Overview

Dataset statistics

Number of variables11
Number of observations252
Missing cells17
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory90.5 B

Variable types

Numeric2
Categorical3
Text4
DateTime2

Alerts

last_load_dttm has constant value ""Constant
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
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 imbalanced (51.0%)Imbalance
gugun_with_walk has 17 (6.7%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 15:19:02.053067
Analysis finished2024-04-16 15:19:03.636059
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct252
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1973.0317
Minimum1332
Maximum2182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T00:19:03.737582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1332
5-th percentile1344.55
Q11901.75
median1992.5
Q32093.25
95-th percentile2169.45
Maximum2182
Range850
Interquartile range (IQR)191.5

Descriptive statistics

Standard deviation182.73563
Coefficient of variation (CV)0.092616669
Kurtosis5.5035462
Mean1973.0317
Median Absolute Deviation (MAD)96
Skewness-2.160754
Sum497204
Variance33392.31
MonotonicityNot monotonic
2024-04-17T00:19:03.886820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1342 1
 
0.4%
1908 1
 
0.4%
1894 1
 
0.4%
1896 1
 
0.4%
1897 1
 
0.4%
1898 1
 
0.4%
1899 1
 
0.4%
1900 1
 
0.4%
1901 1
 
0.4%
1902 1
 
0.4%
Other values (242) 242
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 (%)
2182 1
0.4%
2181 1
0.4%
2180 1
0.4%
2179 1
0.4%
2178 1
0.4%
2177 1
0.4%
2176 1
0.4%
2175 1
0.4%
2174 1
0.4%
2173 1
0.4%

gugun
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
부산광역시 부산진구
38 
부산광역시 해운대구
35 
부산광역시 남구
28 
부산광역시 사하구
27 
부산광역시 연제구
21 
Other values (11)
103 

Length

Max length10
Median length9
Mean length9.1071429
Min length8

Unique

Unique2 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 부산진구 38
15.1%
부산광역시 해운대구 35
13.9%
부산광역시 남구 28
11.1%
부산광역시 사하구 27
10.7%
부산광역시 연제구 21
8.3%
부산광역시 동래구 20
7.9%
부산광역시 기장군 20
7.9%
부산광역시 금정구 14
 
5.6%
부산광역시 북구 14
 
5.6%
부산광역시 강서구 12
 
4.8%
Other values (6) 23
9.1%

Length

2024-04-17T00:19:04.052002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 252
50.0%
부산진구 38
 
7.5%
해운대구 35
 
6.9%
남구 28
 
5.6%
사하구 27
 
5.4%
연제구 21
 
4.2%
동래구 20
 
4.0%
기장군 20
 
4.0%
북구 14
 
2.8%
금정구 14
 
2.8%
Other values (7) 35
 
6.9%

start_spot
Text

Distinct236
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T00:19:04.271418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length29
Mean length11.178571
Min length2

Characters and Unicode

Total characters2817
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

Unique221 ?
Unique (%)87.7%

Sample

1st row송정천
2nd row회동교
3rd row두실역
4th row서동역
5th row부산광역시 동래구 미남로141 (온천동 1422-10)
ValueCountFrequency (%)
부산광역시 28
 
5.0%
동래구 20
 
3.6%
중앙대로 10
 
1.8%
분포로 9
 
1.6%
신평동 9
 
1.6%
좌동 8
 
1.4%
우동 8
 
1.4%
화명동 8
 
1.4%
중동 7
 
1.2%
수영구 7
 
1.2%
Other values (345) 447
79.7%
2024-04-17T00:19:04.643989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
309
 
11.0%
1 190
 
6.7%
동 151
 
5.4%
로 111
 
3.9%
2 92
 
3.3%
- 74
 
2.6%
5 68
 
2.4%
3 68
 
2.4%
대 66
 
2.3%
4 63
 
2.2%
Other values (222) 1625
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1562
55.4%
Decimal Number 738
26.2%
Space Separator 309
 
11.0%
Dash Punctuation 74
 
2.6%
Open Punctuation 57
 
2.0%
Close Punctuation 56
 
2.0%
Uppercase Letter 16
 
0.6%
Other Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 151
 
9.7%
로 111
 
7.1%
대 66
 
4.2%
구 53
 
3.4%
산 48
 
3.1%
시 43
 
2.8%
역 39
 
2.5%
부 34
 
2.2%
교 34
 
2.2%
광 31
 
2.0%
Other values (198) 952
60.9%
Decimal Number
ValueCountFrequency (%)
1 190
25.7%
2 92
12.5%
5 68
 
