Overview

Dataset statistics

Number of variables7
Number of observations28
Missing cells33
Missing cells (%)16.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory66.7 B

Variable types

Text1
Numeric6

Dataset

Description전북스마트쉼센터운영현황20183
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202102

Alerts

2016년 목표 is highly overall correlated with 2016년 실적 and 4 other fieldsHigh correlation
2016년 실적 is highly overall correlated with 2016년 목표 and 4 other fieldsHigh correlation
2017년 목표 is highly overall correlated with 2016년 목표 and 4 other fieldsHigh correlation
2017년 실적 is highly overall correlated with 2016년 목표 and 4 other fieldsHigh correlation
2018년 목표 is highly overall correlated with 2016년 목표 and 4 other fieldsHigh correlation
2018년 3월 현재 실적 is highly overall correlated with 2016년 목표 and 4 other fieldsHigh correlation
2016년 목표 has 9 (32.1%) missing valuesMissing
2016년 실적 has 2 (7.1%) missing valuesMissing
2017년 목표 has 9 (32.1%) missing valuesMissing
2017년 실적 has 2 (7.1%) missing valuesMissing
2018년 목표 has 9 (32.1%) missing valuesMissing
2018년 3월 현재 실적 has 2 (7.1%) missing valuesMissing
구분 has unique valuesUnique
2018년 3월 현재 실적 has 6 (21.4%) zerosZeros

Reproduction

Analysis started2024-03-14 01:20:54.581550
Analysis finished2024-03-14 01:20:58.193396
Duration3.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-03-14T10:20:58.301416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length12.785714
Min length7

Characters and Unicode

Total characters358
Distinct characters62
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row레몬교실(유아)-횟수
2nd row레몬교실(유아)-인원
3rd row레몬교실(청소년)-횟수
4th row레몬교실(청소년)-인원
5th row레몬교실(성인)-횟수
ValueCountFrequency (%)
예방교육 2
 
6.2%
총 2
 
6.2%
레몬교실(유아)-횟수 1
 
3.1%
레몬교실(유아)-인원 1
 
3.1%
예산(국비)-천원 1
 
3.1%
e-클린홍보-인원 1
 
3.1%
e-클린홍보-횟수 1
 
3.1%
놀이치료-인원 1
 
3.1%
놀이치료-횟수 1
 
3.1%
집단상담-인원 1
 
3.1%
Other values (20) 20
62.5%
2024-03-14T10:20:58.599081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 30
 
8.4%
인 23
 
6.4%
( 18
 
5.0%
) 18
 
5.0%
W 16
 
4.5%
원 15
 
4.2%
횟 13
 
3.6%
수 13
 
3.6%
교 10
 
2.8%
실 10
 
2.8%
Other values (52) 192
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 262
73.2%
Dash Punctuation 30
 
8.4%
Uppercase Letter 24
 
6.7%
Open Punctuation 18
 
5.0%
Close Punctuation 18
 
5.0%
Space Separator 4
 
1.1%
Lowercase Letter 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 23
 
8.8%
원 15
 
5.7%
횟 13
 
5.0%
수 13
 
5.0%
교 10
 
3.8%
실 10
 
3.8%
멘 8
 
3.1%
한 8
 
3.1%
터 8
 
3.1%
넷 8
 
3.1%
Other values (45) 146
55.7%
Uppercase Letter
ValueCountFrequency (%)
W 16
66.7%
O 8
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 262
73.2%
Common 70
 
19.6%
Latin 26
 
7.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 23
 
8.8%
원 15
 
5.7%
횟 13
 
5.0%
수 13
 
5.0%
교 10
 
3.8%
실 10
 
3.8%
멘 8
 
3.1%
한 8
 
3.1%
터 8
 
3.1%
넷 8
 
3.1%
Other values (45) 146
55.7%
Common
ValueCountFrequency (%)
- 30
42.9%
( 18
25.7%
) 18
25.7%
4
 
5.7%
Latin
ValueCountFrequency (%)
W 16
61.5%
O 8
30.8%
e 2
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 262
73.2%
ASCII 96
 
26.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 30
31.2%
( 18
18.8%
) 18
18.8%
W 16
16.7%
O 8
 
