Overview

Dataset statistics

Number of variables11
Number of observations40
Missing cells5
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory96.3 B

Variable types

Numeric5
Categorical3
Text3

Alerts

COUNTRY_NM has constant value ""Constant
ARPRT_NM is highly overall correlated with RSTRNT_TO_DSTNC_CO and 3 other fieldsHigh correlation
CTY_NM is highly overall correlated with RSTRNT_TO_DSTNC_CO and 3 other fieldsHigh correlation
RSTRNT_TO_DSTNC_CO is highly overall correlated with ARPRT_NM and 1 other fieldsHigh correlation
RSTRNT_TEL_NO is highly overall correlated with RSTRNT_LAHigh correlation
RSTRNT_LA is highly overall correlated with RSTRNT_TEL_NO and 2 other fieldsHigh correlation
RSTRNT_LO is highly overall correlated with ARPRT_NM and 1 other fieldsHigh correlation
RSTRNT_TEL_NO has 5 (12.5%) missing valuesMissing
RSTRNT_ID has unique valuesUnique
RSTRNT_NM has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:05:38.098738
Analysis finished2023-12-10 10:05:44.388041
Duration6.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RSTRNT_ID
Real number (ℝ)

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130746.82
Minimum14532
Maximum174252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-10T19:05:44.526572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14532
5-th percentile96876.2
Q1116346.75
median135441
Q3156414.75
95-th percentile168544.8
Maximum174252
Range159720
Interquartile range (IQR)40068

Descriptive statistics

Standard deviation35028.797
Coefficient of variation (CV)0.26791317
Kurtosis4.4164509
Mean130746.82
Median Absolute Deviation (MAD)20265
Skewness-1.7476931
Sum5229873
Variance1.2270166 × 109
MonotonicityNot monotonic
2023-12-10T19:05:44.810644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
101201 1
 
2.5%
139368 1
 
2.5%
145132 1
 
2.5%
14532 1
 
2.5%
14705 1
 
2.5%
147774 1
 
2.5%
148765 1
 
2.5%
150614 1
 
2.5%
155958 1
 
2.5%
157785 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
14532 1
2.5%
14705 1
2.5%
101201 1
2.5%
101273 1
2.5%
101328 1
2.5%
103656 1
2.5%
104363 1
2.5%
105432 1
2.5%
108404 1
2.5%
115428 1
2.5%
ValueCountFrequency (%)
174252 1
2.5%
171543 1
2.5%
168387 1
2.5%
167302 1
2.5%
166417 1
2.5%
166045 1
2.5%
163566 1
2.5%
162590 1
2.5%
162422 1
2.5%
157785 1
2.5%

ARPRT_NM
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
포항공항
4 
양양국제공항
4 
울산공항
4 
광주공항
3 
무안국제공항
3 
Other values (9)
22 

Length

Max length6
Median length5
Mean length5
Min length4

Unique

Unique1 ?
Unique (%)2.5%

Sample

1st row광주공항
2nd row무안국제공항
3rd row원주공항
4th row청주국제공항
5th row김해국제공항

Common Values

ValueCountFrequency (%)
포항공항 4
10.0%
양양국제공항 4
10.0%
울산공항 4
10.0%
광주공항 3
7.5%
무안국제공항 3
7.5%
청주국제공항 3
7.5%
김해국제공항 3
7.5%
제주국제공항 3
7.5%
김포국제공항 3
7.5%
여수공항 3
7.5%
Other values (4) 7
17.5%

Length

2023-12-10T19:05:45.190119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
포항공항 4
10.0%
양양국제공항 4
10.0%
울산공항 4
10.0%
광주공항 3
7.5%
무안국제공항 3
7.5%
청주국제공항 3
7.5%
김해국제공항 3
7.5%
제주국제공항 3
7.5%
김포국제공항 3
7.5%
여수공항 3
7.5%
Other values (4) 7
17.5%

RSTRNT_NM
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-10T19:05:45.689422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length10.5
Mean length6.65
Min length2

Characters and Unicode

Total characters266
Distinct characters146
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row송가네왕족발
2nd row무안갯벌낙지전문 8호점
3rd row강해루
4th row천마하나로
5th row아카렌 서부산유통단지점
ValueCountFrequency (%)
서부산유통단지점 2
 
