Overview

Dataset statistics

Number of variables12
Number of observations67
Missing cells120
Missing cells (%)14.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory105.0 B

Variable types

Categorical2
Numeric6
Text3
Boolean1

Alerts

stats_year has constant value ""Constant
trdar_x_crdnt is highly overall correlated with trng_fclty_x_crdnt and 1 other fieldsHigh correlation
trdar_y_crdnt is highly overall correlated with trng_fclty_y_crdntHigh correlation
trng_fclty_x_crdnt is highly overall correlated with trdar_x_crdnt and 2 other fieldsHigh correlation
trng_fclty_y_crdnt is highly overall correlated with trdar_y_crdnt and 2 other fieldsHigh correlation
trdar_kwa_dstnc_km_value is highly overall correlated with nearby_trng_fclty_atHigh correlation
trdar_flag_nm is highly overall correlated with trdar_x_crdnt and 2 other fieldsHigh correlation
nearby_trng_fclty_at is highly overall correlated with trng_fclty_x_crdnt and 2 other fieldsHigh correlation
trng_fclty_nm has 24 (35.8%) missing valuesMissing
trng_fclty_addr has 24 (35.8%) missing valuesMissing
trng_fclty_x_crdnt has 24 (35.8%) missing valuesMissing
trng_fclty_y_crdnt has 24 (35.8%) missing valuesMissing
trdar_kwa_dstnc_km_value has 24 (35.8%) missing valuesMissing

Reproduction

Analysis started2023-12-10 10:11:26.113066
Analysis finished2023-12-10 10:11:34.524230
Duration8.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

stats_year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
2020
67 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 67
100.0%

Length

2023-12-10T19:11:34.649050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:34.830864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 67
100.0%

dynmc_popltn_rank_co
Real number (ℝ)

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.970149
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-10T19:11:35.042848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q120.5
median45
Q358
95-th percentile89.7
Maximum96
Range95
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation26.06575
Coefficient of variation (CV)0.60660134
Kurtosis-0.86576863
Mean42.970149
Median Absolute Deviation (MAD)22
Skewness0.18622519
Sum2879
Variance679.42334
MonotonicityIncreasing
2023-12-10T19:11:35.323233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 5
 
7.5%
15 3
 
4.5%
23 3
 
4.5%
45 2
 
3.0%
52 2
 
3.0%
53 2
 
3.0%
55 2
 
3.0%
57 2
 
3.0%
17 2
 
3.0%
68 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
13 1
1.5%
14 1
1.5%
ValueCountFrequency (%)
96 1
1.5%
95 1
1.5%
92 1
1.5%
90 1
1.5%
89 1
1.5%
82 1
1.5%
79 1
1.5%
77 1
1.5%
74 1
1.5%
70 1
1.5%

trdar_flag_nm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
골목상권
28 
발달상권
25 
관광특구
9 
전통시장
5 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
골목상권 28
41.8%
발달상권 25
37.3%
관광특구 9
 
13.4%
전통시장 5
 
7.5%

Length

2023-12-10T19:11:35.932384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:36.114133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
골목상권 28
41.8%
발달상권 25
37.3%
관광특구 9
 
13.4%
전통시장 5
 
7.5%

trdar_nm
Text

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-10T19:11:36.487221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length13.835821
Min length10

Characters and Unicode

Total characters927
Distinct characters125
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)65.7%

Sample

1st row서울 마포구 홍익대학교 주변
2nd row서울 서대문구 신촌역
3rd row서울 노원구 노원역_3
4th row서울 광진구 건대입구역_2
5th row서울 은평구 연신내역_2
ValueCountFrequency (%)
서울 65
29.8%
관광특구 9
 
4.1%
영등포전통시장 5
 
2.3%
서초구 5
 
2.3%
영등포구 5
 
2.3%
동대문구 4
 
1.8%
송파구 4
 
1.8%
강동구 4
 
1.8%
강남구 3
 
1.4%
동작구 3
 
1.4%
Other values (76) 111
50.9%
2023-12-10T19:11:37.141824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
151
 
