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
Categorical5
Text4

Alerts

last_load_dttm has constant value ""Constant
check_date is highly overall correlated with skey and 3 other fieldsHigh correlation
gugun is highly overall correlated with skey and 4 other fieldsHigh correlation
skey is highly overall correlated with gugun and 2 other fieldsHigh correlation
instt_code is highly overall correlated with gugun and 2 other fieldsHigh correlation
gugun_only_bike is highly overall correlated with skey and 4 other fieldsHigh correlation
gugun_bike_road is highly overall correlated with gugun and 1 other fieldsHigh correlation
gugun_only_bike is highly imbalanced (51.4%)Imbalance
gugun_with_walk has 17 (6.8%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 15:19:59.216610
Analysis finished2024-04-16 15:20:00.351912
Duration1.14 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%
Mean1627.344
Minimum1332
Maximum1789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T00:20:00.422610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1332
5-th percentile1344.45
Q11588.25
median1650.5
Q31712.75
95-th percentile1776.55
Maximum1789
Range457
Interquartile range (IQR)124.5

Descriptive statistics

Standard deviation119.76836
Coefficient of variation (CV)0.073597443
Kurtosis0.53370912
Mean1627.344
Median Absolute Deviation (MAD)62.5
Skewness-1.1271958
Sum406836
Variance14344.46
MonotonicityNot monotonic
2024-04-17T00:20:00.550709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1342 1
 
0.4%
1570 1
 
0.4%
1400 1
 
0.4%
1401 1
 
0.4%
1402 1
 
0.4%
1403 1
 
0.4%
1561 1
 
0.4%
1562 1
 
0.4%
1563 1
 
0.4%
1564 1
 
0.4%
Other values (240) 240
96.0%
ValueCountFrequency (%)
1332 1
0.4%
1333 1
0.4%
1334 1
0.4%
1335 1
0.4%
1336 1
0.4%
1337 1
0.4%
1338 1
0.4%
1339 1
0.4%
1340 1
0.4%
1341 1
0.4%
ValueCountFrequency (%)
1789 1
0.4%
1788 1
0.4%
1787 1
0.4%
1786 1
0.4%
1785 1
0.4%
1784 1
0.4%
1783 1
0.4%
1782 1
0.4%
1781 1
0.4%
1780 1
0.4%

gugun
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
부산광역시 부산진구
38 
부산광역시 해운대구
35 
부산광역시 남구
28 
부산광역시 사하구
26 
부산광역시 연제구
21 
Other values (10)
102 

Length

Max length10
Median length9
Mean length8.824
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row부산광역시 금정구
2nd row부산광역시 금정구
3rd row부산광역시 금정구
4th row부산광역시 금정구
5th row부산광역시 동래구

Common Values

ValueCountFrequency (%)
부산광역시 부산진구 38
15.2%
부산광역시 해운대구 35
14.0%
부산광역시 남구 28
11.2%
부산광역시 사하구 26
10.4%
부산광역시 연제구 21
8.4%
부산광역시 동래구 20
8.0%
부산광역시 기장군 20
8.0%
부산광역시 금정구 14
 
5.6%
부산광역시 북구 14
 
5.6%
강서구 12
 
4.8%
Other values (5) 22
8.8%

Length

2024-04-17T00:20:00.684066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 238
48.8%
부산진구 38
 
7.8%
해운대구 35
 
7.2%
남구 28
 
5.7%
사하구 26
 
5.3%
연제구 21
 
4.3%
동래구 20
 
4.1%
기장군 20
 
4.1%
금정구 14
 
2.9%
북구 14
 
2.9%
Other values (6) 34
 
7.0%

start_spot
Text

Distinct234
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T00:20:00.933199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length29
Mean length10.928
Min length2

Characters and Unicode

Total characters2732
Distinct characters232
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

