Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory810.5 KiB
Average record size in memory83.0 B

Variable types

Categorical2
Text3
Numeric3
Boolean1

Dataset

Description뉴스기반 통계검색 서비스 내의 주요 키워드, 키워드 관계망 그래프 작성을 위한 단어 개수 및 단어 간 상관관계에 대한 자료입니다.
URLhttps://www.data.go.kr/data/15121128/fileData.do

Alerts

사용여부 has constant value ""Constant
단어개수 is highly overall correlated with 관계개수 and 1 other fieldsHigh correlation
관계개수 is highly overall correlated with 단어개수 and 1 other fieldsHigh correlation
상위관계개수 is highly overall correlated with 단어개수 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 18:04:24.533703
Analysis finished2023-12-12 18:04:26.851062
Duration2.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록일자
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-08-06
7626 
2022-08-13
1277 
2022-08-20
966 
2022-08-27
 
131

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-20
2nd row2022-08-06
3rd row2022-08-06
4th row2022-08-06
5th row2022-08-06

Common Values

ValueCountFrequency (%)
2022-08-06 7626
76.3%
2022-08-13 1277
 
12.8%
2022-08-20 966
 
9.7%
2022-08-27 131
 
1.3%

Length

2023-12-13T03:04:26.936354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:04:27.070303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-06 7626
76.3%
2022-08-13 1277
 
12.8%
2022-08-20 966
 
9.7%
2022-08-27 131
 
1.3%

단어
Text

Distinct1609
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:04:27.394393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.3576
Min length1

Characters and Unicode

Total characters33576
Distinct characters571
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)1.9%

Sample

1st row사태
2nd row극대화
3rd row직장인
4th row공업제품
5th row동시간대
ValueCountFrequency (%)
코로나19 44
 
0.4%
확진자 40
 
0.4%
베트남 40
 
0.4%
사망자 39
 
0.4%
대기업 39
 
0.4%
배터리 38
 
0.4%
백신 37
 
0.4%
시스템 37
 
0.4%
치료제 35
 
0.4%
우크라이나 35
 
0.4%
Other values (1599) 9616
96.2%
2023-12-13T03:04:27.911474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
자 800
 
2.4%
기 597
 
1.8%
이 566
 
1.7%
리 546
 
1.6%
스 512
 
1.5%
사 489
 
1.5%
가 475
 
1.4%
업 446
 
1.3%
대 440
 
1.3%
인 422
 
1.3%
Other values (561) 28283
84.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32670
97.3%
Uppercase Letter 735
 
2.2%
Decimal Number 88
 
0.3%
Lowercase Letter 83
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 800
 
2.4%
기 597
 
1.8%
이 566
 
1.7%
리 546
 
1.7%
스 512
 
1.6%
사 489
 
1.5%
가 475
 
1.5%
업 446
 
1.4%
대 440
 
1.3%
인 422
 
1.3%
Other values (534) 27377
83.8%
Uppercase Letter
ValueCountFrequency (%)
S 121
16.5%
G 94
12.8%
D 52
 
7.1%
P 49
 
6.7%
K 49
 
6.7%
L 43
 
5.9%
A 37
 
5.0%
N 35
 
4.8%
C 33
 
4.5%
T 30
 
4.1%
Other values (11) 192
26.1%
Lowercase Letter
ValueCountFrequency (%)
e 33
39.8%
s 22
26.5%
w 22
26.5%
m 6
 
7.2%
Decimal Number
ValueCountFrequency (%)
9 44
50.0%
1 44
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32670
97.3%
Latin 818
 
2.4%
Common 88
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 800
 
2.4%
기 597
 
1.8%
이 566
 
1.7%
리 546
 
1.7%
스 512
 
1.6%
사 489
 
1.5%
가 475
 
1.5%
업 446
 
1.4%
대 440
 
1.3%
인 422
 
1.3%
Other values (534) 27377
83.8%
Latin
ValueCountFrequency (%)
S 121
14.8%
G 94
 
11.5%
D 52
 
6.4%
P 49
 
6.0%
K 49
 
6.0%
L 43
 
5.3%
A 37
 
4.5%
N 35
 
4.3%
C 33
 
4.0%
e 33
 
4.0%
Other values (15) 272
33.3%
Common
ValueCountFrequency (%)
9 44
50.0%
1 44
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32670
97.3%
ASCII 906
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
자 800
 
