Overview

Dataset statistics

Number of variables9
Number of observations3510
Missing cells0
Missing cells (%)0.0%
Duplicate rows59
Duplicate rows (%)1.7%
Total size in memory274.3 KiB
Average record size in memory80.0 B

Variable types

Text1
Numeric8

Dataset

Description농림축산식품부 쌀소득보전직불제 고정직불금/금액별 자료로 관할_읍면동,5만미만_변동,5~10만미만_변동,10~20만미만_변동,20~50만미만_변동,50~100만_변동,100~200만미만_변동,200~500만미만_변동,500만이상_변동 등을 제공합니다.
URLhttps://www.data.go.kr/data/15090525/fileData.do

Alerts

Dataset has 59 (1.7%) duplicate rowsDuplicates
5만미만_변동 is highly overall correlated with 5-10만미만_변동High correlation
5-10만미만_변동 is highly overall correlated with 5만미만_변동 and 6 other fieldsHigh correlation
10-20만미만_변동 is highly overall correlated with 5-10만미만_변동 and 5 other fieldsHigh correlation
20-50만미만_변동 is highly overall correlated with 5-10만미만_변동 and 5 other fieldsHigh correlation
50-100만_변동 is highly overall correlated with 5-10만미만_변동 and 5 other fieldsHigh correlation
100-200만미만_변동 is highly overall correlated with 5-10만미만_변동 and 5 other fieldsHigh correlation
200-500만미만_변동 is highly overall correlated with 5-10만미만_변동 and 5 other fieldsHigh correlation
500만이상_변동 is highly overall correlated with 5-10만미만_변동 and 5 other fieldsHigh correlation
5만미만_변동 is highly skewed (γ1 = 21.84643132)Skewed
5만미만_변동 has 2857 (81.4%) zerosZeros
5-10만미만_변동 has 1738 (49.5%) zerosZeros
10-20만미만_변동 has 1484 (42.3%) zerosZeros
20-50만미만_변동 has 1466 (41.8%) zerosZeros
50-100만_변동 has 1647 (46.9%) zerosZeros
100-200만미만_변동 has 1809 (51.5%) zerosZeros
200-500만미만_변동 has 1972 (56.2%) zerosZeros
500만이상_변동 has 2274 (64.8%) zerosZeros

Reproduction

Analysis started2023-12-12 01:14:52.635904
Analysis finished2023-12-12 01:15:01.483002
Duration8.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관할_읍면동
Text

Distinct3156
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
2023-12-12T10:15:01.805740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.3404558
Min length2

Characters and Unicode

Total characters11725
Distinct characters344
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2917 ?
Unique (%)83.1%

Sample

1st row사직동
2nd row삼청동
3rd row청운효자동
4th row부암동
5th row평창동
ValueCountFrequency (%)
중앙동 31
 
0.9%
남면 12
 
0.3%
서면 10
 
0.3%
북면 8
 
0.2%
송정동 7
 
0.2%
동면 6
 
0.2%
신흥동 5
 
0.1%
금성면 5
 
0.1%
교동 5
 
0.1%
대산면 4
 
0.1%
Other values (3146) 3417
97.4%
2023-12-12T10:15:02.375202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2232
 
19.0%
면 1186
 
10.1%
1 384
 
3.3%
2 376
 
3.2%
산 289
 
2.5%
읍 236
 
2.0%
3 163
 
1.4%
성 156
 
1.3%
신 154
 
1.3%
천 150
 
1.3%
Other values (334) 6399
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10625
90.6%
Decimal Number 1078
 
9.2%
Other Punctuation 21
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2232
 
21.0%
면 1186
 
11.2%
산 289
 
2.7%
읍 236
 
2.2%
성 156
 
1.5%
신 154
 
1.4%
천 150
 
1.4%
남 135
 
1.3%
대 130
 
1.2%
정 121
 
1.1%
Other values (321) 5836
54.9%
Decimal Number
ValueCountFrequency (%)
1 384
35.6%
2 376
34.9%
3 163
15.1%
4 78
 
7.2%
5 34
 
3.2%
6 21
 
1.9%
7 10
 
0.9%
8 6
 
0.6%
9 4
 
0.4%
0 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 18
85.7%
· 3
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10625
90.6%
Common 1100
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2232
 
