Overview

Dataset statistics

Number of variables13
Number of observations284
Missing cells278
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.8 KiB
Average record size in memory107.5 B

Variable types

Text4
Numeric3
Categorical4
DateTime2

Dataset

Description대구광역시 북구_경로당_20230127
Author대구광역시 북구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15028063&dataSetDetailId=150280631b60176464c11_202001081607&provdMethod=FILE

Alerts

관리기관 has constant value ""Constant
관리부서 has constant value ""Constant
데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 행정동명High correlation
경도 is highly overall correlated with 행정동명High correlation
행정동명 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
연락처비고 is highly overall correlated with 행정동명High correlation
연락처비고 is highly imbalanced (80.6%)Imbalance
연락처2 has 278 (97.9%) missing valuesMissing

Reproduction

Analysis started2024-04-19 06:50:13.593923
Analysis finished2024-04-19 06:50:15.032910
Duration1.44 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

경로당명
Text

Distinct282
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-19T15:50:15.163351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length8.4225352
Min length5

Characters and Unicode

Total characters2392
Distinct characters247
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique280 ?
Unique (%)98.6%

Sample

1st row고성2.3가경로당
2nd row고성A경로당
3rd row장수경로당
4th row오페라트루엘경로당
5th row북성경로당
ValueCountFrequency (%)
경로당 10
 
3.4%
동화타운경로당 2
 
0.7%
유성청구a 2
 
0.7%
삼성a경로당 2
 
0.7%
학정1동경로당 1
 
0.3%
태전3차우방a경로당 1
 
0.3%
우방미진경로당 1
 
0.3%
에덴팔공경로당 1
 
0.3%
새마을 1
 
0.3%
샛별경로당 1
 
0.3%
Other values (276) 276
92.6%
2024-04-19T15:50:15.534034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 292
 
12.2%
로 288
 
12.0%
당 287
 
12.0%
A 48
 
2.0%
동 41
 
1.7%
성 39
 
1.6%
산 36
 
1.5%
2 34
 
1.4%
지 33
 
1.4%
아 31
 
1.3%
Other values (237) 1263
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2186
91.4%
Decimal Number 99
 
4.1%
Uppercase Letter 59
 
2.5%
Space Separator 14
 
0.6%
Open Punctuation 12
 
0.5%
Close Punctuation 12
 
0.5%
Lowercase Letter 4
 
0.2%
Other Punctuation 4
 
0.2%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 292
 
13.4%
로 288
 
13.2%
당 287
 
13.1%
동 41
 
1.9%
성 39
 
1.8%
산 36
 
1.6%
지 33
 
1.5%
아 31
 
1.4%
대 30
 
1.4%
운 29
 
1.3%
Other values (215) 1080
49.4%
Decimal Number
ValueCountFrequency (%)
2 34
34.3%
1 30
30.3%
3 21
21.2%
0 4
 
4.0%
5 3
 
3.0%
8 2
 
2.0%
6 2
 
2.0%
7 2
 
2.0%
4 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
A 48
81.4%
L 4
 
6.8%
H 4
 
6.8%
S 1
 
1.7%
D 1
 
1.7%
U 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
@ 2
50.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2186
91.4%
Common 143
 
6.0%
Latin 63
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 292
 
13.4%
로 288
 
13.2%
당 287
 
13.1%
동 41
 
1.9%
성 39
 
1.8%
산 36
 
1.6%
지 33
 
1.5%
아 31
 
1.4%
대 30
 
1.4%
운 29
 
1.3%
Other values (215) 1080
49.4%
Common
ValueCountFrequency (%)
2 34
23.8%
1 30
21.0%
3 21
14.7%
14
9.8%
( 12
 
8.4%
) 12
 
8.4%
0 4
 
2.8%
5 3
 
2.1%
8 2
 
1.4%
6 2
 
1.4%
Other values (5) 9
 
6.3%
Latin
ValueCountFrequency (%)
A 48
76.2%
L 4
 
6.3%
H 4
 
6.3%
e 4
 
6.3%
S 1
 
1.6%
D 1
 
1.6%
U 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2186
91.4%
ASCII 206
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 292
 
