Overview

Dataset statistics

Number of variables16
Number of observations85
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 KiB
Average record size in memory135.6 B

Variable types

Categorical7
Numeric5
Text4

Alerts

FILE_NAME has constant value ""Constant
base_ymd has constant value ""Constant
sale_ratio is highly overall correlated with sales_amtHigh correlation
sales_amt is highly overall correlated with sale_ratio and 1 other fieldsHigh correlation
sample_cnt is highly overall correlated with to_watch and 1 other fieldsHigh correlation
to_watch is highly overall correlated with sample_cntHigh correlation
sa_2 is highly overall correlated with sa_3High correlation
sa_3 is highly overall correlated with sales_amt and 2 other fieldsHigh correlation
sa_1 is highly imbalanced (76.9%)Imbalance
sa_2 is highly imbalanced (70.2%)Imbalance
sale_ratio has 8 (9.4%) zerosZeros
sales_amt has 8 (9.4%) zerosZeros

Reproduction

Analysis started2023-12-10 10:05:53.584148
Analysis finished2023-12-10 10:06:00.441993
Duration6.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

region
Categorical

Distinct17
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
인천
 
5
울산
 
5
경기
 
5
제주
 
5
서울
 
5
Other values (12)
60 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천
2nd row울산
3rd row경기
4th row제주
5th row인천

Common Values

ValueCountFrequency (%)
인천 5
 
5.9%
울산 5
 
5.9%
경기 5
 
5.9%
제주 5
 
5.9%
서울 5
 
5.9%
부산 5
 
5.9%
대구 5
 
5.9%
광주 5
 
5.9%
강원 5
 
5.9%
전북 5
 
5.9%
Other values (7) 35
41.2%

Length

2023-12-10T19:06:00.617912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천 5
 
5.9%
전북 5
 
5.9%
전남 5
 
5.9%
충남 5
 
5.9%
충북 5
 
5.9%
세종 5
 
5.9%
대전 5
 
5.9%
경북 5
 
5.9%
강원 5
 
5.9%
울산 5
 
5.9%
Other values (7) 35
41.2%

gubun
Categorical

Distinct5
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
서양음악
17 
전통예술(국악)
17 
연극
17 
뮤지컬
17 
무용
17 

Length

Max length8
Median length4
Mean length3.8
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서양음악
2nd row서양음악
3rd row서양음악
4th row서양음악
5th row전통예술(국악)

Common Values

ValueCountFrequency (%)
서양음악 17
20.0%
전통예술(국악) 17
20.0%
연극 17
20.0%
뮤지컬 17
20.0%
무용 17
20.0%

Length

2023-12-10T19:06:00.840122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:01.131602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서양음악 17
20.0%
전통예술(국악 17
20.0%
연극 17
20.0%
뮤지컬 17
20.0%
무용 17
20.0%

sale_ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.390588
Minimum0
Maximum87.3
Zeros8
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T19:06:01.463448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.2
median9
Q320.65
95-th percentile77.16
Maximum87.3
Range87.3
Interquartile range (IQR)17.45

Descriptive statistics

Standard deviation22.592176
Coefficient of variation (CV)1.2991036
Kurtosis2.5812399
Mean17.390588
Median Absolute Deviation (MAD)7.5
Skewness1.8419689
Sum1478.2
Variance510.4064
MonotonicityNot monotonic
2023-12-10T19:06:01.725580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
9.4%
3.6 3
 
