Overview

Dataset statistics

Number of variables11
Number of observations229
Missing cells21
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.3 KiB
Average record size in memory90.6 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 3 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 skey and 2 other fieldsHigh correlation
gugun_only_bike is highly imbalanced (51.7%)Imbalance
gugun_with_walk has 21 (9.2%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 15:23:01.249596
Analysis finished2024-04-16 15:23:02.315199
Duration1.07 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct229
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1153.6638
Minimum1033
Maximum1268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-17T00:23:02.378905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1033
5-th percentile1051.4
Q11097
median1154
Q31211
95-th percentile1256.6
Maximum1268
Range235
Interquartile range (IQR)114

Descriptive statistics

Standard deviation66.820954
Coefficient of variation (CV)0.05792065
Kurtosis-1.1633511
Mean1153.6638
Median Absolute Deviation (MAD)57
Skewness-0.0281755
Sum264189
Variance4465.04
MonotonicityNot monotonic
2024-04-17T00:23:02.505913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1042 1
 
0.4%
1248 1
 
0.4%
1250 1
 
0.4%
1251 1
 
0.4%
1058 1
 
0.4%
1059 1
 
0.4%
1060 1
 
0.4%
1061 1
 
0.4%
1062 1
 
0.4%
1063 1
 
0.4%
Other values (219) 219
95.6%
ValueCountFrequency (%)
1033 1
0.4%
1034 1
0.4%
1035 1
0.4%
1036 1
0.4%
1037 1
0.4%
1038 1
0.4%
1039 1
0.4%
1040 1
0.4%
1041 1
0.4%
1042 1
0.4%
ValueCountFrequency (%)
1268 1
0.4%
1267 1
0.4%
1266 1
0.4%
1265 1
0.4%
1264 1
0.4%
1263 1
0.4%
1262 1
0.4%
1261 1
0.4%
1260 1
0.4%
1259 1
0.4%

gugun
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
부산광역시 해운대구
41 
부산광역시 부산진구
33 
부산광역시 남구
28 
부산광역시 사하구
25 
부산광역시 동래구
21 
Other values (9)
81 

Length

Max length10
Median length9
Mean length9.1266376
Min length8

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row부산광역시 강서구
2nd row부산광역시 강서구
3rd row부산광역시 북구
4th row부산광역시 북구
5th row부산광역시 연제구

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 41
17.9%
부산광역시 부산진구 33
14.4%
부산광역시 남구 28
12.2%
부산광역시 사하구 25
10.9%
부산광역시 동래구 21
9.2%
부산광역시 연제구 20
8.7%
부산광역시 북구 14
 
6.1%
부산광역시 금정구 14
 
6.1%
부산광역시 강서구 11
 
4.8%
부산광역시 사상구 10
 
4.4%
Other values (4) 12
 
5.2%

Length

2024-04-17T00:23:02.640255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 229
50.0%
해운대구 41
 
9.0%
부산진구 33
 
7.2%
남구 28
 
6.1%
사하구 25
 
5.5%
동래구 21
 
4.6%
연제구 20
 
4.4%
북구 14
 
3.1%
금정구 14
 
3.1%
강서구 11
 
2.4%
Other values (5) 22
 
4.8%

start_spot
Text

Distinct217
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-04-17T00:23:02.902151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length10.183406
Min length3

Characters and Unicode

Total characters2332
Distinct characters207
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 (%)89.5%

Sample

1st row과학산단로 입구
2nd row미음산단로 입구
3rd row화명동 2315-9(도시철도(수정역)
4th row대동화명대교 중간지점(강서구 경계)
5th row법원북로 16
ValueCountFrequency (%)
부산광역시 18
 
3.8%
동래구 17
 
3.6%
분포로 9
 
1.9%
좌동 9
 
1.9%
우동 9
 
1.9%
중동 8
 
1.7%
화명동 8
 
1.7%
신평동 8
 
1.7%
중앙대로 8
 
1.7%
입구 6
 
1.3%
Other values (291) 368
78.6%
2024-04-17T00:23:03.368050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
243
 
