Overview

Dataset statistics

Number of variables15
Number of observations2260
Missing cells10
Missing cells (%)< 0.1%
Duplicate rows158
Duplicate rows (%)7.0%
Total size in memory289.2 KiB
Average record size in memory131.1 B

Variable types

Categorical5
Text2
Numeric8

Dataset

DescriptionSample
Author㈜한국금융솔루션
URLhttps://www.bigdata-finance.kr/dataset/datasetView.do?datastId=SET1300048

Alerts

Dataset has 158 (7.0%) duplicate rowsDuplicates
중립문서개수 is highly overall correlated with 감성점수값 and 11 other fieldsHigh correlation
분석일시 is highly overall correlated with 감성점수값 and 11 other fieldsHigh correlation
기준일자 is highly overall correlated with 감성점수값 and 11 other fieldsHigh correlation
주식시장명 is highly overall correlated with 전체문서개수 and 8 other fieldsHigh correlation
이전중립문서개수 is highly overall correlated with 감성점수값 and 11 other fieldsHigh correlation
감성점수값 is highly overall correlated with 감성레벨값 and 8 other fieldsHigh correlation
감성레벨값 is highly overall correlated with 감성점수값 and 8 other fieldsHigh correlation
전체문서개수 is highly overall correlated with 감성점수값 and 11 other fieldsHigh correlation
긍정문서개수 is highly overall correlated with 감성점수값 and 9 other fieldsHigh correlation
부정문서개수 is highly overall correlated with 전체문서개수 and 6 other fieldsHigh correlation
이전전체문서개수 is highly overall correlated with 감성점수값 and 11 other fieldsHigh correlation
이전긍정문서개수 is highly overall correlated with 감성점수값 and 9 other fieldsHigh correlation
이전부정문서개수 is highly overall correlated with 전체문서개수 and 7 other fieldsHigh correlation
기준일자 is highly imbalanced (99.4%)Imbalance
주식시장명 is highly imbalanced (62.7%)Imbalance
분석일시 is highly imbalanced (99.4%)Imbalance
중립문서개수 is highly imbalanced (99.4%)Imbalance
이전중립문서개수 is highly imbalanced (99.4%)Imbalance
감성점수값 has 1444 (63.9%) zerosZeros
감성레벨값 has 1404 (62.1%) zerosZeros
전체문서개수 has 190 (8.4%) zerosZeros
긍정문서개수 has 1508 (66.7%) zerosZeros
이전전체문서개수 has 172 (7.6%) zerosZeros
이전긍정문서개수 has 1460 (64.6%) zerosZeros

Reproduction

Analysis started2023-12-10 13:06:01.352749
Analysis finished2023-12-10 13:06:11.098258
Duration9.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
20211022
2259 
2259 217857 20211023024014 F_BBP20_00044
 
1

Length

Max length40
Median length8
Mean length8.0141593
Min length8

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row20211022
2nd row20211022
3rd row20211022
4th row20211022
5th row20211022

Common Values

ValueCountFrequency (%)
20211022 2259
> 99.9%
2259 217857 20211023024014 F_BBP20_00044 1
 
< 0.1%

Length

2023-12-10T22:06:11.160429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:06:11.263450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20211022 2259
99.8%
2259 1
 
< 0.1%
217857 1
 
< 0.1%
20211023024014 1
 
< 0.1%
f_bbp20_00044 1
 
< 0.1%
Distinct251
Distinct (%)11.1%
Missing1
Missing (%)< 0.1%
Memory size17.8 KiB
2023-12-10T22:06:11.533865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.920319
Min length5

Characters and Unicode

Total characters26928
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowKOSPI
3rd rowKOSDAQ
4th rowKR7034830000
5th rowKR7026960005
ValueCountFrequency (%)
total 9
 
0.4%
kr7071050009 9
 
0.4%
kr7069620003 9
 
0.4%
kr7103140000 9
 
0.4%
kr7138930003 9
 
0.4%
kr7139480008 9
 
0.4%
kr7145990008 9
 
0.4%
kr7161390000 9
 
0.4%
kr7161890009 9
 
0.4%
kr7170900005 9
 
0.4%
Other values (241) 2169
96.0%
2023-12-10T22:06:11.989952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10674
39.6%
7 3123
 