3.5%
5.9 2
 
2.4%
4.0 2
 
2.4%
0.3 2
 
2.4%
2.3 2
 
2.4%
9.0 2
 
2.4%
62.0 1
 
1.2%
52.7 1
 
1.2%
35.7 1
 
1.2%
Other values (61) 61
71.8%
ValueCountFrequency (%)
0.0 8
9.4%
0.3 2
 
2.4%
0.4 1
 
1.2%
0.5 1
 
1.2%
0.8 1
 
1.2%
0.9 1
 
1.2%
1.0 1
 
1.2%
1.6 1
 
1.2%
2.0 1
 
1.2%
2.3 2
 
2.4%
ValueCountFrequency (%)
87.3 1
1.2%
86.6 1
1.2%
83.3 1
1.2%
80.0 1
1.2%
79.6 1
1.2%
67.4 1
1.2%
62.0 1
1.2%
58.8 1
1.2%
52.7 1
1.2%
51.8 1
1.2%

sales_amt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96670872
Minimum0
Maximum3.2548327 × 109
Zeros8
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T19:06:01.992572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1731883
median5099175
Q313585880
95-th percentile2.3499064 × 108
Maximum3.2548327 × 109
Range3.2548327 × 109
Interquartile range (IQR)12853997

Descriptive statistics

Standard deviation4.2889182 × 108
Coefficient of variation (CV)4.4366189
Kurtosis38.765368
Mean96670872
Median Absolute Deviation (MAD)4774601
Skewness5.9866239
Sum8.2170242 × 109
Variance1.8394819 × 1017
MonotonicityNot monotonic
2023-12-10T19:06:02.271363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
9.4%
452373 2
 
2.4%
6547531 1
 
1.2%
1013672 1
 
1.2%
4441840 1
 
1.2%
6478025 1
 
1.2%
3093571 1
 
1.2%
2630356 1
 
1.2%
1382541 1
 
1.2%
68838507 1
 
1.2%
Other values (67) 67
78.8%
ValueCountFrequency (%)
0 8
9.4%
69220 1
 
1.2%
92152 1
 
1.2%
184586 1
 
1.2%
222092 1
 
1.2%
296698 1
 
1.2%
324574 1
 
1.2%
334037 1
 
1.2%
352546 1
 
1.2%
360320 1
 
1.2%
ValueCountFrequency (%)
3254832724 1
1.2%
1775363304 1
1.2%
1449880032 1
1.2%
532608991 1
1.2%
266304496 1
1.2%
109735192 1
1.2%
95605013 1
1.2%
68838507 1
1.2%
59098578 1
1.2%
49359652 1
1.2%

sample_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.082353
Minimum2
Maximum278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T19:06:02.544874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q114
median28
Q354
95-th percentile156
Maximum278
Range276
Interquartile range (IQR)40

Descriptive statistics

Standard deviation56.795836
Coefficient of variation (CV)1.2063084
Kurtosis6.8127509
Mean47.082353
Median Absolute Deviation (MAD)18
Skewness2.5524673
Sum4002
Variance3225.7669
MonotonicityNot monotonic
2023-12-10T19:06:02.799207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4
 
4.7%
22 4
 
4.7%
11 4
 
4.7%
8 3
 
3.5%
14 3
 
3.5%
27 3
 
3.5%
39 3
 
3.5%
29 3
 
3.5%
32 3
 
3.5%
3 2
 
2.4%
Other values (43) 53
62.4%
ValueCountFrequency (%)
2 4
4.7%
3 2
2.4%
5 1
 
1.2%
6 2
2.4%
7 2
2.4%
8 3
3.5%
9 1
 
1.2%
11 4
4.7%
13 2
2.4%
14 3
3.5%
ValueCountFrequency (%)
278 1
1.2%
258 1
1.2%
243 1
1.2%
239 1
1.2%
159 1
1.2%
144 1
1.2%
135 1
1.2%
109 2
2.4%
107 1
1.2%
92 1
1.2%

sa_1
Categorical

IMBALANCE 

Distinct9
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size812.0 B
-
77 
2.2
 
1
0.5
 
1
9.1
 
1
2.3
 
1
Other values (4)
 
4

Length

Max length3
Median length1
Mean length1.1882353
Min length1

Unique

Unique8 ?
Unique (%)9.4%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 77
90.6%
2.2 1
 
1.2%
0.5 1
 
1.2%
9.1 1
 
1.2%
2.3 1
 
1.2%
2.4 1
 
1.2%
1.6 1
 
1.2%
3.9 1
 
1.2%
4.7 1
 
1.2%

Length

2023-12-10T19:06:03.046301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:03.258433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
77
90.6%
2.2 1
 
