Overview

Dataset statistics

Number of variables9
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory84.1 B

Variable types

Numeric8
Categorical1

Dataset

Description해당 자료는 외국인촉진법에 근거하여 수집된 외국인직접투자 통계로, 한국에 대한 투자형태별(M&A, Greenfield) 외국인직접투자 실적자료입니다.
Author산업통상자원부
URLhttps://www.data.go.kr/data/15054399/fileData.do

Alerts

신고금액_미지정(백만불) has constant value ""Constant
연도 is highly overall correlated with 신고금액_그린필드(백만불) and 6 other fieldsHigh correlation
신고금액_그린필드(백만불) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
신고금액_인수합병(백만불) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
신고금액_전체(백만불) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
도착금액_그린필드(백만불) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
도착금액_인수합병(백만불) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
도착금액_미지정(백만불) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
도착금액_전체(백만불) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
연도 has unique valuesUnique
신고금액_그린필드(백만불) has unique valuesUnique
신고금액_전체(백만불) has unique valuesUnique
도착금액_전체(백만불) has unique valuesUnique
신고금액_인수합병(백만불) has 16 (37.2%) zerosZeros
도착금액_그린필드(백만불) has 6 (14.0%) zerosZeros
도착금액_인수합병(백만불) has 15 (34.9%) zerosZeros
도착금액_미지정(백만불) has 15 (34.9%) zerosZeros

Reproduction

Analysis started2023-12-11 23:30:40.244025
Analysis finished2023-12-11 23:30:46.469466
Duration6.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001
Minimum1980
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T08:30:46.558054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1982.1
Q11990.5
median2001
Q32011.5
95-th percentile2019.9
Maximum2022
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.556539
Coefficient of variation (CV)0.0062751318
Kurtosis-1.2
Mean2001
Median Absolute Deviation (MAD)11
Skewness0
Sum86043
Variance157.66667
MonotonicityStrictly increasing
2023-12-12T08:30:46.727308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1980 1
 
2.3%
1981 1
 
2.3%
2004 1
 
2.3%
2005 1
 
2.3%
2006 1
 
2.3%
2007 1
 
2.3%
2008 1
 
2.3%
2009 1
 
2.3%
2010 1
 
2.3%
2011 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1980 1
2.3%
1981 1
2.3%
1982 1
2.3%
1983 1
2.3%
1984 1
2.3%
1985 1
2.3%
1986 1
2.3%
1987 1
2.3%
1988 1
2.3%
1989 1
2.3%
ValueCountFrequency (%)
2022 1
2.3%
2021 1
2.3%
2020 1
2.3%
2019 1
2.3%
2018 1
2.3%
2017 1
2.3%
2016 1
2.3%
2015 1
2.3%
2014 1
2.3%
2013 1
2.3%

신고금액_그린필드(백만불)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7046.1163
Minimum143
Maximum22319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T08:30:46.887036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum143
5-th percentile197
Q11077
median6626
Q311380
95-th percentile17873.9
Maximum22319
Range22176
Interquartile range (IQR)10303

Descriptive statistics

Standard deviation6262.0762
Coefficient of variation (CV)0.88872735
Kurtosis-0.5908073
Mean7046.1163
Median Absolute Deviation (MAD)5536
Skewness0.63195324
Sum302983
Variance39213599
MonotonicityNot monotonic
2023-12-12T08:30:47.316720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
143 1
 
2.3%
153 1
 
2.3%
6626 1
 
2.3%
6298 1
 
2.3%
6938 1
 
2.3%
8033 1
 
2.3%
7281 1
 
2.3%
8109 1
 
2.3%
11058 1
 
2.3%
11702 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
143 1
2.3%
153 1
2.3%
189 1
2.3%
269 1
2.3%
358 1
2.3%
422 1
2.3%
532 1
2.3%
803 1
2.3%
895 1
2.3%
1045 1
2.3%
ValueCountFrequency (%)
22319 1
2.3%
20009 1
2.3%
18092 1
2.3%
15911 1
2.3%
15704 1
2.3%
15021 1
2.3%
14514 1
2.3%
14104 1
2.3%
12537 1
2.3%
12400 1
2.3%