16.3%
서 83
 
9.0%
구 76
 
8.2%
울 68
 
7.3%
로 36
 
3.9%
동 31
 
3.3%
길 29
 
3.1%
대 22
 
2.4%
역 21
 
2.3%
1 20
 
2.2%
Other values (115) 390
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 693
74.8%
Space Separator 151
 
16.3%
Decimal Number 66
 
7.1%
Connector Punctuation 14
 
1.5%
Other Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 83
 
12.0%
구 76
 
11.0%
울 68
 
9.8%
로 36
 
5.2%
동 31
 
4.5%
길 29
 
4.2%
대 22
 
3.2%
역 21
 
3.0%
강 15
 
2.2%
포 12
 
1.7%
Other values (102) 300
43.3%
Decimal Number
ValueCountFrequency (%)
1 20
30.3%
2 12
18.2%
6 7
 
10.6%
4 7
 
10.6%
3 6
 
9.1%
7 4
 
6.1%
9 4
 
6.1%
0 2
 
3.0%
8 2
 
3.0%
5 2
 
3.0%
Space Separator
ValueCountFrequency (%)
151
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 14
100.0%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 693
74.8%
Common 234
 
25.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 83
 
12.0%
구 76
 
11.0%
울 68
 
9.8%
로 36
 
5.2%
동 31
 
4.5%
길 29
 
4.2%
대 22
 
3.2%
역 21
 
3.0%
강 15
 
2.2%
포 12
 
1.7%
Other values (102) 300
43.3%
Common
ValueCountFrequency (%)
151
64.5%
1 20
 
8.5%
_ 14
 
6.0%
2 12
 
5.1%
6 7
 
3.0%
4 7
 
3.0%
3 6
 
2.6%
7 4
 
1.7%
9 4
 
1.7%
· 3
 
1.3%
Other values (3) 6
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 693
74.8%
ASCII 231
 
24.9%
None 3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
151
65.4%
1 20
 
8.7%
_ 14
 
6.1%
2 12
 
5.2%
6 7
 
3.0%
4 7
 
3.0%
3 6
 
2.6%
7 4
 
1.7%
9 4
 
1.7%
0 2
 
0.9%
Other values (2) 4
 
1.7%
Hangul
ValueCountFrequency (%)
서 83
 
12.0%
구 76
 
11.0%
울 68
 
9.8%
로 36
 
5.2%
동 31
 
4.5%
길 29
 
4.2%
대 22
 
3.2%
역 21
 
3.0%
강 15
 
2.2%
포 12
 
1.7%
Other values (102) 300
43.3%
None
ValueCountFrequency (%)
· 3
100.0%

trdar_x_crdnt
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.99265
Minimum126.83933
Maximum127.15413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-10T19:11:37.416579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.83933
5-th percentile126.86202
Q1126.9303
median126.99443
Q3127.04041
95-th percentile127.13034
Maximum127.15413
Range0.314799
Interquartile range (IQR)0.11011

Descriptive statistics

Standard deviation0.07652378
Coefficient of variation (CV)0.00060258432
Kurtosis-0.51347181
Mean126.99265
Median Absolute Deviation (MAD)0.053698
Skewness0.073023227
Sum8508.5076
Variance0.0058558889
MonotonicityNot monotonic
2023-12-10T19:11:37.667300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.907037 5
 
7.5%
126.982892 3
 
4.5%
126.982632 3
 
4.5%
126.930301 2
 
3.0%
126.997637 2
 
3.0%
126.91892 2
 
3.0%
127.035516 2
 
3.0%
126.861137 2
 
3.0%
126.965217 2
 
3.0%
126.99443 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
126.839332 1
 
1.5%
126.850522 1
 
1.5%
126.861137 2
 
3.0%
126.864064 1
 
1.5%
126.873992 1
 
1.5%
126.907037 5
7.5%
126.91892 2
 
3.0%
126.92122 1
 
1.5%
126.923966 1
 
1.5%
126.925174 1
 
1.5%
ValueCountFrequency (%)
127.154131 1
1.5%
127.139548 1
1.5%
127.134792 1
1.5%
127.132591 1
1.5%
127.125087 1
1.5%
127.123426 1
1.5%
127.100122 1
1.5%
127.085564 1
1.5%
127.074117 1
1.5%
127.070465 1
1.5%

trdar_y_crdnt
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.550643
Minimum37.48091
Maximum37.661003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-10T19:11:37.923184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.48091
5-th percentile37.484165
Q137.51576
median37.545111
Q337.575363
95-th percentile37.647404
Maximum37.661003
Range0.180093
Interquartile range (IQR)0.0596025