9.2%
3 68
 
9.2%
4 63
 
8.5%
8 58
 
7.9%
6 56
 
7.6%
7 56
 
7.6%
9 46
 
6.2%
0 41
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
L 5
31.2%
G 5
31.2%
N 1
 
6.2%
U 1
 
6.2%
S 1
 
6.2%
D 1
 
6.2%
C 1
 
6.2%
I 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
309
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1562
55.4%
Common 1239
44.0%
Latin 16
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 151
 
9.7%
로 111
 
7.1%
대 66
 
4.2%
구 53
 
3.4%
산 48
 
3.1%
시 43
 
2.8%
역 39
 
2.5%
부 34
 
2.2%
교 34
 
2.2%
광 31
 
2.0%
Other values (198) 952
60.9%
Common
ValueCountFrequency (%)
309
24.9%
1 190
15.3%
2 92
 
7.4%
- 74
 
6.0%
5 68
 
5.5%
3 68
 
5.5%
4 63
 
5.1%
8 58
 
4.7%
( 57
 
4.6%
) 56
 
4.5%
Other values (6) 204
16.5%
Latin
ValueCountFrequency (%)
L 5
31.2%
G 5
31.2%
N 1
 
6.2%
U 1
 
6.2%
S 1
 
6.2%
D 1
 
6.2%
C 1
 
6.2%
I 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1562
55.4%
ASCII 1255
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
309
24.6%
1 190
15.1%
2 92
 
7.3%
- 74
 
5.9%
5 68
 
5.4%
3 68
 
5.4%
4 63
 
5.0%
8 58
 
4.6%
( 57
 
4.5%
) 56
 
4.5%
Other values (14) 220
17.5%
Hangul
ValueCountFrequency (%)
동 151
 
9.7%
로 111
 
7.1%
대 66
 
4.2%
구 53
 
3.4%
산 48
 
3.1%
시 43
 
2.8%
역 39
 
2.5%
부 34
 
2.2%
교 34
 
2.2%
광 31
 
2.0%
Other values (198) 952
60.9%

end_spot
Text

Distinct233
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T00:19:04.890537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length29
Mean length11.253968
Min length3

Characters and Unicode

Total characters2836
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

Unique216 ?
Unique (%)85.7%

Sample

1st row선동상현회관
2nd row동천교
3rd row온천장역
4th row금사역
5th row부산광역시 동래구 미남로27 (사직동 133-9)
ValueCountFrequency (%)
부산광역시 28
 
4.7%
동래구 20
 
3.4%
좌동 12
 
2.0%
우동 10
 
1.7%
신평동 10
 
1.7%
화명동 9
 
1.5%
수영구 7
 
1.2%
중앙대로 6
 
1.0%
사직동 6
 
1.0%
분포로 6
 
1.0%
Other values (374) 479
80.8%
2024-04-17T00:19:05.275089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
342
 
12.1%
1 168
 
5.9%
동 158
 
5.6%
로 105
 
3.7%
2 87
 
3.1%
- 83
 
2.9%
4 77
 
2.7%
3 77
 
2.7%
5 67
 
2.4%
6 59
 
2.1%
Other values (222) 1613
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1533
54.1%
Decimal Number 742
26.2%
Space Separator 342
 
12.1%
Dash Punctuation 83
 
2.9%
Close Punctuation 56
 
2.0%
Open Punctuation 56
 
2.0%
Uppercase Letter 19
 
0.7%
Other Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 158
 
10.3%
로 105
 
6.8%
대 54
 
3.5%
구 50
 
3.3%
산 46
 
3.0%
시 43
 
2.8%
역 39
 
2.5%
부 38
 
2.5%
천 33
 
2.2%
광 32
 
2.1%
Other values (196) 935
61.0%
Decimal Number
ValueCountFrequency (%)
1 168
22.6%
2 87
11.7%
4 77
10.4%
3 77
10.4%
5 67
 
9.0%
6 59
 
8.0%
8 58
 
7.8%
0 53
 
7.1%
7 52
 
7.0%
9 44
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
G 4
21.1%
L 4
21.1%
I 2
10.5%
C 2
10.5%
N 2
10.5%
F 1
 
