Overview

Dataset statistics

Number of variables11
Number of observations41
Missing cells5
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory100.2 B

Variable types

Text2
Numeric7
Categorical2

Dataset

Description소방관서 계급별 정원 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=Z7XDQWY0CTDUJ12A5RN530952417&infSeq=1

Alerts

정원 is highly overall correlated with 소방령 and 6 other fieldsHigh correlation
소방령 is highly overall correlated with 정원 and 6 other fieldsHigh correlation
소방경 is highly overall correlated with 정원 and 6 other fieldsHigh correlation
소방위 is highly overall correlated with 정원 and 6 other fieldsHigh correlation
소방장 is highly overall correlated with 정원 and 6 other fieldsHigh correlation
소방교 is highly overall correlated with 정원 and 5 other fieldsHigh correlation
소방사 is highly overall correlated with 정원 and 7 other fieldsHigh correlation
소방준감 is highly overall correlated with 정원 and 6 other fieldsHigh correlation
소방정 is highly overall correlated with 소방령 and 3 other fieldsHigh correlation
소방준감 is highly imbalanced (65.9%)Imbalance
소방정 is highly imbalanced (59.3%)Imbalance
소방사 has 5 (12.2%) missing valuesMissing
관서명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:56:11.380710
Analysis finished2023-12-10 21:56:16.200282
Duration4.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct32
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-11T06:56:16.327305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0487805
Min length3

Characters and Unicode

Total characters125
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)61.0%

Sample

1st row수원시
2nd row의정부시
3rd row용인시
4th row용인시
5th row의정부시
ValueCountFrequency (%)
수원시 3
 
7.3%
용인시 3
 
7.3%
오산시 2
 
4.9%
성남시 2
 
4.9%
의정부시 2
 
4.9%
고양시 2
 
4.9%
평택시 2
 
4.9%
구리시 1
 
2.4%
양평시 1
 
2.4%
동두시 1
 
2.4%
Other values (22) 22
53.7%
2023-12-11T06:56:16.615179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 42
33.6%
양 6
 
4.8%
천 5
 
4.0%
주 4
 
3.2%
성 4
 
3.2%
남 4
 
3.2%
의 4
 
3.2%
평 4
 
3.2%
안 3
 
2.4%
원 3
 
2.4%
Other values (28) 46
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 125
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 42
33.6%
양 6
 
4.8%
천 5
 
4.0%
주 4
 
3.2%
성 4
 
3.2%
남 4
 
3.2%
의 4
 
3.2%
평 4
 
3.2%
안 3
 
2.4%
원 3
 
2.4%
Other values (28) 46
36.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 125
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 42
33.6%
양 6
 
4.8%
천 5
 
4.0%
주 4
 
3.2%
성 4
 
3.2%
남 4
 
3.2%
의 4
 
3.2%
평 4
 
3.2%
안 3
 
2.4%
원 3
 
2.4%
Other values (28) 46
36.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 125
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 42
33.6%
양 6
 
4.8%
천 5
 
4.0%
주 4
 
3.2%
성 4
 
3.2%
남 4
 
3.2%
의 4
 
3.2%
평 4
 
3.2%
안 3
 
2.4%
원 3
 
2.4%
Other values (28) 46
36.8%

관서명
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-11T06:56:16.821860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.2926829
Min length4

Characters and Unicode

Total characters217
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row소방재난본부
2nd row북부소방재난본부
3rd row소방학교
4th row특수대응단
5th row북부특수대응단
ValueCountFrequency (%)
소방재난본부 1
 
2.4%
김포소방서 1
 
2.4%
안성소방서 1
 
2.4%
하남소방서 1
 
2.4%
의왕소방서 1
 
2.4%
오산소방서 1
 
2.4%
여주소방서 1
 
2.4%
양평소방서 1
 
2.4%
과천소방서 1
 
2.4%
고양소방서 1
 
2.4%
Other values (31) 31
75.6%
2023-12-11T06:56:17.146500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 38
17.5%
방 38
17.5%
서 35
16.1%
부 7
 
3.2%
천 6
 
2.8%
주 5
 
2.3%
양 5
 
2.3%
안 4
 
1.8%
수 4
 
1.8%
남 4
 
1.8%
Other values (52) 71
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 217
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 38
17.5%
방 38
17.5%
서 35
16.1%
부 7
 
3.2%
천 6
 
2.8%
주 5
 
2.3%
양 5
 
2.3%
안 4
 
1.8%
수 4
 
1.8%
남 4
 
1.8%
Other values (52) 71
32.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 217
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 38
17.5%
방 38
17.5%
서 35
16.1%
부 7
 
3.2%
천 6
 
2.8%
주 5
 
2.3%
양 5
 
2.3%
안 4
 
1.8%
수 4
 
1.8%
남 4
 
1.8%
Other values (52) 71
32.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 217
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 38
17.5%
방 38
17.5%
서 35
16.1%
부 7
 