1.2%
0.5 1
 
1.2%
9.1 1
 
1.2%
2.3 1
 
1.2%
2.4 1
 
1.2%
1.6 1
 
1.2%
3.9 1
 
1.2%
4.7 1
 
1.2%

sa_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size812.0 B
-
73 
1.7
 
2
1.6
 
1
5.1
 
1
2.9
 
1
Other values (7)
 
7

Length

Max length3
Median length1
Mean length1.2588235
Min length1

Unique

Unique10 ?
Unique (%)11.8%

Sample

1st row-
2nd row-
3rd row1.6
4th row5.1
5th row-

Common Values

ValueCountFrequency (%)
- 73
85.9%
1.7 2
 
2.4%
1.6 1
 
1.2%
5.1 1
 
1.2%
2.9 1
 
1.2%
8.1 1
 
1.2%
1.2 1
 
1.2%
2.4 1
 
1.2%
0.4 1
 
1.2%
0.5 1
 
1.2%
Other values (2) 2
 
2.4%

Length

2023-12-10T19:06:03.495004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
73
85.9%
1.7 2
 
2.4%
1.6 1
 
1.2%
5.1 1
 
1.2%
2.9 1
 
1.2%
8.1 1
 
1.2%
1.2 1
 
1.2%
2.4 1
 
1.2%
0.4 1
 
1.2%
0.5 1
 
1.2%
Other values (2) 2
 
2.4%

sa_3
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
-
52 
0.7
 
3
1
 
2
0.5
 
2
2.8
 
2
Other values (23)
24 

Length

Max length4
Median length1
Mean length1.7058824
Min length1

Unique

Unique22 ?
Unique (%)25.9%

Sample

1st row-
2nd row-
3rd row1.3
4th row6.5
5th row-

Common Values

ValueCountFrequency (%)
- 52
61.2%
0.7 3
 
3.5%
1 2
 
2.4%
0.5 2
 
2.4%
2.8 2
 
2.4%
0.3 2
 
2.4%
8.4 1
 
1.2%
6.5 1
 
1.2%
2.5 1
 
1.2%
2.9 1
 
1.2%
Other values (18) 18
 
21.2%

Length

2023-12-10T19:06:03.747805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
52
61.2%
0.7 3
 
3.5%
1 2
 
2.4%
0.5 2
 
2.4%
2.8 2
 
2.4%
0.3 2
 
2.4%
1.3 1
 
1.2%
0.2 1
 
1.2%
4 1
 
1.2%
4.7 1
 
1.2%
Other values (18) 18
 
21.2%

sa_4
Text

Distinct56
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
2023-12-10T19:06:04.025798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8352941
Min length1

Characters and Unicode

Total characters241
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)54.1%

Sample

1st row7.2
2nd row-
3rd row17.3
4th row11.9
5th row20.1
ValueCountFrequency (%)
18
 
21.2%
9.7 5
 
5.9%
7.2 2
 
2.4%
6 2
 
2.4%
11.3 2
 
2.4%
16.2 2
 
2.4%
5.2 2
 
2.4%
5.5 2
 
2.4%
11.9 2
 
2.4%
13.5 2
 
2.4%
Other values (46) 46
54.1%
2023-12-10T19:06:04.998654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 62
25.7%
1 35
14.5%
2 26
10.8%
- 18
 
7.5%
6 15
 
6.2%
5 15
 
6.2%
9 14
 
5.8%
3 14
 
5.8%
4 14
 
5.8%
7 11
 
4.6%
Other values (2) 17
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 161
66.8%
Other Punctuation 62
 
25.7%
Dash Punctuation 18
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35
21.7%
2 26
16.1%
6 15
9.3%
5 15
9.3%
9 14
 
8.7%
3 14
 
8.7%
4 14
 
8.7%
7 11
 
6.8%
8 11
 
6.8%
0 6
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 62
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 241
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 62
25.7%
1 35
14.5%
2 26
10.8%
- 18
 
