Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells11
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.5 KiB
Average record size in memory117.3 B

Variable types

Numeric11
Categorical1
Text1

Alerts

seq_no is highly overall correlated with admnstmach_cd and 1 other fieldsHigh correlation
admnstmach_cd is highly overall correlated with seq_no and 1 other fieldsHigh correlation
tot_anexptr_budget_price is highly overall correlated with before_year_anexptr_budget_price and 3 other fieldsHigh correlation
before_year_anexptr_budget_price is highly overall correlated with tot_anexptr_budget_price and 3 other fieldsHigh correlation
cltur_tursm_budget_price is highly overall correlated with tot_anexptr_budget_price and 4 other fieldsHigh correlation
before_year_cltur_tursm_budget_price is highly overall correlated with tot_anexptr_budget_price and 5 other fieldsHigh correlation
cltur_tursm_budget_rate is highly overall correlated with cltur_tursm_budget_price and 2 other fieldsHigh correlation
tot_popltn_co is highly overall correlated with tot_anexptr_budget_price and 1 other fieldsHigh correlation
one_psnby_cltur_tursm_budget_price is highly overall correlated with cltur_tursm_budget_price and 3 other fieldsHigh correlation
finan_year is highly overall correlated with seq_no and 3 other fieldsHigh correlation
finan_year is highly imbalanced (80.6%)Imbalance
cltur_tursm_budget_irds_rt has 11 (11.0%) missing valuesMissing
seq_no has unique valuesUnique
admnstmach_cd has unique valuesUnique
admnstmach_nm has unique valuesUnique
tot_anexptr_budget_price has unique valuesUnique
before_year_anexptr_budget_price has unique valuesUnique
tot_popltn_co has unique valuesUnique
cltur_tursm_budget_price has 13 (13.0%) zerosZeros
before_year_cltur_tursm_budget_price has 11 (11.0%) zerosZeros
cltur_tursm_budget_irds_rt has 2 (2.0%) zerosZeros
cltur_tursm_budget_rate has 15 (15.0%) zerosZeros
one_psnby_cltur_tursm_budget_price has 13 (13.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:03:29.727047
Analysis finished2023-12-10 10:03:55.031195
Duration25.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.17
Minimum1
Maximum1704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:03:55.177277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum1704
Range1703
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation284.52246
Coefficient of variation (CV)2.8123205
Kurtosis29.255162
Mean101.17
Median Absolute Deviation (MAD)25.5
Skewness5.5071801
Sum10117
Variance80953.031
MonotonicityNot monotonic
2023-12-10T19:03:55.446345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
1704 1
1.0%
1703 1
1.0%
1702 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

finan_year
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2016
97 
2022
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2022
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 97
97.0%
2022 3
 
3.0%

Length

2023-12-10T19:03:55.879961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:03:56.039684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 97
97.0%
2022 3
 
3.0%

admnstmach_cd
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.720684 × 109
Minimum1.1 × 109
Maximum5 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:03:56.259440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.122925 × 109
Q12.60825 × 109
median2.8055 × 109
Q33.11475 × 109
95-th percentile4.1391 × 109
Maximum5 × 109
Range3.9 × 109
Interquartile range (IQR)5.065 × 108

Descriptive statistics

Standard deviation1.0668718 × 109
Coefficient of variation (CV)0.39213367
Kurtosis-0.67985849
Mean2.720684 × 109
Median Absolute Deviation (MAD)3.07 × 108
Skewness-0.094444951
Sum2.720684 × 1011
Variance1.1382155 × 1018
MonotonicityNot monotonic
2023-12-10T19:03:56.589084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100000000 1
 
1.0%
2911000000 1
 
1.0%
3023000000 1
 
1.0%
3020000000 1
 
1.0%
3017000000 1
 
1.0%
3014000000 1
 
1.0%
3011000000 1
 
1.0%
3000000000 1
 
1.0%
2920000000 1
 
1.0%
2917000000 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1100000000 1
1.0%
1114000000 1
1.0%
1117000000 1
1.0%
1120000000 1
1.0%
1121500000 1
1.0%
1123000000 1
1.0%
1129000000 1
1.0%
1130500000 1
1.0%
1132000000 1
1.0%
1135000000 1
1.0%
ValueCountFrequency (%)
5000000000 1
1.0%
4889000000 1
1.0%
4888000000 1
1.0%
4143000000 1
1.0%
4141000000 1
1.0%
4139000000 1
1.0%
4137000000 1
1.0%
4136000000 1
1.0%
4131000000 1
1.0%
4129000000 1
1.0%

admnstmach_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:03:57.058028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.09
Min length3

