Overview

Dataset statistics

Number of variables15
Number of observations1301
Missing cells575
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory160.2 KiB
Average record size in memory126.1 B

Variable types

Numeric4
Categorical5
Text2
DateTime4

Dataset

Description서울특별시 금천구의회 의안처리결과정보로 의안코드, 본회의처리결과, 대정보, 회기 회부일, 상정일, 처리일, 보고일, 본회의여부, 상임위처리결과, 상임위대정보 상임위회기, 상임위회부일 상임위상정일을 제공합니다.
Author서울특별시 금천구
URLhttps://www.data.go.kr/data/15064437/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
본회의처리결과 is highly overall correlated with 본회의여부 and 1 other fieldsHigh correlation
상임위처리결과 is highly overall correlated with 본회의처리결과 and 1 other fieldsHigh correlation
본회의여부 is highly overall correlated with 본회의처리결과 and 1 other fieldsHigh correlation
의안코드 is highly overall correlated with 대정보 and 3 other fieldsHigh correlation
대정보 is highly overall correlated with 의안코드 and 2 other fieldsHigh correlation
회기 is highly overall correlated with 의안코드 and 3 other fieldsHigh correlation
회기.1 is highly overall correlated with 의안코드 and 3 other fieldsHigh correlation
대정보.1 is highly overall correlated with 의안코드 and 2 other fieldsHigh correlation
본회의처리결과 is highly imbalanced (76.6%)Imbalance
본회의여부 is highly imbalanced (94.0%)Imbalance
상임위처리결과 is highly imbalanced (62.9%)Imbalance
회부일 has 21 (1.6%) missing valuesMissing
상정일 has 17 (1.3%) missing valuesMissing
처리일 has 17 (1.3%) missing valuesMissing
보고일 has 143 (11.0%) missing valuesMissing
회부일.1 has 187 (14.4%) missing valuesMissing
상정일.1 has 189 (14.5%) missing valuesMissing
대정보 is highly skewed (γ1 = 31.37982769)Skewed
의안코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:55:09.037231
Analysis finished2023-12-12 11:55:12.221823
Duration3.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

의안코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1301
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62412.443
Minimum40658
Maximum82003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-12T20:55:12.310068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40658
5-th percentile40768
Q151028
median61353
Q371678
95-th percentile81938
Maximum82003
Range41345
Interquartile range (IQR)20650

Descriptive statistics

Standard deviation12887.156
Coefficient of variation (CV)0.20648376
Kurtosis-1.0667922
Mean62412.443
Median Absolute Deviation (MAD)10325
Skewness-0.0285095
Sum81198588
Variance1.6607878 × 108
MonotonicityStrictly increasing
2023-12-12T20:55:12.480610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40658 1
 
0.1%
71558 1
 
0.1%
71576 1
 
0.1%
71575 1
 
0.1%
71574 1
 
0.1%
71573 1
 
0.1%
71572 1
 
0.1%
71571 1
 
0.1%
71570 1
 
0.1%
71569 1
 
0.1%
Other values (1291) 1291
99.2%
ValueCountFrequency (%)
40658 1
0.1%
40659 1
0.1%
40660 1
0.1%
40661 1
0.1%
40662 1
0.1%
40663 1
0.1%
40664 1
0.1%
40665 1
0.1%
40666 1
0.1%
40667 1
0.1%
ValueCountFrequency (%)
82003 1
0.1%
82002 1
0.1%
82001 1
0.1%
82000 1
0.1%
81999 1
0.1%
81998 1
0.1%
81997 1
0.1%
81996 1
0.1%
81995 1
0.1%
81994 1
0.1%

본회의처리결과
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
원안가결
1122 
수정가결
 
94
원안채택
 
32
수정의결
 
18
<NA>
 
9
Other values (9)
 
26

Length

Max length6
Median length4
Mean length3.9969254
Min length2

Unique

Unique4 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
원안가결 1122
86.2%
수정가결 94
 