11.6%
K 2250
 
8.4%
R 2232
 
8.3%
1 1332
 
4.9%
3 1242
 
4.6%
2 1080
 
4.0%
4 1062
 
3.9%
5 981
 
3.6%
6 972
 
3.6%
Other values (11) 1980
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22320
82.9%
Uppercase Letter 4608
 
17.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 2250
48.8%
R 2232
48.4%
O 27
 
0.6%
T 18
 
0.4%
S 18
 
0.4%
A 18
 
0.4%
Q 9
 
0.2%
D 9
 
0.2%
L 9
 
0.2%
P 9
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 10674
47.8%
7 3123
 
14.0%
1 1332
 
6.0%
3 1242
 
5.6%
2 1080
 
4.8%
4 1062
 
4.8%
5 981
 
4.4%
6 972
 
4.4%
8 963
 
4.3%
9 891
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22320
82.9%
Latin 4608
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 2250
48.8%
R 2232
48.4%
O 27
 
0.6%
T 18
 
0.4%
S 18
 
0.4%
A 18
 
0.4%
Q 9
 
0.2%
D 9
 
0.2%
L 9
 
0.2%
P 9
 
0.2%
Common
ValueCountFrequency (%)
0 10674
47.8%
7 3123
 
14.0%
1 1332
 
6.0%
3 1242
 
5.6%
2 1080
 
4.8%
4 1062
 
4.8%
5 981
 
4.4%
6 972
 
4.4%
8 963
 
4.3%
9 891
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10674
39.6%
7 3123
 
11.6%
K 2250
 
8.4%
R 2232
 
8.3%
1 1332
 
4.9%
3 1242
 
4.6%
2 1080
 
4.0%
4 1062
 
3.9%
5 981
 
3.6%
6 972
 
3.6%
Other values (11) 1980
 
7.4%
Distinct251
Distinct (%)11.1%
Missing1
Missing (%)< 0.1%
Memory size17.8 KiB
2023-12-10T22:06:12.298660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.3984064
Min length2

Characters and Unicode

Total characters9936
Distinct characters260
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowKOSPI
3rd rowKOSDAQ
4th row한국토지신탁
5th row동서
ValueCountFrequency (%)
total 9
 
0.4%
금호타이어 9
 
0.4%
풍산 9
 
0.4%
bnk금융지주 9
 
0.4%
이마트 9
 
0.4%
삼양사 9
 
0.4%
한국타이어 9
 
0.4%
한국콜마 9
 
0.4%
동아에스티 9
 
0.4%
종근당 9
 
0.4%
Other values (242) 2178
96.0%
2023-12-10T22:06:12.724445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
396
 
4.0%
315
 
3.2%
288
 
2.9%
234
 
2.4%
S 216
 
2.2%
K 153
 
1.5%
144
 
1.4%
135
 
1.4%
G 135
 
1.4%
135
 
1.4%
Other values (250) 7785
78.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8586
86.4%
Uppercase Letter 1260
 
12.7%
Other Punctuation 54
 
0.5%
Lowercase Letter 18
 
0.2%
Space Separator 9
 
0.1%
Dash Punctuation 9
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
396
 
4.6%
315
 
3.7%
288
 
3.4%
234
 
2.7%
144
 
1.7%
135
 
1.6%
135
 
1.6%
126
 
1.5%
126
 
1.5%
126
 
1.5%
Other values (225) 6561
76.4%
Uppercase Letter
ValueCountFrequency (%)
S 216
17.1%
K 153
12.1%
G 135
10.7%
L 126
10.0%
C 117
9.3%
T 81
 
6.4%
O 63
 
5.0%
P 54
 
4.3%
B 54
 
4.3%
J 45
 
3.6%
Other values (10) 216
17.1%
Lowercase Letter
ValueCountFrequency (%)
l 9
50.0%
i 9
50.0%
Other Punctuation
ValueCountFrequency (%)
& 54
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8586
86.4%
Latin 1278
 
12.9%
Common 72
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
396
 
4.6%
315
 
3.7%
288
 
3.4%
234
 
2.7%
144
 
1.7%
135
 
1.6%
135
 
1.6%
126
 
1.5%
126
 
1.5%
126
 
1.5%
Other values (225) 6561
76.4%
Latin
ValueCountFrequency (%)
S 216
16.9%
K 153
12.0%
G 135
10.6%
L 126
9.9%
C 117
9.2%
T 81
 