Unique219 ?
Unique (%)87.6%

Sample

1st row송정천
2nd row회동교
3rd row두실역
4th row서동역
5th row부산광역시 동래구 충렬대로455 (안락동 461-9)
ValueCountFrequency (%)
부산광역시 21
 
3.9%
동래구 20
 
3.7%
중앙대로 10
 
1.8%
분포로 9
 
1.7%
신평동 9
 
1.7%
우동 8
 
1.5%
화명동 8
 
1.5%
좌동 8
 
1.5%
중동 7
 
1.3%
온천동 6
 
1.1%
Other values (342) 438
80.5%
2024-04-17T00:20:01.651113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
294
 
10.8%
1 189
 
6.9%
동 149
 
5.5%
로 111
 
4.1%
2 92
 
3.4%
- 73
 
2.7%
3 68
 
2.5%
5 66
 
2.4%
대 65
 
2.4%
4 63
 
2.3%
Other values (222) 1562
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1498
54.8%
Decimal Number 733
26.8%
Space Separator 294
 
10.8%
Dash Punctuation 73
 
2.7%
Open Punctuation 57
 
2.1%
Close Punctuation 56
 
2.0%
Uppercase Letter 16
 
0.6%
Other Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 149
 
9.9%
로 111
 
7.4%
대 65
 
4.3%
구 45
 
3.0%
산 41
 
2.7%
시 36
 
2.4%
교 33
 
2.2%
역 32
 
2.1%
천 28
 
1.9%
부 27
 
1.8%
Other values (198) 931
62.1%
Decimal Number
ValueCountFrequency (%)
1 189
25.8%
2 92
12.6%
3 68
 
9.3%
5 66
 
9.0%
4 63
 
8.6%
8 57
 
7.8%
7 56
 
7.6%
6 56
 
7.6%
9 45
 
6.1%
0 41
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
L 5
31.2%
G 5
31.2%
D 1
 
6.2%
S 1
 
6.2%
N 1
 
6.2%
U 1
 
6.2%
I 1
 
6.2%
C 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
294
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1498
54.8%
Common 1218
44.6%
Latin 16
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 149
 
9.9%
로 111
 
7.4%
대 65
 
4.3%
구 45
 
3.0%
산 41
 
2.7%
시 36
 
2.4%
교 33
 
2.2%
역 32
 
2.1%
천 28
 
1.9%
부 27
 
1.8%
Other values (198) 931
62.1%
Common
ValueCountFrequency (%)
294
24.1%
1 189
15.5%
2 92
 
7.6%
- 73
 
6.0%
3 68
 
5.6%
5 66
 
5.4%
4 63
 
5.2%
8 57
 
4.7%
( 57
 
4.7%
) 56
 
4.6%
Other values (6) 203
16.7%
Latin
ValueCountFrequency (%)
L 5
31.2%
G 5
31.2%
D 1
 
6.2%
S 1
 
6.2%
N 1
 
6.2%
U 1
 
6.2%
I 1
 
6.2%
C 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1498
54.8%
ASCII 1234
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
294
23.8%
1 189
15.3%
2 92
 
7.5%
- 73
 
5.9%
3 68
 
5.5%
5 66
 
5.3%
4 63
 
5.1%
8 57
 
4.6%
( 57
 
4.6%
) 56
 
4.5%
Other values (14) 219
17.7%
Hangul
ValueCountFrequency (%)
동 149
 
9.9%
로 111
 
7.4%
대 65
 
4.3%
구 45
 
3.0%
산 41
 
2.7%
시 36
 
2.4%
교 33
 
2.2%
역 32
 
2.1%
천 28
 
1.9%
부 27
 
1.8%
Other values (198) 931
62.1%

end_spot
Text

Distinct231
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T00:20:01.894955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length29
Mean length11.004
Min length3

Characters and Unicode

Total characters2751
Distinct characters232
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