3.2%
천 6
 
2.8%
주 5
 
2.3%
양 5
 
2.3%
안 4
 
1.8%
수 4
 
1.8%
남 4
 
1.8%
Other values (52) 71
32.7%

정원
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.34146
Minimum24
Maximum590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:56:17.258304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile75
Q1214
median289
Q3338
95-th percentile445
Maximum590
Range566
Interquartile range (IQR)124

Descriptive statistics

Standard deviation122.18318
Coefficient of variation (CV)0.43739724
Kurtosis0.46408951
Mean279.34146
Median Absolute Deviation (MAD)72
Skewness0.11256899
Sum11453
Variance14928.73
MonotonicityNot monotonic
2023-12-11T06:56:17.376454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
294 2
 
4.9%
289 2
 
4.9%
410 1
 
2.4%
275 1
 
2.4%
223 1
 
2.4%
190 1
 
2.4%
214 1
 
2.4%
240 1
 
2.4%
284 1
 
2.4%
150 1
 
2.4%
Other values (29) 29
70.7%
ValueCountFrequency (%)
24 1
2.4%
32 1
2.4%
75 1
2.4%
77 1
2.4%
150 1
2.4%
151 1
2.4%
167 1
2.4%
190 1
2.4%
194 1
2.4%
203 1
2.4%
ValueCountFrequency (%)
590 1
2.4%
532 1
2.4%
445 1
2.4%
442 1
2.4%
430 1
2.4%
410 1
2.4%
394 1
2.4%
381 1
2.4%
379 1
2.4%
350 1
2.4%

소방준감
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
<NA>
37 
1
 
3
6
 
1

Length

Max length4
Median length4
Mean length3.7073171
Min length1

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st row6
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 37
90.2%
1 3
 
7.3%
6 1
 
2.4%

Length

2023-12-11T06:56:17.501956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:56:17.621480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 37
90.2%
1 3
 
7.3%
6 1
 
2.4%

소방정
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size460.0 B
1
35 
<NA>
 
3
3
 
2
4
 
1

Length

Max length4
Median length1
Mean length1.2195122
Min length1

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st row3
2nd row4
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 35
85.4%
<NA> 3
 
7.3%
3 2
 
4.9%
4 1
 
2.4%

Length

2023-12-11T06:56:17.733392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:56:17.868003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 35
85.4%
na 3
 
7.3%
3 2
 
4.9%
4 1
 
2.4%

소방령
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2195122
Minimum3
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:56:17.949790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q16
median7
Q37
95-th percentile13
Maximum43
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.1012794
Coefficient of variation (CV)0.74229215
Kurtosis27.760907
Mean8.2195122
Median Absolute Deviation (MAD)1
Skewness4.9892456
Sum337
Variance37.22561
MonotonicityNot monotonic
2023-12-11T06:56:18.043874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 20
48.8%
6 10
24.4%
8 3
 
7.3%
11 2
 
4.9%
43 1
 
2.4%
19 1
 
2.4%
13 1
 
2.4%
9 1
 
2.4%
4 1
 
2.4%
3 1
 
2.4%
ValueCountFrequency (%)
3 1
 
2.4%
4 1
 
2.4%
6 10
24.4%
7 20
48.8%
8 3
 
7.3%
9 1
 
2.4%
11 2
 
4.9%
13 1
 
2.4%
19 1
 
2.4%
43 1
 
2.4%
ValueCountFrequency (%)
43 1
 
2.4%
19 1
 
2.4%
13 1
 
2.4%
11 2
 
4.9%
9 1
 
2.4%
8 3
 
7.3%
7 20
48.8%
6 10
24.4%
4 1
 
2.4%
3 1
 
2.4%

소방경
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.439024
Minimum3
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:56:18.161607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q115
median18
Q322
95-th percentile33
Maximum65
Range62
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.002622
Coefficient of variation (CV)0.51456397
Kurtosis10.255892
Mean19.439024
Median Absolute Deviation (MAD)3
Skewness2.4905116
Sum797
Variance100.05244
MonotonicityNot monotonic
2023-12-11T06:56:18.272744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15 6
14.6%
16 4
9.8%
18 4
9.8%
20 4
9.8%
14 3
 
7.3%
24 3
 
7.3%
19 3
 
7.3%
28 2
 
4.9%
22 2
 
4.9%
13 2
 
4.9%
Other values (8) 8
19.5%
ValueCountFrequency (%)
3 1
 
2.4%
4 1
 
2.4%
8 1
 
2.4%
12 1
 
2.4%
13 2
 
4.9%
14 3
7.3%
15 6
14.6%
16 4
9.8%
18 4
9.8%
19 3
7.3%
ValueCountFrequency (%)
65 1
 