6.3%
O 63
 
4.9%
P 54
 
4.2%
B 54
 
4.2%
J 45
 
3.5%
Other values (12) 234
18.3%
Common
ValueCountFrequency (%)
& 54
75.0%
9
 
12.5%
- 9
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8586
86.4%
ASCII 1350
 
13.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
396
 
4.6%
315
 
3.7%
288
 
3.4%
234
 
2.7%
144
 
1.7%
135
 
1.6%
135
 
1.6%
126
 
1.5%
126
 
1.5%
126
 
1.5%
Other values (225) 6561
76.4%
ASCII
ValueCountFrequency (%)
S 216
16.0%
K 153
11.3%
G 135
10.0%
L 126
9.3%
C 117
 
8.7%
T 81
 
6.0%
O 63
 
4.7%
P 54
 
4.0%
& 54
 
4.0%
B 54
 
4.0%
Other values (15) 297
22.0%

주식시장명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
KOSPI
1818 
KOSDAQ
432 
TOTAL
 
9
<NA>
 
1

Length

Max length6
Median length5
Mean length5.190708
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTOTAL
2nd rowKOSPI
3rd rowKOSDAQ
4th rowKOSPI
5th rowKOSPI

Common Values

ValueCountFrequency (%)
KOSPI 1818
80.4%
KOSDAQ 432
 
19.1%
TOTAL 9
 
0.4%
<NA> 1
 
< 0.1%

Length

2023-12-10T22:06:12.879078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:06:12.999667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kospi 1818
80.4%
kosdaq 432
 
19.1%
total 9
 
0.4%
na 1
 
< 0.1%

분석일시
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
202110
2259 
<NA>
 
1

Length

Max length6
Median length6
Mean length5.999115
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row202110
2nd row202110
3rd row202110
4th row202110
5th row202110

Common Values

ValueCountFrequency (%)
202110 2259
> 99.9%
<NA> 1
 
< 0.1%

Length

2023-12-10T22:06:13.137405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:06:13.273006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202110 2259
> 99.9%
na 1
 
< 0.1%

감성점수값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct737
Distinct (%)32.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20.678477
Minimum0
Maximum100
Zeros1444
Zeros (%)63.9%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-10T22:06:13.403576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q342.15
95-th percentile92.714
Maximum100
Range100
Interquartile range (IQR)42.15

Descriptive statistics

Standard deviation32.981819
Coefficient of variation (CV)1.594983
Kurtosis-0.073564863
Mean20.678477
Median Absolute Deviation (MAD)0
Skewness1.24116
Sum46712.68
Variance1087.8004
MonotonicityNot monotonic
2023-12-10T22:06:13.524686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1444
63.9%
100.0 22
 
1.0%
99.98 7
 
0.3%
9.04 5
 
0.2%
99.09 4
 
0.2%
16.56 4
 
0.2%
0.3 3
 
0.1%
0.15 3
 
0.1%
99.99 3
 
0.1%
87.46 2
 
0.1%
Other values (727) 762
33.7%
ValueCountFrequency (%)
0.0 1444
63.9%
0.07 1
 
< 0.1%
0.15 3
 
0.1%
0.22 2
 
0.1%
0.27 2
 
0.1%
0.3 3
 
0.1%
0.31 1
 
< 0.1%
0.33 2
 
0.1%
0.38 1
 
< 0.1%
0.39 1
 
< 0.1%
ValueCountFrequency (%)
100.0 22
1.0%
99.99 3
 
0.1%
99.98 7
 
0.3%
99.97 2
 
0.1%
99.96 2
 
0.1%
99.95 2
 
0.1%
99.85 1
 
< 0.1%
99.74 1
 
< 0.1%
99.41 1
 
< 0.1%
99.37 1
 
< 0.1%

감성레벨값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct800
Distinct (%)35.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean17.542625
Minimum0
Maximum100
Zeros1404
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-10T22:06:13.653682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q327.28
95-th percentile89.621
Maximum100
Range100
Interquartile range (IQR)27.28

Descriptive statistics

Standard deviation29.413018
Coefficient of variation (CV)1.67666
Kurtosis0.97581699
Mean17.542625
Median Absolute Deviation (MAD)0
Skewness1.5324688
Sum39628.79
Variance865.12562
MonotonicityNot monotonic
2023-12-10T22:06:13.784031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1404
62.1%
99.99 4
 
