Overview

Dataset statistics

Number of variables6
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.7 KiB
Average record size in memory52.1 B

Variable types

Numeric4
Text1
Categorical1

Dataset

Description샘플 데이터
Author(주)모토브 / 신재훈
URLhttps://www.bigdata-transportation.kr/frn/prdt/detail?prdtId=PRDTNUM_000000020253

Alerts

register_at has constant value ""Constant
fine_dust_value_id has unique valuesUnique
fine_dust has 165 (8.2%) zerosZeros

Reproduction

Analysis started2024-01-14 00:46:16.173363
Analysis finished2024-01-14 00:46:22.063083
Duration5.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

fine_dust_value_id
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1000.5
Minimum1
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-01-14T09:46:22.197675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100.95
Q1500.75
median1000.5
Q31500.25
95-th percentile1900.05
Maximum2000
Range1999
Interquartile range (IQR)999.5

Descriptive statistics

Standard deviation577.49459
Coefficient of variation (CV)0.57720599
Kurtosis-1.2
Mean1000.5
Median Absolute Deviation (MAD)500
Skewness0
Sum2001000
Variance333500
MonotonicityStrictly increasing
2024-01-14T09:46:22.462489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1331 1
 
0.1%
1344 1
 
0.1%
1343 1
 
0.1%
1342 1
 
0.1%
1341 1
 
0.1%
1340 1
 
0.1%
1339 1
 
0.1%
1338 1
 
0.1%
1337 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
2000 1
0.1%
1999 1
0.1%
1998 1
0.1%
1997 1
0.1%
1996 1
0.1%
1995 1
0.1%
1994 1
0.1%
1993 1
0.1%
1992 1
0.1%
1991 1
0.1%

taxi_id
Text

Distinct61
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2024-01-14T09:46:23.443857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20000
Distinct characters12
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowT_96289981
2nd rowT_73322493
3rd rowT_47791477
4th rowT_97388636
5th rowT_73102763
ValueCountFrequency (%)
t_97608367 34
 
1.7%
t_73468981 34
 
1.7%
t_48230939 34
 
1.7%
t_23798578 34
 
1.7%
t_98633779 33
 
1.7%
t_98047829 33
 
1.7%
t_48084452 33
 
1.7%
t_23945065 33
 
1.7%
t_98267560 33
 
1.7%
t_96289981 33
 
1.7%
Other values (51) 1666
83.3%
2024-01-14T09:46:24.529438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 2298
11.5%
4 2278
11.4%
T 2000
10.0%
_ 2000
10.0%
9 1902
9.5%
2 1866
9.3%
8 1738
8.7%
3 1655
8.3%
5 1156
5.8%
6 1075
5.4%
Other values (2) 2032
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16000
80.0%
Uppercase Letter 2000
 
10.0%
Connector Punctuation 2000
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 2298
14.4%
4 2278
14.2%
9 1902
11.9%
2 1866
11.7%
8 1738
10.9%
3 1655
10.3%
5 1156
7.2%
6 1075
6.7%
1 1057
6.6%
0 975
6.1%
Uppercase Letter
ValueCountFrequency (%)
T 2000
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18000
90.0%
Latin 2000
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 2298
12.8%
4 2278
12.7%
_ 2000
11.1%
9 1902
10.6%
2 1866
10.4%
8 1738
9.7%
3 1655
9.2%
5 1156
6.4%
6 1075
6.0%
1 1057
5.9%
Latin
ValueCountFrequency (%)
T 2000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 2298
11.5%
4 2278
11.4%
T 2000
10.0%
_ 2000
10.0%
9 1902
9.5%
2 1866
9.3%
8 1738
8.7%
3 1655
8.3%
5 1156
5.8%
6 1075
5.4%
Other values (2) 2032
10.2%

latitude
Real number (ℝ)

Distinct1297
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.517553
Minimum37.30882
Maximum37.67605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-01-14T09:46:24.778728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.30882
5-th percentile37.421769
Q137.48688
median37.516973
Q337.540834
95-th percentile37.609883
Maximum37.67605
Range0.36723
Interquartile range (IQR)0.05395425