Characters and Unicode

Total characters809
Distinct characters85
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

Unique100 ?
Unique (%)100.0%

Sample

1st row서울특별시
2nd row경상남도 거창군
3rd row서울특별시 중구
4th row서울특별시 용산구
5th row서울특별시 성동구
ValueCountFrequency (%)
서울특별시 24
 
12.6%
경기도 18
 
9.5%
부산광역시 16
 
8.4%
인천광역시 11
 
5.8%
대구광역시 9
 
4.7%
중구 6
 
3.2%
광주광역시 6
 
3.2%
동구 6
 
3.2%
울산광역시 6
 
3.2%
대전광역시 6
 
3.2%
Other values (70) 82
43.2%
2023-12-10T19:03:58.250363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97
 
12.0%
90
 
11.1%
77
 
9.5%
63
 
7.8%
54
 
6.7%
34
 
4.2%
31
 
3.8%
26
 
3.2%
26
 
3.2%
26
 
3.2%
Other values (75) 285
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 719
88.9%
Space Separator 90
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
97
 
13.5%
77
 
10.7%
63
 
8.8%
54
 
7.5%
34
 
4.7%
31
 
4.3%
26
 
3.6%
26
 
3.6%
26
 
3.6%
23
 
3.2%
Other values (74) 262
36.4%
Space Separator
ValueCountFrequency (%)
90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 719
88.9%
Common 90
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
97
 
13.5%
77
 
10.7%
63
 
8.8%
54
 
7.5%
34
 
4.7%
31
 
4.3%
26
 
3.6%
26
 
3.6%
26
 
3.6%
23
 
3.2%
Other values (74) 262
36.4%
Common
ValueCountFrequency (%)
90
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 719
88.9%
ASCII 90
 
11.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
97
 
13.5%
77
 
10.7%
63
 
8.8%
54
 
7.5%
34
 
4.7%
31
 
4.3%
26
 
3.6%
26
 
3.6%
26
 
3.6%
23
 
3.2%
Other values (74) 262
36.4%
ASCII
ValueCountFrequency (%)
90
100.0%

tot_anexptr_budget_price
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4690091 × 1012
Minimum1.3606396 × 1011
Maximum2.9272794 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:03:58.524082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3606396 × 1011
5-th percentile2.174717 × 1011
Q13.2909601 × 1011
median4.679957 × 1011
Q36.9990381 × 1011
95-th percentile7.0992939 × 1012
Maximum2.9272794 × 1013
Range2.913673 × 1013
Interquartile range (IQR)3.708078 × 1011

Descriptive statistics

Standard deviation3.8487492 × 1012
Coefficient of variation (CV)2.6199629
Kurtosis33.658614
Mean1.4690091 × 1012
Median Absolute Deviation (MAD)1.700085 × 1011
Skewness5.4817436
Sum1.4690091 × 1014
Variance1.481287 × 1025
MonotonicityNot monotonic
2023-12-10T19:03:58.812916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29272794246000 1
 
1.0%
242707550000 1
 
1.0%
294704601000 1
 
1.0%
403205119000 1
 
1.0%
503856598000 1
 
1.0%
331813679000 1
 
1.0%
358203415000 1
 
1.0%
4312802052000 1
 
1.0%
499802478000 1
 
1.0%
504290632000 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
136063957000 1
1.0%
178867322000 1
1.0%
192616093000 1
1.0%
196046933000 1
1.0%
213945473000 1
1.0%
217657287000 1
1.0%
219118791000 1
1.0%
222524011000 1
1.0%
228727696000 1
1.0%
242707550000 1
1.0%
ValueCountFrequency (%)
29272794246000 1
1.0%
20818802530000 1
1.0%
11147652001000 1
1.0%
8531672102000 1
1.0%
7222588409000 1
1.0%
7092804666000 1
1.0%
4312802052000 1
1.0%
4163972594000 1
1.0%
3397293393000 1
1.0%
2572987213000 1
1.0%

before_year_anexptr_budget_price
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3495638 × 1012
Minimum1.2514046 × 1011
Maximum2.6657218 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:03:59.447802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2514046 × 1011
5-th percentile1.9886721 × 1011
Q13.0610003 × 1011
median4.3258112 × 1011
Q36.3352702 × 1011
95-th percentile6.4054346 × 1012
Maximum2.6657218 × 1013
Range2.6532077 × 1013
Interquartile range (IQR)3.2742699 × 1011

