Overview

Dataset statistics

Number of variables34
Number of observations7225
Missing cells9785
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory276.0 B

Variable types

Text10
Numeric4
Categorical19
DateTime1

Alerts

updategbn is highly imbalanced (97.1%)Imbalance
opnsvcnm is highly imbalanced (85.0%)Imbalance
clgstdt is highly imbalanced (97.7%)Imbalance
clgenddt is highly imbalanced (97.6%)Imbalance
ropnymd is highly imbalanced (95.8%)Imbalance
trdstatenm is highly imbalanced (52.8%)Imbalance
dtlstatenm is highly imbalanced (54.8%)Imbalance
uptaenm is highly imbalanced (82.2%)Imbalance
sitetel is highly imbalanced (96.9%)Imbalance
bdngdngnum is highly imbalanced (72.1%)Imbalance
puprsenm is highly imbalanced (97.5%)Imbalance
bupnm is highly imbalanced (93.5%)Imbalance
insurjnyncode is highly imbalanced (56.5%)Imbalance
drmkcobnm is highly imbalanced (93.5%)Imbalance
ldercnt is highly imbalanced (69.4%)Imbalance
memcolltotstfnum is highly imbalanced (97.4%)Imbalance
last_load_dttm is highly imbalanced (99.6%)Imbalance
sitepostno has 1764 (24.4%) missing valuesMissing
sitewhladdr has 143 (2.0%) missing valuesMissing
rdnwhladdr has 349 (4.8%) missing valuesMissing
dcbymd has 3919 (54.2%) missing valuesMissing
x has 133 (1.8%) missing valuesMissing
y has 133 (1.8%) missing valuesMissing
bdngyarea has 3337 (46.2%) missing valuesMissing
opnsfteamcode is highly skewed (γ1 = -21.75812187)Skewed
apvpermymd is highly skewed (γ1 = -59.00075817)Skewed
skey has unique valuesUnique

Reproduction

Analysis started2024-04-17 21:55:18.593141
Analysis finished2024-04-17 21:55:20.473385
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Text

UNIQUE 

Distinct7225
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
2024-04-18T06:55:20.775731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length3.8840138
Min length1

Characters and Unicode

Total characters28062
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7225 ?
Unique (%)100.0%

Sample

1st row4
2nd row5
3rd row6
4th row7
5th row8
ValueCountFrequency (%)
4 1
 
< 0.1%
4852 1
 
< 0.1%
4833 1
 
< 0.1%
4832 1
 
< 0.1%
4831 1
 
< 0.1%
4830 1
 
< 0.1%
4829 1
 
< 0.1%
4828 1
 
< 0.1%
4827 1
 
< 0.1%
4826 1
 
< 0.1%
Other values (7216) 7216
99.9%
2024-04-18T06:55:21.280564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3514
12.5%
3 3261
11.6%
2 3257
11.6%
4 3208
11.4%
5 3207
11.4%
6 2965
10.6%
7 2224
7.9%
8 2141
7.6%
0 2139
7.6%
9 2137
7.6%
Other values (9) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28053
> 99.9%
Lowercase Letter 5
 
< 0.1%
Uppercase Letter 3
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3514
12.5%
3 3261
11.6%
2 3257
11.6%
4 3208
11.4%
5 3207
11.4%
6 2965
10.6%
7 2224
7.9%
8 2141
7.6%
0 2139
7.6%
9 2137
7.6%
Lowercase Letter
ValueCountFrequency (%)
t 1
20.0%
u 1
20.0%
d 1
20.0%
i 1
20.0%
o 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
T 1
33.3%
S 1
33.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28054
> 99.9%
Latin 8
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3514
12.5%
3 3261
11.6%
2 3257
11.6%
4 3208
11.4%
5 3207
11.4%
6 2965
10.6%
7 2224
7.9%
8 2141
7.6%
0 2139
7.6%
9 2137
7.6%
Latin
ValueCountFrequency (%)
P 1
12.5%
T 1
12.5%
S 1
12.5%
t 1
12.5%
u 1
12.5%
d 1
12.5%
i 1
12.5%
o 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3514
12.5%
3 3261
11.6%
2 3257
11.6%
4 3208
11.4%
5 3207
11.4%
6 2965
10.6%
7 2224
7.9%
8 2141
7.6%
0 2139
7.6%
9 2137
7.6%
Other values (9) 9
 
< 0.1%

opnsfteamcode
Real number (ℝ)

SKEWED 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3326792.4
Minimum614853
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-04-18T06:55:21.390323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum614853
5-th percentile3260000
Q13300000
median3330000
Q33350000
95-th percentile3390000
Maximum3400000
Range2785147
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation50197.955
Coefficient of variation (CV)0.015088996
Kurtosis1177.9975
Mean3326792.4
Median Absolute Deviation (MAD)30000
Skewness-21.758122
Sum2.4036075 × 1010
Variance2.5198347 × 109
MonotonicityNot monotonic
2024-04-18T06:55:21.514919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3300000 833
11.5%
3290000 771
10.7%
3330000 701
9.7%
3310000 694
9.6%
3340000 614
8.5%
3350000 588
8.1%
3390000 506
 
7.0%
3320000 481
 
6.7%
3370000 407
 
5.6%
3380000 318
 
4.4%
Other values (7) 1312
18.2%
ValueCountFrequency (%)
614853 1
 
< 0.1%
3250000 205
 
2.8%
3260000 188
 
2.6%
3270000 184
 
2.5%
3280000 235
 
3.3%
3290000 771
10.7%
3300000 833
11.5%
3310000 694
9.6%
3320000 481
6.7%
3330000 701
9.7%
ValueCountFrequency (%)
3400000 275
 
3.8%
3390000 506
7.0%
3380000 318
4.4%
3370000 407
5.6%
3360000 224
 
3.1%
3350000 588
8.1%
3340000 614
8.5%
3330000 701
9.7%
3320000 481
6.7%
3310000 694
9.6%

mgtno
Text

Distinct1778
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
2024-04-18T06:55:21.677485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length20
Mean length20.001107
Min length20

Characters and Unicode

Total characters144508
Distinct characters31
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique694 ?
Unique (%)9.6%

Sample

1st rowCDFH3301051997000002
2nd rowCDFH3301051999000001
3rd rowCDFH3301052003000001
4th rowCDFH3301052003000002
5th rowCDFH3301052004000001
ValueCountFrequency (%)
cdfh3301062019000002 17
 
0.2%
cdfh3301082015000001 16
 
0.2%
cdfh3301082003000002 16
 
0.2%
cdfh3301062015000001 16
 
0.2%
cdfh3301082009000006 16
 
0.2%
cdfh3301082016000001 16
 
0.2%
cdfh3301082009000003 16
 
0.2%
cdfh3301082017000001 16
 
0.2%
cdfh3301082009000001 16
 
0.2%
cdfh3301082003000001 16
 
0.2%
Other values (1772) 7068
97.8%
2024-04-18T06:55:21.988288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58870
40.7%
3 16482
 
11.4%
1 14318
 
9.9%
2 9474
 
6.6%
C 7224
 
5.0%
D 7224
 
5.0%
F 7224
 
5.0%
H 7224
 
5.0%
9 4474
 
3.1%
8 4455
 
3.1%
Other values (21) 7539
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 115589
80.0%
Uppercase Letter 28896
 
20.0%
Other Letter 15
 
< 0.1%
Space Separator 5
 
< 0.1%
Dash Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
13.3%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (3) 3
20.0%
Decimal Number
ValueCountFrequency (%)
0 58870
50.9%
3 16482
 
14.3%
1 14318
 
12.4%
2 9474
 
8.2%
9 4474
 
3.9%
8 4455
 
3.9%
6 2627
 
2.3%
5 2216
 
1.9%
4 1449
 
1.3%
7 1224
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
C 7224
25.0%
D 7224
25.0%
F 7224
25.0%
H 7224
25.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115597
80.0%
Latin 28896
 
20.0%
Hangul 15
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58870
50.9%
3 16482
 
14.3%
1 14318
 
12.4%
2 9474
 
8.2%
9 4474
 
3.9%
8 4455
 
3.9%
6 2627
 
2.3%
5 2216
 
1.9%
4 1449
 
1.3%
7 1224
 
1.1%
Other values (4) 8
 
< 0.1%
Hangul
ValueCountFrequency (%)
2
13.3%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (3) 3
20.0%
Latin
ValueCountFrequency (%)
C 7224
25.0%
D 7224
25.0%
F 7224
25.0%
H 7224
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144493
> 99.9%
Hangul 15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58870
40.7%
3 16482
 
