Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory43.3 B

Variable types

Categorical2
Text1
Numeric2

Alerts

place_nm is highly overall correlated with base_quarterHigh correlation
base_quarter is highly overall correlated with place_nmHigh correlation
card_utiliiza_price is highly overall correlated with card_utiliiza_cas_coHigh correlation
card_utiliiza_cas_co is highly overall correlated with card_utiliiza_priceHigh correlation
place_nm is highly imbalanced (80.6%)Imbalance

Reproduction

Analysis started2023-12-10 09:43:17.222230
Analysis finished2023-12-10 09:43:18.869251
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

place_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
BIFF광장&용두산공원&보수동책방골목
97 
흰여울문화마을
 
3

Length

Max length20
Median length20
Mean length19.61
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBIFF광장&용두산공원&보수동책방골목
2nd row흰여울문화마을
3rd rowBIFF광장&용두산공원&보수동책방골목
4th rowBIFF광장&용두산공원&보수동책방골목
5th rowBIFF광장&용두산공원&보수동책방골목

Common Values

ValueCountFrequency (%)
BIFF광장&용두산공원&보수동책방골목 97
97.0%
흰여울문화마을 3
 
3.0%

Length

2023-12-10T18:43:19.005111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:19.210851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
biff광장&용두산공원&보수동책방골목 97
97.0%
흰여울문화마을 3
 
3.0%

country_nm
Text

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:19.622421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length3.66
Min length1

Characters and Unicode

Total characters366
Distinct characters114
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)43.0%

Sample

1st row일본
2nd row프랑스
3rd row중국
4th row미국
5th row태국
ValueCountFrequency (%)
프랑스 3
 
3.0%
핀란드 3
 
3.0%
인도네시아 3
 
3.0%
스페인 2
 
2.0%
헝가리 2
 
2.0%
스웨덴 2
 
2.0%
오스트리아 2
 
2.0%
아일랜드 2
 
2.0%
브라질 2
 
2.0%
체코 2
 
2.0%
Other values (60) 77
77.0%
2023-12-10T18:43:20.434458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 28
 
7.7%
스 23
 
6.3%
리 15
 
4.1%
이 12
 
3.3%
국 9
 
2.5%
드 9
 
2.5%
레 8
 
2.2%
라 8
 
2.2%
트 8
 
2.2%
일 8
 
2.2%
Other values (104) 238
65.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 366
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 28
 
7.7%
스 23
 
6.3%
리 15
 
4.1%
이 12
 
3.3%
국 9
 
2.5%
드 9
 
2.5%
레 8
 
2.2%
라 8
 
2.2%
트 8
 
2.2%
일 8
 
2.2%
Other values (104) 238
65.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 366
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 28
 
7.7%
스 23
 
6.3%
리 15
 
4.1%
이 12
 
3.3%
국 9
 
2.5%
드 9
 
2.5%
레 8
 
2.2%
라 8
 
2.2%
트 8
 
2.2%
일 8
 
2.2%
Other values (104) 238
65.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 366
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 28
 
7.7%
스 23
 
6.3%
리 15
 
4.1%
이 12
 
3.3%
국 9
 
2.5%
드 9
 
2.5%
레 8
 
2.2%
라 8
 
2.2%
트 8
 
2.2%
일 8
 
2.2%
Other values (104) 238
65.0%

card_utiliiza_price
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4744716.7
Minimum5000
Maximum2.2649 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:20.822335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile12140
Q186300
median200650
Q3930872.5
95-th percentile14228899
Maximum2.2649 × 108
Range2.26485 × 108
Interquartile range (IQR)844572.5

Descriptive statistics

Standard deviation23714479
Coefficient of variation (CV)4.998081
Kurtosis79.282379
Mean4744716.7
Median Absolute Deviation (MAD)164100
Skewness8.5830026
Sum4.7447167 × 108
Variance5.623765 × 1014
MonotonicityNot monotonic
2023-12-10T18:43:21.159638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 2
 
2.0%
226489999 1
 
1.0%
12800 1
 
1.0%
2199950 1
 
1.0%
2523362 1
 
1.0%
3252310 1
 
1.0%
7620500 1
 
1.0%
9149345 1
 
1.0%
10724112 1
 
1.0%
7000 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
5000 1
1.0%
7000 1
1.0%
8000 2
2.0%
11000 1
1.0%
12200 1
1.0%
12500 1
1.0%
12800 1
1.0%
14350 1
1.0%
14740 1
1.0%
20000 1
1.0%
ValueCountFrequency (%)
226489999 1
1.0%
53957560 1
1.0%
48896670 1
1.0%
16107500 1
1.0%
15292413 1
1.0%
14172925 1
1.0%
12343320 1
1.0%
10724112 1
1.0%
9202976 1
1.0%
9149345 1
1.0%

card_utiliiza_cas_co
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.24
Minimum1
Maximum5228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:21.454944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q328
95-th percentile338.55
Maximum5228
Range5227
Interquartile range (IQR)24