10.4%
1 173
 
7.4%
동 129
 
5.5%
로 105
 
4.5%
2 84
 
3.6%
대 64
 
2.7%
3 59
 
2.5%
5 58
 
2.5%
- 57
 
2.4%
6 56
 
2.4%
Other values (197) 1304
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1292
55.4%
Decimal Number 645
27.7%
Space Separator 243
 
10.4%
Dash Punctuation 57
 
2.4%
Open Punctuation 38
 
1.6%
Close Punctuation 37
 
1.6%
Uppercase Letter 14
 
0.6%
Other Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 129
 
10.0%
로 105
 
8.1%
대 64
 
5.0%
구 40
 
3.1%
산 33
 
2.6%
역 30
 
2.3%
시 29
 
2.2%
교 27
 
2.1%
부 24
 
1.9%
중 21
 
1.6%
Other values (176) 790
61.1%
Decimal Number
ValueCountFrequency (%)
1 173
26.8%
2 84
13.0%
3 59
 
9.1%
5 58
 
9.0%
6 56
 
8.7%
4 55
 
8.5%
8 46
 
7.1%
7 43
 
6.7%
0 38
 
5.9%
9 33
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
L 5
35.7%
G 5
35.7%
U 1
 
7.1%
I 1
 
7.1%
C 1
 
7.1%
N 1
 
7.1%
Space Separator
ValueCountFrequency (%)
243
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
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 1292
55.4%
Common 1026
44.0%
Latin 14
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 129
 
10.0%
로 105
 
8.1%
대 64
 
5.0%
구 40
 
3.1%
산 33
 
2.6%
역 30
 
2.3%
시 29
 
2.2%
교 27
 
2.1%
부 24
 
1.9%
중 21
 
1.6%
Other values (176) 790
61.1%
Common
ValueCountFrequency (%)
243
23.7%
1 173
16.9%
2 84
 
8.2%
3 59
 
5.8%
5 58
 
5.7%
- 57
 
5.6%
6 56
 
5.5%
4 55
 
5.4%
8 46
 
4.5%
7 43
 
4.2%
Other values (5) 152
14.8%
Latin
ValueCountFrequency (%)
L 5
35.7%
G 5
35.7%
U 1
 
7.1%
I 1
 
7.1%
C 1
 
7.1%
N 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1292
55.4%
ASCII 1040
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
243
23.4%
1 173
16.6%
2 84
 
8.1%
3 59
 
5.7%
5 58
 
5.6%
- 57
 
5.5%
6 56
 
5.4%
4 55
 
5.3%
8 46
 
4.4%
7 43
 
4.1%
Other values (11) 166
16.0%
Hangul
ValueCountFrequency (%)
동 129
 
10.0%
로 105
 
8.1%
대 64
 
5.0%
구 40
 
3.1%
산 33
 
2.6%
역 30
 
2.3%
시 29
 
2.2%
교 27
 
2.1%
부 24
 
1.9%
중 21
 
1.6%
Other values (176) 790
61.1%

end_spot
Text

Distinct211
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-04-17T00:23:03.626724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length10.069869
Min length3

Characters and Unicode

Total characters2306
Distinct characters212
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

Unique195 ?
Unique (%)85.2%

Sample

1st row신명교
2nd row미음동1543-2
3rd row구포동 166 (덕천교차로-구포시장)
4th row화명IC(램프F) 일원
5th row법원북로 86
ValueCountFrequency (%)
부산광역시 16
 
3.2%
동래구 15
 
3.0%
좌동 13
 
2.6%
우동 12
 
2.4%
신평동 9
 
1.8%
중앙대로 9
 
1.8%
화명동 9
 
1.8%
입구 6
 
1.2%
분포로 6
 
1.2%
월드컵대로 4
 
0.8%
Other values (318) 394
79.9%
2024-04-17T00:23:03.992038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
268
 