신고금액_인수합병(백만불)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2991.6744
Minimum0
Maximum11421
Zeros16
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T08:30:47.456759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2483
Q35209
95-th percentile7924.6
Maximum11421
Range11421
Interquartile range (IQR)5209

Descriptive statistics

Standard deviation3102.7151
Coefficient of variation (CV)1.0371166
Kurtosis-0.46160664
Mean2991.6744
Median Absolute Deviation (MAD)2483
Skewness0.6804607
Sum128642
Variance9626841.3
MonotonicityNot monotonic
2023-12-12T08:30:47.613321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 16
37.2%
2015 1
 
2.3%
8135 1
 
2.3%
11421 1
 
2.3%
6232 1
 
2.3%
7417 1
 
2.3%
6891 1
 
2.3%
7244 1
 
2.3%
6274 1
 
2.3%
6804 1
 
2.3%
Other values (18) 18
41.9%
ValueCountFrequency (%)
0 16
37.2%
23 1
 
2.3%
700 1
 
2.3%
1971 1
 
2.3%
2015 1
 
2.3%
2085 1
 
2.3%
2483 1
 
2.3%
2649 1
 
2.3%
2865 1
 
2.3%
2943 1
 
2.3%
ValueCountFrequency (%)
11421 1
2.3%
8135 1
2.3%
7981 1
2.3%
7417 1
2.3%
7244 1
2.3%
6891 1
2.3%
6804 1
2.3%
6274 1
2.3%
6232 1
2.3%
6170 1
2.3%

신고금액_미지정(백만불)
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
0
43 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 43
100.0%

Length

2023-12-12T08:30:47.750903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:30:47.870461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 43
100.0%

신고금액_전체(백만불)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10037.791
Minimum143
Maximum30454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T08:30:47.997400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum143
5-th percentile197
Q11077
median10516
Q315405
95-th percentile26542.8
Maximum30454
Range30311
Interquartile range (IQR)14328

Descriptive statistics

Standard deviation9075.7834
Coefficient of variation (CV)0.90416145
Kurtosis-0.66934258
Mean10037.791
Median Absolute Deviation (MAD)9198
Skewness0.58494365
Sum431625
Variance82369844
MonotonicityNot monotonic
2023-12-12T08:30:48.178476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
143 1
 
2.3%
153 1
 
2.3%
12796 1
 
2.3%
11566 1
 
2.3%
11248 1
 
2.3%
10516 1
 
2.3%
11712 1
 
2.3%
11484 1
 
2.3%
13073 1
 
2.3%
13673 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
143 1
2.3%
153 1
2.3%
189 1
2.3%
269 1
2.3%
358 1
2.3%
422 1
2.3%
532 1
2.3%
803 1
2.3%
895 1
2.3%
1045 1
2.3%
ValueCountFrequency (%)
30454 1
2.3%
29513 1
2.3%
26900 1
2.3%
23328 1
2.3%
22948 1
2.3%
21295 1
2.3%
20908 1
2.3%
20746 1
2.3%
19000 1
2.3%
16286 1
2.3%

도착금액_그린필드(백만불)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3237.0233
Minimum0
Maximum13099
Zeros6
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T08:30:48.345831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median2163
Q35552
95-th percentile10177.8
Maximum13099
Range13099
Interquartile range (IQR)5547.5

Descriptive statistics

Standard deviation3619.2264
Coefficient of variation (CV)1.1180724
Kurtosis0.27848655
Mean3237.0233
Median Absolute Deviation (MAD)2161
Skewness0.96847928
Sum139192
Variance13098800
MonotonicityNot monotonic
2023-12-12T08:30:48.533083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 6
 
14.0%
4 2
 
4.7%
3 2
 
4.7%
13099 1
 
2.3%
10441 1
 
2.3%
5957 1
 
2.3%
6573 1
 
2.3%
12073 1
 
2.3%
7003 1
 
2.3%
5346 1
 
2.3%
Other values (26) 26
60.5%
ValueCountFrequency (%)
0 6
14.0%
2 1
 
2.3%
3 2
 
4.7%
4 2
 
4.7%
5 1
 
2.3%
11 1
 
2.3%
15 1
 
2.3%
46 1
 
2.3%
47 1
 
2.3%
49 1
 
2.3%
ValueCountFrequency (%)
13099 1
2.3%
12073 1
2.3%
10441 1
2.3%
7809 1
2.3%
7464 1
2.3%
7239 1
2.3%
7003 1
2.3%
6831 1
2.3%
6573 1
2.3%
5957 1
2.3%