Descriptive statistics

Standard deviation3.5228637 × 1012
Coefficient of variation (CV)2.6103721
Kurtosis33.321189
Mean1.3495638 × 1012
Median Absolute Deviation (MAD)1.5249961 × 1011
Skewness5.458957
Sum1.3495638 × 1014
Variance1.2410569 × 1025
MonotonicityNot monotonic
2023-12-10T19:03:59.741816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26657217692000 1
 
1.0%
216526574000 1
 
1.0%
280234420000 1
 
1.0%
390931568000 1
 
1.0%
470121200000 1
 
1.0%
308624620000 1
 
1.0%
334114164000 1
 
1.0%
4108244837000 1
 
1.0%
463822127000 1
 
1.0%
481657600000 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
125140462000 1
1.0%
177446743000 1
1.0%
193339693000 1
1.0%
196728879000 1
1.0%
196792903000 1
1.0%
198976382000 1
1.0%
204299782000 1
1.0%
207404271000 1
1.0%
209313647000 1
1.0%
216526574000 1
1.0%
ValueCountFrequency (%)
26657217692000 1
1.0%
19271679079000 1
1.0%
10020444490000 1
1.0%
8051910458000 1
1.0%
6499212139000 1
1.0%
6400498917000 1
1.0%
4108244837000 1
1.0%
3933062429000 1
1.0%
3090833905000 1
1.0%
2656345953000 1
1.0%

anexptr_budget_irds_rt
Real number (ℝ)

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2596
Minimum-9.08
Maximum29.64
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)3.0%
Memory size1.0 KiB
2023-12-10T19:03:59.994389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.08
5-th percentile2.5875
Q15.62
median8.465
Q310.09
95-th percentile14.8585
Maximum29.64
Range38.72
Interquartile range (IQR)4.47

Descriptive statistics

Standard deviation5.0333068
Coefficient of variation (CV)0.60938869
Kurtosis5.2396944
Mean8.2596
Median Absolute Deviation (MAD)2.305
Skewness0.26872127
Sum825.96
Variance25.334178
MonotonicityNot monotonic
2023-12-10T19:04:00.301680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 2
 
2.0%
6.42 2
 
2.0%
4.98 2
 
2.0%
9.53 2
 
2.0%
5.62 2
 
2.0%
7.42 2
 
2.0%
9.81 1
 
1.0%
1.1 1
 
1.0%
9.92 1
 
1.0%
5.16 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
-9.08 1
1.0%
-8.96 1
1.0%
-3.14 1
1.0%
1.1 1
1.0%
1.4 1
1.0%
2.65 2
2.0%
2.69 1
1.0%
3.14 1
1.0%
3.15 1
1.0%
3.79 1
1.0%
ValueCountFrequency (%)
29.64 1
1.0%
23.45 1
1.0%
18.23 1
1.0%
17.37 1
1.0%
15.78 1
1.0%
14.81 1
1.0%
14.51 1
1.0%
14.49 1
1.0%
13.88 1
1.0%
13.47 1
1.0%

cltur_tursm_budget_price
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1787203 × 109
Minimum0
Maximum5.632196 × 1010
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:00.589255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q152763000
median1.61876 × 108
Q31.0015298 × 109
95-th percentile1.0525531 × 1010
Maximum5.632196 × 1010
Range5.632196 × 1010
Interquartile range (IQR)9.4876675 × 108

Descriptive statistics

Standard deviation7.1396167 × 109
Coefficient of variation (CV)3.2769772
Kurtosis37.531715
Mean2.1787203 × 109
Median Absolute Deviation (MAD)1.61876 × 108
Skewness5.7222345
Sum2.1787203 × 1011
Variance5.0974126 × 1019
MonotonicityNot monotonic
2023-12-10T19:04:00.883792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
13.0%
56321960000 1
 
