Overview

Dataset statistics

Number of variables13
Number of observations3510
Missing cells0
Missing cells (%)0.0%
Duplicate rows59
Duplicate rows (%)1.7%
Total size in memory397.7 KiB
Average record size in memory116.0 B

Variable types

Text1
Numeric7
Categorical5

Dataset

Description쌀소득보전직불제 신청자 유형/금액별 정보로 기관,여자_~100만미만,남자_~100만미만,법인_~100만미만,외국인_~100만미만,여자_100~200만미만,남자_100~200만미만,법인_100~200만미만,외국인_100~200만미만,여자_200만이상,남자_200만이상,법인_200만이상,외국인_200만이상 등의 자료를 제공합니다.
URLhttps://www.data.go.kr/data/15090524/fileData.do

Alerts

Dataset has 59 (1.7%) duplicate rowsDuplicates
여자_100만미만 is highly overall correlated with 남자_100만미만 and 4 other fieldsHigh correlation
남자_100만미만 is highly overall correlated with 여자_100만미만 and 4 other fieldsHigh correlation
여자_100-200만미만 is highly overall correlated with 여자_100만미만 and 4 other fieldsHigh correlation
남자_100-200만미만 is highly overall correlated with 여자_100만미만 and 4 other fieldsHigh correlation
여자_200만이상 is highly overall correlated with 여자_100만미만 and 4 other fieldsHigh correlation
남자_200만이상 is highly overall correlated with 여자_100만미만 and 4 other fieldsHigh correlation
법인_100만미만 is highly imbalanced (90.4%)Imbalance
외국인_100만미만 is highly imbalanced (90.8%)Imbalance
법인_100-200만미만 is highly imbalanced (93.9%)Imbalance
외국인_100-200만미만 is highly imbalanced (96.0%)Imbalance
외국인_200만이상 is highly imbalanced (97.4%)Imbalance
법인_200만이상 is highly skewed (γ1 = 24.66901647)Skewed
여자_100만미만 has 1520 (43.3%) zerosZeros
남자_100만미만 has 1421 (40.5%) zerosZeros
여자_100-200만미만 has 1989 (56.7%) zerosZeros
남자_100-200만미만 has 1759 (50.1%) zerosZeros
여자_200만이상 has 2258 (64.3%) zerosZeros
남자_200만이상 has 1909 (54.4%) zerosZeros
법인_200만이상 has 3362 (95.8%) zerosZeros

Reproduction

Analysis started2023-12-12 00:27:54.284949
Analysis finished2023-12-12 00:28:01.511994
Duration7.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관
Text

Distinct3156
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
2023-12-12T09:28:01.744995image/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%
2023-12-12T09:28:02.145905image/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%

여자_100만미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct296
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.668091
Minimum0
Maximum909
Zeros1520
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T09:28:02.278001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q384
95-th percentile190.55
Maximum909
Range909
Interquartile range (IQR)84

Descriptive statistics

Standard deviation73.632208
Coefficient of variation (CV)1.5129463
Kurtosis10.585717
Mean48.668091
Median Absolute Deviation (MAD)4
Skewness2.3789254
Sum170825
Variance5421.702
MonotonicityNot monotonic
2023-12-12T09:28:02.420071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1520
43.3%
1 121
 
3.4%
2 53
 
1.5%
3 50
 
1.4%
5 29
 
0.8%
7 26
 
0.7%
4 25
 
0.7%
6 21
 
0.6%
74 18
 
0.5%
84 17
 
0.5%
Other values (286) 1630
46.4%
ValueCountFrequency (%)
0 1520
43.3%
1 121
 
3.4%
2 53
 
1.5%
3 50
 
1.4%
4 25
 
0.7%
5 29
 
0.8%
6 21
 
0.6%
7 26
 
0.7%
8 11
 
0.3%
9 16
 
0.5%
ValueCountFrequency (%)
909 1
 
< 0.1%
650 1
 
< 0.1%
535 1
 
< 0.1%
529 1
 
< 0.1%
485 1
 
< 0.1%
464 1
 
< 0.1%
458 1
 
< 0.1%
427 3
0.1%
414 1
 
< 0.1%
411 1
 
< 0.1%

남자_100만미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct504
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.67037
Minimum0
Maximum1155
Zeros1421
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T09:28:02.543661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q3187
95-th percentile398.55
Maximum1155
Range1155
Interquartile range (IQR)187

