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 변동직불금 금액별 자료로 관할읍면동,5만미만_변동,5~10만미만_변동,10~20만미만_변동,20~50만미만_변동,50~100만_변동,100~200만미만_변동,200~500만미만_변동,500만이상_변동 등의 내용을 제공합니다.
URLhttps://www.data.go.kr/data/15090526/fileData.do

Alerts

Dataset has 59 (1.7%) duplicate rowsDuplicates
5만미만_변동 is highly overall correlated with 5-10만미만_변동 and 5 other fieldsHigh correlation
5-10만미만_변동 is highly overall correlated with 5만미만_변동 and 5 other fieldsHigh correlation
10-20만미만_변동 is highly overall correlated with 5만미만_변동 and 5 other fieldsHigh correlation
20-50만미만_변동 is highly overall correlated with 5만미만_변동 and 5 other fieldsHigh correlation
50-100만_변동 is highly overall correlated with 5만미만_변동 and 6 other fieldsHigh correlation
100-200만미만_변동 is highly overall correlated with 5만미만_변동 and 6 other fieldsHigh correlation
200-500만미만_변동 is highly overall correlated with 5만미만_변동 and 6 other fieldsHigh correlation
500만이상_변동 is highly overall correlated with 50-100만_변동 and 2 other fieldsHigh correlation
5만미만_변동 has 1474 (42.0%) zerosZeros
5-10만미만_변동 has 1549 (44.1%) zerosZeros
10-20만미만_변동 has 1579 (45.0%) zerosZeros
20-50만미만_변동 has 1683 (47.9%) zerosZeros
50-100만_변동 has 1938 (55.2%) zerosZeros
100-200만미만_변동 has 2109 (60.1%) zerosZeros
200-500만미만_변동 has 2412 (68.7%) zerosZeros
500만이상_변동 has 3125 (89.0%) zerosZeros

Reproduction

Analysis started2023-12-12 01:57:19.163689
Analysis finished2023-12-12 01:57:28.452181
Duration9.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관할읍면동
Text

Distinct3156
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
2023-12-12T10:57:28.767578image/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%
2023-12-12T10:57:29.355469image/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  ZEROS 

Distinct358
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.321368
Minimum0
Maximum927
Zeros1474
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:57:29.541866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q383
95-th percentile227
Maximum927
Range927
Interquartile range (IQR)83

Descriptive statistics

Standard deviation90.019886
Coefficient of variation (CV)1.6272173
Kurtosis11.782939
Mean55.321368
Median Absolute Deviation (MAD)7
Skewness2.7781313
Sum194178
Variance8103.5798
MonotonicityNot monotonic
2023-12-12T10:57:29.732390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1474
42.0%
1 112
 
3.2%
2 43
 
1.2%
3 40
 
1.1%
4 29
 
0.8%
6 27
 
0.8%
5 23
 
0.7%
11 22
 
0.6%
7 21
 
0.6%
58 20
 
0.6%
Other values (348) 1699
48.4%
ValueCountFrequency (%)
0 1474
42.0%
1 112
 
3.2%
2 43
 
1.2%
3 40
 
1.1%
4 29
 
0.8%
5 23
 
0.7%
6 27
 
0.8%
7 21
 
0.6%
8 15
 
0.4%
9 20
 
0.6%
ValueCountFrequency (%)
927 1
< 0.1%
862 1
< 0.1%
824 1
< 0.1%
691 1
< 0.1%
647 1
< 0.1%
600 1
< 0.1%
562 1
< 0.1%
545 1
< 0.1%
539 2
0.1%
523 1
< 0.1%

5-10만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct248
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.585755
Minimum0
Maximum712
Zeros1549
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:57:29.947237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q361
95-th percentile150
Maximum712
Range712
Interquartile range (IQR)61

Descriptive statistics

Standard deviation57.789783
Coefficient of variation (CV)1.5375448
Kurtosis12.12673
Mean37.585755
Median Absolute Deviation (MAD)3
Skewness2.5544025
Sum131926
Variance3339.6591
MonotonicityNot monotonic
2023-12-12T10:57:30.119458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1549
44.1%
1 120
 