2.4%
기 597
 
1.8%
이 566
 
1.7%
리 546
 
1.7%
스 512
 
1.6%
사 489
 
1.5%
가 475
 
1.5%
업 446
 
1.4%
대 440
 
1.3%
인 422
 
1.3%
Other values (534) 27377
83.8%
ASCII
ValueCountFrequency (%)
S 121
 
13.4%
G 94
 
10.4%
D 52
 
5.7%
P 49
 
5.4%
K 49
 
5.4%
9 44
 
4.9%
1 44
 
4.9%
L 43
 
4.7%
A 37
 
4.1%
N 35
 
3.9%
Other values (17) 338
37.3%

단어개수
Real number (ℝ)

HIGH CORRELATION 

Distinct677
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315.9528
Minimum14
Maximum10789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:04:28.118182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15
Q149
median133
Q3307
95-th percentile1152
Maximum10789
Range10775
Interquartile range (IQR)258

Descriptive statistics

Standard deviation616.68367
Coefficient of variation (CV)1.9518222
Kurtosis85.214594
Mean315.9528
Median Absolute Deviation (MAD)102
Skewness7.3441233
Sum3159528
Variance380298.75
MonotonicityNot monotonic
2023-12-13T03:04:28.324076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 415
 
4.2%
15 405
 
4.0%
16 186
 
1.9%
17 138
 
1.4%
18 127
 
1.3%
123 107
 
1.1%
22 106
 
1.1%
122 106
 
1.1%
20 99
 
1.0%
116 94
 
0.9%
Other values (667) 8217
82.2%
ValueCountFrequency (%)
14 415
4.2%
15 405
4.0%
16 186
1.9%
17 138
 
1.4%
18 127
 
1.3%
19 59
 
0.6%
20 99
 
1.0%
21 75
 
0.8%
22 106
 
1.1%
23 59
 
0.6%
ValueCountFrequency (%)
10789 4
< 0.1%
9367 7
0.1%
6857 6
0.1%
5517 8
0.1%
5471 1
 
< 0.1%
5171 8
0.1%
4418 5
0.1%
4210 4
< 0.1%
4196 1
 
< 0.1%
4124 5
0.1%

관계단어
Text

Distinct1904
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:04:28.690148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.411
Min length1

Characters and Unicode

Total characters34110
Distinct characters667
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique772 ?
Unique (%)7.7%

Sample

1st row계절근로자
2nd row데이터
3rd row이마트
4th row석유류
5th row임시선별검사소
ValueCountFrequency (%)
코로나19 375
 
3.8%
가격 168
 
1.7%
금리 152
 
1.5%
물가 130
 
1.3%
소비자 115
 
1.1%
상승 110
 
1.1%
확진자 100
 
1.0%
삼성전자 98
 
1.0%
추석 91
 
0.9%
인플레이션 85
 
0.9%
Other values (1894) 8576
85.8%
2023-12-13T03:04:29.235312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
자 869
 
2.5%
가 635
 
1.9%
이 621
 
1.8%
리 566
 
1.7%
로 565
 
1.7%
상 484
 
1.4%
나 481
 
1.4%
금 474
 
1.4%
스 453
 
1.3%
코 448
 
1.3%
Other values (657) 28514
83.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32520
95.3%
Uppercase Letter 806
 
2.4%
Decimal Number 750
 
2.2%
Lowercase Letter 34
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 869
 
2.7%
가 635
 
2.0%
이 621
 
1.9%
리 566
 
1.7%
로 565
 
1.7%
상 484
 
1.5%
나 481
 
1.5%
금 474
 
1.5%
스 453
 
1.4%
코 448
 
1.4%
Other values (619) 26924
82.8%
Uppercase Letter
ValueCountFrequency (%)
S 139
17.2%
G 77
 
9.6%
C 62
 
7.7%
D 62
 
7.7%
K 51
 
6.3%
E 46
 
5.7%
A 43
 
5.3%
L 42
 
5.2%
T 37
 
4.6%
M 34
 
4.2%
Other values (15) 213
26.4%
Lowercase Letter
ValueCountFrequency (%)
m 9
26.5%
e 7
20.6%
w 3
 
