.0000.9151.0000.9151.0000.915
ctprvn_chnlng_nm0.9850.8660.8710.4740.9640.9150.9640.9151.0000.9150.9640.9150.964
signgn_chnlng_nm0.9290.8550.7750.5900.9151.0000.9151.0000.9151.0000.9151.0000.915
ctprvn_jlang_nm0.9850.8660.8710.4740.9640.9150.9640.9150.9640.9151.0000.9150.964
signgn_jlang_nm0.9290.8550.7750.5900.9151.0000.9151.0000.9151.0000.9151.0000.915
city_do_cd0.9850.8660.8710.4740.9640.9150.9640.9150.9640.9150.9640.9151.000

Missing values

2023-12-10T18:52:17.462705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:52:17.921709image/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

se_nmentrp_nmrn_adresctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cdcity_gn_gu_cdlolatel_noBASE_YMD
0지하철 수유실경춘선 춘천역 수유방강원 춘천시 공지로 591강원도춘천시Gangwon-doChuncheon-si江原道春川市江原道春川市（チュンチョンシ）3232010127.72583537.876476<NA>2020-12-09
1지하철역 자전거보관소경춘선 춘천역 자전거보관소강원 춘천시 공지로 591강원도춘천시Gangwon-doChuncheon-si江原道春川市江原道春川市（チュンチョンシ）3232010127.72583537.876476<NA>2020-12-09
2지하철 유실물센터춘천역 경춘선 유실물센터강원 춘천시 근화동강원도춘천시Gangwon-doChuncheon-si江原道春川市江原道春川市（チュンチョンシ）3232010127.71249937.880565<NA>2020-12-09
3지하철 수유실경춘선 강촌역 수유방강원 춘천시 남산면 강촌로 150강원도춘천시Gangwon-doChuncheon-si江原道春川市江原道春川市（チュンチョンシ）3232010127.63411237.805779<NA>2020-12-09
4지하철역 자전거보관소경춘선 강촌역 자전거보관소강원 춘천시 남산면 강촌로 150강원도춘천시Gangwon-doChuncheon-si江原道春川市江原道春川市（チュンチョンシ）3232010127.63411237.805779<NA>2020-12-09
5지하철 유실물센터동해역 영동선 유실물센터강원도 동해시 동해역길 69강원도동해시Gangwon-doDonghae-si江原道東海市江原道東海市（トンヘシ）3232040129.11889437.492793<NA>2020-12-09
6지하철역 물품보관함호국이물품보관소강원도 철원군 서면 와수로173번길 24-1강원도철원군Gangwon-doCheorwon-gun江原道鐵原郡江原道鉄原郡（チョルウォングン）3232360127.43692838.238902<NA>2020-12-09
7지하철 관광안내소강촌역강원도 춘천시 남산면 강촌로 150강원도춘천시Gangwon-doChuncheon-si江原道春川市江原道春川市（チュンチョンシ）3232010127.63411237.805779<NA>2020-12-09
8지하철 수유실경춘선 백양리역 수유방강원도 춘천시 남산면 북한강변길 690강원도춘천시Gangwon-doChuncheon-si江原道春川市江原道春川市（チュンチョンシ）3232010127.58929637.83077<NA>2020-12-09
9지하철역 자전거보관소경춘선 백양리역 자전거보관소강원도 춘천시 남산면 북한강변길 690강원도춘천시Gangwon-doChuncheon-si江原道春川市江原道春川市（チュンチョンシ）3232010127.58929637.83077<NA>2020-12-09
se_nmentrp_nmrn_adresctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cdcity_gn_gu_cdlolatel_noBASE_YMD
90지하철 수유실수인선 월곶역 수유방경기도 시흥시 월곶중앙로14번길 56경기도시흥시Gyeonggi-doSiheung-si京畿道始興市京畿道始興市（シフンシ）3131150126.74190237.391191<NA>2020-12-09
91지하철 수유실서해선 선부역 수유방경기도 안산시 단원구 선부동 1074-1경기도안산시 단원구Gyeonggi-doAnsan-si Danwon-gu京畿道安山市 檀園區京畿道安山市（アンサンシ）檀園区（ダンウォンク）3131092126.80990937.335097<NA>2020-12-09
92지하철 수유실서해선 달미역 수유방경기도 안산시 단원구 선부동 481경기도안산시 단원구Gyeonggi-doAnsan-si Danwon-gu京畿道安山市 檀園區京畿道安山市（アンサンシ）檀園区（ダンウォンク）3131092126.80852237.349179<NA>2020-12-09
93지하철 수유실서해선 원시역 수유방경기도 안산시 단원구 원시동 803경기도안산시 단원구Gyeonggi-doAnsan-si Danwon-gu京畿道安山市 檀園區京畿道安山市（アンサンシ）檀園区（ダンウォンク）3131092126.79010837.309956<NA>2020-12-09
94지하철 수유실서해선 원곡역 수유방경기도 안산시 단원구 원시동 804경기도안산시 단원구Gyeonggi-doAnsan-si Danwon-gu京畿道安山市 檀園區京畿道安山市（アンサンシ）檀園区（ダンウォンク）3131092126.7962837.313229<NA>2020-12-09
95지하철 수유실서해선 초지역 수유방경기도 안산시 단원구 초지동 602-2경기도안산시 단원구Gyeonggi-doAnsan-si Danwon-gu京畿道安山市 檀園區京畿道安山市（アンサンシ）檀園区（ダンウォンク）3131092126.80922537.319813<NA>2020-12-09
96지하철 수유실경의중앙선 국수역 수유방경기도 양평군 양서면 국수역길 45경기도양평군Gyeonggi-doYangpyeong-gun京畿道楊平郡京畿道楊平郡（ヤンピョングン）3131380127.39959237.515969<NA>2020-12-09
97지하철 수유실경의중앙선 양수역 수유방경기도 양평군 양서면 목왕로 55-10경기도양평군Gyeonggi-doYangpyeong-gun京畿道楊平郡京畿道楊平郡（ヤンピョングン）3131380127.32906837.545566<NA>2020-12-09
98지하철 수유실경의중앙선 신원역 수유방경기도 양평군 양서면 신원역길 7경기도양평군Gyeonggi-doYangpyeong-gun京畿道楊平郡京畿道楊平郡（ヤンピョングン）3131380127.37260637.525018<NA>2020-12-09
99지하철 수유실경의중앙선 원덕역 수유방경기도 양평군 양평읍 원덕흑천길 138경기도양평군Gyeonggi-doYangpyeong-gun京畿道楊平郡京畿道楊平郡（ヤンピョングン）3131380127.54715637.468333<NA>2020-12-09