Descriptive statistics

Standard deviation0.048552683
Coefficient of variation (CV)0.0012929921
Kurtosis-0.41534489
Mean37.550643
Median Absolute Deviation (MAD)0.031767
Skewness0.5992451
Sum2515.8931
Variance0.002357363
MonotonicityNot monotonic
2023-12-10T19:11:38.202556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.519468 5
 
7.5%
37.569917 3
 
4.5%
37.566696 3
 
4.5%
37.484165 2
 
3.0%
37.481449 2
 
3.0%
37.60898 2
 
3.0%
37.647404 2
 
3.0%
37.524715 2
 
3.0%
37.545111 2
 
3.0%
37.534734 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
37.48091 1
1.5%
37.481449 2
3.0%
37.484165 2
3.0%
37.488272 1
1.5%
37.494068 1
1.5%
37.495386 1
1.5%
37.495973 1
1.5%
37.497983 1
1.5%
37.499516 1
1.5%
37.501872 1
1.5%
ValueCountFrequency (%)
37.661003 1
1.5%
37.655661 1
1.5%
37.653252 1
1.5%
37.647404 2
3.0%
37.638059 1
1.5%
37.621852 1
1.5%
37.619364 1
1.5%
37.613981 1
1.5%
37.60898 2
3.0%
37.597377 1
1.5%

nearby_trng_fclty_at
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size199.0 B
True
43 
False
24 
ValueCountFrequency (%)
True 43
64.2%
False 24
35.8%
2023-12-10T19:11:38.414873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

trng_fclty_nm
Text

MISSING 

Distinct33
Distinct (%)76.7%
Missing24
Missing (%)35.8%
Memory size668.0 B
2023-12-10T19:11:38.799324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length8.5813953
Min length6

Characters and Unicode

Total characters369
Distinct characters82
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)55.8%

Sample

1st row시립창동청소년센터
2nd row갈현청소년문화의집
3rd row역삼청소년수련관
4th row창동청소년문화의집
5th row청소년문화교류센터
ValueCountFrequency (%)
서울청소년센터 3
 
6.7%
시립창동청소년센터 2
 
4.4%
서초유스센터 2
 
4.4%
시립동대문청소년센터 2
 
4.4%
화곡청소년센터 2
 
4.4%
서울유스호스텔 2
 
4.4%
청소년문화교류센터 2
 
4.4%
창동청소년문화의집 2
 
4.4%
목동청소년센터 2
 
4.4%
영등포청소년문화의집 1
 
2.2%
Other values (25) 25
55.6%
2023-12-10T19:11:39.549633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
청 36
 
9.8%
소 36
 
9.8%
년 36
 
9.8%
터 28
 
7.6%
센 27
 
7.3%
문 18
 
4.9%
화 16
 
4.3%
서 12
 
3.3%
동 11
 
3.0%
집 10
 
2.7%
Other values (72) 139
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 367
99.5%
Space Separator 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 36
 
9.8%
소 36
 
9.8%
년 36
 
9.8%
터 28
 
7.6%
센 27
 
7.4%
문 18
 
4.9%
화 16
 
4.4%
서 12
 
3.3%
동 11
 
3.0%
집 10
 
2.7%
Other values (71) 137
37.3%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 367
99.5%
Common 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 36
 
9.8%
소 36
 
9.8%
년 36
 
9.8%
터 28
 
7.6%
센 27
 
7.4%
문 18
 
4.9%
화 16
 
4.4%
서 12
 
3.3%
동 11
 
3.0%
집 10
 
2.7%
Other values (71) 137
37.3%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 367
99.5%
ASCII 2
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
청 36
 