Descriptive statistics

Standard deviation0.057521084
Coefficient of variation (CV)0.0015331779
Kurtosis2.2041823
Mean37.517553
Median Absolute Deviation (MAD)0.0300925
Skewness-0.39069245
Sum75035.105
Variance0.0033086751
MonotonicityNot monotonic
2024-01-14T09:46:25.282356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5649 34
 
1.7%
37.48688 33
 
1.7%
37.473686 29
 
1.5%
37.573574 25
 
1.2%
37.523636 23
 
1.1%
37.53414 21
 
1.1%
37.60681 19
 
0.9%
37.48328 19
 
0.9%
37.523 13
 
0.7%
37.62119 13
 
0.7%
Other values (1287) 1771
88.5%
ValueCountFrequency (%)
37.30882 2
0.1%
37.308823 1
0.1%
37.30883 2
0.1%
37.308846 2
0.1%
37.30886 1
0.1%
37.308876 2
0.1%
37.30889 1
0.1%
37.308907 1
0.1%
37.308914 1
0.1%
37.30892 1
0.1%
ValueCountFrequency (%)
37.67605 1
 
0.1%
37.676044 6
0.3%
37.67604 4
 
0.2%
37.676037 1
 
0.1%
37.676033 3
 
0.1%
37.67603 7
0.4%
37.676025 11
0.5%
37.621807 2
 
0.1%
37.621803 1
 
0.1%
37.6218 1
 
0.1%

longitude
Real number (ℝ)

Distinct976
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97993
Minimum126.70944
Maximum127.16431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-01-14T09:46:25.708506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.70944
5-th percentile126.74964
Q1126.89201
median126.97982
Q3127.08631
95-th percentile127.13107
Maximum127.16431
Range0.45487
Interquartile range (IQR)0.194304

Descriptive statistics

Standard deviation0.11583467
Coefficient of variation (CV)0.00091222818
Kurtosis-0.61849875
Mean126.97993
Median Absolute Deviation (MAD)0.09063
Skewness-0.41629414
Sum253959.87
Variance0.013417672
MonotonicityNot monotonic
2024-01-14T09:46:26.146132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.03322 34
 
1.7%
127.10529 34
 
1.7%
126.83376 34
 
1.7%
126.74964 30
 
1.5%
127.06097 29
 
1.5%
126.97982 27
 
1.4%
127.055176 22
 
1.1%
126.90667 21
 
1.1%
127.12796 21
 
1.1%
126.90562 20
 
1.0%
Other values (966) 1728
86.4%
ValueCountFrequency (%)
126.70944 2
 
0.1%
126.70946 2
 
0.1%
126.70947 3
 
0.1%
126.70949 14
0.7%
126.7095 5
 
0.2%
126.70952 6
0.3%
126.70954 1
 
0.1%
126.724655 1
 
0.1%
126.72468 1
 
0.1%
126.72469 3
 
0.1%
ValueCountFrequency (%)
127.16431 1
0.1%
127.16403 1
0.1%
127.16375 1
0.1%
127.163475 1
0.1%
127.1632 1
0.1%
127.162926 1
0.1%
127.16263 1
0.1%
127.16235 1
0.1%
127.16208 1
0.1%
127.1618 1
0.1%

fine_dust
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.954154
Minimum0
Maximum45.64286
Zeros165
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-01-14T09:46:26.516820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.555804
median18.136162
Q324.716518
95-th percentile28.901787
Maximum45.64286
Range45.64286
Interquartile range (IQR)11.160714

Descriptive statistics

Standard deviation8.6012533
Coefficient of variation (CV)0.45379253
Kurtosis0.92407414
Mean18.954154
Median Absolute Deviation (MAD)5.185268
Skewness-0.12731212
Sum37908.307
Variance73.981558
MonotonicityNot monotonic
2024-01-14T09:46:26.884337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
27.506697 245
12.2%
16.345983 231
11.6%
13.555804 182
9.1%
17.741072 165
8.2%
20.53125 165
8.2%
0.0 165
8.2%
23.32143 123
 
