Overview

Dataset statistics

Number of variables19
Number of observations528
Missing cells2627
Missing cells (%)26.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.5 KiB
Average record size in memory152.2 B

Variable types

Unsupported2
Text6
Categorical11

Dataset

Description대구광역시_보행편의시설물(2)_20220802
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15103064&dataSetDetailId=151030641ffe59a685846&provdMethod=FILE

Alerts

Unnamed: 7 is highly overall correlated with Unnamed: 3 and 7 other fieldsHigh correlation
Unnamed: 5 is highly overall correlated with Unnamed: 3 and 7 other fieldsHigh correlation
Unnamed: 4 is highly overall correlated with Unnamed: 5 and 6 other fieldsHigh correlation
Unnamed: 9 is highly overall correlated with Unnamed: 3 and 6 other fieldsHigh correlation
Unnamed: 10 is highly overall correlated with Unnamed: 3 and 6 other fieldsHigh correlation
Unnamed: 6 is highly overall correlated with Unnamed: 3 and 7 other fieldsHigh correlation
Unnamed: 3 is highly overall correlated with Unnamed: 5 and 6 other fieldsHigh correlation
Unnamed: 8 is highly overall correlated with Unnamed: 3 and 7 other fieldsHigh correlation
Unnamed: 15 is highly overall correlated with Unnamed: 3 and 5 other fieldsHigh correlation
Unnamed: 3 is highly imbalanced (51.0%)Imbalance
Unnamed: 5 is highly imbalanced (52.7%)Imbalance
Unnamed: 6 is highly imbalanced (52.7%)Imbalance
Unnamed: 12 is highly imbalanced (94.7%)Imbalance
Unnamed: 15 is highly imbalanced (94.2%)Imbalance
Unnamed: 16 is highly imbalanced (93.9%)Imbalance
Unnamed: 11 has 522 (98.9%) missing valuesMissing
Unnamed: 13 has 525 (99.4%) missing valuesMissing
Unnamed: 14 has 525 (99.4%) missing valuesMissing
Unnamed: 17 has 526 (99.6%) missing valuesMissing
Unnamed: 18 has 526 (99.6%) missing valuesMissing
음향신호기 현황 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-21 13:57:59.484457
Analysis finished2024-04-21 13:58:02.794337
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

음향신호기 현황
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size4.2 KiB

Unnamed: 1
Text

Distinct527
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Memory size4.2 KiB
2024-04-21T22:58:03.469011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.2258065
Min length3

Characters and Unicode

Total characters3808
Distinct characters330
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique527 ?
Unique (%)100.0%

Sample

1st row설치장소
2nd row525개소
3rd row대평리내과의원 앞
4th row1차대자연아파트
5th row2차대자연아파트
ValueCountFrequency (%)
앞 54
 
7.8%
남편 14
 
2.0%
북편 14
 
2.0%
동편 13
 
1.9%
서편 12
 
1.7%
교차로 8
 
1.2%
남동편 6
 
0.9%
남서편 6
 
0.9%
대구학생문화센터 5
 
0.7%
서남편 4
 
0.6%
Other values (498) 553
80.3%
2024-04-21T22:58:04.485048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 242
 
6.4%
거 219
 
5.8%
166
 
4.4%
네 163
 
4.3%
편 129
 
3.4%
동 104
 
2.7%
교 85
 
2.2%
서 84
 
2.2%
대 77
 
2.0%
앞 73
 
1.9%
Other values (320) 2466
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3567
93.7%
Space Separator 166
 
4.4%
Decimal Number 39
 
1.0%
Uppercase Letter 22
 
0.6%
Open Punctuation 6
 
0.2%
Close Punctuation 6
 
0.2%
Dash Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 242
 
6.8%
거 219
 
6.1%
네 163
 
4.6%
편 129
 
3.6%
동 104
 
2.9%
교 85
 
2.4%
서 84
 
2.4%
대 77
 
2.2%
앞 73
 
2.0%
구 72
 
2.0%
Other values (297) 2319
65.0%
Uppercase Letter
ValueCountFrequency (%)
K 4
18.2%
I 4
18.2%
T 4
18.2%
C 3
13.6%
L 2
9.1%
M 1
 
