Overview

Dataset statistics

Number of variables6
Number of observations38
Missing cells20
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory55.4 B

Variable types

Text1
Categorical1
Numeric4

Dataset

Description산업단지별 생산실적 및 전월대비 증감률, 누계 등 변동사항에 대해 국가산업단지 산업동향정보를 월별로 제공하고 있습니다.
Author한국산업단지공단
URLhttps://www.data.go.kr/data/15085891/fileData.do

Alerts

구분 has constant value ""Constant
당월(억원) is highly overall correlated with 전월(억원) and 1 other fieldsHigh correlation
전월(억원) is highly overall correlated with 당월(억원) and 1 other fieldsHigh correlation
2023누계(억원) is highly overall correlated with 당월(억원) and 1 other fieldsHigh correlation
당월(억원) has 5 (13.2%) missing valuesMissing
전월(억원) has 5 (13.2%) missing valuesMissing
2023누계(억원) has 5 (13.2%) missing valuesMissing
증감률(전월대비)(퍼센트) has 5 (13.2%) missing valuesMissing
산업단지 has unique valuesUnique

Reproduction

Analysis started2024-03-14 21:00:19.512459
Analysis finished2024-03-14 21:00:25.401502
Duration5.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산업단지
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-03-15T06:00:26.383904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length3.2631579
Min length2

Characters and Unicode

Total characters124
Distinct characters79
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row서울
2nd row녹산
3rd row대구
4th row남동
5th row부평
ValueCountFrequency (%)
서울 1
 
2.6%
대불(외 1
 
2.6%
진해 1
 
2.6%
국가식품클러스터(외 1
 
2.6%
군산 1
 
2.6%
군산2 1
 
2.6%
익산 1
 
2.6%
광양 1
 
2.6%
대불 1
 
2.6%
구미 1
 
2.6%
Other values (28) 28
73.7%
2024-03-15T06:00:27.731461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 7
 
5.6%
항 4
 
3.2%
구 3
 
2.4%
미 3
 
2.4%
포 3
 
2.4%
주 3
 
2.4%
) 3
 
2.4%
대 3
 
2.4%
( 3
 
2.4%
외 3
 
2.4%
Other values (69) 89
71.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114
91.9%
Close Punctuation 3
 
2.4%
Open Punctuation 3
 
2.4%
Uppercase Letter 3
 
2.4%
Decimal Number 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 7
 
6.1%
항 4
 
3.5%
구 3
 
2.6%
미 3
 
2.6%
포 3
 
2.6%
주 3
 
2.6%
대 3
 
2.6%
외 3
 
2.6%
스 2
 
1.8%
양 2
 
1.8%
Other values (63) 81
71.1%
Uppercase Letter
ValueCountFrequency (%)
M 1
33.3%
T 1
33.3%
V 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 114
91.9%
Common 7
 
5.6%
Latin 3
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 7
 
6.1%
항 4
 
3.5%
구 3
 
2.6%
미 3
 
2.6%
포 3
 
2.6%
주 3
 
2.6%
대 3
 
2.6%
외 3
 
2.6%
스 2
 
1.8%
양 2
 
1.8%
Other values (63) 81
71.1%
Common
ValueCountFrequency (%)
) 3
42.9%
( 3
42.9%
2 1
 
14.3%
Latin
ValueCountFrequency (%)
M 1
33.3%
T 1
33.3%
V 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
91.1%
ASCII 10
 
8.1%
Compat Jamo 1
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 7
 
6.2%
항 4
 
3.5%
구 3
 
2.7%
미 3
 
2.7%
포 3
 
2.7%
주 3
 
2.7%
대 3
 
2.7%
외 3
 
2.7%
스 2
 
1.8%
양 2
 
1.8%
Other values (62) 80
70.8%
ASCII
ValueCountFrequency (%)
) 3
30.0%
( 3
30.0%
2 1
 
10.0%
M 1
 
10.0%
T 1
 
10.0%
V 1
 
10.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size432.0 B
국가
38 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국가 38
100.0%

Length

2024-03-15T06:00:28.140296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:00:28.462270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국가 38
100.0%

당월(억원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean15832.026
Minimum12.36
Maximum118441.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-03-15T06:00:28.764915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.36
5-th percentile60.828
Q11272.4592
median3482.4649
Q318719.441
95-th percentile56578.44
Maximum118441.16
Range118428.8
Interquartile range (IQR)17446.982

Descriptive statistics

Standard deviation25318.124
Coefficient of variation (CV)1.5991714
Kurtosis7.8306819
Mean15832.026
Median Absolute Deviation (MAD)3335.6644
Skewness2.5633642
Sum522456.86
Variance6.4100739 × 108
MonotonicityNot monotonic
2024-03-15T06:00:29.120280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
173.312 1
 