4.0%
송가네왕족발 1
 
2.0%
청초수 1
 
2.0%
장실장네짜투리구이 1
 
2.0%
정브라더카페 1
 
2.0%
이가짬뽕 1
 
2.0%
뚜레쥬르 1
 
2.0%
이차돌 1
 
2.0%
신방화점 1
 
2.0%
윤이네분식 1
 
2.0%
Other values (39) 39
78.0%
2023-12-10T19:05:46.456884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 10
 
3.8%
점 10
 
3.8%
10
 
3.8%
네 6
 
2.3%
수 6
 
2.3%
지 6
 
2.3%
식 5
 
1.9%
카 4
 
1.5%
라 4
 
1.5%
화 4
 
1.5%
Other values (136) 201
75.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 245
92.1%
Space Separator 10
 
3.8%
Lowercase Letter 8
 
3.0%
Decimal Number 2
 
0.8%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 10
 
4.1%
점 10
 
4.1%
네 6
 
2.4%
수 6
 
2.4%
지 6
 
2.4%
식 5
 
2.0%
카 4
 
1.6%
라 4
 
1.6%
화 4
 
1.6%
산 4
 
1.6%
Other values (128) 186
75.9%
Lowercase Letter
ValueCountFrequency (%)
c 2
25.0%
f 2
25.0%
a 2
25.0%
e 2
25.0%
Decimal Number
ValueCountFrequency (%)
7 1
50.0%
8 1
50.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 245
92.1%
Common 13
 
4.9%
Latin 8
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 10
 
4.1%
점 10
 
4.1%
네 6
 
2.4%
수 6
 
2.4%
지 6
 
2.4%
식 5
 
2.0%
카 4
 
1.6%
라 4
 
1.6%
화 4
 
1.6%
산 4
 
1.6%
Other values (128) 186
75.9%
Common
ValueCountFrequency (%)
10
76.9%
7 1
 
7.7%
8 1
 
7.7%
& 1
 
7.7%
Latin
ValueCountFrequency (%)
c 2
25.0%
f 2
25.0%
a 2
25.0%
e 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 245
92.1%
ASCII 21
 
7.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 10
 
4.1%
점 10
 
4.1%
네 6
 
2.4%
수 6
 
2.4%
지 6
 
2.4%
식 5
 
2.0%
카 4
 
1.6%
라 4
 
1.6%
화 4
 
1.6%
산 4
 
1.6%
Other values (128) 186
75.9%
ASCII
ValueCountFrequency (%)
10
47.6%
c 2
 
9.5%
f 2
 
9.5%
a 2
 
9.5%
e 2
 
9.5%
7 1
 
4.8%
8 1
 
4.8%
& 1
 
4.8%

RSTRNT_LNM_ADDR
Text

Distinct36
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-10T19:05:47.039708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length22
Mean length19.125
Min length14

Characters and Unicode

Total characters765
Distinct characters85
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

Unique33 ?
Unique (%)82.5%

Sample

1st row광주 광산구 소촌동 521-32
2nd row전남 무안군 망운면 피서리 803-9 8호점
3rd row강원 원주시 소초면 의관리 47
4th row충북 청주시 청원구 내수읍 묵방리 491-7
5th row부산 강서구 대저2동 3150-4
ValueCountFrequency (%)
전남 6
 
3.1%
강원 6
 
3.1%
강서구 6
 
3.1%
울산 4
 
2.1%
북구 4
 
2.1%
손양면 4
 
2.1%
양양군 4
 
2.1%
약전리 4
 
2.1%
동해면 4
 
2.1%
남구 4
 
2.1%
Other values (89) 147
76.2%
2023-12-10T19:05:47.832838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
155
20.3%
1 35
 
4.6%
- 31
 
4.1%
동 29
 
3.8%
4 26
 
3.4%
2 23
 
3.0%
구 22
 
2.9%
리 20
 
2.6%
3 19
 
2.5%
시 18
 
2.4%
Other values (75) 387
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 402
52.5%
Decimal Number 177
23.1%
Space Separator 155
 