4.5%
B 1
 
4.5%
H 1
 
4.5%
G 1
 
4.5%
A 1
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 11
28.2%
2 8
20.5%
3 8
20.5%
9 5
12.8%
5 4
 
10.3%
4 1
 
2.6%
6 1
 
2.6%
7 1
 
2.6%
Space Separator
ValueCountFrequency (%)
166
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3567
93.7%
Common 219
 
5.8%
Latin 22
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 242
 
6.8%
거 219
 
6.1%
네 163
 
4.6%
편 129
 
3.6%
동 104
 
2.9%
교 85
 
2.4%
서 84
 
2.4%
대 77
 
2.2%
앞 73
 
2.0%
구 72
 
2.0%
Other values (297) 2319
65.0%
Common
ValueCountFrequency (%)
166
75.8%
1 11
 
5.0%
2 8
 
3.7%
3 8
 
3.7%
( 6
 
2.7%
) 6
 
2.7%
9 5
 
2.3%
5 4
 
1.8%
4 1
 
0.5%
6 1
 
0.5%
Other values (3) 3
 
1.4%
Latin
ValueCountFrequency (%)
K 4
18.2%
I 4
18.2%
T 4
18.2%
C 3
13.6%
L 2
9.1%
M 1
 
4.5%
B 1
 
4.5%
H 1
 
4.5%
G 1
 
4.5%
A 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3567
93.7%
ASCII 241
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 242
 
6.8%
거 219
 
6.1%
네 163
 
4.6%
편 129
 
3.6%
동 104
 
2.9%
교 85
 
2.4%
서 84
 
2.4%
대 77
 
2.2%
앞 73
 
2.0%
구 72
 
2.0%
Other values (297) 2319
65.0%
ASCII
ValueCountFrequency (%)
166
68.9%
1 11
 
4.6%
2 8
 
3.3%
3 8
 
3.3%
( 6
 
2.5%
) 6
 
2.5%
9 5
 
2.1%
5 4
 
1.7%
K 4
 
1.7%
I 4
 
1.7%
Other values (13) 19
 
7.9%

Unnamed: 2
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size4.2 KiB

Unnamed: 3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
*
336 
<NA>
190 
방향
 
1
북동
 
1

Length

Max length4
Median length1
Mean length2.0833333
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
* 336
63.6%
<NA> 190
36.0%
방향 1
 
0.2%
북동 1
 
0.2%

Length

2024-04-21T22:58:04.724186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:04.912283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
336
63.6%
na 190
36.0%
방향 1
 
0.2%
북동 1
 
0.2%

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
*
336 
<NA>
191 
북서
 
1

Length

Max length4
Median length1
Mean length2.0871212
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
* 336
63.6%
<NA> 191
36.2%
북서 1
 
0.2%

Length

2024-04-21T22:58:05.122521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:05.305921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
336
63.6%
na 191
36.2%
북서 1
 
0.2%

Unnamed: 5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
*
355 
<NA>
171 
동남
 
1
 
1

Length

Max length4
Median length1
Mean length1.9734848
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
* 355
67.2%
<NA> 171
32.4%
동남 1
 
0.2%
1
 
0.2%

Length

2024-04-21T22:58:05.503423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:05.690717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
355
67.4%
na 171
32.4%
동남 1
 
0.2%

Unnamed: 6
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
*
355 
<NA>
171 
동북
 
1
 
1

Length

Max length4
Median length1
Mean length1.9734848
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
* 355
67.2%
<NA> 171
32.4%
동북 1
 
0.2%
1
 
0.2%

Length

2024-04-21T22:58:05.895338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:06.082763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
355
67.4%
na 171
32.4%
동북 1
 
0.2%

Unnamed: 7
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
*
288 
<NA>
238 
남동
 
1
 
1

Length

Max length4
Median length1
Mean length2.3541667
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
* 288
54.5%
<NA> 238
45.1%
남동 1
 