5.3%
P 1
 
5.3%
T 1
 
5.3%
B 1
 
5.3%
U 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
342
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1533
54.1%
Common 1284
45.3%
Latin 19
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 158
 
10.3%
로 105
 
6.8%
대 54
 
3.5%
구 50
 
3.3%
산 46
 
3.0%
시 43
 
2.8%
역 39
 
2.5%
부 38
 
2.5%
천 33
 
2.2%
광 32
 
2.1%
Other values (196) 935
61.0%
Common
ValueCountFrequency (%)
342
26.6%
1 168
13.1%
2 87
 
6.8%
- 83
 
6.5%
4 77
 
6.0%
3 77
 
6.0%
5 67
 
5.2%
6 59
 
4.6%
8 58
 
4.5%
) 56
 
4.4%
Other values (6) 210
16.4%
Latin
ValueCountFrequency (%)
G 4
21.1%
L 4
21.1%
I 2
10.5%
C 2
10.5%
N 2
10.5%
F 1
 
5.3%
P 1
 
5.3%
T 1
 
5.3%
B 1
 
5.3%
U 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1533
54.1%
ASCII 1303
45.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
342
26.2%
1 168
12.9%
2 87
 
6.7%
- 83
 
6.4%
4 77
 
5.9%
3 77
 
5.9%
5 67
 
5.1%
6 59
 
4.5%
8 58
 
4.5%
) 56
 
4.3%
Other values (16) 229
17.6%
Hangul
ValueCountFrequency (%)
동 158
 
10.3%
로 105
 
6.8%
대 54
 
3.5%
구 50
 
3.3%
산 46
 
3.0%
시 43
 
2.8%
역 39
 
2.5%
부 38
 
2.5%
천 33
 
2.2%
광 32
 
2.1%
Other values (196) 935
61.0%

total
Text

Distinct155
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T00:19:05.611546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.5555556
Min length1

Characters and Unicode

Total characters896
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 (%)40.9%

Sample

1st row5
2nd row2
3rd row4.8
4th row1.2
5th row1.2
ValueCountFrequency (%)
0.6 6
 
2.4%
0.4 6
 
2.4%
0.2 6
 
2.4%
1.2 5
 
2.0%
2 5
 
2.0%
0.7 5
 
2.0%
0.23 4
 
1.6%
1 4
 
1.6%
2.3 4
 
1.6%
1.17 4
 
1.6%
Other values (145) 203
80.6%
2024-04-17T00:19:06.130544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 239
26.7%
0 128
14.3%
1 112
12.5%
2 91
 
10.2%
5 67
 
7.5%
3 56
 
6.2%
6 53
 
5.9%
4 48
 
5.4%
7 43
 
4.8%
9 29
 
3.2%
Other values (3) 30
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 655
73.1%
Other Punctuation 239
 
26.7%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 128
19.5%
1 112
17.1%
2 91
13.9%
5 67
10.2%
3 56
8.5%
6 53
8.1%
4 48
 
7.3%
7 43
 
6.6%
9 29
 
4.4%
8 28
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 894
99.8%
Latin 2
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 239
26.7%
0 128
14.3%
1 112
12.5%
2 91
 
10.2%
5 67
 
7.5%
3 56
 
6.3%
6 53
 
5.9%
4 48
 
5.4%
7 43
 
4.8%
9 29
 
3.2%
Latin
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 239
26.7%
0 128
14.3%
1 112
12.5%
2 91
 
10.2%
5 67
 
7.5%
3 56
 
6.2%
6 53
 
5.9%
4 48
 
5.4%
7 43
 
4.8%
9 29
 
3.2%
Other values (3) 30
 
3.3%

gugun_only_bike
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct28
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
104 
-
63 
<NA>
58 
0.7
 
2
2
 
2
Other values (23)
23 

Length

Max length5
Median length1
Mean length1.9563492
Min length1

Unique

Unique23 ?
Unique (%)9.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 104
41.3%
- 63
25.0%
<NA> 58
23.0%
0.7 2
 
0.8%
2 2
 
0.8%
0.2 1
 
0.4%
9.4 1
 
0.4%
0.83 1
 
0.4%
2.7 1
 
0.4%
0.38 1
 
0.4%
Other values (18) 18
 
7.1%

Length

2024-04-17T00:19:06.265373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 104
41.3%
63
25.0%
na 58
23.0%
0.7 2
 