11.4%
1 14318
 
9.9%
2 9474
 
6.6%
C 7224
 
5.0%
D 7224
 
5.0%
F 7224
 
5.0%
H 7224
 
5.0%
9 4474
 
3.1%
8 4455
 
3.1%
Other values (8) 7524
 
5.2%
Hangul
ValueCountFrequency (%)
2
13.3%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (3) 3
20.0%

opnsvcid
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
10_32_01_P
3052 
10_41_01_P
1728 
10_42_01_P
1384 
10_31_01_P
919 
10_35_01_P
 
95
Other values (5)
 
47

Length

Max length10
Median length10
Mean length9.999308
Min length5

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row10_31_01_P
2nd row10_31_01_P
3rd row10_31_01_P
4th row10_31_01_P
5th row10_31_01_P

Common Values

ValueCountFrequency (%)
10_32_01_P 3052
42.2%
10_41_01_P 1728
23.9%
10_42_01_P 1384
19.2%
10_31_01_P 919
 
12.7%
10_35_01_P 95
 
1.3%
10_37_01_P 34
 
0.5%
10_33_02_P 8
 
0.1%
10_39_01_P 3
 
< 0.1%
47213 1
 
< 0.1%
10_33_01_P 1
 
< 0.1%

Length

2024-04-18T06:55:22.126467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:22.257003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10_32_01_p 3052
42.2%
10_41_01_p 1728
23.9%
10_42_01_p 1384
19.2%
10_31_01_p 919
 
12.7%
10_35_01_p 95
 
1.3%
10_37_01_p 34
 
0.5%
10_33_02_p 8
 
0.1%
10_39_01_p 3
 
< 0.1%
47213 1
 
< 0.1%
10_33_01_p 1
 
< 0.1%

updategbn
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
I
7189 
U
 
35
부산광역시 부산진구 중앙대로 923-1, 2층 (양정동)
 
1

Length

Max length31
Median length1
Mean length1.0041522
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
I 7189
99.5%
U 35
 
0.5%
부산광역시 부산진구 중앙대로 923-1, 2층 (양정동) 1
 
< 0.1%

Length

2024-04-18T06:55:22.377282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:22.462767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 7189
99.4%
u 35
 
0.5%
부산광역시 1
 
< 0.1%
부산진구 1
 
< 0.1%
중앙대로 1
 
< 0.1%
923-1 1
 
< 0.1%
2층 1
 
< 0.1%
양정동 1
 
< 0.1%
Distinct263
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
Minimum2013-12-05 00:00:00
Maximum2021-02-28 02:40:00
2024-04-18T06:55:22.560388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T06:55:22.706082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

opnsvcnm
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
6746 
체력단련장업
 
183
당구장업
 
107
체육도장업
 
99
골프연습장업
 
63
Other values (5)
 
27

Length

Max length7
Median length4
Mean length4.0853979
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6746
93.4%
체력단련장업 183
 
2.5%
당구장업 107
 
1.5%
체육도장업 99
 
1.4%
골프연습장업 63
 
0.9%
수영장업 9
 
0.1%
무도학원업 8
 
0.1%
종합체육시설업 6
 
0.1%
썰매장업 3
 
< 0.1%
무도장업 1
 
< 0.1%

Length

2024-04-18T06:55:22.832626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:23.012497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6746
93.4%
체력단련장업 183
 
2.5%
당구장업 107
 
1.5%
체육도장업 99
 
1.4%
골프연습장업 63
 
0.9%
수영장업 9
 
0.1%
무도학원업 8
 
0.1%
종합체육시설업 6
 
0.1%
썰매장업 3
 
< 0.1%
무도장업 1
 
< 0.1%

bplcnm
Text

Distinct5429
Distinct (%)75.2%
Missing1
Missing (%)< 0.1%
Memory size56.6 KiB
2024-04-18T06:55:23.350091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length6.6515781
Min length1

Characters and Unicode

Total characters48051
Distinct characters753
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4542 ?
Unique (%)62.9%

Sample

1st row광복실내골프연습장
2nd row마린골프연습장
3rd row포시즌 골프연습장
4th row에스에스 골프연습장
5th row가나다라골프연습장
ValueCountFrequency (%)
당구장 252
 
2.7%
당구클럽 239
 
2.6%
태권도 115
 
1.2%
휘트니스 82
 
0.9%
태권도장 62
 
0.7%
골프 52
 
0.6%
스크린골프 43
 
0.5%
헬스 39
 
0.4%
골프연습장 36
 
0.4%
스크린 35
 
0.4%
Other values (5494) 8362
89.7%
2024-04-18T06:55:23.779234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2974
 
6.2%
2942
 
6.1%
2729
 
5.7%
2167
 
4.5%
2093
 
4.4%
1180
 
2.5%
1179
 
2.5%
1092
 
2.3%
989
 
2.1%
909
 
1.9%
Other values (743) 29797
62.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42588
88.6%
Space Separator 2093
 
4.4%
Uppercase Letter 1880
 
3.9%
Lowercase Letter 469
 
1.0%
Decimal Number 389
 
0.8%
Close Punctuation 231
 
0.5%
Open Punctuation 230
 
0.5%
Other Punctuation 141
 
0.3%
Dash Punctuation 15
 
< 0.1%
Letter Number 6
 
< 0.1%
Other values (4) 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2974
 
7.0%
2942
 
6.9%
2729
 
6.4%
2167
 
5.1%
1180
 
2.8%
1179
 
2.8%
1092
 
2.6%
989
 
2.3%
909
 
2.1%
862
 
2.0%
Other values (660) 25565
60.0%
Uppercase Letter
ValueCountFrequency (%)
M 140
 
7.4%
S 131
 
7.0%
G 127
 
6.8%
P 127
 
6.8%
T 119
 
6.3%
A 117
 
6.2%
K 115
 
6.1%
O 100
 
5.3%
I 97
 
5.2%
J 96
 
5.1%
Other values (16) 711
37.8%
Lowercase Letter
ValueCountFrequency (%)
e 48
 
10.2%
i 47
 
10.0%
o 45
 
9.6%
n 41
 
8.7%
s 33
 
7.0%
l 29
 
6.2%
a 28
 
6.0%
t 23
 
4.9%
r 23
 
4.9%
y 22
 
4.7%
Other values (15) 130
27.7%
Decimal Number
ValueCountFrequency (%)
2 140
36.0%
0 64
16.5%
1 59
15.2%
3 29
 
7.5%
5 28
 
7.2%
7 23
 
5.9%
4 20
 
5.1%
8 14
 
3.6%
9 7
 
1.8%
6 5
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 64
45.4%
& 53
37.6%
, 8
 
5.7%
· 5
 
3.5%
' 4
 
2.8%
# 2
 
1.4%
: 2
 
1.4%
2
 
1.4%
/ 1
 
0.7%
Letter Number
ValueCountFrequency (%)
5
83.3%
1
 
16.7%
Math Symbol
ValueCountFrequency (%)
+ 3
75.0%
1
 
25.0%
Other Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
2093
100.0%
Close Punctuation
ValueCountFrequency (%)
) 231
100.0%
Open Punctuation
ValueCountFrequency (%)
( 230
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42585
88.6%
Common 3108
 
6.5%
Latin 2355
 
4.9%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2974
 
7.0%
2942
 
6.9%
2729
 
6.4%
2167
 
5.1%
1180
 
2.8%
1179
 
2.8%
1092
 
2.6%
989
 
2.3%
909
 
2.1%
862
 
2.0%
Other values (657) 25562
60.0%
Latin
ValueCountFrequency (%)
M 140
 
5.9%
S 131
 
5.6%
G 127
 
5.4%
P 127
 
5.4%
T 119
 
5.1%
A 117
 
5.0%
K 115
 
4.9%
O 100
 
4.2%
I 97
 
4.1%
J 96
 
4.1%
Other values (43) 1186
50.4%
Common
ValueCountFrequency (%)
2093
67.3%
) 231
 
7.4%
( 230
 
7.4%
2 140
 
4.5%
0 64
 
2.1%
. 64
 
2.1%
1 59
 
1.9%
& 53
 
1.7%
3 29
 
0.9%
5 28
 
0.9%
Other values (20) 117
 
3.8%
Han
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42585
88.6%
ASCII 5445
 
11.3%
None 8
 
< 0.1%
Number Forms 6
 
< 0.1%
CJK 3
 
< 0.1%
Specials 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%
Arrows 1
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2974
 
7.0%
2942
 
6.9%
2729
 
6.4%
2167
 
5.1%
1180
 
2.8%
1179
 
2.8%
1092
 
2.6%
989
 
2.3%
909
 
2.1%
862
 
2.0%
Other values (657) 25562
60.0%
ASCII
ValueCountFrequency (%)
2093
38.4%
) 231
 