Descriptive statistics

Standard deviation145.89811
Coefficient of variation (CV)1.3806909
Kurtosis2.9740964
Mean105.67037
Median Absolute Deviation (MAD)13
Skewness1.6097919
Sum370903
Variance21286.26
MonotonicityNot monotonic
2023-12-12T09:28:02.667920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1421
40.5%
1 110
 
3.1%
2 49
 
1.4%
3 37
 
1.1%
4 32
 
0.9%
5 23
 
0.7%
9 16
 
0.5%
11 14
 
0.4%
7 14
 
0.4%
139 11
 
0.3%
Other values (494) 1783
50.8%
ValueCountFrequency (%)
0 1421
40.5%
1 110
 
3.1%
2 49
 
1.4%
3 37
 
1.1%
4 32
 
0.9%
5 23
 
0.7%
6 10
 
0.3%
7 14
 
0.4%
8 8
 
0.2%
9 16
 
0.5%
ValueCountFrequency (%)
1155 1
< 0.1%
911 1
< 0.1%
904 1
< 0.1%
892 1
< 0.1%
837 1
< 0.1%
782 1
< 0.1%
729 1
< 0.1%
721 2
0.1%
716 1
< 0.1%
710 1
< 0.1%

법인_100만미만
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
0
3402 
1
 
92
2
 
11
3
 
4
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3402
96.9%
1 92
 
2.6%
2 11
 
0.3%
3 4
 
0.1%
5 1
 
< 0.1%

Length

2023-12-12T09:28:02.803231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:28:02.920129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3402
96.9%
1 92
 
2.6%
2 11
 
0.3%
3 4
 
0.1%
5 1
 
< 0.1%

외국인_100만미만
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
0
3440 
1
 
67
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3440
98.0%
1 67
 
1.9%
2 3
 
0.1%

Length

2023-12-12T09:28:03.026942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:28:03.119750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3440
98.0%
1 67
 
1.9%
2 3
 
0.1%

여자_100-200만미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5692308
Minimum0
Maximum123
Zeros1989
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T09:28:03.260885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile42
Maximum123
Range123
Interquartile range (IQR)11

Descriptive statistics

Standard deviation16.003425
Coefficient of variation (CV)1.8675452
Kurtosis8.0669956
Mean8.5692308
Median Absolute Deviation (MAD)0
Skewness2.6187604
Sum30078
Variance256.10963
MonotonicityNot monotonic
2023-12-12T09:28:03.410797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1989
56.7%
1 127
 
3.6%
2 93
 
2.6%
5 63
 
1.8%
3 60
 
1.7%
6 56
 
1.6%
4 53
 
1.5%
9 43
 
1.2%
7 43
 
1.2%
13 43
 
1.2%
Other values (86) 940
26.8%
ValueCountFrequency (%)
0 1989
56.7%
1 127
 
3.6%
2 93
 
2.6%
3 60
 
1.7%
4 53
 
1.5%
5 63
 
1.8%
6 56
 
1.6%
7 43
 
1.2%
8 38
 
1.1%
9 43
 
1.2%
ValueCountFrequency (%)
123 1
 
< 0.1%
112 1
 
< 0.1%
109 1
 
< 0.1%
107 1
 
< 0.1%
103 1
 
< 0.1%
100 1
 
< 0.1%
94 2
0.1%
93 1
 
< 0.1%
91 1
 
< 0.1%
90 4
0.1%

남자_100-200만미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct233
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.637322
Minimum0
Maximum337
Zeros1759
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T09:28:03.553365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q355
95-th percentile145
Maximum337
Range337
Interquartile range (IQR)55