0.2%
100.0 4
 
0.2%
0.08 4
 
0.2%
7.91 3
 
0.1%
44.62 3
 
0.1%
95.98 3
 
0.1%
0.01 3
 
0.1%
83.41 3
 
0.1%
27.28 2
 
0.1%
Other values (790) 826
36.5%
ValueCountFrequency (%)
0.0 1404
62.1%
0.01 3
 
0.1%
0.02 1
 
< 0.1%
0.03 2
 
0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.08 4
 
0.2%
0.09 1
 
< 0.1%
0.1 1
 
< 0.1%
0.12 2
 
0.1%
ValueCountFrequency (%)
100.0 4
0.2%
99.99 4
0.2%
99.98 1
 
< 0.1%
99.93 1
 
< 0.1%
99.92 1
 
< 0.1%
99.91 1
 
< 0.1%
99.83 1
 
< 0.1%
99.76 1
 
< 0.1%
99.58 1
 
< 0.1%
99.52 2
0.1%

전체문서개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct409
Distinct (%)18.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean311.67596
Minimum0
Maximum35443
Zeros190
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-10T22:06:13.915526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median18
Q370
95-th percentile546.2
Maximum35443
Range35443
Interquartile range (IQR)66

Descriptive statistics

Standard deviation2234.0305
Coefficient of variation (CV)7.1677984
Kurtosis136.6604
Mean311.67596
Median Absolute Deviation (MAD)17
Skewness11.340446
Sum704076
Variance4990892.1
MonotonicityNot monotonic
2023-12-10T22:06:14.114988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 190
 
8.4%
1 149
 
6.6%
2 97
 
4.3%
3 94
 
4.2%
4 76
 
3.4%
6 49
 
2.2%
12 47
 
2.1%
5 47
 
2.1%
9 44
 
1.9%
8 44
 
1.9%
Other values (399) 1422
62.9%
ValueCountFrequency (%)
0 190
8.4%
1 149
6.6%
2 97
4.3%
3 94
4.2%
4 76
 
3.4%
5 47
 
2.1%
6 49
 
2.2%
7 43
 
1.9%
8 44
 
1.9%
9 44
 
1.9%
ValueCountFrequency (%)
35443 1
< 0.1%
32782 1
< 0.1%
30681 1
< 0.1%
29241 1
< 0.1%
28672 1
< 0.1%
27284 1
< 0.1%
26305 1
< 0.1%
25692 1
< 0.1%
24081 1
< 0.1%
23294 1
< 0.1%

긍정문서개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct287
Distinct (%)12.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean235.68659
Minimum0
Maximum35134
Zeros1508
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-10T22:06:14.262285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile270.1
Maximum35134
Range35134
Interquartile range (IQR)14

Descriptive statistics

Standard deviation2184.817
Coefficient of variation (CV)9.27001
Kurtosis145.7591
Mean235.68659
Median Absolute Deviation (MAD)0
Skewness11.844217
Sum532416
Variance4773425.4
MonotonicityNot monotonic
2023-12-10T22:06:14.390360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1508
66.7%
1 41
 
1.8%
4 19
 
0.8%
5 18
 
0.8%
2 17
 
0.8%
11 16
 
0.7%
3 15
 
0.7%
9 13
 
0.6%
19 13
 
0.6%
6 11
 
0.5%
Other values (277) 588
 
26.0%
ValueCountFrequency (%)
0 1508
66.7%
1 41
 
1.8%
2 17
 
0.8%
3 15
 
0.7%
4 19
 
0.8%
5 18
 
0.8%
6 11
 
0.5%
7 8
 
0.4%
8 4
 
0.2%
9 13
 
0.6%
ValueCountFrequency (%)
35134 1
< 0.1%
32532 1
< 0.1%
30401 1
< 0.1%
28992 1
< 0.1%
28391 1
< 0.1%
27076 1
< 0.1%
26049 1
< 0.1%
25458 1
< 0.1%
23861 1
< 0.1%
23075 1
< 0.1%

중립문서개수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
0
2259 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0013274
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2259
> 99.9%
<NA> 1
 
< 0.1%

Length

2023-12-10T22:06:14.611374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:06:14.703092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2259
> 99.9%
na 1
 
< 0.1%

부정문서개수
Real number (ℝ)

HIGH CORRELATION 

Distinct278
Distinct (%)12.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean76.638336
Minimum1
Maximum4684
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-10T22:06:14.850233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median12
Q340
95-th percentile244.3
Maximum4684
Range4683
Interquartile range (IQR)36

