Overview

Dataset statistics

Number of variables7
Number of observations38
Missing cells25
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory65.5 B

Variable types

Text1
Numeric6

Dataset

Description산업단지별, 공장규모별 가동업체수 및 가동률 현황등에 관한 국가산업단지 산업동향정보를 월별로 제공하고 있습니다.
Author한국산업단지공단
URLhttps://www.data.go.kr/data/15085889/fileData.do

Alerts

제조업가동업체수(개사) is highly overall correlated with 생산(백만원)_최대생산능력 and 1 other fieldsHigh correlation
생산(백만원)_최대생산능력 is highly overall correlated with 제조업가동업체수(개사) and 1 other fieldsHigh correlation
생산(백만원)_당월생산액 is highly overall correlated with 제조업가동업체수(개사) and 1 other fieldsHigh correlation
가동률(퍼센트)_당월 is highly overall correlated with 가동률(퍼센트)_전월High correlation
가동률(퍼센트)_전월 is highly overall correlated with 가동률(퍼센트)_당월High correlation
생산(백만원)_최대생산능력 has 5 (13.2%) missing valuesMissing
생산(백만원)_당월생산액 has 5 (13.2%) missing valuesMissing
가동률(퍼센트)_당월 has 5 (13.2%) missing valuesMissing
가동률(퍼센트)_전월 has 5 (13.2%) missing valuesMissing
가동률(퍼센트)_전월대비(퍼센트포인트) has 5 (13.2%) missing valuesMissing
산업단지 has unique valuesUnique
제조업가동업체수(개사) has 2 (5.3%) zerosZeros

Reproduction

Analysis started2024-04-06 08:29:40.047232
Analysis finished2024-04-06 08:29:49.798425
Duration9.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산업단지
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2024-04-06T17:29:50.153311image/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%
2024-04-06T17:29:50.905208image/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%

제조업가동업체수(개사)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1151.1842
Minimum0
Maximum10805
Zeros2
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-04-06T17:29:51.173465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.85
Q123.75
median162.5
Q3966.5
95-th percentile7566.85
Maximum10805
Range10805
Interquartile range (IQR)942.75

Descriptive statistics

Standard deviation2452.4421
Coefficient of variation (CV)2.1303646
Kurtosis8.109434
Mean1151.1842
Median Absolute Deviation (MAD)154
Skewness2.9058032
Sum43745
Variance6014472.2
MonotonicityNot monotonic
2024-04-06T17:29:51.411126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 2
 
5.3%
3740 1
 
2.6%
235 1
 
2.6%
166 1
 
2.6%
477 1
 
2.6%
226 1
 
2.6%
122 1
 
2.6%
285 1
 
2.6%
22 1
 
2.6%
2119 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
0 2
5.3%
1 1
2.6%
2 1
2.6%
3 1
2.6%
7 1
2.6%
10 1
2.6%
14 1
2.6%
22 1
2.6%
23 1
2.6%
26 1
2.6%
ValueCountFrequency (%)
10805 1
2.6%
8297 1
2.6%
7438 1
2.6%
3740 1
2.6%
2448 1
2.6%
2119 1
2.6%
1432 1
2.6%
1229 1
2.6%
1151 1
2.6%
1071 1
2.6%

생산(백만원)_최대생산능력
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean1970079
Minimum5914
Maximum13991747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-04-06T17:29:51.662693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5914
5-th percentile9856.4
Q1169244
median455785.88
Q31907668.9
95-th percentile7249270.9
Maximum13991747
Range13985833
Interquartile range (IQR)1738424.9

Descriptive statistics

Standard deviation3100384.5
Coefficient of variation (CV)1.5737361
Kurtosis6.4095364
Mean1970079
Median Absolute Deviation (MAD)435356.48
Skewness2.3810736
Sum65012608
Variance9.6123843 × 1012
MonotonicityNot monotonic
2024-04-06T17:29:51.999129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
20429.4 1
 