21.0%
면 1186
 
11.2%
산 289
 
2.7%
읍 236
 
2.2%
성 156
 
1.5%
신 154
 
1.4%
천 150
 
1.4%
남 135
 
1.3%
대 130
 
1.2%
정 121
 
1.1%
Other values (321) 5836
54.9%
Common
ValueCountFrequency (%)
1 384
34.9%
2 376
34.2%
3 163
14.8%
4 78
 
7.1%
5 34
 
3.1%
6 21
 
1.9%
. 18
 
1.6%
7 10
 
0.9%
8 6
 
0.5%
9 4
 
0.4%
Other values (3) 6
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10625
90.6%
ASCII 1097
 
9.4%
None 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2232
 
21.0%
면 1186
 
11.2%
산 289
 
2.7%
읍 236
 
2.2%
성 156
 
1.5%
신 154
 
1.4%
천 150
 
1.4%
남 135
 
1.3%
대 130
 
1.2%
정 121
 
1.1%
Other values (321) 5836
54.9%
ASCII
ValueCountFrequency (%)
1 384
35.0%
2 376
34.3%
3 163
14.9%
4 78
 
7.1%
5 34
 
3.1%
6 21
 
1.9%
. 18
 
1.6%
7 10
 
0.9%
8 6
 
0.5%
9 4
 
0.4%
Other values (2) 3
 
0.3%
None
ValueCountFrequency (%)
· 3
100.0%

5만미만_변동
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33475783
Minimum0
Maximum50
Zeros2857
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:15:02.555771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1979439
Coefficient of variation (CV)3.5785389
Kurtosis848.03573
Mean0.33475783
Median Absolute Deviation (MAD)0
Skewness21.846431
Sum1175
Variance1.4350697
MonotonicityNot monotonic
2023-12-12T10:15:02.686056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 2857
81.4%
1 391
 
11.1%
2 153
 
4.4%
3 53
 
1.5%
4 34
 
1.0%
5 9
 
0.3%
6 6
 
0.2%
7 2
 
0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
0 2857
81.4%
1 391
 
11.1%
2 153
 
4.4%
3 53
 
1.5%
4 34
 
1.0%
5 9
 
0.3%
6 6
 
0.2%
7 2
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
0.1%
6 6
 
0.2%
5 9
 
0.3%
4 34
1.0%
3 53
1.5%

5-10만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3897436
Minimum0
Maximum86
Zeros1738
Zeros (%)49.5%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:15:02.845706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.75
95-th percentile19
Maximum86
Range86
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation7.3468951
Coefficient of variation (CV)1.6736502
Kurtosis15.820804
Mean4.3897436
Median Absolute Deviation (MAD)1
Skewness3.1231561
Sum15408
Variance53.976868
MonotonicityNot monotonic
2023-12-12T10:15:03.272297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1738
49.5%
1 187
 
5.3%
3 154
 
4.4%
5 149
 
4.2%
2 142
 
4.0%
4 133
 
3.8%
6 129
 
3.7%
8 117
 
3.3%
7 109
 
3.1%
10 87
 
2.5%
Other values (44) 565
 
16.1%
ValueCountFrequency (%)
0 1738
49.5%
1 187
 
5.3%
2 142
 
4.0%
3 154
 
4.4%
4 133
 
3.8%
5 149
 
4.2%
6 129
 
3.7%
7 109
 
3.1%
8 117
 
3.3%
9 80
 
2.3%
ValueCountFrequency (%)
86 1
< 0.1%
75 1
< 0.1%
64 1
< 0.1%
60 1
< 0.1%
59 1
< 0.1%
58 1
< 0.1%
57 1
< 0.1%
53 1
< 0.1%
48 1
< 0.1%
45 1
< 0.1%

10-20만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct219
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.469231
Minimum0
Maximum507
Zeros1484
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:15:03.432342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q354
95-th percentile131
Maximum507
Range507
Interquartile range (IQR)54

