Overview

Dataset statistics

Number of variables14
Number of observations46
Missing cells118
Missing cells (%)18.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory114.9 B

Variable types

Text6
Categorical8

Dataset

Description영등포구 공영(노상) 주차장 현황 운영 데이터(2014)
Author영등포구시설관리공단
URLhttps://www.data.go.kr/data/15044354/fileData.do

Alerts

Unnamed: 4 is highly overall correlated with Unnamed: 3 and 6 other fieldsHigh correlation
Unnamed: 6 is highly overall correlated with Unnamed: 3 and 6 other fieldsHigh correlation
Unnamed: 3 is highly overall correlated with Unnamed: 4 and 3 other fieldsHigh correlation
Unnamed: 10 is highly overall correlated with Unnamed: 3 and 6 other fieldsHigh correlation
Unnamed: 7 is highly overall correlated with Unnamed: 4 and 4 other fieldsHigh correlation
Unnamed: 5 is highly overall correlated with Unnamed: 3 and 6 other fieldsHigh correlation
Unnamed: 8 is highly overall correlated with Unnamed: 4 and 4 other fieldsHigh correlation
Unnamed: 9 is highly overall correlated with Unnamed: 4 and 3 other fieldsHigh correlation
영등포구 공영 노상주차장 일반현황 has 3 (6.5%) missing valuesMissing
Unnamed: 1 has 4 (8.7%) missing valuesMissing
Unnamed: 2 has 3 (6.5%) missing valuesMissing
Unnamed: 11 has 31 (67.4%) missing valuesMissing
Unnamed: 12 has 34 (73.9%) missing valuesMissing
Unnamed: 13 has 43 (93.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 18:05:33.737818
Analysis finished2023-12-12 18:05:35.518762
Duration1.78 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영등포구 공영 노상주차장 일반현황
Text

MISSING 

Distinct43
Distinct (%)100.0%
Missing3
Missing (%)6.5%
Memory size500.0 B
2023-12-13T03:05:35.693794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.7674419
Min length1

Characters and Unicode

Total characters76
Distinct characters13
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

Unique43 ?
Unique (%)100.0%

Sample

1st row연번
2nd row1
3rd row2
4th row3
5th row4
ValueCountFrequency (%)
연번 1
 
2.3%
32 1
 
2.3%
23 1
 
2.3%
24 1
 
2.3%
25 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
Other values (33) 33
76.7%
2023-12-13T03:05:36.112365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15
19.7%
2 14
18.4%
3 14
18.4%
4 6
 
7.9%
9 4
 
5.3%
5 4
 
5.3%
6 4
 
5.3%
7 4
 
5.3%
8 4
 
5.3%
0 4
 
5.3%
Other values (3) 3
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73
96.1%
Other Letter 3
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
20.5%
2 14
19.2%
3 14
19.2%
4 6
 
8.2%
9 4
 
5.5%
5 4
 
5.5%
6 4
 
5.5%
7 4
 
5.5%
8 4
 
5.5%
0 4
 
5.5%
Other Letter
ValueCountFrequency (%)
연 1
33.3%
번 1
33.3%
계 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 73
96.1%
Hangul 3
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
20.5%
2 14
19.2%
3 14
19.2%
4 6
 
8.2%
9 4
 
5.5%
5 4
 
5.5%
6 4
 
5.5%
7 4
 
5.5%
8 4
 
5.5%
0 4
 
5.5%
Hangul
ValueCountFrequency (%)
연 1
33.3%
번 1
33.3%
계 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73
96.1%
Hangul 3
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15
20.5%
2 14
19.2%
3 14
19.2%
4 6
 
8.2%
9 4
 
5.5%
5 4
 
5.5%
6 4
 
5.5%
7 4
 
5.5%
8 4
 
5.5%
0 4
 
5.5%
Hangul
ValueCountFrequency (%)
연 1
33.3%
번 1
33.3%
계 1
33.3%

Unnamed: 1
Text

MISSING 

Distinct42
Distinct (%)100.0%
Missing4
Missing (%)8.7%
Memory size500.0 B
2023-12-13T03:05:36.317452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.9047619
Min length4

