Overview

Dataset statistics

Number of variables16
Number of observations175
Missing cells403
Missing cells (%)14.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.6 KiB
Average record size in memory143.8 B

Variable types

DateTime1
Numeric3
Categorical12

Dataset

Description2020년도에 서울특별시 동작구에서 발생한 코로나 19 확진자 수를 동별, 일별로 나타내고 있는 csv 파일 데이터 입니다.
Author서울특별시 동작구
URLhttps://www.data.go.kr/data/15075903/fileData.do

Alerts

흑석동 is highly overall correlated with 사당3동 and 4 other fieldsHigh correlation
상도2동 is highly overall correlated with 노량진2동 and 1 other fieldsHigh correlation
노량진2동 is highly overall correlated with 사당3동 and 6 other fieldsHigh correlation
신대방1동 is highly overall correlated with 사당3동 and 1 other fieldsHigh correlation
사당4동 is highly overall correlated with 노량진1동 and 7 other fieldsHigh correlation
사당2동 is highly overall correlated with 노량진1동 and 1 other fieldsHigh correlation
상도3동 is highly overall correlated with 노량진2동 and 1 other fieldsHigh correlation
사당1동 is highly overall correlated with 노량진2동 and 2 other fieldsHigh correlation
대방동 is highly overall correlated with 상도3동 and 1 other fieldsHigh correlation
신대방2동 is highly overall correlated with 사당3동 and 5 other fieldsHigh correlation
상도4동 is highly overall correlated with 노량진2동 and 2 other fieldsHigh correlation
노량진1동 is highly overall correlated with 사당2동 and 2 other fieldsHigh correlation
사당3동 is highly overall correlated with 노량진2동 and 5 other fieldsHigh correlation
상도1동 is highly overall correlated with 신대방2동High correlation
사당5동 is highly overall correlated with 노량진1동 and 3 other fieldsHigh correlation
노량진2동 is highly imbalanced (63.5%)Imbalance
대방동 is highly imbalanced (61.2%)Imbalance
사당1동 is highly imbalanced (54.5%)Imbalance
사당2동 is highly imbalanced (54.1%)Imbalance
사당4동 is highly imbalanced (70.9%)Imbalance
사당5동 is highly imbalanced (64.9%)Imbalance
상도2동 is highly imbalanced (54.8%)Imbalance
상도3동 is highly imbalanced (51.5%)Imbalance
상도4동 is highly imbalanced (50.3%)Imbalance
신대방1동 is highly imbalanced (52.5%)Imbalance
신대방2동 is highly imbalanced (63.8%)Imbalance
흑석동 is highly imbalanced (58.9%)Imbalance
노량진1동 has 132 (75.4%) missing valuesMissing
사당3동 has 145 (82.9%) missing valuesMissing
상도1동 has 126 (72.0%) missing valuesMissing
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:18:20.012696
Analysis finished2023-12-12 23:18:22.392289
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

UNIQUE 

Distinct175
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2020-02-25 00:00:00
Maximum2020-12-31 00:00:00
2023-12-13T08:18:22.471457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:18:22.616028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

노량진1동
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)16.3%
Missing132
Missing (%)75.4%
Infinite0
Infinite (%)0.0%
Mean1.9069767
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:18:22.734007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4.9
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5401601
Coefficient of variation (CV)0.80764491
Kurtosis5.6262664
Mean1.9069767
Median Absolute Deviation (MAD)0
Skewness2.2544369
Sum82
Variance2.372093
MonotonicityNot monotonic
2023-12-13T08:18:22.834219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 26
 
14.9%
2 7
 
4.0%
3 5
 
2.9%
4 2
 
1.1%
8 1
 
0.6%
6 1
 
0.6%
5 1
 
0.6%
(Missing) 132
75.4%
ValueCountFrequency (%)
1 26
14.9%
2 7
 
4.0%
3 5
 
2.9%
4 2
 
1.1%
5 1
 
0.6%
6 1
 
0.6%
8 1
 
0.6%
ValueCountFrequency (%)
8 1
 
0.6%
6 1
 
0.6%
5 1
 
0.6%
4 2
 
1.1%
3 5
 
2.9%
2 7
 
4.0%
1 26
14.9%

노량진2동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
153 
1
21 
2
 
1

Length

Max length4
Median length4
Mean length3.6228571
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 153
87.4%
1 21
 
12.0%
2 1
 
0.6%

Length

2023-12-13T08:18:22.988177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:23.092414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 153
87.4%
1 21
 
12.0%
2 1
 
0.6%

대방동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
135 
1
33 
2
 
3
4
 
2
3
 
1

Length

Max length4
Median length4
Mean length3.3142857
Min length1

Unique

Unique2 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 135
77.1%
1 33
 
18.9%
2 3
 
1.7%
4 2
 
1.1%
3 1
 
0.6%
8 1
 
0.6%

Length

2023-12-13T08:18:23.207702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:23.316873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 135
77.1%
1 33
 
