Overview

Dataset statistics

Number of variables4
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory37.3 B

Variable types

Text1
Numeric3

Dataset

Description인천 광역시 지하철 노선 별 수송객수와 여객수입 등에 대한 인천광역시 기본 통계(2019년도)에 대한 데이터 입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15066566&srcSe=7661IVAWM27C61E190

Alerts

승차여객수 (명) is highly overall correlated with 하차여객수 (명) and 1 other fieldsHigh correlation
하차여객수 (명) is highly overall correlated with 승차여객수 (명) and 1 other fieldsHigh correlation
여객수입 (천원) is highly overall correlated with 승차여객수 (명) and 1 other fieldsHigh correlation
승차여객수 (명) has unique valuesUnique
하차여객수 (명) has unique valuesUnique
여객수입 (천원) has unique valuesUnique

Reproduction

Analysis started2024-01-28 05:09:00.888420
Analysis finished2024-01-28 05:09:02.045825
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역명
Text

Distinct56
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-01-28T14:09:02.186530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.6842105
Min length2

Characters and Unicode

Total characters210
Distinct characters102
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)96.5%

Sample

1st row계양
2nd row귤현
3rd row박촌
4th row임학
5th row계산
ValueCountFrequency (%)
인천시청 2
 
3.4%
국제업무지구 1
 
1.7%
석남 1
 
1.7%
왕길 1
 
1.7%
검단사거리 1
 
1.7%
마전 1
 
1.7%
완정 1
 
1.7%
독정 1
 
1.7%
검암 1
 
1.7%
검바위 1
 
1.7%
Other values (47) 47
81.0%
2024-01-28T14:09:02.479240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 9
 
4.3%
시 8
 
3.8%
천 8
 
3.8%
구 6
 
2.9%
장 6
 
2.9%
청 5
 
2.4%
가 5
 
2.4%
정 5
 
2.4%
부 5
 
2.4%
거 4
 
1.9%
Other values (92) 149
71.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 208
99.0%
Decimal Number 1
 
0.5%
Space Separator 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 9
 
4.3%
시 8
 
3.8%
천 8
 
3.8%
구 6
 
2.9%
장 6
 
2.9%
청 5
 
2.4%
가 5
 
2.4%
정 5
 
2.4%
부 5
 
2.4%
거 4
 
1.9%
Other values (90) 147
70.7%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 208
99.0%
Common 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 9
 
4.3%
시 8
 
3.8%
천 8
 
3.8%
구 6
 
2.9%
장 6
 
2.9%
청 5
 
2.4%
가 5
 
2.4%
정 5
 
2.4%
부 5
 
2.4%
거 4
 
1.9%
Other values (90) 147
70.7%
Common
ValueCountFrequency (%)
2 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 208
99.0%
ASCII 2
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 9
 
4.3%
시 8
 
3.8%
천 8
 
3.8%
구 6
 
2.9%
장 6
 
2.9%
청 5
 
2.4%
가 5
 
2.4%
정 5
 
2.4%
부 5
 
2.4%
거 4
 
1.9%
Other values (90) 147
70.7%
ASCII
ValueCountFrequency (%)
2 1
50.0%
1
50.0%

승차여객수 (명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2757936
Minimum367971
Maximum41387613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-01-28T14:09:02.597062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum367971
5-th percentile611788.8
Q11106456
median1721027
Q32719021
95-th percentile4785563.8
Maximum41387613
Range41019642
Interquartile range (IQR)1612565

Descriptive statistics

Standard deviation5366317.9
Coefficient of variation (CV)1.9457732
Kurtosis50.220765
Mean2757936
Median Absolute Deviation (MAD)675852
Skewness6.896341
Sum1.5720235 × 108
Variance2.8797368 × 1013
MonotonicityNot monotonic
2024-01-28T14:09:02.720598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1045175 1
 
1.8%
1499715 1
 
1.8%
658230 1
 
1.8%
2917119 1
 
1.8%
1255251 1
 
1.8%
2022684 1
 
1.8%
1042440 1
 
1.8%
1140652 1
 
1.8%
861335 1
 
1.8%
1570254 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
367971 1
1.8%
412869 1
1.8%
610600 1
1.8%
612086 1
1.8%
658230 1
1.8%
658588 1
1.8%
784232 1
1.8%
861335 1
1.8%
978431 1
1.8%
1042440 1
1.8%
ValueCountFrequency (%)
41387613 1
1.8%
6489616 1
1.8%
5508711 1
1.8%
4604777 1
1.8%
4506524 1
1.8%
4387643 1
1.8%
4176728 1
1.8%
3648231 1
1.8%
3404293 1
1.8%
3174545 1
1.8%