Unique214 ?
Unique (%)85.6%

Sample

1st row선동상현회관
2nd row동천교
3rd row온천장역
4th row금사역
5th row부산광역시 동래구 충렬대로171 (명륜동 533-230)
ValueCountFrequency (%)
부산광역시 21
 
3.6%
동래구 20
 
3.5%
좌동 12
 
2.1%
신평동 10
 
1.7%
우동 10
 
1.7%
화명동 9
 
1.6%
사직동 6
 
1.0%
분포로 6
 
1.0%
중앙대로 6
 
1.0%
입구 5
 
0.9%
Other values (371) 471
81.8%
2024-04-17T00:20:02.254775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
327
 
11.9%
1 167
 
6.1%
동 157
 
5.7%
로 105
 
3.8%
2 86
 
3.1%
- 82
 
3.0%
3 77
 
2.8%
4 76
 
2.8%
5 66
 
2.4%
6 59
 
2.1%
Other values (222) 1549
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1469
53.4%
Decimal Number 737
26.8%
Space Separator 327
 
11.9%
Dash Punctuation 82
 
3.0%
Open Punctuation 56
 
2.0%
Close Punctuation 56
 
2.0%
Uppercase Letter 19
 
0.7%
Other Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 157
 
10.7%
로 105
 
7.1%
대 54
 
3.7%
구 42
 
2.9%
산 39
 
2.7%
시 36
 
2.5%
천 33
 
2.2%
역 32
 
2.2%
부 31
 
2.1%
교 28
 
1.9%
Other values (196) 912
62.1%
Decimal Number
ValueCountFrequency (%)
1 167
22.7%
2 86
11.7%
3 77
10.4%
4 76
10.3%
5 66
 
9.0%
6 59
 
8.0%
8 58
 
7.9%
0 53
 
7.2%
7 51
 
6.9%
9 44
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
G 4
21.1%
L 4
21.1%
N 2
10.5%
C 2
10.5%
I 2
10.5%
F 1
 
5.3%
P 1
 
5.3%
U 1
 
5.3%
T 1
 
5.3%
B 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
327
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1469
53.4%
Common 1263
45.9%
Latin 19
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 157
 
10.7%
로 105
 
7.1%
대 54
 
3.7%
구 42
 
2.9%
산 39
 
2.7%
시 36
 
2.5%
천 33
 
2.2%
역 32
 
2.2%
부 31
 
2.1%
교 28
 
1.9%
Other values (196) 912
62.1%
Common
ValueCountFrequency (%)
327
25.9%
1 167
13.2%
2 86
 
6.8%
- 82
 
6.5%
3 77
 
6.1%
4 76
 
6.0%
5 66
 
5.2%
6 59
 
4.7%
8 58
 
4.6%
( 56
 
4.4%
Other values (6) 209
16.5%
Latin
ValueCountFrequency (%)
G 4
21.1%
L 4
21.1%
N 2
10.5%
C 2
10.5%
I 2
10.5%
F 1
 
5.3%
P 1
 
5.3%
U 1
 
5.3%
T 1
 
5.3%
B 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1469
53.4%
ASCII 1282
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
327
25.5%
1 167
13.0%
2 86
 
6.7%
- 82
 
6.4%
3 77
 
6.0%
4 76
 
5.9%
5 66
 
5.1%
6 59
 
4.6%
8 58
 
4.5%
( 56
 
4.4%
Other values (16) 228
17.8%
Hangul
ValueCountFrequency (%)
동 157
 
10.7%
로 105
 
7.1%
대 54
 
3.7%
구 42
 
2.9%
산 39
 
2.7%
시 36
 
2.5%
천 33
 
2.2%
역 32
 
2.2%
부 31
 
2.1%
교 28
 
1.9%
Other values (196) 912
62.1%

total
Text

Distinct155
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T00:20:02.606540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.56
Min length1

Characters and Unicode

Total characters890
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

Unique103 ?
Unique (%)41.2%

Sample

1st row5
2nd row2
3rd row4.8
4th row1.2
5th row2.94
ValueCountFrequency (%)
0.6 6
 
