Overview

Dataset statistics

Number of variables15
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.6 KiB
Average record size in memory130.3 B

Variable types

Numeric8
Categorical4
Text3

Dataset

DescriptionSample
Author한국인터넷진흥원
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KIS0000032

Alerts

2020 has constant value ""Constant
X has constant value ""Constant
3099 is highly overall correlated with 3071 and 3 other fieldsHigh correlation
3071 is highly overall correlated with 3099 and 3 other fieldsHigh correlation
2463 is highly overall correlated with 3099 and 3 other fieldsHigh correlation
4184 is highly overall correlated with 3099 and 3 other fieldsHigh correlation
3938 is highly overall correlated with 3099 and 3 other fieldsHigh correlation
1 has unique valuesUnique
3071 has unique valuesUnique
4184 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:42:44.719210
Analysis finished2023-12-10 06:42:54.894046
Duration10.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

1
Real number (ℝ)

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:42:55.014654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.9
Q126.5
median51
Q375.5
95-th percentile95.1
Maximum100
Range98
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.722813
Coefficient of variation (CV)0.56319242
Kurtosis-1.2
Mean51
Median Absolute Deviation (MAD)25
Skewness0
Sum5049
Variance825
MonotonicityStrictly increasing
2023-12-10T15:42:55.550192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 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 (89) 89
89.9%
ValueCountFrequency (%)
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
ValueCountFrequency (%)
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%
93 1
1.0%
92 1
1.0%
91 1
1.0%

2020
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2020
99 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 99
100.0%

Length

2023-12-10T15:42:55.712693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:55.826783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 99
100.0%

3099
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2516.9899
Minimum5
Maximum9556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:42:55.980747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile55.2
Q11334
median2218
Q33567.5
95-th percentile5526.1
Maximum9556
Range9551
Interquartile range (IQR)2233.5

Descriptive statistics

Standard deviation1791.6055
Coefficient of variation (CV)0.71180479
Kurtosis2.2892413
Mean2516.9899
Median Absolute Deviation (MAD)1094
Skewness1.1404259
Sum249182
Variance3209850.1
MonotonicityNot monotonic
2023-12-10T15:42:56.168747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
815 2
 
2.0%
2218 1
 
1.0%
218 1
 
1.0%
2033 1
 
1.0%
3312 1
 
1.0%
1979 1
 
1.0%
3105 1
 
1.0%
379 1
 
1.0%
3844 1
 
1.0%
2672 1
 
1.0%
Other values (88) 88
88.9%
ValueCountFrequency (%)
5 1
1.0%
10 1
1.0%
38 1
1.0%
42 1
1.0%
48 1
1.0%
56 1
1.0%
63 1
1.0%
147 1
1.0%
162 1
1.0%
218 1
1.0%
ValueCountFrequency (%)
9556 1
1.0%
8049 1
1.0%
7569 1
1.0%
6246 1
1.0%
5626 1
1.0%
5515 1
1.0%
5156 1
1.0%
5028 1
1.0%
4514 1
1.0%
4484 1
1.0%

주식
Text

Distinct60
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-10T15:42:56.438695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.1313131
Min length2

Characters and Unicode

Total characters211
Distinct characters91
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

Unique45 ?
Unique (%)45.5%

Sample

1st row급등
2nd row수익
3rd row종목
4th row국내외
5th row클릭
ValueCountFrequency (%)
수익 9
 
9.1%
신청 7
 
7.1%
주식 5
 
5.1%
진행 5
 
5.1%
종목 4
 
4.0%
상담 3
 
3.0%
투자 3
 
3.0%
코로나 3
 
3.0%
상품 3
 
3.0%
수신 2
 
2.0%
Other values (50) 55
55.6%
2023-12-10T15:42:56.938844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 14
 
6.6%
신 10
 
4.7%
상 9
 
4.3%
익 9
 
4.3%
청 7
 
3.3%
주 6
 
2.8%
진 6
 
2.8%
인 5
 
2.4%
로 5
 
2.4%
식 5
 
2.4%
Other values (81) 135
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 211
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 14
 
