Overview

Dataset statistics

Number of variables11
Number of observations250
Missing cells17
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.1 KiB
Average record size in memory90.5 B

Variable types

Numeric2
Categorical3
Text4
DateTime2

Alerts

last_load_dttm has constant value ""Constant
skey is highly overall correlated with gugunHigh correlation
instt_code is highly overall correlated with gugun and 1 other fieldsHigh correlation
gugun is highly overall correlated with skey and 3 other fieldsHigh correlation
gugun_only_bike is highly overall correlated with instt_code and 2 other fieldsHigh correlation
gugun_bike_road is highly overall correlated with gugun and 1 other fieldsHigh correlation
gugun_only_bike is highly imbalanced (51.7%)Imbalance
gugun_with_walk has 17 (6.8%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 15:22:15.337099
Analysis finished2024-04-16 15:22:16.367950
Duration1.03 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1406.352
Minimum1051
Maximum1560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T00:22:16.436102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1051
5-th percentile1274.45
Q11345.25
median1407.5
Q31469.75
95-th percentile1547.55
Maximum1560
Range509
Interquartile range (IQR)124.5

Descriptive statistics

Standard deviation99.067601
Coefficient of variation (CV)0.070442963
Kurtosis2.4778131
Mean1406.352
Median Absolute Deviation (MAD)62.5
Skewness-0.98889891
Sum351588
Variance9814.3897
MonotonicityNot monotonic
2024-04-17T00:22:16.585856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1304 1
 
0.4%
1393 1
 
0.4%
1376 1
 
0.4%
1377 1
 
0.4%
1378 1
 
0.4%
1379 1
 
0.4%
1380 1
 
0.4%
1381 1
 
0.4%
1382 1
 
0.4%
1387 1
 
0.4%
Other values (240) 240
96.0%
ValueCountFrequency (%)
1051 1
0.4%
1052 1
0.4%
1053 1
0.4%
1054 1
0.4%
1055 1
0.4%
1056 1
0.4%
1057 1
0.4%
1269 1
0.4%
1270 1
0.4%
1271 1
0.4%
ValueCountFrequency (%)
1560 1
0.4%
1559 1
0.4%
1558 1
0.4%
1557 1
0.4%
1556 1
0.4%
1555 1
0.4%
1554 1
0.4%
1553 1
0.4%
1552 1
0.4%
1551 1
0.4%

gugun
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
부산광역시 해운대구
35 
부산광역시 부산진구
34 
부산광역시 남구
28 
부산광역시 사하구
25 
부산광역시 연제구
21 
Other values (10)
107 

Length

Max length10
Median length9
Mean length8.64
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row부산광역시 부산진구
2nd row부산광역시 부산진구
3rd row부산광역시 부산진구
4th row부산광역시 부산진구
5th row부산광역시 부산진구

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 35
14.0%
부산광역시 부산진구 34
13.6%
부산광역시 남구 28
11.2%
부산광역시 사하구 25
10.0%
부산광역시 연제구 21
8.4%
부산광역시 동래구 20
8.0%
기장군 19
7.6%
부산광역시 금정구 14
 
5.6%
부산광역시 북구 14
 
5.6%
부산광역시 수영구 14
 
5.6%
Other values (5) 26
10.4%

Length

2024-04-17T00:22:16.728942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 231
48.0%
해운대구 35
 
7.3%
부산진구 34
 
7.1%
남구 28
 
5.8%
사하구 25
 
5.2%
연제구 21
 
4.4%
동래구 20
 
4.2%
기장군 19
 
4.0%
금정구 14
 
2.9%
북구 14
 
2.9%
Other values (6) 40
 
8.3%

start_spot
Text

Distinct227
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T00:22:16.982898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.812
Min length2

Characters and Unicode

Total characters2453
Distinct characters218
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)82.0%

Sample

1st row대학로46번길 5
2nd row대학로46번길 2
3rd row가야대로 725
4th row신천대로 291
5th row새싹로 132
ValueCountFrequency (%)
부산광역시 17
 
3.4%
동래구 16
 
3.2%
신평동 9
 
1.8%
분포로 9
 
1.8%
중앙대로 9
 
1.8%
좌동 8
 
1.6%
우동 8
 
1.6%
화명동 8
 
1.6%
중동 7
 
1.4%
맞은편 6
 
1.2%
Other values (308) 403
80.6%
2024-04-17T00:22:17.345301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
250
 
10.2%
1 177
 
7.2%
동 128
 
5.2%
로 108
 
4.4%
2 85
 
3.5%
대 68
 
2.8%
5 58
 
2.4%
3 57
 
2.3%
- 56
 
2.3%
6 55
 
2.2%
Other values (208) 1411
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1403
57.2%
Decimal Number 649
26.5%
Space Separator 250
 