7.2%
원안채택 32
 
2.5%
수정의결 18
 
1.4%
<NA> 9
 
0.7%
의견서채택 8
 
0.6%
계류중 6
 
0.5%
임기만료폐기 3
 
0.2%
부결 3
 
0.2%
원안의결 2
 
0.2%
Other values (4) 4
 
0.3%

Length

2023-12-12T20:55:12.652136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
원안가결 1122
86.2%
수정가결 94
 
7.2%
원안채택 32
 
2.5%
수정의결 18
 
1.4%
na 9
 
0.7%
의견서채택 8
 
0.6%
계류중 6
 
0.5%
임기만료폐기 3
 
0.2%
부결 3
 
0.2%
원안의결 2
 
0.2%
Other values (4) 4
 
0.3%

대정보
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.217525
Minimum4
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-12T20:55:12.794438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum151
Range147
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.2076341
Coefficient of variation (CV)0.67673779
Kurtosis1080.3698
Mean6.217525
Median Absolute Deviation (MAD)1
Skewness31.379828
Sum8089
Variance17.704185
MonotonicityNot monotonic
2023-12-12T20:55:12.931402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 324
24.9%
7 316
24.3%
6 307
23.6%
8 213
16.4%
4 140
10.8%
151 1
 
0.1%
ValueCountFrequency (%)
4 140
10.8%
5 324
24.9%
6 307
23.6%
7 316
24.3%
8 213
16.4%
151 1
 
0.1%
ValueCountFrequency (%)
151 1
 
0.1%
8 213
16.4%
7 316
24.3%
6 307
23.6%
5 324
24.9%
4 140
10.8%

회기
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)10.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean161.51154
Minimum1
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-12T20:55:13.118356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96
Q1127
median163
Q3200
95-th percentile219
Maximum223
Range222
Interquartile range (IQR)73

Descriptive statistics

Standard deviation41.250757
Coefficient of variation (CV)0.2554044
Kurtosis-1.0087465
Mean161.51154
Median Absolute Deviation (MAD)37
Skewness-0.21952879
Sum209965
Variance1701.625
MonotonicityNot monotonic
2023-12-12T20:55:13.302673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 41
 
3.2%
138 33
 
2.5%
110 33
 
2.5%
218 29
 
2.2%
155 27
 
2.1%
201 27
 
2.1%
119 26
 
2.0%
219 26
 
2.0%
191 25
 
1.9%
164 25
 
1.9%
Other values (124) 1008
77.5%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
85 4
 
0.3%
86 2
 
0.2%
87 7
0.5%
88 4
 
0.3%
89 14
1.1%
90 4
 
0.3%
94 6
0.5%
95 9
0.7%
ValueCountFrequency (%)
223 21
1.6%
222 6
 
0.5%
221 16
1.2%
220 3
 
0.2%
219 26
2.0%
218 29
2.2%
217 1
 
0.1%
216 24
1.8%
215 22
1.7%
214 8
 
0.6%

회부일
Text

MISSING 

Distinct237
Distinct (%)18.5%
Missing21
Missing (%)1.6%
Memory size10.3 KiB
2023-12-12T20:55:13.683878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters12800
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

Unique78 ?
Unique (%)6.1%

Sample

1st row2004-01-13
2nd row2004-01-13
3rd row2004-01-13
4th row2004-01-13
5th row2004-03-11
ValueCountFrequency (%)
2010-12-15 40
 
3.1%
2009-12-15 31
 
2.4%
2006-12-15 30
 
2.3%
2019-09-23 29
 
2.3%
2007-12-14 25
 
2.0%
2017-04-26 25
 
2.0%
2012-12-13 24
 
1.9%
2019-12-17 24
 
1.9%
2015-12-16 24
 
1.9%
2011-12-14 24
 
1.9%
Other values (227) 1004
78.4%
2023-12-12T20:55:14.223657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2942
23.0%
- 2558
20.0%
2 2438
19.0%
1 2117
16.5%
9 499
 