4.2%
( 230
 
4.2%
2 140
 
2.6%
M 140
 
2.6%
S 131
 
2.4%
G 127
 
2.3%
P 127
 
2.3%
T 119
 
2.2%
A 117
 
2.1%
Other values (64) 1990
36.5%
Number Forms
ValueCountFrequency (%)
5
83.3%
1
 
16.7%
None
ValueCountFrequency (%)
· 5
62.5%
2
 
25.0%
1
 
12.5%
CJK
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Specials
ValueCountFrequency (%)
1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
1
100.0%
Arrows
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

sitepostno
Text

MISSING 

Distinct871
Distinct (%)15.9%
Missing1764
Missing (%)24.4%
Memory size56.6 KiB
2024-04-18T06:55:24.063841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters32766
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique233 ?
Unique (%)4.3%

Sample

1st row600092
2nd row600100
3rd row600092
4th row600031
5th row600816
ValueCountFrequency (%)
608805 66
 
1.2%
지번우편번호 55
 
1.0%
616852 53
 
1.0%
604851 52
 
1.0%
609839 49
 
0.9%
619963 43
 
0.8%
608810 42
 
0.8%
607815 41
 
0.8%
619905 39
 
0.7%
619903 39
 
0.7%
Other values (861) 4982
91.2%
2024-04-18T06:55:24.448501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 6630
20.2%
8 5711
17.4%
0 5429
16.6%
1 4835
14.8%
2 2309
 
7.0%
4 1888
 
5.8%
7 1787
 
5.5%
3 1715
 
5.2%
9 1268
 
3.9%
5 862
 
2.6%
Other values (6) 332
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32434
99.0%
Other Letter 330
 
1.0%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 6630
20.4%
8 5711
17.6%
0 5429
16.7%
1 4835
14.9%
2 2309
 
7.1%
4 1888
 
5.8%
7 1787
 
5.5%
3 1715
 
5.3%
9 1268
 
3.9%
5 862
 
2.7%
Other Letter
ValueCountFrequency (%)
110
33.3%
55
16.7%
55
16.7%
55
16.7%
55
16.7%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32436
99.0%
Hangul 330
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 6630
20.4%
8 5711
17.6%
0 5429
16.7%
1 4835
14.9%
2 2309
 
7.1%
4 1888
 
5.8%
7 1787
 
5.5%
3 1715
 
5.3%
9 1268
 
3.9%
5 862
 
2.7%
Hangul
ValueCountFrequency (%)
110
33.3%
55
16.7%
55
16.7%
55
16.7%
55
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32436
99.0%
Hangul 330
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 6630
20.4%
8 5711
17.6%
0 5429
16.7%
1 4835
14.9%
2 2309
 
7.1%
4 1888
 
5.8%
7 1787
 
5.5%
3 1715
 
5.3%
9 1268
 
3.9%
5 862
 
2.7%
Hangul
ValueCountFrequency (%)
110
33.3%
55
16.7%
55
16.7%
55
16.7%
55
16.7%

sitewhladdr
Text

MISSING 

Distinct6016
Distinct (%)84.9%
Missing143
Missing (%)2.0%
Memory size56.6 KiB
2024-04-18T06:55:24.748044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length51
Mean length25.175798
Min length14

Characters and Unicode

Total characters178295
Distinct characters460
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5203 ?
Unique (%)73.5%

Sample

1st row부산광역시 중구 대청동2가 34-1번지
2nd row부산광역시 중구 중앙동4가 79-1번지 마린센터 지하107호,110호
3rd row부산광역시 중구 대창동2가 36-5번지
4th row부산광역시 중구 대청동2가 7-1번지 5층
5th row부산광역시 중구 신창동3가 13-1번지
ValueCountFrequency (%)
부산광역시 7082
 
21.6%
동래구 833
 
2.5%
부산진구 770
 
2.3%
해운대구 691
 
2.1%
남구 682
 
2.1%
금정구 585
 
1.8%
사하구 571
 
1.7%
사상구 500
 
1.5%
북구 477
 
1.5%
3층 460
 
1.4%
Other values (6259) 20193
61.5%
2024-04-18T06:55:25.178011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32344
18.1%
8394
 
4.7%
8363
 
4.7%
8320
 
4.7%
1 7534
 
4.2%
7376
 
4.1%
7253
 
4.1%
7152
 
4.0%
7087
 
4.0%
7060
 
4.0%
Other values (450) 77412
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102661
57.6%
Decimal Number 36042
 
20.2%
Space Separator 32344
 
18.1%
Dash Punctuation 6389
 
3.6%
Uppercase Letter 252
 
0.1%
Other Punctuation 243
 
0.1%
Open Punctuation 161
 
0.1%
Close Punctuation 160
 
0.1%
Math Symbol 35
 
< 0.1%
Lowercase Letter 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8394
 
8.2%
8363
 
8.1%
8320
 
8.1%
7376
 
7.2%
7253
 
7.1%
7152
 
7.0%
7087
 
6.9%
7060
 
6.9%
6899
 
6.7%
1812
 
1.8%
Other values (399) 32945
32.1%
Uppercase Letter
ValueCountFrequency (%)
B 70
27.8%
A 28
 
11.1%
S 19
 
7.5%
C 16
 
6.3%
K 14
 
5.6%
I 13
 
5.2%
E 10
 
4.0%
Z 9
 
3.6%
G 8
 
3.2%
T 8
 
3.2%
Other values (12) 57
22.6%
Decimal Number
ValueCountFrequency (%)
1 7534
20.9%
2 5131
14.2%
3 4348
12.1%
4 3625
10.1%
5 3221
8.9%
0 2866
 
8.0%
6 2535
 
7.0%
7 2505
 
7.0%
8 2250
 
6.2%
9 2027
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 197
81.1%
. 24
 
9.9%
@ 7
 
2.9%
/ 7
 
2.9%
& 3
 
1.2%
? 2
 
0.8%
2
 
0.8%
· 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 3
42.9%
s 1
 
14.3%
k 1
 
14.3%
b 1
 
14.3%
g 1
 
14.3%
Space Separator
ValueCountFrequency (%)
32344
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6389
100.0%
Open Punctuation
ValueCountFrequency (%)
( 161
100.0%
Close Punctuation
ValueCountFrequency (%)
) 160
100.0%
Math Symbol
ValueCountFrequency (%)
~ 35
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102660
57.6%
Common 75374
42.3%
Latin 260
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8394
 
8.2%
8363
 
8.1%
8320
 
8.1%
7376
 
7.2%
7253
 
7.1%
7152
 
7.0%
7087
 
6.9%
7060
 
6.9%
6899
 
6.7%
1812
 
1.8%
Other values (398) 32944
32.1%
Latin
ValueCountFrequency (%)
B 70
26.9%
A 28
 
10.8%
S 19
 
7.3%
C 16
 
6.2%
K 14
 
5.4%
I 13
 
5.0%
E 10
 
3.8%
Z 9
 
3.5%
G 8
 
3.1%
T 8
 
3.1%
Other values (18) 65
25.0%
Common
ValueCountFrequency (%)
32344
42.9%
1 7534
 
10.0%
- 6389
 
8.5%
2 5131
 
6.8%
3 4348
 
5.8%
4 3625
 
4.8%
5 3221
 
4.3%
0 2866
 
3.8%
6 2535
 
3.4%
7 2505
 
3.3%
Other values (13) 4876
 
6.5%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102660
57.6%
ASCII 75630
42.4%
None 3
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32344
42.8%
1 7534
 
10.0%
- 6389
 
8.4%
2 5131
 
6.8%
3 4348
 
5.7%
4 3625
 
4.8%
5 3221
 
4.3%
0 2866
 
3.8%
6 2535
 
3.4%
7 2505
 
3.3%
Other values (38) 5132
 
6.8%
Hangul
ValueCountFrequency (%)
8394
 
8.2%
8363
 
8.1%
8320
 
8.1%
7376
 
7.2%
7253
 
7.1%
7152
 
7.0%
7087
 
6.9%
7060
 
6.9%
6899
 
6.7%
1812
 
1.8%
Other values (398) 32944
32.1%
None
ValueCountFrequency (%)
2
66.7%
· 1
33.3%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%

rdnpostno
Real number (ℝ)

Distinct1161
Distinct (%)16.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean48366.815
Minimum13
Maximum49524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-04-18T06:55:25.298096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile46278
Q147803
median48947
Q348947
95-th percentile48984
Maximum49524
Range49511
Interquartile range (IQR)1144

