Overview

Dataset statistics

Number of variables10
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory82.2 KiB
Average record size in memory84.1 B

Variable types

Categorical1
Numeric4
Text5

Alerts

CTY_NM has constant value ""Constant
RSTRNT_ID has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:55:47.939109
Analysis finished2023-12-10 09:55:53.241449
Duration5.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CTY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
seoul
1000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
seoul 1000
100.0%

Length

2023-12-10T18:55:53.405195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:55:53.593578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seoul 1000
100.0%

RSTRNT_ID
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5648.17
Minimum1088
Maximum9743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:55:53.936744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1088
5-th percentile1574.9
Q13256.5
median5734
Q37843.5
95-th percentile9637.2
Maximum9743
Range8655
Interquartile range (IQR)4587

Descriptive statistics

Standard deviation2713.1837
Coefficient of variation (CV)0.48036508
Kurtosis-1.2686303
Mean5648.17
Median Absolute Deviation (MAD)2132.5
Skewness0.015609224
Sum5648170
Variance7361365.5
MonotonicityStrictly increasing
2023-12-10T18:55:54.329318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1088 1
 
0.1%
7729 1
 
0.1%
7707 1
 
0.1%
7708 1
 
0.1%
7710 1
 
0.1%
7711 1
 
0.1%
7713 1
 
0.1%
7715 1
 
0.1%
7717 1
 
0.1%
7719 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1088 1
0.1%
1115 1
0.1%
1116 1
0.1%
1117 1
0.1%
1121 1
0.1%
1123 1
0.1%
1126 1
0.1%
1130 1
0.1%
1134 1
0.1%
1156 1
0.1%
ValueCountFrequency (%)
9743 1
0.1%
9742 1
0.1%
9741 1
0.1%
9740 1
0.1%
9739 1
0.1%
9738 1
0.1%
9734 1
0.1%
9733 1
0.1%
9732 1
0.1%
9731 1
0.1%

RSTRNT_NM
Text

Distinct928
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T18:55:54.838058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length14.575
Min length1

Characters and Unicode

Total characters14575
Distinct characters86
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique874 ?
Unique (%)87.4%

Sample

1st rowpanncoffee
2nd rowtodamtodam
3rd rowttowachamsutdwaeji galbi
4th rowdongseonggak
5th rowhurendeu chicken
ValueCountFrequency (%)
sikdang 73
 
4.2%
coffee 24
 
1.4%
chicken 23
 
1.3%
galbi 21
 
1.2%
kalguksu 16
 
0.9%
hof 16
 
0.9%
jokbal 13
 
0.7%
bulgalbi 11
 
0.6%
hanmari 11
 
0.6%
gamjatang 11
 
0.6%
Other values (1137) 1521
87.4%
2023-12-10T18:55:55.860720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1546
 
10.6%
a 1420
 
9.7%
g 1245
 
8.5%
o 1166
 
8.0%
e 1052
 
7.2%
743
 
5.1%
i 711
 
4.9%
u 664
 
4.6%
s 600
 
4.1%
j 511
 
3.5%
Other values (76) 4917
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12704
87.2%
Uppercase Letter 1046
 
7.2%
Space Separator 743
 
5.1%
Decimal Number 26
 
0.2%
Other Punctuation 19
 
0.1%
Control 18
 
0.1%
Dash Punctuation 7
 
< 0.1%
Modifier Symbol 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Other Symbol 2
 
< 0.1%
Other values (6) 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1546
12.2%
a 1420
11.2%
g 1245
 
9.8%
o 1166
 
9.2%
e 1052
 
8.3%
i 711
 
5.6%
u 664
 
5.2%
s 600
 
4.7%
j 511
 
4.0%
k 492
 
3.9%
Other values (21) 3297
26.0%
Uppercase Letter
ValueCountFrequency (%)
A 112
 
10.7%
E 109
 
10.4%
O 73
 
7.0%
S 69
 
6.6%
T 68
 
6.5%
C 62
 
5.9%
R 61
 
5.8%
N 56
 
5.4%
I 55
 
5.3%
B 49
 
4.7%
Other values (16) 332
31.7%
Decimal Number
ValueCountFrequency (%)
2 6
23.1%
1 4
15.4%
0 4
15.4%
9 4
15.4%
4 3
11.5%
7 2
 
