Overview

Dataset statistics

Number of variables4
Number of observations100
Missing cells100
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory34.3 B

Variable types

Text3
Unsupported1

Alerts

장소 타입 has 100 (100.0%) missing valuesMissing
장소 아이디 has unique valuesUnique
장소 타입 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:16:08.880020
Analysis finished2023-12-10 10:16:09.919332
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

장소 아이디
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:16:10.204530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters2400
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 row5f28cf7ea42fcd6d828e0137
2nd row5f28d167a42fcd6d828e013b
3rd row5f28db25ee39d47796f08a01
4th row5f28dc6eee39d47796f08a05
5th row5f28f15b4de0bb7ebabe6db8
ValueCountFrequency (%)
5f28cf7ea42fcd6d828e0137 1
 
1.0%
5f929cf7e687616c801a19be 1
 
1.0%
5f92a0e1d3522b13c574bdd7 1
 
1.0%
5f92a0e1d3522b13c574bdd3 1
 
1.0%
5f92a0e0d3522b13c574bdcf 1
 
1.0%
5f92a0e0d3522b13c574bdcb 1
 
1.0%
5f92a0dfd3522b13c574bd75 1
 
1.0%
5f92a0dbd3522b13c574bd71 1
 
1.0%
5f92a0d8d3522b13c574bd6d 1
 
1.0%
5f92a0d2d3522b13c574bd3d 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:16:10.972792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 224
 
9.3%
7 206
 
8.6%
f 191
 
8.0%
d 173
 
7.2%
1 170
 
7.1%
e 168
 
7.0%
2 155
 
6.5%
8 144
 
6.0%
9 138
 
5.8%
6 134
 
5.6%
Other values (6) 697
29.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1508
62.8%
Lowercase Letter 892
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 224
14.9%
7 206
13.7%
1 170
11.3%
2 155
10.3%
8 144
9.5%
9 138
9.2%
6 134
8.9%
3 119
7.9%
4 115
7.6%
0 103
6.8%
Lowercase Letter
ValueCountFrequency (%)
f 191
21.4%
d 173
19.4%
e 168
18.8%
b 133
14.9%
c 126
14.1%
a 101
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1508
62.8%
Latin 892
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 224
14.9%
7 206
13.7%
1 170
11.3%
2 155
10.3%
8 144
9.5%
9 138
9.2%
6 134
8.9%
3 119
7.9%
4 115
7.6%
0 103
6.8%
Latin
ValueCountFrequency (%)
f 191
21.4%
d 173
19.4%
e 168
18.8%
b 133
14.9%
c 126
14.1%
a 101
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 224
 
9.3%
7 206
 
8.6%
f 191
 
8.0%
d 173
 
7.2%
1 170
 
7.1%
e 168
 
7.0%
2 155
 
6.5%
8 144
 
6.0%
9 138
 
5.8%
6 134
 
5.6%
Other values (6) 697
29.0%

장소 타입
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

장소 이름
Text

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:16:11.469891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.76
Min length2

Characters and Unicode

Total characters676
Distinct characters47
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)73.0%

Sample

1st row100동 100호
2nd row7208동 1204호
3rd row205동 604호
4th row316동 2304호
5th row105동 2101호
ValueCountFrequency (%)
301 8
 
5.8%
123 5
 
3.6%
107동 5
 
3.6%
111 4
 
2.9%
102동 4
 
2.9%
1111 3
 
2.2%
304 3
 
2.2%
604호 2
 
1.4%
1층 2
 
1.4%
106동 2
 
1.4%
Other values (93) 100
72.5%
2023-12-10T19:16:12.278033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 167
24.7%
0 136
20.1%
2 60
 
8.9%
3 49
 
7.2%
38
 
5.6%
호 36
 
5.3%
동 35
 
5.2%
4 34
 
5.0%
& 22
 
3.3%
5 18
 
2.7%
Other values (37) 81
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
74.0%
Other Letter 106
 