3.4%
2 62
 
1.8%
3 32
 
0.9%
4 29
 
0.8%
6 28
 
0.8%
7 24
 
0.7%
56 23
 
0.7%
8 22
 
0.6%
58 21
 
0.6%
Other values (238) 1600
45.6%
ValueCountFrequency (%)
0 1549
44.1%
1 120
 
3.4%
2 62
 
1.8%
3 32
 
0.9%
4 29
 
0.8%
5 20
 
0.6%
6 28
 
0.8%
7 24
 
0.7%
8 22
 
0.6%
9 16
 
0.5%
ValueCountFrequency (%)
712 1
< 0.1%
545 1
< 0.1%
506 1
< 0.1%
438 1
< 0.1%
409 1
< 0.1%
379 1
< 0.1%
357 1
< 0.1%
353 1
< 0.1%
338 1
< 0.1%
332 1
< 0.1%

10-20만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct321
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.145014
Minimum0
Maximum739
Zeros1579
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:57:30.309508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q388
95-th percentile209
Maximum739
Range739
Interquartile range (IQR)88

Descriptive statistics

Standard deviation78.246589
Coefficient of variation (CV)1.5005574
Kurtosis6.1062157
Mean52.145014
Median Absolute Deviation (MAD)3
Skewness2.0557212
Sum183029
Variance6122.5287
MonotonicityNot monotonic
2023-12-12T10:57:30.483025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1579
45.0%
1 101
 
2.9%
2 54
 
1.5%
3 26
 
0.7%
4 22
 
0.6%
14 22
 
0.6%
7 20
 
0.6%
6 20
 
0.6%
10 18
 
0.5%
87 17
 
0.5%
Other values (311) 1631
46.5%
ValueCountFrequency (%)
0 1579
45.0%
1 101
 
2.9%
2 54
 
1.5%
3 26
 
0.7%
4 22
 
0.6%
5 14
 
0.4%
6 20
 
0.6%
7 20
 
0.6%
8 13
 
0.4%
9 15
 
0.4%
ValueCountFrequency (%)
739 1
< 0.1%
648 1
< 0.1%
543 1
< 0.1%
522 1
< 0.1%
480 1
< 0.1%
475 1
< 0.1%
449 1
< 0.1%
433 1
< 0.1%
430 1
< 0.1%
416 1
< 0.1%

20-50만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct340
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.291453
Minimum0
Maximum590
Zeros1683
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:57:30.934303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q383.75
95-th percentile228.55
Maximum590
Range590
Interquartile range (IQR)83.75

Descriptive statistics

Standard deviation83.20071
Coefficient of variation (CV)1.6221165
Kurtosis4.09989
Mean51.291453
Median Absolute Deviation (MAD)1
Skewness1.9808264
Sum180033
Variance6922.3582
MonotonicityNot monotonic
2023-12-12T10:57:31.078010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1683
47.9%
1 102
 
2.9%
3 42
 
1.2%
2 38
 
1.1%
6 37
 
1.1%
4 32
 
0.9%
5 30
 
0.9%
8 26
 
0.7%
113 19
 
0.5%
10 19
 
0.5%
Other values (330) 1482
42.2%
ValueCountFrequency (%)
0 1683
47.9%
1 102
 
2.9%
2 38
 
1.1%
3 42
 
1.2%
4 32
 
0.9%
5 30
 
0.9%
6 37
 
1.1%
7 12
 
0.3%
8 26
 
0.7%
9 17
 
0.5%
ValueCountFrequency (%)
590 1
< 0.1%
500 1
< 0.1%
476 1
< 0.1%
458 1
< 0.1%
451 1
< 0.1%
445 1
< 0.1%
441 1
< 0.1%
439 1
< 0.1%
432 1
< 0.1%
431 1
< 0.1%

50-100만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct193
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.254131
Minimum0
Maximum376
Zeros1938
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:57:31.213844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324
95-th percentile101
Maximum376
Range376
Interquartile range (IQR)24