20.3%
Dash Punctuation 31
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 29
 
7.2%
구 22
 
5.5%
리 20
 
5.0%
시 18
 
4.5%
면 14
 
3.5%
서 14
 
3.5%
주 13
 
3.2%
산 13
 
3.2%
북 13
 
3.2%
강 12
 
3.0%
Other values (63) 234
58.2%
Decimal Number
ValueCountFrequency (%)
1 35
19.8%
4 26
14.7%
2 23
13.0%
3 19
10.7%
0 17
9.6%
6 14
 
7.9%
8 13
 
7.3%
9 11
 
6.2%
5 11
 
6.2%
7 8
 
4.5%
Space Separator
ValueCountFrequency (%)
155
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 402
52.5%
Common 363
47.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 29
 
7.2%
구 22
 
5.5%
리 20
 
5.0%
시 18
 
4.5%
면 14
 
3.5%
서 14
 
3.5%
주 13
 
3.2%
산 13
 
3.2%
북 13
 
3.2%
강 12
 
3.0%
Other values (63) 234
58.2%
Common
ValueCountFrequency (%)
155
42.7%
1 35
 
9.6%
- 31
 
8.5%
4 26
 
7.2%
2 23
 
6.3%
3 19
 
5.2%
0 17
 
4.7%
6 14
 
3.9%
8 13
 
3.6%
9 11
 
3.0%
Other values (2) 19
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 402
52.5%
ASCII 363
47.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
155
42.7%
1 35
 
9.6%
- 31
 
8.5%
4 26
 
7.2%
2 23
 
6.3%
3 19
 
5.2%
0 17
 
4.7%
6 14
 
3.9%
8 13
 
3.6%
9 11
 
3.0%
Other values (2) 19
 
5.2%
Hangul
ValueCountFrequency (%)
동 29
 
7.2%
구 22
 
5.5%
리 20
 
5.0%
시 18
 
4.5%
면 14
 
3.5%
서 14
 
3.5%
주 13
 
3.2%
산 13
 
3.2%
북 13
 
3.2%
강 12
 
3.0%
Other values (63) 234
58.2%

RSTRNT_ROAD_NM_ADDR
Text

Distinct37
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-10T19:05:48.451430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length26
Mean length20.85
Min length12

Characters and Unicode

Total characters834
Distinct characters119
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

Unique35 ?
Unique (%)87.5%

Sample

1st row광주 광산구 상무대로 303
2nd row전남 무안군 망운면 청운로 869 8호점
3rd row강원 원주시 소초면 북원로 3376
4th row충북 청주시 청원구 내수읍 내수로 538
5th row부산 강서구 유통단지1로49번길 6 104호
ValueCountFrequency (%)
전남 6
 
2.9%
강서구 6
 
2.9%
강원 6
 
2.9%
남구 4
 
1.9%
동해면 4
 
1.9%
일월로 4
 
1.9%
손양면 4
 
1.9%
양양군 4
 
1.9%
경북 4
 
1.9%
포항시 4
 
1.9%
Other values (104) 163
78.0%
2023-12-10T19:05:49.234800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
169
 
20.3%
1 36
 
4.3%
로 36
 
4.3%
구 25
 
3.0%
시 21
 
2.5%
2 16
 
1.9%
산 16
 
1.9%
7 16
 
1.9%
북 14
 
1.7%
면 13
 
1.6%
Other values (109) 472
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 516
61.9%
Space Separator 169
 
20.3%
Decimal Number 143
 
17.1%
Dash Punctuation 6
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 36
 
7.0%
구 25
 
4.8%
시 21
 
4.1%
산 16
 
3.1%
북 14
 
2.7%
면 13
 
2.5%
주 13
 
2.5%
남 13
 
2.5%
강 12
 
2.3%
원 12
 
2.3%
Other values (97) 341
66.1%
Decimal Number
ValueCountFrequency (%)
1 36
25.2%
2 16
11.2%
7 16
11.2%
0 13
 
9.1%
9 13
 
9.1%
8 11
 
7.7%
3 11
 
7.7%
6 10
 
7.0%
5 10
 
7.0%
4 7
 
4.9%
Space Separator
ValueCountFrequency (%)
169
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 516
61.9%
Common 318
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 36
 
