Overview

Dataset statistics

Number of variables9
Number of observations35
Missing cells196
Missing cells (%)62.2%
Duplicate rows1
Duplicate rows (%)2.9%
Total size in memory2.9 KiB
Average record size in memory83.8 B

Variable types

Text1
Numeric6
Unsupported2

Dataset

Description보건복지부 사회복지사 각 지역별 급수가 다르거나 성별이 다른 사회복지사 현황 자료를 제공합니다. 사회복지사, 자격증 등 자료를 제공합니다.
Author보건복지부
URLhttps://www.data.go.kr/data/15065415/fileData.do

Alerts

Dataset has 1 (2.9%) duplicate rowsDuplicates
1급 남 is highly overall correlated with 1급 여 and 4 other fieldsHigh correlation
1급 여 is highly overall correlated with 1급 남 and 4 other fieldsHigh correlation
2급 남 is highly overall correlated with 1급 남 and 4 other fieldsHigh correlation
2급 여 is highly overall correlated with 1급 남 and 4 other fieldsHigh correlation
3급 남 is highly overall correlated with 1급 남 and 4 other fieldsHigh correlation
3급 여 is highly overall correlated with 1급 남 and 4 other fieldsHigh correlation
구분 has 18 (51.4%) missing valuesMissing
1급 남 has 18 (51.4%) missing valuesMissing
1급 여 has 18 (51.4%) missing valuesMissing
2급 남 has 18 (51.4%) missing valuesMissing
2급 여 has 18 (51.4%) missing valuesMissing
3급 남 has 18 (51.4%) missing valuesMissing
3급 여 has 18 (51.4%) missing valuesMissing
Unnamed: 7 has 35 (100.0%) missing valuesMissing
Unnamed: 8 has 35 (100.0%) missing valuesMissing
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
3급 남 has 1 (2.9%) zerosZeros
3급 여 has 1 (2.9%) zerosZeros

Reproduction

Analysis started2023-12-12 23:32:36.053334
Analysis finished2023-12-12 23:32:39.846601
Duration3.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing18
Missing (%)51.4%
Memory size412.0 B
2023-12-13T08:32:39.977122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.7647059
Min length6

Characters and Unicode

Total characters149
Distinct characters47
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

Unique17 ?
Unique (%)100.0%

Sample

1st row서울Seoul
2nd row부산Busan
3rd row대구Daegu
4th row인천Incheon
5th row광주Gwangju
ValueCountFrequency (%)
부산busan 1
 
5.6%
대구daegu 1
 
5.6%
제주jeju 1
 
5.6%
경남gyeongnam 1
 
5.6%
경북gyeongbuk 1
 
5.6%
전남jeonnam 1
 
5.6%
전북jeonbuk 1
 
5.6%
충남chungnam 1
 
5.6%
충북chungbuk 1
 
5.6%
서울seoul 1
 
5.6%
Other values (8) 8
44.4%
2023-12-13T08:32:40.392864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 19
 
12.8%
e 12
 
8.1%
u 10
 
6.7%
g 10
 
6.7%
o 10
 
6.7%
a 9
 
6.0%
G 5
 
3.4%
j 4
 
2.7%
전 3
 
2.0%
k 3
 
2.0%
Other values (37) 64
43.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 97
65.1%
Other Letter 34
 
22.8%
Uppercase Letter 17
 
11.4%
Space Separator 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 3
 
8.8%
경 3
 
8.8%
북 3
 
8.8%
남 3
 
8.8%
충 2
 
5.9%
울 2
 
5.9%
산 2
 
5.9%
대 2
 
5.9%
주 2
 
5.9%
제 1
 
2.9%
Other values (11) 11
32.4%
Lowercase Letter
ValueCountFrequency (%)
n 19
19.6%
e 12
12.4%
u 10
10.3%
g 10
10.3%
o 10
10.3%
a 9
9.3%
j 4
 
4.1%
k 3
 
3.1%
b 3
 
3.1%
y 3
 
3.1%
Other values (7) 14
14.4%
Uppercase Letter
ValueCountFrequency (%)
G 5
29.4%
J 3
17.6%
S 2
 
11.8%
C 2
 
11.8%
D 2
 
11.8%
U 1
 
5.9%
I 1
 
5.9%
B 1
 
5.9%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 114
76.5%
Hangul 34
 
22.8%
Common 1
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 19
16.7%
e 12
10.5%
u 10
 