Descriptive statistics

Standard deviation51.850695
Coefficient of variation (CV)1.5886933
Kurtosis3.5535742
Mean32.637322
Median Absolute Deviation (MAD)0
Skewness1.8874025
Sum114557
Variance2688.4945
MonotonicityNot monotonic
2023-12-12T09:28:03.683182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1759
50.1%
1 86
 
2.5%
2 63
 
1.8%
3 35
 
1.0%
4 35
 
1.0%
5 34
 
1.0%
83 21
 
0.6%
8 21
 
0.6%
6 19
 
0.5%
34 18
 
0.5%
Other values (223) 1419
40.4%
ValueCountFrequency (%)
0 1759
50.1%
1 86
 
2.5%
2 63
 
1.8%
3 35
 
1.0%
4 35
 
1.0%
5 34
 
1.0%
6 19
 
0.5%
7 18
 
0.5%
8 21
 
0.6%
9 18
 
0.5%
ValueCountFrequency (%)
337 1
< 0.1%
284 1
< 0.1%
273 1
< 0.1%
272 1
< 0.1%
270 1
< 0.1%
269 2
0.1%
268 2
0.1%
264 1
< 0.1%
263 1
< 0.1%
261 1
< 0.1%

법인_100-200만미만
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
0
3455 
1
 
52
2
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3455
98.4%
1 52
 
1.5%
2 2
 
0.1%
4 1
 
< 0.1%

Length

2023-12-12T09:28:03.815881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:28:03.904792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3455
98.4%
1 52
 
1.5%
2 2
 
0.1%
4 1
 
< 0.1%

외국인_100-200만미만
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
0
3495 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3495
99.6%
1 15
 
0.4%

Length

2023-12-12T09:28:04.010572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:28:04.100218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3495
99.6%
1 15
 
0.4%

여자_200만이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0376068
Minimum0
Maximum119
Zeros2258
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T09:28:04.198212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile23
Maximum119
Range119
Interquartile range (IQR)3

Descriptive statistics

Standard deviation10.004488
Coefficient of variation (CV)2.4778262
Kurtosis27.191283
Mean4.0376068
Median Absolute Deviation (MAD)0
Skewness4.4340402
Sum14172
Variance100.08978
MonotonicityNot monotonic
2023-12-12T09:28:04.325109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2258
64.3%
1 198
 
5.6%
2 120
 
3.4%
3 98
 
2.8%
4 96
 
2.7%
6 61
 
1.7%
7 59
 
1.7%
5 57
 
1.6%
8 51
 
1.5%
9 48
 
1.4%
Other values (62) 464
 
13.2%
ValueCountFrequency (%)
0 2258
64.3%
1 198
 
5.6%
2 120
 
3.4%
3 98
 
2.8%
4 96
 
2.7%
5 57
 
1.6%
6 61
 
1.7%
7 59
 
1.7%
8 51
 
1.5%
9 48
 
1.4%
ValueCountFrequency (%)
119 1
< 0.1%
110 1
< 0.1%
99 1
< 0.1%
96 1
< 0.1%
94 1
< 0.1%
88 1
< 0.1%
85 1
< 0.1%
79 1
< 0.1%
76 1
< 0.1%
71 2
0.1%

남자_200만이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct269
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.689459
Minimum0
Maximum736
Zeros1909
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T09:28:04.473557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q338
95-th percentile168
Maximum736
Range736
Interquartile range (IQR)38

Descriptive statistics

Standard deviation62.028803
Coefficient of variation (CV)1.9573955
Kurtosis12.917662
Mean31.689459
Median Absolute Deviation (MAD)0
Skewness2.9980321
Sum111230
Variance3847.5724
MonotonicityNot monotonic
2023-12-12T09:28:04.598651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1909
54.4%
1 96
 