7.7%
8 1
 
3.8%
5 1
 
3.8%
6 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
& 9
47.4%
' 4
21.1%
. 4
21.1%
… 1
 
5.3%
, 1
 
5.3%
Control
ValueCountFrequency (%)
17
94.4%
 1
 
5.6%
Open Punctuation
ValueCountFrequency (%)
( 1
50.0%
‚ 1
50.0%
Other Symbol
ValueCountFrequency (%)
° 1
50.0%
© 1
50.0%
Space Separator
ValueCountFrequency (%)
743
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Modifier Symbol
ValueCountFrequency (%)
¸ 2
100.0%
Math Symbol
ValueCountFrequency (%)
¬ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
” 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Currency Symbol
ValueCountFrequency (%)
€ 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13750
94.3%
Common 825
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1546
 
11.2%
a 1420
 
10.3%
g 1245
 
9.1%
o 1166
 
8.5%
e 1052
 
7.7%
i 711
 
5.2%
u 664
 
4.8%
s 600
 
4.4%
j 511
 
3.7%
k 492
 
3.6%
Other values (47) 4343
31.6%
Common
ValueCountFrequency (%)
743
90.1%
17
 
2.1%
& 9
 
1.1%
- 7
 
0.8%
2 6
 
0.7%
' 4
 
0.5%
1 4
 
0.5%
0 4
 
0.5%
. 4
 
0.5%
9 4
 
0.5%
Other values (19) 23
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14557
99.9%
None 13
 
0.1%
Punctuation 4
 
< 0.1%
Currency Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1546
 
10.6%
a 1420
 
9.8%
g 1245
 
8.6%
o 1166
 
8.0%
e 1052
 
7.2%
743
 
5.1%
i 711
 
4.9%
u 664
 
4.6%
s 600
 
4.1%
j 511
 
3.5%
Other values (59) 4899
33.7%
None
ValueCountFrequency (%)
¸ 2
15.4%
ë 1
7.7%
¬ 1
7.7%
ì 1
7.7%
ä 1
7.7%
° 1
7.7%
ê 1
7.7%
© 1
7.7%
Š 1
7.7%
ã 1
7.7%
Other values (2) 2
15.4%
Punctuation
ValueCountFrequency (%)
‚ 1
25.0%
” 1
25.0%
… 1
25.0%
“ 1
25.0%
Currency Symbols
ValueCountFrequency (%)
€ 1
100.0%

LNM_ADDR
Text

Distinct912
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T18:55:56.334563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length77
Median length52
Mean length35.112
Min length2

Characters and Unicode

Total characters35112
Distinct characters67
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique868 ?
Unique (%)86.8%

Sample

1st row198-10 Gwanhun-dong Jongno-gu Seoul
2nd row218-1 Nagwon-dong Jongno-gu Seoul
3rd row166 Waryong-dong Jongno-gu Seoul
4th row18 Dangju-dong Jongno-gu Seoul
5th row225-112 Itaewon-dong Yongsan-gu Seoul
ValueCountFrequency (%)
seoul 969
23.2%
jung-gu 253
 
6.1%
jongno-gu 159
 
3.8%
yeongdeungpo-gu 89
 
2.1%
2(i)-ga 69
 
1.7%
3(sam)-ga 64
 
1.5%
yongsan-gu 55
 
1.3%
seodaemun-gu 45
 
1.1%
dongdaemun-gu 42
 
1.0%
5(o)-ga 39
 
0.9%
Other values (1085) 2389
57.2%
2023-12-10T18:55:57.287235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 3765
 
10.7%
o 3630
 
10.3%
n 3337
 
9.5%
3173
 
9.0%
u 2878
 
8.2%
- 2864
 
8.2%
e 1919
 
5.5%
a 1304
 
3.7%
S 1240
 
3.5%
d 1177
 
3.4%
Other values (57) 9825
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21644
61.6%
Decimal Number 3917
 
11.2%
Space Separator 3173
 
9.0%
Uppercase Letter 2943
 
8.4%
Dash Punctuation 2864
 
8.2%
Open Punctuation 263
 
0.7%
Close Punctuation 260
 
0.7%
Other Punctuation 39
 
0.1%
Modifier Symbol 3
 
< 0.1%
Other Number 2
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 3765
17.4%
o 3630
16.8%
n 3337
15.4%
u 2878
13.3%
e 1919
8.9%
a 1304
 