6.6%
신 10
 
4.7%
상 9
 
4.3%
익 9
 
4.3%
청 7
 
3.3%
주 6
 
2.8%
진 6
 
2.8%
인 5
 
2.4%
로 5
 
2.4%
식 5
 
2.4%
Other values (81) 135
64.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 211
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 14
 
6.6%
신 10
 
4.7%
상 9
 
4.3%
익 9
 
4.3%
청 7
 
3.3%
주 6
 
2.8%
진 6
 
2.8%
인 5
 
2.4%
로 5
 
2.4%
식 5
 
2.4%
Other values (81) 135
64.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 211
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 14
 
6.6%
신 10
 
4.7%
상 9
 
4.3%
익 9
 
4.3%
청 7
 
3.3%
주 6
 
2.8%
진 6
 
2.8%
인 5
 
2.4%
로 5
 
2.4%
식 5
 
2.4%
Other values (81) 135
64.0%

3071
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2222.9394
Minimum5
Maximum9370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:42:57.284433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile51.4
Q11214.5
median2010
Q32901
95-th percentile5157.7
Maximum9370
Range9365
Interquartile range (IQR)1686.5

Descriptive statistics

Standard deviation1569.4064
Coefficient of variation (CV)0.70600502
Kurtosis3.7748463
Mean2222.9394
Median Absolute Deviation (MAD)858
Skewness1.3370505
Sum220071
Variance2463036.4
MonotonicityNot monotonic
2023-12-10T15:42:57.485166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1758 1
 
1.0%
3029 1
 
1.0%
209 1
 
1.0%
1711 1
 
1.0%
802 1
 
1.0%
2868 1
 
1.0%
1845 1
 
1.0%
2483 1
 
1.0%
350 1
 
1.0%
3800 1
 
1.0%
Other values (89) 89
89.9%
ValueCountFrequency (%)
5 1
1.0%
10 1
1.0%
37 1
1.0%
38 1
1.0%
46 1
1.0%
52 1
1.0%
63 1
1.0%
133 1
1.0%
136 1
1.0%
209 1
1.0%
ValueCountFrequency (%)
9370 1
1.0%
6486 1
1.0%
6044 1
1.0%
5516 1
1.0%
5173 1
1.0%
5156 1
1.0%
4833 1
1.0%
4350 1
1.0%
4080 1
1.0%
3970 1
1.0%

진행
Text

Distinct65
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-10T15:42:57.825775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.0606061
Min length2

Characters and Unicode

Total characters204
Distinct characters97
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

Unique46 ?
Unique (%)46.5%

Sample

1st row밴드
2nd row안내
3rd row폐렴
4th row지원
5th row스팸
ValueCountFrequency (%)
수신 5
 
5.1%
투자 4
 
4.0%
확인 4
 
4.0%
종목 4
 
4.0%
대출 3
 
3.0%
신청 3
 
3.0%
고객 3
 
3.0%
국제 3
 
3.0%
운영 3
 
3.0%
진행 3
 
3.0%
Other values (55) 64
64.6%
2023-12-10T15:42:58.399146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 9
 
4.4%
인 7
 
3.4%
대 7
 
3.4%
금 7
 
3.4%
수 5
 
2.5%
자 5
 
2.5%
진 4
 
2.0%
종 4
 
2.0%
목 4
 
2.0%
투 4
 
2.0%
Other values (87) 148
72.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 204
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 9
 
4.4%
인 7
 
3.4%
대 7
 
3.4%
금 7
 
3.4%
수 5
 
2.5%
자 5
 
2.5%
진 4
 
2.0%
종 4
 
2.0%
목 4
 
2.0%
투 4
 
2.0%
Other values (87) 148
72.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 204
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 9
 
4.4%
인 7
 
3.4%
대 7
 
3.4%
금 7
 
3.4%
수 5
 
2.5%
자 5
 
2.5%
진 4
 
2.0%
종 4
 
2.0%
목 4
 
2.0%
투 4
 
2.0%
Other values (87) 148
72.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 204
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 9
 