18.9%
2 3
 
1.7%
4 2
 
1.1%
3 1
 
0.6%
8 1
 
0.6%

사당1동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
130 
1
37 
2
 
5
3
 
2
4
 
1

Length

Max length4
Median length4
Mean length3.2285714
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 130
74.3%
1 37
 
21.1%
2 5
 
2.9%
3 2
 
1.1%
4 1
 
0.6%

Length

2023-12-13T08:18:23.446539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:23.565249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 130
74.3%
1 37
 
21.1%
2 5
 
2.9%
3 2
 
1.1%
4 1
 
0.6%

사당2동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
134 
1
24 
4
 
7
2
 
5
3
 
4

Length

Max length4
Median length4
Mean length3.2971429
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 134
76.6%
1 24
 
13.7%
4 7
 
4.0%
2 5
 
2.9%
3 4
 
2.3%
5 1
 
0.6%

Length

2023-12-13T08:18:23.746865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:23.876014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 134
76.6%
1 24
 
13.7%
4 7
 
4.0%
2 5
 
2.9%
3 4
 
2.3%
5 1
 
0.6%

사당3동
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)20.0%
Missing145
Missing (%)82.9%
Infinite0
Infinite (%)0.0%
Mean1.8
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:18:23.965919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5.1
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6060231
Coefficient of variation (CV)0.89223508
Kurtosis8.2805871
Mean1.8
Median Absolute Deviation (MAD)0
Skewness2.8105143
Sum54
Variance2.5793103
MonotonicityNot monotonic
2023-12-13T08:18:24.065637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 19
 
10.9%
2 7
 
4.0%
8 1
 
0.6%
6 1
 
0.6%
3 1
 
0.6%
4 1
 
0.6%
(Missing) 145
82.9%
ValueCountFrequency (%)
1 19
10.9%
2 7
 
4.0%
3 1
 
0.6%
4 1
 
0.6%
6 1
 
0.6%
8 1
 
0.6%
ValueCountFrequency (%)
8 1
 
0.6%
6 1
 
0.6%
4 1
 
0.6%
3 1
 
0.6%
2 7
 
4.0%
1 19
10.9%

사당4동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
154 
1
 
15
2
 
4
4
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.64
Min length1

Unique

Unique2 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 154
88.0%
1 15
 
8.6%
2 4
 
2.3%
4 1
 
0.6%
3 1
 
0.6%

Length

2023-12-13T08:18:24.214465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:24.663990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 154
88.0%
1 15
 
8.6%
2 4
 
2.3%
4 1
 
0.6%
3 1
 
0.6%

사당5동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
149 
1
16 
2
 
7
3
 
2
5
 
1

Length

Max length4
Median length4
Mean length3.5542857
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 149
85.1%
1 16
 
9.1%
2 7
 
4.0%
3 2
 
1.1%
5 1
 
0.6%

Length

2023-12-13T08:18:24.798599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:24.912838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 149
85.1%
1 16
 
9.1%
2 7
 
4.0%
3 2
 
1.1%
5 1
 
0.6%

상도1동
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)12.2%
Missing126
Missing (%)72.0%
Infinite0
Infinite (%)0.0%
Mean1.5918367
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:18:25.009262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3.6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1532268
Coefficient of variation (CV)0.72446297
Kurtosis10.633102
Mean1.5918367
Median Absolute Deviation (MAD)0
Skewness2.9951731
Sum78
Variance1.329932
MonotonicityNot monotonic
2023-12-13T08:18:25.104375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 32
 
18.3%
2 12
 
6.9%
3 2
 
1.1%
4 1
 
0.6%
5 1
 
0.6%
7 1
 
0.6%
(Missing) 126
72.0%
ValueCountFrequency (%)
1 32
18.3%
2 12
 
6.9%
3 2
 
1.1%
4 1
 
0.6%
5 1
 
0.6%
7 1
 
0.6%
ValueCountFrequency (%)
7 1
 
0.6%
5 1
 
0.6%
4 1
 
0.6%
3 2
 
1.1%
2 12
 
6.9%
1 32
18.3%

상도2동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
145 
1
17 
2
 
7
3
 
6

Length

Max length4
Median length4
Mean length3.4857143
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 145
82.9%
1 17
 
9.7%
2 7
 
4.0%
3 6
 
3.4%

Length

2023-12-13T08:18:25.220303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:25.342763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 145
82.9%
1 17
 
9.7%
2 7
 
4.0%
3 6
 
3.4%

상도3동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
140 
1
22 
2
 
10
3
 
3

Length

Max length4
Median length4
Mean length3.4
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> 140
80.0%
1 22
 