1.0%
58250000 1
 
1.0%
2311700000 1
 
1.0%
5323460000 1
 
1.0%
466430000 1
 
1.0%
100305000 1
 
1.0%
163571000 1
 
1.0%
84493000 1
 
1.0%
2762162000 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0 13
13.0%
1200000 1
 
1.0%
16350000 1
 
1.0%
21700000 1
 
1.0%
24000000 1
 
1.0%
28704000 1
 
1.0%
35920000 1
 
1.0%
42370000 1
 
1.0%
44460000 1
 
1.0%
46000000 1
 
1.0%
ValueCountFrequency (%)
56321960000 1
1.0%
34509025000 1
1.0%
18513595000 1
1.0%
17816313000 1
1.0%
12385261000 1
1.0%
10427650000 1
1.0%
7869613000 1
1.0%
6884235000 1
1.0%
5323460000 1
1.0%
4814060000 1
1.0%

before_year_cltur_tursm_budget_price
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8088392 × 109
Minimum0
Maximum4.0738312 × 1010
Zeros11
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:01.133300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141440000
median1.48498 × 108
Q36.6342525 × 108
95-th percentile8.239973 × 109
Maximum4.0738312 × 1010
Range4.0738312 × 1010
Interquartile range (IQR)6.2198525 × 108

Descriptive statistics

Standard deviation6.0212681 × 109
Coefficient of variation (CV)3.3288022
Kurtosis32.018362
Mean1.8088392 × 109
Median Absolute Deviation (MAD)1.277435 × 108
Skewness5.4643656
Sum1.8088392 × 1011
Variance3.6255669 × 1019
MonotonicityNot monotonic
2023-12-10T19:04:01.400493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
11.0%
40738312000 1
 
1.0%
41720000 1
 
1.0%
4332355000 1
 
1.0%
87360000 1
 
1.0%
129600000 1
 
1.0%
151280000 1
 
1.0%
171960000 1
 
1.0%
2617765000 1
 
1.0%
409000000 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
0 11
11.0%
14000000 1
 
1.0%
20000000 1
 
1.0%
20068000 1
 
1.0%
21700000 1
 
1.0%
25000000 1
 
1.0%
28302000 1
 
1.0%
30229000 1
 
1.0%
31995000 1
 
1.0%
32000000 1
 
1.0%
ValueCountFrequency (%)
40738312000 1
1.0%
39095847000 1
1.0%
17467059000 1
1.0%
9754356000 1
1.0%
9413109000 1
1.0%
8178229000 1
1.0%
7110598000 1
1.0%
4555900000 1
1.0%
4424019000 1
1.0%
4338331000 1
1.0%

cltur_tursm_budget_irds_rt
Real number (ℝ)

MISSING  ZEROS 

Distinct84
Distinct (%)94.4%
Missing11
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean103.6391
Minimum-100
Maximum3104.54
Zeros2
Zeros (%)2.0%
Negative30
Negative (%)30.0%
Memory size1.0 KiB
2023-12-10T19:04:01.652424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-99.064
Q1-17.38
median14.08
Q376.02
95-th percentile545.49
Maximum3104.54
Range3204.54
Interquartile range (IQR)93.4

Descriptive statistics

Standard deviation368.27281
Coefficient of variation (CV)3.5534157
Kurtosis50.874254
Mean103.6391
Median Absolute Deviation (MAD)40.39
Skewness6.4734656
Sum9223.88
Variance135624.87
MonotonicityNot monotonic
2023-12-10T19:04:01.909676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100.0 5
 
5.0%
0.0 2
 
2.0%
-75.91 1
 
1.0%
12.25 1
 
1.0%
2.88 1
 
1.0%
22.88 1
 
1.0%
433.92 1
 
1.0%
-22.6 1
 
1.0%
8.12 1
 
1.0%
-50.86 1
 
1.0%
Other values (74) 74
74.0%
(Missing) 11
 
11.0%
ValueCountFrequency (%)
-100.0 5
5.0%
-97.66 1
 
1.0%
-95.2 1
 
1.0%
-92.4 1
 
1.0%
-88.69 1
 
1.0%
-80.06 1
 
1.0%
-75.91 1
 
1.0%
-73.5 1
 
1.0%
-62.96 1
 
1.0%
-50.86 1
 
1.0%
ValueCountFrequency (%)
3104.54 1
1.0%
769.88 1
1.0%
681.25 1
1.0%
590.1 1
1.0%
546.89 1
1.0%
543.39 1
1.0%
490.0 1
1.0%
433.92 1
1.0%
419.25 1
1.0%
377.57 1
1.0%