2.6%
30732.27789 1
 
2.6%
405891.8916 1
 
2.6%
370146.2469 1
 
2.6%
179511.9117 1
 
2.6%
1907668.941 1
 
2.6%
213390.1157 1
 
2.6%
8506703.111 1
 
2.6%
1438217.142 1
 
2.6%
5536233.491 1
 
2.6%
Other values (23) 23
60.5%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
5914.0 1
2.6%
5940.5 1
2.6%
12467.0 1
2.6%
20429.4 1
2.6%
21243.52071 1
2.6%
30732.27789 1
2.6%
43337.0 1
2.6%
119617.0822 1
2.6%
169244.0 1
2.6%
179511.9117 1
2.6%
ValueCountFrequency (%)
13991747.04 1
2.6%
8506703.111 1
2.6%
6410982.814 1
2.6%
6195108.91 1
2.6%
5536233.491 1
2.6%
3911672.133 1
2.6%
3724957.197 1
2.6%
3447688.852 1
2.6%
1907668.941 1
2.6%
1828470.308 1
2.6%

생산(백만원)_당월생산액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean1649142.2
Minimum4589.5
Maximum12661407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-04-06T17:29:52.386153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4589.5
5-th percentile5435
Q1125460
median346722.53
Q31745758.3
95-th percentile6495244.9
Maximum12661407
Range12656818
Interquartile range (IQR)1620298.3

Descriptive statistics

Standard deviation2734587.8
Coefficient of variation (CV)1.658188
Kurtosis7.844182
Mean1649142.2
Median Absolute Deviation (MAD)330735.48
Skewness2.6199349
Sum54421693
Variance7.4779707 × 1012
MonotonicityNot monotonic
2024-04-06T17:29:52.638637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
19114.8 1
 
2.6%
25516.64949 1
 
2.6%
353347.6266 1
 
2.6%
270950.8292 1
 
2.6%
134168.5919 1
 
2.6%
1745758.271 1
 
2.6%
171129.1863 1
 
2.6%
7604575.947 1
 
2.6%
1038490.981 1
 
2.6%
3527594.485 1
 
2.6%
Other values (23) 23
60.5%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
4589.5 1
2.6%
4682.0 1
2.6%
5937.0 1
2.6%
15987.05087 1
2.6%
18337.0 1
2.6%
19114.8 1
2.6%
25516.64949 1
2.6%
107400.4139 1
2.6%
125460.0 1
2.6%
134168.5919 1
2.6%
ValueCountFrequency (%)
12661407.27 1
2.6%
7604575.947 1
2.6%
5755690.922 1
2.6%
5228945.91 1
2.6%
3527594.485 1
2.6%
3113168.76 1
2.6%
3038146.953 1
2.6%
2803192.891 1
2.6%
1745758.271 1
2.6%
1526054.832 1
2.6%

가동률(퍼센트)_당월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean78.375173
Minimum42.312574
Maximum97.686772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-04-06T17:29:52.900095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.312574
5-th percentile56.152443
Q174.740774
median79.586649
Q387.054616
95-th percentile93.170273
Maximum97.686772
Range55.374198
Interquartile range (IQR)12.313842

Descriptive statistics

Standard deviation12.360043
Coefficient of variation (CV)0.15770355
Kurtosis1.7424764
Mean78.375173
Median Absolute Deviation (MAD)6.3856285
Skewness-1.1078124
Sum2586.3807
Variance152.77066
MonotonicityNot monotonic
2024-04-06T17:29:53.137111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
93.5651561 1
 
2.6%
83.02882585 1
 
2.6%
87.05461575 1
 
2.6%
73.20102027 1
 
2.6%
74.74077382 1
 
2.6%
91.51264321 1
 
2.6%
80.19546066 1
 
2.6%
89.39510229 1
 
2.6%
72.2068282 1
 
2.6%
63.71831122 1
 
2.6%
Other values (23) 23
60.5%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
42.31257355 1
2.6%
47.62172134 1
2.6%
61.83959157 1
2.6%
63.71831122 1
2.6%
64.89724344 1
2.6%
68.25343899 1
2.6%
72.2068282 1
2.6%
73.20102027 1
2.6%
74.74077382 1
2.6%
75.25612676 1
2.6%
ValueCountFrequency (%)
97.68677176 1
2.6%
93.5651561 1
2.6%
92.90701755 1
2.6%
92.75363115 1
2.6%
91.51264321 1
2.6%
90.49196812 1
2.6%
89.78685308 1
2.6%
89.39510229 1
2.6%
87.05461575 1
2.6%
84.72559592 1
2.6%