Descriptive statistics

Standard deviation1085.1179
Coefficient of variation (CV)0.022435173
Kurtosis545.10578
Mean48366.815
Median Absolute Deviation (MAD)0
Skewness-12.995669
Sum3.4930514 × 108
Variance1177480.8
MonotonicityNot monotonic
2024-04-18T06:55:25.414983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48947 4072
56.4%
46726 60
 
0.8%
46759 23
 
0.3%
46764 20
 
0.3%
48111 17
 
0.2%
46061 16
 
0.2%
46015 16
 
0.2%
46765 15
 
0.2%
46230 15
 
0.2%
47229 14
 
0.2%
Other values (1151) 2954
40.9%
ValueCountFrequency (%)
13 1
 
< 0.1%
46004 3
 
< 0.1%
46006 1
 
< 0.1%
46007 1
 
< 0.1%
46008 13
0.2%
46011 1
 
< 0.1%
46012 8
0.1%
46013 2
 
< 0.1%
46014 2
 
< 0.1%
46015 16
0.2%
ValueCountFrequency (%)
49524 1
 
< 0.1%
49523 1
 
< 0.1%
49521 8
0.1%
49520 1
 
< 0.1%
49519 1
 
< 0.1%
49518 4
0.1%
49516 3
 
< 0.1%
49515 1
 
< 0.1%
49514 2
 
< 0.1%
49511 6
0.1%

rdnwhladdr
Text

MISSING 

Distinct6242
Distinct (%)90.8%
Missing349
Missing (%)4.8%
Memory size56.6 KiB
2024-04-18T06:55:25.729944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length55
Mean length29.304392
Min length3

Characters and Unicode

Total characters201497
Distinct characters528
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5727 ?
Unique (%)83.3%

Sample

1st row부산광역시 중구 광복중앙로 28-1 (대청동2가)
2nd row부산광역시 중구 충장대로9번길 52, 지1층 (중앙동4가, 마린센터빌딩)
3rd row부산광역시 중구 중앙대로 133 (대창동2가)
4th row부산광역시 중구 대청로 107, 5층 (대청동2가)
5th row부산광역시 중구 광복로35번길 18 (신창동3가)
ValueCountFrequency (%)
부산광역시 6875
 
17.8%
동래구 787
 
2.0%
부산진구 753
 
1.9%
해운대구 683
 
1.8%
남구 651
 
1.7%
사하구 597
 
1.5%
금정구 570
 
1.5%
사상구 474
 
1.2%
북구 462
 
1.2%
3층 459
 
1.2%
Other values (4836) 26308
68.1%
2024-04-18T06:55:26.209179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34514
 
17.1%
8798
 
4.4%
8264
 
4.1%
8230
 
4.1%
7277
 
3.6%
7248
 
3.6%
6982
 
3.5%
6881
 
3.4%
6787
 
3.4%
( 6767
 
3.4%
Other values (518) 99749
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118329
58.7%
Space Separator 34514
 
17.1%
Decimal Number 29570
 
14.7%
Open Punctuation 6769
 
3.4%
Close Punctuation 6767
 
3.4%
Other Punctuation 4426
 
2.2%
Dash Punctuation 817
 
0.4%
Uppercase Letter 245
 
0.1%
Math Symbol 41
 
< 0.1%
Lowercase Letter 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8798
 
7.4%
8264
 
7.0%
8230
 
7.0%
7277
 
6.1%
7248
 
6.1%
6982
 
5.9%
6881
 
5.8%
6787
 
5.7%
3129
 
2.6%
2542
 
2.1%
Other values (458) 52191
44.1%
Uppercase Letter
ValueCountFrequency (%)
B 87
35.5%
A 23
 
9.4%
S 20
 
8.2%
K 18
 
7.3%
C 14
 
5.7%
I 12
 
4.9%
E 8
 
3.3%
G 6
 
2.4%
N 6
 
2.4%
P 6
 
2.4%
Other values (13) 45
18.4%
Decimal Number
ValueCountFrequency (%)
1 5923
20.0%
2 4757
16.1%
3 3730
12.6%
4 2815
9.5%
0 2578
8.7%
5 2433
8.2%
6 2146
 
7.3%
7 1856
 
6.3%
8 1735
 
5.9%
9 1597
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
e 4
22.2%
b 3
16.7%
s 2
11.1%
k 2
11.1%
a 2
11.1%
z 1
 
5.6%
l 1
 
5.6%
w 1
 
5.6%
i 1
 
5.6%
v 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 4386
99.1%
. 18
 
0.4%
@ 6
 
0.1%
/ 4
 
0.1%
· 3
 
0.1%
& 3
 
0.1%
? 2
 
< 0.1%
2
 
< 0.1%
* 2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 6767
> 99.9%
[ 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 6765
> 99.9%
] 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
34514
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 817
100.0%
Math Symbol
ValueCountFrequency (%)
~ 41
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118328
58.7%
Common 82904
41.1%
Latin 264
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8798
 
7.4%
8264
 
7.0%
8230
 
7.0%
7277
 
6.1%
7248
 
6.1%
6982
 
5.9%
6881
 
5.8%
6787
 
5.7%
3129
 
2.6%
2542
 
2.1%
Other values (457) 52190
44.1%
Latin
ValueCountFrequency (%)
B 87
33.0%
A 23
 
8.7%
S 20
 
7.6%
K 18
 
6.8%
C 14
 
5.3%
I 12
 
4.5%
E 8
 
3.0%
G 6
 
2.3%
N 6
 
2.3%
P 6
 
2.3%
Other values (24) 64
24.2%
Common
ValueCountFrequency (%)
34514
41.6%
( 6767
 
8.2%
) 6765
 
8.2%
1 5923
 
7.1%
2 4757
 
5.7%
, 4386
 
5.3%
3 3730
 
4.5%
4 2815
 
3.4%
0 2578
 
3.1%
5 2433
 
2.9%
Other values (16) 8236
 
9.9%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118328
58.7%
ASCII 83162
41.3%
None 5
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34514
41.5%
( 6767
 
8.1%
) 6765
 
8.1%
1 5923
 
7.1%
2 4757
 
5.7%
, 4386
 
5.3%
3 3730
 
4.5%
4 2815
 
3.4%
0 2578
 
3.1%
5 2433
 
2.9%
Other values (47) 8494
 
10.2%
Hangul
ValueCountFrequency (%)
8798
 
7.4%
8264
 
7.0%
8230
 
7.0%
7277
 
6.1%
7248
 
6.1%
6982
 
5.9%
6881
 
5.8%
6787
 
5.7%
3129
 
2.6%
2542
 
2.1%
Other values (457) 52190
44.1%
None
ValueCountFrequency (%)
· 3
60.0%
2
40.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%

apvpermymd
Real number (ℝ)

SKEWED 

Distinct4243
Distinct (%)58.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20052823
Minimum388631.59
Maximum20210226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-04-18T06:55:26.339007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum388631.59
5-th percentile19921041
Q120000406
median20051123
Q320111210
95-th percentile20190412
Maximum20210226
Range19821594
Interquartile range (IQR)110804.5

Descriptive statistics

Standard deviation272486.25
Coefficient of variation (CV)0.013588424
Kurtosis4011.7069
Mean20052823
Median Absolute Deviation (MAD)59693
Skewness-59.000758
Sum1.4486159 × 1011
Variance7.4248759 × 1010
MonotonicityNot monotonic
2024-04-18T06:55:26.461723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030204.0 124
 
1.7%
20030206.0 66
 
0.9%
20030203.0 55
 
0.8%
20030205.0 36
 
0.5%
19890731.0 31
 
0.4%
20030123.0 21
 
0.3%
20030124.0 19
 
0.3%
19891116.0 12
 
0.2%
20030210.0 12
 
0.2%
20191213.0 11
 
0.2%
Other values (4233) 6837
94.6%
ValueCountFrequency (%)
388631.593406 1
< 0.1%
10001126.0 1
< 0.1%
19711022.0 1
< 0.1%
19720503.0 2
< 0.1%
19730112.0 1
< 0.1%
19750416.0 1
< 0.1%
19750503.0 1
< 0.1%
19750519.0 1
< 0.1%
19751001.0 1
< 0.1%
19770203.0 1
< 0.1%
ValueCountFrequency (%)
20210226.0 6
0.1%
20210224.0 2
 
< 0.1%
20210223.0 1
 
< 0.1%
20210222.0 1
 
< 0.1%
20210218.0 1
 
< 0.1%
20210217.0 2
 
< 0.1%
20210216.0 2
 
< 0.1%
20210209.0 1
 
< 0.1%
20210205.0 2
 
< 0.1%
20210203.0 1
 
< 0.1%

dcbymd
Text

MISSING 

Distinct1861
Distinct (%)56.3%
Missing3919
Missing (%)54.2%
Memory size56.6 KiB
2024-04-18T06:55:26.697594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length8
Mean length7.9370841
Min length4