cltur_tursm_budget_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1038
Minimum0
Maximum1.18
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:02.161530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.04
Q30.12
95-th percentile0.3355
Maximum1.18
Range1.18
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.17708515
Coefficient of variation (CV)1.7060227
Kurtosis16.692748
Mean0.1038
Median Absolute Deviation (MAD)0.03
Skewness3.6975384
Sum10.38
Variance0.031359152
MonotonicityNot monotonic
2023-12-10T19:04:02.381310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 15
15.0%
0.02 14
14.0%
0.03 12
12.0%
0.01 7
 
7.0%
0.04 6
 
6.0%
0.07 5
 
5.0%
0.16 5
 
5.0%
0.06 4
 
4.0%
0.12 3
 
3.0%
0.05 3
 
3.0%
Other values (20) 26
26.0%
ValueCountFrequency (%)
0.0 15
15.0%
0.01 7
7.0%
0.02 14
14.0%
0.03 12
12.0%
0.04 6
 
6.0%
0.05 3
 
3.0%
0.06 4
 
4.0%
0.07 5
 
5.0%
0.08 1
 
1.0%
0.09 3
 
3.0%
ValueCountFrequency (%)
1.18 1
1.0%
0.83 1
1.0%
0.66 1
1.0%
0.62 1
1.0%
0.44 1
1.0%
0.33 1
1.0%
0.32 1
1.0%
0.31 1
1.0%
0.3 2
2.0%
0.26 1
1.0%

tot_popltn_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean673915.19
Minimum21351
Maximum12716780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:02.653566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21351
5-th percentile67798.4
Q1205327.75
median340099
Q3495319.75
95-th percentile1562879.3
Maximum12716780
Range12695429
Interquartile range (IQR)289992

Descriptive statistics

Standard deviation1625939.1
Coefficient of variation (CV)2.4126762
Kurtosis40.289895
Mean673915.19
Median Absolute Deviation (MAD)145575
Skewness6.1290798
Sum67391519
Variance2.6436781 × 1012
MonotonicityNot monotonic
2023-12-10T19:04:03.005020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9857426 1
 
1.0%
95791 1
 
1.0%
192688 1
 
1.0%
343222 1
 
1.0%
491011 1
 
1.0%
252490 1
 
1.0%
234959 1
 
1.0%
1514370 1
 
1.0%
403049 1
 
1.0%
441066 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
21351 1
1.0%
45208 1
1.0%
48026 1
1.0%
63308 1
1.0%
63778 1
1.0%
68010 1
1.0%
71014 1
1.0%
79712 1
1.0%
89826 1
1.0%
95791 1
1.0%
ValueCountFrequency (%)
12716780 1
1.0%
9857426 1
1.0%
3498529 1
1.0%
2943069 1
1.0%
2484557 1
1.0%
1514370 1
1.0%
1469214 1
1.0%
1194041 1
1.0%
1172304 1
1.0%
1039684 1
1.0%

one_psnby_cltur_tursm_budget_price
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3311.37
Minimum0
Maximum64332
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:03.394222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1211.25
median626
Q32640.25
95-th percentile9845.85
Maximum64332
Range64332
Interquartile range (IQR)2429

Descriptive statistics

Standard deviation8823.1307
Coefficient of variation (CV)2.6644956
Kurtosis30.427263
Mean3311.37
Median Absolute Deviation (MAD)626
Skewness5.2336065
Sum331137
Variance77847636
MonotonicityNot monotonic
2023-12-10T19:04:03.757569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
13.0%
596 2
 
2.0%
302 2
 
2.0%
1401 1
 
1.0%
9531 1
 
1.0%
4541 1
 
1.0%
2421 1
 
1.0%
292 1
 
1.0%
333 1
 
1.0%
360 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0 13
13.0%
3 1
 
1.0%
63 1
 
1.0%
68 1
 
1.0%
79 1
 
1.0%
87 1
 
1.0%
94 1
 
1.0%
115 1
 
1.0%
141 1
 
1.0%
152 1
 
1.0%
ValueCountFrequency (%)
64332 1
1.0%
49328 1
1.0%
28855 1
1.0%
21956 1
1.0%
15828 1
1.0%
9531 1
1.0%
8733 1
1.0%
8430 1
1.0%
7622 1
1.0%
6418 1
1.0%