9.8%
소 36
 
9.8%
년 36
 
9.8%
터 28
 
7.6%
센 27
 
7.4%
문 18
 
4.9%
화 16
 
4.4%
서 12
 
3.3%
동 11
 
3.0%
집 10
 
2.7%
Other values (71) 137
37.3%
ASCII
ValueCountFrequency (%)
2
100.0%

trng_fclty_addr
Text

MISSING 

Distinct30
Distinct (%)69.8%
Missing24
Missing (%)35.8%
Memory size668.0 B
2023-12-10T19:11:40.119096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length17.418605
Min length15

Characters and Unicode

Total characters749
Distinct characters90
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

Unique19 ?
Unique (%)44.2%

Sample

1st row서울시 도봉구 노해로69길 132
2nd row서울시 은평구 통일로 89길 6-20
3rd row서울시 강남구 논현로64길 7
4th row서울시 도봉구 덕릉로62길 89
5th row서울시 중구 퇴계로26가길 6 2층
ValueCountFrequency (%)
서울시 43
23.6%
중구 7
 
3.8%
영등포구 5
 
2.7%
도봉구 5
 
2.7%
퇴계로26가길 4
 
2.2%
6 4
 
2.2%
을지로 3
 
1.6%
서초구 3
 
1.6%
은평구 3
 
1.6%
강서구 3
 
1.6%
Other values (68) 102
56.0%
2023-12-10T19:11:41.033789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
139
18.6%
서 54
 
7.2%
울 44
 
5.9%
시 44
 
5.9%
구 43
 
5.7%
로 42
 
5.6%
2 36
 
4.8%
길 29
 
3.9%
3 21
 
2.8%
6 21
 
2.8%
Other values (80) 276
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 452
60.3%
Decimal Number 155
 
20.7%
Space Separator 139
 
18.6%
Dash Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 54
 
11.9%
울 44
 
9.7%
시 44
 
9.7%
구 43
 
9.5%
로 42
 
9.3%
길 29
 
6.4%
영 10
 
2.2%
동 10
 
2.2%
대 10
 
2.2%
포 9
 
2.0%
Other values (68) 157
34.7%
Decimal Number
ValueCountFrequency (%)
2 36
23.2%
3 21
13.5%
6 21
13.5%
1 20
12.9%
0 12
 
7.7%
7 11
 
7.1%
5 11
 
7.1%
4 9
 
5.8%
9 8
 
5.2%
8 6
 
3.9%
Space Separator
ValueCountFrequency (%)
139
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 452
60.3%
Common 297
39.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 54
 
11.9%
울 44
 
9.7%
시 44
 
9.7%
구 43
 
9.5%
로 42
 
9.3%
길 29
 
6.4%
영 10
 
2.2%
동 10
 
2.2%
대 10
 
2.2%
포 9
 
2.0%
Other values (68) 157
34.7%
Common
ValueCountFrequency (%)
139
46.8%
2 36
 
12.1%
3 21
 
7.1%
6 21
 
7.1%
1 20
 
6.7%
0 12
 
4.0%
7 11
 
3.7%
5 11
 
3.7%
4 9
 
3.0%
9 8
 
2.7%
Other values (2) 9
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 452
60.3%
ASCII 297
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
139
46.8%
2 36
 
12.1%
3 21
 
7.1%
6 21
 
7.1%
1 20
 
6.7%
0 12
 
4.0%
7 11
 
3.7%
5 11
 
3.7%
4 9
 
3.0%
9 8
 
2.7%
Other values (2) 9
 
3.0%
Hangul
ValueCountFrequency (%)
서 54
 
11.9%
울 44
 
9.7%
시 44
 
9.7%
구 43
 
9.5%
로 42
 
9.3%
길 29
 
6.4%
영 10
 
2.2%
동 10
 
2.2%
대 10
 
2.2%
포 9
 
2.0%
Other values (68) 157
34.7%

trng_fclty_x_crdnt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)67.4%
Missing24
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean126.97339
Minimum126.84429
Maximum127.11536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-10T19:11:41.299390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.84429
5-th percentile126.86208
Q1126.91828
median126.99056
Q3127.03284
95-th percentile127.05343
Maximum127.11536
Range0.271075
Interquartile range (IQR)0.114559