11.6%
1 147
 
6.4%
동 130
 
5.6%
로 103
 
4.5%
2 73
 
3.2%
4 71
 
3.1%
- 59
 
2.6%
6 56
 
2.4%
3 56
 
2.4%
대 55
 
2.4%
Other values (202) 1288
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1244
53.9%
Decimal Number 640
27.8%
Space Separator 268
 
11.6%
Dash Punctuation 59
 
2.6%
Close Punctuation 35
 
1.5%
Open Punctuation 35
 
1.5%
Uppercase Letter 19
 
0.8%
Other Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 130
 
10.5%
로 103
 
8.3%
대 55
 
4.4%
구 36
 
2.9%
시 30
 
2.4%
산 27
 
2.2%
역 27
 
2.2%
부 24
 
1.9%
천 23
 
1.8%
평 22
 
1.8%
Other values (177) 767
61.7%
Decimal Number
ValueCountFrequency (%)
1 147
23.0%
2 73
11.4%
4 71
11.1%
6 56
 
8.8%
3 56
 
8.8%
8 54
 
8.4%
5 50
 
7.8%
7 47
 
7.3%
0 46
 
7.2%
9 40
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
L 4
21.1%
G 4
21.1%
N 2
10.5%
I 2
10.5%
C 2
10.5%
P 1
 
5.3%
U 1
 
5.3%
T 1
 
5.3%
B 1
 
5.3%
F 1
 
5.3%
Space Separator
ValueCountFrequency (%)
268
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 1244
53.9%
Common 1043
45.2%
Latin 19
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 130
 
10.5%
로 103
 
8.3%
대 55
 
4.4%
구 36
 
2.9%
시 30
 
2.4%
산 27
 
2.2%
역 27
 
2.2%
부 24
 
1.9%
천 23
 
1.8%
평 22
 
1.8%
Other values (177) 767
61.7%
Common
ValueCountFrequency (%)
268
25.7%
1 147
14.1%
2 73
 
7.0%
4 71
 
6.8%
- 59
 
5.7%
6 56
 
5.4%
3 56
 
5.4%
8 54
 
5.2%
5 50
 
4.8%
7 47
 
4.5%
Other values (5) 162
15.5%
Latin
ValueCountFrequency (%)
L 4
21.1%
G 4
21.1%
N 2
10.5%
I 2
10.5%
C 2
10.5%
P 1
 
5.3%
U 1
 
5.3%
T 1
 
5.3%
B 1
 
5.3%
F 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1244
53.9%
ASCII 1062
46.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
268
25.2%
1 147
13.8%
2 73
 
6.9%
4 71
 
6.7%
- 59
 
5.6%
6 56
 
5.3%
3 56
 
5.3%
8 54
 
5.1%
5 50
 
4.7%
7 47
 
4.4%
Other values (15) 181
17.0%
Hangul
ValueCountFrequency (%)
동 130
 
10.5%
로 103
 
8.3%
대 55
 
4.4%
구 36
 
2.9%
시 30
 
2.4%
산 27
 
2.2%
역 27
 
2.2%
부 24
 
1.9%
천 23
 
1.8%
평 22
 
1.8%
Other values (177) 767
61.7%

total
Text

Distinct146
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-04-17T00:23:04.304748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.6244541
Min length1

Characters and Unicode

Total characters830
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 (%)44.1%

Sample

1st row6.9
2nd row10
3rd row1.5
4th row1.159
5th row0.75
ValueCountFrequency (%)
0.6 6
 
2.6%
0.2 5
 
2.2%
1.2 5
 
2.2%
0.4 5
 
2.2%
0.25 4
 
1.7%
0.23 4
 
1.7%
2.3 4
 
1.7%
0.45 4
 
1.7%
0.7 4
 
1.7%
0.5 3
 
1.3%
Other values (136) 185
80.8%
2024-04-17T00:23:04.718014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 219
26.4%
0 123
14.8%
1 99
11.9%
2 84
 