8.8%
a 3
 
8.8%
l 3
 
8.8%
i 3
 
8.8%
s 2
 
5.9%
t 1
 
2.9%
v 1
 
2.9%
p 1
 
2.9%
Decimal Number
ValueCountFrequency (%)
9 375
50.0%
1 375
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32520
95.3%
Latin 840
 
2.5%
Common 750
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 869
 
2.7%
가 635
 
2.0%
이 621
 
1.9%
리 566
 
1.7%
로 565
 
1.7%
상 484
 
1.5%
나 481
 
1.5%
금 474
 
1.5%
스 453
 
1.4%
코 448
 
1.4%
Other values (619) 26924
82.8%
Latin
ValueCountFrequency (%)
S 139
16.5%
G 77
 
9.2%
C 62
 
7.4%
D 62
 
7.4%
K 51
 
6.1%
E 46
 
5.5%
A 43
 
5.1%
L 42
 
5.0%
T 37
 
4.4%
M 34
 
4.0%
Other values (26) 247
29.4%
Common
ValueCountFrequency (%)
9 375
50.0%
1 375
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32520
95.3%
ASCII 1590
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
자 869
 
2.7%
가 635
 
2.0%
이 621
 
1.9%
리 566
 
1.7%
로 565
 
1.7%
상 484
 
1.5%
나 481
 
1.5%
금 474
 
1.5%
스 453
 
1.4%
코 448
 
1.4%
Other values (619) 26924
82.8%
ASCII
ValueCountFrequency (%)
9 375
23.6%
1 375
23.6%
S 139
 
8.7%
G 77
 
4.8%
C 62
 
3.9%
D 62
 
3.9%
K 51
 
3.2%
E 46
 
2.9%
A 43
 
2.7%
L 42
 
2.6%
Other values (28) 318
20.0%

관계개수
Real number (ℝ)

HIGH CORRELATION 

Distinct579
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.8885
Minimum2
Maximum5455
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:04:29.405757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q132
median63
Q3126
95-th percentile330
Maximum5455
Range5453
Interquartile range (IQR)94

Descriptive statistics

Standard deviation167.81228
Coefficient of variation (CV)1.5699751
Kurtosis350.37034
Mean106.8885
Median Absolute Deviation (MAD)38
Skewness13.518669
Sum1068885
Variance28160.961
MonotonicityNot monotonic
2023-12-13T03:04:29.569191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 141
 
1.4%
20 136
 
1.4%
33 132
 
1.3%
26 126
 
1.3%
27 126
 
1.3%
29 116
 
1.2%
15 114
 
1.1%
17 110
 
1.1%
35 109
 
1.1%
28 109
 
1.1%
Other values (569) 8781
87.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 6
 
0.1%
4 8
 
0.1%
5 21
 
0.2%
6 24
 
0.2%
7 28
 
0.3%
8 44
0.4%
9 55
0.5%
10 65
0.7%
11 72
0.7%
ValueCountFrequency (%)
5455 2
< 0.1%
5171 1
< 0.1%
4392 1
< 0.1%
2394 1
< 0.1%
2196 1
< 0.1%
2154 1
< 0.1%
2030 1
< 0.1%
1996 1
< 0.1%
1821 2
< 0.1%
1820 1
< 0.1%

상위관계단어
Text

Distinct4670
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:04:29.925702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length3.6047
Min length1

Characters and Unicode

Total characters36047
Distinct characters792
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2931 ?
Unique (%)29.3%

Sample

1st row외국인
2nd row낸드플래시
3rd row샐러드
4th row외환위기
5th row대구중구
ValueCountFrequency (%)
소비자 46
 
0.5%
고물가 29
 
0.3%
상승세 28
 
0.3%
중소기업 28
 
0.3%
대형마트 28
 
0.3%
상승률 24
 
0.2%
기준금리 22
 
0.2%
대출 22
 
0.2%
프로그램 22
 
0.2%
국제원유가격 21
 
0.2%
Other values (4660) 9730
97.3%
2023-12-13T03:04:30.516373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
자 705
 