Descriptive statistics

Standard deviation48.906056
Coefficient of variation (CV)1.4612244
Kurtosis7.0179138
Mean33.469231
Median Absolute Deviation (MAD)5
Skewness2.1509505
Sum117477
Variance2391.8023
MonotonicityNot monotonic
2023-12-12T10:15:03.587013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1484
42.3%
1 121
 
3.4%
2 58
 
1.7%
3 36
 
1.0%
5 33
 
0.9%
36 27
 
0.8%
34 27
 
0.8%
4 26
 
0.7%
49 25
 
0.7%
65 25
 
0.7%
Other values (209) 1648
47.0%
ValueCountFrequency (%)
0 1484
42.3%
1 121
 
3.4%
2 58
 
1.7%
3 36
 
1.0%
4 26
 
0.7%
5 33
 
0.9%
6 21
 
0.6%
7 18
 
0.5%
8 22
 
0.6%
9 23
 
0.7%
ValueCountFrequency (%)
507 1
< 0.1%
345 1
< 0.1%
333 1
< 0.1%
314 1
< 0.1%
301 1
< 0.1%
297 1
< 0.1%
288 1
< 0.1%
284 1
< 0.1%
281 2
0.1%
280 1
< 0.1%

20-50만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct417
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.482906
Minimum0
Maximum1155
Zeros1466
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:15:03.754377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q3140
95-th percentile302.55
Maximum1155
Range1155
Interquartile range (IQR)140

Descriptive statistics

Standard deviation115.08761
Coefficient of variation (CV)1.4299634
Kurtosis5.8436937
Mean80.482906
Median Absolute Deviation (MAD)8
Skewness1.9319654
Sum282495
Variance13245.158
MonotonicityNot monotonic
2023-12-12T10:15:03.966072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1466
41.8%
1 126
 
3.6%
2 52
 
1.5%
3 31
 
0.9%
7 24
 
0.7%
4 23
 
0.7%
6 16
 
0.5%
9 14
 
0.4%
111 14
 
0.4%
5 12
 
0.3%
Other values (407) 1732
49.3%
ValueCountFrequency (%)
0 1466
41.8%
1 126
 
3.6%
2 52
 
1.5%
3 31
 
0.9%
4 23
 
0.7%
5 12
 
0.3%
6 16
 
0.5%
7 24
 
0.7%
8 10
 
0.3%
9 14
 
0.4%
ValueCountFrequency (%)
1155 1
< 0.1%
897 1
< 0.1%
838 1
< 0.1%
791 1
< 0.1%
673 1
< 0.1%
648 1
< 0.1%
631 1
< 0.1%
630 1
< 0.1%
623 1
< 0.1%
616 1
< 0.1%

50-100만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct322
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.617094
Minimum0
Maximum617
Zeros1647
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:15:04.201589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q396
95-th percentile218
Maximum617
Range617
Interquartile range (IQR)96

Descriptive statistics

Standard deviation80.00951
Coefficient of variation (CV)1.4922388
Kurtosis3.301632
Mean53.617094
Median Absolute Deviation (MAD)2
Skewness1.7411587
Sum188196
Variance6401.5216
MonotonicityNot monotonic
2023-12-12T10:15:04.367201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1647
46.9%
1 102
 
2.9%
2 42
 
1.2%
4 26
 
0.7%
5 25
 
0.7%
3 24
 
0.7%
9 23
 
0.7%
6 23
 
0.7%
7 19
 
0.5%
73 18
 
0.5%
Other values (312) 1561
44.5%
ValueCountFrequency (%)
0 1647
46.9%
1 102
 
2.9%
2 42
 
1.2%
3 24
 
0.7%
4 26
 
0.7%
5 25
 
0.7%
6 23
 
0.7%
7 19
 
0.5%
8 18
 
0.5%
9 23
 
0.7%
ValueCountFrequency (%)
617 1
< 0.1%
484 1
< 0.1%
468 2
0.1%
462 1
< 0.1%
456 1
< 0.1%
430 1
< 0.1%
416 1
< 0.1%
411 2
0.1%
390 1
< 0.1%
389 1
< 0.1%

100-200만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct251
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.945299
Minimum0
Maximum419
Zeros1809
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:15:04.551689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q353
95-th percentile158.55
Maximum419
Range419
Interquartile range (IQR)53