13.4%
로 288
 
13.2%
당 287
 
13.1%
동 41
 
1.9%
성 39
 
1.8%
산 36
 
1.6%
지 33
 
1.5%
아 31
 
1.4%
대 30
 
1.4%
운 29
 
1.3%
Other values (215) 1080
49.4%
ASCII
ValueCountFrequency (%)
A 48
23.3%
2 34
16.5%
1 30
14.6%
3 21
10.2%
14
 
6.8%
( 12
 
5.8%
) 12
 
5.8%
L 4
 
1.9%
H 4
 
1.9%
e 4
 
1.9%
Other values (12) 23
11.2%

소재지도로명주소
Text

Distinct279
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-19T15:50:15.800903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length22.672535
Min length19

Characters and Unicode

Total characters6439
Distinct characters103
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

Unique274 ?
Unique (%)96.5%

Sample

1st row대구광역시 북구 고성북로 35-13(고성동3가)
2nd row대구광역시 북구 고성북로 3(고성동2가)
3rd row대구광역시 북구 칠성남로 41-8(고성동2가)
4th row대구광역시 북구 고성북로 34(고성동3가)
5th row대구광역시 북구 칠성남로20길 22-2(칠성동2가)
ValueCountFrequency (%)
대구광역시 284
25.2%
북구 284
25.2%
복현로 9
 
0.8%
검단로 7
 
0.6%
서변로 6
 
0.5%
동북로 5
 
0.4%
칠곡중앙대로 5
 
0.4%
태전로7길 4
 
0.4%
침산남로 4
 
0.4%
학남로 4
 
0.4%
Other values (426) 513
45.6%
2024-04-19T15:50:16.183275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
841
 
13.1%
구 618
 
9.6%
대 346
 
5.4%
동 340
 
5.3%
북 302
 
4.7%
시 287
 
4.5%
광 284
 
4.4%
역 284
 
4.4%
( 281
 
4.4%
) 281
 
4.4%
Other values (93) 2575
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4019
62.4%
Decimal Number 955
 
14.8%
Space Separator 841
 
13.1%
Open Punctuation 281
 
4.4%
Close Punctuation 281
 
4.4%
Dash Punctuation 56
 
0.9%
Other Punctuation 5
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 618
15.4%
대 346
 
8.6%
동 340
 
8.5%
북 302
 
7.5%
시 287
 
7.1%
광 284
 
7.1%
역 284
 
7.1%
로 271
 
6.7%
길 147
 
3.7%
산 87
 
2.2%
Other values (76) 1053
26.2%
Decimal Number
ValueCountFrequency (%)
1 221
23.1%
2 156
16.3%
5 108
11.3%
3 96
10.1%
4 84
 
8.8%
0 77
 
8.1%
7 64
 
6.7%
6 62
 
6.5%
8 46
 
4.8%
9 41
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
/ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
841
100.0%
Open Punctuation
ValueCountFrequency (%)
( 281
100.0%
Close Punctuation
ValueCountFrequency (%)
) 281
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4019
62.4%
Common 2419
37.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 618
15.4%
대 346
 
8.6%
동 340
 
8.5%
북 302
 
7.5%
시 287
 
7.1%
광 284
 
7.1%
역 284
 
7.1%
로 271
 
6.7%
길 147
 
3.7%
산 87
 
2.2%
Other values (76) 1053
26.2%
Common
ValueCountFrequency (%)
841
34.8%
( 281
 
11.6%
) 281
 
11.6%
1 221
 
9.1%
2 156
 
6.4%
5 108
 
4.5%
3 96
 
4.0%
4 84
 
3.5%
0 77
 
3.2%
7 64
 
2.6%
Other values (6) 210
 
8.7%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4019
62.4%
ASCII 2420
37.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
841
34.8%
( 281
 
11.6%
) 281
 
11.6%
1 221
 
9.1%
2 156
 
6.4%
5 108
 
4.5%
3 96
 
4.0%
4 84
 
3.5%
0 77
 
3.2%
7 64
 
2.6%
Other values (7) 211
 
8.7%
Hangul
ValueCountFrequency (%)
구 618
15.4%
대 346
 
8.6%
동 340
 
8.5%
북 302
 
7.5%
시 287
 
7.1%
광 284
 
7.1%
역 284
 
7.1%
로 271
 
6.7%
길 147
 
3.7%
산 87
 
2.2%
Other values (76) 1053
26.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct279
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.913681
Minimum35.874805
Maximum35.970057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-19T15:50:16.327607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.874805
5-th percentile35.880782
Q135.893645
median35.910405
Q335.9344
95-th percentile35.949544
Maximum35.970057
Range0.09525192
Interquartile range (IQR)0.040754542