Descriptive statistics

Standard deviation347.86786
Coefficient of variation (CV)4.5390843
Kurtosis116.69697
Mean76.638336
Median Absolute Deviation (MAD)10
Skewness10.270848
Sum173126
Variance121012.05
MonotonicityNot monotonic
2023-12-10T22:06:15.023401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 250
 
11.1%
2 169
 
7.5%
3 125
 
5.5%
4 95
 
4.2%
7 78
 
3.5%
6 75
 
3.3%
5 72
 
3.2%
8 65
 
2.9%
10 58
 
2.6%
11 55
 
2.4%
Other values (268) 1217
53.8%
ValueCountFrequency (%)
1 250
11.1%
2 169
7.5%
3 125
5.5%
4 95
 
4.2%
5 72
 
3.2%
6 75
 
3.3%
7 78
 
3.5%
8 65
 
2.9%
9 54
 
2.4%
10 58
 
2.6%
ValueCountFrequency (%)
4684 1
< 0.1%
4659 1
< 0.1%
4625 1
< 0.1%
4586 1
< 0.1%
4501 1
< 0.1%
4388 1
< 0.1%
4224 1
< 0.1%
4015 1
< 0.1%
3839 1
< 0.1%
3718 1
< 0.1%

이전전체문서개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct403
Distinct (%)17.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean247.14343
Minimum0
Maximum31250
Zeros172
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-10T22:06:15.155282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median17
Q369
95-th percentile518.2
Maximum31250
Range31250
Interquartile range (IQR)64

Descriptive statistics

Standard deviation1774.8713
Coefficient of variation (CV)7.1815435
Kurtosis162.94576
Mean247.14343
Median Absolute Deviation (MAD)16
Skewness12.253493
Sum558297
Variance3150168
MonotonicityNot monotonic
2023-12-10T22:06:15.284019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 172
 
7.6%
1 133
 
5.9%
2 110
 
4.9%
3 75
 
3.3%
7 74
 
3.3%
4 68
 
3.0%
6 62
 
2.7%
5 58
 
2.6%
9 56
 
2.5%
11 50
 
2.2%
Other values (393) 1401
62.0%
ValueCountFrequency (%)
0 172
7.6%
1 133
5.9%
2 110
4.9%
3 75
3.3%
4 68
 
3.0%
5 58
 
2.6%
6 62
 
2.7%
7 74
3.3%
8 50
 
2.2%
9 56
 
2.5%
ValueCountFrequency (%)
31250 1
< 0.1%
28614 1
< 0.1%
25904 1
< 0.1%
24919 1
< 0.1%
23415 1
< 0.1%
22895 1
< 0.1%
20842 1
< 0.1%
20425 1
< 0.1%
18858 1
< 0.1%
17448 1
< 0.1%

이전긍정문서개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct266
Distinct (%)11.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean109.6857
Minimum0
Maximum21896
Zeros1460
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-10T22:06:15.450429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile248.8
Maximum21896
Range21896
Interquartile range (IQR)11

Descriptive statistics

Standard deviation1011.47
Coefficient of variation (CV)9.2215298
Kurtosis262.84606
Mean109.6857
Median Absolute Deviation (MAD)0
Skewness15.476554
Sum247780
Variance1023071.5
MonotonicityNot monotonic
2023-12-10T22:06:15.622356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1460
64.6%
1 42
 
1.9%
5 32
 
1.4%
3 29
 
1.3%
2 28
 
1.2%
6 18
 
0.8%
4 18
 
0.8%
10 17
 
0.8%
8 17
 
0.8%
30 15
 
0.7%
Other values (256) 583
 
25.8%
ValueCountFrequency (%)
0 1460
64.6%
1 42
 
1.9%
2 28
 
1.2%
3 29
 
1.3%
4 18
 
0.8%
5 32
 
1.4%
6 18
 
0.8%
7 13
 
0.6%
8 17
 
0.8%
9 12
 
0.5%
ValueCountFrequency (%)
21896 1
< 0.1%
18607 1
< 0.1%
17864 1
< 0.1%
15653 1
< 0.1%
15280 1
< 0.1%
12943 1
< 0.1%
12909 1
< 0.1%
10790 1
< 0.1%
7320 1
< 0.1%
6151 1
< 0.1%

이전중립문서개수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
0
2259 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0013274
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2259
> 99.9%
<NA> 1
 