6.0%
d 1177
 
5.4%
l 1100
 
5.1%
i 377
 
1.7%
m 363
 
1.7%
Other values (13) 1794
8.3%
Uppercase Letter
ValueCountFrequency (%)
S 1240
42.1%
J 525
17.8%
Y 255
 
8.7%
G 231
 
7.8%
D 139
 
4.7%
N 106
 
3.6%
M 104
 
3.5%
C 88
 
3.0%
H 80
 
2.7%
E 60
 
2.0%
Other values (7) 115
 
3.9%
Decimal Number
ValueCountFrequency (%)
1 800
20.4%
2 626
16.0%
3 509
13.0%
4 403
10.3%
5 343
8.8%
6 294
 
7.5%
0 247
 
6.3%
7 241
 
6.2%
8 231
 
5.9%
9 223
 
5.7%
Other Punctuation
ValueCountFrequency (%)
\ 31
79.5%
, 4
 
10.3%
• 2
 
5.1%
‡ 1
 
2.6%
§ 1
 
2.6%
Open Punctuation
ValueCountFrequency (%)
( 260
98.9%
„ 3
 
1.1%
Modifier Symbol
ValueCountFrequency (%)
˜ 2
66.7%
¸ 1
33.3%
Space Separator
ValueCountFrequency (%)
3173
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2864
100.0%
Close Punctuation
ValueCountFrequency (%)
) 260
100.0%
Other Number
ValueCountFrequency (%)
¼ 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Currency Symbol
ValueCountFrequency (%)
€ 1
100.0%
Control
ValueCountFrequency (%)
 1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24587
70.0%
Common 10525
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 3765
15.3%
o 3630
14.8%
n 3337
13.6%
u 2878
11.7%
e 1919
7.8%
a 1304
 
5.3%
S 1240
 
5.0%
d 1177
 
4.8%
l 1100
 
4.5%
J 525
 
2.1%
Other values (30) 3712
15.1%
Common
ValueCountFrequency (%)
3173
30.1%
- 2864
27.2%
1 800
 
7.6%
2 626
 
5.9%
3 509
 
4.8%
4 403
 
3.8%
5 343
 
3.3%
6 294
 
2.8%
) 260
 
2.5%
( 260
 
2.5%
Other values (17) 993
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35088
99.9%
None 14
 
< 0.1%
Punctuation 7
 
< 0.1%
Modifier Letters 2
 
< 0.1%
Currency Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 3765
 
10.7%
o 3630
 
10.3%
n 3337
 
9.5%
3173
 
9.0%
u 2878
 
8.2%
- 2864
 
8.2%
e 1919
 
5.5%
a 1304
 
3.7%
S 1240
 
3.5%
d 1177
 
3.4%
Other values (44) 9801
27.9%
None
ValueCountFrequency (%)
ì 4
28.6%
í 4
28.6%
¼ 2
14.3%
¸ 1
 
7.1%
§ 1
 
7.1%
 1
 
7.1%
° 1
 
7.1%
Punctuation
ValueCountFrequency (%)
„ 3
42.9%
• 2
28.6%
‘ 1
 
14.3%
‡ 1
 
14.3%
Modifier Letters
ValueCountFrequency (%)
˜ 2
100.0%
Currency Symbols
ValueCountFrequency (%)
€ 1
100.0%

RDNMADR_NM
Text

Distinct915
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T18:55:57.814677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length44
Mean length33.953
Min length2

Characters and Unicode

Total characters33953
Distinct characters63
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique867 ?
Unique (%)86.7%

Sample

1st row12 Insadong 5-gil Jongno-gu Seoul
2nd row121 Supyo-ro Jongno-gu Seoul
3rd row48 Donhwamun-ro 11ga-gil Jongno-gu Seoul
4th row29-2 Saemunan-ro 9-gil Jongno-gu Seoul
5th row10 Hoenamu-ro 13-gil Yongsan-gu Seoul
ValueCountFrequency (%)
seoul 977
 