Descriptive statistics

Standard deviation0.022947163
Coefficient of variation (CV)0.00063895324
Kurtosis-1.1747178
Mean35.913681
Median Absolute Deviation (MAD)0.019950395
Skewness0.23344672
Sum10199.485
Variance0.00052657229
MonotonicityNot monotonic
2024-04-19T15:50:16.476418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.90703787 2
 
0.7%
35.9222584 2
 
0.7%
35.90627346 2
 
0.7%
35.953249 2
 
0.7%
35.9146165 2
 
0.7%
35.92953659 1
 
0.4%
35.91880928 1
 
0.4%
35.91828246 1
 
0.4%
35.91864516 1
 
0.4%
35.92257733 1
 
0.4%
Other values (269) 269
94.7%
ValueCountFrequency (%)
35.87480515 1
0.4%
35.87781845 1
0.4%
35.87798653 1
0.4%
35.87843 1
0.4%
35.87856363 1
0.4%
35.87860643 1
0.4%
35.87982822 1
0.4%
35.879854 1
0.4%
35.88023409 1
0.4%
35.88028547 1
0.4%
ValueCountFrequency (%)
35.97005707 1
0.4%
35.96357474 1
0.4%
35.96143125 1
0.4%
35.95848056 1
0.4%
35.95708985 1
0.4%
35.95436723 1
0.4%
35.95400168 1
0.4%
35.95332231 1
0.4%
35.953249 2
0.7%
35.95270043 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct279
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.57751
Minimum128.50807
Maximum128.62816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-19T15:50:16.610229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.50807
5-th percentile128.54155
Q1128.5528
median128.5802
Q3128.60134
95-th percentile128.62079
Maximum128.62816
Range0.1200879
Interquartile range (IQR)0.048542375

Descriptive statistics

Standard deviation0.02866267
Coefficient of variation (CV)0.00022292133
Kurtosis-0.9306525
Mean128.57751
Median Absolute Deviation (MAD)0.02432755
Skewness-0.093379284
Sum36516.014
Variance0.00082154868
MonotonicityNot monotonic
2024-04-19T15:50:16.747446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.6196296 2
 
0.7%
128.6006893 2
 
0.7%
128.5626569 2
 
0.7%
128.5551534 2
 
0.7%
128.6268353 2
 
0.7%
128.5509976 1
 
0.4%
128.5513552 1
 
0.4%
128.5502767 1
 
0.4%
128.5498028 1
 
0.4%
128.549276 1
 
0.4%
Other values (269) 269
94.7%
ValueCountFrequency (%)
128.5080685 1
0.4%
128.5090026 1
0.4%
128.5118296 1
0.4%
128.5121023 1
0.4%
128.5124043 1
0.4%
128.5140712 1
0.4%
128.5142384 1
0.4%
128.5150119 1
0.4%
128.5250625 1
0.4%
128.5359321 1
0.4%
ValueCountFrequency (%)
128.6281564 1
0.4%
128.627359 1
0.4%
128.6268353 2
0.7%
128.6266659 1
0.4%
128.62592 1
0.4%
128.624233 1
0.4%
128.6239323 1
0.4%
128.6233406 1
0.4%
128.6229112 1
0.4%
128.622443 1
0.4%

행정동명
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
읍내동
24 
복현2동
23 
무태조야동
22 
구암동
21 
관문동
21 
Other values (18)
173 

Length

Max length5
Median length3
Mean length3.5704225
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고성동
2nd row고성동
3rd row고성동
4th row고성동
5th row칠성동

Common Values

ValueCountFrequency (%)
읍내동 24
 
8.5%
복현2동 23
 
8.1%
무태조야동 22
 
7.7%
구암동 21
 
7.4%
관문동 21
 
7.4%
침산3동 17
 
6.0%
태전1동 17
 
6.0%
국우동 16
 
5.6%
태전2동 15
 
5.3%
침산2동 13
 
4.6%
Other values (13) 95
33.5%

Length

2024-04-19T15:50:16.878319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
읍내동 24
 