2.4%
38 1
 
2.4%
33 1
 
2.4%
31 1
 
2.4%
28 2
4.9%
24 3
7.3%
22 2
4.9%
20 4
9.8%
19 3
7.3%
18 4
9.8%

소방위
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.804878
Minimum3
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:56:18.379710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile18
Q118
median19
Q321
95-th percentile32
Maximum116
Range113
Interquartile range (IQR)3

Descriptive statistics

Standard deviation15.9534
Coefficient of variation (CV)0.73164363
Kurtosis32.19567
Mean21.804878
Median Absolute Deviation (MAD)1
Skewness5.3398752
Sum894
Variance254.51098
MonotonicityNot monotonic
2023-12-11T06:56:18.491754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
18 12
29.3%
19 11
26.8%
20 5
12.2%
21 4
 
9.8%
22 2
 
4.9%
3 2
 
4.9%
116 1
 
2.4%
34 1
 
2.4%
23 1
 
2.4%
32 1
 
2.4%
ValueCountFrequency (%)
3 2
 
4.9%
18 12
29.3%
19 11
26.8%
20 5
12.2%
21 4
 
9.8%
22 2
 
4.9%
23 1
 
2.4%
30 1
 
2.4%
32 1
 
2.4%
34 1
 
2.4%
ValueCountFrequency (%)
116 1
 
2.4%
34 1
 
2.4%
32 1
 
2.4%
30 1
 
2.4%
23 1
 
2.4%
22 2
 
4.9%
21 4
 
9.8%
20 5
12.2%
19 11
26.8%
18 12
29.3%

소방장
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.439024
Minimum3
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:56:18.597909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile19
Q133
median40
Q344
95-th percentile67
Maximum115
Range112
Interquartile range (IQR)11

Descriptive statistics

Standard deviation18.264787
Coefficient of variation (CV)0.44076296
Kurtosis6.2157129
Mean41.439024
Median Absolute Deviation (MAD)7
Skewness1.7086389
Sum1699
Variance333.60244
MonotonicityNot monotonic
2023-12-11T06:56:18.703748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
42 3
 
7.3%
31 3
 
7.3%
44 3
 
7.3%
33 2
 
4.9%
48 2
 
4.9%
47 2
 
4.9%
39 2
 
4.9%
43 2
 
4.9%
41 2
 
4.9%
40 2
 
4.9%
Other values (17) 18
43.9%
ValueCountFrequency (%)
3 1
 
2.4%
14 1
 
2.4%
19 1
 
2.4%
21 1
 
2.4%
27 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 3
7.3%
33 2
4.9%
34 1
 
2.4%
ValueCountFrequency (%)
115 1
 
2.4%
82 1
 
2.4%
67 1
 
2.4%
65 1
 
2.4%
60 1
 
2.4%
57 1
 
2.4%
48 2
4.9%
47 2
4.9%
44 3
7.3%
43 2
4.9%

소방교
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.121951
Minimum6
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:56:18.811244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q149
median64
Q375
95-th percentile109
Maximum138
Range132
Interquartile range (IQR)26

Descriptive statistics

Standard deviation29.296924
Coefficient of variation (CV)0.44987786
Kurtosis0.54318658
Mean65.121951
Median Absolute Deviation (MAD)15
Skewness0.18402813
Sum2670
Variance858.30976
MonotonicityNot monotonic
2023-12-11T06:56:18.930852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
64 2
 
4.9%
69 2
 
4.9%
54 2
 
4.9%
68 2
 
4.9%
74 2
 
4.9%
45 2
 
4.9%
100 2
 
4.9%
63 2
 
4.9%
95 2
 
4.9%
75 1
 
2.4%
Other values (22) 22
53.7%
ValueCountFrequency (%)
6 1
2.4%
11 1
2.4%
12 1
2.4%
13 1
2.4%
30 1
2.4%
37 1
2.4%
40 1
2.4%
45 2
4.9%
47 1
2.4%
49 1
2.4%
ValueCountFrequency (%)
138 1
2.4%
131 1
2.4%
109 1
2.4%
100 2
4.9%
98 1
2.4%
95 2
4.9%
83 1
2.4%
82 1
2.4%
75 1
2.4%
74 2
4.9%

소방사
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)91.7%
Missing5
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean138.94444
Minimum6
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T06:56:19.052476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile58
Q193
median140.5
Q3167.5
95-th percentile232.5
Maximum288
Range282
Interquartile range (IQR)74.5

Descriptive statistics

Standard deviation59.024423
Coefficient of variation (CV)0.42480593
Kurtosis0.47597858
Mean138.94444
Median Absolute Deviation (MAD)42.5
Skewness0.31640864
Sum5002
Variance3483.8825
MonotonicityNot monotonic
2023-12-11T06:56:19.153913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
141 2
 
