Overview

Dataset statistics

Number of variables5
Number of observations1743
Missing cells20
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory71.6 KiB
Average record size in memory42.1 B

Variable types

Categorical2
Text1
Numeric2

Dataset

Description행복택시 이용정보는 합천군에서 교통수단 취약계층의 불편함 해소를 위해 운영하는 행복택시의 월별, 마을별 탑승객수와 이용횟수를 제공하고 있다.
Author경상남도 합천군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15038387

Alerts

운행횟수 is highly overall correlated with 이용인원High correlation
이용인원 is highly overall correlated with 운행횟수High correlation
운행횟수 has 49 (2.8%) zerosZeros
이용인원 has 38 (2.2%) zerosZeros

Reproduction

Analysis started2023-12-10 23:24:16.471449
Analysis finished2023-12-10 23:24:17.528914
Duration1.06 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Categorical

Distinct11
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
2023-01
201 
2022-09
156 
2022-10
156 
2022-11
156 
2022-12
156 
Other values (6)
918 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-07
2nd row2022-07
3rd row2022-07
4th row2022-07
5th row2022-07

Common Values

ValueCountFrequency (%)
2023-01 201
11.5%
2022-09 156
9.0%
2022-10 156
9.0%
2022-11 156
9.0%
2022-12 156
9.0%
2023-03 155
8.9%
2023-05 154
8.8%
2022-07 153
8.8%
2023-02 153
8.8%
2023-04 153
8.8%

Length

2023-12-11T08:24:17.616322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-01 201
11.5%
2022-09 156
9.0%
2022-10 156
9.0%
2022-11 156
9.0%
2022-12 156
9.0%
2023-03 155
8.9%
2023-05 154
8.8%
2022-07 153
8.8%
2023-02 153
8.8%
2023-04 153
8.8%

읍면
Categorical

Distinct18
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
가야면
268 
야로면
199 
대양면
166 
율곡면
151 
가회면
124 
Other values (13)
835 

Length

Max length4
Median length3
Mean length3.0034423
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가야면 268
15.4%
야로면 199
11.4%
대양면 166
9.5%
율곡면 151
8.7%
가회면 124
 
7.1%
쌍책면 123
 
7.1%
합천읍 106
 
6.1%
묘산면 95
 
5.5%
용주면 90
 
5.2%
봉산면 77
 
4.4%
Other values (8) 344
19.7%

Length

2023-12-11T08:24:17.777632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가야면 268
15.4%
야로면 205
11.8%
대양면 166
9.5%
율곡면 151
8.7%
가회면 124
 
7.1%
쌍책면 123
 
7.1%
합천읍 106
 
6.1%
묘산면 95
 
5.5%
용주면 90
 
5.2%
봉산면 77
 
4.4%
Other values (7) 338
19.4%

마을명
Text

Distinct197
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
2023-12-11T08:24:18.129853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length5.7349398
Min length2

Characters and Unicode

Total characters9996
Distinct characters172
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.7%

Sample

1st row가천(덕방)
2nd row구미2구(학계)
3rd row구원1구(터서리)
4th row대전(비계산)
5th row대전(황령)
ValueCountFrequency (%)
가호(가호 14
 
0.8%
야로2(돈평 14
 
0.8%
화양(아랫마 14
 
0.8%
본곡(죽림동 14
 
0.8%
구평 13
 
0.7%
거산(음지 13
 
0.7%
와리(지릿터 13
 
0.7%
치인(마장 13
 
0.7%
멱곡(멱실 12
 
0.7%
묵촌1(묵촌 12
 
0.7%
Other values (189) 1616
92.4%
2023-12-11T08:24:18.632759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 1258
 
12.6%
) 1258
 
12.6%
구 419
 
4.2%
곡 285
 
2.9%
동 263
 
2.6%
2 247
 
2.5%
1 216
 
2.2%
계 184
 
1.8%
하 145
 
1.5%
대 136
 
1.4%
Other values (162) 5585
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6927
69.3%
Open Punctuation 1258
 
12.6%
Close Punctuation 1258
 
12.6%
Decimal Number 534
 
5.3%
Other Punctuation 7
 
0.1%
Math Symbol 7
 
0.1%
Space Separator 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 419
 
6.0%
곡 285
 
4.1%
동 263
 
3.8%
계 184
 
2.7%
하 145
 
2.1%
대 136
 
2.0%
실 125
 
1.8%
나 122
 
1.8%
촌 114
 
1.6%
가 114
 
1.6%
Other values (152) 5020
72.5%
Decimal Number
ValueCountFrequency (%)
2 247
46.3%
1 216
40.4%
3 59
 
11.0%
4 12
 
2.2%
Math Symbol
ValueCountFrequency (%)
~ 5
71.4%
→ 2
 
28.6%
Open Punctuation
ValueCountFrequency (%)
( 1258
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1258
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6927
69.3%
Common 3069
30.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 419
 
6.0%
곡 285
 
4.1%
동 263
 
3.8%
계 184
 
2.7%
하 145
 
2.1%
대 136
 
2.0%
실 125
 
1.8%
나 122
 
1.8%
촌 114
 
1.6%
가 114
 
1.6%
Other values (152) 5020
72.5%
Common
ValueCountFrequency (%)
( 1258
41.0%
) 1258
41.0%
2 247
 
