Overview

Dataset statistics

Number of variables10
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory91.6 B

Variable types

Categorical4
Text1
Numeric5

Alerts

BASE_YM has constant value ""Constant
SIGNGU_CD is highly overall correlated with SEEM_RATEHigh correlation
LTRS_INCOME_NMPR_RATE is highly overall correlated with GNRL_INCOME_NMPR_RATE and 1 other fieldsHigh correlation
GNRL_INCOME_NMPR_RATE is highly overall correlated with LTRS_INCOME_NMPR_RATE and 1 other fieldsHigh correlation
SEEM_RATE is highly overall correlated with SIGNGU_CD and 2 other fieldsHigh correlation
PRFSN_INCOME_NMPR_RATE is highly overall correlated with REPRSNT_RATE and 1 other fieldsHigh correlation
REPRSNT_RATE is highly overall correlated with PRFSN_INCOME_NMPR_RATE and 1 other fieldsHigh correlation
PRFSN_SEEM_RATE is highly overall correlated with PRFSN_INCOME_NMPR_RATE and 1 other fieldsHigh correlation
PRFSN_INCOME_NMPR_RATE is highly imbalanced (59.8%)Imbalance
PRFSN_SEEM_RATE is highly imbalanced (53.1%)Imbalance
SIGNGU_CD has unique valuesUnique
LTRS_INCOME_NMPR_RATE has 7 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:17:24.439404
Analysis finished2023-12-10 10:17:28.291966
Duration3.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BASE_YM
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
202106
50 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202106
2nd row202106
3rd row202106
4th row202106
5th row202106

Common Values

ValueCountFrequency (%)
202106 50
100.0%

Length

2023-12-10T19:17:28.551400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:28.910573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202106 50
100.0%

SIGNGU_NM
Text

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-10T19:17:29.202032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.62
Min length2

Characters and Unicode

Total characters181
Distinct characters60
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)88.0%

Sample

1st row무주군
2nd row울주군
3rd row노원구
4th row음성군
5th row제주시
ValueCountFrequency (%)
남구 3
 
5.2%
수원시 2
 
3.4%
중구 2
 
3.4%
포항시 2
 
3.4%
강서구 2
 
3.4%
북구 1
 
1.7%
목포시 1
 
1.7%
군위군 1
 
1.7%
해남군 1
 
1.7%
예천군 1
 
1.7%
Other values (42) 42
72.4%
2023-12-10T19:17:29.978772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 23
 
12.7%
시 20
 
11.0%
군 18
 
9.9%
8
 
4.4%
천 7
 
3.9%
남 5
 
2.8%
주 5
 
2.8%
안 4
 
2.2%
양 4
 
2.2%
원 4
 
2.2%
Other values (50) 83
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 173
95.6%
Space Separator 8
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 23
 
13.3%
시 20
 
11.6%
군 18
 
10.4%
천 7
 
4.0%
남 5
 
2.9%
주 5
 
2.9%
안 4
 
2.3%
양 4
 
2.3%
원 4
 
2.3%
부 4
 
2.3%
Other values (49) 79
45.7%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 173
95.6%
Common 8
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 23
 
13.3%
시 20
 
11.6%
군 18
 
10.4%
천 7
 
4.0%
남 5
 
2.9%
주 5
 
2.9%
안 4
 
2.3%
양 4
 
2.3%
원 4
 
2.3%
부 4
 
2.3%
Other values (49) 79
45.7%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 173
95.6%
ASCII 8
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 23
 
13.3%
시 20
 
11.6%
군 18
 
10.4%
천 7
 
4.0%
남 5
 
2.9%
주 5
 
2.9%
안 4
 
2.3%
양 4
 
2.3%
원 4
 
2.3%
부 4
 
2.3%
Other values (49) 79
45.7%
ASCII
ValueCountFrequency (%)
8
100.0%

SIGNGU_CD
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37337
Minimum11110
Maximum50110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:17:30.190991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11300.5
Q128821.25
median42725
Q346140
95-th percentile48817
Maximum50110
Range39000
Interquartile range (IQR)17318.75

Descriptive statistics

Standard deviation12103.35
Coefficient of variation (CV)0.32416503
Kurtosis0.077072724
Mean37337
Median Absolute Deviation (MAD)4416.5
Skewness-1.1271197
Sum1866850
Variance1.4649107 × 108
MonotonicityNot monotonic
2023-12-10T19:17:30.438502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45730 1
 
