Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory76.3 B

Variable types

Numeric3
Categorical6

Alerts

goods_ty_nm has constant value ""Constant
crtfc_str_nm is highly overall correlated with seq_no and 6 other fieldsHigh correlation
brand_nm is highly overall correlated with seq_no and 6 other fieldsHigh correlation
crtfc_occrrnc_addr is highly overall correlated with seq_no and 6 other fieldsHigh correlation
goods_nm is highly overall correlated with seq_no and 6 other fieldsHigh correlation
crtfc_occrrnc_gugun_klang_nm is highly overall correlated with seq_no and 6 other fieldsHigh correlation
seq_no is highly overall correlated with str_visit_dt and 5 other fieldsHigh correlation
goods_online_sle_dt is highly overall correlated with crtfc_occrrnc_addr and 4 other fieldsHigh correlation
str_visit_dt is highly overall correlated with seq_no and 5 other fieldsHigh correlation
crtfc_occrrnc_addr is highly imbalanced (86.0%)Imbalance
crtfc_occrrnc_gugun_klang_nm is highly imbalanced (86.0%)Imbalance
goods_nm is highly imbalanced (61.3%)Imbalance
crtfc_str_nm is highly imbalanced (86.0%)Imbalance
brand_nm is highly imbalanced (80.6%)Imbalance
seq_no has unique valuesUnique
str_visit_dt has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:08:44.047061
Analysis finished2023-12-10 10:08:46.996342
Duration2.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.88
Minimum1
Maximum13061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:47.149734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum13061
Range13060
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation2230.4193
Coefficient of variation (CV)5.047568
Kurtosis29.887264
Mean441.88
Median Absolute Deviation (MAD)25.5
Skewness5.5932229
Sum44188
Variance4974770.4
MonotonicityNot monotonic
2023-12-10T19:08:47.459165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
13061 1
1.0%
13060 1
1.0%
13059 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

crtfc_occrrnc_addr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
東京都文京区水道1-3-3 2F
97 
神戸市兵庫区湊町1-1-17
 
2
神戸市灘区備後町3-7
 
1

Length

Max length16
Median length16
Mean length15.91
Min length11

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row東京都文京区水道1-3-3 2F
2nd row神戸市灘区備後町3-7
3rd row東京都文京区水道1-3-3 2F
4th row東京都文京区水道1-3-3 2F
5th row東京都文京区水道1-3-3 2F

Common Values

ValueCountFrequency (%)
東京都文京区水道1-3-3 2F 97
97.0%
神戸市兵庫区湊町1-1-17 2
 
2.0%
神戸市灘区備後町3-7 1
 
1.0%

Length

2023-12-10T19:08:47.784299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:47.993878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
東京都文京区水道1-3-3 97
49.2%
2f 97
49.2%
神戸市兵庫区湊町1-1-17 2
 
1.0%
神戸市灘区備後町3-7 1
 
0.5%

crtfc_occrrnc_gugun_klang_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
도쿄도 분쿄구
97 
고베시 효고구
 
2
고베시 나다구
 
1

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row도쿄도 분쿄구
2nd row고베시 나다구
3rd row도쿄도 분쿄구
4th row도쿄도 분쿄구
5th row도쿄도 분쿄구

Common Values

ValueCountFrequency (%)
도쿄도 분쿄구 97
97.0%
고베시 효고구 2
 
2.0%
고베시 나다구 1
 
1.0%

Length

2023-12-10T19:08:48.170469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:48.354015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도쿄도 97
48.5%
분쿄구 97
48.5%
고베시 3
 
1.5%
효고구 2
 
1.0%
나다구 1
 
0.5%

goods_ty_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
回数券・セット券
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row回数券・セット券
2nd row回数券・セット券
3rd row回数券・セット券
4th row回数券・セット券
5th row回数券・セット券

Common Values

ValueCountFrequency (%)
回数券・セット券 100
100.0%

Length

2023-12-10T19:08:48.609514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:48.769864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
回数券・セット券 100
100.0%

goods_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
【Ticket A】コーヒー・紅茶
84 
【Ticket B】お好きなドリンク
13 
入浴助成チケット（大人）
 
2
入浴助成チケット（幼児）
 
1

Length

Max length18
Median length17
Mean length16.98
Min length12

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row【Ticket B】お好きなドリンク
2nd row入浴助成チケット（大人）
3rd row【Ticket A】コーヒー・紅茶
4th row【Ticket A】コーヒー・紅茶
5th row【Ticket B】お好きなドリンク

Common Values

ValueCountFrequency (%)
【Ticket A】コーヒー・紅茶 84
84.0%
【Ticket B】お好きなドリンク 13
 
13.0%
入浴助成チケット（大人） 2
 
2.0%
入浴助成チケット（幼児） 1
 
1.0%

Length

2023-12-10T19:08:48.943449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:49.138186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
【ticket 97
49.2%
a】コーヒー・紅茶 84
42.6%
b】お好きなドリンク 13
 