< 0.1%

Length

2023-12-10T22:06:15.768423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:06:15.875497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2259
> 99.9%
na 1
 
< 0.1%

이전부정문서개수
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)13.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean138.08101
Minimum1
Maximum13105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-10T22:06:15.978903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median12
Q348
95-th percentile272.3
Maximum13105
Range13104
Interquartile range (IQR)44

Descriptive statistics

Standard deviation861.79658
Coefficient of variation (CV)6.241239
Kurtosis115.46243
Mean138.08101
Median Absolute Deviation (MAD)10
Skewness10.424376
Sum311925
Variance742693.35
MonotonicityNot monotonic
2023-12-10T22:06:16.106752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 206
 
9.1%
2 162
 
7.2%
3 126
 
5.6%
4 114
 
5.0%
5 90
 
4.0%
8 78
 
3.5%
7 76
 
3.4%
6 62
 
2.7%
10 61
 
2.7%
12 57
 
2.5%
Other values (301) 1227
54.3%
ValueCountFrequency (%)
1 206
9.1%
2 162
7.2%
3 126
5.6%
4 114
5.0%
5 90
4.0%
6 62
 
2.7%
7 76
 
3.4%
8 78
 
3.5%
9 53
 
2.3%
10 61
 
2.7%
ValueCountFrequency (%)
13105 1
< 0.1%
11904 1
< 0.1%
10472 1
< 0.1%
10418 1
< 0.1%
10251 1
< 0.1%
10200 1
< 0.1%
10007 1
< 0.1%
9387 1
< 0.1%
9354 1
< 0.1%
8496 1
< 0.1%