2.0%
48740 1
 
2.0%
47900 1
 
2.0%
11230 1
 
2.0%
41281 1
 
2.0%
28237 1
 
2.0%
44810 1
 
2.0%
29155 1
 
2.0%
26110 1
 
2.0%
11500 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
11110 1
2.0%
11230 1
2.0%
11260 1
2.0%
11350 1
2.0%
11470 1
2.0%
11500 1
2.0%
26110 1
2.0%
26230 1
2.0%
26350 1
2.0%
26440 1
2.0%
ValueCountFrequency (%)
50110 1
2.0%
48890 1
2.0%
48880 1
2.0%
48740 1
2.0%
48129 1
2.0%
47900 1
2.0%
47720 1
2.0%
47170 1
2.0%
47113 1
2.0%
47111 1
2.0%

LTRS_INCOME_NMPR_RATE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015
Minimum0
Maximum0.07
Zeros7
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:17:30.623540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.01
Q30.02
95-th percentile0.0355
Maximum0.07
Range0.07
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.012494897
Coefficient of variation (CV)0.83299313
Kurtosis6.7550178
Mean0.015
Median Absolute Deviation (MAD)0.01
Skewness2.0269713
Sum0.75
Variance0.00015612245
MonotonicityNot monotonic
2023-12-10T19:17:30.817200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.01 24
48.0%
0.02 12
24.0%
0.0 7
 
14.0%
0.03 4
 
8.0%
0.04 2
 
4.0%
0.07 1
 
2.0%
ValueCountFrequency (%)
0.0 7
 
14.0%
0.01 24
48.0%
0.02 12
24.0%
0.03 4
 
8.0%
0.04 2
 
4.0%
0.07 1
 
2.0%
ValueCountFrequency (%)
0.07 1
 
2.0%
0.04 2
 
4.0%
0.03 4
 
8.0%
0.02 12
24.0%
0.01 24
48.0%
0.0 7
 
14.0%

GNRL_INCOME_NMPR_RATE
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3706
Minimum0.23
Maximum0.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:17:31.026602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.249
Q10.32
median0.4
Q30.42
95-th percentile0.4455
Maximum0.46
Range0.23
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.06296776
Coefficient of variation (CV)0.16990761
Kurtosis-0.62611807
Mean0.3706
Median Absolute Deviation (MAD)0.03
Skewness-0.71258539
Sum18.53
Variance0.0039649388
MonotonicityNot monotonic
2023-12-10T19:17:31.211873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.41 8
16.0%
0.42 6
12.0%
0.4 5
 
10.0%
0.31 4
 
8.0%
0.43 3
 
6.0%
0.26 2
 
4.0%
0.32 2
 
4.0%
0.37 2
 
4.0%
0.34 2
 
4.0%
0.3 2
 
4.0%
Other values (10) 14
28.0%
ValueCountFrequency (%)
0.23 1
 
2.0%
0.24 2
4.0%
0.26 2
4.0%
0.28 1
 
2.0%
0.3 2
4.0%
0.31 4
8.0%
0.32 2
4.0%
0.33 2
4.0%
0.34 2
4.0%
0.36 1
 
2.0%
ValueCountFrequency (%)
0.46 1
 
2.0%
0.45 2
 
4.0%
0.44 2
 
4.0%
0.43 3
 
6.0%
0.42 6
12.0%
0.41 8
16.0%
0.4 5
10.0%
0.39 1
 
2.0%
0.38 1
 
2.0%
0.37 2
 
4.0%

PRFSN_INCOME_NMPR_RATE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0.0
46 
0.01
 
4

Length

Max length4
Median length3
Mean length3.08
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46
92.0%
0.01 4
 
8.0%

Length

2023-12-10T19:17:31.397132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:31.536354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46
92.0%
0.01 4
 
8.0%

REPRSNT_RATE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0.01
30 
0.02
15 
0.03
4 
0.05
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.01
2nd row0.01
3rd row0.02
4th row0.01
5th row0.02

Common Values

ValueCountFrequency (%)
0.01 30
60.0%
0.02 15
30.0%
0.03 4
 
8.0%
0.05 1
 
2.0%

Length

2023-12-10T19:17:31.673689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:31.885513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01 30
60.0%
0.02 15
30.0%
0.03 4
 