Descriptive statistics

Standard deviation39.795857
Coefficient of variation (CV)1.9648267
Kurtosis11.031939
Mean20.254131
Median Absolute Deviation (MAD)0
Skewness2.9334832
Sum71092
Variance1583.7103
MonotonicityNot monotonic
2023-12-12T10:57:31.369431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1938
55.2%
1 113
 
3.2%
2 61
 
1.7%
4 45
 
1.3%
3 43
 
1.2%
5 36
 
1.0%
8 36
 
1.0%
6 30
 
0.9%
7 29
 
0.8%
10 28
 
0.8%
Other values (183) 1151
32.8%
ValueCountFrequency (%)
0 1938
55.2%
1 113
 
3.2%
2 61
 
1.7%
3 43
 
1.2%
4 45
 
1.3%
5 36
 
1.0%
6 30
 
0.9%
7 29
 
0.8%
8 36
 
1.0%
9 23
 
0.7%
ValueCountFrequency (%)
376 1
 
< 0.1%
323 1
 
< 0.1%
310 1
 
< 0.1%
275 2
0.1%
264 1
 
< 0.1%
261 1
 
< 0.1%
260 1
 
< 0.1%
238 1
 
< 0.1%
236 2
0.1%
233 3
0.1%

100-200만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct132
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7025641
Minimum0
Maximum302
Zeros2109
Zeros (%)60.1%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:57:31.499497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile51.55
Maximum302
Range302
Interquartile range (IQR)8

Descriptive statistics

Standard deviation23.220292
Coefficient of variation (CV)2.3932119
Kurtosis29.113762
Mean9.7025641
Median Absolute Deviation (MAD)0
Skewness4.4988383
Sum34056
Variance539.18195
MonotonicityNot monotonic
2023-12-12T10:57:31.626061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2109
60.1%
1 138
 
3.9%
2 97
 
2.8%
3 78
 
2.2%
4 52
 
1.5%
5 48
 
1.4%
8 47
 
1.3%
9 43
 
1.2%
6 38
 
1.1%
7 36
 
1.0%
Other values (122) 824
 
23.5%
ValueCountFrequency (%)
0 2109
60.1%
1 138
 
3.9%
2 97
 
2.8%
3 78
 
2.2%
4 52
 
1.5%
5 48
 
1.4%
6 38
 
1.1%
7 36
 
1.0%
8 47
 
1.3%
9 43
 
1.2%
ValueCountFrequency (%)
302 1
< 0.1%
237 1
< 0.1%
229 1
< 0.1%
220 1
< 0.1%
215 1
< 0.1%
205 1
< 0.1%
204 1
< 0.1%
197 1
< 0.1%
190 1
< 0.1%
175 1
< 0.1%

200-500만미만_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4279202
Minimum0
Maximum173
Zeros2412
Zeros (%)68.7%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:57:31.767517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile26
Maximum173
Range173
Interquartile range (IQR)2

Descriptive statistics

Standard deviation13.590993
Coefficient of variation (CV)3.0693853
Kurtosis44.343058
Mean4.4279202
Median Absolute Deviation (MAD)0
Skewness5.7718999
Sum15542
Variance184.71509
MonotonicityNot monotonic
2023-12-12T10:57:31.935718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2412
68.7%
1 199
 
5.7%
2 98
 
2.8%
3 82
 
2.3%
5 73
 
2.1%
4 67
 
1.9%
6 52
 
1.5%
7 42
 
1.2%
10 33
 
0.9%
8 29
 
0.8%
Other values (79) 423
 
12.1%
ValueCountFrequency (%)
0 2412
68.7%
1 199
 
5.7%
2 98
 
2.8%
3 82
 
2.3%
4 67
 
1.9%
5 73
 
2.1%
6 52
 
1.5%
7 42
 
1.2%
8 29
 
0.8%
9 28
 
0.8%
ValueCountFrequency (%)
173 1
< 0.1%
165 1
< 0.1%
156 1
< 0.1%
148 1
< 0.1%
144 1
< 0.1%
134 1
< 0.1%
128 2
0.1%
125 2
0.1%
119 1
< 0.1%
107 1
< 0.1%