Characters and Unicode

Total characters248
Distinct characters115
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row주차장명
2nd row가든예식장앞
3rd row경남아파트앞
4th row경도상가앞
5th row경호빌딩앞
ValueCountFrequency (%)
도림고가 1
 
2.4%
양남사거리앞 1
 
2.4%
여의도공원앞2 1
 
2.4%
여의도공원앞3 1
 
2.4%
여의도백화점앞 1
 
2.4%
영등포중앙시장 1
 
2.4%
중마루공원앞 1
 
2.4%
중소기업중앙회앞 1
 
2.4%
진주아파트옆 1
 
2.4%
태양빌딩앞 1
 
2.4%
Other values (32) 32
76.2%
2023-12-13T03:05:36.690778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
앞 31
 
12.5%
도 7
 
2.8%
딩 7
 
2.8%
빌 7
 
2.8%
원 5
 
2.0%
K 5
 
2.0%
B 5
 
2.0%
S 5
 
2.0%
장 4
 
1.6%
보 4
 
1.6%
Other values (105) 168
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
91.9%
Uppercase Letter 17
 
6.9%
Decimal Number 3
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 31
 
13.6%
도 7
 
3.1%
딩 7
 
3.1%
빌 7
 
3.1%
원 5
 
2.2%
장 4
 
1.8%
보 4
 
1.8%
의 4
 
1.8%
여 4
 
1.8%
중 4
 
1.8%
Other values (97) 151
66.2%
Uppercase Letter
ValueCountFrequency (%)
K 5
29.4%
B 5
29.4%
S 5
29.4%
C 1
 
5.9%
M 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
2 1
33.3%
1 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
91.9%
Latin 17
 
6.9%
Common 3
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 31
 
13.6%
도 7
 
3.1%
딩 7
 
3.1%
빌 7
 
3.1%
원 5
 
2.2%
장 4
 
1.8%
보 4
 
1.8%
의 4
 
1.8%
여 4
 
1.8%
중 4
 
1.8%
Other values (97) 151
66.2%
Latin
ValueCountFrequency (%)
K 5
29.4%
B 5
29.4%
S 5
29.4%
C 1
 
5.9%
M 1
 
5.9%
Common
ValueCountFrequency (%)
3 1
33.3%
2 1
33.3%
1 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
91.9%
ASCII 20
 
8.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
앞 31
 
13.6%
도 7
 
3.1%
딩 7
 
3.1%
빌 7
 
3.1%
원 5
 
2.2%
장 4
 
1.8%
보 4
 
1.8%
의 4
 
1.8%
여 4
 
1.8%
중 4
 
1.8%
Other values (97) 151
66.2%
ASCII
ValueCountFrequency (%)
K 5
25.0%
B 5
25.0%
S 5
25.0%
3 1
 
5.0%
C 1
 
5.0%
M 1
 
5.0%
2 1
 
5.0%
1 1
 
5.0%

Unnamed: 2
Text

MISSING 

Distinct30
Distinct (%)69.8%
Missing3
Missing (%)6.5%
Memory size500.0 B
2023-12-13T03:05:36.888274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.9302326
Min length1

Characters and Unicode

Total characters83
Distinct characters13
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

Unique21 ?
Unique (%)48.8%

Sample

1st row주차면
2nd row7
3rd row26
4th row17
5th row10
ValueCountFrequency (%)
12 3
 
7.0%
13 3
 
7.0%
16 3
 
7.0%
33 3
 
7.0%
15 2
 
4.7%
14 2
 
4.7%
21 2
 
4.7%
11 2
 
4.7%
26 2
 
4.7%
59 1
 
2.3%
Other values (20) 20
46.5%
2023-12-13T03:05:37.226865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22
26.5%
3 16
19.3%
2 12
14.5%
6 9
10.8%
5 6
 