4.4%
인 7
 
3.4%
대 7
 
3.4%
금 7
 
3.4%
수 5
 
2.5%
자 5
 
2.5%
진 4
 
2.0%
종 4
 
2.0%
목 4
 
2.0%
투 4
 
2.0%
Other values (87) 148
72.5%

2463
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2072.7071
Minimum5
Maximum8979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:42:58.644085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile48.7
Q11155
median1908
Q32781
95-th percentile4694.3
Maximum8979
Range8974
Interquartile range (IQR)1626

Descriptive statistics

Standard deviation1461.9057
Coefficient of variation (CV)0.70531225
Kurtosis4.2660736
Mean2072.7071
Median Absolute Deviation (MAD)855
Skewness1.3785492
Sum205198
Variance2137168.2
MonotonicityNot monotonic
2023-12-10T15:42:58.891213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2236 2
 
2.0%
1733 1
 
1.0%
3022 1
 
1.0%
173 1
 
1.0%
1626 1
 
1.0%
626 1
 
1.0%
1704 1
 
1.0%
2198 1
 
1.0%
325 1
 
1.0%
3050 1
 
1.0%
Other values (88) 88
88.9%
ValueCountFrequency (%)
5 1
1.0%
9 1
1.0%
32 1
1.0%
36 1
1.0%
46 1
1.0%
49 1
1.0%
63 1
1.0%
130 1
1.0%
131 1
1.0%
173 1
1.0%
ValueCountFrequency (%)
8979 1
1.0%
5513 1
1.0%
5369 1
1.0%
5248 1
1.0%
5156 1
1.0%
4643 1
1.0%
4414 1
1.0%
4350 1
1.0%
4060 1
1.0%
3810 1
1.0%

1.1
Real number (ℝ)

Distinct11
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2020202
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:42:59.079398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile10.1
Maximum11
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7663422
Coefficient of variation (CV)0.53178228
Kurtosis-0.58703799
Mean5.2020202
Median Absolute Deviation (MAD)2
Skewness0.38502984
Sum515
Variance7.6526489
MonotonicityNot monotonic
2023-12-10T15:42:59.284453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 14
14.1%
6 14
14.1%
3 13
13.1%
4 11
11.1%
1 9
9.1%
2 9
9.1%
7 9
9.1%
8 7
7.1%
10 5
 
5.1%
11 5
 
5.1%
ValueCountFrequency (%)
1 9
9.1%
2 9
9.1%
3 13
13.1%
4 11
11.1%
5 14
14.1%
6 14
14.1%
7 9
9.1%
8 7
7.1%
9 3
 
3.0%
10 5
 
5.1%
ValueCountFrequency (%)
11 5
 
5.1%
10 5
 
5.1%
9 3
 
3.0%
8 7
7.1%
7 9
9.1%
6 14
14.1%
5 14
14.1%
4 11
11.1%
3 13
13.1%
2 9
9.1%

2
Real number (ℝ)

Distinct30
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.868687
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:42:59.492520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q18
median17
Q323
95-th percentile28.1
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.5650529
Coefficient of variation (CV)0.53974554
Kurtosis-1.2514674
Mean15.868687
Median Absolute Deviation (MAD)7
Skewness-0.045657331
Sum1571
Variance73.360132
MonotonicityNot monotonic
2023-12-10T15:42:59.704160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6 6
 
6.1%
23 6
 
6.1%
21 6
 
6.1%
8 5
 
5.1%
9 5
 
5.1%
27 5
 
5.1%
20 5
 
5.1%
22 4
 
4.0%
24 4
 
4.0%
28 4
 
4.0%
Other values (20) 49
49.5%
ValueCountFrequency (%)
1 1
 
1.0%
2 4
4.0%
3 2
 
2.0%
4 3
3.0%
5 4
4.0%
6 6
6.1%
7 2
 
2.0%
8 5
5.1%
9 5
5.1%
10 2
 
2.0%
ValueCountFrequency (%)
31 2
 
2.0%
30 2
 
2.0%
29 1
 
1.0%
28 4
4.0%
27 5
5.1%
25 3
3.0%
24 4
4.0%
23 6
6.1%
22 4
4.0%
21 6
6.1%

X
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
X
99 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
X 99
100.0%