12.6%
2 10
 
5.7%
3 3
 
1.7%

Length

2023-12-13T08:18:25.503887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:25.613912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 140
80.0%
1 22
 
12.6%
2 10
 
5.7%
3 3
 
1.7%

상도4동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
133 
1
26 
2
 
8
3
 
6
4
 
2

Length

Max length4
Median length4
Mean length3.28
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> 133
76.0%
1 26
 
14.9%
2 8
 
4.6%
3 6
 
3.4%
4 2
 
1.1%

Length

2023-12-13T08:18:25.739503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:25.856636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 133
76.0%
1 26
 
14.9%
2 8
 
4.6%
3 6
 
3.4%
4 2
 
1.1%

신대방1동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
130 
1
32 
2
 
11
4
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.2285714
Min length1

Unique

Unique2 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 130
74.3%
1 32
 
18.3%
2 11
 
6.3%
4 1
 
0.6%
3 1
 
0.6%

Length

2023-12-13T08:18:25.989536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:26.100540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 130
74.3%
1 32
 
18.3%
2 11
 
6.3%
4 1
 
0.6%
3 1
 
0.6%

신대방2동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
145 
1
23 
2
 
5
3
 
1
4
 
1

Length

Max length4
Median length4
Mean length3.4857143
Min length1

Unique

Unique2 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 145
82.9%
1 23
 
13.1%
2 5
 
2.9%
3 1
 
0.6%
4 1
 
0.6%

Length

2023-12-13T08:18:26.216823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:26.330817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 145
82.9%
1 23
 
13.1%
2 5
 
2.9%
3 1
 
0.6%
4 1
 
0.6%

흑석동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
144 
1
25 
2
 
5
4
 
1

Length

Max length4
Median length4
Mean length3.4685714
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 144
82.3%
1 25
 
14.3%
2 5
 
2.9%
4 1
 
0.6%

Length

2023-12-13T08:18:26.458286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:18:26.572680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 144
82.3%
1 25
 