7.0%
구 25
 
4.8%
시 21
 
4.1%
산 16
 
3.1%
북 14
 
2.7%
면 13
 
2.5%
주 13
 
2.5%
남 13
 
2.5%
강 12
 
2.3%
원 12
 
2.3%
Other values (97) 341
66.1%
Common
ValueCountFrequency (%)
169
53.1%
1 36
 
11.3%
2 16
 
5.0%
7 16
 
5.0%
0 13
 
4.1%
9 13
 
4.1%
8 11
 
3.5%
3 11
 
3.5%
6 10
 
3.1%
5 10
 
3.1%
Other values (2) 13
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 516
61.9%
ASCII 318
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
169
53.1%
1 36
 
11.3%
2 16
 
5.0%
7 16
 
5.0%
0 13
 
4.1%
9 13
 
4.1%
8 11
 
3.5%
3 11
 
3.5%
6 10
 
3.1%
5 10
 
3.1%
Other values (2) 13
 
4.1%
Hangul
ValueCountFrequency (%)
로 36
 
7.0%
구 25
 
4.8%
시 21
 
4.1%
산 16
 
3.1%
북 14
 
2.7%
면 13
 
2.5%
주 13
 
2.5%
남 13
 
2.5%
강 12
 
2.3%
원 12
 
2.3%
Other values (97) 341
66.1%

RSTRNT_TO_DSTNC_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6473645
Minimum1.00106
Maximum3.33049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-10T19:05:49.530217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.00106
5-th percentile1.0243735
Q11.0491875
median1.166295
Q32.356105
95-th percentile3.23886
Maximum3.33049
Range2.32943
Interquartile range (IQR)1.3069175

Descriptive statistics

Standard deviation0.77291152
Coefficient of variation (CV)0.46918063
Kurtosis-0.45848892
Mean1.6473645
Median Absolute Deviation (MAD)0.1563
Skewness1.001043
Sum65.89458
Variance0.59739222
MonotonicityNot monotonic
2023-12-10T19:05:49.816175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.23886 2
 
5.0%
1.13835 2
 
5.0%
1.07868 1
 
2.5%
3.11243 1
 
2.5%
1.02918 1
 
2.5%
1.08502 1
 
2.5%
1.34908 1
 
2.5%
2.55975 1
 
2.5%
1.02477 1
 
2.5%
1.01893 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
1.00106 1
2.5%
1.01893 1
2.5%
1.02466 1
2.5%
1.02477 1
2.5%
1.0248 1
2.5%
1.02918 1
2.5%
1.0352 1
2.5%
1.03592 1
2.5%
1.04208 1
2.5%
1.0421 1
2.5%
ValueCountFrequency (%)
3.33049 1
2.5%
3.23886 2
5.0%
3.11243 1
2.5%
2.79588 1
2.5%
2.63239 1
2.5%
2.60502 1
2.5%
2.55975 1
2.5%
2.49494 1
2.5%
2.3668 1
2.5%
2.35254 1
2.5%

RSTRNT_TEL_NO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)100.0%
Missing5
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean2.1019192 × 109
Minimum2.2661229 × 108
Maximum5.0405449 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-10T19:05:50.024094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.2661229 × 108
5-th percentile2.2664919 × 108
Q13.8480295 × 108
median5.3984222 × 108
Q36.1685096 × 108
95-th percentile2.5654352 × 109
Maximum5.0405449 × 1010
Range5.0178837 × 1010
Interquartile range (IQR)2.32048 × 108

Descriptive statistics

Standard deviation8.4785161 × 109
Coefficient of variation (CV)4.0337021
Kurtosis33.692753
Mean2.1019192 × 109
Median Absolute Deviation (MAD)89587072
Skewness5.7672911
Sum7.3567173 × 1010
Variance7.1885236 × 1019
MonotonicityNot monotonic
2023-12-10T19:05:50.265452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
614546668 1
 