Interactions

2023-12-10T22:06:09.379958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:02.823010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:03.695643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:05.098075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.005059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.858297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:07.716707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:08.560997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:09.465738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:02.929671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:03.825346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:05.261575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.136482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.972070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:07.825558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:08.644052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:09.574767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:03.046717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:03.967535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:05.350737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.236689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:07.056742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:07.926983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:08.755915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:09.698560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:03.150294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:04.102929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:05.451449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.361006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:07.169352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:08.029071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:08.900042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:09.843489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:03.250632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:04.258404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:05.582609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.458396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:07.284962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:08.124425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:09.013323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:09.954521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:03.372920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:04.376005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:05.711879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.545499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:07.421489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:08.222609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:09.110130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:10.072016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:03.480916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:04.482976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:05.806940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.640477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:07.537300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:08.336919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:09.194524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:10.160280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:03.577646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:04.620900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:05.909016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:06.748265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:07.616588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:08.460941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:06:09.282888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:06:16.455999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자주식시장명감성점수값감성레벨값전체문서개수긍정문서개수부정문서개수이전전체문서개수이전긍정문서개수이전부정문서개수
기준일자1.000NaNNaNNaNNaNNaNNaNNaNNaNNaN
주식시장명NaN1.0000.3480.3230.6840.6610.2760.7080.7080.867
감성점수값NaN0.3481.0000.6170.2630.2520.1720.4020.3110.313
감성레벨값NaN0.3230.6171.0000.2440.2490.1790.3150.3750.197
전체문서개수NaN0.6840.2630.2441.0001.0000.6310.9400.9120.792
긍정문서개수NaN0.6610.2520.2491.0001.0000.0000.9450.9270.770
부정문서개수NaN0.2760.1720.1790.6310.0001.0000.5920.0000.921
이전전체문서개수NaN0.7080.4020.3150.9400.9450.5921.0000.9780.880
이전긍정문서개수NaN0.7080.3110.3750.9120.9270.0000.9781.0000.815
이전부정문서개수NaN0.8670.3130.1970.7920.7700.9210.8800.8151.000
2023-12-10T22:06:16.580125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중립문서개수분석일시기준일자주식시장명이전중립문서개수
중립문서개수1.0001.0001.0001.0001.000
분석일시1.0001.0001.0001.0001.000
기준일자1.0001.0001.0001.0001.000
주식시장명1.0001.0001.0001.0001.000
이전중립문서개수1.0001.0001.0001.0001.000
2023-12-10T22:06:16.684706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
감성점수값감성레벨값전체문서개수긍정문서개수부정문서개수이전전체문서개수이전긍정문서개수이전부정문서개수기준일자주식시장명분석일시중립문서개수이전중립문서개수
감성점수값1.0000.6170.5560.9460.1770.5240.6460.4041.0000.2231.0001.0001.000
감성레벨값0.6171.0000.5220.6400.3190.5870.9460.2631.0000.2041.0001.0001.000
전체문서개수0.5560.5221.0000.6360.8630.9130.6010.8141.0000.5651.0001.0001.000
긍정문서개수0.9460.6400.6361.0000.3050.5980.7080.4691.0000.5651.0001.0001.000
부정문서개수0.1770.3190.8630.3051.0000.7750.3860.7211.0000.1261.0001.0001.000
이전전체문서개수0.5240.5870.9130.5980.7751.0000.6680.8871.0000.5671.0001.0001.000
이전긍정문서개수0.6460.9460.6010.7080.3860.6681.0000.3921.0000.5661.0001.0001.000
이전부정문서개수0.4040.2630.8140.4690.7210.8870.3921.0001.0000.5871.0001.0001.000
기준일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주식시장명0.2230.2040.5650.5650.1260.5670.5660.5871.0001.0001.0001.0001.000
분석일시1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중립문서개수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
이전중립문서개수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T22:06:10.316705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:06:10.523724image/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.
2023-12-10T22:06:10.936939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준일자주식종목값주식종목명주식시장명분석일시감성점수값감성레벨값전체문서개수긍정문서개수중립문서개수부정문서개수이전전체문서개수이전긍정문서개수이전중립문서개수이전부정문서개수
020211022TOTALTOTALTOTAL20211098.6335.091809717849024811843415507688
120211022KOSPIKOSPIKOSPI20211099.4136.3616041159460959816356906247
220211022KOSDAQKOSDAQKOSDAQ20211028.7818.84205659101465202738101646
320211022KR7034830000한국토지신탁KOSPI2021100.00.000010001
420211022KR7026960005동서KOSPI20211063.2654.64159062513012
520211022KR7035720002카카오KOSPI202110100.094.98515101228216012
620211022KR7000080002하이트진로KOSPI2021100.046.78500612507
720211022KR7000100008유한양행KOSPI2021100.00.01000118009
820211022KR7000120006CJ대한통운KOSPI2021100.00.012000121170018
920211022KR7000140004하이트진로홀딩스KOSPI2021100.00.000010001
기준일자주식종목값주식종목명주식시장명분석일시감성점수값감성레벨값전체문서개수긍정문서개수중립문서개수부정문서개수이전전체문서개수이전긍정문서개수이전중립문서개수이전부정문서개수
225020211022KR7091700005파트론KOSDAQ2021100.00.0210022170018
225120211022KR7096530001씨젠KOSDAQ20211052.8881.955462880258397325072
225220211022KR7102940004코오롱생명과학KOSDAQ2021100.00.021002290010
225320211022KR7108790007인터파크KOSDAQ2021100.00.079008090010
225420211022KR7112040001위메이드KOSDAQ2021100.024.05122600122712342960938
225520211022KR7122870009와이지엔터테인먼트KOSDAQ20211069.110.010069031970098
225620211022KR7007390008네이처셀KOSDAQ2021100.00.03070030831700318
225720211022KR7108230004톱텍KOSDAQ2021100.00.0510052300031
225820211022KR7215600008신라젠KOSDAQ2021100.00.0250026290030
22592259 217857 20211023024014 F_BBP20_00044<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

기준일자주식종목값주식종목명주식시장명분석일시감성점수값감성레벨값전체문서개수긍정문서개수중립문서개수부정문서개수이전전체문서개수이전긍정문서개수이전중립문서개수이전부정문서개수# duplicates
620211022KR7000140004하이트진로홀딩스KOSPI2021100.00.0000100019
1020211022KR7000480004조선내화KOSPI2021100.00.0000110029
2620211022KR7003240009태광산업KOSPI2021100.00.0000110029
2720211022KR7003300001한일시멘트KOSPI2021100.00.0000100019
3920211022KR7004700001조광피혁KOSPI2021100.00.0000100019
4620211022KR7005830005DB손해보험KOSPI2021100.00.0000100019
8020211022KR7016100000산성앨엔에스KOSDAQ2021100.00.0100210029
13420211022KR7084870005TBH글로벌KOSPI2021100.00.0100200019
14120211022KR7108790007인터파크KOSDAQ2021100.00.0530054900108
1220211022KR7000670000영풍KOSPI2021100.00.0100200017