10.1%
5 65
 
7.8%
3 50
 
6.0%
4 48
 
5.8%
6 47
 
5.7%
7 40
 
4.8%
9 27
 
3.3%
Other values (3) 28
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
73.4%
Other Punctuation 219
 
26.4%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 123
20.2%
1 99
16.3%
2 84
13.8%
5 65
10.7%
3 50
8.2%
4 48
 
7.9%
6 47
 
7.7%
7 40
 
6.6%
9 27
 
4.4%
8 26
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 219
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 828
99.8%
Latin 2
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 219
26.4%
0 123
14.9%
1 99
12.0%
2 84
 
10.1%
5 65
 
7.9%
3 50
 
6.0%
4 48
 
5.8%
6 47
 
5.7%
7 40
 
4.8%
9 27
 
3.3%
Latin
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 219
26.4%
0 123
14.8%
1 99
11.9%
2 84
 
10.1%
5 65
 
7.8%
3 50
 
6.0%
4 48
 
5.8%
6 47
 
5.7%
7 40
 
4.8%
9 27
 
3.3%
Other values (3) 28
 
3.4%

gugun_only_bike
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct30
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
<NA>
93 
0
81 
-
27 
0.83
 
2
2
 
1
Other values (25)
25 

Length

Max length6
Median length5
Mean length2.558952
Min length1

Unique

Unique26 ?
Unique (%)11.4%

Sample

1st row<NA>
2nd row<NA>
3rd row-
4th row-
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 93
40.6%
0 81
35.4%
- 27
 
11.8%
0.83 2
 
0.9%
2 1
 
0.4%
0.2 1
 
0.4%
0.69 1
 
0.4%
1.2 1
 
0.4%
1.1 1
 
0.4%
0.24 1
 
0.4%
Other values (20) 20
 
8.7%

Length

2024-04-17T00:23:04.849159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 93
40.6%
0 81
35.4%
27
 
11.8%
0.83 2
 
0.9%
0.796 1
 
0.4%
0.6 1
 
0.4%
0.7 1
 
0.4%
0.816 1
 
0.4%
0.4 1
 
0.4%
2.05 1
 
0.4%
Other values (20) 20
 
8.7%

gugun_with_walk
Text

MISSING 

Distinct133
Distinct (%)63.9%
Missing21
Missing (%)9.2%
Memory size1.9 KiB
2024-04-17T00:23:05.118180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.4807692
Min length1

Characters and Unicode

Total characters724
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

Unique93 ?
Unique (%)44.7%

Sample

1st row6.9
2nd row10
3rd row1.5
4th row1.159
5th row0.75
ValueCountFrequency (%)
0 8
 
3.8%
0.6 5
 
2.4%
0.2 4
 
1.9%
2.3 4
 
1.9%
0.4 4
 
1.9%
1.2 4
 
1.9%
0.45 4
 
1.9%
0.23 4
 
1.9%
0.12 3
 
1.4%
0.26 3
 
1.4%
Other values (123) 165
79.3%
2024-04-17T00:23:05.533679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 190
26.2%
0 110
15.2%
1 89
12.3%
2 74
 
10.2%
5 58
 
8.0%
3 42
 
5.8%
4 42
 
5.8%
6 41
 
5.7%
7 32
 
4.4%
9 26
 
3.6%
Other values (4) 20
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 531
73.3%
Other Punctuation 190
 
26.2%
Lowercase Letter 2
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 110
20.7%
1 89
16.8%
2 74
13.9%
5 58
10.9%
3 42
 
7.9%
4 42
 
7.9%
6 41
 
7.7%
7 32
 
6.0%
9 26
 
4.9%
8 17
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 190
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 722
99.7%
Latin 2
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
. 190
26.3%
0 110
15.2%
1 89
12.3%
2 74
 
10.2%
5 58
 
8.0%
3 42
 
5.8%
4 42
 
5.8%
6 41
 
5.7%
7 32
 
4.4%
9 26
 
3.6%
Other values (2) 18
 
2.5%
Latin
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 190
26.2%
0 110
15.2%
1 89
12.3%
2 74
 