Length

2023-12-10T15:42:59.932373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:43:00.078316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
x 99
100.0%

종목
Categorical

Distinct37
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Memory size924.0 B
국제
21 
투자
11 
수익
6 
신청
 
5
상품
 
5
Other values (32)
51 

Length

Max length3
Median length2
Mean length2.030303
Min length2

Unique

Unique23 ?
Unique (%)23.2%

Sample

1st row수익
2nd row고객
3rd row수익
4th row인증
5th row문자

Common Values

ValueCountFrequency (%)
국제 21
21.2%
투자 11
 
11.1%
수익 6
 
6.1%
신청 5
 
5.1%
상품 5
 
5.1%
인증 5
 
5.1%
시장 4
 
4.0%
정보 4
 
4.0%
고객 4
 
4.0%
입장 3
 
3.0%
Other values (27) 31
31.3%

Length

2023-12-10T15:43:00.244452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
국제 21
21.2%
투자 11
 
11.1%
수익 6
 
6.1%
신청 5
 
5.1%
상품 5
 
5.1%
인증 5
 
5.1%
시장 4
 
4.0%
정보 4
 
4.0%
고객 4
 
4.0%
입장 3
 
3.0%
Other values (27) 31
31.3%

4184
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3701.4747
Minimum10
Maximum12299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:43:00.444837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile86
Q11763
median3452
Q34702.5
95-th percentile9292.8
Maximum12299
Range12289
Interquartile range (IQR)2939.5

Descriptive statistics

Standard deviation2667.5982
Coefficient of variation (CV)0.72068522
Kurtosis1.1670039
Mean3701.4747
Median Absolute Deviation (MAD)1505
Skewness1.0247551
Sum366446
Variance7116079.9
MonotonicityNot monotonic
2023-12-10T15:43:00.718348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7580 1
 
1.0%
4957 1
 
1.0%
397 1
 
1.0%
2149 1
 
1.0%
1496 1
 
1.0%
3452 1
 
1.0%
3267 1
 
1.0%
4001 1
 
1.0%
531 1
 
1.0%
4317 1
 
1.0%
Other values (89) 89
89.9%
ValueCountFrequency (%)
10 1
1.0%
14 1
1.0%
45 1
1.0%
56 1
1.0%
68 1
1.0%
88 1
1.0%
128 1
1.0%
171 1
1.0%
181 1
1.0%
397 1
1.0%
ValueCountFrequency (%)
12299 1
1.0%
11703 1
1.0%
10391 1
1.0%
10158 1
1.0%
9570 1
1.0%
9262 1
1.0%
8656 1
1.0%
8056 1
1.0%
7870 1
1.0%
7580 1
1.0%

투자
Categorical

Distinct49
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Memory size924.0 B
투자
10 
종목
 
6
국제
 
5
인증
 
5
수익
 
5
Other values (44)
68 

Length

Max length3
Median length2
Mean length2.0808081
Min length2

Unique

Unique31 ?
Unique (%)31.3%

Sample

1st row종목
2nd row문자
3rd row투자
4th row제공
5th row유도

Common Values

ValueCountFrequency (%)
투자 10
 
10.1%
종목 6
 
6.1%
국제 5
 
5.1%
인증 5
 
5.1%
수익 5
 
5.1%
정보 4
 
4.0%
신청 4
 
4.0%
시장 4
 
4.0%
문자 4
 
4.0%
대출 4
 
4.0%
Other values (39) 48
48.5%

Length

2023-12-10T15:43:00.949416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
투자 10
 
10.1%
종목 6
 
6.1%
국제 5
 
5.1%
인증 5
 
5.1%
수익 5
 
5.1%
정보 4
 
4.0%
신청 4
 
4.0%
시장 4
 
4.0%
문자 4
 
4.0%
대출 4
 
4.0%
Other values (39) 48
48.5%

3938
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2923.7273
Minimum6
Maximum11254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:43:01.157185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile67.8
Q11565
median2603
Q34074
95-th percentile7182.9
Maximum11254
Range11248
Interquartile range (IQR)2509