10.2%
Dash Punctuation 56
 
2.3%
Open Punctuation 38
 
1.5%
Close Punctuation 37
 
1.5%
Uppercase Letter 14
 
0.6%
Other Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 128
 
9.1%
로 108
 
7.7%
대 68
 
4.8%
구 42
 
3.0%
산 36
 
2.6%
교 34
 
2.4%
시 30
 
2.1%
역 29
 
2.1%
장 23
 
1.6%
부 23
 
1.6%
Other values (187) 882
62.9%
Decimal Number
ValueCountFrequency (%)
1 177
27.3%
2 85
13.1%
5 58
 
8.9%
3 57
 
8.8%
6 55
 
8.5%
4 55
 
8.5%
8 48
 
7.4%
7 42
 
6.5%
0 37
 
5.7%
9 35
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
L 5
35.7%
G 5
35.7%
U 1
 
7.1%
N 1
 
7.1%
I 1
 
7.1%
C 1
 
7.1%
Space Separator
ValueCountFrequency (%)
250
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1403
57.2%
Common 1036
42.2%
Latin 14
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 128
 
9.1%
로 108
 
7.7%
대 68
 
4.8%
구 42
 
3.0%
산 36
 
2.6%
교 34
 
2.4%
시 30
 
2.1%
역 29
 
2.1%
장 23
 
1.6%
부 23
 
1.6%
Other values (187) 882
62.9%
Common
ValueCountFrequency (%)
250
24.1%
1 177
17.1%
2 85
 
8.2%
5 58
 
5.6%
3 57
 
5.5%
- 56
 
5.4%
6 55
 
5.3%
4 55
 
5.3%
8 48
 
4.6%
7 42
 
4.1%
Other values (5) 153
14.8%
Latin
ValueCountFrequency (%)
L 5
35.7%
G 5
35.7%
U 1
 
7.1%
N 1
 
7.1%
I 1
 
7.1%
C 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1403
57.2%
ASCII 1050
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
250
23.8%
1 177
16.9%
2 85
 
8.1%
5 58
 
5.5%
3 57
 
5.4%
- 56
 
5.3%
6 55
 
5.2%
4 55
 
5.2%
8 48
 
4.6%
7 42
 
4.0%
Other values (11) 167
15.9%
Hangul
ValueCountFrequency (%)
동 128
 
9.1%
로 108
 
7.7%
대 68
 
4.8%
구 42
 
3.0%
산 36
 
2.6%
교 34
 
2.4%
시 30
 
2.1%
역 29
 
2.1%
장 23
 
1.6%
부 23
 
1.6%
Other values (187) 882
62.9%

end_spot
Text

Distinct224
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T00:22:17.641797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length9.756
Min length3

Characters and Unicode

Total characters2439
Distinct characters227
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)80.0%

Sample

1st row대학로46번길 41
2nd row대학로46번길 42
3rd row신천대로 292
4th row신천대로 281
5th row동평로 218
ValueCountFrequency (%)
부산광역시 15
 
2.9%
동래구 14
 
2.7%
좌동 12
 
2.3%
신평동 11
 
2.1%
우동 10
 
1.9%
화명동 9
 
1.7%
중앙대로 9
 
1.7%
남천동 6
 
1.1%
입구 6
 
1.1%
분포로 6
 
1.1%
Other values (339) 428
81.4%
2024-04-17T00:22:18.053415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
279
 
11.4%
1 152
 
6.2%
동 130
 
5.3%
로 108
 
4.4%
2 77
 
3.2%
4 66
 
2.7%
- 59
 
2.4%
대 57
 
2.3%
3 57
 
2.3%
6 56
 
2.3%
Other values (217) 1398
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1360
55.8%
Decimal Number 646
26.5%
Space Separator 279
 
11.4%
Dash Punctuation 59
 
2.4%
Close Punctuation 35
 
1.4%
Open Punctuation 35
 
1.4%
Uppercase Letter 19
 
0.8%
Other Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 130
 
9.6%
로 108
 
7.9%
대 57
 
4.2%
구 35
 
2.6%
산 33
 
2.4%
시 30
 
2.2%
교 28
 
2.1%
역 27
 
2.0%
천 26
 
1.9%
평 25
 
1.8%
Other values (192) 861
63.3%
Decimal Number
ValueCountFrequency (%)
1 152
23.5%
2 77
11.9%
4 66
10.2%
3 57
 
