Overview

Dataset statistics

Number of variables13
Number of observations21
Missing cells16
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory121.3 B

Variable types

Text1
Numeric5
Categorical7

Dataset

Description인천광역시 미추홀구의 출산축하금 지원실적 현황에 대한 데이터로 월별 출산축하금 지원인원에 대한 데이터 등을 제공합니다.
Author인천광역시 미추홀구
URLhttps://www.data.go.kr/data/15081981/fileData.do

Alerts

기준일 has constant value ""Constant
2022년6월_명 is highly overall correlated with 2021년9월_명 and 7 other fieldsHigh correlation
2022년7월_명 is highly overall correlated with 2021년9월_명 and 5 other fieldsHigh correlation
2022년5월_명 is highly overall correlated with 2021년9월_명 and 6 other fieldsHigh correlation
2022년2월_명 is highly overall correlated with 2022년5월_명 and 2 other fieldsHigh correlation
2021년9월_명 is highly overall correlated with 2021년10월_명 and 5 other fieldsHigh correlation
2021년10월_명 is highly overall correlated with 2021년9월_명 and 3 other fieldsHigh correlation
2021년11월_명 is highly overall correlated with 2021년9월_명 and 4 other fieldsHigh correlation
2021년12월_명 is highly overall correlated with 2021년11월_명 and 2 other fieldsHigh correlation
2022년1월_명 is highly overall correlated with 2021년9월_명 and 5 other fieldsHigh correlation
2022년2월_명 is highly imbalanced (50.6%)Imbalance
2022년3월_명 is highly imbalanced (72.4%)Imbalance
2022년4월_명 is highly imbalanced (72.4%)Imbalance
2021년9월_명 has 1 (4.8%) missing valuesMissing
2021년10월_명 has 1 (4.8%) missing valuesMissing
2021년11월_명 has 4 (19.0%) missing valuesMissing
2021년12월_명 has 8 (38.1%) missing valuesMissing
2022년1월_명 has 2 (9.5%) missing valuesMissing
동별 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:54:24.036032
Analysis finished2023-12-12 08:54:28.072709
Duration4.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동별
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T17:54:28.226043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1904762
Min length3

Characters and Unicode

Total characters88
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row숭의1_3동
2nd row숭의2동
3rd row숭의4동
4th row용현1_4동
5th row용현2동
ValueCountFrequency (%)
숭의1_3동 1
 
4.8%
주안1동 1
 
4.8%
관교동 1
 
4.8%
주안8동 1
 
4.8%
주안7동 1
 
4.8%
주안6동 1
 
4.8%
주안5동 1
 
4.8%
주안4동 1
 
4.8%
주안3동 1
 
4.8%
주안2동 1
 
4.8%
Other values (11) 11
52.4%
2023-12-12T17:54:28.624087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
23.9%
안 8
 
9.1%
주 8
 
9.1%
1 5
 
5.7%
2 5
 
5.7%
3 4
 
4.5%
용 4
 
4.5%
현 4
 
4.5%
숭 3
 
3.4%
의 3
 
3.4%
Other values (13) 23
26.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
71.6%
Decimal Number 22
 
25.0%
Connector Punctuation 3
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
Decimal Number
ValueCountFrequency (%)
1 5
22.7%
2 5
22.7%
3 4
18.2%
4 3
13.6%
5 2
 
9.1%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
71.6%
Common 25
 
28.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
Common
ValueCountFrequency (%)
1 5
20.0%
2 5
20.0%
3 4
16.0%
4 3
12.0%
_ 3
12.0%
5 2
 
8.0%
6 1
 
4.0%
7 1
 
4.0%
8 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
71.6%
ASCII 25
 
28.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
ASCII
ValueCountFrequency (%)
1 5
20.0%
2 5
20.0%
3 4
16.0%
4 3
12.0%
_ 3
12.0%
5 2
 
8.0%
6 1
 
4.0%
7 1
 
4.0%
8 1
 
4.0%

2021년9월_명
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)50.0%
Missing1
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean7.7
Minimum3
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T17:54:28.813504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13.75
median5
Q39.25
95-th percentile17.5
Maximum27
Range24
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation6.2752647
Coefficient of variation (CV)0.81496944
Kurtosis3.6824329
Mean7.7
Median Absolute Deviation (MAD)2
Skewness1.8875068
Sum154
Variance39.378947
MonotonicityNot monotonic
2023-12-12T17:54:28.984850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 5
23.8%
5 4
19.0%
4 3
14.3%
9 2
 