22.0%
jung-gu 254
 
5.7%
jongno-gu 157
 
3.5%
yeongdeungpo-gu 86
 
1.9%
jong-ro 59
 
1.3%
yongsan-gu 52
 
1.2%
seodaemun-gu 47
 
1.1%
dongdaemun-gu 44
 
1.0%
eulji-ro 44
 
1.0%
gwangjin-gu 37
 
0.8%
Other values (802) 2685
60.4%
2023-12-10T18:55:58.643137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3605
 
10.6%
3443
 
10.1%
g 3376
 
9.9%
u 2944
 
8.7%
- 2647
 
7.8%
n 2546
 
7.5%
e 2135
 
6.3%
l 1667
 
4.9%
S 1267
 
3.7%
a 1200
 
3.5%
Other values (53) 9123
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21682
63.9%
Space Separator 3443
 
10.1%
Decimal Number 3197
 
9.4%
Uppercase Letter 2954
 
8.7%
Dash Punctuation 2647
 
7.8%
Other Punctuation 23
 
0.1%
Modifier Symbol 2
 
< 0.1%
Other Letter 1
 
< 0.1%
Control 1
 
< 0.1%
Other Symbol 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3605
16.6%
g 3376
15.6%
u 2944
13.6%
n 2546
11.7%
e 2135
9.8%
l 1667
7.7%
a 1200
 
5.5%
r 977
 
4.5%
i 813
 
3.7%
d 487
 
2.2%
Other values (14) 1932
8.9%
Uppercase Letter
ValueCountFrequency (%)
S 1267
42.9%
J 524
17.7%
G 222
 
7.5%
Y 216
 
7.3%
D 164
 
5.6%
M 115
 
3.9%
E 94
 
3.2%
N 77
 
2.6%
C 57
 
1.9%
T 57
 
1.9%
Other values (10) 161
 
5.5%
Decimal Number
ValueCountFrequency (%)
1 761
23.8%
2 466
14.6%
3 357
11.2%
4 344
10.8%
5 249
 
7.8%
7 222
 
6.9%
8 209
 
6.5%
0 208
 
6.5%
6 206
 
6.4%
9 175
 
5.5%
Space Separator
ValueCountFrequency (%)
3443
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2647
100.0%
Other Punctuation
ValueCountFrequency (%)
\ 23
100.0%
Modifier Symbol
ValueCountFrequency (%)
¸ 2
100.0%
Other Letter
ValueCountFrequency (%)
º 1
100.0%
Control
ValueCountFrequency (%)
 1
100.0%
Other Symbol
ValueCountFrequency (%)
© 1
100.0%
Final Punctuation
ValueCountFrequency (%)
› 1
100.0%
Currency Symbol
ValueCountFrequency (%)
¤ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24637
72.6%
Common 9316
 
27.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3605
14.6%
g 3376
13.7%
u 2944
11.9%
n 2546
10.3%
e 2135
8.7%
l 1667
6.8%
S 1267
 
5.1%
a 1200
 
4.9%
r 977
 
4.0%
i 813
 
3.3%
Other values (35) 4107
16.7%
Common
ValueCountFrequency (%)
3443
37.0%
- 2647
28.4%
1 761
 
8.2%
2 466
 
5.0%
3 357
 
3.8%
4 344
 
3.7%
5 249
 
2.7%
7 222
 
2.4%
8 209
 
2.2%
0 208
 
2.2%
Other values (8) 410
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33941
> 99.9%
None 11
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3605
 
10.6%
3443
 
10.1%
g 3376
 
9.9%
u 2944
 
8.7%
- 2647
 
7.8%
n 2546
 
7.5%
e 2135
 
6.3%
l 1667
 
4.9%
S 1267
 
3.7%
a 1200
 
3.5%
Other values (43) 9111
26.8%
None
ValueCountFrequency (%)
¸ 2
18.2%
é 2
18.2%
Š 1
9.1%
æ 1
9.1%
º 1
9.1%
 1
9.1%
© 1
9.1%
ê 1
9.1%
¤ 1
9.1%
Punctuation
ValueCountFrequency (%)
› 1
100.0%

RSTRNT_TEL_NO
Text

Distinct971
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T18:55:59.091543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.184
Min length2