6.6%
入浴助成チケット（大人） 2
 
1.0%
入浴助成チケット（幼児） 1
 
0.5%

crtfc_str_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
小石川テラス
97 
湊湯
 
2
灘温泉　六甲道店
 
1

Length

Max length8
Median length6
Mean length5.94
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row小石川テラス
2nd row灘温泉　六甲道店
3rd row小石川テラス
4th row小石川テラス
5th row小石川テラス

Common Values

ValueCountFrequency (%)
小石川テラス 97
97.0%
湊湯 2
 
2.0%
灘温泉　六甲道店 1
 
1.0%

Length

2023-12-10T19:08:49.330983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:49.479627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
小石川テラス 97
96.0%
湊湯 2
 
2.0%
灘温泉 1
 
1.0%
六甲道店 1
 
1.0%

goods_online_sle_dt
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0191303 × 1013
Minimum2.0190703 × 1013
Maximum2.0210726 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:49.679828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190703 × 1013
5-th percentile2.0190703 × 1013
Q12.0190703 × 1013
median2.0190703 × 1013
Q32.0190703 × 1013
95-th percentile2.0190703 × 1013
Maximum2.0210726 × 1013
Range2.0023061 × 1010
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation3.427125 × 109
Coefficient of variation (CV)0.00016973273
Kurtosis29.898581
Mean2.0191303 × 1013
Median Absolute Deviation (MAD)8.5
Skewness5.5947016
Sum2.0191303 × 1015
Variance1.1745186 × 1019
MonotonicityNot monotonic
2023-12-10T19:08:49.942569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
20190703091830 8
 
8.0%
20190703091828 5
 
5.0%
20190703091832 5
 
5.0%
20190703091821 5
 
5.0%
20190703091836 5
 
5.0%
20190703091835 4
 
4.0%
20190703091806 4
 
4.0%
20190703091820 4
 
4.0%
20190703091811 4
 
4.0%
20190703091803 4
 
4.0%
Other values (23) 52
52.0%
ValueCountFrequency (%)
20190703091803 4
4.0%
20190703091804 3
3.0%
20190703091805 1
 
1.0%
20190703091806 4
4.0%
20190703091810 3
3.0%
20190703091811 4
4.0%
20190703091812 3
3.0%
20190703091813 3
3.0%
20190703091814 3
3.0%
20190703091816 2
2.0%
ValueCountFrequency (%)
20210726152624 1
 
1.0%
20210726151229 1
 
1.0%
20210625174151 1
 
1.0%
20190703091837 2
 
2.0%
20190703091836 5
5.0%
20190703091835 4
4.0%
20190703091834 1
 
1.0%
20190703091833 4
4.0%
20190703091832 5
5.0%
20190703091831 2
 
2.0%

str_visit_dt
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0191311 × 1013
Minimum2.0190705 × 1013
Maximum2.0210901 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:50.232423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190705 × 1013
5-th percentile2.0190705 × 1013
Q12.0190705 × 1013
median2.0190705 × 1013
Q32.0190705 × 1013
95-th percentile2.0190708 × 1013
Maximum2.0210901 × 1013
Range2.0196047 × 1010
Interquartile range (IQR)14418.25

Descriptive statistics

Standard deviation3.4584408 × 109
Coefficient of variation (CV)0.00017128362
Kurtosis29.898146
Mean2.0191311 × 1013
Median Absolute Deviation (MAD)7787.5
Skewness5.5946729
Sum2.0191311 × 1015
Variance1.1960813 × 1019
MonotonicityNot monotonic
2023-12-10T19:08:50.737706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190705082711 1
 
1.0%
20190705101024 1
 
1.0%
20190705103446 1
 
1.0%
20190705102818 1
 
1.0%
20190705102655 1
 
1.0%
20190705101904 1
 
1.0%
20190705101827 1
 
1.0%
20190705101806 1
 
1.0%
20190705101758 1
 
1.0%
20190705101739 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
20190705082711 1
1.0%
20190705082815 1
1.0%
20190705083022 1
1.0%
20190705083103 1
1.0%
20190705083133 1
1.0%
20190705083207 1
1.0%
20190705083942 1
1.0%
20190705084033 1
1.0%
20190705084351 1
1.0%
20190705084817 1
1.0%
ValueCountFrequency (%)
20210901130149 1
1.0%
20210901130133 1
1.0%
20210831221735 1
1.0%
20190708090633 1
1.0%
20190708090615 1
1.0%
20190708090601 1
1.0%
20190708090530 1
1.0%
20190708090259 1
1.0%
20190708085848 1
1.0%
20190708085832 1
1.0%

brand_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
小石川テラス
97 
銭湯利用促進
 
3

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row小石川テラス
2nd row銭湯利用促進
3rd row小石川テラス
4th row小石川テラス
5th row小石川テラス