4.9%
93 2
 
4.9%
193 2
 
4.9%
73 1
 
2.4%
108 1
 
2.4%
131 1
 
2.4%
46 1
 
2.4%
169 1
 
2.4%
122 1
 
2.4%
134 1
 
2.4%
Other values (23) 23
56.1%
(Missing) 5
 
12.2%
ValueCountFrequency (%)
6 1
2.4%
46 1
2.4%
62 1
2.4%
73 1
2.4%
79 1
2.4%
80 1
2.4%
84 1
2.4%
92 1
2.4%
93 2
4.9%
100 1
2.4%
ValueCountFrequency (%)
288 1
2.4%
261 1
2.4%
223 1
2.4%
219 1
2.4%
206 1
2.4%
193 2
4.9%
185 1
2.4%
169 1
2.4%
167 1
2.4%
161 1
2.4%

Interactions

2023-12-11T06:56:15.370000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:11.821013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.341497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.887644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.440538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.984236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.758570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.455677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:11.893960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.416098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.969388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.524095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.293857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.851155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.536531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:11.975853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.499112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.049495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.602613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.375150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.954206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.620541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.048567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.572118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.121712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.677737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.444276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.033123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.713297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.122694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.645773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.194239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.753164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.510493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.114315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.774976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.197025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.719612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.262176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.825307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.583162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.191323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.857737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.271765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:12.806112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.352143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:13.911907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:14.673166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:15.280439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:56:19.260208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명관서명정원소방준감소방정소방령소방경소방위소방장소방교소방사
시군명1.0001.0000.0000.0000.0000.0000.0000.0000.0000.6700.724
관서명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
정원0.0001.0001.0001.0000.3050.7800.9020.7470.8750.9560.947
소방준감0.0001.0001.0001.000NaN1.0001.0001.0001.0001.0001.000
소방정0.0001.0000.305NaN1.0000.8780.8160.6420.6250.9271.000
소방령0.0001.0000.7801.0000.8781.0000.8130.8040.8100.8600.976
소방경0.0001.0000.9021.0000.8160.8131.0000.8600.9330.7760.947
소방위0.0001.0000.7471.0000.6420.8040.8601.0000.9920.7941.000
소방장0.0001.0000.8751.0000.6250.8100.9330.9921.0000.7840.919
소방교0.6701.0000.9561.0000.9270.8600.7760.7940.7841.0000.851
소방사0.7241.0000.9471.0001.0000.9760.9471.0000.9190.8511.000
2023-12-11T06:56:19.367436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방정소방준감
소방정1.000NaN
소방준감NaN1.000
2023-12-11T06:56:19.686369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정원소방령소방경소방위소방장소방교소방사소방준감소방정
정원1.0000.5020.8860.5440.9140.9240.8760.7070.174
소방령0.5021.0000.6440.7080.6030.4070.6210.7070.971
소방경0.8860.6441.0000.6780.9400.7960.7430.7070.476
소방위0.5440.7080.6781.0000.6430.4430.5150.7070.655
소방장0.9140.6030.9400.6431.0000.7890.7920.7070.488
소방교0.9240.4070.7960.4430.7891.0000.9300.7070.621
소방사0.8760.6210.7430.5150.7920.9301.0000.7070.880
소방준감0.7070.7070.7070.7070.7070.7070.7071.000NaN
소방정0.1740.9710.4760.6550.4880.6210.880NaN1.000

Missing values

2023-12-11T06:56:15.993912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:56:16.135849image/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

시군명관서명정원소방준감소방정소방령소방경소방위소방장소방교소방사
0수원시소방재난본부410634365116115566
1의정부시북부소방재난본부151<NA>41920344430<NA>
2용인시소방학교75<NA>3138191913<NA>
3용인시특수대응단77<NA>1912222112<NA>
4의정부시북부특수대응단32<NA>1443146<NA>
5오산시국민안전체험관24<NA>1333311<NA>
6수원시수원소방서2941<NA>720184362143
7수원시수원남부소방서317<NA>1719194170160
8성남시성남소방서329<NA>1720214469167
9성남시분당소방서295<NA>1718204267140
시군명관서명정원소방준감소방정소방령소방경소방위소방장소방교소방사
31고양시일산소방서350<NA>1722214783169
32의정시의정부소방서275<NA>1822203963122
33남양시남양주소방서442<NA>17282157109219
34파주시파주소방서394<NA>17242148100193
35구리시구리소방서194<NA>161418314579
36포천시포천소방서381<NA>1719194795193
37양주시양주소방서307<NA>1718194274146
38동두시동두천소방서167<NA>161318274062
39가평시가평소방서222<NA>161518355493
40연천시연천소방서231<NA>1615183160100