Characters and Unicode

Total characters9184
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique963 ?
Unique (%)96.3%

Sample

1st row027250062
2nd row027447934
3rd row0236760399
4th row027350107
5th row027964642
ValueCountFrequency (%)
n 22
 
2.2%
0237898088 3
 
0.3%
027765348 2
 
0.2%
027773131 2
 
0.2%
027775668 2
 
0.2%
0222679396 2
 
0.2%
025711110 2
 
0.2%
027523177 2
 
0.2%
027396942 1
 
0.1%
027351933 1
 
0.1%
Other values (961) 961
96.1%
2023-12-10T18:56:00.366478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2030
22.1%
0 1481
16.1%
7 1025
11.2%
3 782
 
8.5%
6 752
 
8.2%
5 689
 
7.5%
4 657
 
7.2%
9 621
 
6.8%
8 572
 
6.2%
1 531
 
5.8%
Other values (2) 44
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9140
99.5%
Other Punctuation 22
 
0.2%
Uppercase Letter 22
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2030
22.2%
0 1481
16.2%
7 1025
11.2%
3 782
 
8.6%
6 752
 
8.2%
5 689
 
7.5%
4 657
 
7.2%
9 621
 
6.8%
8 572
 
6.3%
1 531
 
5.8%
Other Punctuation
ValueCountFrequency (%)
\ 22
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9162
99.8%
Latin 22
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2030
22.2%
0 1481
16.2%
7 1025
11.2%
3 782
 
8.5%
6 752
 
8.2%
5 689
 
7.5%
4 657
 
7.2%
9 621
 
6.8%
8 572
 
6.2%
1 531
 
5.8%
Latin
ValueCountFrequency (%)
N 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2030
22.1%
0 1481
16.1%
7 1025
11.2%
3 782
 
8.5%
6 752
 
8.2%
5 689
 
7.5%
4 657
 
7.2%
9 621
 
6.8%
8 572
 
6.2%
1 531
 
5.8%
Other values (2) 44
 
0.5%

SUBWAYST_NM
Text

Distinct170
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T18:56:00.907765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length29
Mean length18.539
Min length11

Characters and Unicode

Total characters18539
Distinct characters56
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)4.8%

Sample

1st rowAnguk Station
2nd rowAnguk Station
3rd rowAnguk Station
4th rowGwanghwamun Station
5th rowItaewon Station
ValueCountFrequency (%)
station 975
41.4%
euljiro 101
 
4.3%
4(sa)ga 74
 
3.1%
hoehyeon 63
 
2.7%
gwanghwamun 56
 
2.4%
myeongdong 53
 
2.3%
univ 49
 
2.1%
yeongdeungpo 35
 
1.5%
park 34
 
1.4%
seoul 30
 
1.3%
Other values (179) 885
37.6%
2023-12-10T18:56:01.809392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2208
11.9%
o 2081
11.2%
t 2079
11.2%
a 1826
9.8%
i 1460
 
7.9%
1376
 
7.4%
S 1159
 
6.3%
g 945
 
5.1%
e 786
 
4.2%
u 616
 
3.3%
Other values (46) 4003
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14449
77.9%
Uppercase Letter 2243
 
12.1%
Space Separator 1376
 
7.4%
Open Punctuation 132
 
0.7%
Close Punctuation 132
 
0.7%
Decimal Number 107
 
0.6%
Other Punctuation 78
 
0.4%
Dash Punctuation 18
 
0.1%
Initial Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2208
15.3%
o 2081
14.4%
t 2079
14.4%
a 1826
12.6%
i 1460
10.1%
g 945
6.5%
e 786
 
5.4%
u 616
 
4.3%
s 267
 
1.8%
y 255
 
1.8%
Other values (14) 1926
13.3%
Uppercase Letter
ValueCountFrequency (%)
S 1159
51.7%
G 160
 
7.1%
H 146
 
6.5%
E 110
 
4.9%
Y 97
 
4.3%
D 96
 
4.3%
M 91
 
4.1%
C 67
 
3.0%
U 52
 
2.3%
N 40
 
1.8%
Other values (11) 225
 
10.0%
Decimal Number
ValueCountFrequency (%)
4 74
69.2%
3 27
 
25.2%
5 6
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 49
62.8%
& 22
28.2%
' 7
 