0.2%
1
 
0.2%

Length

2024-04-21T22:58:06.285936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:06.471953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
288
54.6%
na 238
45.2%
남동 1
 
0.2%

Unnamed: 8
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
*
288 
<NA>
238 
남서
 
1
 
1

Length

Max length4
Median length1
Mean length2.3541667
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
* 288
54.5%
<NA> 238
45.1%
남서 1
 
0.2%
1
 
0.2%

Length

2024-04-21T22:58:06.673539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:06.858415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
288
54.6%
na 238
45.2%
남서 1
 
0.2%

Unnamed: 9
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
*
309 
<NA>
217 
서남
 
1
 
1

Length

Max length4
Median length1
Mean length2.2348485
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
* 309
58.5%
<NA> 217
41.1%
서남 1
 
0.2%
1
 
0.2%

Length

2024-04-21T22:58:07.059331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:07.244108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
309
58.6%
na 217
41.2%
서남 1
 
0.2%

Unnamed: 10
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
*
309 
<NA>
217 
서북
 
1
 
1

Length

Max length4
Median length1
Mean length2.2348485
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
* 309
58.5%
<NA> 217
41.1%
서북 1
 
0.2%
1
 
0.2%

Length

2024-04-21T22:58:07.445108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:07.629944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
309
58.6%
na 217
41.2%
서북 1
 
0.2%

Unnamed: 11
Text

MISSING 

Distinct3
Distinct (%)50.0%
Missing522
Missing (%)98.9%
Memory size4.2 KiB
2024-04-21T22:58:07.748739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.3333333
Min length1

Characters and Unicode

Total characters8
Distinct characters5
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

Unique2 ?
Unique (%)33.3%

Sample

1st row방향
2nd row북동
3rd row*
4th row*
5th row*
ValueCountFrequency (%)
4
66.7%
방향 1
 
16.7%
북동 1
 
16.7%
2024-04-21T22:58:08.095162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 4
50.0%
방 1
 
12.5%
향 1
 
12.5%
북 1
 
12.5%
동 1
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 4
50.0%
Other Letter 4
50.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
방 1
25.0%
향 1
25.0%
북 1
25.0%
동 1
25.0%
Other Punctuation
ValueCountFrequency (%)
* 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
50.0%
Hangul 4
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
방 1
25.0%
향 1
25.0%
북 1
25.0%
동 1
25.0%
Common
ValueCountFrequency (%)
* 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
50.0%
Hangul 4
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 4
100.0%
Hangul
ValueCountFrequency (%)
방 1
25.0%
향 1
25.0%
북 1
25.0%
동 1
25.0%

Unnamed: 12
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
<NA>
523 
*
 
4
북서
 
1

Length

Max length4
Median length4
Mean length3.9734848
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 523
99.1%
* 4
 
0.8%
북서 1
 
0.2%

Length

2024-04-21T22:58:08.313828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:08.578928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 523
99.1%
4
 
0.8%
북서 1
 
0.2%

Unnamed: 13
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing525
Missing (%)99.4%
Memory size4.2 KiB
2024-04-21T22:58:08.688351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.3333333
Min length1

Characters and Unicode

Total characters4
Distinct characters3
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

Unique1 ?
Unique (%)33.3%

Sample

1st row동남
2nd row*
3rd row*
ValueCountFrequency (%)
2
66.7%
동남 1
33.3%
2024-04-21T22:58:09.083923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 2
50.0%
동 1
25.0%
남 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 2
50.0%
Other Letter 2
50.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1
50.0%
남 1
50.0%
Other Punctuation
ValueCountFrequency (%)
* 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2
50.0%
Hangul 2
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1
50.0%
남 1
50.0%
Common
ValueCountFrequency (%)
* 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2
50.0%
Hangul 2
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 2
100.0%
Hangul
ValueCountFrequency (%)
동 1
50.0%
남 1
50.0%

Unnamed: 14
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing525
Missing (%)99.4%
Memory size4.2 KiB
2024-04-21T22:58:09.217293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.3333333
Min length1