7.2%
9 5
 
6.0%
4 4
 
4.8%
7 2
 
2.4%
8 2
 
2.4%
0 2
 
2.4%
Other values (3) 3
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
96.4%
Other Letter 3
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
27.5%
3 16
20.0%
2 12
15.0%
6 9
11.2%
5 6
 
7.5%
9 5
 
6.2%
4 4
 
5.0%
7 2
 
2.5%
8 2
 
2.5%
0 2
 
2.5%
Other Letter
ValueCountFrequency (%)
주 1
33.3%
차 1
33.3%
면 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 80
96.4%
Hangul 3
 
3.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22
27.5%
3 16
20.0%
2 12
15.0%
6 9
11.2%
5 6
 
7.5%
9 5
 
6.2%
4 4
 
5.0%
7 2
 
2.5%
8 2
 
2.5%
0 2
 
2.5%
Hangul
ValueCountFrequency (%)
주 1
33.3%
차 1
33.3%
면 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80
96.4%
Hangul 3
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22
27.5%
3 16
20.0%
2 12
15.0%
6 9
11.2%
5 6
 
7.5%
9 5
 
6.2%
4 4
 
5.0%
7 2
 
2.5%
8 2
 
2.5%
0 2
 
2.5%
Hangul
ValueCountFrequency (%)
주 1
33.3%
차 1
33.3%
면 1
33.3%

Unnamed: 3
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
1
24 
2
13 
<NA>
5 
인력
 
1
3
 
1
Other values (2)
 
2

Length

Max length4
Median length1
Mean length1.3695652
Min length1

Unique

Unique4 ?
Unique (%)8.7%

Sample

1st row<NA>
2nd row인력
3rd row<NA>
4th row<NA>
5th row1

Common Values

ValueCountFrequency (%)
1 24
52.2%
2 13
28.3%
<NA> 5
 
10.9%
인력 1
 
2.2%
3 1
 
2.2%
6 1
 
2.2%
60 1
 
2.2%

Length

2023-12-13T03:05:37.357981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:05:37.467399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 24
52.2%
2 13
28.3%
na 5
 
10.9%
인력 1
 
2.2%
3 1
 
2.2%
6 1
 
2.2%
60 1
 
2.2%

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size500.0 B
1
29 
2
8 
<NA>
4 
4
4 
급지
 
1

Length

Max length4
Median length1
Mean length1.2826087
Min length1

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row급지
3rd row<NA>
4th row<NA>
5th row1

Common Values

ValueCountFrequency (%)
1 29
63.0%
2 8
 
17.4%
<NA> 4
 
8.7%
4 4
 
8.7%
급지 1
 
2.2%

Length

2023-12-13T03:05:37.584659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:05:37.680427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 29
63.0%
2 8
 
17.4%
na 4
 
8.7%
4 4
 
8.7%
급지 1
 
2.2%

Unnamed: 5
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size500.0 B
1
15 
2
14 
3
12 
<NA>
4 
반
 
1

Length

Max length4
Median length1
Mean length1.2608696
Min length1

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row반
3rd row<NA>
4th row<NA>
5th row2

Common Values

ValueCountFrequency (%)
1 15
32.6%
2 14
30.4%
3 12
26.1%
<NA> 4
 
8.7%
반 1
 
2.2%

Length

2023-12-13T03:05:37.783606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:05:37.876184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 15
32.6%
2 14
30.4%
3 12
26.1%
na 4
 
8.7%
반 1
 
2.2%

Unnamed: 6
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
500
30 
250
7 
100
4 
<NA>
 
2
주 차 요 금
 
1
Other values (2)
 
2

Length

Max length7
Median length3
Mean length3.0869565
Min length1

Unique

Unique3 ?
Unique (%)6.5%

Sample

1st row<NA>
2nd row주 차 요 금
3rd row시간권
4th row5
5th row500

Common Values

ValueCountFrequency (%)
500 30
65.2%
250 7
 
15.2%
100 4
 
8.7%
<NA> 2
 
4.3%
주 차 요 금 1
 
2.2%
시간권 1
 
2.2%
5 1
 
2.2%

Length

2023-12-13T03:05:37.971658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:05:38.064956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
500 30
61.2%
250 7
 