Descriptive statistics

Standard deviation57.427327
Coefficient of variation (CV)1.6917608
Kurtosis5.0186972
Mean33.945299
Median Absolute Deviation (MAD)0
Skewness2.1421998
Sum119148
Variance3297.8978
MonotonicityNot monotonic
2023-12-12T10:15:04.737581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1809
51.5%
1 93
 
2.6%
2 59
 
1.7%
3 42
 
1.2%
4 30
 
0.9%
6 24
 
0.7%
5 23
 
0.7%
13 22
 
0.6%
8 20
 
0.6%
30 19
 
0.5%
Other values (241) 1369
39.0%
ValueCountFrequency (%)
0 1809
51.5%
1 93
 
2.6%
2 59
 
1.7%
3 42
 
1.2%
4 30
 
0.9%
5 23
 
0.7%
6 24
 
0.7%
7 16
 
0.5%
8 20
 
0.6%
9 12
 
0.3%
ValueCountFrequency (%)
419 1
< 0.1%
392 1
< 0.1%
353 1
< 0.1%
323 1
< 0.1%
318 1
< 0.1%
313 2
0.1%
308 1
< 0.1%
304 1
< 0.1%
300 1
< 0.1%
292 1
< 0.1%

200-500만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct187
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.797721
Minimum0
Maximum452
Zeros1972
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:15:04.918334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q322
95-th percentile97
Maximum452
Range452
Interquartile range (IQR)22

Descriptive statistics

Standard deviation36.870234
Coefficient of variation (CV)1.9614205
Kurtosis13.71471
Mean18.797721
Median Absolute Deviation (MAD)0
Skewness3.0415812
Sum65980
Variance1359.4142
MonotonicityNot monotonic
2023-12-12T10:15:05.069007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1972
56.2%
1 109
 
3.1%
2 47
 
1.3%
4 44
 
1.3%
7 42
 
1.2%
6 40
 
1.1%
5 39
 
1.1%
3 38
 
1.1%
8 30
 
0.9%
14 26
 
0.7%
Other values (177) 1123
32.0%
ValueCountFrequency (%)
0 1972
56.2%
1 109
 
3.1%
2 47
 
1.3%
3 38
 
1.1%
4 44
 
1.3%
5 39
 
1.1%
6 40
 
1.1%
7 42
 
1.2%
8 30
 
0.9%
9 24
 
0.7%
ValueCountFrequency (%)
452 1
< 0.1%
299 1
< 0.1%
271 1
< 0.1%
270 1
< 0.1%
257 1
< 0.1%
238 1
< 0.1%
233 1
< 0.1%
230 1
< 0.1%
225 2
0.1%
223 1
< 0.1%

500만이상_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct108
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3410256
Minimum0
Maximum241
Zeros2274
Zeros (%)64.8%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:15:05.233575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile36
Maximum241
Range241
Interquartile range (IQR)4

Descriptive statistics

Standard deviation17.422113
Coefficient of variation (CV)2.7475228
Kurtosis41.163998
Mean6.3410256
Median Absolute Deviation (MAD)0
Skewness5.3245303
Sum22257
Variance303.53001
MonotonicityNot monotonic
2023-12-12T10:15:05.406785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2274
64.8%
1 165
 
4.7%
2 102
 
2.9%
3 88
 
2.5%
6 60
 
1.7%
5 54
 
1.5%
4 54
 
1.5%
8 47
 
1.3%
7 45
 
1.3%
15 34
 
1.0%
Other values (98) 587
 
16.7%
ValueCountFrequency (%)
0 2274
64.8%
1 165
 
4.7%
2 102
 
2.9%
3 88
 
2.5%
4 54
 
1.5%
5 54
 
1.5%
6 60
 
1.7%
7 45
 
1.3%
8 47
 
1.3%
9 33
 
0.9%
ValueCountFrequency (%)
241 1
< 0.1%
239 1
< 0.1%
189 1
< 0.1%
179 1
< 0.1%
171 1
< 0.1%
159 1
< 0.1%
155 1
< 0.1%
149 1
< 0.1%
137 1
< 0.1%
132 1
< 0.1%