Characters and Unicode

Total characters26240
Distinct characters16
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

Unique1314 ?
Unique (%)39.7%

Sample

1st row20020727
2nd row20040228
3rd row20130408
4th row20150608
5th row20070605
ValueCountFrequency (%)
20180302 96
 
2.9%
20040504 65
 
2.0%
폐업일자 55
 
1.7%
20140411 54
 
1.6%
20151231 51
 
1.5%
20070801 39
 
1.2%
20030613 28
 
0.8%
20021212 24
 
0.7%
20061215 20
 
0.6%
20180703 19
 
0.6%
Other values (1851) 2855
86.4%
2024-04-18T06:55:27.079597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8555
32.6%
2 5319
20.3%
1 5088
19.4%
3 1291
 
4.9%
4 1032
 
3.9%
9 996
 
3.8%
7 988
 
3.8%
8 965
 
3.7%
5 951
 
3.6%
6 832
 
3.2%
Other values (6) 223
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26017
99.2%
Other Letter 220
 
0.8%
Space Separator 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8555
32.9%
2 5319
20.4%
1 5088
19.6%
3 1291
 
5.0%
4 1032
 
4.0%
9 996
 
3.8%
7 988
 
3.8%
8 965
 
3.7%
5 951
 
3.7%
6 832
 
3.2%
Other Letter
ValueCountFrequency (%)
55
25.0%
55
25.0%
55
25.0%
55
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26020
99.2%
Hangul 220
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8555
32.9%
2 5319
20.4%
1 5088
19.6%
3 1291
 
5.0%
4 1032
 
4.0%
9 996
 
3.8%
7 988
 
3.8%
8 965
 
3.7%
5 951
 
3.7%
6 832
 
3.2%
Other values (2) 3
 
< 0.1%
Hangul
ValueCountFrequency (%)
55
25.0%
55
25.0%
55
25.0%
55
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26020
99.2%
Hangul 220
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8555
32.9%
2 5319
20.4%
1 5088
19.6%
3 1291
 
5.0%
4 1032
 
4.0%
9 996
 
3.8%
7 988
 
3.8%
8 965
 
3.7%
5 951
 
3.7%
6 832
 
3.2%
Other values (2) 3
 
< 0.1%
Hangul
ValueCountFrequency (%)
55
25.0%
55
25.0%
55
25.0%
55
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
7155 
휴업시작일자
 
55
20180808
 
2
20180701
 
1
20090701
 
1
Other values (11)
 
11

Length

Max length14
Median length4
Mean length4.0243599
Min length4

Unique

Unique13 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7155
99.0%
휴업시작일자 55
 
0.8%
20180808 2
 
< 0.1%
20180701 1
 
< 0.1%
20090701 1
 
< 0.1%
20140115112341 1
 
< 0.1%
20070801 1
 
< 0.1%
20030108 1
 
< 0.1%
20171025 1
 
< 0.1%
20130122 1
 
< 0.1%
Other values (6) 6
 
0.1%

Length

2024-04-18T06:55:27.209465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7155
99.0%
휴업시작일자 55
 
0.8%
20180808 2
 
< 0.1%
20180701 1
 
< 0.1%
20090701 1
 
< 0.1%
20140115112341 1
 
< 0.1%
20070801 1
 
< 0.1%
20030108 1
 
< 0.1%
20171025 1
 
< 0.1%
20130122 1
 
< 0.1%
Other values (6) 6
 
0.1%

clgenddt
Categorical

IMBALANCE 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
7156 
휴업종료일자
 
55
20190630
 
2
20181231
 
1
20110630
 
1
Other values (10)
 
10

Length

Max length8
Median length4
Mean length4.0229758
Min length4

Unique

Unique12 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7156
99.0%
휴업종료일자 55
 
0.8%
20190630 2
 
< 0.1%
20181231 1
 
< 0.1%
20110630 1
 
< 0.1%
20421031 1
 
< 0.1%
20031231 1
 
< 0.1%
20181025 1
 
< 0.1%
20130714 1
 
< 0.1%
20100123 1
 
< 0.1%
Other values (5) 5
 
0.1%

Length

2024-04-18T06:55:27.325963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7156
99.0%
휴업종료일자 55
 
0.8%
20190630 2
 
< 0.1%
20181231 1
 
< 0.1%
20110630 1
 
< 0.1%
20421031 1
 
< 0.1%
20031231 1
 
< 0.1%
20181025 1
 
< 0.1%
20130714 1
 
< 0.1%
20100123 1
 
< 0.1%
Other values (5) 5
 
0.1%

ropnymd
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
7169 
재개업일자
 
55
051-123-1234
 
1

Length

Max length12
Median length4
Mean length4.0087197
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7169
99.2%
재개업일자 55
 
0.8%
051-123-1234 1
 
< 0.1%

Length

2024-04-18T06:55:27.447361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:27.544509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7169
99.2%
재개업일자 55
 
0.8%
051-123-1234 1
 
< 0.1%

trdstatenm
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
13
3364 
03
2962 
영업/정상
467 
35
402 
02
 
14
Other values (5)
 
16

Length

Max length5
Median length2
Mean length2.1964014
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row03
2nd row13
3rd row03
4th row13
5th row13

Common Values

ValueCountFrequency (%)
13 3364
46.6%
03 2962
41.0%
영업/정상 467
 
6.5%
35 402
 
5.6%
02 14
 
0.2%
<NA> 8
 
0.1%
폐업 5
 
0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
영업상태 1
 
< 0.1%

Length

2024-04-18T06:55:27.642147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:27.742126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13 3364
46.6%
03 2962
41.0%
영업/정상 467
 
6.5%
35 402
 
5.6%
02 14
 
0.2%
na 8
 
0.1%
폐업 5
 
0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
영업상태 1
 
< 0.1%

dtlstatenm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
영업중
3838 
폐업
2967 
직권말소
402 
휴업
 
14
<NA>
 
2
Other values (2)
 
2

Length

Max length4
Median length3
Mean length2.6435986
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row폐업
2nd row영업중
3rd row폐업
4th row영업중
5th row영업중

Common Values

ValueCountFrequency (%)
영업중 3838
53.1%
폐업 2967
41.1%
직권말소 402
 
5.6%
휴업 14
 
0.2%
<NA> 2
 
< 0.1%
신고취소 1
 
< 0.1%
지정취소 1
 
< 0.1%

Length

2024-04-18T06:55:27.879407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:27.988163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 3838
53.1%
폐업 2967
41.1%
직권말소 402
 
5.6%
휴업 14
 
0.2%
na 2
 
< 0.1%
신고취소 1
 
< 0.1%
지정취소 1
 
< 0.1%

x
Text

MISSING 

Distinct5051
Distinct (%)71.2%
Missing133
Missing (%)1.8%
Memory size56.6 KiB
2024-04-18T06:55:28.182664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.993796
Min length2

Characters and Unicode

Total characters141796
Distinct characters21
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3687 ?
Unique (%)52.0%

Sample

1st row385198.60018500000
2nd row385898.66824500000
3rd row385707.34088600000
4th row385250.02502800000
5th row385004.37293600000
ValueCountFrequency (%)
380613.87795500000 11
 
0.2%
385599.32170700000 9
 
0.1%
373579.04761500000 9
 
0.1%
392607.54573000000 8
 
0.1%
390129.64468900000 8
 
0.1%
393357.80475400000 8
 
0.1%
395308.61045334 8
 
0.1%
383255.80791500000 7
 
0.1%
387962.62914200000 7
 
0.1%
387811.67602200000 7
 
0.1%
Other values (5041) 7010
98.8%
2024-04-18T06:55:28.885236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 38737
27.3%
17651
12.4%
3 13955
 
9.8%
8 11180
 
7.9%
9 9793
 
6.9%
7 7768
 
5.5%
1 7489
 
5.3%
2 7253
 
5.1%
4 7179
 
5.1%
. 7010
 
4.9%
Other values (11) 13781
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117119
82.6%
Space Separator 17651
 
12.4%
Other Punctuation 7010
 
4.9%
Other Letter 10
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38737
33.1%
3 13955
 
11.9%
8 11180
 
9.5%
9 9793
 
8.4%
7 7768
 
6.6%
1 7489
 
6.4%
2 7253
 
6.2%
4 7179
 
6.1%
5 7006
 
6.0%
6 6759
 
5.8%
Other Letter
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
1
10.0%
1
10.0%
Space Separator
ValueCountFrequency (%)
17651
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7010
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 141784
> 99.9%
Hangul 10
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38737
27.3%
17651
12.4%
3 13955
 