2.5%
519882200 1
 
2.5%
226612288 1
 
2.5%
432143130 1
 
2.5%
226660666 1
 
2.5%
629461113 1
 
2.5%
616851125 1
 
2.5%
333437918 1
 
2.5%
558542296 1
 
2.5%
336726654 1
 
2.5%
Other values (25) 25
62.5%
(Missing) 5
 
12.5%
ValueCountFrequency (%)
226612288 1
2.5%
226622421 1
2.5%
226660666 1
2.5%
333437918 1
2.5%
336710001 1
2.5%
336710769 1
2.5%
336720758 1
2.5%
336726654 1
2.5%
337462774 1
2.5%
432143130 1
2.5%
ValueCountFrequency (%)
50405449093 1
2.5%
7041496900 1
2.5%
647123066 1
2.5%
629461113 1
2.5%
629459997 1
2.5%
629429292 1
2.5%
618183330 1
2.5%
616911282 1
2.5%
616851125 1
2.5%
616850788 1
2.5%

RSTRNT_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.877861
Minimum33.509811
Maximum38.078624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-10T19:05:50.566972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.509811
5-th percentile33.512217
Q135.083123
median35.588621
Q336.714809
95-th percentile38.059712
Maximum38.078624
Range4.5688128
Interquartile range (IQR)1.6316859

Descriptive statistics

Standard deviation1.2620457
Coefficient of variation (CV)0.035176168
Kurtosis-0.50925121
Mean35.877861
Median Absolute Deviation (MAD)0.6098891
Skewness0.22918726
Sum1435.1144
Variance1.5927593
MonotonicityNot monotonic
2023-12-10T19:05:50.801909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
34.8266555 2
 
5.0%
35.9878324 2
 
5.0%
35.1437425 1
 
2.5%
37.4863099 1
 
2.5%
35.1672267 1
 
2.5%
37.565845 1
 
2.5%
35.9878738 1
 
2.5%
36.717408 1
 
2.5%
37.56494 1
 
2.5%
35.1383682 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
33.5098114 1
2.5%
33.5099391 1
2.5%
33.512337 1
2.5%
34.826082 1
2.5%
34.8266555 2
5.0%
34.9776079 1
2.5%
34.978722 1
2.5%
34.9787416 1
2.5%
35.0829257 1
2.5%
35.0831883 1
2.5%
ValueCountFrequency (%)
38.0786242 1
2.5%
38.0638131 1
2.5%
38.0594961 1
2.5%
38.0580963 1
2.5%
37.565845 1
2.5%
37.56494 1
2.5%
37.5562289 1
2.5%
37.4863099 1
2.5%
37.4380524 1
2.5%
36.717408 1
2.5%

RSTRNT_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.89745
Minimum126.37963
Maximum129.44942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-10T19:05:51.041074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.37963
5-th percentile126.3805
Q1126.79939
median127.8124
Q3128.95182
95-th percentile129.44607
Maximum129.44942
Range3.0697837
Interquartile range (IQR)2.1524228

Descriptive statistics

Standard deviation1.1126109
Coefficient of variation (CV)0.0086992424
Kurtosis-1.5298347
Mean127.89745
Median Absolute Deviation (MAD)1.0133762
Skewness0.057434852
Sum5115.8979
Variance1.237903
MonotonicityNot monotonic
2023-12-10T19:05:51.299491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
127.6451709 2
 
5.0%
129.4459513 2
 
5.0%
126.7988098 1
 
2.5%
127.9857248 1
 
2.5%
128.9551919 1
 
2.5%
126.8120748 1
 
2.5%
129.4484193 1
 
2.5%
127.52393 1
 
2.5%
126.81213 1
 
2.5%
126.7992405 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
126.3796345 1
2.5%
126.3804482 1
2.5%
126.3805045 1
2.5%
126.5042078 1
2.5%
126.5058371 1
2.5%
126.5071677 1
2.5%
126.6445921 1
2.5%
126.6448991 1
2.5%
126.7988098 1
2.5%
126.7992405 1
2.5%
ValueCountFrequency (%)
129.4494182 1
2.5%
129.4484193 1
2.5%
129.4459513 2
5.0%
129.3668453 1
2.5%
129.3668118 1
2.5%
129.3656036 1
2.5%
129.365526 1
2.5%
128.9552828 1
2.5%
128.9551919 1
2.5%
128.9506922 1
2.5%