도착금액_인수합병(백만불)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2280.4186
Minimum0
Maximum8862
Zeros15
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T08:30:48.691419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1161
Q34130
95-th percentile6800.9
Maximum8862
Range8862
Interquartile range (IQR)4130

Descriptive statistics

Standard deviation2623.4798
Coefficient of variation (CV)1.1504378
Kurtosis-0.33543866
Mean2280.4186
Median Absolute Deviation (MAD)1161
Skewness0.88111384
Sum98058
Variance6882646.1
MonotonicityNot monotonic
2023-12-12T08:30:48.826900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 15
34.9%
25 1
 
2.3%
5217 1
 
2.3%
8159 1
 
2.3%
5149 1
 
2.3%
6800 1
 
2.3%
5223 1
 
2.3%
6801 1
 
2.3%
3381 1
 
2.3%
8862 1
 
2.3%
Other values (19) 19
44.2%
ValueCountFrequency (%)
0 15
34.9%
3 1
 
2.3%
6 1
 
2.3%
25 1
 
2.3%
189 1
 
2.3%
1049 1
 
2.3%
1071 1
 
2.3%
1161 1
 
2.3%
1502 1
 
2.3%
1901 1
 
2.3%
ValueCountFrequency (%)
8862 1
2.3%
8159 1
2.3%
6801 1
2.3%
6800 1
2.3%
5529 1
2.3%
5515 1
2.3%
5480 1
2.3%
5223 1
2.3%
5217 1
2.3%
5149 1
2.3%

도착금액_미지정(백만불)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean809.51163
Minimum0
Maximum8356
Zeros15
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T08:30:48.996032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median131
Q3813.5
95-th percentile4147
Maximum8356
Range8356
Interquartile range (IQR)813.5

Descriptive statistics

Standard deviation1641.593
Coefficient of variation (CV)2.0278807
Kurtosis11.419782
Mean809.51163
Median Absolute Deviation (MAD)131
Skewness3.2222668
Sum34809
Variance2694827.6
MonotonicityNot monotonic
2023-12-12T08:30:49.147892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 15
34.9%
1 2
 
4.7%
131 1
 
2.3%
1340 1
 
2.3%
7 1
 
2.3%
6 1
 
2.3%
173 1
 
2.3%
816 1
 
2.3%
757 1
 
2.3%
5407 1
 
2.3%
Other values (18) 18
41.9%
ValueCountFrequency (%)
0 15
34.9%
1 2
 
4.7%
6 1
 
2.3%
7 1
 
2.3%
123 1
 
2.3%
129 1
 
2.3%
131 1
 
2.3%
152 1
 
2.3%
173 1
 
2.3%
193 1
 
2.3%
ValueCountFrequency (%)
8356 1
2.3%
5407 1
2.3%
4270 1
2.3%
3040 1
2.3%
2279 1
2.3%
1340 1
2.3%
1177 1
2.3%
992 1
2.3%
895 1
2.3%
891 1
2.3%

도착금액_전체(백만불)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6326.9535
Minimum123
Maximum18600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T08:30:49.349552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123
5-th percentile133.1
Q1873.5
median5348
Q310504
95-th percentile17233.5
Maximum18600
Range18477
Interquartile range (IQR)9630.5

Descriptive statistics

Standard deviation5716.8614
Coefficient of variation (CV)0.90357253
Kurtosis-0.74278166
Mean6326.9535
Median Absolute Deviation (MAD)4541
Skewness0.59264429
Sum272059
Variance32682504
MonotonicityNot monotonic
2023-12-12T08:30:49.535529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
131 1
 
2.3%
152 1
 
2.3%
9246 1
 
2.3%
9623 1
 
2.3%
9128 1
 
2.3%
7877 1
 
2.3%
8405 1
 
2.3%
6759 1
 
2.3%
5450 1
 
2.3%
6655 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
123 1
2.3%
129 1
2.3%
131 1
2.3%
152 1
2.3%
196 1
2.3%
236 1
2.3%
482 1
2.3%
625 1
2.3%
736 1
2.3%
813 1
2.3%
ValueCountFrequency (%)
18600 1
2.3%
18316 1
2.3%
17296 1
2.3%
16671 1
2.3%
13804 1
2.3%
13373 1
2.3%
12346 1
2.3%
11106 1
2.3%
11031 1
2.3%
10845 1
2.3%