7.5%
6 15
 
6.2%
5 15
 
6.2%
9 14
 
5.8%
3 14
 
5.8%
4 14
 
5.8%
7 11
 
4.6%
Other values (2) 17
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 62
25.7%
1 35
14.5%
2 26
10.8%
- 18
 
7.5%
6 15
 
6.2%
5 15
 
6.2%
9 14
 
5.8%
3 14
 
5.8%
4 14
 
5.8%
7 11
 
4.6%
Other values (2) 17
 
7.1%

sa_5
Text

Distinct78
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size812.0 B
2023-12-10T19:06:05.462614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6941176
Min length1

Characters and Unicode

Total characters314
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)83.5%

Sample

1st row39.3
2nd row38.8
3rd row41.6
4th row27.8
5th row42.3
ValueCountFrequency (%)
30.1 2
 
2.4%
14.1 2
 
2.4%
26.4 2
 
2.4%
38.3 2
 
2.4%
19.8 2
 
2.4%
2
 
2.4%
41.1 2
 
2.4%
29.3 1
 
1.2%
41.5 1
 
1.2%
52.6 1
 
1.2%
Other values (68) 68
80.0%
2023-12-10T19:06:06.160795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 76
24.2%
1 38
12.1%
3 36
11.5%
4 33
10.5%
2 31
9.9%
7 28
 
8.9%
8 21
 
6.7%
6 17
 
5.4%
9 15
 
4.8%
5 15
 
4.8%
Other values (2) 4
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236
75.2%
Other Punctuation 76
 
24.2%
Dash Punctuation 2
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38
16.1%
3 36
15.3%
4 33
14.0%
2 31
13.1%
7 28
11.9%
8 21
8.9%
6 17
7.2%
9 15
 
6.4%
5 15
 
6.4%
0 2
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 76
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 76
24.2%
1 38
12.1%
3 36
11.5%
4 33
10.5%
2 31
9.9%
7 28
 
8.9%
8 21
 
6.7%
6 17
 
5.4%
9 15
 
4.8%
5 15
 
4.8%
Other values (2) 4
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 76
24.2%
1 38
12.1%
3 36
11.5%
4 33
10.5%
2 31
9.9%
7 28
 
8.9%
8 21
 
6.7%
6 17
 
5.4%
9 15
 
4.8%
5 15
 
4.8%
Other values (2) 4
 
1.3%

sa_6
Text

Distinct77
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size812.0 B
2023-12-10T19:06:06.664851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6588235
Min length1

Characters and Unicode

Total characters311
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)82.4%

Sample

1st row24.4
2nd row61.2
3rd row36.4
4th row23.8
5th row30.6
ValueCountFrequency (%)
46.1 3
 
3.5%
33.9 2
 
2.4%
31.9 2
 
2.4%
27.1 2
 
2.4%
32.4 2
 
2.4%
27.8 2
 
2.4%
33 2
 
2.4%
45.6 1
 
1.2%
18.4 1
 
1.2%
82.3 1
 
1.2%
Other values (67) 67
78.8%
2023-12-10T19:06:07.360417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 72
23.2%
3 49
15.8%
4 37
11.9%
2 30
9.6%
6 27
 
8.7%
1 27
 
8.7%
9 20
 
6.4%
8 17
 
5.5%
7 15
 
4.8%
5 10
 
3.2%
Other values (2) 7
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 238
76.5%
Other Punctuation 72
 
23.2%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 49
20.6%
4 37
15.5%
2 30
12.6%
6 27
11.3%
1 27
11.3%
9 20
8.4%
8 17
 
7.1%
7 15
 
6.3%
5 10
 
4.2%
0 6
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 311
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 72
23.2%
3 49
15.8%
4 37
11.9%
2 30
9.6%
6 27
 
8.7%
1 27
 
8.7%
9 20
 
6.4%
8 17
 
5.5%
7 15
 
4.8%
5 10
 
3.2%
Other values (2) 7
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 72
23.2%
3 49
15.8%
4 37
11.9%
2 30
9.6%
6 27
 