9.5%
10 1
 
4.8%
27 1
 
4.8%
16 1
 
4.8%
17 1
 
4.8%
13 1
 
4.8%
6 1
 
4.8%
(Missing) 1
 
4.8%
ValueCountFrequency (%)
3 5
23.8%
4 3
14.3%
5 4
19.0%
6 1
 
4.8%
9 2
 
9.5%
10 1
 
4.8%
13 1
 
4.8%
16 1
 
4.8%
17 1
 
4.8%
27 1
 
4.8%
ValueCountFrequency (%)
27 1
 
4.8%
17 1
 
4.8%
16 1
 
4.8%
13 1
 
4.8%
10 1
 
4.8%
9 2
 
9.5%
6 1
 
4.8%
5 4
19.0%
4 3
14.3%
3 5
23.8%

2021년10월_명
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)65.0%
Missing1
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean9.7
Minimum2
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T17:54:29.132724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.95
Q15
median7.5
Q316
95-th percentile22
Maximum22
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.4244598
Coefficient of variation (CV)0.66231545
Kurtosis-0.70305034
Mean9.7
Median Absolute Deviation (MAD)3
Skewness0.77902981
Sum194
Variance41.273684
MonotonicityNot monotonic
2023-12-12T17:54:29.287195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
5 3
14.3%
7 2
9.5%
22 2
9.5%
16 2
9.5%
3 2
9.5%
9 2
9.5%
8 1
 
4.8%
17 1
 
4.8%
4 1
 
4.8%
18 1
 
4.8%
Other values (3) 3
14.3%
ValueCountFrequency (%)
2 1
 
4.8%
3 2
9.5%
4 1
 
4.8%
5 3
14.3%
6 1
 
4.8%
7 2
9.5%
8 1
 
4.8%
9 2
9.5%
10 1
 
4.8%
16 2
9.5%
ValueCountFrequency (%)
22 2
9.5%
18 1
 
4.8%
17 1
 
4.8%
16 2
9.5%
10 1
 
4.8%
9 2
9.5%
8 1
 
4.8%
7 2
9.5%
6 1
 
4.8%
5 3
14.3%

2021년11월_명
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)35.3%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean3.3529412
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T17:54:29.461195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6.4
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2062745
Coefficient of variation (CV)0.65801169
Kurtosis-0.71900663
Mean3.3529412
Median Absolute Deviation (MAD)2
Skewness0.67796206
Sum57
Variance4.8676471
MonotonicityNot monotonic
2023-12-12T17:54:29.586998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 4
19.0%
1 4
19.0%
6 3
14.3%
3 3
14.3%
5 2
9.5%
8 1
 
4.8%
(Missing) 4
19.0%
ValueCountFrequency (%)
1 4
19.0%
2 4
19.0%
3 3
14.3%
5 2
9.5%
6 3
14.3%
8 1
 
4.8%
ValueCountFrequency (%)
8 1
 
4.8%
6 3
14.3%
5 2
9.5%
3 3
14.3%
2 4
19.0%
1 4
19.0%

2021년12월_명
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)46.2%
Missing8
Missing (%)38.1%
Infinite0
Infinite (%)0.0%
Mean2.5384615
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T17:54:29.745425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5.4
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6641006
Coefficient of variation (CV)0.65555478
Kurtosis-0.094654882
Mean2.5384615
Median Absolute Deviation (MAD)1
Skewness0.88241922
Sum33
Variance2.7692308
MonotonicityNot monotonic
2023-12-12T17:54:29.884672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 5
23.8%
3 3
 
14.3%
2 2
 
9.5%
6 1
 
4.8%
4 1
 
4.8%
5 1
 
4.8%
(Missing) 8
38.1%
ValueCountFrequency (%)
1 5
23.8%
2 2
 
9.5%
3 3
14.3%
4 1
 
4.8%
5 1
 
4.8%
6 1
 
4.8%
ValueCountFrequency (%)
6 1
 
4.8%
5 1
 
4.8%
4 1
 
4.8%
3 3
14.3%
2 2
 
9.5%
1 5
23.8%

2022년1월_명
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)63.2%
Missing2
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean8.1578947
Minimum2
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T17:54:29.992512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12.5
median7
Q312
95-th percentile18.1
Maximum19
Range17
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation5.7856447
Coefficient of variation (CV)0.70920806
Kurtosis-0.89841982
Mean8.1578947
Median Absolute Deviation (MAD)5
Skewness0.56924862
Sum155
Variance33.473684
MonotonicityNot monotonic
2023-12-12T17:54:30.086543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 5
23.8%
7 2
 