9.0%
Space Separator
ValueCountFrequency (%)
1376
100.0%
Open Punctuation
ValueCountFrequency (%)
( 132
100.0%
Close Punctuation
ValueCountFrequency (%)
) 132
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16692
90.0%
Common 1847
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2208
13.2%
o 2081
12.5%
t 2079
12.5%
a 1826
10.9%
i 1460
8.7%
S 1159
 
6.9%
g 945
 
5.7%
e 786
 
4.7%
u 616
 
3.7%
s 267
 
1.6%
Other values (35) 3265
19.6%
Common
ValueCountFrequency (%)
1376
74.5%
( 132
 
7.1%
) 132
 
7.1%
4 74
 
4.0%
. 49
 
2.7%
3 27
 
1.5%
& 22
 
1.2%
- 18
 
1.0%
' 7
 
0.4%
5 6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18535
> 99.9%
Punctuation 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2208
11.9%
o 2081
11.2%
t 2079
11.2%
a 1826
9.9%
i 1460
 
7.9%
1376
 
7.4%
S 1159
 
6.3%
g 945
 
5.1%
e 786
 
4.2%
u 616
 
3.3%
Other values (45) 3999
21.6%
Punctuation
ValueCountFrequency (%)
‘ 4
100.0%

SUBWAYST_NM.1
Real number (ℝ)

Distinct936
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean413.77836
Minimum0.009446
Maximum699.54752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:56:02.166816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.009446
5-th percentile98.42562
Q1259.30608
median430.40847
Q3575.79551
95-th percentile676.28204
Maximum699.54752
Range699.53807
Interquartile range (IQR)316.48943

Descriptive statistics

Standard deviation184.4123
Coefficient of variation (CV)0.44567895
Kurtosis-1.1090928
Mean413.77836
Median Absolute Deviation (MAD)153.88229
Skewness-0.24843532
Sum413778.36
Variance34007.898
MonotonicityNot monotonic
2023-12-10T18:56:02.616459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.77552 6
 
0.6%
257.409397 6
 
0.6%
612.785909 4
 
0.4%
462.410271 4
 
0.4%
677.616009 4
 
0.4%
676.21183 3
 
0.3%
461.551236 3
 
0.3%
109.059737 2
 
0.2%
559.154933 2
 
0.2%
213.346422 2
 
0.2%
Other values (926) 964
96.4%
ValueCountFrequency (%)
0.009446 1
0.1%
18.673123 1
0.1%
18.967334 1
0.1%
25.132352 1
0.1%
25.359498 1
0.1%
39.684891 1
0.1%
41.966893 1
0.1%
46.021078 1
0.1%
46.034054 1
0.1%
47.776278 1
0.1%
ValueCountFrequency (%)
699.547518 1
0.1%
698.483019 1
0.1%
698.192538 1
0.1%
696.924441 1
0.1%
696.670583 1
0.1%
696.317406 1
0.1%
695.72837 1
0.1%
693.632945 1
0.1%
693.52106 1
0.1%
692.39651 1
0.1%

RSTRNT_LA
Real number (ℝ)

Distinct935
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.555564
Minimum37.453428
Maximum37.68503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:56:02.924215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.453428
5-th percentile37.492714
Q137.539114
median37.562906
Q337.570897
95-th percentile37.603853
Maximum37.68503
Range0.2316017
Interquartile range (IQR)0.031782475

Descriptive statistics

Standard deviation0.031949077
Coefficient of variation (CV)0.00085071488
Kurtosis1.1729098
Mean37.555564
Median Absolute Deviation (MAD)0.0109993
Skewness-0.17347138
Sum37555.564
Variance0.0010207435
MonotonicityNot monotonic
2023-12-10T18:56:03.339132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5214704 6
 