Characters and Unicode

Total characters4
Distinct characters3
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

Unique1 ?
Unique (%)33.3%

Sample

1st row동북
2nd row*
3rd row*
ValueCountFrequency (%)
2
66.7%
동북 1
33.3%
2024-04-21T22:58:09.556946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 2
50.0%
동 1
25.0%
북 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 2
50.0%
Other Letter 2
50.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1
50.0%
북 1
50.0%
Other Punctuation
ValueCountFrequency (%)
* 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2
50.0%
Hangul 2
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1
50.0%
북 1
50.0%
Common
ValueCountFrequency (%)
* 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2
50.0%
Hangul 2
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 2
100.0%
Hangul
ValueCountFrequency (%)
동 1
50.0%
북 1
50.0%

Unnamed: 15
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
<NA>
521 
*
 
5
남동
 
1
 
1

Length

Max length4
Median length4
Mean length3.9621212
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 521
98.7%
* 5
 
0.9%
남동 1
 
0.2%
1
 
0.2%

Length

2024-04-21T22:58:09.771549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:09.955585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 521
98.9%
5
 
0.9%
남동 1
 
0.2%

Unnamed: 16
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
<NA>
522 
*
 
5
남서
 
1

Length

Max length4
Median length4
Mean length3.967803
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 522
98.9%
* 5
 
0.9%
남서 1
 
0.2%

Length

2024-04-21T22:58:10.147516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:10.337903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 522
98.9%
5
 
0.9%
남서 1
 
0.2%

Unnamed: 17
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing526
Missing (%)99.6%
Memory size4.2 KiB
2024-04-21T22:58:10.521379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length1.5
Mean length1.5
Min length1

Characters and Unicode

Total characters3
Distinct characters3
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

Unique2 ?
Unique (%)100.0%

Sample

1st row서남
2nd row*
ValueCountFrequency (%)
서남 1
50.0%
1
50.0%
2024-04-21T22:58:10.936270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 1
33.3%
남 1
33.3%
* 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 1
50.0%
남 1
50.0%
Other Punctuation
ValueCountFrequency (%)
* 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
66.7%
Common 1
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 1
50.0%
남 1
50.0%
Common
ValueCountFrequency (%)
* 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
66.7%
ASCII 1
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 1
50.0%
남 1
50.0%
ASCII
ValueCountFrequency (%)
* 1
100.0%

Unnamed: 18
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing526
Missing (%)99.6%
Memory size4.2 KiB
2024-04-21T22:58:11.144149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length1.5
Mean length1.5
Min length1

Characters and Unicode

Total characters3
Distinct characters3
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

Unique2 ?
Unique (%)100.0%

Sample

1st row서북
2nd row*
ValueCountFrequency (%)
서북 1
50.0%
1
50.0%
2024-04-21T22:58:11.556908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 1
33.3%
북 1
33.3%
* 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 1
50.0%
북 1
50.0%
Other Punctuation
ValueCountFrequency (%)
* 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
66.7%
Common 1
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 1
50.0%
북 1
50.0%
Common
ValueCountFrequency (%)
* 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
66.7%
ASCII 1
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 1
50.0%
북 1
50.0%
ASCII
ValueCountFrequency (%)
* 1
100.0%