9.5%
6 2
 
9.5%
12 2
 
9.5%
11 1
 
4.8%
10 1
 
4.8%
19 1
 
4.8%
17 1
 
4.8%
18 1
 
4.8%
3 1
 
4.8%
Other values (2) 2
 
9.5%
(Missing) 2
 
9.5%
ValueCountFrequency (%)
2 5
23.8%
3 1
 
4.8%
4 1
 
4.8%
6 2
 
9.5%
7 2
 
9.5%
10 1
 
4.8%
11 1
 
4.8%
12 2
 
9.5%
13 1
 
4.8%
17 1
 
4.8%
ValueCountFrequency (%)
19 1
4.8%
18 1
4.8%
17 1
4.8%
13 1
4.8%
12 2
9.5%
11 1
4.8%
10 1
4.8%
7 2
9.5%
6 2
9.5%
4 1
4.8%

2022년2월_명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
17 
1
2 
3
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.4285714
Min length1

Unique

Unique2 ?
Unique (%)9.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
81.0%
1 2
 
9.5%
3 1
 
4.8%
2 1
 
4.8%

Length

2023-12-12T17:54:30.212547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:30.319207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
81.0%
1 2
 
9.5%
3 1
 
4.8%
2 1
 
4.8%

2022년3월_명
Categorical

IMBALANCE 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
20 
1
 
1

Length

Max length4
Median length4
Mean length3.8571429
Min length1

Unique

Unique1 ?
Unique (%)4.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 20
95.2%
1 1
 
4.8%

Length

2023-12-12T17:54:30.418030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:30.793919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
95.2%
1 1
 
4.8%

2022년4월_명
Categorical

IMBALANCE 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
20 
1
 
1

Length

Max length4
Median length4
Mean length3.8571429
Min length1

Unique

Unique1 ?
Unique (%)4.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 20
95.2%
1 1
 
4.8%

Length

2023-12-12T17:54:30.916360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:31.036776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
95.2%
1 1
 
4.8%

2022년5월_명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
17 
1
2 
2
2 

Length

Max length4
Median length4
Mean length3.4285714
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
81.0%
1 2
 
9.5%
2 2
 
9.5%

Length

2023-12-12T17:54:31.178820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:31.341252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
81.0%
1 2
 
9.5%
2 2
 
9.5%

2022년6월_명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
18 
1
3 

Length

Max length4
Median length4
Mean length3.5714286
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row1
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 18
85.7%
1 3
 
14.3%

Length

2023-12-12T17:54:31.509427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:31.654528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 18
85.7%
1 3
 
14.3%

2022년7월_명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
16 
1
3 
2
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.2857143
Min length1

Unique

Unique2 ?
Unique (%)9.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 16
76.2%
1 3
 
14.3%
2 1
 
4.8%
3 1
 
4.8%

Length

2023-12-12T17:54:31.802673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:31.961411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
76.2%
1 3
 
14.3%
2 1
 
4.8%
3 1
 
4.8%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
2022-08-09
21 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-09
2nd row2022-08-09
3rd row2022-08-09
4th row2022-08-09
5th row2022-08-09

Common Values

ValueCountFrequency (%)
2022-08-09 21
100.0%

Length

2023-12-12T17:54:32.120198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:32.248382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-09 21
100.0%