10.2%
5 58
 
8.0%
3 42
 
5.8%
4 42
 
5.8%
6 41
 
5.7%
7 32
 
4.4%
9 26
 
3.6%
Other values (4) 20
 
2.8%

gugun_bike_road
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0
82 
-
78 
<NA>
66 
0.39
 
1
0.73
 
1

Length

Max length4
Median length1
Mean length1.9039301
Min length1

Unique

Unique3 ?
Unique (%)1.3%

Sample

1st row<NA>
2nd row<NA>
3rd row-
4th row-
5th row<NA>

Common Values

ValueCountFrequency (%)
0 82
35.8%
- 78
34.1%
<NA> 66
28.8%
0.39 1
 
0.4%
0.73 1
 
0.4%
0.13 1
 
0.4%

Length

2024-04-17T00:23:05.665972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T00:23:05.766457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 82
35.8%
78
34.1%
na 66
28.8%
0.39 1
 
0.4%
0.73 1
 
0.4%
0.13 1
 
0.4%

check_date
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2020-07-31
161 
2020-08-31
61 
2020-08-21
 
7

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-07-31
2nd row2020-07-31
3rd row2020-07-31
4th row2020-07-31
5th row2020-08-31

Common Values

ValueCountFrequency (%)
2020-07-31 161
70.3%
2020-08-31 61
 
26.6%
2020-08-21 7
 
3.1%

Length

2024-04-17T00:23:05.868688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T00:23:05.956529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-07-31 161
70.3%
2020-08-31 61
 
26.6%
2020-08-21 7
 
3.1%

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3328427.9
Minimum3250000
Maximum3390000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-17T00:23:06.044395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3290000
Q13300000
median3330000
Q33350000
95-th percentile3380000
Maximum3390000
Range140000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation30235.484
Coefficient of variation (CV)0.0090840133
Kurtosis-0.62040864
Mean3328427.9
Median Absolute Deviation (MAD)20000
Skewness0.21896644
Sum7.6221 × 108
Variance9.1418448 × 108
MonotonicityNot monotonic
2024-04-17T00:23:06.141331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3330000 41
17.9%
3290000 33
14.4%
3310000 28
12.2%
3340000 25
10.9%
3300000 21
9.2%
3370000 20
8.7%
3320000 14
 
6.1%
3350000 14
 
6.1%
3360000 11
 
4.8%
3390000 10
 
4.4%
Other values (4) 12
 
5.2%
ValueCountFrequency (%)
3250000 1
 
0.4%
3260000 2
 
0.9%
3280000 2
 
0.9%
3290000 33
14.4%
3300000 21
9.2%
3310000 28
12.2%
3320000 14
 
6.1%
3330000 41
17.9%
3340000 25
10.9%
3350000 14
 
6.1%
ValueCountFrequency (%)
3390000 10
 
4.4%
3380000 7
 
3.1%
3370000 20
8.7%
3360000 11
 
4.8%
3350000 14
 
6.1%
3340000 25
10.9%
3330000 41
17.9%
3320000 14
 
6.1%
3310000 28
12.2%
3300000 21
9.2%

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2020-12-22 14:34:13
229 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-22 14:34:13
2nd row2020-12-22 14:34:13
3rd row2020-12-22 14:34:13
4th row2020-12-22 14:34:13
5th row2020-12-22 14:34:13

Common Values

ValueCountFrequency (%)
2020-12-22 14:34:13 229
100.0%

Length

2024-04-17T00:23:06.253419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T00:23:06.326830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-22 229
50.0%
14:34:13 229
50.0%