Descriptive statistics

Standard deviation2132.2019
Coefficient of variation (CV)0.72927522
Kurtosis2.3572864
Mean2923.7273
Median Absolute Deviation (MAD)1301
Skewness1.2213397
Sum289449
Variance4546284.8
MonotonicityNot monotonic
2023-12-10T15:43:01.398213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1887 2
 
2.0%
4127 1
 
1.0%
1594 1
 
1.0%
2058 1
 
1.0%
1047 1
 
1.0%
3443 1
 
1.0%
2118 1
 
1.0%
3266 1
 
1.0%
531 1
 
1.0%
4191 1
 
1.0%
Other values (88) 88
88.9%
ValueCountFrequency (%)
6 1
1.0%
12 1
1.0%
43 1
1.0%
52 1
1.0%
66 1
1.0%
68 1
1.0%
71 1
1.0%
155 1
1.0%
170 1
1.0%
245 1
1.0%
ValueCountFrequency (%)
11254 1
1.0%
9576 1
1.0%
8108 1
1.0%
8022 1
1.0%
7695 1
1.0%
7126 1
1.0%
6785 1
1.0%
5744 1
1.0%
5427 1
1.0%
5156 1
1.0%

수익
Text

Distinct59
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-10T15:43:01.756494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.1212121
Min length2

Characters and Unicode

Total characters210
Distinct characters90
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

Unique41 ?
Unique (%)41.4%

Sample

1st row투자
2nd row클릭
3rd row고객
4th row보증
5th row주소
ValueCountFrequency (%)
투자 10
 
10.1%
국제 6
 
6.1%
상품 6
 
6.1%
유도 4
 
4.0%
수익 4
 
4.0%
종목 3
 
3.0%
고객 3
 
3.0%
신청 2
 
2.0%
주소 2
 
2.0%
확인 2
 
2.0%
Other values (49) 57
57.6%
2023-12-10T15:43:02.227408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
자 11
 
5.2%
상 10
 
4.8%
투 10
 
4.8%
국 7
 
3.3%
품 6
 
2.9%
제 6
 
2.9%
인 6
 
2.9%
수 6
 
2.9%
대 5
 
2.4%
신 4
 
1.9%
Other values (80) 139
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 210
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 11
 
5.2%
상 10
 
4.8%
투 10
 
4.8%
국 7
 
3.3%
품 6
 
2.9%
제 6
 
2.9%
인 6
 
2.9%
수 6
 
2.9%
대 5
 
2.4%
신 4
 
1.9%
Other values (80) 139
66.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 210
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 11
 
5.2%
상 10
 
4.8%
투 10
 
4.8%
국 7
 
3.3%
품 6
 
2.9%
제 6
 
2.9%
인 6
 
2.9%
수 6
 
2.9%
대 5
 
2.4%
신 4
 
1.9%
Other values (80) 139
66.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 210
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
자 11
 