2.0%
이 587
 
1.6%
기 575
 
1.6%
스 573
 
1.6%
리 532
 
1.5%
대 477
 
1.3%
가 475
 
1.3%
업 449
 
1.2%
사 442
 
1.2%
수 434
 
1.2%
Other values (782) 30798
85.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34731
96.3%
Uppercase Letter 1003
 
2.8%
Lowercase Letter 277
 
0.8%
Decimal Number 36
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 705
 
2.0%
이 587
 
1.7%
기 575
 
1.7%
스 573
 
1.6%
리 532
 
1.5%
대 477
 
1.4%
가 475
 
1.4%
업 449
 
1.3%
사 442
 
1.3%
수 434
 
1.2%
Other values (731) 29482
84.9%
Uppercase Letter
ValueCountFrequency (%)
S 135
13.5%
G 84
 
8.4%
C 79
 
7.9%
D 69
 
6.9%
M 64
 
6.4%
K 55
 
5.5%
T 55
 
5.5%
P 52
 
5.2%
L 48
 
4.8%
I 44
 
4.4%
Other values (16) 318
31.7%
Lowercase Letter
ValueCountFrequency (%)
e 44
15.9%
a 24
 
8.7%
i 21
 
7.6%
o 20
 
7.2%
s 19
 
6.9%
r 18
 
6.5%
n 18
 
6.5%
m 16
 
5.8%
l 14
 
5.1%
w 13
 
4.7%
Other values (13) 70
25.3%
Decimal Number
ValueCountFrequency (%)
9 18
50.0%
1 18
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34731
96.3%
Latin 1280
 
3.6%
Common 36
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 705
 
2.0%
이 587
 
1.7%
기 575
 
1.7%
스 573
 
1.6%
리 532
 
1.5%
대 477
 
1.4%
가 475
 
1.4%
업 449
 
1.3%
사 442
 
1.3%
수 434
 
1.2%
Other values (731) 29482
84.9%
Latin
ValueCountFrequency (%)
S 135
 
10.5%
G 84
 
6.6%
C 79
 
6.2%
D 69
 
5.4%
M 64
 
5.0%
K 55
 
4.3%
T 55
 
4.3%
P 52
 
4.1%
L 48
 
3.8%
I 44
 
3.4%
Other values (39) 595
46.5%
Common
ValueCountFrequency (%)
9 18
50.0%
1 18
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34731
96.3%
ASCII 1316
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
자 705
 
2.0%
이 587
 
1.7%
기 575
 
1.7%
스 573
 
1.6%
리 532
 
1.5%
대 477
 
1.4%
가 475
 
1.4%
업 449
 
1.3%
사 442
 
1.3%
수 434
 
1.2%
Other values (731) 29482
84.9%
ASCII
ValueCountFrequency (%)
S 135
 
10.3%
G 84
 
6.4%
C 79
 
6.0%
D 69
 
5.2%
M 64
 
4.9%
K 55
 
4.2%
T 55
 
4.2%
P 52
 
4.0%
L 48
 
3.6%
I 44
 
3.3%
Other values (41) 631
47.9%

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
10000 
ValueCountFrequency (%)
True 10000
100.0%
2023-12-13T03:04:30.657064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

상위관계개수
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.1596
Minimum1
Maximum425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:04:30.786459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median15
Q327
95-th percentile59
Maximum425
Range424
Interquartile range (IQR)18

Descriptive statistics

Standard deviation20.689005
Coefficient of variation (CV)0.97775974
Kurtosis33.181346
Mean21.1596
Median Absolute Deviation (MAD)8
Skewness3.8118569
Sum211596
Variance428.03493
MonotonicityNot monotonic
2023-12-13T03:04:30.951672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 496
 
5.0%
10 483
 
4.8%
6 458
 
4.6%
9 443
 
4.4%
7 416
 
4.2%
12 415
 
4.2%
11 410
 
4.1%
13 381
 
3.8%
5 374
 
3.7%
14 339
 
3.4%
Other values (144) 5785
57.9%
ValueCountFrequency (%)
1 21
 
0.2%
2 108
 
1.1%
3 247
2.5%
4 332
3.3%
5 374
3.7%
6 458
4.6%
7 416
4.2%
8 496
5.0%
9 443
4.4%
10 483
4.8%
ValueCountFrequency (%)
425 1
< 0.1%
338 1
< 0.1%
252 1
< 0.1%
247 1
< 0.1%
240 1
< 0.1%
206 2
< 0.1%
204 1
< 0.1%
202 1
< 0.1%
198 1
< 0.1%
194 1
< 0.1%