Interactions

2024-04-17T00:23:01.878343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:23:01.715185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:23:01.975698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T00:23:01.791542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T00:23:06.375943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeygugungugun_only_bikegugun_bike_roadcheck_dateinstt_code
skey1.0000.9390.8810.8730.7550.883
gugun0.9391.0000.9440.8531.0001.000
gugun_only_bike0.8810.9441.0000.9070.8360.899
gugun_bike_road0.8730.8530.9071.0000.5120.627
check_date0.7551.0000.8360.5121.0000.979
instt_code0.8831.0000.8990.6270.9791.000
2024-04-17T00:23:06.787820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gugun_only_bikecheck_dategugungugun_bike_road
gugun_only_bike1.0000.5780.6480.819
check_date0.5781.0000.9750.446
gugun0.6480.9751.0000.670
gugun_bike_road0.8190.4460.6701.000
2024-04-17T00:23:06.876695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codegugungugun_only_bikegugun_bike_roadcheck_date
skey1.000-0.0380.7560.5150.5380.611
instt_code-0.0381.0000.9890.6230.4590.815
gugun0.7560.9891.0000.6480.6700.975
gugun_only_bike0.5150.6230.6481.0000.8190.578
gugun_bike_road0.5380.4590.6700.8191.0000.446
check_date0.6110.8150.9750.5780.4461.000

Missing values

2024-04-17T00:23:02.114112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T00:23:02.261042image/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
01042부산광역시 강서구과학산단로 입구신명교6.9<NA>6.9<NA>2020-07-3133600002020-12-22 14:34:13
11043부산광역시 강서구미음산단로 입구미음동1543-210<NA>10<NA>2020-07-3133600002020-12-22 14:34:13
21072부산광역시 북구화명동 2315-9(도시철도(수정역)구포동 166 (덕천교차로-구포시장)1.5-1.5-2020-07-3133200002020-12-22 14:34:13
31073부산광역시 북구대동화명대교 중간지점(강서구 경계)화명IC(램프F) 일원1.159-1.159-2020-07-3133200002020-12-22 14:34:13
41086부산광역시 연제구법원북로 16법원북로 860.75<NA>0.75<NA>2020-08-3133700002020-12-22 14:34:13
51087부산광역시 연제구법원북로 13법원북로 810.75<NA>0.75<NA>2020-08-3133700002020-12-22 14:34:13
61088부산광역시 연제구법원로 31법원로 50.4<NA>0.4<NA>2020-08-3133700002020-12-22 14:34:13
71089부산광역시 연제구법원로 42법원로 20.4<NA>0.4<NA>2020-08-3133700002020-12-22 14:34:13
81090부산광역시 연제구미남로 1미남로 200.20.2<NA><NA>2020-08-3133700002020-12-22 14:34:13
91091부산광역시 연제구여고로 74여고로 1380.690.69<NA><NA>2020-08-3133700002020-12-22 14:34:13
skeygugunstart_spotend_spottotalgugun_only_bikegugun_with_walkgugun_bike_roadcheck_dateinstt_codelast_load_dttm
2191211부산광역시 남구범일교하구교0.13<NA><NA>0.132020-07-3133100002020-12-22 14:34:13
2201212부산광역시 남구용당동 532-26(동명대입구 맞은편)신선로 3010.8160.816<NA><NA>2020-07-3133100002020-12-22 14:34:13
2211261부산광역시 사상구사하구경계감전교차로3.0403.0402020-07-3133900002020-12-22 14:34:13
2221262부산광역시 사상구괘법교사상지하철역0.70.7002020-07-3133900002020-12-22 14:34:13
2231263부산광역시 사상구사상구청감전중천공영P1.0601.0602020-07-3133900002020-12-22 14:34:13
2241264부산광역시 사상구사상구청교차로낙동제방1.7601.7602020-07-3133900002020-12-22 14:34:13
2251265부산광역시 사상구감전지하철역사상구청교차로0.7400.7402020-07-3133900002020-12-22 14:34:13
2261266부산광역시 사상구사상구청교차로학장교차로방향0.2500.2502020-07-3133900002020-12-22 14:34:13
2271267부산광역시 사상구학장교차로사상구청교차로0.600.602020-07-3133900002020-12-22 14:34:13
2281268부산광역시 사상구북구경계사하구경계9.49.4002020-07-3133900002020-12-22 14:34:13