COUNTRY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
KOR
40 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KOR 40
100.0%

Length

2023-12-10T19:05:51.548825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:51.728182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kor 40
100.0%

CTY_NM
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
pohang
4 
yangyang
4 
ulsan
4 
gwangju
3 
muan
3 
Other values (9)
22 

Length

Max length8
Median length7
Mean length5.775
Min length4

Unique

Unique1 ?
Unique (%)2.5%

Sample

1st rowgwangju
2nd rowmuan
3rd rowwonju
4th rowcheongju
5th rowbusan

Common Values

ValueCountFrequency (%)
pohang 4
10.0%
yangyang 4
10.0%
ulsan 4
10.0%
gwangju 3
7.5%
muan 3
7.5%
cheongju 3
7.5%
busan 3
7.5%
jeju 3
7.5%
seoul 3
7.5%
yeosu 3
7.5%
Other values (4) 7
17.5%

Length

2023-12-10T19:05:51.974783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pohang 4
10.0%
yangyang 4
10.0%
ulsan 4
10.0%
gwangju 3
7.5%
muan 3
7.5%
cheongju 3
7.5%
busan 3
7.5%
jeju 3
7.5%
seoul 3
7.5%
yeosu 3
7.5%
Other values (4) 7
17.5%

Interactions

2023-12-10T19:05:42.972715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:39.157596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:40.061676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:41.058758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:42.157019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:43.139400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:39.343842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:40.293357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:41.208386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:42.341504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:43.276927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:39.504781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:40.463923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:41.354598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:42.486242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:43.456721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:39.708482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:40.652754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:41.835807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:42.671080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:43.603958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:39.869729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:40.883843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:42.003711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:42.811128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:05:52.148477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDARPRT_NMRSTRNT_NMRSTRNT_LNM_ADDRRSTRNT_ROAD_NM_ADDRRSTRNT_TO_DSTNC_CORSTRNT_TEL_NORSTRNT_LARSTRNT_LOCTY_NM
RSTRNT_ID1.0000.0001.0000.8000.8950.2840.1460.1460.1600.000
ARPRT_NM0.0001.0001.0001.0001.0000.8700.0001.0001.0001.000
RSTRNT_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
RSTRNT_LNM_ADDR0.8001.0001.0001.0001.0001.0000.0001.0001.0001.000
RSTRNT_ROAD_NM_ADDR0.8951.0001.0001.0001.0001.0000.0001.0001.0001.000
RSTRNT_TO_DSTNC_CO0.2840.8701.0001.0001.0001.0000.0000.8750.8700.870
RSTRNT_TEL_NO0.1460.0001.0000.0000.0000.0001.0000.0000.1610.000
RSTRNT_LA0.1461.0001.0001.0001.0000.8750.0001.0000.9681.000
RSTRNT_LO0.1601.0001.0001.0001.0000.8700.1610.9681.0001.000
CTY_NM0.0001.0001.0001.0001.0000.8700.0001.0001.0001.000
2023-12-10T19:05:52.390632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ARPRT_NMCTY_NM
ARPRT_NM1.0001.000
CTY_NM1.0001.000
2023-12-10T19:05:52.563073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_TO_DSTNC_CORSTRNT_TEL_NORSTRNT_LARSTRNT_LOARPRT_NMCTY_NM
RSTRNT_ID1.000-0.0190.008-0.176-0.0580.0000.000
RSTRNT_TO_DSTNC_CO-0.0191.000-0.1410.158-0.1790.5620.562
RSTRNT_TEL_NO0.008-0.1411.000-0.832-0.1440.0000.000
RSTRNT_LA-0.1760.158-0.8321.0000.3820.9010.901
RSTRNT_LO-0.058-0.179-0.1440.3821.0000.9010.901
ARPRT_NM0.0000.5620.0000.9010.9011.0001.000
CTY_NM0.0000.5620.0000.9010.9011.0001.000

Missing values

2023-12-10T19:05:43.809183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:05:44.177071image/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