8.8%
g 10
 
8.8%
o 10
 
8.8%
a 9
 
7.9%
G 5
 
4.4%
j 4
 
3.5%
k 3
 
2.6%
b 3
 
2.6%
Other values (15) 29
25.4%
Hangul
ValueCountFrequency (%)
전 3
 
8.8%
경 3
 
8.8%
북 3
 
8.8%
남 3
 
8.8%
충 2
 
5.9%
울 2
 
5.9%
산 2
 
5.9%
대 2
 
5.9%
주 2
 
5.9%
제 1
 
2.9%
Other values (11) 11
32.4%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115
77.2%
Hangul 34
 
22.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 19
16.5%
e 12
 
10.4%
u 10
 
8.7%
g 10
 
8.7%
o 10
 
8.7%
a 9
 
7.8%
G 5
 
4.3%
j 4
 
3.5%
k 3
 
2.6%
b 3
 
2.6%
Other values (16) 30
26.1%
Hangul
ValueCountFrequency (%)
전 3
 
8.8%
경 3
 
8.8%
북 3
 
8.8%
남 3
 
8.8%
충 2
 
5.9%
울 2
 
5.9%
산 2
 
5.9%
대 2
 
5.9%
주 2
 
5.9%
제 1
 
2.9%
Other values (11) 11
32.4%

1급 남
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing18
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean2155.5294
Minimum18
Maximum12430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T08:32:40.527845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile237.2
Q11078
median1279
Q32088
95-th percentile6365.2
Maximum12430
Range12412
Interquartile range (IQR)1010

Descriptive statistics

Standard deviation2864.6115
Coefficient of variation (CV)1.3289596
Kurtosis11.55394
Mean2155.5294
Median Absolute Deviation (MAD)595
Skewness3.240155
Sum36644
Variance8205998.8
MonotonicityNot monotonic
2023-12-13T08:32:40.650232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2572 1
 
2.9%
331 1
 
2.9%
1167 1
 
2.9%
2215 1
 
2.9%
1078 1
 
2.9%
1874 1
 
2.9%
1192 1
 
2.9%
1279 1
 
2.9%
12430 1
 
2.9%
4849 1
 
2.9%
Other values (7) 7
 
20.0%
(Missing) 18
51.4%
ValueCountFrequency (%)
18 1
2.9%
292 1
2.9%
331 1
2.9%
1036 1
2.9%
1078 1
2.9%
1167 1
2.9%
1192 1
2.9%
1269 1
2.9%
1279 1
2.9%
1319 1
2.9%
ValueCountFrequency (%)
12430 1
2.9%
4849 1
2.9%
2572 1
2.9%
2215 1
2.9%
2088 1
2.9%
1874 1
2.9%
1635 1
2.9%
1319 1
2.9%
1279 1
2.9%
1269 1
2.9%

1급 여
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing18
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean6439.7059
Minimum48
Maximum38636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T08:32:40.764887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile757.6
Q13548
median4174
Q35430
95-th percentile18532
Maximum38636
Range38588
Interquartile range (IQR)1882

Descriptive statistics

Standard deviation8816.1041
Coefficient of variation (CV)1.3690228
Kurtosis12.63766
Mean6439.7059
Median Absolute Deviation (MAD)1256
Skewness3.4109814
Sum109475
Variance77723691
MonotonicityNot monotonic
2023-12-13T08:32:40.888070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7468 1
 
2.9%
935 1
 
2.9%
4712 1
 
2.9%
5726 1
 
2.9%
3548 1
 
2.9%
5191 1
 
2.9%
3855 1
 
2.9%
3767 1
 
2.9%
38636 1
 
2.9%
13506 1
 
2.9%
Other values (7) 7
 
20.0%
(Missing) 18
51.4%
ValueCountFrequency (%)
48 1
2.9%
935 1
2.9%
1134 1
2.9%
2642 1
2.9%
3548 1
2.9%
3767 1
2.9%
3855 1
2.9%
4082 1
2.9%
4174 1
2.9%
4621 1
2.9%
ValueCountFrequency (%)
38636 1
2.9%
13506 1
2.9%
7468 1
2.9%
5726 1
2.9%
5430 1
2.9%
5191 1
2.9%
4712 1
2.9%
4621 1
2.9%
4174 1
2.9%
4082 1
2.9%

2급 남
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing18
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean13206
Minimum276
Maximum43360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T08:32:41.049366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum276
5-th percentile2176
Q18101
median11793
Q313747
95-th percentile35703.2
Maximum43360
Range43084
Interquartile range (IQR)5646