2.7%
2 52
 
1.5%
3 39
 
1.1%
6 31
 
0.9%
4 30
 
0.9%
5 29
 
0.8%
11 28
 
0.8%
12 23
 
0.7%
7 23
 
0.7%
Other values (259) 1250
35.6%
ValueCountFrequency (%)
0 1909
54.4%
1 96
 
2.7%
2 52
 
1.5%
3 39
 
1.1%
4 30
 
0.9%
5 29
 
0.8%
6 31
 
0.9%
7 23
 
0.7%
8 19
 
0.5%
9 21
 
0.6%
ValueCountFrequency (%)
736 1
< 0.1%
487 1
< 0.1%
481 1
< 0.1%
420 1
< 0.1%
413 1
< 0.1%
403 1
< 0.1%
400 1
< 0.1%
396 1
< 0.1%
382 1
< 0.1%
380 1
< 0.1%

법인_200만이상
Real number (ℝ)

SKEWED  ZEROS 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.086609687
Minimum0
Maximum33
Zeros3362
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-12T09:28:04.743261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.86304561
Coefficient of variation (CV)9.96477
Kurtosis777.11875
Mean0.086609687
Median Absolute Deviation (MAD)0
Skewness24.669016
Sum304
Variance0.74484772
MonotonicityNot monotonic
2023-12-12T09:28:04.862682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 3362
95.8%
1 115
 
3.3%
2 14
 
0.4%
3 6
 
0.2%
4 2
 
0.1%
8 2
 
0.1%
5 2
 
0.1%
9 2
 
0.1%
15 1
 
< 0.1%
22 1
 
< 0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
0 3362
95.8%
1 115
 
3.3%
2 14
 
0.4%
3 6
 
0.2%
4 2
 
0.1%
5 2
 
0.1%
7 1
 
< 0.1%
8 2
 
0.1%
9 2
 
0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
22 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
9 2
 
0.1%
8 2
 
0.1%
7 1
 
< 0.1%
5 2
 
0.1%
4 2
 
0.1%
3 6
0.2%

외국인_200만이상
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
0
3501 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3501
99.7%
1 9
 
0.3%

Length

2023-12-12T09:28:04.975672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:28:05.072854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3501
99.7%
1 9
 