14.3%
100 4
 
8.2%
na 2
 
4.1%
주 1
 
2.0%
차 1
 
2.0%
요 1
 
2.0%
금 1
 
2.0%
시간권 1
 
2.0%
5 1
 
2.0%

Unnamed: 7
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size500.0 B
20000
29 
15000
8 
<NA>
4 
6000
4 
일주차권
 
1

Length

Max length5
Median length5
Mean length4.8043478
Min length4

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row<NA>
3rd row일주차권
4th row<NA>
5th row20000

Common Values

ValueCountFrequency (%)
20000 29
63.0%
15000 8
 
17.4%
<NA> 4
 
8.7%
6000 4
 
8.7%
일주차권 1
 
2.2%

Length

2023-12-13T03:05:38.178061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:05:38.283060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20000 29
63.0%
15000 8
 
17.4%
na 4
 
8.7%
6000 4
 
8.7%
일주차권 1
 
2.2%

Unnamed: 8
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size500.0 B
200000
29 
100000
8 
<NA>
4 
50000
4 
월정기권
 
1

Length

Max length6
Median length6
Mean length5.6956522
Min length4

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row<NA>
3rd row월정기권
4th row<NA>
5th row200000

Common Values

ValueCountFrequency (%)
200000 29
63.0%
100000 8
 
17.4%
<NA> 4
 
8.7%
50000 4
 
8.7%
월정기권 1
 
2.2%

Length

2023-12-13T03:05:38.388760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:05:38.484661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
200000 29
63.0%
100000 8
 
17.4%
na 4
 
8.7%
50000 4
 
8.7%
월정기권 1
 
2.2%

Unnamed: 9
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size500.0 B
2005-01-01
23 
2006-12-04
7 
2004-07-01
5 
<NA>
4 
2012-04-01
4 
Other values (3)
3 

Length

Max length10
Median length10
Mean length9.3478261
Min length4

Unique

Unique3 ?
Unique (%)6.5%

Sample

1st row<NA>
2nd row인수현황
3rd row<NA>
4th row<NA>
5th row2004-10-01

Common Values

ValueCountFrequency (%)
2005-01-01 23
50.0%
2006-12-04 7
 
15.2%
2004-07-01 5
 
10.9%
<NA> 4
 
8.7%
2012-04-01 4
 
8.7%
인수현황 1
 
2.2%
2004-10-01 1
 
2.2%
2005-11-01 1
 
2.2%

Length

2023-12-13T03:05:38.581187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:05:38.682737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2005-01-01 23
50.0%
2006-12-04 7
 
15.2%
2004-07-01 5
 
10.9%
na 4
 
8.7%
2012-04-01 4
 
8.7%
인수현황 1
 
2.2%
2004-10-01 1
 
2.2%
2005-11-01 1
 
2.2%

Unnamed: 10
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size500.0 B
<NA>
35 
거주자
10 
야간거주자(10개소 327면)
 
1

Length

Max length16
Median length4
Mean length4.0434783
Min length3

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row야간거주자(10개소 327면)
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 35
76.1%
거주자 10
 
21.7%
야간거주자(10개소 327면) 1
 
2.2%

Length

2023-12-13T03:05:38.815451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:05:38.908759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 35
74.5%
거주자 10
 
21.3%
야간거주자(10개소 1
 
2.1%
327면 1
 
2.1%

Unnamed: 11
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing31
Missing (%)67.4%
Memory size500.0 B
2023-12-13T03:05:39.074876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5
Min length3

Characters and Unicode

Total characters75
Distinct characters64
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row기존명칭
2nd row대우아파트앞
3rd row장기신용은행
4th row맨하탄호텔뒤
5th row제일은행
ValueCountFrequency (%)
기존명칭 1
 