Descriptive statistics

Standard deviation0.067370574
Coefficient of variation (CV)0.00053058814
Kurtosis-0.87195441
Mean126.97339
Median Absolute Deviation (MAD)0.056309
Skewness-0.11072034
Sum5459.8558
Variance0.0045387943
MonotonicityNot monotonic
2023-12-10T19:11:41.604566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
126.990722 4
 
6.0%
126.902977 3
 
4.5%
126.99056 3
 
4.5%
127.050032 2
 
3.0%
126.875505 2
 
3.0%
126.860588 2
 
3.0%
126.919865 2
 
3.0%
127.010367 2
 
3.0%
127.050904 2
 
3.0%
127.042915 2
 
3.0%
Other values (19) 19
28.4%
(Missing) 24
35.8%
ValueCountFrequency (%)
126.844288 1
 
1.5%
126.860588 2
3.0%
126.875505 2
3.0%
126.8941 1
 
1.5%
126.902977 3
4.5%
126.916618 1
 
1.5%
126.917084 1
 
1.5%
126.919474 1
 
1.5%
126.919865 2
3.0%
126.934251 1
 
1.5%
ValueCountFrequency (%)
127.115363 1
1.5%
127.080097 1
1.5%
127.053709 1
1.5%
127.050904 2
3.0%
127.050032 2
3.0%
127.042915 2
3.0%
127.040913 1
1.5%
127.036578 1
1.5%
127.029098 1
1.5%
127.010367 2
3.0%

trng_fclty_y_crdnt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)65.1%
Missing24
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean37.55424
Minimum37.478294
Maximum37.657606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-10T19:11:41.884033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.478294
5-th percentile37.485517
Q137.520364
median37.540987
Q337.583143
95-th percentile37.652592
Maximum37.657606
Range0.179312
Interquartile range (IQR)0.062779

Descriptive statistics

Standard deviation0.05035143
Coefficient of variation (CV)0.0013407655
Kurtosis-0.49213043
Mean37.55424
Median Absolute Deviation (MAD)0.026492
Skewness0.561558
Sum1614.8323
Variance0.0025352666
MonotonicityNot monotonic
2023-12-10T19:11:42.173823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
37.558894 4
 
6.0%
37.525232 3
 
4.5%
37.567479 3
 
4.5%
37.587255 2
 
3.0%
37.530731 2
 
3.0%
37.534787 2
 
3.0%
37.485517 2
 
3.0%
37.520364 2
 
3.0%
37.657606 2
 
3.0%
37.493275 2
 
3.0%
Other values (18) 19
28.4%
(Missing) 24
35.8%
ValueCountFrequency (%)
37.478294 1
 
1.5%
37.485402 1
 
1.5%
37.485517 2
3.0%
37.493275 2
3.0%
37.493977 1
 
1.5%
37.509634 1
 
1.5%
37.513559 1
 
1.5%
37.51886 1
 
1.5%
37.520364 2
3.0%
37.525232 3
4.5%
ValueCountFrequency (%)
37.657606 2
3.0%
37.654203 1
 
1.5%
37.638097 2
3.0%
37.62539 1
 
1.5%
37.615656 1
 
1.5%
37.608396 1
 
1.5%
37.604352 1
 
1.5%
37.587255 2
3.0%
37.579031 1
 
1.5%
37.567479 3
4.5%

trdar_kwa_dstnc_km_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)88.4%
Missing24
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean0.98949161
Minimum0.3727634
Maximum1.493393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-10T19:11:42.458905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3727634
5-th percentile0.52292743
Q10.7259462
median1.0718841
Q31.2428378
95-th percentile1.4301652
Maximum1.493393
Range1.1206296
Interquartile range (IQR)0.51689155

Descriptive statistics

Standard deviation0.32660708
Coefficient of variation (CV)0.33007565
Kurtosis-1.1692088
Mean0.98949161
Median Absolute Deviation (MAD)0.2970418
Skewness-0.23385974
Sum42.548139
Variance0.10667219
MonotonicityNot monotonic
2023-12-10T19:11:42.760902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.7341596 3
 