8.7%
1 27
 
8.7%
9 20
 
6.4%
8 17
 
5.5%
7 15
 
4.8%
5 10
 
3.2%
Other values (2) 7
 
2.3%

sa_7
Text

Distinct71
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
2023-12-10T19:06:07.785238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.1411765
Min length1

Characters and Unicode

Total characters267
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)75.3%

Sample

1st row29
2nd row-
3rd row1.9
4th row24.9
5th row7
ValueCountFrequency (%)
9
 
10.6%
7 2
 
2.4%
3.9 2
 
2.4%
36.9 2
 
2.4%
19 2
 
2.4%
8.7 2
 
2.4%
24.9 2
 
2.4%
29 1
 
1.2%
42.4 1
 
1.2%
15.3 1
 
1.2%
Other values (61) 61
71.8%
2023-12-10T19:06:08.390856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 62
23.2%
1 36
13.5%
2 29
10.9%
3 28
10.5%
4 20
 
7.5%
7 20
 
7.5%
9 18
 
6.7%
8 15
 
5.6%
5 14
 
5.2%
6 11
 
4.1%
Other values (2) 14
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 196
73.4%
Other Punctuation 62
 
23.2%
Dash Punctuation 9
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36
18.4%
2 29
14.8%
3 28
14.3%
4 20
10.2%
7 20
10.2%
9 18
9.2%
8 15
7.7%
5 14
 
7.1%
6 11
 
5.6%
0 5
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 62
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 267
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 62
23.2%
1 36
13.5%
2 29
10.9%
3 28
10.5%
4 20
 
7.5%
7 20
 
7.5%
9 18
 
6.7%
8 15
 
5.6%
5 14
 
5.2%
6 11
 
4.1%
Other values (2) 14
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 62
23.2%
1 36
13.5%
2 29
10.9%
3 28
10.5%
4 20
 
7.5%
7 20
 
7.5%
9 18
 
6.7%
8 15
 
5.6%
5 14
 
5.2%
6 11
 
4.1%
Other values (2) 14
 
5.2%

avr
Real number (ℝ)

Distinct62
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6170588
Minimum4.86
Maximum6.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T19:06:08.702667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.86
5-th percentile5.092
Q15.32
median5.59
Q35.84
95-th percentile6.278
Maximum6.56
Range1.7
Interquartile range (IQR)0.52

Descriptive statistics

Standard deviation0.3590723
Coefficient of variation (CV)0.063925322
Kurtosis-0.28845408
Mean5.6170588
Median Absolute Deviation (MAD)0.27
Skewness0.39249085
Sum477.45
Variance0.12893291
MonotonicityNot monotonic
2023-12-10T19:06:08.957274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.61 4
 
4.7%
5.57 3
 
3.5%
5.72 3
 
3.5%
5.31 3
 
3.5%
5.7 2
 
2.4%
5.3 2
 
2.4%
5.25 2
 
2.4%
6.0 2
 
2.4%
5.59 2
 
2.4%
5.67 2
 
2.4%
Other values (52) 60
70.6%
ValueCountFrequency (%)
4.86 1
1.2%
4.97 1
1.2%
5.07 1
1.2%
5.08 2
2.4%
5.14 1
1.2%
5.16 1
1.2%
5.17 1
1.2%
5.19 1
1.2%
5.2 1
1.2%
5.21 1
1.2%
ValueCountFrequency (%)
6.56 1
1.2%
6.39 1
1.2%
6.31 2
2.4%
6.29 1
1.2%
6.23 1
1.2%
6.22 1
1.2%
6.18 1
1.2%
6.11 1
1.2%
6.1 1
1.2%
6.09 1
1.2%

to_watch
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.856471
Minimum1.1
Maximum29.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T19:06:09.208055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.84
Q15.6
median9.4
Q317.2
95-th percentile26.56
Maximum29.9
Range28.8
Interquartile range (IQR)11.6