2.6%
293.1697359 1
 
2.6%
3482.464928 1
 
2.6%
2917.396997 1
 
2.6%
1339.080619 1
 
2.6%
18719.44103 1
 
2.6%
1656.333684 1
 
2.6%
66734.72068 1
 
2.6%
10942.47405 1
 
2.6%
38781.52853 1
 
2.6%
Other values (23) 23
60.5%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
12.36 1
2.6%
42.375 1
2.6%
73.13 1
2.6%
146.8005087 1
2.6%
170.68 1
2.6%
173.312 1
2.6%
258.56 1
2.6%
293.1697359 1
2.6%
1272.459201 1
2.6%
1339.080619 1
2.6%
ValueCountFrequency (%)
118441.1589 1
2.6%
66734.72068 1
2.6%
49807.58659 1
2.6%
48993.50555 1
2.6%
38781.52853 1
2.6%
31061.87927 1
2.6%
30731.91878 1
2.6%
26828.95518 1
2.6%
18719.44103 1
2.6%
18052.74933 1
2.6%

전월(억원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean15689.246
Minimum29.87
Maximum118387.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-03-15T06:00:29.341455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.87
5-th percentile63.264
Q11219.6592
median3687.7275
Q319275.456
95-th percentile58058.239
Maximum118387.11
Range118357.24
Interquartile range (IQR)18055.797

Descriptive statistics

Standard deviation25056.822
Coefficient of variation (CV)1.5970698
Kurtosis8.2818993
Mean15689.246
Median Absolute Deviation (MAD)3527.3069
Skewness2.6262663
Sum517745.12
Variance6.2784431 × 108
MonotonicityNot monotonic
2024-03-15T06:00:29.764405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
206.884 1
 
2.6%
299.2221039 1
 
2.6%
3732.085265 1
 
2.6%
3054.551261 1
 
2.6%
1423.177047 1
 
2.6%
19275.45604 1
 
2.6%
1706.826101 1
 
2.6%
66065.0368 1
 
2.6%
11095.45061 1
 
2.6%
35071.15483 1
 
2.6%
Other values (23) 23
60.5%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
29.87 1
2.6%
44.775 1
2.6%
75.59 1
2.6%
160.4205087 1
2.6%
169.95 1
2.6%
206.884 1
2.6%
273.3 1
2.6%
299.2221039 1
2.6%
1219.659228 1
2.6%
1359.14 1
2.6%
ValueCountFrequency (%)
118387.1051 1
2.6%
66065.0368 1
2.6%
52720.37374 1
2.6%
42197.37435 1
2.6%
35071.15483 1
2.6%
31493.31466 1
2.6%
31000.57407 1
2.6%
28132.70639 1
2.6%
19275.45604 1
2.6%
17580.0511 1
2.6%

2023누계(억원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean111471.13
Minimum66.08
Maximum852061.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-03-15T06:00:30.166075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66.08
5-th percentile341.028
Q18227.1077
median24304.918
Q3132394.6
95-th percentile401761.42
Maximum852061.53
Range851995.45
Interquartile range (IQR)124167.49

Descriptive statistics

Standard deviation180498.03
Coefficient of variation (CV)1.6192357
Kurtosis8.3159353
Mean111471.13
Median Absolute Deviation (MAD)23139.188
Skewness2.6379079
Sum3678547.3
Variance3.2579539 × 1010
MonotonicityNot monotonic
2024-03-15T06:00:30.507576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1493.256 1
 
2.6%
2012.898999 1
 
2.6%
24304.91816 1
 
2.6%
21529.57305 1
 
2.6%
9802.743541 1
 
2.6%
132394.5975 1
 
2.6%
11377.52323 1
 
2.6%
472637.882 1
 
2.6%
75271.02398 1
 
2.6%
263364.3807 1
 
2.6%
Other values (23) 23
60.5%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
66.08 1
2.6%
303.12 1
2.6%
366.3 1
2.6%
1165.73 1
2.6%
1187.411561 1
2.6%
1493.256 1
2.6%
1852.22 1
2.6%
2012.898999 1
2.6%
8227.107656 1
2.6%
9425.7 1
2.6%
ValueCountFrequency (%)
852061.5257 1
2.6%
472637.882 1
2.6%
354510.443 1
2.6%
344273.9999 1
2.6%
263364.3807 1
2.6%
214902.7878 1
2.6%
214500.8855 1
2.6%
186847.4994 1
2.6%
132394.5975 1
2.6%
125119.2099 1
2.6%