3.9%
5 435
 
3.4%
6 415
 
3.2%
7 411
 
3.2%
3 358
 
2.8%
4 344
 
2.7%
Other values (2) 283
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10240
80.0%
Dash Punctuation 2558
 
20.0%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2942
28.7%
2 2438
23.8%
1 2117
20.7%
9 499
 
4.9%
5 435
 
4.2%
6 415
 
4.1%
7 411
 
4.0%
3 358
 
3.5%
4 344
 
3.4%
8 281
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 2558
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2942
23.0%
- 2558
20.0%
2 2438
19.0%
1 2117
16.5%
9 499
 
3.9%
5 435
 
3.4%
6 415
 
3.2%
7 411
 
3.2%
3 358
 
2.8%
4 344
 
2.7%
Other values (2) 283
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2942
23.0%
- 2558
20.0%
2 2438
19.0%
1 2117
16.5%
9 499
 
3.9%
5 435
 
3.4%
6 415
 
3.2%
7 411
 
3.2%
3 358
 
2.8%
4 344
 
2.7%
Other values (2) 283
 
2.2%

상정일
Text

MISSING 

Distinct237
Distinct (%)18.5%
Missing17
Missing (%)1.3%
Memory size10.3 KiB
2023-12-12T20:55:14.556331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9984424
Min length8

Characters and Unicode

Total characters12838
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

Unique75 ?
Unique (%)5.8%

Sample

1st row2004-01-13
2nd row2005-01-13
3rd row2004-01-13
4th row2004-01-13
5th row2004-03-11
ValueCountFrequency (%)
2010-12-15 40
 
3.1%
2009-12-15 31
 
2.4%
2006-12-15 30
 
2.3%
2019-09-23 29
 
2.3%
2017-04-26 25
 
1.9%
2007-12-14 25
 
1.9%
2019-12-17 24
 
1.9%
2012-12-13 24
 
1.9%
2011-12-14 24
 
1.9%
2015-12-16 24
 
1.9%
Other values (227) 1008
78.5%
2023-12-12T20:55:15.047404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2956
23.0%
- 2566
20.0%
2 2442
19.0%
1 2110
16.4%
9 497
 
3.9%
5 437
 
3.4%
6 427
 
3.3%
7 414
 
3.2%
3 352
 
2.7%
4 351
 
2.7%
Other values (2) 286
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10270
80.0%
Dash Punctuation 2566
 
20.0%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2956
28.8%
2 2442
23.8%
1 2110
20.5%
9 497
 
4.8%
5 437
 
4.3%
6 427
 
4.2%
7 414
 
4.0%
3 352
 
3.4%
4 351
 
3.4%
8 284
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 2566
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12838
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2956
23.0%
- 2566
20.0%
2 2442
19.0%
1 2110
16.4%
9 497
 
3.9%
5 437
 
3.4%
6 427
 
3.3%
7 414
 
3.2%
3 352
 
2.7%
4 351
 
2.7%
Other values (2) 286
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2956
23.0%
- 2566
20.0%
2 2442
19.0%
1 2110
16.4%
9 497
 
3.9%
5 437
 
3.4%
6 427
 
3.3%
7 414
 
3.2%
3 352
 
2.7%
4 351
 
2.7%
Other values (2) 286
 
2.2%

처리일
Date

MISSING 

Distinct234
Distinct (%)18.2%
Missing17
Missing (%)1.3%
Memory size10.3 KiB
Minimum2004-01-13 00:00:00
Maximum2020-06-29 00:00:00
2023-12-12T20:55:15.222219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:15.380802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

보고일
Date

MISSING 

Distinct205
Distinct (%)17.7%
Missing143
Missing (%)11.0%
Memory size10.3 KiB
Minimum2005-03-24 00:00:00
Maximum2020-06-29 00:00:00
2023-12-12T20:55:15.556579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:15.756939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

본회의여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
1
1292 
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1292
99.3%
0 9
 
0.7%

Length

2023-12-12T20:55:15.974254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:55:16.113189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1292
99.3%
0 9
 