8.3%
4
 
4.2%
e 2
 
2.1%
Hangul
ValueCountFrequency (%)
인 23
 
8.8%
원 15
 
5.7%
횟 13
 
5.0%
수 13
 
5.0%
교 10
 
3.8%
실 10
 
3.8%
멘 8
 
3.1%
한 8
 
3.1%
터 8
 
3.1%
넷 8
 
3.1%
Other values (45) 146
55.7%

2016년 목표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)89.5%
Missing9
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean12760.158
Minimum10
Maximum89640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-14T10:20:58.707429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q1150
median1720
Q315780
95-th percentile44964
Maximum89640
Range89630
Interquartile range (IQR)15630

Descriptive statistics

Standard deviation23380.485
Coefficient of variation (CV)1.8323037
Kurtosis5.8600568
Mean12760.158
Median Absolute Deviation (MAD)1570
Skewness2.3195085
Sum242443
Variance5.4664707 × 108
MonotonicityNot monotonic
2024-03-14T10:20:58.800707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 2
 
7.1%
150 2
 
7.1%
28540 1
 
3.6%
40000 1
 
3.6%
89640 1
 
3.6%
2500 1
 
3.6%
2000 1
 
3.6%
125 1
 
3.6%
2590 1
 
3.6%
35870 1
 
3.6%
Other values (7) 7
25.0%
(Missing) 9
32.1%
ValueCountFrequency (%)
10 2
7.1%
57 1
3.6%
125 1
3.6%
150 2
7.1%
427 1
3.6%
484 1
3.6%
1000 1
3.6%
1720 1
3.6%
2000 1
3.6%
2500 1
3.6%
ValueCountFrequency (%)
89640 1
3.6%
40000 1
3.6%
35870 1
3.6%
34150 1
3.6%
28540 1
3.6%
3020 1
3.6%
2590 1
3.6%
2500 1
3.6%
2000 1
3.6%
1720 1
3.6%

2016년 실적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing2
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean4706.3846
Minimum7
Maximum38053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-14T10:20:58.886708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10.75
Q141.25
median386.5
Q32258
95-th percentile34722.5
Maximum38053
Range38046
Interquartile range (IQR)2216.75

Descriptive statistics

Standard deviation11199.669
Coefficient of variation (CV)2.3796757
Kurtosis5.1836501
Mean4706.3846
Median Absolute Deviation (MAD)369
Skewness2.5701254
Sum122366
Variance1.2543259 × 108
MonotonicityNot monotonic
2024-03-14T10:20:58.979213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
57 1
 
3.6%
2974 1
 
3.6%
10 1
 
3.6%
13 1
 
3.6%
155 1
 
3.6%
2403 1
 
3.6%
152 1
 
3.6%
126 1
 
3.6%
949 1
 
3.6%
38053 1
 
3.6%
Other values (16) 16
57.1%
(Missing) 2
 
7.1%
ValueCountFrequency (%)
7 1
3.6%
10 1
3.6%
13 1
3.6%
22 1
3.6%
27 1
3.6%
28 1
3.6%
36 1
3.6%
57 1
3.6%
126 1
3.6%
152 1
3.6%
ValueCountFrequency (%)
38053 1
3.6%
36230 1
3.6%
30200 1
3.6%
3171 1
3.6%
2974 1
3.6%
2859 1
3.6%
2403 1
3.6%
1823 1
3.6%
949 1
3.6%
924 1
3.6%

2017년 목표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)94.7%
Missing9
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean11311.579
Minimum10
Maximum65900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-14T10:20:59.097958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q1130
median1300
Q315245
95-th percentile42590
Maximum65900
Range65890
Interquartile range (IQR)15115

Descriptive statistics

Standard deviation19188.175
Coefficient of variation (CV)1.6963303
Kurtosis2.3480286
Mean11311.579
Median Absolute Deviation (MAD)1240
Skewness1.7427643
Sum214920
Variance3.6818604 × 108
MonotonicityNot monotonic
2024-03-14T10:20:59.202166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
10 2
 