9.8%
8 11180
 
7.9%
9 9793
 
6.9%
7 7768
 
5.5%
1 7489
 
5.3%
2 7253
 
5.1%
4 7179
 
5.1%
. 7010
 
4.9%
Other values (4) 13769
 
9.7%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
1
10.0%
1
10.0%
Latin
ValueCountFrequency (%)
X 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141786
> 99.9%
Hangul 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38737
27.3%
17651
12.4%
3 13955
 
9.8%
8 11180
 
7.9%
9 9793
 
6.9%
7 7768
 
5.5%
1 7489
 
5.3%
2 7253
 
5.1%
4 7179
 
5.1%
. 7010
 
4.9%
Other values (5) 13771
 
9.7%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
1
10.0%
1
10.0%

y
Text

MISSING 

Distinct5050
Distinct (%)71.2%
Missing133
Missing (%)1.8%
Memory size56.6 KiB
2024-04-18T06:55:29.090399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.99436
Min length6

Characters and Unicode

Total characters141800
Distinct characters25
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3687 ?
Unique (%)52.0%

Sample

1st row180287.48839500000
2nd row181213.88172000000
3rd row181152.27052700000
4th row180424.29203400000
5th row180162.69448800000
ValueCountFrequency (%)
175596.00351700000 11
 
0.2%
178017.84297300000 9
 
0.1%
187606.00717200000 9
 
0.1%
191804.07825600000 8
 
0.1%
186564.639113751 8
 
0.1%
183803.94132300000 8
 
0.1%
197260.59223600000 8
 
0.1%
188403.67614400000 7
 
0.1%
194331.52968600000 7
 
0.1%
190262.68201300000 7
 
0.1%
Other values (5040) 7010
98.8%
2024-04-18T06:55:29.413212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 38529
27.2%
17626
12.4%
1 14274
 
10.1%
8 10913
 
7.7%
9 9758
 
6.9%
7 8241
 
5.8%
6 7308
 
5.2%
4 7208
 
5.1%
5 7042
 
5.0%
. 7010
 
4.9%
Other values (15) 13891
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117144
82.6%
Space Separator 17626
 
12.4%
Other Punctuation 7010
 
4.9%
Other Letter 14
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38529
32.9%
1 14274
 
12.2%
8 10913
 
9.3%
9 9758
 
8.3%
7 8241
 
7.0%
6 7308
 
6.2%
4 7208
 
6.2%
5 7042
 
6.0%
2 6936
 
5.9%
3 6935
 
5.9%
Other Letter
ValueCountFrequency (%)
2
14.3%
2
14.3%
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Space Separator
ValueCountFrequency (%)
17626
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7010
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 141784
> 99.9%
Hangul 14
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38529
27.2%
17626
12.4%
1 14274
 
10.1%
8 10913
 
7.7%
9 9758
 
6.9%
7 8241
 
5.8%
6 7308
 
5.2%
4 7208
 
5.1%
5 7042
 
5.0%
. 7010
 
4.9%
Other values (4) 13875
 
9.8%
Hangul
ValueCountFrequency (%)
2
14.3%
2
14.3%
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Latin
ValueCountFrequency (%)
Y 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141786
> 99.9%
Hangul 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38529
27.2%
17626
12.4%
1 14274
 
10.1%
8 10913
 
7.7%
9 9758
 
6.9%
7 8241
 
5.8%
6 7308
 
5.2%
4 7208
 
5.1%
5 7042
 
5.0%
. 7010
 
4.9%
Other values (5) 13877
 
9.8%
Hangul
ValueCountFrequency (%)
2
14.3%
2
14.3%
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%

lastmodts
Real number (ℝ)

Distinct7040
Distinct (%)97.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.0131401 × 1013
Minimum2.0021018 × 1013
Maximum2.0210226 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-04-18T06:55:29.547656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0021018 × 1013
5-th percentile2.0031112 × 1013
Q12.0110111 × 1013
median2.0140515 × 1013
Q32.0170418 × 1013
95-th percentile2.019051 × 1013
Maximum2.0210226 × 1013
Range1.8920804 × 1011
Interquartile range (IQR)6.0306953 × 1010

Descriptive statistics

Standard deviation4.6909171 × 1010
Coefficient of variation (CV)0.0023301493
Kurtosis-0.51100903
Mean2.0131401 × 1013
Median Absolute Deviation (MAD)3.019198 × 1010
Skewness-0.64279765
Sum1.4540911 × 1017
Variance2.2004703 × 1021
MonotonicityNot monotonic
2024-04-18T06:55:29.665285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20021018132120 62
 
0.9%
20190809140635 3
 
< 0.1%
20200508134225 3
 
< 0.1%
20190809132354 3
 
< 0.1%
20200710161620 3
 
< 0.1%
20191018101519 3
 
< 0.1%
20191108144420 3
 
< 0.1%
20191115090310 3
 
< 0.1%
20191115162448 3
 
< 0.1%
20191115140601 3
 
< 0.1%
Other values (7030) 7134
98.7%
ValueCountFrequency (%)
20021018132120 62
0.9%
20021226152409 1
 
< 0.1%
20021226155826 1
 
< 0.1%
20021226160855 1
 
< 0.1%
20021226163050 1
 
< 0.1%
20021227103144 1
 
< 0.1%
20021227115048 1
 
< 0.1%
20021227135543 1
 
< 0.1%
20021227140112 1
 
< 0.1%
20021227140309 1
 
< 0.1%
ValueCountFrequency (%)
20210226174329 1
< 0.1%
20210226153908 2
< 0.1%
20210226140220 1
< 0.1%
20210226111447 2
< 0.1%
20210226103639 1
< 0.1%
20210226094133 2
< 0.1%
20210226092516 1
< 0.1%
20210226092444 1
< 0.1%
20210226091820 1
< 0.1%
20210224155610 1
< 0.1%

uptaenm
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
6636 
태권도
 
366
권투
 
52
업태구분명
 
43
유도
 
38
Other values (4)
 
90

Length

Max length5
Median length4
Mean length3.9118339
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6636
91.8%
태권도 366
 
5.1%
권투 52
 
0.7%
업태구분명 43
 
0.6%
유도 38
 
0.5%
합기도 38
 
0.5%
검도 37
 
0.5%
레슬링 8
 
0.1%
우슈 7
 
0.1%

Length

2024-04-18T06:55:29.787211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:29.913458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6636
91.8%
태권도 366
 
5.1%
권투 52
 
0.7%
업태구분명 43
 
0.6%
유도 38
 
0.5%
합기도 38
 
0.5%
검도 37
 
0.5%
레슬링 8
 
0.1%
우슈 7
 
0.1%

sitetel
Categorical

IMBALANCE 

Distinct40
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
051-123-1234
7113 
<NA>
 
58
전화번호
 
11
051-731-1469
 
2
051)515-1369
 
2
Other values (35)
 
39

Length

Max length13
Median length12
Mean length11.920554
Min length4

Unique

Unique31 ?
Unique (%)0.4%

Sample

1st row051-123-1234
2nd row051-123-1234
3rd row051-123-1234
4th row051-123-1234
5th row051-123-1234

Common Values

ValueCountFrequency (%)
051-123-1234 7113
98.4%
<NA> 58
 
0.8%
전화번호 11
 
0.2%
051-731-1469 2
 
< 0.1%
051)515-1369 2
 
< 0.1%
051-905-0444 2
 
< 0.1%
051-582-8779 2
 
< 0.1%
051-925-0909 2
 
< 0.1%
051-723-5896 2
 
< 0.1%
051-758-1012 1
 
< 0.1%
Other values (30) 30
 
0.4%

Length

2024-04-18T06:55:30.033537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
051-123-1234 7113
98.4%
na 58
 
0.8%
전화번호 11
 
0.2%
051-731-1469 2
 
< 0.1%
051)515-1369 2
 
< 0.1%
051-905-0444 2
 
< 0.1%
051-582-8779 2
 
< 0.1%
051-925-0909 2
 
< 0.1%
051-723-5896 2
 
< 0.1%
051-703-7274 1
 
< 0.1%
Other values (30) 30
 
0.4%

bdngdngnum
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
5780 
1
1037 
0
 
309
건축물동수
 
54
2
 
24
Other values (6)
 
21

Length

Max length5
Median length4
Mean length3.4303114
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5780
80.0%
1 1037
 
14.4%
0 309
 
4.3%
건축물동수 54
 
0.7%
2 24
 
0.3%
3 8
 
0.1%
4 6
 
0.1%
5 4
 
0.1%
302 1
 
< 0.1%
12 1
 
< 0.1%

Length

2024-04-18T06:55:30.139863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 5780
80.0%
1 1037
 