2.4%
0.2 6
 
2.4%
0.4 6
 
2.4%
1.2 5
 
2.0%
2 5
 
2.0%
2.3 4
 
1.6%
1.17 4
 
1.6%
0.23 4
 
1.6%
1 4
 
1.6%
0.7 4
 
1.6%
Other values (145) 202
80.8%
2024-04-17T00:20:03.073310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 237
26.6%
0 127
14.3%
1 112
12.6%
2 90
 
10.1%
5 67
 
7.5%
3 56
 
6.3%
6 53
 
6.0%
4 47
 
5.3%
7 42
 
4.7%
9 29
 
3.3%
Other values (3) 30
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 651
73.1%
Other Punctuation 237
 
26.6%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127
19.5%
1 112
17.2%
2 90
13.8%
5 67
10.3%
3 56
8.6%
6 53
8.1%
4 47
 
7.2%
7 42
 
6.5%
9 29
 
4.5%
8 28
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 888
99.8%
Latin 2
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 237
26.7%
0 127
14.3%
1 112
12.6%
2 90
 
10.1%
5 67
 
7.5%
3 56
 
6.3%
6 53
 
6.0%
4 47
 
5.3%
7 42
 
4.7%
9 29
 
3.3%
Latin
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 237
26.6%
0 127
14.3%
1 112
12.6%
2 90
 
10.1%
5 67
 
7.5%
3 56
 
6.3%
6 53
 
6.0%
4 47
 
5.3%
7 42
 
4.7%
9 29
 
3.3%
Other values (3) 30
 
3.4%

gugun_only_bike
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct28
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
104 
-
63 
<NA>
57 
2
 
2
0.2
 
1
Other values (23)
23 

Length

Max length6
Median length1
Mean length1.952
Min length1

Unique

Unique24 ?
Unique (%)9.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 104
41.6%
- 63
25.2%
<NA> 57
22.8%
2 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 (18) 18
 
7.2%

Length

2024-04-17T00:20:03.204569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 104
41.6%
63
25.2%
na 57
22.8%
2 2
 
0.8%
0.38km 1
 
0.4%
0.69 1
 
0.4%
0.816 1
 
0.4%
1.1 1
 
0.4%
0.24 1
 
0.4%
0.73 1
 
0.4%
Other values (18) 18
 
7.2%

gugun_with_walk
Text

MISSING 

Distinct144
Distinct (%)61.8%
Missing17
Missing (%)6.8%
Memory size2.1 KiB
2024-04-17T00:20:03.473597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.4291845
Min length1

Characters and Unicode

Total characters799
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

Unique95 ?
Unique (%)40.8%

Sample

1st row5
2nd row2
3rd row4.8
4th row1.2
5th row2.94
ValueCountFrequency (%)
0 10
 
4.3%
0.2 5
 
2.1%
0.4 5
 
2.1%
0.6 5
 
2.1%
2.3 4
 
1.7%
1.17 4
 
1.7%
0.23 4
 
1.7%
1.2 4
 
1.7%
1 4
 
1.7%
0.45 3
 
1.3%
Other values (134) 185
79.4%
2024-04-17T00:20:03.892903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 211
26.4%
0 117
14.6%
1 104
13.0%
2 80
 
10.0%
5 60
 
7.5%
3 48
 
6.0%
6 47
 
5.9%
4 42
 
5.3%
7 37
 
4.6%
9 28
 
3.5%
Other values (4) 25
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 585
73.2%
Other Punctuation 211
 
26.4%
Lowercase Letter 2
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117
20.0%
1 104
17.8%
2 80
13.7%
5 60
10.3%
3 48
8.2%
6 47
8.0%
4 42
 
7.2%
7 37
 
6.3%
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 797
99.7%
Latin 2
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
. 211
26.5%
0 117
14.7%
1 104
13.0%
2 80
 