15.7%
Space Separator 38
 
5.6%
Other Punctuation 22
 
3.3%
Dash Punctuation 4
 
0.6%
Lowercase Letter 3
 
0.4%
Uppercase Letter 2
 
0.3%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 36
34.0%
동 35
33.0%
층 3
 
2.8%
이 3
 
2.8%
어 3
 
2.8%
집 2
 
1.9%
린 2
 
1.9%
학 2
 
1.9%
님 1
 
0.9%
방 1
 
0.9%
Other values (18) 18
17.0%
Decimal Number
ValueCountFrequency (%)
1 167
33.4%
0 136
27.2%
2 60
 
12.0%
3 49
 
9.8%
4 34
 
6.8%
5 18
 
3.6%
7 17
 
3.4%
8 9
 
1.8%
6 7
 
1.4%
9 3
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
f 1
33.3%
d 1
33.3%
a 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
38
100.0%
Other Punctuation
ValueCountFrequency (%)
& 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 565
83.6%
Hangul 106
 
15.7%
Latin 5
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 36
34.0%
동 35
33.0%
층 3
 
2.8%
이 3
 
2.8%
어 3
 
2.8%
집 2
 
1.9%
린 2
 
1.9%
학 2
 
1.9%
님 1
 
0.9%
방 1
 
0.9%
Other values (18) 18
17.0%
Common
ValueCountFrequency (%)
1 167
29.6%
0 136
24.1%
2 60
 
10.6%
3 49
 
8.7%
38
 
6.7%
4 34
 
6.0%
& 22
 
3.9%
5 18
 
3.2%
7 17
 
3.0%
8 9
 
1.6%
Other values (4) 15
 
2.7%
Latin
ValueCountFrequency (%)
B 1
20.0%
f 1
20.0%
d 1
20.0%
a 1
20.0%
A 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
84.3%
Hangul 101
 
14.9%
Compat Jamo 5
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 167
29.3%
0 136
23.9%
2 60
 
10.5%
3 49
 
8.6%
38
 
6.7%
4 34
 
6.0%
& 22
 
3.9%
5 18
 
3.2%
7 17
 
3.0%
8 9
 
1.6%
Other values (9) 20
 
3.5%
Hangul
ValueCountFrequency (%)
호 36
35.6%
동 35
34.7%
층 3
 
3.0%
이 3
 
3.0%
어 3
 
3.0%
집 2
 
2.0%
린 2
 
2.0%
학 2
 
2.0%
님 1
 
1.0%
방 1
 
1.0%
Other values (13) 13
 
12.9%
Compat Jamo
ValueCountFrequency (%)
ㅂ 1
20.0%
ㄹ 1
20.0%
ㅇ 1
20.0%
ㄴ 1
20.0%
ㅁ 1
20.0%

건물 아이디
Text

Distinct51
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:16:12.795339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters2400
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)38.0%

Sample

1st row5f06a67b153b58ec1eb3ea5b
2nd row5f06a1b2153b58ec1eb3ea29
3rd row5f06a67b153b58ec1eb3ea5b
4th row5f06a67b153b58ec1eb3ea5b
5th row5f06a658153b58ec1eb3ea59
ValueCountFrequency (%)
5f77ed1f39518d739336219b 22
22.0%
5f92a0d2d3522b13c574bd3c 8
 
8.0%
5f2b9e80fcc931639f20d589 6
 
6.0%
5f067dd8153b58ec1eb3ea12 4
 
4.0%
5f06a4e9153b58ec1eb3ea4d 3
 
3.0%
5f8d465df61d2e318b09fcf5 3
 
3.0%
5f4eeeaf706250145618bb53 3
 
3.0%
5f06a67b153b58ec1eb3ea5b 3
 
3.0%
5f06a658153b58ec1eb3ea59 2
 
2.0%
5f06a478153b58ec1eb3ea47 2
 
2.0%
Other values (41) 44
44.0%
2023-12-10T19:16:13.414880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 282
11.8%
3 236
 