6.7%
대우아파트앞 1
 
6.7%
장기신용은행 1
 
6.7%
맨하탄호텔뒤 1
 
6.7%
제일은행 1
 
6.7%
문래고가바이더웨이 1
 
6.7%
조흥화학 1
 
6.7%
서울방송 1
 
6.7%
선명회 1
 
6.7%
목화예식장 1
 
6.7%
Other values (5) 5
33.3%
2023-12-13T03:05:39.437828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 2
 
2.7%
앞 2
 
2.7%
래 2
 
2.7%
가 2
 
2.7%
행 2
 
2.7%
은 2
 
2.7%
이 2
 
2.7%
장 2
 
2.7%
화 2
 
2.7%
명 2
 
2.7%
Other values (54) 55
73.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71
94.7%
Uppercase Letter 3
 
4.0%
Decimal Number 1
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 2
 
2.8%
앞 2
 
2.8%
래 2
 
2.8%
가 2
 
2.8%
행 2
 
2.8%
은 2
 
2.8%
이 2
 
2.8%
장 2
 
2.8%
화 2
 
2.8%
명 2
 
2.8%
Other values (50) 51
71.8%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
B 1
33.3%
M 1
33.3%
Decimal Number
ValueCountFrequency (%)
7 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71
94.7%
Latin 3
 
4.0%
Common 1
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 2
 
2.8%
앞 2
 
2.8%
래 2
 
2.8%
가 2
 
2.8%
행 2
 
2.8%
은 2
 
2.8%
이 2
 
2.8%
장 2
 
2.8%
화 2
 
2.8%
명 2
 
2.8%
Other values (50) 51
71.8%
Latin
ValueCountFrequency (%)
I 1
33.3%
B 1
33.3%
M 1
33.3%
Common
ValueCountFrequency (%)
7 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71
94.7%
ASCII 4
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 2
 
2.8%
앞 2
 
2.8%
래 2
 
2.8%
가 2
 
2.8%
행 2
 
2.8%
은 2
 
2.8%
이 2
 
2.8%
장 2
 
2.8%
화 2
 
2.8%
명 2
 
2.8%
Other values (50) 51
71.8%
ASCII
ValueCountFrequency (%)
7 1
25.0%
I 1
25.0%
B 1
25.0%
M 1
25.0%

Unnamed: 12
Text

MISSING 

Distinct10
Distinct (%)83.3%
Missing34
Missing (%)73.9%
Memory size500.0 B
2023-12-13T03:05:39.667916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length18.5
Mean length15.333333
Min length3

Characters and Unicode

Total characters184
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)75.0%

Sample

1st row비 고
2nd row2010.5.3 신설(6면)
3rd row2012.4.1 신설
4th row2012.4.1 신설
5th row2012.4.1 신설
ValueCountFrequency (%)
신설 5
15.2%
2012.4.1 4
 
12.1%
4
 
12.1%
2012.11.5 3
 
9.1%
상인회위탁 2
 
6.1%
2010.1.1 1
 
3.0%
8명관리반장 1
 
3.0%
대직인력 1
 
3.0%
문래동주민센터(13면 1
 
3.0%
16면 1
 
3.0%
Other values (10) 10
30.3%
2023-12-13T03:05:40.028202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 25
13.6%
22
 
12.0%
. 20
 
10.9%
2 18
 
9.8%
0 13
 
7.1%
신 8
 
4.3%
설 8
 
4.3%
4 5
 
2.7%
면 5
 
2.7%
3 4
 
2.2%
Other values (36) 56
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75
40.8%
Other Letter 56
30.4%
Other Punctuation 23
 