Correlations

2024-04-21T22:58:11.725440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18
Unnamed: 31.0000.7031.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0000.0000.0000.000
Unnamed: 40.7031.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.0000.0000.000
Unnamed: 51.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.000NaNNaN
Unnamed: 61.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.000NaNNaN
Unnamed: 71.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.000NaNNaN
Unnamed: 81.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.000NaNNaN
Unnamed: 91.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.000NaNNaN
Unnamed: 101.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.000NaNNaN
Unnamed: 111.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
Unnamed: 120.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
Unnamed: 130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
Unnamed: 140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
Unnamed: 151.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
Unnamed: 160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
Unnamed: 170.0000.000NaNNaNNaNNaNNaNNaN0.0000.0000.0000.0000.0000.0001.0000.000
Unnamed: 180.0000.000NaNNaNNaNNaNNaNNaN0.0000.0000.0000.0000.0000.0000.0001.000
2024-04-21T22:58:12.022203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 16Unnamed: 12Unnamed: 7Unnamed: 5Unnamed: 4Unnamed: 15Unnamed: 9Unnamed: 10Unnamed: 6Unnamed: 3Unnamed: 8
Unnamed: 161.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Unnamed: 120.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Unnamed: 70.0000.0001.0001.0000.9980.8161.0001.0001.0000.9981.000
Unnamed: 50.0000.0001.0001.0000.9980.8161.0001.0001.0000.9981.000
Unnamed: 40.0000.0000.9980.9981.0000.8160.9980.9980.9980.4960.998
Unnamed: 150.0000.0000.8160.8160.8161.0000.0000.0000.8160.8160.816
Unnamed: 90.0000.0001.0001.0000.9980.0001.0001.0001.0000.9981.000
Unnamed: 100.0000.0001.0001.0000.9980.0001.0001.0001.0000.9981.000
Unnamed: 60.0000.0001.0001.0000.9980.8161.0001.0001.0000.9981.000
Unnamed: 30.0000.0000.9980.9980.4960.8160.9980.9980.9981.0000.998
Unnamed: 80.0000.0001.0001.0000.9980.8161.0001.0001.0000.9981.000
2024-04-21T22:58:12.257624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 12Unnamed: 15Unnamed: 16
Unnamed: 31.0000.4960.9980.9980.9980.9980.9980.9980.0000.8160.000
Unnamed: 40.4961.0000.9980.9980.9980.9980.9980.9980.0000.8160.000
Unnamed: 50.9980.9981.0001.0001.0001.0001.0001.0000.0000.8160.000
Unnamed: 60.9980.9981.0001.0001.0001.0001.0001.0000.0000.8160.000
Unnamed: 70.9980.9981.0001.0001.0001.0001.0001.0000.0000.8160.000
Unnamed: 80.9980.9981.0001.0001.0001.0001.0001.0000.0000.8160.000
Unnamed: 90.9980.9981.0001.0001.0001.0001.0001.0000.0000.0000.000
Unnamed: 100.9980.9981.0001.0001.0001.0001.0001.0000.0000.0000.000
Unnamed: 120.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
Unnamed: 150.8160.8160.8160.8160.8160.8160.0000.0000.0001.0000.000
Unnamed: 160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-21T22:58:01.400557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T22:58:01.815365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-21T22:58:02.420966image/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

음향신호기 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18
0NaN<NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1연번설치장소수량방향<NA><NA><NA><NA><NA><NA><NA>방향<NA><NA><NA><NA><NA><NA><NA>
2계525개소2606북동북서동남동북남동남서서남서북북동북서동남동북남동남서서남서북
31대평리내과의원 앞8********<NA><NA><NA><NA><NA><NA><NA><NA>
421차대자연아파트4<NA><NA>****<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
532차대자연아파트2**<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
64산격양우아파트2<NA><NA><NA><NA><NA><NA>**<NA><NA><NA><NA><NA><NA><NA><NA>
75러브산업안전 앞2**<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
86포산중앙사거리 북편8********<NA><NA><NA><NA><NA><NA><NA><NA>
97제일풍경채북서편8********<NA><NA><NA><NA><NA><NA><NA><NA>
음향신호기 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18
518516호림네거리 북편2**<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
519517고산역화성파크드림4****<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
520518신매초등학교교차로4**<NA><NA>**<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
521519범어네거리 남편4<NA><NA>**<NA><NA>**<NA><NA><NA><NA><NA><NA><NA><NA>
522520청운휘트니스클럽8********<NA><NA><NA><NA><NA><NA><NA><NA>
523521삼덕119안전센터2**<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
524522중앙네거리 북편4**<NA><NA>**<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
525523수성구청역4****<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
526524경대교교차로6<NA><NA>******<NA><NA><NA><NA><NA><NA><NA><NA>
527525매천고네거리2<NA><NA>**<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>