8.0%
1 216
 
7.0%
3 59
 
1.9%
4 12
 
0.4%
, 7
 
0.2%
5
 
0.2%
~ 5
 
0.2%
→ 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6927
69.3%
ASCII 3067
30.7%
Arrows 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 1258
41.0%
) 1258
41.0%
2 247
 
8.1%
1 216
 
7.0%
3 59
 
1.9%
4 12
 
0.4%
, 7
 
0.2%
5
 
0.2%
~ 5
 
0.2%
Hangul
ValueCountFrequency (%)
구 419
 
6.0%
곡 285
 
4.1%
동 263
 
3.8%
계 184
 
2.7%
하 145
 
2.1%
대 136
 
2.0%
실 125
 
1.8%
나 122
 
1.8%
촌 114
 
1.6%
가 114
 
1.6%
Other values (152) 5020
72.5%
Arrows
ValueCountFrequency (%)
→ 2
100.0%

운행횟수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)4.1%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean30.522714
Minimum0
Maximum150
Zeros49
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-12-11T08:24:18.820913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q120
median29
Q344
95-th percentile58
Maximum150
Range150
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.486697
Coefficient of variation (CV)0.5401452
Kurtosis7.1240376
Mean30.522714
Median Absolute Deviation (MAD)12
Skewness1.3812238
Sum53079
Variance271.81119
MonotonicityNot monotonic
2023-12-11T08:24:19.005080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 218
 
12.5%
45 147
 
8.4%
15 116
 
6.7%
29 96
 
5.5%
28 72
 
4.1%
23 67
 
3.8%
27 57
 
3.3%
22 54
 
3.1%
44 54
 
3.1%
21 51
 
2.9%
Other values (61) 807
46.3%
ValueCountFrequency (%)
0 49
2.8%
1 5
 
0.3%
2 6
 
0.3%
3 8
 
0.5%
4 5
 
0.3%
5 4
 
0.2%
6 5
 
0.3%
7 4
 
0.2%
8 20
1.1%
9 11
 
0.6%
ValueCountFrequency (%)
150 1
0.1%
148 1
0.1%
146 2
0.1%
142 1
0.1%
117 1
0.1%
115 1
0.1%
114 1
0.1%
109 1
0.1%
68 1
0.1%
61 1
0.1%

이용인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct145
Distinct (%)8.4%
Missing16
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean45.537927
Minimum0
Maximum296
Zeros38
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-12-11T08:24:19.150156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q126
median39
Q359
95-th percentile104.7
Maximum296
Range296
Interquartile range (IQR)33

Descriptive statistics

Standard deviation31.065114
Coefficient of variation (CV)0.68218111
Kurtosis11.765712
Mean45.537927
Median Absolute Deviation (MAD)16
Skewness2.3049758
Sum78644
Variance965.04129
MonotonicityNot monotonic
2023-12-11T08:24:19.291271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 91
 
5.2%
29 58
 
3.3%
60 47
 
2.7%
28 45
 
2.6%
45 41
 
2.4%
31 41
 
2.4%
44 40
 
2.3%
15 40
 
2.3%
0 38
 
2.2%
23 38
 
2.2%
Other values (135) 1248
71.6%
ValueCountFrequency (%)
0 38
2.2%
1 2
 
0.1%
2 7
 
0.4%
3 3
 
0.2%
4 6
 
0.3%
5 6
 
0.3%
6 4
 
0.2%
7 2
 
0.1%
8 7
 
0.4%
9 13
 
0.7%
ValueCountFrequency (%)
296 2
0.1%
294 1
0.1%
290 1
0.1%
284 1
0.1%
175 1
0.1%
163 1
0.1%
156 1
0.1%
154 1
0.1%
152 1
0.1%
150 1
0.1%

Interactions

2023-12-11T08:24:16.989447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:24:16.744250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:24:17.119741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:24:16.868743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:24:19.387020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월읍면운행횟수이용인원
연월1.0000.0000.1210.067
읍면0.0001.0000.4690.627
운행횟수0.1210.4691.0000.901
이용인원0.0670.6270.9011.000
2023-12-11T08:24:19.750290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면연월
읍면1.0000.000
연월0.0001.000
2023-12-11T08:24:19.852741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운행횟수이용인원연월읍면
운행횟수1.0000.7940.0460.218
이용인원0.7941.0000.0330.339
연월0.0460.0331.0000.000
읍면0.2180.3390.0001.000

Missing values

2023-12-11T08:24:17.260777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:24:17.375851image/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.
2023-12-11T08:24:17.480701image/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

연월읍면마을명운행횟수이용인원
02022-07가야면가천(덕방)4147
12022-07가야면구미2구(학계)2324
22022-07가야면구원1구(터서리)6096
32022-07가야면대전(비계산)3033
42022-07가야면대전(황령)5886
52022-07가야면매화(맬리)4355
62022-07가야면사촌2구(내사)2427
72022-07가야면성기1구(나부동)1515
82022-07가야면성기2구(상두동)3842
92022-07가야면성기2구(하두동)1617
연월읍면마을명운행횟수이용인원
17332023-05청덕면적포(상적포)4561
17342023-05합천읍계림(굼마)1531
17352023-05합천읍관자(신촌)3060
17362023-05합천읍남암(남실)2929
17372023-05합천읍상용계2324
17382023-05합천읍안계(내안계)2427
17392023-05합천읍안계(외안계)5459
17402023-05합천읍인곡(영하동)3144
17412023-05합천읍장계(육정)2729
17422023-05합천읍하용계1427