14.4%
0 309
 
4.3%
건축물동수 54
 
0.7%
2 24
 
0.3%
3 8
 
0.1%
4 6
 
0.1%
5 4
 
0.1%
302 1
 
< 0.1%
12 1
 
< 0.1%

bdngyarea
Text

MISSING 

Distinct2881
Distinct (%)74.1%
Missing3337
Missing (%)46.2%
Memory size56.6 KiB
2024-04-18T06:55:30.460374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.1296296
Min length1

Characters and Unicode

Total characters19944
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2541 ?
Unique (%)65.4%

Sample

1st row20120.65
2nd row4130.27
3rd row1155.7
4th row5404.88
5th row8245.54
ValueCountFrequency (%)
0 306
 
7.9%
1 85
 
2.2%
건축물연면적 37
 
1.0%
150 36
 
0.9%
160 25
 
0.6%
120 23
 
0.6%
140 16
 
0.4%
99 14
 
0.4%
158 12
 
0.3%
165 11
 
0.3%
Other values (2871) 3323
85.5%
2024-04-18T06:55:30.882909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2935
14.7%
1 2822
14.1%
2 1916
9.6%
9 1657
8.3%
4 1632
8.2%
3 1562
7.8%
8 1525
7.6%
5 1465
7.3%
0 1419
7.1%
6 1409
7.1%
Other values (7) 1602
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16787
84.2%
Other Punctuation 2935
 
14.7%
Other Letter 222
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2822
16.8%
2 1916
11.4%
9 1657
9.9%
4 1632
9.7%
3 1562
9.3%
8 1525
9.1%
5 1465
8.7%
0 1419
8.5%
6 1409
8.4%
7 1380
8.2%
Other Letter
ValueCountFrequency (%)
37
16.7%
37
16.7%
37
16.7%
37
16.7%
37
16.7%
37
16.7%
Other Punctuation
ValueCountFrequency (%)
. 2935
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19722
98.9%
Hangul 222
 
1.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2935
14.9%
1 2822
14.3%
2 1916
9.7%
9 1657
8.4%
4 1632
8.3%
3 1562
7.9%
8 1525
7.7%
5 1465
7.4%
0 1419
7.2%
6 1409
7.1%
Hangul
ValueCountFrequency (%)
37
16.7%
37
16.7%
37
16.7%
37
16.7%
37
16.7%
37
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19722
98.9%
Hangul 222
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2935
14.9%
1 2822
14.3%
2 1916
9.7%
9 1657
8.4%
4 1632
8.3%
3 1562
7.9%
8 1525
7.7%
5 1465
7.4%
0 1419
7.2%
6 1409
7.1%
Hangul
ValueCountFrequency (%)
37
16.7%
37
16.7%
37
16.7%
37
16.7%
37
16.7%
37
16.7%

puprsenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
사립
7183 
<NA>
 
33
공립
 
6
공사립구분명
 
2
2021-03-01 05:22:03
 
1

Length

Max length19
Median length2
Mean length2.0125952
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
사립 7183
99.4%
<NA> 33
 
0.5%
공립 6
 
0.1%
공사립구분명 2
 
< 0.1%
2021-03-01 05:22:03 1
 
< 0.1%

Length

2024-04-18T06:55:31.033076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:31.161726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사립 7183
99.4%
na 33
 
0.5%
공립 6
 
0.1%
공사립구분명 2
 
< 0.1%
2021-03-01 1
 
< 0.1%
05:22:03 1
 
< 0.1%

culphyedcobnm
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
당구장업
3052 
체육도장업
1728 
체력단련장업
1383 
골프연습장업
919 
수영장업
 
95
Other values (5)
 
48

Length

Max length7
Median length6
Mean length4.8783391
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
당구장업 3052
42.2%
체육도장업 1728
23.9%
체력단련장업 1383
19.1%
골프연습장업 919
 
12.7%
수영장업 95
 
1.3%
<NA> 34
 
0.5%
무도학원업 8
 
0.1%
썰매장업 3
 
< 0.1%
문화체육업종명 2
 
< 0.1%
무도장업 1
 
< 0.1%

Length

2024-04-18T06:55:31.293281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:31.402265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당구장업 3052
42.2%
체육도장업 1728
23.9%
체력단련장업 1383
19.1%
골프연습장업 919
 
12.7%
수영장업 95
 
1.3%
na 34
 
0.5%
무도학원업 8
 
0.1%
썰매장업 3
 
< 0.1%
문화체육업종명 2
 
< 0.1%
무도장업 1
 
< 0.1%

bupnm
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
7170 
법인명
 
55

Length

Max length4
Median length4
Mean length3.9923875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7170
99.2%
법인명 55
 
0.8%

Length

2024-04-18T06:55:31.537130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:31.620992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7170
99.2%
법인명 55
 
0.8%

insurjnyncode
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
5189 
0
1860 
Y
 
102
 
53
1
 
21

Length

Max length4
Median length4
Mean length3.1546021
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row<NA>
3rd row0
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5189
71.8%
0 1860
 
25.7%
Y 102
 
1.4%
53
 
0.7%
1 21
 
0.3%

Length

2024-04-18T06:55:31.705203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:31.794287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5189
71.8%
0 1860
 
25.7%
y 102
 
1.4%
53
 
0.7%
1 21
 
0.3%

drmkcobnm
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
7170 
세부업종명
 
55

Length

Max length5
Median length4
Mean length4.0076125
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7170
99.2%
세부업종명 55
 
0.8%

Length

2024-04-18T06:55:31.894340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:31.985742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7170
99.2%
세부업종명 55
 
0.8%

ldercnt
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
5800 
1
1023 
2
 
179
0
 
178
지도자수
 
38
Other values (4)
 
7

Length

Max length4
Median length4
Mean length3.424083
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5800
80.3%
1 1023
 
14.2%
2 179
 
2.5%
0 178
 
2.5%
지도자수 38
 
0.5%
3 4
 
0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%

Length

2024-04-18T06:55:32.076181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:32.184499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5800
80.3%
1 1023
 
14.2%
2 179
 
2.5%
0 178
 
2.5%
지도자수 38
 
0.5%
3 4
 
0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%

memcolltotstfnum
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
<NA>
7150 
회원모집총인원
 
55
60
 
3
100
 
2
20
 
2
Other values (11)
 
13

Length

Max length7
Median length4
Mean length4.0178547
Min length1

Unique

Unique9 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7150
99.0%
회원모집총인원 55
 
0.8%
60 3
 
< 0.1%
100 2
 
< 0.1%
20 2
 
< 0.1%
50 2
 
< 0.1%
30 2
 
< 0.1%
300 1
 
< 0.1%
70 1
 
< 0.1%
400 1
 
< 0.1%
Other values (6) 6
 
0.1%

Length

2024-04-18T06:55:32.317334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7150
99.0%
회원모집총인원 55
 
0.8%
60 3
 
< 0.1%
100 2
 
< 0.1%
20 2
 
< 0.1%
50 2
 
< 0.1%
30 2
 
< 0.1%
300 1
 
< 0.1%
70 1
 
< 0.1%
400 1
 
< 0.1%
Other values (6) 6
 
0.1%

last_load_dttm
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.6 KiB
2021-03-01 05:22:03
7223 
<NA>
 
2

Length

Max length19
Median length19
Mean length18.995848
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01 05:22:03
2nd row2021-03-01 05:22:03
3rd row2021-03-01 05:22:03
4th row2021-03-01 05:22:03
5th row2021-03-01 05:22:03

Common Values

ValueCountFrequency (%)
2021-03-01 05:22:03 7223
> 99.9%
<NA> 2
 
< 0.1%

Length

2024-04-18T06:55:32.447944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:55:32.549980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-01 7223
50.0%
05:22:03 7223
50.0%
na 2
 