500만이상_변동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63960114
Minimum0
Maximum92
Zeros3125
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T10:57:32.086956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum92
Range92
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.2017074
Coefficient of variation (CV)6.5692618
Kurtosis224.03342
Mean0.63960114
Median Absolute Deviation (MAD)0
Skewness13.549346
Sum2245
Variance17.654345
MonotonicityNot monotonic
2023-12-12T10:57:32.216579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 3125
89.0%
1 150
 
4.3%
2 56
 
1.6%
3 41
 
1.2%
4 23
 
0.7%
5 19
 
0.5%
6 16
 
0.5%
9 12
 
0.3%
10 12
 
0.3%
7 9
 
0.3%
Other values (29) 47
 
1.3%
ValueCountFrequency (%)
0 3125
89.0%
1 150
 
4.3%
2 56
 
1.6%
3 41
 
1.2%
4 23
 
0.7%
5 19
 
0.5%
6 16
 
0.5%
7 9
 
0.3%
8 8
 
0.2%
9 12
 
0.3%
ValueCountFrequency (%)
92 1
< 0.1%
84 1
< 0.1%
77 1
< 0.1%
73 1
< 0.1%
69 1
< 0.1%
64 1
< 0.1%
56 1
< 0.1%
47 1
< 0.1%
45 1
< 0.1%
41 1
< 0.1%

Interactions

2023-12-12T10:57:27.185097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:20.240368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:21.242683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:22.268514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:23.675783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.541059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.355160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:26.204706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:27.288164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:20.335104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:21.366105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:22.367750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:23.786760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.628398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.442887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:26.297242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:27.404823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:20.468806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:21.501643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:22.501151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:23.916372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.726409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.546557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:26.419580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:27.540462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:20.594397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:21.627714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:22.636664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.029299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.844858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.651194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:26.533661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:27.672971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:20.707165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:21.751343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:22.812384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.149696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.945565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.751016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:26.650825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:27.785644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:20.829057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:21.887357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:22.966221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.256250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.048817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.876894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:26.771034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:27.904351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:20.964019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:22.015547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:23.098877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.354561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.152855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.987118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:26.893576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:28.024282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:21.105835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:22.137544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:23.241351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:24.448535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:25.256717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:26.106326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:57:27.057265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:57:32.305853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5만미만_변동5-10만미만_변동10-20만미만_변동20-50만미만_변동50-100만_변동100-200만미만_변동200-500만미만_변동500만이상_변동
5만미만_변동1.0000.5330.6190.4810.2920.0220.0000.000
5-10만미만_변동0.5331.0000.8810.6210.3350.2750.0600.000
10-20만미만_변동0.6190.8811.0000.9180.6500.3520.3470.010
20-50만미만_변동0.4810.6210.9181.0000.8570.5320.5470.354
50-100만_변동0.2920.3350.6500.8571.0000.8260.8700.632
100-200만미만_변동0.0220.2750.3520.5320.8261.0000.8330.692
200-500만미만_변동0.0000.0600.3470.5470.8700.8331.0000.877
500만이상_변동0.0000.0000.0100.3540.6320.6920.8771.000
2023-12-12T10:57:32.424272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5만미만_변동5-10만미만_변동10-20만미만_변동20-50만미만_변동50-100만_변동100-200만미만_변동200-500만미만_변동500만이상_변동
5만미만_변동1.0000.9370.9190.8800.8140.7550.6240.292
5-10만미만_변동0.9371.0000.9590.9210.8610.8070.6810.323
10-20만미만_변동0.9190.9591.0000.9620.9120.8630.7500.394
20-50만미만_변동0.8800.9210.9621.0000.9510.9100.8120.466
50-100만_변동0.8140.8610.9120.9511.0000.9480.8580.518
100-200만미만_변동0.7550.8070.8630.9100.9481.0000.8900.554
200-500만미만_변동0.6240.6810.7500.8120.8580.8901.0000.616
500만이상_변동0.2920.3230.3940.4660.5180.5540.6161.000

Missing values

2023-12-12T10:57:28.197411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:57:28.371937image/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

관할읍면동5만미만_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
관할읍면동5만미만_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

관할읍면동5만미만_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