Interactions

2023-12-10T19:03:52.134316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:30.558078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:32.836772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:34.869411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:37.095500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:39.081461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.134618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:43.061106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:45.257061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:48.087308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:50.124149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:52.310765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:30.751260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:32.998553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:35.036564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:37.252780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:39.258437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.380394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:43.237453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:45.431069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:48.303596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:50.301838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:52.496671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:30.949271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:33.171617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:35.228152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:37.437692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:39.444655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.563164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:43.414734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:45.632237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:48.485387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:50.483201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:52.689999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:31.124232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:33.421389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:35.395894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:37.639940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:39.660752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.733758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:43.586195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:45.820284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:48.662344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:50.676237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:52.856438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:31.347835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:33.622951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:35.581850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:37.815675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:39.829738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.884567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:43.815096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:46.125404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:48.848330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:50.842218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:53.071535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:31.576515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:33.798960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:35.757209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:38.076398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:40.011824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:42.057100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:44.164328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:46.322031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:49.034063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:51.034424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:53.279861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:31.793151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:33.956064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:35.914509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:38.218055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:40.193362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:42.205445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:44.340176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:46.481709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:49.214422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:51.208656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:53.470246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:32.002391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:34.163457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:36.102033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:38.390666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:40.390431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:42.373524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:44.528184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:46.831639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:49.418383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:51.434433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:53.676663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:32.202463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:34.345035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:36.264041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:38.555827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:40.562316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:42.522876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:44.700695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:47.398751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:49.582494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:51.594336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:53.955442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:32.413895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:34.510670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:36.769828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:38.714666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:40.745066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:42.684748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:44.878708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:47.638474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:49.753587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:51.762300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:54.163581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:32.667030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:34.680447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:36.930002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:38.914376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:40.930534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:42.881210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:45.050252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:47.903252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:49.943288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:51.959108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:04:04.022380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nofinan_yearadmnstmach_cdadmnstmach_nmtot_anexptr_budget_pricebefore_year_anexptr_budget_priceanexptr_budget_irds_rtcltur_tursm_budget_pricebefore_year_cltur_tursm_budget_pricecltur_tursm_budget_irds_rtcltur_tursm_budget_ratetot_popltn_coone_psnby_cltur_tursm_budget_price
seq_no1.0000.9631.0001.0000.3130.3130.0000.4630.4510.0000.3640.0000.738
finan_year0.9631.0001.0001.0000.3130.3130.0000.4630.4510.0000.3640.0000.738
admnstmach_cd1.0001.0001.0001.0000.1850.1850.6680.1530.4120.0000.5170.0000.702
admnstmach_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
tot_anexptr_budget_price0.3130.3130.1851.0001.0001.0000.0000.9860.8110.0000.0000.9260.237
before_year_anexptr_budget_price0.3130.3130.1851.0001.0001.0000.0000.9860.8110.0000.0000.9260.237
anexptr_budget_irds_rt0.0000.0000.6681.0000.0000.0001.0000.0000.0000.0000.3680.0000.584
cltur_tursm_budget_price0.4630.4630.1531.0000.9860.9860.0001.0000.8460.0000.4580.8950.505
before_year_cltur_tursm_budget_price0.4510.4510.4121.0000.8110.8110.0000.8461.0000.0000.5970.9020.760
cltur_tursm_budget_irds_rt0.0000.0000.0001.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
cltur_tursm_budget_rate0.3640.3640.5171.0000.0000.0000.3680.4580.5970.0001.0000.0000.957
tot_popltn_co0.0000.0000.0001.0000.9260.9260.0000.8950.9020.0000.0001.0000.000
one_psnby_cltur_tursm_budget_price0.7380.7380.7021.0000.2370.2370.5840.5050.7600.0000.9570.0001.000
2023-12-10T19:04:04.394579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noadmnstmach_cdtot_anexptr_budget_pricebefore_year_anexptr_budget_priceanexptr_budget_irds_rtcltur_tursm_budget_pricebefore_year_cltur_tursm_budget_pricecltur_tursm_budget_irds_rtcltur_tursm_budget_ratetot_popltn_coone_psnby_cltur_tursm_budget_pricefinan_year
seq_no1.0001.0000.1980.213-0.0950.4620.4070.1290.441-0.0440.4920.826
admnstmach_cd1.0001.0000.1980.213-0.0950.4620.4070.1290.441-0.0440.4920.974
tot_anexptr_budget_price0.1980.1981.0000.9970.1460.5520.5500.0830.2800.8060.2940.221
before_year_anexptr_budget_price0.2130.2130.9971.0000.0980.5490.5490.0820.2770.8080.2920.221
anexptr_budget_irds_rt-0.095-0.0950.1460.0981.0000.1500.139-0.0180.1400.0450.1230.000
cltur_tursm_budget_price0.4620.4620.5520.5490.1501.0000.8020.4070.9340.3270.9170.326
before_year_cltur_tursm_budget_price0.4070.4070.5500.5490.1390.8021.000-0.1490.7090.3150.7500.539
cltur_tursm_budget_irds_rt0.1290.1290.0830.082-0.0180.407-0.1491.0000.4260.0240.3800.000
cltur_tursm_budget_rate0.4410.4410.2800.2770.1400.9340.7090.4261.0000.0730.9560.379
tot_popltn_co-0.044-0.0440.8060.8080.0450.3270.3150.0240.0731.000-0.0130.000
one_psnby_cltur_tursm_budget_price0.4920.4920.2940.2920.1230.9170.7500.3800.956-0.0131.0000.778
finan_year0.8260.9740.2210.2210.0000.3260.5390.0000.3790.0000.7781.000