8.0%
0.05 1
 
2.0%

SEEM_RATE
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1968
Minimum0.11
Maximum0.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:17:32.038574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile0.1145
Q10.13
median0.155
Q30.2675
95-th percentile0.3555
Maximum0.4
Range0.29
Interquartile range (IQR)0.1375

Descriptive statistics

Standard deviation0.085175449
Coefficient of variation (CV)0.43280208
Kurtosis-0.37395573
Mean0.1968
Median Absolute Deviation (MAD)0.035
Skewness0.93080384
Sum9.84
Variance0.0072548571
MonotonicityNot monotonic
2023-12-10T19:17:32.214207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.14 8
16.0%
0.12 7
14.0%
0.13 4
 
8.0%
0.11 3
 
6.0%
0.16 3
 
6.0%
0.15 3
 
6.0%
0.4 2
 
4.0%
0.25 2
 
4.0%
0.3 2
 
4.0%
0.28 2
 
4.0%
Other values (11) 14
28.0%
ValueCountFrequency (%)
0.11 3
 
6.0%
0.12 7
14.0%
0.13 4
8.0%
0.14 8
16.0%
0.15 3
 
6.0%
0.16 3
 
6.0%
0.17 1
 
2.0%
0.18 1
 
2.0%
0.19 2
 
4.0%
0.2 1
 
2.0%
ValueCountFrequency (%)
0.4 2
4.0%
0.36 1
2.0%
0.35 1
2.0%
0.34 1
2.0%
0.31 2
4.0%
0.3 2
4.0%
0.28 2
4.0%
0.27 2
4.0%
0.26 1
2.0%
0.25 2
4.0%

PRFSN_SEEM_RATE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0.0
45 
0.01
5 

Length

Max length4
Median length3
Mean length3.1
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45
90.0%
0.01 5
 
10.0%

Length

2023-12-10T19:17:32.388775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:32.531830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45
90.0%
0.01 5
 
10.0%

ETC_RATE
Real number (ℝ)

Distinct14
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.396
Minimum0.32
Maximum0.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:17:32.653710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile0.35
Q10.3725
median0.39
Q30.41
95-th percentile0.452
Maximum0.49
Range0.17
Interquartile range (IQR)0.0375

Descriptive statistics

Standard deviation0.033442
Coefficient of variation (CV)0.084449494
Kurtosis1.0556656
Mean0.396
Median Absolute Deviation (MAD)0.02
Skewness0.53816347
Sum19.8
Variance0.0011183673
MonotonicityNot monotonic
2023-12-10T19:17:32.797194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.39 10
20.0%
0.41 7
14.0%
0.43 5
10.0%
0.37 5
10.0%
0.38 4
 