9.8%
1 205
 
8.5%
f 183
 
7.6%
e 174
 
7.2%
b 162
 
6.8%
9 157
 
6.5%
8 136
 
5.7%
7 134
 
5.6%
d 133
 
5.5%
Other values (6) 598
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1547
64.5%
Lowercase Letter 853
35.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 282
18.2%
3 236
15.3%
1 205
13.3%
9 157
10.1%
8 136
8.8%
7 134
8.7%
2 122
7.9%
6 113
7.3%
0 93
 
6.0%
4 69
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
f 183
21.5%
e 174
20.4%
b 162
19.0%
d 133
15.6%
a 103
12.1%
c 98
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1547
64.5%
Latin 853
35.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 282
18.2%
3 236
15.3%
1 205
13.3%
9 157
10.1%
8 136
8.8%
7 134
8.7%
2 122
7.9%
6 113
7.3%
0 93
 
6.0%
4 69
 
4.5%
Latin
ValueCountFrequency (%)
f 183
21.5%
e 174
20.4%
b 162
19.0%
d 133
15.6%
a 103
12.1%
c 98
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 282
11.8%
3 236
 
9.8%
1 205
 
8.5%
f 183
 
7.6%
e 174
 
7.2%
b 162
 
6.8%
9 157
 
6.5%
8 136
 
5.7%
7 134
 
5.6%
d 133
 
5.5%
Other values (6) 598
24.9%

Correlations

2023-12-10T19:16:13.578295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장소 아이디장소 이름건물 아이디
장소 아이디1.0001.0001.000
장소 이름1.0001.0000.987
건물 아이디1.0000.9871.000

Missing values

2023-12-10T19:16:09.600008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:16:09.866101image/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

장소 아이디장소 타입장소 이름건물 아이디
05f28cf7ea42fcd6d828e0137<NA>100동 100호5f06a67b153b58ec1eb3ea5b
15f28d167a42fcd6d828e013b<NA>7208동 1204호5f06a1b2153b58ec1eb3ea29
25f28db25ee39d47796f08a01<NA>205동 604호5f06a67b153b58ec1eb3ea5b
35f28dc6eee39d47796f08a05<NA>316동 2304호5f06a67b153b58ec1eb3ea5b
45f28f15b4de0bb7ebabe6db8<NA>105동 2101호5f06a658153b58ec1eb3ea59
55f28f1bb4de0bb7ebabe6dbc<NA>107동 503호5f06a658153b58ec1eb3ea59
65f28f21a4de0bb7ebabe6dc0<NA>A동 402호5f0844b80e917c7f792a2df4
75f28f3324de0bb7ebabe6dc6<NA>2413동 401호5f06a5b2153b58ec1eb3ea53
85f28f3804de0bb7ebabe6dca<NA>102동 301호5f06a5d8153b58ec1eb3ea55
95f28f3f44de0bb7ebabe6dcd<NA>102동 1004호5f06a627153b58ec1eb3ea57
장소 아이디장소 타입장소 이름건물 아이디
905f962e189dc73747d6e71c91<NA>102&13015f77ed1f39518d739336219b
915f962e189dc73747d6e71c94<NA>102&3015f77ed1f39518d739336219b
925f962e189dc73747d6e71c97<NA>102&10025f77ed1f39518d739336219b
935f962e189dc73747d6e71c9a<NA>101&7025f77ed1f39518d739336219b
945f962e189dc73747d6e71c9d<NA>101&12055f77ed1f39518d739336219b
955f962e189dc73747d6e71ca0<NA>103&7045f77ed1f39518d739336219b
965f962e189dc73747d6e71ca3<NA>101&12045f77ed1f39518d739336219b
975f962e189dc73747d6e71ca6<NA>101&7015f77ed1f39518d739336219b
985f962e189dc73747d6e71cb0<NA>102&4015f77ed1f39518d739336219b
995f962e189dc73747d6e71cb3<NA>103&8055f77ed1f39518d739336219b