0.8%
2 2
 
0.8%
0.68 1
 
0.4%
0.69 1
 
0.4%
0.816 1
 
0.4%
1.1 1
 
0.4%
0.24 1
 
0.4%
Other values (18) 18
 
7.1%

gugun_with_walk
Text

MISSING 

Distinct145
Distinct (%)61.7%
Missing17
Missing (%)6.7%
Memory size2.1 KiB
2024-04-17T00:19:06.541885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.4170213
Min length1

Characters and Unicode

Total characters803
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 (%)40.9%

Sample

1st row5
2nd row2
3rd row4.8
4th row1.2
5th row1.2
ValueCountFrequency (%)
0 11
 
4.7%
0.6 5
 
2.1%
0.2 5
 
2.1%
0.4 5
 
2.1%
2.3 4
 
1.7%
0.23 4
 
1.7%
1.2 4
 
1.7%
1.17 4
 
1.7%
1 4
 
1.7%
0.7 3
 
1.3%
Other values (135) 186
79.1%
2024-04-17T00:19:07.011069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 212
26.4%
0 118
14.7%
1 104
13.0%
2 81
 
10.1%
5 60
 
7.5%
3 48
 
6.0%
6 47
 
5.9%
4 43
 
5.4%
7 37
 
4.6%
9 28
 
3.5%
Other values (4) 25
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 588
73.2%
Other Punctuation 212
 
26.4%
Lowercase Letter 2
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118
20.1%
1 104
17.7%
2 81
13.8%
5 60
10.2%
3 48
8.2%
6 47
 
8.0%
4 43
 
7.3%
7 37
 
6.3%
9 28
 
4.8%
8 22
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 212
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 801
99.8%
Latin 2
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 212
26.5%
0 118
14.7%
1 104
13.0%
2 81
 
10.1%
5 60
 
7.5%
3 48
 
6.0%
6 47
 
5.9%
4 43
 
5.4%
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 803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 212
26.4%
0 118
14.7%
1 104
13.0%
2 81
 
10.1%
5 60
 
7.5%
3 48
 
6.0%
6 47
 
5.9%
4 43
 
5.4%
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
108 
<NA>
75 
-
66 
0.39
 
1
0.73
 
1

Length

Max length4
Median length1
Mean length1.9285714
Min length1

Unique

Unique3 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 108
42.9%
<NA> 75
29.8%
- 66
26.2%
0.39 1
 
0.4%
0.73 1
 
0.4%
0.13 1
 
0.4%

Length

2024-04-17T00:19:07.151178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T00:19:07.250671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 108
42.9%
na 75
29.8%
66
26.2%
0.39 1
 
0.4%
0.73 1
 
0.4%
0.13 1
 
0.4%

check_date
Date

Distinct6
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2022-12-31 00:00:00
Maximum2023-07-14 00:00:00
2024-04-17T00:19:07.362222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:19:07.865085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3333571.4
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T00:19:08.002069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3290000
Q13300000
median3330000
Q33360000
95-th percentile3400000
Maximum3400000
Range150000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation35548.391
Coefficient of variation (CV)0.010663756
Kurtosis-0.79032218
Mean3333571.4
Median Absolute Deviation (MAD)30000
Skewness0.3055709
Sum8.4006 × 108
Variance1.2636881 × 109
MonotonicityNot monotonic
2024-04-17T00:19:08.141158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3290000 38
15.1%
3330000 35
13.9%
3310000 28
11.1%
3340000 27
10.7%
3370000 21
8.3%
3400000 20
7.9%
3300000 20
7.9%
3350000 14
 
5.6%
3320000 14
 
5.6%
3360000 12
 
4.8%
Other values (6) 23
9.1%
ValueCountFrequency (%)
3250000 1
 
0.4%
3260000 2
 
0.8%
3270000 1
 
0.4%
3280000 2
 
0.8%
3290000 38
15.1%
3300000 20
7.9%
3310000 28
11.1%
3320000 14
 
5.6%
3330000 35
13.9%
3340000 27
10.7%
ValueCountFrequency (%)
3400000 20
7.9%
3390000 10
 