8.5%
복현2동 23
 
8.1%
무태조야동 22
 
7.7%
구암동 21
 
7.4%
관문동 21
 
7.4%
침산3동 17
 
6.0%
태전1동 17
 
6.0%
국우동 16
 
5.6%
태전2동 15
 
5.3%
동천동 13
 
4.6%
Other values (13) 95
33.5%

등록일자
Date

Distinct204
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum1909-03-04 00:00:00
Maximum2099-11-15 00:00:00
2024-04-19T15:50:17.031578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:17.171490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

건물면적(제곱미터)
Real number (ℝ)

Distinct252
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.495394
Minimum21.06
Maximum291.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-19T15:50:17.305947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.06
5-th percentile33.01
Q161.7175
median90.345
Q3122.625
95-th percentile199.916
Maximum291.51
Range270.45
Interquartile range (IQR)60.9075

Descriptive statistics

Standard deviation50.189849
Coefficient of variation (CV)0.50444394
Kurtosis0.86114224
Mean99.495394
Median Absolute Deviation (MAD)30.345
Skewness0.95853008
Sum28256.692
Variance2519.021
MonotonicityNot monotonic
2024-04-19T15:50:17.782525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.0 4
 
1.4%
50.0 4
 
1.4%
80.0 4
 
1.4%
45.0 3
 
1.1%
135.0 3
 
1.1%
66.0 2
 
0.7%
102.0 2
 
0.7%
26.4 2
 
0.7%
90.0 2
 
0.7%
77.01 2
 
0.7%
Other values (242) 256
90.1%
ValueCountFrequency (%)
21.06 1
0.4%
22.7 1
0.4%
23.04 1
0.4%
23.1 1
0.4%
23.22 1
0.4%
23.7 1
0.4%
24.57 1
0.4%
25.0 1
0.4%
25.12 1
0.4%
25.5 1
0.4%
ValueCountFrequency (%)
291.51 1
0.4%
266.64 1
0.4%
258.91 1
0.4%
238.062 1
0.4%
229.0 1
0.4%
221.82 1
0.4%
218.15 2
0.7%
216.0 1
0.4%
209.85 1
0.4%
207.0 1
0.4%

연락처1
Text

Distinct275
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-19T15:50:18.013051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters3408
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

Unique274 ?
Unique (%)96.5%

Sample

1st row053-357-7265
2nd row053-356-3868
3rd row053-358-1117
4th row000-000-0000
5th row053-351-2196
ValueCountFrequency (%)
000-000-0000 10
 
3.5%
053-311-3934 1
 
0.4%
053-325-8885 1
 
0.4%
053-312-2177 1
 
0.4%
053-314-3319 1
 
0.4%
053-312-6779 1
 
0.4%
053-311-1555 1
 
0.4%
053-314-3312 1
 
0.4%
053-323-7444 1
 
0.4%
053-321-0818 1
 
0.4%
Other values (265) 265
93.3%
2024-04-19T15:50:18.378009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 623
18.3%
- 568
16.7%
0 501
14.7%
5 484
14.2%
2 269
7.9%
1 220
 
6.5%
4 171
 
5.0%
9 162
 
4.8%
8 158
 
4.6%
6 138
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2840
83.3%
Dash Punctuation 568
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 623
21.9%
0 501
17.6%
5 484
17.0%
2 269
9.5%
1 220
 
7.7%
4 171
 
6.0%
9 162
 
5.7%
8 158
 
5.6%
6 138
 
4.9%
7 114
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3408
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 623
18.3%
- 568
16.7%
0 501
14.7%
5 484
14.2%
2 269
7.9%
1 220
 
6.5%
4 171
 
5.0%
9 162
 
4.8%
8 158
 
4.6%
6 138
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 623
18.3%
- 568
16.7%
0 501
14.7%
5 484
14.2%
2 269
7.9%
1 220
 
6.5%
4 171
 
5.0%
9 162
 
4.8%
8 158
 
4.6%
6 138
 
4.0%

연락처2
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing278
Missing (%)97.9%
Memory size2.3 KiB
2024-04-19T15:50:18.551883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters72
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