키워드별코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
ECO_KWD
3598 
FRMPRD_KWD
3225 
COVID_ECO_KWD
1318 
COVID_SOC_KWD
1045 
INSTITUTE_KWD
814 

Length

Max length13
Median length10
Mean length9.8737
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFRMPRD_KWD
2nd rowECO_KWD
3rd rowECO_KWD
4th rowECO_KWD
5th rowCOVID_SOC_KWD

Common Values

ValueCountFrequency (%)
ECO_KWD 3598
36.0%
FRMPRD_KWD 3225
32.2%
COVID_ECO_KWD 1318
 
13.2%
COVID_SOC_KWD 1045
 
10.4%
INSTITUTE_KWD 814
 
8.1%

Length

2023-12-13T03:04:31.126156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:04:31.262106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
eco_kwd 3598
36.0%
frmprd_kwd 3225
32.2%
covid_eco_kwd 1318
 
13.2%
covid_soc_kwd 1045
 
10.4%
institute_kwd 814
 
8.1%

Interactions

2023-12-13T03:04:26.338738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:04:25.765733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:04:26.047363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:04:26.418364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:04:25.870261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:04:26.152529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:04:26.505481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:04:25.952656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:04:26.248812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:04:31.370303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록일자단어개수관계개수상위관계개수키워드별코드
등록일자1.0000.1010.0060.0530.187
단어개수0.1011.0000.5290.6170.422
관계개수0.0060.5291.0000.6470.153
상위관계개수0.0530.6170.6471.0000.320
키워드별코드0.1870.4220.1530.3201.000
2023-12-13T03:04:31.485558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
키워드별코드등록일자
키워드별코드1.0000.153
등록일자0.1531.000
2023-12-13T03:04:31.581981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단어개수관계개수상위관계개수등록일자키워드별코드
단어개수1.0000.7620.7010.0650.260
관계개수0.7621.0000.7960.0040.098
상위관계개수0.7010.7961.0000.0240.202
등록일자0.0650.0040.0241.0000.153
키워드별코드0.2600.0980.2020.1531.000

Missing values

2023-12-13T03:04:26.628421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:04:26.764278image/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

등록일자단어단어개수관계단어관계개수상위관계단어사용여부상위관계개수키워드별코드
420462022-08-20사태18계절근로자32외국인Y13FRMPRD_KWD
505872022-08-06극대화398데이터104낸드플래시Y40ECO_KWD
538262022-08-06직장인273이마트73샐러드Y23ECO_KWD
47682022-08-06공업제품151석유류223외환위기Y62ECO_KWD
812482022-08-06동시간대91임시선별검사소17대구중구Y4COVID_SOC_KWD
388932022-08-13토마토113농작물35서울Y13FRMPRD_KWD
678492022-08-06의약품117바이오109CMOY14COVID_ECO_KWD
183632022-08-20한의원134브로커159알선수수료Y11ECO_KWD
287622022-08-06사업부문123카카오톡39오픈채팅Y21ECO_KWD
189272022-08-06도입64가격100만톤Y18FRMPRD_KWD
등록일자단어단어개수관계단어관계개수상위관계단어사용여부상위관계개수키워드별코드
524112022-08-06상승률1066석유류291근원물가Y48INSTITUTE_KWD
272102022-08-06지배구조381친환경189지속가능경영보고서Y44ECO_KWD
337012022-08-06강진14벌떼3팔Y1FRMPRD_KWD
825162022-08-06다중채무자128대출71외국인Y6ECO_KWD
866642022-08-20총파업137근무시간28은행문Y4ECO_KWD
256532022-08-06정상화318둔촌주공95시공사업단Y41ECO_KWD
562352022-08-06산업평균지수122인플레이션109연방준비은행Y20ECO_KWD
458482022-08-06입주민247관리비56리얼하우스Y20ECO_KWD
168922022-08-06기자회견1051초등학교273취학연령Y125INSTITUTE_KWD
847612022-08-13해외유입223질병관리청58지역발생Y14COVID_SOC_KWD