가동률(퍼센트)_전월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean79.960816
Minimum27.291717
Maximum99.625408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-04-06T17:29:53.407970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27.291717
5-th percentile69.362457
Q175.939204
median79.402212
Q389.457841
95-th percentile93.216481
Maximum99.625408
Range72.333691
Interquartile range (IQR)13.518637

Descriptive statistics

Standard deviation12.451428
Coefficient of variation (CV)0.15571912
Kurtosis9.3322369
Mean79.960816
Median Absolute Deviation (MAD)6.9139763
Skewness-2.2480588
Sum2638.7069
Variance155.03806
MonotonicityNot monotonic
2024-04-06T17:29:53.650418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
90.24566551 1
 
2.6%
79.40221192 1
 
2.6%
85.54775985 1
 
2.6%
77.7422809 1
 
2.6%
73.90601037 1
 
2.6%
91.37886476 1
 
2.6%
80.93305435 1
 
2.6%
87.97773414 1
 
2.6%
72.42035389 1
 
2.6%
69.24725774 1
 
2.6%
Other values (23) 23
60.5%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
27.29171688 1
2.6%
69.24725774 1
2.6%
69.4392567 1
2.6%
69.63784774 1
2.6%
70.64761584 1
2.6%
72.42035389 1
2.6%
72.48823566 1
2.6%
73.90601037 1
2.6%
75.93920386 1
2.6%
77.61142446 1
2.6%
ValueCountFrequency (%)
99.62540804 1
2.6%
93.48895169 1
2.6%
93.03483436 1
2.6%
92.96967024 1
2.6%
91.37886476 1
2.6%
91.1362921 1
2.6%
90.24566551 1
2.6%
89.89354281 1
2.6%
89.45784072 1
2.6%
87.97773414 1
2.6%

가동률(퍼센트)_전월대비(퍼센트포인트)
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean-1.585643
Minimum-57.312834
Maximum20.330004
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)44.7%
Memory size474.0 B
2024-04-06T17:29:53.899451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-57.312834
5-th percentile-13.966936
Q1-1.3188879
median-0.034239911
Q32.1299855
95-th percentile5.1800342
Maximum20.330004
Range77.642839
Interquartile range (IQR)3.4488734

Descriptive statistics

Standard deviation11.501168
Coefficient of variation (CV)-7.253315
Kurtosis18.097641
Mean-1.585643
Median Absolute Deviation (MAD)1.9920856
Skewness-3.6074235
Sum-52.326218
Variance132.27687
MonotonicityNot monotonic
2024-04-06T17:29:54.230478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
3.319490591 1
 
2.6%
3.626613937 1
 
2.6%
1.506855903 1
 
2.6%
-4.541260623 1
 
2.6%
0.834763453 1
 
2.6%
0.133778451 1
 
2.6%
-0.73759369 1
 
2.6%
1.417368146 1
 
2.6%
-0.213525688 1
 
2.6%
-5.52894652 1
 
2.6%
Other values (23) 23
60.5%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
-57.31283449 1
2.6%
-14.09961229 1
2.6%
-13.87848552 1
2.6%
-5.7503724 1
2.6%
-5.52894652 1
2.6%
-4.541260623 1
2.6%
-2.355297693 1
2.6%
-1.876130178 1
2.6%
-1.318887916 1
2.6%
-1.185817709 1
2.6%
ValueCountFrequency (%)
20.33000446 1
2.6%
6.65335527 1
2.6%
4.197820071 1
2.6%
3.626613937 1
2.6%
3.583127899 1
2.6%
3.319490591 1
2.6%
2.834068502 1
2.6%
2.68132021 1
2.6%
2.129985527 1
2.6%
1.957845737 1
2.6%