Unique6 ?
Unique (%)100.0%

Sample

1st row053-357-7447
2nd row053-427-7050
3rd row053-351-5376
4th row053-292-8830
5th row053-322-8966
ValueCountFrequency (%)
053-357-7447 1
16.7%
053-427-7050 1
16.7%
053-351-5376 1
16.7%
053-292-8830 1
16.7%
053-322-8966 1
16.7%
053-323-5871 1
16.7%
2024-04-19T15:50:18.820014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 13
18.1%
- 12
16.7%
5 11
15.3%
0 9
12.5%
7 7
9.7%
2 6
8.3%
8 4
 
5.6%
4 3
 
4.2%
6 3
 
4.2%
1 2
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
83.3%
Dash Punctuation 12
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 13
21.7%
5 11
18.3%
0 9
15.0%
7 7
11.7%
2 6
10.0%
8 4
 
6.7%
4 3
 
5.0%
6 3
 
5.0%
1 2
 
3.3%
9 2
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 13
18.1%
- 12
16.7%
5 11
15.3%
0 9
12.5%
7 7
9.7%
2 6
8.3%
8 4
 
5.6%
4 3
 
4.2%
6 3
 
4.2%
1 2
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 13
18.1%
- 12
16.7%
5 11
15.3%
0 9
12.5%
7 7
9.7%
2 6
8.3%
8 4
 
5.6%
4 3
 
4.2%
6 3
 
4.2%
1 2
 
2.8%

연락처비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
264 
관리사무소
 
15
경비실
 
2
회
 
2
남, 여
 
1

Length

Max length5
Median length4
Mean length4.0246479
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 264
93.0%
관리사무소 15
 
5.3%
경비실 2
 
0.7%
회 2
 
0.7%
남, 여 1
 
0.4%

Length

2024-04-19T15:50:18.952857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:50:19.066266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 264
92.6%
관리사무소 15
 
5.3%
경비실 2
 
0.7%
회 2
 
0.7%
남 1
 
0.4%
여 1
 
0.4%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
대구광역시 북구
284 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 북구
2nd row대구광역시 북구
3rd row대구광역시 북구
4th row대구광역시 북구
5th row대구광역시 북구

Common Values

ValueCountFrequency (%)
대구광역시 북구 284
100.0%

Length

2024-04-19T15:50:19.166623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:50:19.249382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 284
50.0%
북구 284
50.0%

관리부서
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
복지정책과
284 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
복지정책과 284
100.0%

Length

2024-04-19T15:50:19.343187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:50:19.454183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
복지정책과 284
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2023-01-27 00:00:00
Maximum2023-01-27 00:00:00
2024-04-19T15:50:19.521254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:19.609291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-19T15:50:14.535486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:14.042530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:14.280693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:14.612937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:14.125964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:14.354118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:14.699209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:14.201903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:50:14.440860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T15:50:19.691702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동명건물면적(제곱미터)연락처2연락처비고
위도1.0000.7500.8900.3071.0000.422
경도0.7501.0000.9350.1921.0000.303
행정동명0.8900.9351.0000.1271.0000.949
건물면적(제곱미터)0.3070.1920.1271.0001.0000.440
연락처21.0001.0001.0001.0001.0001.000
연락처비고0.4220.3030.9490.4401.0001.000
2024-04-19T15:50:19.801578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명연락처비고
행정동명1.0000.671
연락처비고0.6711.000
2024-04-19T15:50:19.878182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도건물면적(제곱미터)행정동명연락처비고
위도1.000-0.426-0.0700.5850.093
경도-0.4261.0000.0070.6990.137
건물면적(제곱미터)-0.0700.0071.0000.0430.252
행정동명0.5850.6990.0431.0000.671
연락처비고0.0930.1370.2520.6711.000

Missing values

2024-04-19T15:50:14.815336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T15:50:14.970568image/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