8.8%
6 56
 
8.7%
8 55
 
8.5%
5 53
 
8.2%
0 48
 
7.4%
7 45
 
7.0%
9 37
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
L 4
21.1%
G 4
21.1%
N 2
10.5%
C 2
10.5%
I 2
10.5%
F 1
 
5.3%
U 1
 
5.3%
B 1
 
5.3%
T 1
 
5.3%
P 1
 
5.3%
Space Separator
ValueCountFrequency (%)
279
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1360
55.8%
Common 1060
43.5%
Latin 19
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 130
 
9.6%
로 108
 
7.9%
대 57
 
4.2%
구 35
 
2.6%
산 33
 
2.4%
시 30
 
2.2%
교 28
 
2.1%
역 27
 
2.0%
천 26
 
1.9%
평 25
 
1.8%
Other values (192) 861
63.3%
Common
ValueCountFrequency (%)
279
26.3%
1 152
14.3%
2 77
 
7.3%
4 66
 
6.2%
- 59
 
5.6%
3 57
 
5.4%
6 56
 
5.3%
8 55
 
5.2%
5 53
 
5.0%
0 48
 
4.5%
Other values (5) 158
14.9%
Latin
ValueCountFrequency (%)
L 4
21.1%
G 4
21.1%
N 2
10.5%
C 2
10.5%
I 2
10.5%
F 1
 
5.3%
U 1
 
5.3%
B 1
 
5.3%
T 1
 
5.3%
P 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1360
55.8%
ASCII 1079
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
279
25.9%
1 152
14.1%
2 77
 
7.1%
4 66
 
6.1%
- 59
 
5.5%
3 57
 
5.3%
6 56
 
5.2%
8 55
 
5.1%
5 53
 
4.9%
0 48
 
4.4%
Other values (15) 177
16.4%
Hangul
ValueCountFrequency (%)
동 130
 
9.6%
로 108
 
7.9%
대 57
 
4.2%
구 35
 
2.6%
산 33
 
2.4%
시 30
 
2.2%
교 28
 
2.1%
역 27
 
2.0%
천 26
 
1.9%
평 25
 
1.8%
Other values (192) 861
63.3%

total
Text

Distinct154
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T00:22:18.359579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.548
Min length1

Characters and Unicode

Total characters887
Distinct characters13
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

Unique101 ?
Unique (%)40.4%

Sample

1st row0.2
2nd row0.21
3rd row1.17
4th row0.12
5th row0.12
ValueCountFrequency (%)
0.4 6
 
2.4%
0.6 6
 
2.4%
1.2 5
 
2.0%
1 5
 
2.0%
0.2 5
 
2.0%
2 4
 
1.6%
0.45 4
 
1.6%
0.23 4
 
1.6%
0.7 4
 
1.6%
2.3 4
 
1.6%
Other values (144) 203
81.2%
2024-04-17T00:22:18.804676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 237
26.7%
0 126
14.2%
1 114
12.9%
2 89
 
10.0%
5 68
 
7.7%
6 52
 
5.9%
3 51
 
5.7%
4 49
 
5.5%
7 42
 
4.7%
9 30
 
3.4%
Other values (3) 29
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 648
73.1%
Other Punctuation 237
 
26.7%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 126
19.4%
1 114
17.6%
2 89
13.7%
5 68
10.5%
6 52
8.0%
3 51
7.9%
4 49
 
7.6%
7 42
 
6.5%
9 30
 
4.6%
8 27
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 885
99.8%
Latin 2
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 237
26.8%
0 126
14.2%
1 114
12.9%
2 89
 
10.1%
5 68
 
7.7%
6 52
 
5.9%
3 51
 
5.8%
4 49
 
5.5%
7 42
 
4.7%
9 30
 
3.4%
Latin
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 237
26.7%
0 126
14.2%
1 114
12.9%
2 89
 
10.0%
5 68
 
7.7%
6 52
 
5.9%
3 51
 
5.7%
4 49
 
5.5%
7 42
 
4.7%
9 30
 
3.4%
Other values (3) 29
 
3.3%

gugun_only_bike
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
106 
-
62 
<NA>
57 
2.7
 
2
0.2
 
1
Other values (22)
22 

Length

Max length6
Median length1
Mean length1.956
Min length1

Unique

Unique23 ?
Unique (%)9.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106
42.4%
- 62
24.8%
<NA> 57
22.8%
2.7 2
 
0.8%
0.2 1
 
0.4%
9.4 1
 
0.4%
0.7 1
 
0.4%
1.2 1
 
0.4%
0.796 1
 
0.4%
0.83 1
 
0.4%
Other values (17) 17
 
6.8%

Length

2024-04-17T00:22:18.948850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 106
42.4%
62
24.8%
na 57
22.8%
2.7 2
 