14.3%
2 5
 
2.9%
4 1
 
0.6%

Interactions

2023-12-13T08:18:21.459288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:18:20.912320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:18:21.168715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:18:21.551849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:18:21.010501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:18:21.252574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:18:21.636858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:18:21.088967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:18:21.348184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:18:26.678535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노량진1동노량진2동대방동사당1동사당2동사당3동사당4동사당5동상도1동상도2동상도3동상도4동신대방1동신대방2동흑석동
노량진1동1.0000.0000.0000.0000.7830.0001.0000.8240.2250.2040.7380.3050.0000.5040.000
노량진2동0.0001.0000.000NaNNaNNaN0.0000.1930.000NaNNaNNaN0.000NaN0.000
대방동0.0000.0001.0000.4960.0000.0000.5520.5640.0000.000NaN0.0000.000NaN0.000
사당1동0.000NaN0.4961.0000.5370.6160.6410.7760.0000.4870.3100.0000.0000.0000.579
사당2동0.783NaN0.0000.5371.0000.0000.0000.0000.1070.0000.3140.4300.0000.0000.000
사당3동0.000NaN0.0000.6160.0001.0001.0000.7110.0000.0000.4470.0000.702NaN1.000
사당4동1.0000.0000.5520.6410.0001.0001.0000.8840.4350.9460.0000.720NaN0.0000.607
사당5동0.8240.1930.5640.7760.0000.7110.8841.0000.5870.5150.0000.4080.0001.0000.000
상도1동0.2250.0000.0000.0000.1070.0000.4350.5871.0000.1920.0000.0000.0000.9230.000
상도2동0.204NaN0.0000.4870.0000.0000.9460.5150.1921.0000.0000.0000.4460.6450.000
상도3동0.738NaNNaN0.3100.3140.4470.0000.0000.0000.0001.0000.0000.0000.0000.000
상도4동0.305NaN0.0000.0000.4300.0000.7200.4080.0000.0000.0001.0000.2820.7440.678
신대방1동0.0000.0000.0000.0000.0000.702NaN0.0000.0000.4460.0000.2821.0000.0000.000
신대방2동0.504NaNNaN0.0000.000NaN0.0001.0000.9230.6450.0000.7440.0001.000NaN
흑석동0.0000.0000.0000.5790.0001.0000.6070.0000.0000.0000.0000.6780.000NaN1.000
2023-12-13T08:18:26.852423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
흑석동상도2동노량진2동신대방1동사당4동사당2동사당5동상도3동사당1동대방동신대방2동상도4동
흑석동1.0000.0000.0000.0000.5530.0000.0000.0000.5400.0001.0000.673
상도2동0.0001.0001.0000.3950.7010.0000.3980.0000.1270.0000.2720.000
노량진2동0.0001.0001.0000.0000.0001.0000.0001.0001.0000.0001.0001.000
신대방1동0.0000.3950.0001.0001.0000.0000.0000.0000.0000.0000.0000.227
사당4동0.5530.7010.0001.0001.0000.0000.5450.0000.5570.4870.0000.658
사당2동0.0000.0001.0000.0000.0001.0000.0000.1760.3220.0000.0000.407
사당5동0.0000.3980.0000.0000.5450.0001.0000.0000.3400.2830.5770.333
상도3동0.0000.0001.0000.0000.0000.1760.0001.0000.0001.0000.0000.000
사당1동0.5400.1271.0000.0000.5570.3220.3400.0001.0000.4560.0000.000
대방동0.0000.0000.0000.0000.4870.0000.2831.0000.4561.0001.0000.000
신대방2동1.0000.2721.0000.0000.0000.0000.5770.0000.0001.0001.0000.370
상도4동0.6730.0001.0000.2270.6580.4070.3330.0000.0000.0000.3701.000
2023-12-13T08:18:27.030650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노량진1동사당3동상도1동노량진2동대방동사당1동사당2동사당4동사당5동상도2동상도3동상도4동신대방1동신대방2동흑석동
노량진1동1.000-0.2170.1640.0000.0000.0000.5390.6320.7300.0000.3150.1880.0000.3830.000
사당3동-0.2171.000-0.1871.0000.0000.3800.0000.8660.6120.0000.1110.0000.6161.0000.775
상도1동0.164-0.1871.0000.0000.0000.0000.0000.2140.1850.1290.0000.0000.0000.6490.000
노량진2동0.0001.0000.0001.0000.0001.0001.0000.0000.0001.0001.0001.0000.0001.0000.000
대방동0.0000.0000.0000.0001.0000.4560.0000.4870.2830.0001.0000.0000.0001.0000.000
사당1동0.0000.3800.0001.0000.4561.0000.3220.5570.3400.1270.0000.0000.0000.0000.540
사당2동0.5390.0000.0001.0000.0000.3221.0000.0000.0000.0000.1760.4070.0000.0000.000
사당4동0.6320.8660.2140.0000.4870.5570.0001.0000.5450.7010.0000.6581.0000.0000.553
사당5동0.7300.6120.1850.0000.2830.3400.0000.5451.0000.3980.0000.3330.0000.5770.000
상도2동0.0000.0000.1291.0000.0000.1270.0000.7010.3981.0000.0000.0000.3950.2720.000
상도3동0.3150.1110.0001.0001.0000.0000.1760.0000.0000.0001.0000.0000.0000.0000.000
상도4동0.1880.0000.0001.0000.0000.0000.4070.6580.3330.0000.0001.0000.2270.3700.673
신대방1동0.0000.6160.0000.0000.0000.0000.0001.0000.0000.3950.0000.2271.0000.0000.000
신대방2동0.3831.0000.6491.0001.0000.0000.0000.0000.5770.2720.0000.3700.0001.0001.000
흑석동0.0000.7750.0000.0000.0000.5400.0000.5530.0000.0000.0000.6730.0001.0001.000

Missing values

2023-12-13T08:18:21.758172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:18:22.002592image/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.
2023-12-13T08:18:22.210422image/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

구분노량진1동노량진2동대방동사당1동사당2동사당3동사당4동사당5동상도1동상도2동상도3동상도4동신대방1동신대방2동흑석동
02020-02-25<NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA><NA>
12020-03-09<NA><NA>1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
22020-03-103<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32020-03-13<NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA>
42020-03-16<NA><NA><NA>1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
52020-03-18<NA><NA>2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
62020-03-19<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA><NA><NA>1<NA>
72020-03-20<NA><NA>1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1
82020-03-24<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1
92020-03-25<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA><NA><NA><NA>1
구분노량진1동노량진2동대방동사당1동사당2동사당3동사당4동사당5동상도1동상도2동상도3동상도4동신대방1동신대방2동흑석동
1652020-12-221<NA>21<NA><NA>1<NA><NA>3<NA><NA>1<NA>1
1662020-12-23<NA>1<NA><NA>1<NA>2<NA>1331<NA><NA>2
1672020-12-2451<NA><NA>5<NA>1<NA>213<NA>11<NA>
1682020-12-2511<NA><NA><NA><NA><NA><NA>11<NA><NA>24<NA>
1692020-12-26<NA><NA>1<NA><NA><NA>12<NA>1<NA><NA>1<NA>2
1702020-12-27<NA><NA>1111<NA><NA>11<NA>211<NA>
1712020-12-28<NA><NA>43<NA><NA><NA><NA><NA><NA><NA>31<NA>1
1722020-12-291<NA>1<NA>2<NA><NA>223<NA>231<NA>
1732020-12-30411<NA>31<NA>12<NA>112<NA><NA>
1742020-12-31211<NA>4<NA>121311<NA><NA>1