7.1%
110 1
 
3.6%
40000 1
 
3.6%
65900 1
 
3.6%
2500 1
 
3.6%
150 1
 
3.6%
1300 1
 
3.6%
100 1
 
3.6%
2780 1
 
3.6%
27550 1
 
3.6%
Other values (8) 8
28.6%
(Missing) 9
32.1%
ValueCountFrequency (%)
10 2
7.1%
60 1
3.6%
100 1
3.6%
110 1
3.6%
150 1
3.6%
335 1
3.6%
395 1
3.6%
880 1
3.6%
1300 1
3.6%
1680 1
3.6%
ValueCountFrequency (%)
65900 1
3.6%
40000 1
3.6%
34950 1
3.6%
33270 1
3.6%
27550 1
3.6%
2940 1
3.6%
2780 1
3.6%
2500 1
3.6%
1680 1
3.6%
1300 1
3.6%

2017년 실적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)96.2%
Missing2
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean4670.3462
Minimum12
Maximum36710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-14T10:20:59.304807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile13
Q146.5
median359
Q31786.75
95-th percentile33298
Maximum36710
Range36698
Interquartile range (IQR)1740.25

Descriptive statistics

Standard deviation10738.45
Coefficient of variation (CV)2.2992835
Kurtosis5.0724054
Mean4670.3462
Median Absolute Deviation (MAD)335
Skewness2.5264014
Sum121429
Variance1.153143 × 108
MonotonicityNot monotonic
2024-03-14T10:20:59.422112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
12 2
 
7.1%
60 1
 
3.6%
7093 1
 
3.6%
16 1
 
3.6%
154 1
 
3.6%
1809 1
 
3.6%
100 1
 
3.6%
114 1
 
3.6%
834 1
 
3.6%
36710 1
 
3.6%
Other values (15) 15
53.6%
(Missing) 2
 
7.1%
ValueCountFrequency (%)
12 2
7.1%
16 1
3.6%
20 1
3.6%
28 1
3.6%
39 1
3.6%
42 1
3.6%
60 1
3.6%
100 1
3.6%
114 1
3.6%
154 1
3.6%
ValueCountFrequency (%)
36710 1
3.6%
34990 1
3.6%
28222 1
3.6%
7093 1
3.6%
3561 1
3.6%
3207 1
3.6%
1809 1
3.6%
1720 1
3.6%
834 1
3.6%
607 1
3.6%

2018년 목표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)89.5%
Missing9
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean8236.9474
Minimum10
Maximum49480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-14T10:20:59.610513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q1150
median1040
Q310305
95-th percentile40948
Maximum49480
Range49470
Interquartile range (IQR)10155

Descriptive statistics

Standard deviation14338.784
Coefficient of variation (CV)1.7407886
Kurtosis3.7342637
Mean8236.9474
Median Absolute Deviation (MAD)1030
Skewness2.0815085
Sum156502
Variance2.0560073 × 108
MonotonicityNot monotonic
2024-03-14T10:20:59.740827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 2
 
7.1%
150 2
 
7.1%
13610 1
 
3.6%
40000 1
 
3.6%
49480 1
 
3.6%
7000 1
 
3.6%
2200 1
 
3.6%
80 1
 
3.6%
2100 1
 
3.6%
19100 1
 
3.6%
Other values (7) 7
25.0%
(Missing) 9
32.1%
ValueCountFrequency (%)
10 2
7.1%
35 1
3.6%
80 1
3.6%
150 2
7.1%
226 1
3.6%
261 1
3.6%
640 1
3.6%
1040 1
3.6%
2100 1
3.6%
2200 1
3.6%
ValueCountFrequency (%)
49480 1
3.6%
40000 1
3.6%
19100 1
3.6%
18060 1
3.6%
13610 1
3.6%
7000 1
3.6%
2350 1
3.6%
2200 1
3.6%
2100 1
3.6%
1040 1
3.6%

2018년 3월 현재 실적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)69.2%
Missing2
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean225.80769
Minimum0
Maximum1854
Zeros6
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-14T10:20:59.839013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median10.5
Q3141.5
95-th percentile1540.75
Maximum1854
Range1854
Interquartile range (IQR)139

Descriptive statistics

Standard deviation521.50268
Coefficient of variation (CV)2.3094992
Kurtosis5.1768803
Mean225.80769
Median Absolute Deviation (MAD)10.5
Skewness2.5386043
Sum5871
Variance271965.04
MonotonicityNot monotonic
2024-03-14T10:20:59.930791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 6
21.4%
15 2
 