Interactions

2023-12-12T10:15:00.235768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:53.489095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:54.496514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:55.494161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.310317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:57.213846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:58.230067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:59.171623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:00.364347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:53.581814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:54.613480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:55.607880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.409570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:57.329341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:58.347666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:59.307566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:00.478505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:53.662225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:54.733314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:55.718748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.516467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:57.434476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:58.456384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:59.437808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:00.577851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:53.747183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:54.889075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:55.821189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.625047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:57.564427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:58.590661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:59.568449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:00.713112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:53.835844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:55.021037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:55.921675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.737407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:57.705343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:58.693605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:59.670064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:00.841245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:53.922959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:55.133708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.016160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.852248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:57.885314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:58.818999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:59.836563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:00.969232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:54.004252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:55.267128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.113643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.992522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:58.011714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:58.952748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:59.973572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:01.091910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:54.108962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:55.380903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:56.205902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:57.103047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:58.123337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:14:59.056836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:00.106453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:15:05.536054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5만미만_변동5-10만미만_변동10-20만미만_변동20-50만미만_변동50-100만_변동100-200만미만_변동200-500만미만_변동500만이상_변동
5만미만_변동1.0000.4250.3040.2230.0630.0000.0000.000
5-10만미만_변동0.4251.0000.7600.5960.4080.3900.1490.000
10-20만미만_변동0.3040.7601.0000.8240.6000.4280.3570.103
20-50만미만_변동0.2230.5960.8241.0000.9000.5640.3560.280
50-100만_변동0.0630.4080.6000.9001.0000.7550.5170.448
100-200만미만_변동0.0000.3900.4280.5640.7551.0000.7490.570
200-500만미만_변동0.0000.1490.3570.3560.5170.7491.0000.740
500만이상_변동0.0000.0000.1030.2800.4480.5700.7401.000
2023-12-12T10:15:05.655899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5만미만_변동5-10만미만_변동10-20만미만_변동20-50만미만_변동50-100만_변동100-200만미만_변동200-500만미만_변동500만이상_변동
5만미만_변동1.0000.5090.4980.4930.4810.4570.4210.363
5-10만미만_변동0.5091.0000.9320.9030.8720.8280.7770.662
10-20만미만_변동0.4980.9321.0000.9660.9270.8770.8240.706
20-50만미만_변동0.4930.9030.9661.0000.9610.9170.8690.758
50-100만_변동0.4810.8720.9270.9611.0000.9620.9230.823
100-200만미만_변동0.4570.8280.8770.9170.9621.0000.9590.874
200-500만미만_변동0.4210.7770.8240.8690.9230.9591.0000.911
500만이상_변동0.3630.6620.7060.7580.8230.8740.9111.000

Missing values

2023-12-12T10:15:01.232237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:15:01.415374image/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

관할_읍면동5만미만_변동5-10만미만_변동10-20만미만_변동20-50만미만_변동50-100만_변동100-200만미만_변동200-500만미만_변동500만이상_변동
0사직동00000000
1삼청동00000000
2청운효자동00000000
3부암동00000000
4평창동00000000
5무악동00000000
6교남동00000000
7가회동00000000
8종로1.2.3.4가동00000000
9종로5.6가동00000000
관할_읍면동5만미만_변동5-10만미만_변동10-20만미만_변동20-50만미만_변동50-100만_변동100-200만미만_변동200-500만미만_변동500만이상_변동
3500아름동00000000
3501종촌동00000000
3502고운동00000000
3503보람동00000000
3504대평동00000000
3505소담동00000000
3506다정동00000000
3507반곡동00000000
3508해밀동00000000
3509새롬동00000000

Duplicate rows

Most frequently occurring

관할_읍면동5만미만_변동5-10만미만_변동10-20만미만_변동20-50만미만_변동50-100만_변동100-200만미만_변동200-500만미만_변동500만이상_변동# duplicates
51중앙동0000000019
34신촌동000000003
36신흥동000000003
46위례동000000003
54충무동000000003
0가양2동000000002
1고등동000000002
2금곡동000000002
3남현동000000002
4논현1동000000002