Descriptive statistics

Standard deviation7.911105
Coefficient of variation (CV)0.66723946
Kurtosis-0.74018093
Mean11.856471
Median Absolute Deviation (MAD)5.8
Skewness0.56217858
Sum1007.8
Variance62.585583
MonotonicityNot monotonic
2023-12-10T19:06:09.458294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 3
 
3.5%
5.6 3
 
3.5%
7.6 3
 
3.5%
4.7 2
 
2.4%
22.8 2
 
2.4%
16.1 2
 
2.4%
6.6 2
 
2.4%
9.4 1
 
1.2%
16.8 1
 
1.2%
2.1 1
 
1.2%
Other values (65) 65
76.5%
ValueCountFrequency (%)
1.1 1
1.2%
1.2 1
1.2%
1.5 1
1.2%
1.7 1
1.2%
1.8 1
1.2%
2.0 1
1.2%
2.1 1
1.2%
2.2 1
1.2%
2.3 1
1.2%
2.6 1
1.2%
ValueCountFrequency (%)
29.9 1
1.2%
29.3 1
1.2%
28.0 1
1.2%
27.4 1
1.2%
26.7 1
1.2%
26.0 1
1.2%
25.2 1
1.2%
24.0 1
1.2%
23.8 1
1.2%
22.9 1
1.2%

FILE_NAME
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
KC_602_PLAY_TYPE_CUST_EXP_MAP_2019
85 

Length

Max length34
Median length34
Mean length34
Min length34

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_602_PLAY_TYPE_CUST_EXP_MAP_2019
2nd rowKC_602_PLAY_TYPE_CUST_EXP_MAP_2019
3rd rowKC_602_PLAY_TYPE_CUST_EXP_MAP_2019
4th rowKC_602_PLAY_TYPE_CUST_EXP_MAP_2019
5th rowKC_602_PLAY_TYPE_CUST_EXP_MAP_2019

Common Values

ValueCountFrequency (%)
KC_602_PLAY_TYPE_CUST_EXP_MAP_2019 85
100.0%

Length

2023-12-10T19:06:09.685741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:09.853189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_602_play_type_cust_exp_map_2019 85
100.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
20200221
85 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200221
2nd row20200221
3rd row20200221
4th row20200221
5th row20200221

Common Values

ValueCountFrequency (%)
20200221 85
100.0%

Length

2023-12-10T19:06:10.122425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:10.418149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200221 85
100.0%