Descriptive statistics

Standard deviation10694.398
Coefficient of variation (CV)0.8098136
Kurtosis3.7049409
Mean13206
Median Absolute Deviation (MAD)3282
Skewness1.8296044
Sum224502
Variance1.1437016 × 108
MonotonicityNot monotonic
2023-12-13T08:32:41.149863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13299 1
 
2.9%
3790 1
 
2.9%
11793 1
 
2.9%
18100 1
 
2.9%
13994 1
 
2.9%
12623 1
 
2.9%
7977 1
 
2.9%
8739 1
 
2.9%
43360 1
 
2.9%
33789 1
 
2.9%
Other values (7) 7
 
20.0%
(Missing) 18
51.4%
ValueCountFrequency (%)
276 1
2.9%
2651 1
2.9%
3790 1
2.9%
7977 1
2.9%
8101 1
2.9%
8511 1
2.9%
8739 1
2.9%
10535 1
2.9%
11793 1
2.9%
12623 1
2.9%
ValueCountFrequency (%)
43360 1
2.9%
33789 1
2.9%
18100 1
2.9%
13994 1
2.9%
13747 1
2.9%
13299 1
2.9%
13217 1
2.9%
12623 1
2.9%
11793 1
2.9%
10535 1
2.9%

2급 여
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing18
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean37380.118
Minimum736
Maximum117919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T08:32:41.278074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum736
5-th percentile7175.2
Q123770
median33476
Q339319
95-th percentile105162.2
Maximum117919
Range117183
Interquartile range (IQR)15549

Descriptive statistics

Standard deviation30309.974
Coefficient of variation (CV)0.81085818
Kurtosis3.1121115
Mean37380.118
Median Absolute Deviation (MAD)9454
Skewness1.7492432
Sum635462
Variance9.1869453 × 108
MonotonicityNot monotonic
2023-12-13T08:32:41.404181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
41958 1
 
2.9%
8785 1
 
2.9%
34256 1
 
2.9%
49855 1
 
2.9%
38915 1
 
2.9%
33476 1
 
2.9%
23770 1
 
2.9%
26018 1
 
2.9%
117919 1
 
2.9%
101973 1
 
2.9%
Other values (7) 7
 
20.0%
(Missing) 18
51.4%
ValueCountFrequency (%)
736 1
2.9%
8785 1
2.9%
9983 1
2.9%
19900 1
2.9%
23770 1
2.9%
24022 1
2.9%
26018 1
2.9%
26313 1
2.9%
33476 1
2.9%
34256 1
2.9%
ValueCountFrequency (%)
117919 1
2.9%
101973 1
2.9%
49855 1
2.9%
41958 1
2.9%
39319 1
2.9%
38915 1
2.9%
38264 1
2.9%
34256 1
2.9%
33476 1
2.9%
26313 1
2.9%

3급 남
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct14
Distinct (%)82.4%
Missing18
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean228.11765
Minimum0
Maximum3285
Zeros1
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T08:32:41.510137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q116
median36
Q343
95-th percentile817
Maximum3285
Range3285
Interquartile range (IQR)27

Descriptive statistics

Standard deviation789.02748
Coefficient of variation (CV)3.4588621
Kurtosis16.86665
Mean228.11765
Median Absolute Deviation (MAD)18
Skewness4.1008807
Sum3878
Variance622564.36
MonotonicityNot monotonic
2023-12-13T08:32:41.622457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
36 2
 
5.7%
39 2
 
5.7%
16 2
 
5.7%
3285 1
 
2.9%
200 1
 
2.9%
54 1
 
2.9%
6 1
 
2.9%
8 1
 
2.9%
0 1
 
2.9%
44 1
 
2.9%
Other values (4) 4
 
11.4%
(Missing) 18
51.4%
ValueCountFrequency (%)
0 1
2.9%
3 1
2.9%
6 1
2.9%
8 1
2.9%
16 2
5.7%
20 1
2.9%
33 1
2.9%
36 2
5.7%
39 2
5.7%
43 1
2.9%
ValueCountFrequency (%)
3285 1
2.9%
200 1
2.9%
54 1
2.9%
44 1
2.9%
43 1
2.9%
39 2
5.7%
36 2
5.7%
33 1
2.9%
20 1
2.9%
16 2
5.7%

3급 여
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)100.0%
Missing18
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean569.76471
Minimum0
Maximum8617
Zeros1
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T08:32:41.741274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.2
Q129
median59
Q3103
95-th percentile1917.8
Maximum8617
Range8617
Interquartile range (IQR)74