4.0%
3380000 7
 
2.8%
3370000 21
8.3%
3360000 12
 
4.8%
3350000 14
 
5.6%
3340000 27
10.7%
3330000 35
13.9%
3320000 14
 
5.6%
3310000 28
11.1%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2023-09-01 05:41:03
Maximum2023-09-01 05:41:03
2024-04-17T00:19:08.258570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:19:08.341960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T00:19:03.175110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:19:03.004404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:19:03.259910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:19:03.080449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T00:19:08.411531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeygugungugun_only_bikegugun_bike_roadcheck_dateinstt_code
skey1.0000.9920.6380.7700.7670.986
gugun0.9921.0000.9170.8541.0001.000
gugun_only_bike0.6380.9171.0001.0000.3140.841
gugun_bike_road0.7700.8541.0001.0000.6210.569
check_date0.7671.0000.3140.6211.0000.923
instt_code0.9861.0000.8410.5690.9231.000
2024-04-17T00:19:08.541962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gugun_only_bikegugungugun_bike_road
gugun_only_bike1.0000.5760.967
gugun0.5761.0000.673
gugun_bike_road0.9670.6731.000
2024-04-17T00:19:08.644985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codegugungugun_only_bikegugun_bike_road
skey1.000-0.0890.9580.3510.392
instt_code-0.0891.0000.9880.5560.403
gugun0.9580.9881.0000.5760.673
gugun_only_bike0.3510.5560.5761.0000.967
gugun_bike_road0.3920.4030.6730.9671.000

Missing values

2024-04-17T00:19:03.382151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T00:19:03.551629image/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 금정구송정천선동상현회관5-5-2022-12-3133500002023-09-01 05:41:03
11343부산광역시 금정구회동교동천교2-2-2022-12-3133500002023-09-01 05:41:03
21344부산광역시 금정구두실역온천장역4.8-4.8-2022-12-3133500002023-09-01 05:41:03
31345부산광역시 금정구서동역금사역1.2-1.2-2022-12-3133500002023-09-01 05:41:03
41865부산광역시 동래구부산광역시 동래구 미남로141 (온천동 1422-10)부산광역시 동래구 미남로27 (사직동 133-9)1.201.202023-06-3033000002023-09-01 05:41:03
51891부산광역시 북구금곡동 1882(율리마을주공 301동)화명동 2318 (한국방송통신대)2.85-2.85-2023-06-2133200002023-09-01 05:41:03
61892부산광역시 북구화명동 2264(롯데낙천대아파트 101동)화명동 2315-9 (도시철도 수정역)2.15-2.15-2023-06-2133200002023-09-01 05:41:03
71980부산광역시 사상구북구경계사하구경계9.49.4002023-07-0533900002023-09-01 05:41:03
81981부산광역시 사상구괘법교사상지하철역0.70.7002023-07-0533900002023-09-01 05:41:03
91991부산광역시 기장군해운대송정2호교기장곰장어0.800.802023-07-1434000002023-09-01 05:41:03
skeygugunstart_spotend_spottotalgugun_only_bikegugun_with_walkgugun_bike_roadcheck_dateinstt_codelast_load_dttm
2422121부산광역시 남구수영로 184(하이마트)천제등로 280.285<NA>0.29<NA>2023-06-3033100002023-09-01 05:41:03
2432122부산광역시 남구수영로 185수영로 73-1 (지게골역2번출구)1.16<NA>1.16<NA>2023-06-3033100002023-09-01 05:41:03
2442123부산광역시 남구수영로 72(지게골역1번출구)수영로 184 (하이마트)1.17<NA>1.17<NA>2023-06-3033100002023-09-01 05:41:03
2452124부산광역시 남구수영로 72(지게골역1번출구)지게골로 40.73<NA><NA>0.732023-06-3033100002023-09-01 05:41:03
2462125부산광역시 남구지게골로 4범일교0.295<NA>0.295<NA>2023-06-3033100002023-09-01 05:41:03
2472126부산광역시 남구범일교하구교0.13<NA><NA>0.132023-06-3033100002023-09-01 05:41:03
2482127부산광역시 남구용당동 532-26(동명대입구 맞은편)신선로 3010.8160.816<NA><NA>2023-06-3033100002023-09-01 05:41:03
2492180부산광역시 사하구신평동 582-2신평동 690.2100.2102023-06-3033400002023-09-01 05:41:03
2502181부산광역시 사하구감천동 487-4감천동 449-20.320.32002023-06-3033400002023-09-01 05:41:03
2512182부산광역시 사하구다대동 1598-5구평동 472-150.70.7002023-06-3033400002023-09-01 05:41:03