Descriptive statistics

Standard deviation548.66967
Coefficient of variation (CV)4.7611044
Kurtosis78.277077
Mean115.24
Median Absolute Deviation (MAD)7
Skewness8.5229771
Sum11524
Variance301038.41
MonotonicityNot monotonic
2023-12-10T18:43:21.806768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 10
 
10.0%
2 9
 
9.0%
5 7
 
7.0%
4 5
 
5.0%
6 5
 
5.0%
3 5
 
5.0%
8 5
 
5.0%
14 4
 
4.0%
10 4
 
4.0%
9 3
 
3.0%
Other values (35) 43
43.0%
ValueCountFrequency (%)
1 10
10.0%
2 9
9.0%
3 5
5.0%
4 5
5.0%
5 7
7.0%
6 5
5.0%
7 2
 
2.0%
8 5
5.0%
9 3
 
3.0%
10 4
 
4.0%
ValueCountFrequency (%)
5228 1
1.0%
1477 1
1.0%
864 1
1.0%
419 1
1.0%
406 1
1.0%
335 1
1.0%
330 1
1.0%
293 1
1.0%
251 1
1.0%
224 1
1.0%

base_quarter
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020년 1분기
66 
2020년 2분기
31 
2021년 2분기
 
3

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020년 1분기
2nd row2021년 2분기
3rd row2020년 1분기
4th row2020년 1분기
5th row2020년 1분기

Common Values

ValueCountFrequency (%)
2020년 1분기 66
66.0%
2020년 2분기 31
31.0%
2021년 2분기 3
 
3.0%

Length

2023-12-10T18:43:22.024445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:22.210503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020년 97
48.5%
1분기 66
33.0%
2분기 34
 
17.0%
2021년 3
 
1.5%

Interactions

2023-12-10T18:43:17.997055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:17.594936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:18.177423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:17.779007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:43:22.361232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
place_nmcountry_nmcard_utiliiza_pricecard_utiliiza_cas_cobase_quarter
place_nm1.0000.0000.0000.0001.000
country_nm0.0001.0000.0000.0000.000
card_utiliiza_price0.0000.0001.0001.0000.000
card_utiliiza_cas_co0.0000.0001.0001.0000.000
base_quarter1.0000.0000.0000.0001.000
2023-12-10T18:43:22.564976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
place_nmbase_quarter
place_nm1.0000.995
base_quarter0.9951.000
2023-12-10T18:43:22.744859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
card_utiliiza_pricecard_utiliiza_cas_coplace_nmbase_quarter
card_utiliiza_price1.0000.8960.0000.000
card_utiliiza_cas_co0.8961.0000.0000.000
place_nm0.0000.0001.0000.995
base_quarter0.0000.0000.9951.000

Missing values

2023-12-10T18:43:18.593387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:43:18.799898image/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

place_nmcountry_nmcard_utiliiza_pricecard_utiliiza_cas_cobase_quarter
0BIFF광장&용두산공원&보수동책방골목일본22648999952282020년 1분기
1흰여울문화마을프랑스119700112021년 2분기
2BIFF광장&용두산공원&보수동책방골목중국539575608642020년 1분기
3BIFF광장&용두산공원&보수동책방골목미국4889667014772020년 1분기
4BIFF광장&용두산공원&보수동책방골목태국161075003352020년 1분기
5BIFF광장&용두산공원&보수동책방골목홍콩152924132512020년 1분기
6BIFF광장&용두산공원&보수동책방골목말레이지아141729252932020년 1분기
7흰여울문화마을핀란드1250012021년 2분기
8BIFF광장&용두산공원&보수동책방골목싱가폴123433202242020년 1분기
9BIFF광장&용두산공원&보수동책방골목으스트레일리아92029763302020년 1분기
place_nmcountry_nmcard_utiliiza_pricecard_utiliiza_cas_cobase_quarter
90BIFF광장&용두산공원&보수동책방골목홍콩14100062020년 2분기
91BIFF광장&용두산공원&보수동책방골목핀란드133200142020년 2분기
92BIFF광장&용두산공원&보수동책방골목카자흐스탄10810042020년 2분기
93BIFF광장&용두산공원&보수동책방골목칠레10540052020년 2분기
94BIFF광장&용두산공원&보수동책방골목브라질9790042020년 2분기
95BIFF광장&용두산공원&보수동책방골목싸이프러스8715032020년 2분기
96BIFF광장&용두산공원&보수동책방골목우크라이나87100102020년 2분기
97BIFF광장&용두산공원&보수동책방골목오스트리아8680032020년 2분기
98BIFF광장&용두산공원&보수동책방골목체코7550082020년 2분기
99BIFF광장&용두산공원&보수동책방골목필리핀7472052020년 2분기