하차여객수 (명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2735716.7
Minimum315871
Maximum41234241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-01-28T14:09:02.838463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum315871
5-th percentile611056.6
Q11044671
median1660873
Q32716043
95-th percentile4959006.2
Maximum41234241
Range40918370
Interquartile range (IQR)1671372

Descriptive statistics

Standard deviation5356239.4
Coefficient of variation (CV)1.9578925
Kurtosis49.899127
Mean2735716.7
Median Absolute Deviation (MAD)721904
Skewness6.8668067
Sum1.5593585 × 108
Variance2.86893 × 1013
MonotonicityNot monotonic
2024-01-28T14:09:02.954398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960102 1
 
1.8%
1403800 1
 
1.8%
610459 1
 
1.8%
2716043 1
 
1.8%
1284340 1
 
1.8%
1930847 1
 
1.8%
1040016 1
 
1.8%
917241 1
 
1.8%
933542 1
 
1.8%
1361992 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
315871 1
1.8%
573076 1
1.8%
610459 1
1.8%
611206 1
1.8%
670582 1
1.8%
699645 1
1.8%
786992 1
1.8%
917241 1
1.8%
933542 1
1.8%
938969 1
1.8%
ValueCountFrequency (%)
41234241 1
1.8%
6868080 1
1.8%
5215287 1
1.8%
4894936 1
1.8%
4636834 1
1.8%
4022433 1
1.8%
3978695 1
1.8%
3891923 1
1.8%
3432604 1
1.8%
3379565 1
1.8%

여객수입 (천원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2460028.1
Minimum250343
Maximum35715167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-01-28T14:09:03.078761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250343
5-th percentile617187
Q11082970
median1479624
Q32385898
95-th percentile4559997.6
Maximum35715167
Range35464824
Interquartile range (IQR)1302928

Descriptive statistics

Standard deviation4643197.8
Coefficient of variation (CV)1.8874572
Kurtosis49.151642
Mean2460028.1
Median Absolute Deviation (MAD)646740
Skewness6.7921237
Sum1.402216 × 108
Variance2.1559286 × 1013
MonotonicityNot monotonic
2024-01-28T14:09:03.194864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
780509 1
 
1.8%
1373070 1
 
1.8%
698816 1
 
1.8%
2830747 1
 
1.8%
1298323 1
 
1.8%
2035655 1
 
1.8%
1096365 1
 
1.8%
1018262 1
 
1.8%
815377 1
 
1.8%
1479624 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
250343 1
1.8%
269857 1
1.8%
592227 1
1.8%
623427 1
1.8%
624282 1
1.8%
636867 1
1.8%
658012 1
1.8%
698816 1
1.8%
727851 1
1.8%
780509 1
1.8%
ValueCountFrequency (%)
35715167 1
1.8%
5816657 1
1.8%
4965676 1
1.8%
4458578 1
1.8%
4112209 1
1.8%
3935260 1
1.8%
3681416 1
1.8%
3513541 1
1.8%
3507385 1
1.8%
3140282 1
1.8%

Interactions

2024-01-28T14:09:01.688400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:09:01.285887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:09:01.483886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:09:01.774776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:09:01.353299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:09:01.544597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:09:01.846352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:09:01.415099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:09:01.611423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T14:09:03.289830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명승차여객수 (명)하차여객수 (명)여객수입 (천원)
역명1.0001.0001.0001.000
승차여객수 (명)1.0001.0001.0000.998
하차여객수 (명)1.0001.0001.0000.998
여객수입 (천원)1.0000.9980.9981.000
2024-01-28T14:09:03.366966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승차여객수 (명)하차여객수 (명)여객수입 (천원)
승차여객수 (명)1.0000.9850.974
하차여객수 (명)0.9851.0000.970
여객수입 (천원)0.9740.9701.000

Missing values

2024-01-28T14:09:01.944991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T14:09:02.018246image/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

역명승차여객수 (명)하차여객수 (명)여객수입 (천원)
0계양1045175960102780509
1귤현412869573076269857
2박촌140607512077571236943
3임학307581529848173140282
4계산417672840224334112209
5경인교대입구229326621678272222837
6작전550871152152874965676
7갈산317454530948362841775
8부평구청272179428353232385898
9부평시장460477746368343935260
역명승차여객수 (명)하차여객수 (명)여객수입 (천원)
47주안236505433795652126364
48시민공원271902125863771925644
49석바위시장155990116608731082970
50인천시청10834601218418870422
51석천사거리152415114162391295070
52모래내시장257523226479321760894
53만수172102716247671269312
54남동구청154835214088831115742
55인천대공원10680971030397623427
56운연367971315871250343