0.6%
37.504154 6
 
0.6%
37.5208744 4
 
0.4%
37.5644687 4
 
0.4%
37.5121721 4
 
0.4%
37.5641656 3
 
0.3%
37.5196767 3
 
0.3%
37.5210051 2
 
0.2%
37.5703041 2
 
0.2%
37.562291 2
 
0.2%
Other values (925) 964
96.4%
ValueCountFrequency (%)
37.4534281 1
0.1%
37.4559852 1
0.1%
37.4672522 2
0.2%
37.4685471 1
0.1%
37.4755582 1
0.1%
37.4789287 1
0.1%
37.4801437 1
0.1%
37.4802585 1
0.1%
37.4802634 1
0.1%
37.4804543 1
0.1%
ValueCountFrequency (%)
37.6850298 1
0.1%
37.6846059 1
0.1%
37.6773506 1
0.1%
37.6713881 1
0.1%
37.665656 1
0.1%
37.6638325 1
0.1%
37.6516194 1
0.1%
37.6513711 1
0.1%
37.6475992 1
0.1%
37.6357937 1
0.1%

RSTRNT_LO
Real number (ℝ)

Distinct936
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98345
Minimum126.80745
Maximum127.15365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:56:03.647139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80745
5-th percentile126.88923
Q1126.94847
median126.98542
Q3127.01215
95-th percentile127.07999
Maximum127.15365
Range0.3461964
Interquartile range (IQR)0.063678875

Descriptive statistics

Standard deviation0.057537291
Coefficient of variation (CV)0.00045310859
Kurtosis0.49320298
Mean126.98345
Median Absolute Deviation (MAD)0.0304145
Skewness-0.18079765
Sum126983.45
Variance0.0033105398
MonotonicityNot monotonic
2023-12-10T18:56:03.973217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9249738 6
 
0.6%
126.879538 6
 
0.6%
126.9313093 4
 
0.4%
127.0653425 4
 
0.4%
126.9800463 4
 
0.4%
126.9811637 3
 
0.3%
126.928942 3
 
0.3%
126.9238388 2
 
0.2%
126.9824573 2
 
0.2%
126.9078716 2
 
0.2%
Other values (926) 964
96.4%
ValueCountFrequency (%)
126.8074545 1
0.1%
126.8079647 1
0.1%
126.8080279 2
0.2%
126.8081111 1
0.1%
126.8091358 1
0.1%
126.8099671 1
0.1%
126.8120516 1
0.1%
126.8121299 1
0.1%
126.82014 1
0.1%
126.8409761 1
0.1%
ValueCountFrequency (%)
127.1536509 1
0.1%
127.1528684 1
0.1%
127.1343437 1
0.1%
127.1313787 1
0.1%
127.1294834 1
0.1%
127.1267408 1
0.1%
127.1265552 1
0.1%
127.1263565 1
0.1%
127.1263319 1
0.1%
127.126184 1
0.1%

Interactions

2023-12-10T18:55:51.625969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:48.789698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.737621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:50.632344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:51.865426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.085096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.951505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:50.853062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:52.151438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.316313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:50.196263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:51.110006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:52.367275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.510095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:50.388924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:51.306556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:56:04.190749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDSUBWAYST_NM.1RSTRNT_LARSTRNT_LO
RSTRNT_ID1.0000.1010.1910.313
SUBWAYST_NM.10.1011.0000.2130.299
RSTRNT_LA0.1910.2131.0000.709
RSTRNT_LO0.3130.2990.7091.000
2023-12-10T18:56:04.443197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDSUBWAYST_NM.1RSTRNT_LARSTRNT_LO
RSTRNT_ID1.000-0.067-0.1650.056
SUBWAYST_NM.1-0.0671.0000.0260.010
RSTRNT_LA-0.1650.0261.0000.369
RSTRNT_LO0.0560.0100.3691.000

Missing values

2023-12-10T18:55:52.705157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:55:53.091463image/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