6.2%
26.111609 119
 
5.9%
10.765625 100
 
5.0%
19.136162 99
 
5.0%
Other values (9) 406
20.3%
ValueCountFrequency (%)
0.0 165
8.2%
10.765625 100
5.0%
12.160715 66
 
3.3%
13.555804 182
9.1%
14.950893 67
 
3.4%
16.345983 231
11.6%
17.741072 165
8.2%
18.136162 33
 
1.7%
19.136162 99
5.0%
20.53125 165
8.2%
ValueCountFrequency (%)
45.64286 14
 
0.7%
44.24777 19
 
0.9%
34.482143 66
 
3.3%
28.901787 33
 
1.7%
27.506697 245
12.2%
26.111609 119
5.9%
24.716518 66
 
3.3%
23.32143 123
6.2%
21.92634 42
 
2.1%
20.53125 165
8.2%

register_at
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2020-09-10 22:00
2000 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-09-10 22:00
2nd row2020-09-10 22:00
3rd row2020-09-10 22:00
4th row2020-09-10 22:00
5th row2020-09-10 22:00

Common Values

ValueCountFrequency (%)
2020-09-10 22:00 2000
100.0%

Length

2024-01-14T09:46:27.267161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T09:46:27.577094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-09-10 2000
50.0%
22:00 2000
50.0%

Interactions

2024-01-14T09:46:20.567924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:16.924364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:18.180887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:19.373239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:20.741502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:17.304409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:18.481267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:19.660377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:20.918108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:17.607048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:18.778268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:19.958278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:21.119551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:17.889872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:19.101108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T09:46:20.291831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T09:46:27.718062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
fine_dust_value_idtaxi_idlatitudelongitudefine_dust
fine_dust_value_id1.0000.0000.0000.0000.000
taxi_id0.0001.0001.0000.9990.999
latitude0.0001.0001.0000.7710.543
longitude0.0000.9990.7711.0000.585
fine_dust0.0000.9990.5430.5851.000
2024-01-14T09:46:27.975784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
fine_dust_value_idlatitudelongitudefine_dust
fine_dust_value_id1.0000.013-0.0060.009
latitude0.0131.000-0.2940.171
longitude-0.006-0.2941.000-0.093
fine_dust0.0090.171-0.0931.000

Missing values

2024-01-14T09:46:21.454311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T09:46:21.817558image/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

fine_dust_value_idtaxi_idlatitudelongitudefine_dustregister_at
01T_9628998137.610664126.72465517.7410722020-09-10 22:00
12T_7332249337.621788127.0875524.7165182020-09-10 22:00
23T_4779147737.502796127.0418323.321432020-09-10 22:00
34T_9738863637.527405126.9056334.4821432020-09-10 22:00
45T_7310276337.55852126.85976410.7656252020-09-10 22:00
56T_7434790537.309208127.131120.531252020-09-10 22:00
67T_7412817437.483276127.105310.02020-09-10 22:00
78T_2372533437.522526126.92573520.531252020-09-10 22:00
89T_4705904037.512455126.8866927.5066972020-09-10 22:00
910T_7266330037.503887126.9473340.02020-09-10 22:00
fine_dust_value_idtaxi_idlatitudelongitudefine_dustregister_at
19901991T_9687593137.50764127.03390521.926342020-09-10 22:00
19911992T_4874364537.523792126.88167620.531252020-09-10 22:00
19921993T_9804782937.573574126.9798212.1607152020-09-10 22:00
19931994T_4808445237.53414126.9066719.1361622020-09-10 22:00
19941995T_9826756037.51667126.9394214.9508932020-09-10 22:00
19951996T_4808445237.53414126.9066719.1361622020-09-10 22:00
19961997T_7449439237.569824126.8193716.3459832020-09-10 22:00
19971998T_2379857837.525528126.83491513.5558042020-09-10 22:00
19981999T_9760836737.5649126.8337626.1116092020-09-10 22:00
19992000T_7346898137.540596126.9733410.7656252020-09-10 22:00