0.8%
0.73 1
 
0.4%
1.1 1
 
0.4%
0.816 1
 
0.4%
2 1
 
0.4%
10.79 1
 
0.4%
0.68 1
 
0.4%
Other values (17) 17
 
6.8%

gugun_with_walk
Text

MISSING 

Distinct144
Distinct (%)61.8%
Missing17
Missing (%)6.8%
Memory size2.1 KiB
2024-04-17T00:22:19.272418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.4120172
Min length1

Characters and Unicode

Total characters795
Distinct characters14
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

Unique96 ?
Unique (%)41.2%

Sample

1st row0.2
2nd row0.21
3rd row1.17
4th row0.12
5th row0.12
ValueCountFrequency (%)
0 9
 
3.9%
1 5
 
2.1%
0.6 5
 
2.1%
0.4 5
 
2.1%
1.2 4
 
1.7%
0.45 4
 
1.7%
2.3 4
 
1.7%
0.23 4
 
1.7%
0.5 4
 
1.7%
0.2 4
 
1.7%
Other values (134) 185
79.4%
2024-04-17T00:22:19.660320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 211
26.5%
0 115
14.5%
1 106
13.3%
2 80
 
10.1%
5 61
 
7.7%
6 46
 
5.8%
4 44
 
5.5%
3 44
 
5.5%
7 35
 
4.4%
9 28
 
3.5%
Other values (4) 25
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581
73.1%
Other Punctuation 211
 
26.5%
Lowercase Letter 2
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115
19.8%
1 106
18.2%
2 80
13.8%
5 61
10.5%
6 46
 
7.9%
4 44
 
7.6%
3 44
 
7.6%
7 35
 
6.0%
9 28
 
4.8%
8 22
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 793
99.7%
Latin 2
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
. 211
26.6%
0 115
14.5%
1 106
13.4%
2 80
 
10.1%
5 61
 
7.7%
6 46
 
5.8%
4 44
 
5.5%
3 44
 
5.5%
7 35
 
4.4%
9 28
 
3.5%
Other values (2) 23
 
2.9%
Latin
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 211
26.5%
0 115
14.5%
1 106
13.3%
2 80
 
10.1%
5 61
 
7.7%
6 46
 
5.8%
4 44
 
5.5%
3 44
 
5.5%
7 35
 
4.4%
9 28
 
3.5%
Other values (4) 25
 
3.1%

gugun_bike_road
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
101 
-
80 
<NA>
66 
0.39
 
1
0.73
 
1

Length

Max length4
Median length1
Mean length1.828
Min length1

Unique

Unique3 ?
Unique (%)1.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101
40.4%
- 80
32.0%
<NA> 66
26.4%
0.39 1
 
0.4%
0.73 1
 
0.4%
0.13 1
 
0.4%

Length

2024-04-17T00:22:19.800400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T00:22:19.910017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 101
40.4%
80
32.0%
na 66
26.4%
0.39 1
 
0.4%
0.73 1
 
0.4%
0.13 1
 
0.4%

check_date
Date

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2020-08-21 00:00:00
Maximum2021-01-01 00:00:00
2024-04-17T00:22:20.262181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:22:20.347965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3335400
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T00:22:20.441627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3290000
Q13310000
median3330000
Q33370000
95-th percentile3400000
Maximum3400000
Range150000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation35545.66
Coefficient of variation (CV)0.010657091
Kurtosis-0.88485443
Mean3335400
Median Absolute Deviation (MAD)30000
Skewness0.24040551
Sum8.3385 × 108
Variance1.263494 × 109
MonotonicityNot monotonic
2024-04-17T00:22:20.543006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3330000 35
14.0%
3290000 34
13.6%
3310000 28
11.2%
3340000 25
10.0%
3370000 21
8.4%
3300000 20
8.0%
3400000 19
7.6%
3350000 14
 