CTY_NMRSTRNT_IDRSTRNT_NMLNM_ADDRRDNMADR_NMRSTRNT_TEL_NOSUBWAYST_NMSUBWAYST_NM.1RSTRNT_LARSTRNT_LO
0seoul1088panncoffee198-10 Gwanhun-dong Jongno-gu Seoul12 Insadong 5-gil Jongno-gu Seoul027250062Anguk Station429.32820737.572857126.985577
1seoul1115todamtodam218-1 Nagwon-dong Jongno-gu Seoul121 Supyo-ro Jongno-gu Seoul027447934Anguk Station588.60089337.571998126.988855
2seoul1116ttowachamsutdwaeji galbi166 Waryong-dong Jongno-gu Seoul48 Donhwamun-ro 11ga-gil Jongno-gu Seoul0236760399Anguk Station480.51917337.574727126.990652
3seoul1117dongseonggak18 Dangju-dong Jongno-gu Seoul29-2 Saemunan-ro 9-gil Jongno-gu Seoul027350107Gwanghwamun Station135.40508237.571444126.975272
4seoul1121hurendeu chicken225-112 Itaewon-dong Yongsan-gu Seoul10 Hoenamu-ro 13-gil Yongsan-gu Seoul027964642Itaewon Station691.73399537.539777126.989591
5seoul1123donghosutbul barbecue651 Sanggye-dong Nowon-gu Seoul1541 Dongil-ro Nowon-gu Seoul029321090Madeul Station82.55110737.665656127.057086
6seoul1126Brasserie159 Samseong-dong Gangnam-gu Seoul524 Bongeunsa-ro Gangnam-gu Seoul0234308585Bongeunsa Station358.33778237.512878127.057291
7seoul1130coffee Amarelo552-12 Seongnae-dong Gangdong-gu Seoul14-26 Seongnae-ro 6-gil Gangdong-gu Seoul024886394Gangdong-gu Office Station122.3476937.529883127.12154
8seoul1134LINNE'S GARDEN24-2 Yeouido-dong Yeongdeungpo-gu Seoul77-1 Yeouinaru-ro Yeongdeungpo-gu Seoul027834877Yeouinaru Station585.87504737.524395126.927161
9seoul1156maeha281-52 Bulgwang-dong Eunpyeong-gu Seoul11 Tongil-ro 66-gil Eunpyeong-gu Seoul\NBulgwang Station138.9523737.61083126.931351
CTY_NMRSTRNT_IDRSTRNT_NMLNM_ADDRRDNMADR_NMRSTRNT_TEL_NOSUBWAYST_NMSUBWAYST_NM.1RSTRNT_LARSTRNT_LO
990seoul9731cheongdammyeonok8-1 Samseong-dong Gangnam-gu Seoul664 Seolleung-ro Gangnam-gu Seoul025483777Seonjeongneung Station571.35410237.515896127.042335
991seoul9732duruchigi yuseonsaeng166-6 Samseong-dong Gangnam-gu Seoul42 Bongeunsa-ro 114-gil Gangnam-gu Seoul025631159Bongeunsa Station462.41027137.512172127.065342
992seoul9733ucheuwa9-6 Samseong-dong Gangnam-gu Seoul32 Hakdong-ro 56-gil Gangnam-gu Seoul025114956Seonjeongneung Station486.3521637.51519127.042846
993seoul9734samhwanso hanmari107-5 Samseong-dong Gangnam-gu Seoul10 Yeongdong-daero 112-gil Gangnam-gu Seoul025452429Bongeunsa Station71.13774637.514992127.061034
994seoul9738COFFEE ONLY164-11 Nonhyeon-dong Gangnam-gu Seoul506 Gangnam-daero Gangnam-gu Seoul0260840015Nonhyeon Station443.28190837.50749127.023517
995seoul9739insaengkacheu615-1 Sinsa-dong Gangnam-gu Seoul216 Apgujeong-ro Gangnam-gu Seoul025453442Apgujeong Station292.37846137.528095127.031045
996seoul974060nyeon jeontong sinchon hwang sogopchang144 Nonhyeon-dong Gangnam-gu Seoul10 Gangnam-daero 128-gil Gangnam-gu Seoul025114632Nonhyeon Station198.03923337.509822127.023083
997seoul9741hanul dakgalbi144-1 Nonhyeon-dong Gangnam-gu Seoul29 Hakdong-ro 2-gil Gangnam-gu Seoul025488970Nonhyeon Station221.58882537.509577127.023119
998seoul9742sammi143-10 Nonhyeon-dong Gangnam-gu Seoul30 Hakdong-ro 2-gil Gangnam-gu Seoul025499485Nonhyeon Station222.15376837.509475127.022935
999seoul9743Mr.dolsoe166-6 Samseong-dong Gangnam-gu Seoul42 Bongeunsa-ro 114-gil Gangnam-gu Seoul025670933Bongeunsa Station462.41027137.512172127.065342