Interactions

2023-12-12T08:30:45.574210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:40.506513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:41.259480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.301422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.005144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.764843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.458036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.018522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.642769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:40.600133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:41.344017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.398571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.089385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.873874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.524346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.091661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.712828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:40.702971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:41.731250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.478497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.180057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.949037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.588683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.156103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.787999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:40.823899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:41.810988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.552670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.274233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.052518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.654639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.226110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.874279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:40.923768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:41.902504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.631037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.379360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.137286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.729118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.299310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.963909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:41.013989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:41.997242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.735881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.485830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.235488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.799563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.368208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:46.039414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:41.087712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.091087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.818800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.572575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.306634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.865792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.434454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:46.120090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:41.163930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.185792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:42.914078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:43.649382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.378711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:44.934670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:45.497496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:30:49.679213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도신고금액_그린필드(백만불)신고금액_인수합병(백만불)신고금액_전체(백만불)도착금액_그린필드(백만불)도착금액_인수합병(백만불)도착금액_미지정(백만불)도착금액_전체(백만불)
연도1.0000.8650.6900.8920.7120.6510.2950.834
신고금액_그린필드(백만불)0.8651.0000.8760.9710.7800.8290.5340.947
신고금액_인수합병(백만불)0.6900.8761.0000.8310.8360.9170.0000.798
신고금액_전체(백만불)0.8920.9710.8311.0000.8320.7800.7230.951
도착금액_그린필드(백만불)0.7120.7800.8360.8321.0000.8070.0000.787
도착금액_인수합병(백만불)0.6510.8290.9170.7800.8071.0000.0000.837
도착금액_미지정(백만불)0.2950.5340.0000.7230.0000.0001.0000.234
도착금액_전체(백만불)0.8340.9470.7980.9510.7870.8370.2341.000
2023-12-12T08:30:49.841466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도신고금액_그린필드(백만불)신고금액_인수합병(백만불)신고금액_전체(백만불)도착금액_그린필드(백만불)도착금액_인수합병(백만불)도착금액_미지정(백만불)도착금액_전체(백만불)
연도1.0000.9560.9020.9580.9720.920-0.6710.945
신고금액_그린필드(백만불)0.9561.0000.8780.9870.9310.866-0.5370.953
신고금액_인수합병(백만불)0.9020.8781.0000.9220.9040.951-0.6020.945
신고금액_전체(백만불)0.9580.9870.9221.0000.9390.902-0.5440.979
도착금액_그린필드(백만불)0.9720.9310.9040.9391.0000.925-0.6740.949
도착금액_인수합병(백만불)0.9200.8660.9510.9020.9251.000-0.6870.927
도착금액_미지정(백만불)-0.671-0.537-0.602-0.544-0.674-0.6871.000-0.537
도착금액_전체(백만불)0.9450.9530.9450.9790.9490.927-0.5371.000

Missing values

2023-12-12T08:30:46.250984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:30:46.400820image/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

연도신고금액_그린필드(백만불)신고금액_인수합병(백만불)신고금액_미지정(백만불)신고금액_전체(백만불)도착금액_그린필드(백만불)도착금액_인수합병(백만불)도착금액_미지정(백만불)도착금액_전체(백만불)
019801430014300131131
119811530015300152152
219821890018900129129
319832690026900123123
419844220042230193196
519855320053200236236
619863580035850477482
71987106400106400625625
81988128400128440891895
91989109000109020811813
연도신고금액_그린필드(백만불)신고금액_인수합병(백만불)신고금액_미지정(백만불)신고금액_전체(백만불)도착금액_그린필드(백만불)도착금액_인수합병(백만불)도착금액_미지정(백만불)도착금액_전체(백만불)
332013956749790145465758413109889
34201411019798101900068315515012346
35201514104680402090878098862016671
36201615021627402129574643381010845
37201715704724402294870036801013804
382018200096891026900120735223017296
39201915911741702332865736800013373
40202014514623202074659575149011106
4120211809211421029513104418159018600
422022223198135030454130995217018316