7.1%
6 2
 
7.1%
5 2
 
7.1%
272 1
 
3.6%
192 1
 
3.6%
11 1
 
3.6%
4 1
 
3.6%
1854 1
 
3.6%
25 1
 
3.6%
Other values (8) 8
28.6%
(Missing) 2
 
7.1%
ValueCountFrequency (%)
0 6
21.4%
2 1
 
3.6%
4 1
 
3.6%
5 2
 
7.1%
6 2
 
7.1%
10 1
 
3.6%
11 1
 
3.6%
13 1
 
3.6%
15 2
 
7.1%
25 1
 
3.6%
ValueCountFrequency (%)
1854 1
3.6%
1582 1
3.6%
1417 1
3.6%
272 1
3.6%
192 1
3.6%
165 1
3.6%
147 1
3.6%
125 1
3.6%
25 1
3.6%
15 2
7.1%

Interactions

2024-03-14T10:20:57.146966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:54.770078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.321678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.793555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.245267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.713656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:57.243486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:54.838272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.393652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.852780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.334118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.772448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:57.357451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:54.912218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.491415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.924584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.422688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.839051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:57.447111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:54.991295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.577038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.007358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.493776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.925873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:57.520655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.130763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.647159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.082897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.560083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:57.010775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:57.629166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.221976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:55.719098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.156483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:56.632076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:20:57.076015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T10:21:00.003959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2016년 목표2016년 실적2017년 목표2017년 실적2018년 목표2018년 3월 현재 실적
구분1.0001.0001.0001.0001.0001.0001.000
2016년 목표1.0001.0000.9610.8910.7010.9671.000
2016년 실적1.0000.9611.0001.0001.0001.0001.000
2017년 목표1.0000.8911.0001.0001.0001.0001.000
2017년 실적1.0000.7011.0001.0001.0001.0000.815
2018년 목표1.0000.9671.0001.0001.0001.0000.796
2018년 3월 현재 실적1.0001.0001.0001.0000.8150.7961.000
2024-03-14T10:21:00.109594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2016년 목표2016년 실적2017년 목표2017년 실적2018년 목표2018년 3월 현재 실적
2016년 목표1.0000.9960.9950.9880.9910.554
2016년 실적0.9961.0000.9920.9860.9940.544
2017년 목표0.9950.9921.0000.9900.9850.552
2017년 실적0.9880.9860.9901.0000.9930.541
2018년 목표0.9910.9940.9850.9931.0000.532
2018년 3월 현재 실적0.5540.5440.5520.5410.5321.000

Missing values

2024-03-14T10:20:57.941642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:20:58.028324image/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.
2024-03-14T10:20:58.122863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분2016년 목표2016년 실적2017년 목표2017년 실적2018년 목표2018년 3월 현재 실적
0레몬교실(유아)-횟수<NA>36<NA>42<NA>2
1레몬교실(유아)-인원25902859278032072100165
2레몬교실(청소년)-횟수<NA>355<NA>248<NA>13
3레몬교실(청소년)-인원28540302002755028222136101417
4레몬교실(성인)-횟수<NA>27<NA>39<NA>0
5레몬교실(성인)-인원302031712940356123500
6레몬교실(종합)-횟수42741833532922615
7레몬교실(종합)-인원34150362303327034990180601582
8WOW건강한인터넷멘토링(초등학생)-횟수<NA>28<NA>28<NA>5
9WOW건강한인터넷멘토링(초등학생)-인원<NA>924<NA>539<NA>125
구분2016년 목표2016년 실적2017년 목표2017년 실적2018년 목표2018년 3월 현재 실적
18가정방문상담-횟수10009498808346404
19가정방문상담-인원125126110114806
20집단상담-횟수15015210010015011
21집단상담-인원20002403130018092200192
22놀이치료-횟수15015515015415015
23놀이치료-인원10131012106
24e-클린홍보-횟수10101016100
25e-클린홍보-인원250029742500709370000
26예산(국비)-천원89640<NA>65900<NA>49480<NA>
27예산(도비)-천원40000<NA>40000<NA>40000<NA>