0.7%

상임위처리결과
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
원안가결
965 
수정가결
 
91
<NA>
 
82
계류중
 
74
원안채택
 
24
Other values (13)
 
65

Length

Max length5
Median length4
Mean length3.9508071
Min length2

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
원안가결 965
74.2%
수정가결 91
 
7.0%
<NA> 82
 
6.3%
계류중 74
 
5.7%
원안채택 24
 
1.8%
본회의직상 19
 
1.5%
수정의결 16
 
1.2%
의견서채택 7
 
0.5%
폐기 6
 
0.5%
위원회안 3
 
0.2%
Other values (8) 14
 
1.1%

Length

2023-12-12T20:55:16.575100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
원안가결 965
74.2%
수정가결 91
 
7.0%
na 82
 
6.3%
계류중 74
 
5.7%
원안채택 24
 
1.8%
본회의직상 19
 
1.5%
수정의결 16
 
1.2%
의견서채택 7
 
0.5%
폐기 6
 
0.5%
보류 3
 
0.2%
Other values (8) 14
 
1.1%

대정보.1
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
5
324 
7
316 
6
308 
8
213 
4
140 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
5 324
24.9%
7 316
24.3%
6 308
23.7%
8 213
16.4%
4 140
10.8%

Length

2023-12-12T20:55:16.709203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:55:16.843386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 324
24.9%
7 316
24.3%
6 308
23.7%
8 213
16.4%
4 140
10.8%

회기.1
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.66487
Minimum1
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-12T20:55:16.988883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96
Q1127
median163
Q3200
95-th percentile219
Maximum223
Range222
Interquartile range (IQR)73

Descriptive statistics

Standard deviation41.011254
Coefficient of variation (CV)0.25368068
Kurtosis-1.1367346
Mean161.66487
Median Absolute Deviation (MAD)37
Skewness-0.18767772
Sum210326
Variance1681.923
MonotonicityNot monotonic
2023-12-12T20:55:17.149363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 41
 
3.2%
110 33
 
2.5%
138 31
 
2.4%
218 29
 
2.2%
201 27
 
2.1%
155 27
 
2.1%
119 26
 
2.0%
219 26
 
2.0%
128 25
 
1.9%
191 25
 
1.9%
Other values (123) 1011
77.7%
ValueCountFrequency (%)
1 1
 
0.1%
85 4
 
0.3%
86 2
 
0.2%
87 7
 
0.5%
88 4
 
0.3%
89 14
1.1%
90 4
 
0.3%
94 6
 
0.5%
95 9
0.7%
96 19
1.5%
ValueCountFrequency (%)
223 21
1.6%
222 6
 
0.5%
221 16
1.2%
220 3
 
0.2%
219 26
2.0%
218 29
2.2%
217 1
 
0.1%
216 24
1.8%
215 22
1.7%
214 8
 
0.6%

회부일.1
Date

MISSING 

Distinct245
Distinct (%)22.0%
Missing187
Missing (%)14.4%
Memory size10.3 KiB
Minimum2004-05-01 00:00:00
Maximum2020-06-23 00:00:00
2023-12-12T20:55:17.298751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:17.438627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

상정일.1
Date

MISSING 

Distinct283
Distinct (%)25.4%
Missing189
Missing (%)14.5%
Memory size10.3 KiB
Minimum2004-05-01 00:00:00
Maximum2020-06-23 00:00:00
2023-12-12T20:55:17.607761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:17.755823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2020-09-04
1301 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-09-04
2nd row2020-09-04
3rd row2020-09-04
4th row2020-09-04
5th row2020-09-04

Common Values

ValueCountFrequency (%)
2020-09-04 1301
100.0%

Length

2023-12-12T20:55:17.906450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:55:18.014401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-09-04 1301
100.0%