5.2%
상 10
 
4.8%
투 10
 
4.8%
국 7
 
3.3%
품 6
 
2.9%
제 6
 
2.9%
인 6
 
2.9%
수 6
 
2.9%
대 5
 
2.4%
신 4
 
1.9%
Other values (80) 139
66.2%

Interactions

2023-12-10T15:42:53.494335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:45.902876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:46.794603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:48.212193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:49.237585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:50.271779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:51.344090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:52.439252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:53.619261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:46.010975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:47.237232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:48.339419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:49.373203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:50.402880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:51.485127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:52.563585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:53.752862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:46.119623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:47.367687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:48.489674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:49.497998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:50.532291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:51.616086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:52.689785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:53.872527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:46.222219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:47.556071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:48.614042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:49.614381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:50.658454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:51.740868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:52.831886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:54.014710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:46.344498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:47.686199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:48.729569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:49.733009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:50.785120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:51.870690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:52.978632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:54.117188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:46.448200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:47.810816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:48.861357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:49.847283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:50.913848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:51.997773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:53.104330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:54.257905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:46.556334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:47.954276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:49.005879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:49.997191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:51.065938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:52.144101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:53.221622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:54.371275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:46.679926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:48.087909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:49.124249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:50.137767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:51.214185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:52.296786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:53.365931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:43:02.389622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
13099주식3071진행24631.12종목4184투자3938수익
11.0000.3830.3950.2170.3720.0000.9490.0000.6390.3620.3980.1320.000
30990.3831.0000.7120.9440.0000.9330.6500.2230.0000.9380.0000.9760.796
주식0.3950.7121.0000.5500.9290.6600.5980.0000.9500.0000.9450.0000.959
30710.2170.9440.5501.0000.0000.9900.5100.0000.3370.8250.0000.9090.730
진행0.3720.0000.9290.0001.0000.0000.2950.0000.9560.7670.9340.0000.899
24630.0000.9330.6600.9900.0001.0000.4930.0000.0000.8650.0000.9220.804
1.10.9490.6500.5980.5100.2950.4931.0000.0000.6450.6000.6860.5930.677
20.0000.2230.0000.0000.0000.0000.0001.0000.2590.0000.0000.0000.000
종목0.6390.0000.9500.3370.9560.0000.6450.2591.0000.5050.9140.0000.824
41840.3620.9380.0000.8250.7670.8650.6000.0000.5051.0000.0000.9600.763
투자0.3980.0000.9450.0000.9340.0000.6860.0000.9140.0001.0000.0000.947
39380.1320.9760.0000.9090.0000.9220.5930.0000.0000.9600.0001.0000.760
수익0.0000.7960.9590.7300.8990.8040.6770.0000.8240.7630.9470.7601.000
2023-12-10T15:43:02.598642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
투자종목
투자1.0000.342
종목0.3421.000
2023-12-10T15:43:02.740721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
13099307124631.1241843938종목투자
11.0000.1430.1430.1380.298-0.1440.0400.0910.2310.000
30990.1431.0000.9740.9740.114-0.0910.9080.9690.0000.000
30710.1430.9741.0000.9940.144-0.0940.8880.9510.0940.000
24630.1380.9740.9941.0000.140-0.0910.8910.9530.0000.000
1.10.2980.1140.1440.1401.000-0.3220.0770.0970.2870.092
2-0.144-0.091-0.094-0.091-0.3221.000-0.030-0.0790.0000.000
41840.0400.9080.8880.8910.077-0.0301.0000.9460.1560.000
39380.0910.9690.9510.9530.097-0.0790.9461.0000.0000.000
종목0.2310.0000.0940.0000.2870.0000.1560.0001.0000.342
투자0.0000.0000.0000.0000.0920.0000.0000.0000.3421.000

Missing values

2023-12-10T15:42:54.531664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:42:54.803426image/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

120203099주식3071진행24631.12X종목4184투자3938수익
0220202218급등1758밴드173316X수익7580종목4127투자
1320202646수익2626안내2246123X고객4548문자4322클릭
2420202221종목1835폐렴1732128X수익2910투자2284고객
3520201819국내외1646지원1646131X인증3558제공1887보증
4620205156클릭5156스팸515625X문자5169유도5156주소
5720205028신청3970전화3455221X정보5164거나5030유도
6820201591기한1458대표1422222X코드1627신규1627초대
7920205통합5경선538X방문10고객6미래
810202038진행37금리36310X투자68신청66시장
91120201671인증1595투자1383313X신청2971확인1674진행
120203099주식3071진행24631.12X종목4184투자3938수익
899120202145해외2010운영2010620X보증4020국제2775공식
909220201585신뢰1528사정1498627X국제1976해외1658업계
919320202509럭키2242투자2066630X국제3445시장2577안정
929420202495접수2473확인237176X국제3898입장3359신청
939520201887진입1887국제1693719X수돗물1887유충1887관련
949620203985투자2884방법2763721X국제4857수익4067입장
95972020953내일813준비64582X가난1047국제1032자산
969820202958방법2789투자277885X국제3547수신3168시장
979920203753수신3005증권291786X시장5252투자4868주식
9810020201046하루853가난78588X국제2438결혼1196국외