Interactions

2023-12-12T17:54:26.765887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:24.546897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:25.013034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:25.556449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:26.145284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:26.863546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:24.630684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:25.106251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:25.676704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:26.243928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:27.012432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:24.747882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:25.236183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:25.791216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:26.369956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:27.147508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:24.839229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:25.337527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:25.923093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:26.511831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:27.267874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:24.926295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:25.445155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:26.036156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:26.635887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:54:32.335093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동별2021년9월_명2021년10월_명2021년11월_명2021년12월_명2022년1월_명2022년2월_명2022년5월_명2022년7월_명
동별1.0001.0001.0001.0001.0001.0001.0001.0001.000
2021년9월_명1.0001.0000.8850.6190.0000.7370.8271.0001.000
2021년10월_명1.0000.8851.0000.4150.4220.0000.8271.0000.416
2021년11월_명1.0000.6190.4151.0000.6490.6840.8271.0000.416
2021년12월_명1.0000.0000.4220.6491.0000.5390.0000.0001.000
2022년1월_명1.0000.7370.0000.6840.5391.0000.8271.0001.000
2022년2월_명1.0000.8270.8270.8270.0000.8271.0000.0001.000
2022년5월_명1.0001.0001.0001.0000.0001.0000.0001.0001.000
2022년7월_명1.0001.0000.4160.4161.0001.0001.0001.0001.000
2023-12-12T17:54:32.467140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2022년3월_명2022년4월_명2022년6월_명2022년7월_명2022년5월_명2022년2월_명
2022년3월_명1.000NaNNaNNaNNaNNaN
2022년4월_명NaN1.000NaNNaNNaNNaN
2022년6월_명NaNNaN1.0001.0001.0001.000
2022년7월_명NaNNaN1.0001.0001.0001.000
2022년5월_명NaNNaN1.0001.0001.0001.000
2022년2월_명NaNNaN1.0001.0001.0001.000
2023-12-12T17:54:32.600078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2021년9월_명2021년10월_명2021년11월_명2021년12월_명2022년1월_명2022년2월_명2022년3월_명2022년4월_명2022년5월_명2022년6월_명2022년7월_명
2021년9월_명1.0000.7400.554-0.3240.6900.000NaNNaN1.0001.0001.000
2021년10월_명0.7401.0000.463-0.1150.6010.000NaNNaN1.0001.0000.000
2021년11월_명0.5540.4631.000-0.7040.6630.000NaN0.0001.0001.0000.000
2021년12월_명-0.324-0.115-0.7041.000-0.4230.000NaNNaN0.0001.0001.000
2022년1월_명0.6900.6010.663-0.4231.0000.000NaNNaN0.7071.0001.000
2022년2월_명0.0000.0000.0000.0000.0001.000NaN0.0001.0001.0001.000
2022년3월_명NaNNaNNaNNaNNaNNaN1.0000.000NaNNaNNaN
2022년4월_명NaNNaN0.000NaNNaN0.0000.0001.0000.0000.0000.000
2022년5월_명1.0001.0001.0000.0000.7071.000NaN0.0001.0001.0001.000
2022년6월_명1.0001.0001.0001.0001.0001.000NaN0.0001.0001.0001.000
2022년7월_명1.0000.0000.0001.0001.0001.000NaN0.0001.0001.0001.000

Missing values

2023-12-12T17:54:27.429966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:54:27.658898image/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.
2023-12-12T17:54:27.903542image/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

동별2021년9월_명2021년10월_명2021년11월_명2021년12월_명2022년1월_명2022년2월_명2022년3월_명2022년4월_명2022년5월_명2022년6월_명2022년7월_명기준일
0숭의1_3동57637<NA><NA><NA><NA><NA><NA>2022-08-09
1숭의2동482611<NA><NA><NA><NA>1<NA>2022-08-09
2숭의4동55136<NA><NA><NA><NA><NA><NA>2022-08-09
3용현1_4동3<NA>3<NA>2<NA><NA><NA><NA><NA><NA>2022-08-09
4용현2동10172310<NA><NA><NA><NA><NA><NA>2022-08-09
5용현3동342<NA><NA><NA><NA><NA><NA><NA><NA>2022-08-09
6용현5동271861193<NA><NA>1122022-08-09
7학익1동55321211<NA>2132022-08-09
8학익2동361<NA>2<NA><NA><NA><NA><NA><NA>2022-08-09
9도화1동162252122<NA><NA><NA><NA><NA>2022-08-09
동별2021년9월_명2021년10월_명2021년11월_명2021년12월_명2022년1월_명2022년2월_명2022년3월_명2022년4월_명2022년5월_명2022년6월_명2022년7월_명기준일
11주안1동9165118<NA><NA><NA>1<NA><NA>2022-08-09
12주안2동3103<NA>3<NA><NA><NA><NA><NA><NA>2022-08-09
13주안3동53<NA>42<NA><NA><NA><NA><NA><NA>2022-08-09
14주안4동<NA>52<NA>4<NA><NA><NA><NA><NA>12022-08-09
15주안5동1398<NA>13<NA><NA><NA>2<NA>12022-08-09
16주안6동9161<NA>6<NA><NA><NA><NA><NA><NA>2022-08-09
17주안7동42<NA>12<NA><NA><NA><NA><NA><NA>2022-08-09
18주안8동4715<NA><NA><NA><NA><NA><NA><NA>2022-08-09
19관교동33<NA>17<NA><NA>1<NA><NA><NA>2022-08-09
20문학동69<NA><NA>2<NA><NA><NA><NA><NA><NA>2022-08-09