Overview

Dataset statistics

Number of variables12
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory191.5 KiB
Average record size in memory98.1 B

Variable types

Numeric2
Boolean9
Categorical1

Alerts

MEISHILIN_RSTRNT_CN has constant value ""Constant
MULTILINGUAL_MENU_INFO_PROVD_AT is highly overall correlated with FGGG_MENU_INFO_ATHigh correlation
FGGG_MENU_INFO_AT is highly overall correlated with MULTILINGUAL_MENU_INFO_PROVD_ATHigh correlation
MULTILINGUAL_MENU_INFO_PROVD_AT is highly imbalanced (82.3%)Imbalance
DSPSN_FCLTY_AT is highly imbalanced (91.6%)Imbalance
MICHELIN_RSTRNT_CN is highly imbalanced (99.4%)Imbalance
FGGG_MENU_INFO_AT is highly imbalanced (82.3%)Imbalance
RSTRNT_ID has unique valuesUnique
REVIEW_CO has 425 (21.2%) zerosZeros

Reproduction

Analysis started2023-12-10 09:39:21.218510
Analysis finished2023-12-10 09:39:24.239882
Duration3.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RSTRNT_ID
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26868.932
Minimum17
Maximum58328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-10T18:39:24.367257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile469.9
Q19812.75
median27149
Q343039.25
95-th percentile55102.6
Maximum58328
Range58311
Interquartile range (IQR)33226.5

Descriptive statistics

Standard deviation18361.382
Coefficient of variation (CV)0.6833685
Kurtosis-1.3290186
Mean26868.932
Median Absolute Deviation (MAD)16303
Skewness0.015633865
Sum53737864
Variance3.3714034 × 108
MonotonicityStrictly increasing
2023-12-10T18:39:24.605238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 1
 
0.1%
38835 1
 
0.1%
39158 1
 
0.1%
39146 1
 
0.1%
39132 1
 
0.1%
39129 1
 
0.1%
39060 1
 
0.1%
39053 1
 
0.1%
39019 1
 
0.1%
39008 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
17 1
0.1%
21 1
0.1%
33 1
0.1%
35 1
0.1%
51 1
0.1%
54 1
0.1%
83 1
0.1%
90 1
0.1%
92 1
0.1%
94 1
0.1%
ValueCountFrequency (%)
58328 1
0.1%
58305 1
0.1%
58278 1
0.1%
58239 1
0.1%
58116 1
0.1%
58108 1
0.1%
58076 1
0.1%
58052 1
0.1%
58029 1
0.1%
57989 1
0.1%

MULTILINGUAL_MENU_INFO_PROVD_AT
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1947 
True
 
53
ValueCountFrequency (%)
False 1947
97.4%
True 53
 
2.6%
2023-12-10T18:39:24.804929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

SAFETY_RSTRNT_AT
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1762 
True
238 
ValueCountFrequency (%)
False 1762
88.1%
True 238
 
11.9%
2023-12-10T18:39:24.945660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

PCRNGE_FLAG_CD
Categorical

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
99999
1026 
1만원 대
360 
2만원 대
272 
5만원 대
148 
7만원 대
110 
Other values (2)
 
84

Length

Max length6
Median length5
Mean length5.007
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2만원 대
2nd row5만원 대
3rd row99999
4th row99999
5th row99999

Common Values

ValueCountFrequency (%)
99999 1026
51.3%
1만원 대 360
 
18.0%
2만원 대 272
 
13.6%
5만원 대 148
 
7.4%
7만원 대 110
 
5.5%
3만원 대 70
 
3.5%
10만원 대 14
 
0.7%

Length

2023-12-10T18:39:25.167847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:25.440156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99999 1026
34.5%
대 974
32.8%
1만원 360
 
12.1%
2만원 272
 
9.1%
5만원 148
 
5.0%
7만원 110
 
3.7%
3만원 70
 
2.4%
10만원 14
 
0.5%

PARKNG_POSBL_AT
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1413 
True
587 
ValueCountFrequency (%)
False 1413
70.7%
True 587
29.3%
2023-12-10T18:39:25.756318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

DSPSN_FCLTY_AT
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1979 
True
 
21
ValueCountFrequency (%)
False 1979
99.0%
True 21
 
1.1%
2023-12-10T18:39:25.925758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

REVIEW_CO
Real number (ℝ)

ZEROS 

Distinct231
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.2395
Minimum0
Maximum848
Zeros425
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-10T18:39:26.128723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q323
95-th percentile168.1
Maximum848
Range848
Interquartile range (IQR)22