Interactions

2023-12-10T19:05:58.824396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:55.445724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:56.230235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:57.100882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:57.959052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:58.996408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:55.594822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:56.388419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:57.260979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:58.154986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:59.199268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:55.748862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:56.582418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:57.431371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:58.320662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:59.377814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:55.905131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:56.733631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:57.584597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:58.486172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:59.543559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:56.064935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:56.907775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:57.799893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:58.662571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:06:10.658970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
regiongubunsale_ratiosales_amtsample_cntsa_1sa_2sa_3sa_4sa_5sa_6sa_7avrto_watch
region1.0000.0000.0120.2060.4470.2110.0870.6530.7070.7230.6780.6770.4720.188
gubun0.0001.0000.6560.0330.3810.0680.0000.0520.5280.4990.6370.8500.2930.686
sale_ratio0.0120.6561.0000.2720.5240.0000.6880.1570.8210.7520.8470.9780.3260.274
sales_amt0.2060.0330.2721.0000.6690.6160.5870.9760.9570.0001.0001.0000.0000.672
sample_cnt0.4470.3810.5240.6691.0000.2480.7430.9520.9120.0000.8170.9780.0000.688
sa_10.2110.0680.0000.6160.2481.0000.0000.0000.8860.8930.0000.9720.0000.142
sa_20.0870.0000.6880.5870.7430.0001.0000.9560.8830.0000.7780.0000.3110.364
sa_30.6530.0520.1570.9760.9520.0000.9561.0000.9850.0000.8770.9900.3900.755
sa_40.7070.5280.8210.9570.9120.8860.8830.9851.0000.8210.9330.9960.4850.865
sa_50.7230.4990.7520.0000.0000.8930.0000.0000.8211.0000.9340.9360.8120.915
sa_60.6780.6370.8471.0000.8170.0000.7780.8770.9330.9341.0000.9210.2120.823
sa_70.6770.8500.9781.0000.9780.9720.0000.9900.9960.9360.9211.0000.8510.926
avr0.4720.2930.3260.0000.0000.0000.3110.3900.4850.8120.2120.8511.0000.284
to_watch0.1880.6860.2740.6720.6880.1420.3640.7550.8650.9150.8230.9260.2841.000
2023-12-10T19:06:11.064773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
sa_2regiongubunsa_3sa_1
sa_21.0000.0000.0000.6450.000
region0.0001.0000.0000.2120.064
gubun0.0000.0001.0000.0000.016
sa_30.6450.2120.0001.0000.000
sa_10.0000.0640.0160.0001.000
2023-12-10T19:06:11.274013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
sale_ratiosales_amtsample_cntavrto_watchregiongubunsa_1sa_2sa_3
sale_ratio1.0000.7920.2450.2840.3270.0000.4410.0000.3680.000
sales_amt0.7921.0000.3230.1900.2300.0860.0000.4040.3560.761
sample_cnt0.2450.3231.000-0.1600.8290.1860.2380.1180.4140.656
avr0.2840.190-0.1601.0000.0290.2530.1140.0000.1190.097
to_watch0.3270.2300.8290.0291.0000.0500.3400.0520.1540.333
region0.0000.0860.1860.2530.0501.0000.0000.0640.0000.212
gubun0.4410.0000.2380.1140.3400.0001.0000.0160.0000.000
sa_10.0000.4040.1180.0000.0520.0640.0161.0000.0000.000
sa_20.3680.3560.4140.1190.1540.0000.0000.0001.0000.645
sa_30.0000.7610.6560.0970.3330.2120.0000.0000.6451.000

Missing values

2023-12-10T19:05:59.800920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:06:00.242474image/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

regiongubunsale_ratiosales_amtsample_cntsa_1sa_2sa_3sa_4sa_5sa_6sa_7avrto_watchFILE_NAMEbase_ymd
0인천서양음악8.15654753139---7.239.324.4295.7511.9KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
1울산서양음악4.65306587511----38.861.2-5.614.3KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
2경기서양음악11.654935965266-1.61.317.341.636.41.95.167.7KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
3제주서양음악5.7573188332-5.16.511.927.823.824.95.3315.1KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
4인천전통예술(국악)1.040168988---20.142.330.675.2418.8KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
5울산전통예술(국악)0.0017---5.513.263.118.35.948.4KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
6경기전통예술(국악)3.36990852144--2.514.549.327.85.85.212.7KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
7제주전통예술(국악)0.00362.2-2.933.917.519.424.15.1917.0KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
8인천연극21.98796989159--0.514.441.441.12.65.3129.9KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
9울산연극9.5313180822---013.84937.26.2316.1KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
regiongubunsale_ratiosales_amtsample_cntsa_1sa_2sa_3sa_4sa_5sa_6sa_7avrto_watchFILE_NAMEbase_ymd
75충북뮤지컬86.61998147840---5.237.739.717.35.6914.3KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
76충남뮤지컬45.11182373247--0.76.124.945.62275.6317.2KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
77전남뮤지컬42.1927635628----8.643.747.76.3915.3KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
78경남뮤지컬87.348471579234.7---56.534.34.55.256.9KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
79대전무용18.6275354038---13.558.98.918.75.333.4KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
80세종무용3.63340372----33.666.4-5.661.2KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
81충북무용0.81845867---26.851.87.913.55.081.7KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
82충남무용21.0550550722-1.7-11.943.838.83.95.34.7KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
83전남무용1.63525462----48.751.3-5.511.1KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221
84경남무용0.42220923----18.681.4-5.816.6KC_602_PLAY_TYPE_CUST_EXP_MAP_201920200221