Interactions

2023-12-12T20:55:10.976390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:09.741407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:10.139698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:10.582806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:11.122118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:09.846044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:10.252393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:10.676672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:11.253228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:09.959929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:10.361485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:10.790730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:11.403825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:10.045492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:10.473606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:55:10.879677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:55:18.096528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의안코드본회의처리결과대정보회기본회의여부상임위처리결과대정보.1회기.1
의안코드1.0000.2330.0540.9270.0680.3391.0000.927
본회의처리결과0.2331.0000.0260.252NaN0.9530.2450.241
대정보0.0540.0261.0000.8810.0000.0000.0000.000
회기0.9270.2520.8811.0000.0290.3580.9011.000
본회의여부0.068NaN0.0000.0291.0000.9630.0410.029
상임위처리결과0.3390.9530.0000.3580.9631.0000.3550.358
대정보.11.0000.2450.0000.9010.0410.3551.0000.901
회기.10.9270.2410.0001.0000.0290.3580.9011.000
2023-12-12T20:55:18.224011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본회의처리결과상임위처리결과본회의여부대정보.1
본회의처리결과1.0000.7681.0000.134
상임위처리결과0.7681.0000.9560.190
본회의여부1.0000.9561.0000.050
대정보.10.1340.1900.0501.000
2023-12-12T20:55:18.366738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의안코드대정보회기회기.1본회의처리결과본회의여부상임위처리결과대정보.1
의안코드1.0000.9740.9991.0000.1170.0490.1661.000
대정보0.9741.0000.9710.9740.0240.0000.0000.000
회기0.9990.9711.0000.9990.1160.0220.1540.824
회기.11.0000.9740.9991.0000.1110.0210.1540.824
본회의처리결과0.1170.0240.1160.1111.0001.0000.7680.134
본회의여부0.0490.0000.0220.0211.0001.0000.9560.050
상임위처리결과0.1660.0000.1540.1540.7680.9561.0000.190
대정보.11.0000.0000.8240.8240.1340.0500.1901.000

Missing values

2023-12-12T20:55:11.597326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:55:11.894355image/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.
2023-12-12T20:55:12.108961image/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

의안코드본회의처리결과대정보회기회부일상정일처리일보고일본회의여부상임위처리결과대정보.1회기.1회부일.1상정일.1데이터기준일자
040658원안가결4852004-01-132004-01-132004-01-13<NA>1<NA>485<NA><NA>2020-09-04
140659원안가결4852004-01-132005-01-132005-01-13<NA>1<NA>485<NA><NA>2020-09-04
240660원안가결4852004-01-132004-01-132004-01-13<NA>1<NA>485<NA><NA>2020-09-04
340661원안가결4852004-01-132004-01-132004-01-13<NA>1<NA>485<NA><NA>2020-09-04
440662원안가결4962004-03-112004-03-112004-03-11<NA>1<NA>496<NA><NA>2020-09-04
540663원안가결4862004-03-052004-03-052004-03-05<NA>1<NA>486<NA><NA>2020-09-04
640664원안가결4862004-03-112004-03-112004-03-11<NA>1<NA>486<NA><NA>2020-09-04
740665원안가결4972004-04-292004-04-292004-04-29<NA>1<NA>497<NA><NA>2020-09-04
840666원안가결4872004-05-062004-05-062004-05-06<NA>1<NA>487<NA><NA>2020-09-04
940667원안가결4872004-05-062004-05-062004-05-06<NA>1<NA>487<NA><NA>2020-09-04
의안코드본회의처리결과대정보회기회부일상정일처리일보고일본회의여부상임위처리결과대정보.1회기.1회부일.1상정일.1데이터기준일자
129181994원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-05-292020-06-182020-09-04
129281995원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-05-292020-06-182020-09-04
129381996원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-05-292020-06-182020-09-04
129481997원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-05-292020-06-182020-09-04
129581998원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-05-292020-06-182020-09-04
129681999원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-05-292020-06-182020-09-04
129782000원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-05-292020-06-182020-09-04
129882001원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-05-292020-06-182020-09-04
129982002원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-05-292020-06-182020-09-04
130082003원안가결82232020-06-292020-06-292020-06-292020-06-291원안가결82232020-06-232020-06-232020-09-04