0.3%

Interactions

2023-12-12T09:28:00.394105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:55.624797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:56.317111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:57.214461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:58.021567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:58.817073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:59.619982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:00.489300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:55.714499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:56.440769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:57.336400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:58.112565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:58.927731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:59.716860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:00.574656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:55.806388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:56.531268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:57.467430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:58.204392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:59.043335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:59.848215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:00.666684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:55.901252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:56.637922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:57.590538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:58.328653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:59.156079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:59.980870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:00.775723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:56.012157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:56.816392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:57.715355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:58.465814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:59.282382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:00.095968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:00.899179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:56.100819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:56.995965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:57.820579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:58.576462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:59.394616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:00.187976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:01.061920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:56.204601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:57.118027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:57.918929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:58.691211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:27:59.509082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:00.292714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:28:05.137321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
여자_100만미만남자_100만미만법인_100만미만외국인_100만미만여자_100-200만미만남자_100-200만미만법인_100-200만미만외국인_100-200만미만여자_200만이상남자_200만이상법인_200만이상외국인_200만이상
여자_100만미만1.0000.8550.1490.1970.6270.6330.1860.1130.2920.4600.0000.066
남자_100만미만0.8551.0000.1910.2640.5660.6780.1510.0880.3180.3700.0530.058
법인_100만미만0.1490.1911.0000.0000.2130.2580.3860.0230.2170.1600.0000.000
외국인_100만미만0.1970.2640.0001.0000.2560.1980.0000.0940.1790.1380.2690.000
여자_100-200만미만0.6270.5660.2130.2561.0000.8890.2250.2160.8080.6750.3170.162
남자_100-200만미만0.6330.6780.2580.1980.8891.0000.2110.2290.6960.6630.2760.179
법인_100-200만미만0.1860.1510.3860.0000.2250.2111.0000.0000.2330.2640.1080.000
외국인_100-200만미만0.1130.0880.0230.0940.2160.2290.0001.0000.0430.1060.0000.057
여자_200만이상0.2920.3180.2170.1790.8080.6960.2330.0431.0000.8270.5430.144
남자_200만이상0.4600.3700.1600.1380.6750.6630.2640.1060.8271.0000.4590.109
법인_200만이상0.0000.0530.0000.2690.3170.2760.1080.0000.5430.4591.0000.000
외국인_200만이상0.0660.0580.0000.0000.1620.1790.0000.0570.1440.1090.0001.000
2023-12-12T09:28:05.278600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인_100-200만미만법인_100-200만미만외국인_100만미만외국인_200만이상법인_100만미만
외국인_100-200만미만1.0000.0000.1560.0360.028
법인_100-200만미만0.0001.0000.0000.0000.324
외국인_100만미만0.1560.0001.0000.0000.000
외국인_200만이상0.0360.0000.0001.0000.000
법인_100만미만0.0280.3240.0000.0001.000
2023-12-12T09:28:05.377738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
여자_100만미만남자_100만미만여자_100-200만미만남자_100-200만미만여자_200만이상남자_200만이상법인_200만이상법인_100만미만외국인_100만미만법인_100-200만미만외국인_100-200만미만외국인_200만이상
여자_100만미만1.0000.9660.8930.9320.7970.8870.2390.0910.1270.0840.0850.049
남자_100만미만0.9661.0000.8720.9260.7690.8700.2260.1110.1200.0970.0870.057
여자_100-200만미만0.8930.8721.0000.9460.9120.9540.3060.0900.1580.1360.1650.124
남자_100-200만미만0.9320.9260.9461.0000.8810.9570.2870.1100.1200.1270.1760.137
여자_200만이상0.7970.7690.9120.8811.0000.9170.3310.0920.1080.1410.0330.110
남자_200만이상0.8870.8700.9540.9570.9171.0000.3120.0980.0870.1210.0800.082
법인_200만이상0.2390.2260.3060.2870.3310.3121.0000.0000.1150.0700.0000.000
법인_100만미만0.0910.1110.0900.1100.0920.0980.0001.0000.0000.3240.0280.000
외국인_100만미만0.1270.1200.1580.1200.1080.0870.1150.0001.0000.0000.1560.000
법인_100-200만미만0.0840.0970.1360.1270.1410.1210.0700.3240.0001.0000.0000.000
외국인_100-200만미만0.0850.0870.1650.1760.0330.0800.0000.0280.1560.0001.0000.036
외국인_200만이상0.0490.0570.1240.1370.1100.0820.0000.0000.0000.0000.0361.000

Missing values

2023-12-12T09:28:01.247196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:28:01.440210image/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

기관여자_100만미만남자_100만미만법인_100만미만외국인_100만미만여자_100-200만미만남자_100-200만미만법인_100-200만미만외국인_100-200만미만여자_200만이상남자_200만이상법인_200만이상외국인_200만이상
0사직동000000000000
1삼청동000000000000
2청운효자동000000000000
3부암동000000000000
4평창동000000000000
5무악동000000000000
6교남동000000000000
7가회동000000000000
8종로1.2.3.4가동000000000000
9종로5.6가동000000000000
기관여자_100만미만남자_100만미만법인_100만미만외국인_100만미만여자_100-200만미만남자_100-200만미만법인_100-200만미만외국인_100-200만미만여자_200만이상남자_200만이상법인_200만이상외국인_200만이상
3500아름동000000000000
3501종촌동000000000000
3502고운동000000000000
3503보람동000000000000
3504대평동000000000000
3505소담동000000000000
3506다정동000000000000
3507반곡동000000000000
3508해밀동000000000000
3509새롬동000000000000

Duplicate rows

Most frequently occurring

기관여자_100만미만남자_100만미만법인_100만미만외국인_100만미만여자_100-200만미만남자_100-200만미만법인_100-200만미만외국인_100-200만미만여자_200만이상남자_200만이상법인_200만이상외국인_200만이상# duplicates
51중앙동00000000000019
34신촌동0000000000003
36신흥동0000000000003
46위례동0000000000003
54충무동0000000000003
0가양2동0000000000002
1고등동0000000000002
2금곡동0000000000002
3남현동0000000000002
4논현1동0000000000002