Descriptive statistics

Standard deviation2074.6044
Coefficient of variation (CV)3.6411599
Kurtosis16.965388
Mean569.76471
Median Absolute Deviation (MAD)38
Skewness4.1172092
Sum9686
Variance4303983.3
MonotonicityNot monotonic
2023-12-13T08:32:41.855472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
243 1
 
2.9%
9 1
 
2.9%
37 1
 
2.9%
47 1
 
2.9%
21 1
 
2.9%
89 1
 
2.9%
63 1
 
2.9%
64 1
 
2.9%
8617 1
 
2.9%
103 1
 
2.9%
Other values (7) 7
 
20.0%
(Missing) 18
51.4%
ValueCountFrequency (%)
0 1
2.9%
9 1
2.9%
14 1
2.9%
21 1
2.9%
29 1
2.9%
33 1
2.9%
37 1
2.9%
47 1
2.9%
59 1
2.9%
63 1
2.9%
ValueCountFrequency (%)
8617 1
2.9%
243 1
2.9%
153 1
2.9%
105 1
2.9%
103 1
2.9%
89 1
2.9%
64 1
2.9%
63 1
2.9%
59 1
2.9%
47 1
2.9%

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing35
Missing (%)100.0%
Memory size447.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing35
Missing (%)100.0%
Memory size447.0 B

Interactions

2023-12-13T08:32:38.981642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.273189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.720654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.204563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.610343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:38.085386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:39.076643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.344892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.797994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.274092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.681671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:38.210619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:39.165492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.431652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.881228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.347926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.760781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:38.304908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:39.232044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.503890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.963051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.408552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.823207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:38.387531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:39.302550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.581663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.039807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.474112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.898738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:38.476594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:39.382577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:36.656117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.130256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.549871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:37.980192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:38.887357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:32:41.949461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1급 남1급 여2급 남2급 여3급 남3급 여
구분1.0001.0001.0001.0001.0001.0001.000
1급 남1.0001.0000.8950.7580.7761.0001.000
1급 여1.0000.8951.0000.9100.8741.0001.000
2급 남1.0000.7580.9101.0000.9991.0001.000
2급 여1.0000.7760.8740.9991.0001.0001.000
3급 남1.0001.0001.0001.0001.0001.0000.605
3급 여1.0001.0001.0001.0001.0000.6051.000
2023-12-13T08:32:42.063743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1급 남1급 여2급 남2급 여3급 남3급 여
1급 남1.0000.9510.8140.8630.8280.887
1급 여0.9511.0000.8310.8970.8340.850
2급 남0.8140.8311.0000.9780.6430.686
2급 여0.8630.8970.9781.0000.7070.750
3급 남0.8280.8340.6430.7071.0000.920
3급 여0.8870.8500.6860.7500.9201.000

Missing values

2023-12-13T08:32:39.488801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:32:39.624150image/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.
2023-12-13T08:32:39.773764image/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

구분1급 남1급 여2급 남2급 여3급 남3급 여Unnamed: 7Unnamed: 8
0서울Seoul12430386364336011791932858617<NA><NA>
1부산Busan257274681329941958200243<NA><NA>
2대구Daegu20885430132173826454153<NA><NA>
3인천Incheon12694082810124022633<NA><NA>
4광주Gwangju13194621137473931936105<NA><NA>
5대전Daejeon1635417410535263133959<NA><NA>
6울산Ulsan292113426519983814<NA><NA>
7세종 Sejong184827673600<NA><NA>
8경기Gyeonggi4849135063378910197344103<NA><NA>
9강원Gangwon103626428511199001629<NA><NA>
구분1급 남1급 여2급 남2급 여3급 남3급 여Unnamed: 7Unnamed: 8
25<NA><NA><NA><NA><NA><NA><NA><NA><NA>
26<NA><NA><NA><NA><NA><NA><NA><NA><NA>
27<NA><NA><NA><NA><NA><NA><NA><NA><NA>
28<NA><NA><NA><NA><NA><NA><NA><NA><NA>
29<NA><NA><NA><NA><NA><NA><NA><NA><NA>
30<NA><NA><NA><NA><NA><NA><NA><NA><NA>
31<NA><NA><NA><NA><NA><NA><NA><NA><NA>
32<NA><NA><NA><NA><NA><NA><NA><NA><NA>
33<NA><NA><NA><NA><NA><NA><NA><NA><NA>
34<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구분1급 남1급 여2급 남2급 여3급 남3급 여# duplicates
0<NA><NA><NA><NA><NA><NA><NA>18