Common Values

ValueCountFrequency (%)
小石川テラス 97
97.0%
銭湯利用促進 3
 
3.0%

Length

2023-12-10T19:08:51.156820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:51.419460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
小石川テラス 97
97.0%
銭湯利用促進 3
 
3.0%

Interactions

2023-12-10T19:08:46.020693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:44.762263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.263799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:46.185213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:44.907856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.553837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:46.386256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.089804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.822654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:08:51.607126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nocrtfc_occrrnc_addrcrtfc_occrrnc_gugun_klang_nmgoods_nmcrtfc_str_nmgoods_online_sle_dtstr_visit_dtbrand_nm
seq_no1.0001.0001.0001.0001.0000.9630.9630.963
crtfc_occrrnc_addr1.0001.0001.0000.7371.0001.0001.0001.000
crtfc_occrrnc_gugun_klang_nm1.0001.0001.0000.7371.0001.0001.0001.000
goods_nm1.0000.7370.7371.0000.7371.0001.0001.000
crtfc_str_nm1.0001.0001.0000.7371.0001.0001.0001.000
goods_online_sle_dt0.9631.0001.0001.0001.0001.0000.9630.963
str_visit_dt0.9631.0001.0001.0001.0000.9631.0000.963
brand_nm0.9631.0001.0001.0001.0000.9630.9631.000
2023-12-10T19:08:51.928839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
crtfc_str_nmbrand_nmcrtfc_occrrnc_addrgoods_nmcrtfc_occrrnc_gugun_klang_nm
crtfc_str_nm1.0000.9951.0000.7791.000
brand_nm0.9951.0000.9950.9900.995
crtfc_occrrnc_addr1.0000.9951.0000.7791.000
goods_nm0.7790.9900.7791.0000.779
crtfc_occrrnc_gugun_klang_nm1.0000.9951.0000.7791.000
2023-12-10T19:08:52.199985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nogoods_online_sle_dtstr_visit_dtcrtfc_occrrnc_addrcrtfc_occrrnc_gugun_klang_nmgoods_nmcrtfc_str_nmbrand_nm
seq_no1.0000.0731.0000.9950.9950.9900.9950.826
goods_online_sle_dt0.0731.0000.0730.9950.9950.9900.9950.826
str_visit_dt1.0000.0731.0000.9950.9950.9900.9950.826
crtfc_occrrnc_addr0.9950.9950.9951.0001.0000.7791.0000.995
crtfc_occrrnc_gugun_klang_nm0.9950.9950.9951.0001.0000.7791.0000.995
goods_nm0.9900.9900.9900.7790.7791.0000.7790.990
crtfc_str_nm0.9950.9950.9951.0001.0000.7791.0000.995
brand_nm0.8260.8260.8260.9950.9950.9900.9951.000

Missing values

2023-12-10T19:08:46.623702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:08:46.895304image/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

seq_nocrtfc_occrrnc_addrcrtfc_occrrnc_gugun_klang_nmgoods_ty_nmgoods_nmcrtfc_str_nmgoods_online_sle_dtstr_visit_dtbrand_nm
01東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket B】お好きなドリンク小石川テラス2019070309183020190705082711小石川テラス
113059神戸市灘区備後町3-7고베시 나다구回数券・セット券入浴助成チケット（大人）灘温泉　六甲道店2021062517415120210831221735銭湯利用促進
23東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309182920190705082815小石川テラス
34東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309181820190705083022小石川テラス
45東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket B】お好きなドリンク小石川テラス2019070309183220190705083103小石川テラス
56東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309181320190705083133小石川テラス
67東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309182120190705083207小石川テラス
713060神戸市兵庫区湊町1-1-17고베시 효고구回数券・セット券入浴助成チケット（幼児）湊湯2021072615262420210901130133銭湯利用促進
89東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309182220190705083942小石川テラス
910東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309181720190705084033小石川テラス
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9091東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309181020190708085602小石川テラス
9192東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309183320190708085622小石川テラス
9293東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309180320190708085736小石川テラス
9394東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309182120190708085832小石川テラス
9495東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309181120190708085848小石川テラス
9596東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309182420190708090259小石川テラス
9697東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309181820190708090530小石川テラス
9798東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309180620190708090601小石川テラス
9899東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket B】お好きなドリンク小石川テラス2019070309183320190708090615小石川テラス
99100東京都文京区水道1-3-3 2F도쿄도 분쿄구回数券・セット券【Ticket A】コーヒー・紅茶小石川テラス2019070309182820190708090633小石川テラス