4.5%
1.4066415 2
 
3.0%
1.3689259 2
 
3.0%
1.1229892 2
 
3.0%
1.4478013 1
 
1.5%
1.0718841 1
 
1.5%
1.2319477 1
 
1.5%
0.5423241 1
 
1.5%
1.2537278 1
 
1.5%
1.3520421 1
 
1.5%
Other values (28) 28
41.8%
(Missing) 24
35.8%
ValueCountFrequency (%)
0.3727634 1
1.5%
0.3742118 1
1.5%
0.5212664 1
1.5%
0.5378767 1
1.5%
0.5423241 1
1.5%
0.5483962 1
1.5%
0.5531232 1
1.5%
0.6796514 1
1.5%
0.6874792 1
1.5%
0.7041578 1
1.5%
ValueCountFrequency (%)
1.493393 1
1.5%
1.4478013 1
1.5%
1.4327789 1
1.5%
1.4066415 2
3.0%
1.3689259 2
3.0%
1.3565245 1
1.5%
1.3520421 1
1.5%
1.3156303 1
1.5%
1.2537278 1
1.5%
1.2319477 1
1.5%

Interactions

2023-12-10T19:11:32.603254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:27.159162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:28.158053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:29.092411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:30.334751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.449580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.758204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:27.316913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:28.295604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:29.246483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:30.477280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.603648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.928033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:27.507904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:28.454000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:29.395055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:30.653466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.802192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:33.129257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:27.697783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:28.631576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:29.548828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:30.939460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.996111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:33.317775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:27.837953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:28.776102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:29.716308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.103578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.194113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:33.504984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:27.997502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:28.941046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:29.998336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.283997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.382853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:11:42.962394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
dynmc_popltn_rank_cotrdar_flag_nmtrdar_nmtrdar_x_crdnttrdar_y_crdntnearby_trng_fclty_attrng_fclty_nmtrng_fclty_addrtrng_fclty_x_crdnttrng_fclty_y_crdnttrdar_kwa_dstnc_km_value
dynmc_popltn_rank_co1.0000.6501.0000.7370.5250.5800.0000.7270.6790.8070.710
trdar_flag_nm0.6501.0001.0000.7640.6410.0730.9420.9590.7710.8080.353
trdar_nm1.0001.0001.0001.0001.0001.0000.7180.9320.9580.9720.903
trdar_x_crdnt0.7370.7641.0001.0000.6390.4600.9000.9380.9070.6760.382
trdar_y_crdnt0.5250.6411.0000.6391.0000.1130.8770.9530.8450.9740.112
nearby_trng_fclty_at0.5800.0731.0000.4600.1131.000NaNNaNNaNNaNNaN
trng_fclty_nm0.0000.9420.7180.9000.877NaN1.0001.0001.0001.0000.000
trng_fclty_addr0.7270.9590.9320.9380.953NaN1.0001.0001.0001.0000.841
trng_fclty_x_crdnt0.6790.7710.9580.9070.845NaN1.0001.0001.0000.8380.154
trng_fclty_y_crdnt0.8070.8080.9720.6760.974NaN1.0001.0000.8381.0000.000
trdar_kwa_dstnc_km_value0.7100.3530.9030.3820.112NaN0.0000.8410.1540.0001.000
2023-12-10T19:11:43.350039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
nearby_trng_fclty_attrdar_flag_nm
nearby_trng_fclty_at1.0000.040
trdar_flag_nm0.0401.000
2023-12-10T19:11:43.550767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
dynmc_popltn_rank_cotrdar_x_crdnttrdar_y_crdnttrng_fclty_x_crdnttrng_fclty_y_crdnttrdar_kwa_dstnc_km_valuetrdar_flag_nmnearby_trng_fclty_at
dynmc_popltn_rank_co1.000-0.0360.025-0.183-0.069-0.0020.4270.418
trdar_x_crdnt-0.0361.0000.1840.9850.248-0.0630.5470.329
trdar_y_crdnt0.0250.1841.0000.2500.986-0.0920.4180.067
trng_fclty_x_crdnt-0.1830.9850.2501.0000.223-0.0080.5351.000
trng_fclty_y_crdnt-0.0690.2480.9860.2231.000-0.1250.5811.000
trdar_kwa_dstnc_km_value-0.002-0.063-0.092-0.008-0.1251.0000.1881.000
trdar_flag_nm0.4270.5470.4180.5350.5810.1881.0000.040
nearby_trng_fclty_at0.4180.3290.0671.0001.0001.0000.0401.000