증감률(전월대비)(퍼센트)
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean-3.8912472
Minimum-58.62069
Maximum16.105578
Zeros0
Zeros (%)0.0%
Negative23
Negative (%)60.5%
Memory size470.0 B
2024-03-15T06:00:30.991554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-58.62069
5-th percentile-11.728174
Q1-5.5249744
median-2.4958699
Q30.19775506
95-th percentile6.8292693
Maximum16.105578
Range74.726268
Interquartile range (IQR)5.7227295

Descriptive statistics

Standard deviation11.346171
Coefficient of variation (CV)-2.9158187
Kurtosis17.648397
Mean-3.8912472
Median Absolute Deviation (MAD)2.925408
Skewness-3.4988877
Sum-128.41116
Variance128.73561
MonotonicityNot monotonic
2024-03-15T06:00:31.261433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
-16.22745113 1
 
2.6%
-2.022700837 1
 
2.6%
-6.688495018 1
 
2.6%
-4.490160821 1
 
2.6%
-5.909062977 1
 
2.6%
-2.884575094 1
 
2.6%
-2.95826374 1
 
2.6%
1.013673672 1
 
2.6%
-1.378732324 1
 
2.6%
10.5795595 1
 
2.6%
Other values (23) 23
60.5%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
-58.62068966 1
2.6%
-16.22745113 1
2.6%
-8.728655424 1
2.6%
-8.661779979 1
2.6%
-8.490186266 1
2.6%
-6.688495018 1
2.6%
-5.909062977 1
2.6%
-5.575557552 1
2.6%
-5.524974397 1
2.6%
-5.393340651 1
2.6%
ValueCountFrequency (%)
16.10557838 1
2.6%
10.5795595 1
2.6%
4.329075843 1
2.6%
2.688833094 1
2.6%
1.563574466 1
2.6%
1.013673672 1
2.6%
0.871483471 1
2.6%
0.429538099 1
2.6%
0.197755057 1
2.6%
0.045658476 1
2.6%

Interactions

2024-03-15T06:00:23.084873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:19.732273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:20.814783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:22.015852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:23.429862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:19.959435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:21.097788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:22.301837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:23.856040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:20.217408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:21.451220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:22.528429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:24.112459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:20.514882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:21.697057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:00:22.745935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T06:00:31.428988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산업단지당월(억원)전월(억원)2023누계(억원)증감률(전월대비)(퍼센트)
산업단지1.0001.0001.0001.0001.000
당월(억원)1.0001.0000.9921.0000.353
전월(억원)1.0000.9921.0000.9950.000
2023누계(억원)1.0001.0000.9951.0000.025
증감률(전월대비)(퍼센트)1.0000.3530.0000.0251.000
2024-03-15T06:00:31.606556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당월(억원)전월(억원)2023누계(억원)증감률(전월대비)(퍼센트)
당월(억원)1.0000.9990.9990.493
전월(억원)0.9991.0000.9990.482
2023누계(억원)0.9990.9991.0000.486
증감률(전월대비)(퍼센트)0.4930.4820.4861.000

Missing values

2024-03-15T06:00:24.485113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T06:00:24.960674image/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.
2024-03-15T06:00:25.234515image/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

산업단지구분당월(억원)전월(억원)2023누계(억원)증감률(전월대비)(퍼센트)
0서울국가11926.6395811743.0285880184.944081.563574
1녹산국가10942.4740511095.4506175271.02398-1.378732
2대구국가5640.8561045776.11362337955.22246-2.34167
3남동국가26828.9551828132.70639186847.4994-4.63429
4부평국가2755.7003693019.2393719532.61121-8.728655
5주안국가3268.0954453313.3462722587.92755-1.365714
6광주첨단국가5907.3431766256.15891741059.27204-5.575558
7빛그린국가146.800509160.4205091187.411561-8.490186
8온산국가48993.5055542197.37435354510.44316.105578
9울산ㆍ미포국가118441.1589118387.1051852061.52570.045658
산업단지구분당월(억원)전월(억원)2023누계(억원)증감률(전월대비)(퍼센트)
28여수국가66734.7206866065.0368472637.8821.013674
29구미국가38781.5285335071.15483263364.380710.57956
30구미(외)국가1348.111359.149425.7-0.811543
31포항국가18052.7493317580.0511125119.20992.688833
32포항블루밸리국가12.3629.8766.08-58.62069
33경남항공국가<NA><NA><NA><NA>
34밀양나노국가<NA><NA><NA><NA>
35안정국가258.56273.31852.22-5.393341
36진해국가<NA><NA><NA><NA>
37창원국가49807.5865952720.37374344273.9999-5.524974