5.6%
3320000 14
 
5.6%
3380000 14
 
5.6%
Other values (5) 26
10.4%
ValueCountFrequency (%)
3250000 1
 
0.4%
3260000 2
 
0.8%
3280000 2
 
0.8%
3290000 34
13.6%
3300000 20
8.0%
3310000 28
11.2%
3320000 14
 
5.6%
3330000 35
14.0%
3340000 25
10.0%
3350000 14
 
5.6%
ValueCountFrequency (%)
3400000 19
7.6%
3390000 10
 
4.0%
3380000 14
 
5.6%
3370000 21
8.4%
3360000 11
 
4.4%
3350000 14
 
5.6%
3340000 25
10.0%
3330000 35
14.0%
3320000 14
 
5.6%
3310000 28
11.2%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2021-07-01 05:41:03
Maximum2021-07-01 05:41:03
2024-04-17T00:22:20.659504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:22:20.745856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T00:22:15.964674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:22:15.811035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:22:16.043495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:22:15.882279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T00:22:20.813570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeygugungugun_only_bikegugun_bike_roadcheck_dateinstt_code
skey1.0000.9460.5160.4070.8450.838
gugun0.9461.0000.9570.8490.9841.000
gugun_only_bike0.5160.9571.0000.8750.8610.875
gugun_bike_road0.4070.8490.8751.0000.1420.597
check_date0.8450.9840.8610.1421.0000.828
instt_code0.8381.0000.8750.5970.8281.000
2024-04-17T00:22:20.910205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gugun_only_bikegugungugun_bike_road
gugun_only_bike1.0000.6160.835
gugun0.6161.0000.671
gugun_bike_road0.8350.6711.000
2024-04-17T00:22:21.003652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codegugungugun_only_bikegugun_bike_road
skey1.0000.1560.8000.2320.275
instt_code0.1561.0000.9900.6100.411
gugun0.8000.9901.0000.6160.671
gugun_only_bike0.2320.6100.6161.0000.835
gugun_bike_road0.2750.4110.6710.8351.000

Missing values

2024-04-17T00:22:16.185225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T00:22:16.308871image/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

skeygugunstart_spotend_spottotalgugun_only_bikegugun_with_walkgugun_bike_roadcheck_dateinstt_codelast_load_dttm
01304부산광역시 부산진구대학로46번길 5대학로46번길 410.200.202020-12-3132900002021-07-01 05:41:03
11305부산광역시 부산진구대학로46번길 2대학로46번길 420.2100.2102020-12-3132900002021-07-01 05:41:03
21306부산광역시 부산진구가야대로 725신천대로 2921.1701.1702020-12-3132900002021-07-01 05:41:03
31307부산광역시 부산진구신천대로 291신천대로 2810.1200.1202020-12-3132900002021-07-01 05:41:03
41308부산광역시 부산진구새싹로 132동평로 2180.1200.1202020-12-3132900002021-07-01 05:41:03
51309부산광역시 부산진구부산시민공원 북문부산시민공원 북2문0.5500.5502020-12-3132900002021-07-01 05:41:03
61310부산광역시 부산진구연지화인아파트 맞은편양정현대아파트0.4500.4502020-12-3132900002021-07-01 05:41:03
71311부산광역시 부산진구중앙대로 883-2중앙대로 9891.2501.2502020-12-3132900002021-07-01 05:41:03
81312부산광역시 부산진구연수로 13-1연수로 710.5500.5502020-12-3132900002021-07-01 05:41:03
91313부산광역시 부산진구중앙대로 884연수로 701.201.202020-12-3132900002021-07-01 05:41:03
skeygugunstart_spotend_spottotalgugun_only_bikegugun_with_walkgugun_bike_roadcheck_dateinstt_codelast_load_dttm
2401527부산광역시 남구유엔로 127-2(대성맨션)천제등로 280.39<NA><NA>0.392020-12-3133100002021-07-01 05:41:03
2411528부산광역시 남구천제등로 28 맞은편수영로 186 (삼성리빙프라자)0.23<NA>0.23<NA>2020-12-3133100002021-07-01 05:41:03
2421529부산광역시 남구수영로 184(하이마트)천제등로 280.285<NA>0.29<NA>2020-12-3133100002021-07-01 05:41:03
2431530부산광역시 남구수영로 185수영로 73-1 (지게골역2번출구)1.16<NA>1.16<NA>2020-12-3133100002021-07-01 05:41:03
2441531부산광역시 남구수영로 72(지게골역1번출구)수영로 184 (하이마트)1.17<NA>1.17<NA>2020-12-3133100002021-07-01 05:41:03
2451532부산광역시 남구수영로 72(지게골역1번출구)지게골로 40.73<NA><NA>0.732020-12-3133100002021-07-01 05:41:03
2461533부산광역시 남구지게골로 4범일교0.295<NA>0.295<NA>2020-12-3133100002021-07-01 05:41:03
2471534부산광역시 남구범일교하구교0.13<NA><NA>0.132020-12-3133100002021-07-01 05:41:03
2481535부산광역시 남구용당동 532-26(동명대입구 맞은편)신선로 3010.8160.816<NA><NA>2020-12-3133100002021-07-01 05:41:03
2491560부산광역시 사하구신평동 643,강서경계신평동 645-2,신평역0.40.4002020-12-3133400002021-07-01 05:41:03