Descriptive statistics

Standard deviation92.549645
Coefficient of variation (CV)2.6263042
Kurtosis29.947231
Mean35.2395
Median Absolute Deviation (MAD)5
Skewness4.9968102
Sum70479
Variance8565.4369
MonotonicityNot monotonic
2023-12-10T18:39:26.464962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 425
21.2%
1 233
 
11.7%
2 133
 
6.7%
3 106
 
5.3%
4 82
 
4.1%
5 72
 
3.6%
6 52
 
2.6%
7 45
 
2.2%
8 41
 
2.1%
9 37
 
1.8%
Other values (221) 774
38.7%
ValueCountFrequency (%)
0 425
21.2%
1 233
11.7%
2 133
 
6.7%
3 106
 
5.3%
4 82
 
4.1%
5 72
 
3.6%
6 52
 
2.6%
7 45
 
2.2%
8 41
 
2.1%
9 37
 
1.8%
ValueCountFrequency (%)
848 1
0.1%
821 1
0.1%
819 1
0.1%
797 1
0.1%
795 1
0.1%
789 1
0.1%
780 1
0.1%
764 1
0.1%
722 1
0.1%
681 1
0.1%

DLVR_POSBL_AT
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1762 
True
238 
ValueCountFrequency (%)
False 1762
88.1%
True 238
 
11.9%
2023-12-10T18:39:26.932617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

PACKNG_POSBL_AT
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1273 
True
727 
ValueCountFrequency (%)
False 1273
63.6%
True 727
36.4%
2023-12-10T18:39:27.115675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

MICHELIN_RSTRNT_CN
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1999 
True
 
1
ValueCountFrequency (%)
False 1999
> 99.9%
True 1
 
0.1%
2023-12-10T18:39:27.396410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

MEISHILIN_RSTRNT_CN
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
2000 
ValueCountFrequency (%)
False 2000
100.0%
2023-12-10T18:39:27.564886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FGGG_MENU_INFO_AT
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1947 
True
 