경로당명소재지도로명주소위도경도행정동명등록일자건물면적(제곱미터)연락처1연락처2연락처비고관리기관관리부서데이터기준일자
0고성2.3가경로당대구광역시 북구 고성북로 35-13(고성동3가)35.88306128.582506고성동1990-04-0632.8053-357-7265053-357-7447남, 여대구광역시 북구복지정책과2023-01-27
1고성A경로당대구광역시 북구 고성북로 3(고성동2가)35.880649128.58148고성동1993-11-2999.0053-356-3868<NA><NA>대구광역시 북구복지정책과2023-01-27
2장수경로당대구광역시 북구 칠성남로 41-8(고성동2가)35.879828128.585339고성동2012-05-1472.6053-358-1117<NA><NA>대구광역시 북구복지정책과2023-01-27
3오페라트루엘경로당대구광역시 북구 고성북로 34(고성동3가)35.882025128.583667고성동2021-08-04120.2000-000-0000<NA><NA>대구광역시 북구복지정책과2023-01-27
4북성경로당대구광역시 북구 칠성남로20길 22-2(칠성동2가)35.87843128.589935칠성동1990-04-0699.45053-351-2196<NA><NA>대구광역시 북구복지정책과2023-01-27
5삼성A경로당대구광역시 북구 호암로 40(칠성동2가)35.881905128.593783칠성동1996-06-1592.25053-356-7475<NA><NA>대구광역시 북구복지정책과2023-01-27
6성광우방타운경로당대구광역시 북구 호암로 20(칠성동2가)35.882662128.592028칠성동1996-06-15106.0053-353-1747<NA><NA>대구광역시 북구복지정책과2023-01-27
7칠성경로당대구광역시 북구 칠성남로29길 11(칠성동2가)35.877987128.59691칠성동1990-04-0675.54053-423-7564<NA><NA>대구광역시 북구복지정책과2023-01-27
8칠성1가경로당대구광역시 북구 칠성시장로1길 60-3(칠성동1가)35.874805128.602257칠성동1994-04-06199.44053-427-4144<NA><NA>대구광역시 북구복지정책과2023-01-27
9칠성2가2동경로당대구광역시 북구 공평로 179-6(칠성동2가)35.880285128.600405칠성동1991-01-26169.49053-421-5966<NA><NA>대구광역시 북구복지정책과2023-01-27
경로당명소재지도로명주소위도경도행정동명등록일자건물면적(제곱미터)연락처1연락처2연락처비고관리기관관리부서데이터기준일자
274동호경로당대구광역시 북구 동호길 72-13(동호동)35.963575128.557798국우동1997-04-0185.02053-311-8507<NA><NA>대구광역시 북구복지정책과2023-01-27
275부영1단지A경로당대구광역시 북구 학정동로 7(국우동)35.944488128.564835국우동2002-10-1083.6053-321-0131<NA><NA>대구광역시 북구복지정책과2023-01-27
276칠곡현대아파트경로당대구광역시 북구 구리로 254(국우동)35.947781128.577384국우동2001-11-09122.0053-326-7587<NA><NA>대구광역시 북구복지정책과2023-01-27
277학수경로당대구광역시 북구 학남로 35(학정동)35.948769128.568667국우동1992-09-18135.18053-322-4751<NA><NA>대구광역시 북구복지정책과2023-01-27
278학정1동경로당대구광역시 북구 학정로109길 16(학정동)35.9527128.564528국우동1994-06-2050.0053-321-0772<NA><NA>대구광역시 북구복지정책과2023-01-27
279칠곡1차한라하우젠트경로당대구광역시 북구 학남로 10(학정동)35.948551128.56534국우동2007-07-04104.12053-324-7080<NA><NA>대구광역시 북구복지정책과2023-01-27
280칠곡7주공경로당대구광역시 북구 학남로 60(학정동)35.94756128.568991국우동2007-11-2089.79053-290-8146<NA><NA>대구광역시 북구복지정책과2023-01-27
281학정청아람경로당대구광역시 북구 학정로110길 29(학정동)35.954367128.566568국우동2008-08-19179.02053-312-0191<NA><NA>대구광역시 북구복지정책과2023-01-27
282노본경로당대구광역시 북구 노본길22-3(학정동)35.958481128.567762국우동2011-11-2436.0053-321-2468<NA><NA>대구광역시 북구복지정책과2023-01-27
283남문고을경로당대구광역시 북구 구리로34길 57(국우동)35.947804128.57632국우동2014-07-2436.0053-593-5033<NA>회대구광역시 북구복지정책과2023-01-27