Missing values

2023-12-10T19:03:54.436872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:03:54.797432image/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

seq_nofinan_yearadmnstmach_cdadmnstmach_nmtot_anexptr_budget_pricebefore_year_anexptr_budget_priceanexptr_budget_irds_rtcltur_tursm_budget_pricebefore_year_cltur_tursm_budget_pricecltur_tursm_budget_irds_rtcltur_tursm_budget_ratetot_popltn_coone_psnby_cltur_tursm_budget_price
0120161100000000서울특별시29272794246000266572176920009.81563219600004073831200038.250.1998574265714
1170220224888000000경상남도 거창군8519056430008299434720002.6530052000024854400020.910.04633084747
2320161114000000서울특별시 중구41458557200036212067400014.4913848900020068000590.10.031252491106
3420161117000000서울특별시 용산구3669454360003428220050007.0421700000217000000.00.0123024194
4520161120000000서울특별시 성동구4167870940003916280420006.4246000000460000000.00.01299259154
5620161121500000서울특별시 광진구4322070050003947457890009.4952928400082265000543.390.123572151482
6720161123000000서울특별시 동대문구4443270450004179571500006.310146000000-100.00.03550690
7170320224889000000경상남도 합천군75100363400067652124700011.012369050000199323000018.850.324802649328
8920161129000000서울특별시 성북구5308141750004944455960007.3636696000027200000034.910.07450355815
91020161130500000서울특별시 강북구4861291320004438206320009.53349200000170000000105.410.073271951067
seq_nofinan_yearadmnstmach_cdadmnstmach_nmtot_anexptr_budget_pricebefore_year_anexptr_budget_priceanexptr_budget_irds_rtcltur_tursm_budget_pricebefore_year_cltur_tursm_budget_pricecltur_tursm_budget_irds_rtcltur_tursm_budget_ratetot_popltn_coone_psnby_cltur_tursm_budget_price
909120164125000000경기도 동두천시35012613000030241003800015.782008880001962710002.350.06982772044
919220164127000000경기도 안산시162286194100015293573180006.11134964400079797200069.130.086898591956
929320164128000000경기도 고양시155605650000015153660900002.6924500470003320756000-26.220.1610396842357
939420164129000000경기도 과천시289797445000318303121000-8.9626231900016981500054.470.09637784113
949520164131000000경기도 구리시4548648570004339325680004.8228560600021189500034.790.061937631474
959620164136000000경기도 남양주시114945441700010494194670009.5313639770001415006000-3.610.126621542060
969720164137000000경기도 오산시4339770530003972539710009.2454853000027555500099.060.132086562629
979820164139000000경기도 시흥시1480128676000129262770200014.5162135700049658900025.130.044028881542
989920164141000000경기도 군포시6513737500006192129440005.19294511000341374000-13.730.052848901034
9910020164143000000경기도 의왕시3209429990003073300290004.431006119000115662000769.880.311567636418