53
ValueCountFrequency (%)
False 1947
97.4%
True 53
 
2.6%
2023-12-10T18:39:27.734773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-10T18:39:23.087032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:22.741293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:23.254962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:22.921171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:39:27.881138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDMULTILINGUAL_MENU_INFO_PROVD_ATSAFETY_RSTRNT_ATPCRNGE_FLAG_CDPARKNG_POSBL_ATDSPSN_FCLTY_ATREVIEW_CODLVR_POSBL_ATPACKNG_POSBL_ATMICHELIN_RSTRNT_CNFGGG_MENU_INFO_AT
RSTRNT_ID1.0000.3420.4450.1080.0080.0500.2850.0910.0970.0000.342
MULTILINGUAL_MENU_INFO_PROVD_AT0.3421.0000.3270.0540.0410.0000.3520.0460.0000.0971.000
SAFETY_RSTRNT_AT0.4450.3271.0000.1410.0000.0000.3460.1050.0940.0220.327
PCRNGE_FLAG_CD0.1080.0540.1411.0000.2780.0840.2040.1410.3170.0000.054
PARKNG_POSBL_AT0.0080.0410.0000.2781.0000.2210.1840.1430.6380.0000.041
DSPSN_FCLTY_AT0.0500.0000.0000.0840.2211.0000.1040.0000.1370.0000.000
REVIEW_CO0.2850.3520.3460.2040.1840.1041.0000.0000.0820.0910.352
DLVR_POSBL_AT0.0910.0460.1050.1410.1430.0000.0001.0000.6090.0000.046
PACKNG_POSBL_AT0.0970.0000.0940.3170.6380.1370.0820.6091.0000.0000.000
MICHELIN_RSTRNT_CN0.0000.0970.0220.0000.0000.0000.0910.0000.0001.0000.097
FGGG_MENU_INFO_AT0.3421.0000.3270.0540.0410.0000.3520.0460.0000.0971.000
2023-12-10T18:39:28.135212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MICHELIN_RSTRNT_CNSAFETY_RSTRNT_ATDLVR_POSBL_ATDSPSN_FCLTY_ATMULTILINGUAL_MENU_INFO_PROVD_ATPARKNG_POSBL_ATPACKNG_POSBL_ATPCRNGE_FLAG_CDFGGG_MENU_INFO_AT
MICHELIN_RSTRNT_CN1.0000.0140.0000.0000.0620.0000.0000.0000.062
SAFETY_RSTRNT_AT0.0141.0000.0670.0000.2120.0000.0600.1510.212
DLVR_POSBL_AT0.0000.0671.0000.0000.0290.0910.4170.1510.029
DSPSN_FCLTY_AT0.0000.0000.0001.0000.0000.1420.0880.0900.000
MULTILINGUAL_MENU_INFO_PROVD_AT0.0620.2120.0290.0001.0000.0260.0000.0580.990
PARKNG_POSBL_AT0.0000.0000.0910.1420.0261.0000.4400.2970.026
PACKNG_POSBL_AT0.0000.0600.4170.0880.0000.4401.0000.3390.000
PCRNGE_FLAG_CD0.0000.1510.1510.0900.0580.2970.3391.0000.058
FGGG_MENU_INFO_AT0.0620.2120.0290.0000.9900.0260.0000.0581.000
2023-12-10T18:39:28.358548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDREVIEW_COMULTILINGUAL_MENU_INFO_PROVD_ATSAFETY_RSTRNT_ATPCRNGE_FLAG_CDPARKNG_POSBL_ATDSPSN_FCLTY_ATDLVR_POSBL_ATPACKNG_POSBL_ATMICHELIN_RSTRNT_CNFGGG_MENU_INFO_AT
RSTRNT_ID1.000-0.2040.2620.3420.0540.0060.0390.0700.0750.0000.262
REVIEW_CO-0.2041.0000.2690.2650.1040.1410.0800.0000.0620.0690.269
MULTILINGUAL_MENU_INFO_PROVD_AT0.2620.2691.0000.2120.0580.0260.0000.0290.0000.0620.990
SAFETY_RSTRNT_AT0.3420.2650.2121.0000.1510.0000.0000.0670.0600.0140.212
PCRNGE_FLAG_CD0.0540.1040.0580.1511.0000.2970.0900.1510.3390.0000.058
PARKNG_POSBL_AT0.0060.1410.0260.0000.2971.0000.1420.0910.4400.0000.026
DSPSN_FCLTY_AT0.0390.0800.0000.0000.0900.1421.0000.0000.0880.0000.000
DLVR_POSBL_AT0.0700.0000.0290.0670.1510.0910.0001.0000.4170.0000.029
PACKNG_POSBL_AT0.0750.0620.0000.0600.3390.4400.0880.4171.0000.0000.000
MICHELIN_RSTRNT_CN0.0000.0690.0620.0140.0000.0000.0000.0000.0001.0000.062
FGGG_MENU_INFO_AT0.2620.2690.9900.2120.0580.0260.0000.0290.0000.0621.000

Missing values

2023-12-10T18:39:23.769998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:39:24.116562image/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

RSTRNT_IDMULTILINGUAL_MENU_INFO_PROVD_ATSAFETY_RSTRNT_ATPCRNGE_FLAG_CDPARKNG_POSBL_ATDSPSN_FCLTY_ATREVIEW_CODLVR_POSBL_ATPACKNG_POSBL_ATMICHELIN_RSTRNT_CNMEISHILIN_RSTRNT_CNFGGG_MENU_INFO_AT
017NN2만원 대NN1NNNNN
121NN5만원 대NN38NNNNN
233NN99999NN167NNNNN
335NN99999NN18NNNNN
451NN99999YN764NNNNN
554NN99999YN24NYNNN
683NN5만원 대NN559NNNNN
790NN99999NN17NNNNN
892NN99999NN23NNNNN
994NN2만원 대YN355NYNNN
RSTRNT_IDMULTILINGUAL_MENU_INFO_PROVD_ATSAFETY_RSTRNT_ATPCRNGE_FLAG_CDPARKNG_POSBL_ATDSPSN_FCLTY_ATREVIEW_CODLVR_POSBL_ATPACKNG_POSBL_ATMICHELIN_RSTRNT_CNMEISHILIN_RSTRNT_CNFGGG_MENU_INFO_AT
199057989NN2만원 대YN91NNNNN
199158029NN1만원 대YN2NYNNN
199258052NN2만원 대YN1YYNNN
199358076NN99999NN1NNNNN
199458108NN1만원 대NN3NNNNN
199558116NN2만원 대YN13NYNNN
199658239NN99999NN8NYNNN
199758278NN5만원 대NN32NNNNN
199858305NN99999NN5NNNNN
199958328NN99999NN0NNNNN