RSTRNT_IDARPRT_NMRSTRNT_NMRSTRNT_LNM_ADDRRSTRNT_ROAD_NM_ADDRRSTRNT_TO_DSTNC_CORSTRNT_TEL_NORSTRNT_LARSTRNT_LOCOUNTRY_NMCTY_NM
0101201광주공항송가네왕족발광주 광산구 소촌동 521-32광주 광산구 상무대로 3031.0786862942929235.143743126.79881KORgwangju
1101273무안국제공항무안갯벌낙지전문 8호점전남 무안군 망운면 피서리 803-9 8호점전남 무안군 망운면 청운로 869 8호점1.7463761454666834.978742126.380448KORmuan
2101328원주공항강해루강원 원주시 소초면 의관리 47강원 원주시 소초면 북원로 33762.3525433746277437.438052127.978867KORwonju
3103656청주국제공항천마하나로충북 청주시 청원구 내수읍 묵방리 491-7충북 청주시 청원구 내수읍 내수로 5382.4949443273000136.713942127.521913KORcheongju
4104363김해국제공항아카렌 서부산유통단지점부산 강서구 대저2동 3150-4부산 강서구 유통단지1로49번길 6 104호1.0359251832151535.167222128.955283KORbusan
5105432포항공항피자모자이크도구점경북 포항시 남구 동해면 약전리 421-14경북 포항시 남구 동해면 일월로 1511.4474654286253335.988248129.449418KORpohang
6108404양양국제공항비치얼스동호 cafe강원 양양군 손양면 동호리 141-7 1층강원 양양군 손양면 선사유적로 327 1층1.6087333671000138.059496128.681191KORyangyang
7115428포항공항후크경북 포항시 남구 동해면 약전리 403-8경북 포항시 남구 동해면 일월로 1181.1383554278262235.987832129.445951KORpohang
8116653포항공항와보라카이경북 포항시 남구 동해면 약전리 403-8 가동 2층 110호경북 포항시 남구 동해면 일월로 118 가동 2층 110호1.1383554275915535.987832129.445951KORpohang
9117706무안국제공항화이트카페블랙라운지전남 무안군 망운면 피서리 803-47전남 무안군 망운면 청운로 857-101.8913661818333034.977608126.379634KORmuan
RSTRNT_IDARPRT_NMRSTRNT_NMRSTRNT_LNM_ADDRRSTRNT_ROAD_NM_ADDRRSTRNT_TO_DSTNC_CORSTRNT_TEL_NORSTRNT_LARSTRNT_LOCOUNTRY_NMCTY_NM
30157785원주공항청초수 물회강원 횡성군 횡성읍 읍상리 671-5강원 횡성군 횡성읍 앞들동로10번길 93.1124333343791837.48631127.985725KORwonju
31162422사천공항디올경남 사천시 사천읍 수석리 258-14경남 사천시 사천읍 진삼로 14771.0010655854229635.083188128.08583KORsacheon
32162590군산공항해뜨는집연탄구이전북 군산시 옥서면 옥봉리 56-29전북 군산시 옥서면 옥구저수지로 214 즐거운하루2.60502<NA>35.924856126.644592KORgunsan
33163566양양국제공항맛골강원 양양군 손양면 동호리 212강원 양양군 손양면 선사유적로 3891.3732433672665438.063813128.677039KORyangyang
34166045사천공항식껍데기경남 사천시 사천읍 평화리 52-5경남 사천시 사천읍 평화2길 35 남해숯불장어구이1.035255852889035.082926128.087956KORsacheon
35166417김포국제공항청수원잔치국수서울 강서구 공항동 669-8서울특별시 강서구 방화대로7길 381.0521222662242137.556229126.814142KORseoul
36167302여수공항산단짜장짬봉전문점전남 여수시 화치동 1431전남 여수시 산단중앙로 673.2388661685078834.826656127.645171KORyeosu
37168387여수공항수정분식전남 여수시 화치동 1431전남 여수시 산단중앙로 673.2388661691128234.826656127.645171KORyeosu
38171543울산공항커피밭울산 북구 연암동 401-3울산 북구 화봉로 58 신화프라자1.0246652283234435.587674129.365526KORulsan
39174252울산공항자유시간 라운지울산 북구 화봉동 449-8울산 북구 화봉로 711.0633452288570035.588618129.366812KORulsan