12.5%
Space Separator 22
 
12.0%
Open Punctuation 3
 
1.6%
Close Punctuation 3
 
1.6%
Dash Punctuation 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 8
 
14.3%
설 8
 
14.3%
면 5
 
8.9%
인 3
 
5.4%
명 3
 
5.4%
탁 2
 
3.6%
회 2
 
3.6%
위 2
 
3.6%
상 2
 
3.6%
센 1
 
1.8%
Other values (20) 20
35.7%
Decimal Number
ValueCountFrequency (%)
1 25
33.3%
2 18
24.0%
0 13
17.3%
4 5
 
6.7%
3 4
 
5.3%
5 4
 
5.3%
7 2
 
2.7%
6 2
 
2.7%
8 1
 
1.3%
9 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 20
87.0%
/ 3
 
13.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 128
69.6%
Hangul 56
30.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 8
 
14.3%
설 8
 
14.3%
면 5
 
8.9%
인 3
 
5.4%
명 3
 
5.4%
탁 2
 
3.6%
회 2
 
3.6%
위 2
 
3.6%
상 2
 
3.6%
센 1
 
1.8%
Other values (20) 20
35.7%
Common
ValueCountFrequency (%)
1 25
19.5%
22
17.2%
. 20
15.6%
2 18
14.1%
0 13
10.2%
4 5
 
3.9%
3 4
 
3.1%
5 4
 
3.1%
( 3
 
2.3%
) 3
 
2.3%
Other values (6) 11
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128
69.6%
Hangul 56
30.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25
19.5%
22
17.2%
. 20
15.6%
2 18
14.1%
0 13
10.2%
4 5
 
3.9%
3 4
 
3.1%
5 4
 
3.1%
( 3
 
2.3%
) 3
 
2.3%
Other values (6) 11
8.6%
Hangul
ValueCountFrequency (%)
신 8
 
14.3%
설 8
 
14.3%
면 5
 
8.9%
인 3
 
5.4%
명 3
 
5.4%
탁 2
 
3.6%
회 2
 
3.6%
위 2
 
3.6%
상 2
 
3.6%
센 1
 
1.8%
Other values (20) 20
35.7%

Unnamed: 13
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing43
Missing (%)93.5%
Memory size500.0 B
2023-12-13T03:05:40.233890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.3333333
Min length2

Characters and Unicode

Total characters10
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row비고
2nd row민간위탁
3rd row민간위탁
ValueCountFrequency (%)
민간위탁 2
66.7%
비고 1
33.3%
2023-12-13T03:05:40.603888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
민 2
20.0%
간 2
20.0%
위 2
20.0%
탁 2
20.0%
비 1
10.0%
고 1
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
민 2
20.0%
간 2
20.0%
위 2
20.0%
탁 2
20.0%
비 1
10.0%
고 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
민 2
20.0%
간 2
20.0%
위 2
20.0%
탁 2
20.0%
비 1
10.0%
고 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
민 2
20.0%
간 2
20.0%
위 2
20.0%
탁 2
20.0%
비 1
10.0%
고 1
10.0%