Interactions

2024-04-06T17:29:47.604134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:40.499317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:42.064860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:43.518783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:45.098444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:46.275868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:47.891736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:40.721572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:42.334401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:43.703406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:45.253898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:46.448824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:48.106545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:40.960042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:42.588853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:43.891936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:45.426440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:46.734607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:48.381637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:41.226009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:42.758343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:44.096605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:45.618065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:46.984062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:48.560971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:41.542669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:42.970560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:44.674998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:45.777428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:47.264051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:48.727710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:41.809316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:43.235918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:44.917185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:46.002582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:47.423945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:29:54.512002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산업단지제조업가동업체수(개사)생산(백만원)_최대생산능력생산(백만원)_당월생산액가동률(퍼센트)_당월가동률(퍼센트)_전월가동률(퍼센트)_전월대비(퍼센트포인트)
산업단지1.0001.0001.0001.0001.0001.0001.000
제조업가동업체수(개사)1.0001.0000.8020.4670.0000.0000.000
생산(백만원)_최대생산능력1.0000.8021.0000.9670.1140.0000.000
생산(백만원)_당월생산액1.0000.4670.9671.0000.0000.0000.000
가동률(퍼센트)_당월1.0000.0000.1140.0001.0000.8790.624
가동률(퍼센트)_전월1.0000.0000.0000.0000.8791.0000.881
가동률(퍼센트)_전월대비(퍼센트포인트)1.0000.0000.0000.0000.6240.8811.000
2024-04-06T17:29:54.974855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제조업가동업체수(개사)생산(백만원)_최대생산능력생산(백만원)_당월생산액가동률(퍼센트)_당월가동률(퍼센트)_전월가동률(퍼센트)_전월대비(퍼센트포인트)
제조업가동업체수(개사)1.0000.7280.720-0.012-0.2480.053
생산(백만원)_최대생산능력0.7281.0000.9940.2510.0180.073
생산(백만원)_당월생산액0.7200.9941.0000.3280.0590.136
가동률(퍼센트)_당월-0.0120.2510.3281.0000.7360.447
가동률(퍼센트)_전월-0.2480.0180.0590.7361.000-0.010
가동률(퍼센트)_전월대비(퍼센트포인트)0.0530.0730.1360.447-0.0101.000

Missing values

2024-04-06T17:29:48.968233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:29:49.394924image/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.
2024-04-06T17:29:49.642461image/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

산업단지제조업가동업체수(개사)생산(백만원)_최대생산능력생산(백만원)_당월생산액가동률(퍼센트)_당월가동률(퍼센트)_전월가동률(퍼센트)_전월대비(퍼센트포인트)
0서울37401501975.751207840.16880.41675678.458911.957846
1녹산12291438217.1421038490.98172.20682872.420354-0.213526
2대구159831654.044567632.485668.25343969.439257-1.185818
3남동74383447688.8522803192.89181.30643578.6251152.68132
4부평1432344701.6367272828.651479.14921878.3432020.806016
5주안1071455785.8754346722.530376.07136472.4882363.583128
6광주첨단653676889.426573498.599984.72559684.759836-0.03424
7빛그린4921243.5207115987.0508775.25612777.611424-2.355298
8온산2456195108.915755690.92292.90701893.034834-0.127817
9울산ㆍ미포62013991747.0412661407.2790.49196889.4578411.034127
산업단지제조업가동업체수(개사)생산(백만원)_최대생산능력생산(백만원)_당월생산액가동률(퍼센트)_당월가동률(퍼센트)_전월가동률(퍼센트)_전월대비(퍼센트포인트)
28여수2358506703.1117604575.94789.39510287.9777341.417368
29구미21195536233.4913527594.48563.71831169.247258-5.528947
30구미(외)23193321.0125460.064.89724370.647616-5.750372
31포항861645277.7241526054.83292.75363192.96967-0.216039
32포항블루밸리712467.05937.047.62172127.29171720.330004
33경남항공0<NA><NA><NA><NA><NA>
34밀양나노2<NA><NA><NA><NA><NA>
35안정10169244.0165329.097.68677293.4889524.19782
36진해3<NA><NA><NA><NA><NA>
37창원24486410982.8145228945.9181.56231478.7282452.834069