10.0%
5 60
 
7.5%
3 48
 
6.0%
6 47
 
5.9%
4 42
 
5.3%
7 37
 
4.6%
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 799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 211
26.4%
0 117
14.6%
1 104
13.0%
2 80
 
10.0%
5 60
 
7.5%
3 48
 
6.0%
6 47
 
5.9%
4 42
 
5.3%
7 37
 
4.6%
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
107 
-
73 
<NA>
67 
0.39
 
1
0.73
 
1

Length

Max length4
Median length1
Mean length1.84
Min length1

Unique

Unique3 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 107
42.8%
- 73
29.2%
<NA> 67
26.8%
0.39 1
 
0.4%
0.73 1
 
0.4%
0.13 1
 
0.4%

Length

2024-04-17T00:20:04.033563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T00:20:04.162977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 107
42.8%
73
29.2%
na 67
26.8%
0.39 1
 
0.4%
0.73 1
 
0.4%
0.13 1
 
0.4%

check_date
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2022-12-31
171 
2023-01-10
38 
2021-01-01
21 
2023-01-20
20 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 171
68.4%
2023-01-10 38
 
15.2%
2021-01-01 21
 
8.4%
2023-01-20 20
 
8.0%

Length

2024-04-17T00:20:04.269841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T00:20:04.359631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 171
68.4%
2023-01-10 38
 
15.2%
2021-01-01 21
 
8.4%
2023-01-20 20
 
8.0%

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3333800
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T00:20:04.443140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3290000
Q13302500
median3330000
Q33360000
95-th percentile3400000
Maximum3400000
Range150000
Interquartile range (IQR)57500

Descriptive statistics

Standard deviation35459.689
Coefficient of variation (CV)0.010636418
Kurtosis-0.80038982
Mean3333800
Median Absolute Deviation (MAD)30000
Skewness0.3141719
Sum8.3345 × 108
Variance1.2573896 × 109
MonotonicityNot monotonic
2024-04-17T00:20:04.562924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3290000 38
15.2%
3330000 35
14.0%
3310000 28
11.2%
3340000 26
10.4%
3370000 21
8.4%
3300000 20
8.0%
3400000 20
8.0%
3350000 14
 
5.6%
3320000 14
 
5.6%
3360000 12
 
4.8%
Other values (5) 22
8.8%
ValueCountFrequency (%)
3250000 1
 
0.4%
3260000 2
 
0.8%
3280000 2
 
0.8%
3290000 38
15.2%
3300000 20
8.0%
3310000 28
11.2%
3320000 14
 
5.6%
3330000 35
14.0%
3340000 26
10.4%
3350000 14
 
5.6%
ValueCountFrequency (%)
3400000 20
8.0%
3390000 10
 
4.0%
3380000 7
 
2.8%
3370000 21
8.4%
3360000 12
 
4.8%
3350000 14
 
5.6%
3340000 26
10.4%
3330000 35
14.0%
3320000 14
 
5.6%
3310000 28
11.2%

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-03-01 05:41:03
250 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-01 05:41:03
2nd row2023-03-01 05:41:03
3rd row2023-03-01 05:41:03
4th row2023-03-01 05:41:03
5th row2023-03-01 05:41:03

Common Values

ValueCountFrequency (%)
2023-03-01 05:41:03 250
100.0%

Length

2024-04-17T00:20:04.686773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T00:20:04.773017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-01 250
50.0%
05:41:03 250
50.0%