Correlations

2023-12-13T03:05:40.758317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영등포구 공영 노상주차장 일반현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
영등포구 공영 노상주차장 일반현황1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 11.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0000.9790.8370.8450.8690.4850.4850.7251.0001.0001.0001.000
Unnamed: 31.0001.0000.9791.0000.6130.6380.6080.0000.0000.6191.0001.0000.848NaN
Unnamed: 41.0001.0000.8370.6131.0000.9781.0001.0001.0000.7431.0001.0000.8421.000
Unnamed: 51.0001.0000.8450.6380.9781.0000.9720.9350.9350.7231.0001.0001.0000.000
Unnamed: 61.0001.0000.8690.6081.0000.9721.0001.0001.0000.7391.0001.0000.8421.000
Unnamed: 71.0001.0000.4850.0001.0000.9351.0001.0001.0000.613NaN1.0000.705NaN
Unnamed: 81.0001.0000.4850.0001.0000.9351.0001.0001.0000.613NaN1.0000.705NaN
Unnamed: 91.0001.0000.7250.6190.7430.7230.7390.6130.6131.0001.0001.0001.0000.000
Unnamed: 101.0001.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.0001.000NaN
Unnamed: 111.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 121.0001.0001.0000.8480.8421.0000.8420.7050.7051.0001.0001.0001.0001.000
Unnamed: 131.0001.0001.000NaN1.0000.0001.000NaNNaN0.000NaN1.0001.0001.000
2023-12-13T03:05:40.959309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 6Unnamed: 3Unnamed: 9Unnamed: 10Unnamed: 7Unnamed: 5Unnamed: 8
Unnamed: 41.0000.9720.5320.5930.8821.0000.8001.000
Unnamed: 60.9721.0000.5270.5880.8820.9720.7740.972
Unnamed: 30.5320.5271.0000.4430.9430.0000.5580.000
Unnamed: 90.5930.5880.4431.0000.8160.2960.5690.296
Unnamed: 100.8820.8820.9430.8161.0001.0000.9431.000
Unnamed: 71.0000.9720.0000.2961.0001.0000.6881.000
Unnamed: 50.8000.7740.5580.5690.9430.6881.0000.688
Unnamed: 81.0000.9720.0000.2961.0001.0000.6881.000
2023-12-13T03:05:41.130621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
Unnamed: 31.0000.5320.5580.5270.0000.0000.4430.943
Unnamed: 40.5321.0000.8000.9721.0001.0000.5930.882
Unnamed: 50.5580.8001.0000.7740.6880.6880.5690.943
Unnamed: 60.5270.9720.7741.0000.9720.9720.5880.882
Unnamed: 70.0001.0000.6880.9721.0001.0000.2961.000
Unnamed: 80.0001.0000.6880.9721.0001.0000.2961.000
Unnamed: 90.4430.5930.5690.5880.2960.2961.0000.816
Unnamed: 100.9430.8820.9430.8821.0001.0000.8161.000

Missing values

2023-12-13T03:05:34.604974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:05:34.842933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T03:05:35.343502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

영등포구 공영 노상주차장 일반현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1연번주차장명주차면인력급지반주 차 요 금<NA><NA>인수현황야간거주자(10개소 327면)기존명칭비 고비고
2<NA><NA><NA><NA><NA><NA>시간권일주차권월정기권<NA><NA><NA><NA><NA>
3<NA><NA><NA><NA><NA><NA>5<NA><NA><NA><NA><NA><NA><NA>
41가든예식장앞7112500200002000002004-10-01<NA><NA><NA><NA>
52경남아파트앞262431006000500002005-01-01거주자<NA><NA><NA>
63경도상가앞17212500200002000002005-01-01<NA><NA><NA><NA>
74경호빌딩앞10111500200002000002005-01-01<NA><NA><NA><NA>
85교보생명앞35123250150001000002005-01-01거주자대우아파트앞2010.5.3 신설(6면)<NA>
96교보생명옆13123250150001000002012-04-01거주자<NA>2012.4.1 신설<NA>
영등포구 공영 노상주차장 일반현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
3633호성빌딩앞22212500200002000002004-07-01<NA><NA><NA><NA>
3734홍우빌딩앞11112500200002000002005-01-01<NA><NA><NA><NA>
3835화재보험협회앞16212500200002000002006-12-04<NA><NA><NA><NA>
3936KBS별관뒤26212500200002000002005-01-01<NA><NA><NA><NA>
4037KBS별관옆21212500200002000002005-01-01<NA><NA><NA><NA>
4138KBS본관앞69611500200002000002005-01-01<NA><NA>2012.11.5 / 16면 신설<NA>
4239KBS연구관앞59211500200002000002005-01-01<NA><NA><NA><NA>
4340MBC방송국뒤63212500200002000002005-01-01거주자<NA><NA><NA>
4441SK리더스뷰도로변30123250150001000002005-01-01거주자홈플러스뒤문래동주민센터(13면)<NA>
45계<NA>93360<NA><NA><NA><NA><NA><NA><NA><NA>대직인력 8명관리반장 3명(총74명)<NA>