Missing values

2023-12-10T19:11:33.739052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:11:34.140136image/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.
2023-12-10T19:11:34.374451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

stats_yeardynmc_popltn_rank_cotrdar_flag_nmtrdar_nmtrdar_x_crdnttrdar_y_crdntnearby_trng_fclty_attrng_fclty_nmtrng_fclty_addrtrng_fclty_x_crdnttrng_fclty_y_crdnttrdar_kwa_dstnc_km_value
020201발달상권서울 마포구 홍익대학교 주변126.92396637.552584N<NA><NA><NA><NA><NA>
120202발달상권서울 서대문구 신촌역126.9424137.559975N<NA><NA><NA><NA><NA>
220203발달상권서울 노원구 노원역_3127.06065237.655661Y시립창동청소년센터서울시 도봉구 노해로69길 132127.05090437.6576060.884965
320204발달상권서울 광진구 건대입구역_2127.07046537.540194N<NA><NA><NA><NA><NA>
420205발달상권서울 은평구 연신내역_2126.9212237.619364Y갈현청소년문화의집서울시 은평구 통일로 89길 6-20126.91947437.625390.687479
520206발달상권서울 서초구 강남역127.02688137.497983Y역삼청소년수련관서울시 강남구 논현로64길 7127.04091337.4939771.31563
620207발달상권서울 성북구 성신여대입구역127.01658437.592893N<NA><NA><NA><NA><NA>
720208골목상권서울 강남구 삼성로63길127.05820537.499516N<NA><NA><NA><NA><NA>
8202013발달상권서울 강남구 신사동 가로수길127.0225837.518177N<NA><NA><NA><NA><NA>
9202014발달상권서울 강북구 수유역_2127.02595537.638059Y창동청소년문화의집서울시 도봉구 덕릉로62길 89127.04291537.6380971.493393
stats_yeardynmc_popltn_rank_cotrdar_flag_nmtrdar_nmtrdar_x_crdnttrdar_y_crdntnearby_trng_fclty_attrng_fclty_nmtrng_fclty_addrtrng_fclty_x_crdnttrng_fclty_y_crdnttrdar_kwa_dstnc_km_value
57202070골목상권서울 노원구 한글비석로22길127.07411737.661003N<NA><NA><NA><NA><NA>
58202074발달상권서울 도붕구 창동역127.04812837.653252Y시립창동청소년센터서울시 도봉구 노해로69길 132127.05090437.6576060.542324
59202077발달상권서울 강북구 미아삼거리역_1127.0301937.613981N<NA><NA><NA><NA><NA>
60202079골목상권서울 양천구 신목로6길126.87399237.519717Y목동청소년센터서울시 양천구 목동서로 143126.87550537.5307311.231948
61202082골목상권서울 강북구 삼양로41길127.0178237.621852Y성북청소년문화의집서울시 성북구 솔샘로 107127.00849737.6156561.071884
62202089골목상권서울 송파구 양재대로71길127.12508737.509874Y서울올림픽파크텔서울시 송파구 올림픽로 448127.11536337.5203641.447801
63202090골목상권서울 성동구 마조로1길127.03938937.560332Y시립성동청소년센터서울시 성동구 고산자로 260127.03657837.5628540.374212
64202092골목상권서울 강동구 강동대로53길127.13259137.525919N<NA><NA><NA><NA><NA>
65202095골목상권서울 동대문구 전농로29길127.05491537.580604Y시립동대문청소년센터서울시 동대문구 제기로33길 25127.05003237.5872550.855619
66202096골목상권서울 서초구 고무래로8길127.00998937.501872N<NA><NA><NA><NA><NA>