Interactions

2024-04-17T00:19:59.890878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:19:59.713177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:19:59.988117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:19:59.783907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T00:20:04.832562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeygugungugun_only_bikegugun_bike_roadcheck_dateinstt_code
skey1.0000.9840.8570.6270.8480.913
gugun0.9841.0000.9180.8491.0001.000
gugun_only_bike0.8570.9181.0000.9930.8330.842
gugun_bike_road0.6270.8490.9931.0000.4510.591
check_date0.8481.0000.8330.4511.0000.939
instt_code0.9131.0000.8420.5910.9391.000
2024-04-17T00:20:04.940752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gugun_only_bikecheck_dategugungugun_bike_road
gugun_only_bike1.0000.5700.5790.895
check_date0.5701.0000.9770.380
gugun0.5790.9771.0000.670
gugun_bike_road0.8950.3800.6701.000
2024-04-17T00:20:05.038725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codegugungugun_only_bikegugun_bike_roadcheck_date
skey1.000-0.4620.9220.5440.4860.766
instt_code-0.4621.0000.9900.5580.4060.844
gugun0.9220.9901.0000.5790.6700.977
gugun_only_bike0.5440.5580.5791.0000.8950.570
gugun_bike_road0.4860.4060.6700.8951.0000.380
check_date0.7660.8440.9770.5700.3801.000

Missing values

2024-04-17T00:20:00.118186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T00:20:00.290632image/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
01342부산광역시 금정구송정천선동상현회관5-5-2022-12-3133500002023-03-01 05:41:03
11343부산광역시 금정구회동교동천교2-2-2022-12-3133500002023-03-01 05:41:03
21344부산광역시 금정구두실역온천장역4.8-4.8-2022-12-3133500002023-03-01 05:41:03
31345부산광역시 금정구서동역금사역1.2-1.2-2022-12-3133500002023-03-01 05:41:03
41574부산광역시 동래구부산광역시 동래구 충렬대로455 (안락동 461-9)부산광역시 동래구 충렬대로171 (명륜동 533-230)2.9402.9402022-12-3133000002023-03-01 05:41:03
51581부산광역시 사상구북구경계사하구경계9.49.4002022-12-3133900002023-03-01 05:41:03
61582부산광역시 사상구괘법교사상지하철역0.70.7002022-12-3133900002023-03-01 05:41:03
71591부산광역시 기장군해운대송정2호교기장곰장어0.800.802023-01-2034000002023-03-01 05:41:03
81592부산광역시 기장군기장죽성교차로기장체육관1.1201.1202023-01-2034000002023-03-01 05:41:03
91593부산광역시 기장군대변 무양삼거리기장죽성교차로1.801.802023-01-2034000002023-03-01 05:41:03
skeygugunstart_spotend_spottotalgugun_only_bikegugun_with_walkgugun_bike_roadcheck_dateinstt_codelast_load_dttm
2401679부산광역시 남구범일교하구교0.13<NA><NA>0.132022-12-3133100002023-03-01 05:41:03
2411680부산광역시 남구용당동 532-26(동명대입구 맞은편)신선로 3010.8160.816<NA><NA>2022-12-3133100002023-03-01 05:41:03
2421727부산광역시 부산진구범전로 19 맞은편범전로 60.12600.12602023-01-1032900002023-03-01 05:41:03
2431728부산광역시 부산진구새싹로 127새싹로 950.3300.3302023-01-1032900002023-03-01 05:41:03
2441729부산광역시 부산진구부암동 87-10새싹로 130-70.3300.3302023-01-1032900002023-03-01 05:41:03
2451730부산광역시 부산진구전포동 587-21전포동 665-291.07501.07502023-01-1032900002023-03-01 05:41:03
2461731부산광역시 부산진구전포동 170-5전포대로 2401.07501.07502023-01-1032900002023-03-01 05:41:03
2471757부산광역시 사하구감천동 487-4감천동 449-20.320.32002022-12-3133400002023-03-01 05:41:03
2481787부산광역시 북구화명동 190-1(양달로 진입 삼거리)화명동 310 (코오롱아파트 103동)1.8-1.8-2022-12-3133200002023-03-01 05:41:03
2491789부산광역시 서구인터불고 냉동창고 맞은편동양시멘트1.56<NA>1.56<NA>2022-12-3132600002023-03-01 05:41:03