8.0%
0.42 4
 
8.0%
0.4 4
 
8.0%
0.35 3
 
6.0%
0.36 3
 
6.0%
0.48 1
 
2.0%
Other values (4) 4
 
8.0%
ValueCountFrequency (%)
0.32 1
 
2.0%
0.34 1
 
2.0%
0.35 3
 
6.0%
0.36 3
 
6.0%
0.37 5
10.0%
0.38 4
 
8.0%
0.39 10
20.0%
0.4 4
 
8.0%
0.41 7
14.0%
0.42 4
 
8.0%
ValueCountFrequency (%)
0.49 1
 
2.0%
0.48 1
 
2.0%
0.47 1
 
2.0%
0.43 5
10.0%
0.42 4
 
8.0%
0.41 7
14.0%
0.4 4
 
8.0%
0.39 10
20.0%
0.38 4
 
8.0%
0.37 5
10.0%

Interactions

2023-12-10T19:17:27.399703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:24.845544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:25.521729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:26.242313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:26.864546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:27.502479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:25.010896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:25.679893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:26.369344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:27.000237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:27.597070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:25.149256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:25.824718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:26.496434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:27.111684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:27.680277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:25.267464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:25.975859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:26.611937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:27.211771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:27.796357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:25.391497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:26.092528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:26.742184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:17:27.316389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:17:32.931887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIGNGU_NMSIGNGU_CDLTRS_INCOME_NMPR_RATEGNRL_INCOME_NMPR_RATEPRFSN_INCOME_NMPR_RATEREPRSNT_RATESEEM_RATEPRFSN_SEEM_RATEETC_RATE
SIGNGU_NM1.0000.0000.0000.9571.0000.9790.9750.0000.000
SIGNGU_CD0.0001.0000.2750.2660.1510.3940.2360.0670.537
LTRS_INCOME_NMPR_RATE0.0000.2751.0000.6440.0000.5500.3720.0000.317
GNRL_INCOME_NMPR_RATE0.9570.2660.6441.0000.0000.0000.9010.0000.633
PRFSN_INCOME_NMPR_RATE1.0000.1510.0000.0001.0000.9840.3010.9280.000
REPRSNT_RATE0.9790.3940.5500.0000.9841.0000.0000.9380.000
SEEM_RATE0.9750.2360.3720.9010.3010.0001.0000.0000.412
PRFSN_SEEM_RATE0.0000.0670.0000.0000.9280.9380.0001.0000.000
ETC_RATE0.0000.5370.3170.6330.0000.0000.4120.0001.000
2023-12-10T19:17:33.092854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRFSN_SEEM_RATEPRFSN_INCOME_NMPR_RATEREPRSNT_RATE
PRFSN_SEEM_RATE1.0000.7560.759
PRFSN_INCOME_NMPR_RATE0.7561.0000.866
REPRSNT_RATE0.7590.8661.000
2023-12-10T19:17:33.212454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIGNGU_CDLTRS_INCOME_NMPR_RATEGNRL_INCOME_NMPR_RATESEEM_RATEETC_RATEPRFSN_INCOME_NMPR_RATEREPRSNT_RATEPRFSN_SEEM_RATE
SIGNGU_CD1.000-0.285-0.4720.709-0.4620.2830.3000.217
LTRS_INCOME_NMPR_RATE-0.2851.0000.780-0.621-0.1300.0000.3760.000
GNRL_INCOME_NMPR_RATE-0.4720.7801.000-0.7940.0500.0000.0000.000
SEEM_RATE0.709-0.621-0.7941.000-0.4350.2020.0000.000
ETC_RATE-0.462-0.1300.050-0.4351.0000.0000.0000.000
PRFSN_INCOME_NMPR_RATE0.2830.0000.0000.2020.0001.0000.8660.756
REPRSNT_RATE0.3000.3760.0000.0000.0000.8661.0000.759
PRFSN_SEEM_RATE0.2170.0000.0000.0000.0000.7560.7591.000

Missing values

2023-12-10T19:17:27.981791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:17:28.191231image/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

BASE_YMSIGNGU_NMSIGNGU_CDLTRS_INCOME_NMPR_RATEGNRL_INCOME_NMPR_RATEPRFSN_INCOME_NMPR_RATEREPRSNT_RATESEEM_RATEPRFSN_SEEM_RATEETC_RATE
0202106무주군457300.00.260.00.010.360.00.35
1202106울주군317100.030.410.00.010.160.00.38
2202106노원구113500.020.420.00.020.110.00.43
3202106음성군437700.010.380.00.010.190.00.41
4202106제주시501100.010.370.00.020.20.00.41
5202106종로구111100.010.340.010.030.120.010.48
6202106거창군488800.00.30.00.010.310.00.36
7202106남양주시413600.010.40.00.020.170.00.39
8202106합천군488900.00.240.00.010.40.00.35
9202106부산진구262300.010.420.00.010.130.00.42
BASE_YMSIGNGU_NMSIGNGU_CDLTRS_INCOME_NMPR_RATEGNRL_INCOME_NMPR_RATEPRFSN_INCOME_NMPR_RATEREPRSNT_RATESEEM_RATEPRFSN_SEEM_RATEETC_RATE
40202106대덕구302300.020.410.00.010.140.00.41
41202106의정부시411500.010.410.00.010.140.00.42
42202106시흥시413900.010.430.00.020.140.00.4
43202106과천시412900.030.410.010.050.120.010.37
44202106논산시442300.010.320.00.010.250.00.4
45202106수원시 장안구411110.020.410.00.020.120.00.43
46202106안동시471700.010.340.00.010.240.00.39
47202106군산시451300.020.40.00.010.160.00.4
48202106목포시461100.010.390.00.010.150.00.43
49202106무안군468400.010.330.00.010.260.00.39