< 0.1%

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngdngnumbdngyareapuprsenmculphyedcobnmbupnminsurjnyncodedrmkcobnmldercntmemcolltotstfnumlast_load_dttm
043250000CDFH330105199700000210_31_01_PI2018-08-31 23:59:59.0<NA>광복실내골프연습장600092부산광역시 중구 대청동2가 34-1번지48947부산광역시 중구 광복중앙로 28-1 (대청동2가)19971211.020020727<NA><NA><NA>03폐업385198.60018500000180287.4883950000020040727102048<NA>051-123-1234<NA><NA>사립골프연습장업<NA>0<NA><NA><NA>2021-03-01 05:22:03
153250000CDFH330105199900000110_31_01_PI2018-08-31 23:59:59.0<NA>마린골프연습장<NA>부산광역시 중구 중앙동4가 79-1번지 마린센터 지하107호,110호48936부산광역시 중구 충장대로9번길 52, 지1층 (중앙동4가, 마린센터빌딩)19990513.0<NA><NA><NA><NA>13영업중385898.66824500000181213.8817200000020170124143354<NA>051-123-1234<NA>20120.65사립골프연습장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
263250000CDFH330105200300000110_31_01_PI2018-08-31 23:59:59.0<NA>포시즌 골프연습장600100부산광역시 중구 대창동2가 36-5번지48947부산광역시 중구 중앙대로 133 (대창동2가)20030305.020040228<NA><NA><NA>03폐업385707.34088600000181152.2705270000020040709121811<NA>051-123-1234<NA><NA>사립골프연습장업<NA>0<NA><NA><NA>2021-03-01 05:22:03
373250000CDFH330105200300000210_31_01_PI2018-08-31 23:59:59.0<NA>에스에스 골프연습장<NA>부산광역시 중구 대청동2가 7-1번지 5층48933부산광역시 중구 대청로 107, 5층 (대청동2가)20031124.0<NA><NA><NA><NA>13영업중385250.02502800000180424.2920340000020161205154150<NA>051-123-1234<NA>4130.27사립골프연습장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
483250000CDFH330105200400000110_31_01_PI2018-08-31 23:59:59.0<NA>가나다라골프연습장<NA>부산광역시 중구 신창동3가 13-1번지48946부산광역시 중구 광복로35번길 18 (신창동3가)20040413.0<NA><NA><NA><NA>13영업중385004.37293600000180162.6944880000020180202153954<NA>051-123-1234<NA>1155.7사립골프연습장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
593250000CDFH330105200400000210_31_01_PI2018-08-31 23:59:59.0<NA>테일러메이드 실내골프 연습장600092부산광역시 중구 대청동2가 34-1번지48947부산광역시 중구 광복중앙로 28-1 (대청동2가)20040727.020130408<NA><NA><NA>35직권말소385198.60018500000180287.4883950000020130408094631<NA>051-123-1234<NA><NA>사립골프연습장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
6103250000CDFH330105200400000310_31_01_PI2018-08-31 23:59:59.0<NA>(주)이지알앤에스 실내골프연습장600031부산광역시 중구 광복동1가 7번지48947부산광역시 중구 광복로85번길 5-10 (광복동1가)20040811.020150608<NA><NA><NA>03폐업385448.75499800000179977.3282280000020150608132227<NA>051-123-1234<NA><NA>사립골프연습장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
7113250000CDFH330105200500000110_31_01_PI2018-08-31 23:59:59.0<NA>리드 실내골프연습장600816부산광역시 중구 중앙동4가 76-23번지48947부산광역시 중구 중앙대로 148 (중앙동4가)20050328.020070605<NA><NA><NA>03폐업385805.96072100000181267.3847210000020070605140559<NA>051-123-1234<NA><NA>사립골프연습장업<NA>0<NA><NA><NA>2021-03-01 05:22:03
8123250000CDFH330105201400000210_31_01_PI2018-08-31 23:59:59.0<NA>우리동네스크린골프<NA>부산광역시 중구 동광동2가 1번지48955부산광역시 중구 광복로97번길 26-2, 2층 (동광동2가)20140429.0<NA><NA><NA><NA>13영업중385546.63989500000180191.5029580000020180126153016<NA>051-123-1234<NA>5404.88사립골프연습장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
9133250000CDFH330105201400000310_31_01_PI2018-08-31 23:59:59.0<NA>광복스크린600042부산광역시 중구 남포동2가 25-10번지48954부산광역시 중구 구덕로34번길 4 (남포동2가)20140508.0<NA><NA><NA><NA>13영업중385314.95814000000179888.7646780000020161125110922<NA>051-123-1234<NA>8245.54사립골프연습장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngdngnumbdngyareapuprsenmculphyedcobnmbupnminsurjnyncodedrmkcobnmldercntmemcolltotstfnumlast_load_dttm
7215137833320000CDFH330102202100000510_41_01_PI2021-02-25 00:23:01.0체육도장업동천백산무림관지번우편번호부산광역시 북구 구포동 897-2746636부산광역시 북구 백양대로 1024, 4층 (구포동)20210223.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중381658.194682511190465.95756508720210223101618합기도051-333-2823건축물동수1094.76사립체육도장업법인명세부업종명지도자수회원모집총인원2021-03-01 05:22:03
7216137843350000CDFH330102202100000310_41_01_PI2021-02-26 00:23:01.0체육도장업부산대 팀매드지번우편번호부산광역시 금정구 장전동 219-446283부산광역시 금정구 금강로 338, 3층 (장전동)20210224.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중389876.019381244195192.89421052820210224155610권투전화번호건축물동수1022.57사립체육도장업법인명세부업종명지도자수회원모집총인원2021-03-01 05:22:03
7217137853360000CDFH330105202100000110_31_01_PI2021-02-26 00:23:01.0골프연습장업원스윙 골프아카데미<NA>부산광역시 강서구 명지동 3589-6 208호46726부산광역시 강서구 명지국제8로10번길 16, KB타워 2층 208호 (명지동)20210224.0<NA><NA><NA><NA>영업/정상영업중373444.976539446178660.57735539220210224103122<NA><NA><NA><NA>사립골프연습장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
7218137863330000CDFH330106202100000510_42_01_PI2021-02-28 00:23:01.0체력단련장업플레이 돔지번우편번호부산광역시 해운대구 중동 1090-5 3층48097부산광역시 해운대구 달맞이길 52, 3층 (중동)20210226.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중397690.481074299186809.65780825820210226094133업태구분명051-731-1469건축물동수1770.43사립체력단련장업법인명세부업종명1회원모집총인원2021-03-01 05:22:03
7219137873300000CDFH330106202100000410_42_01_PI2021-02-28 00:23:01.0체력단련장업몸이 좋아지는 PTT운동 트레이닝 샵<NA>부산광역시 동래구 안락동 629-8147895부산광역시 동래구 연안로51번길 77, 3층 (안락동)20210226.0<NA><NA><NA><NA>영업/정상영업중391522.27387775190296.57479298920210226111447<NA><NA><NA>491.88사립체력단련장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
7220137883400000CDFH330106202100000310_42_01_PI2021-02-28 00:23:01.0체력단련장업골드웨이브<NA>부산광역시 기장군 일광면 삼성리 809-1 일광골드타워 601~3호46048부산광역시 기장군 일광면 해빛1로 74, 일광골드타워 6층 601~603호20210226.0<NA><NA><NA><NA>영업/정상영업중<NA><NA>20210226153908<NA>051-723-589618098.57사립체력단련장업<NA><NA><NA>1<NA>2021-03-01 05:22:03
7221137893330000CDFH330106202100000510_42_01_PI2021-02-28 00:23:01.0체력단련장업플레이 돔지번우편번호부산광역시 해운대구 중동 1090-5 3층48097부산광역시 해운대구 달맞이길 52, 3층 (중동)20210226.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중397690.481074299186809.65780825820210226094133업태구분명051-731-1469건축물동수1770.43사립체력단련장업법인명세부업종명1회원모집총인원2021-03-01 05:22:03
7222137903300000CDFH330106202100000410_42_01_PI2021-02-28 00:23:01.0체력단련장업몸이 좋아지는 PTT운동 트레이닝 샵<NA>부산광역시 동래구 안락동 629-8147895부산광역시 동래구 연안로51번길 77, 3층 (안락동)20210226.0<NA><NA><NA><NA>영업/정상영업중391522.27387775190296.57479298920210226111447<NA><NA><NA>491.88사립체력단련장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03
7223137913400000CDFH330106202100000310_42_01_PI2021-02-28 00:23:01.0체력단련장업골드웨이브<NA>부산광역시 기장군 일광면 삼성리 809-1 일광골드타워 601~3호46048부산광역시 기장군 일광면 해빛1로 74, 일광골드타워 6층 601~603호20210226.0<NA><NA><NA><NA>영업/정상영업중<NA><NA>20210226153908<NA>051-723-589618098.57사립체력단련장업<NA><NA><NA>1<NA>2021-03-01 05:22:03
7224137783340000CDFH330108202100000210_32_01_PI2021-02-24 00:23:01.0당구장업해오름 당구클럽<NA>부산광역시 사하구 다대동 1552-549506부산광역시 사하구 다대로 694-2, 5층 (다대동)20210222.0<NA><NA><NA><NA>영업/정상영업중379257.467705859173968.57461559120210222090253<NA><NA><NA><NA>사립당구장업<NA><NA><NA><NA><NA>2021-03-01 05:22:03