Overview

Dataset statistics

Number of variables16
Number of observations77
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory141.7 B

Variable types

Categorical3
Text1
Numeric12

Dataset

Description인천광역시 주요관광지점 입장객 현황에 데이터로 시도명, 군구명, 관광지명, 내외국인 구분, 월별 입장객수를 제공합니다.
URLhttps://www.data.go.kr/data/15119916/fileData.do

Alerts

시도 has constant value ""Constant
2022-01 is highly overall correlated with 2022-02 and 10 other fieldsHigh correlation
2022-02 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-03 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-04 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-05 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-06 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-07 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-08 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-09 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-10 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-11 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-12 is highly overall correlated with 2022-01 and 10 other fieldsHigh correlation
2022-01 has 21 (27.3%) zerosZeros
2022-02 has 21 (27.3%) zerosZeros
2022-03 has 21 (27.3%) zerosZeros
2022-04 has 17 (22.1%) zerosZeros
2022-05 has 14 (18.2%) zerosZeros
2022-06 has 15 (19.5%) zerosZeros
2022-07 has 15 (19.5%) zerosZeros
2022-08 has 14 (18.2%) zerosZeros
2022-09 has 14 (18.2%) zerosZeros
2022-10 has 15 (19.5%) zerosZeros
2022-11 has 15 (19.5%) zerosZeros
2022-12 has 14 (18.2%) zerosZeros

Reproduction

Analysis started2023-12-12 07:02:13.293349
Analysis finished2023-12-12 07:02:28.925285
Duration15.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size748.0 B
인천광역시
77 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 77
100.0%

Length

2023-12-12T16:02:28.999492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:02:29.105812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 77
100.0%

군구
Categorical

Distinct10
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size748.0 B
강화군
22 
중구
19 
옹진군
연수구
부평구
Other values (5)
14 

Length

Max length4
Median length3
Mean length2.6753247
Min length2

Unique

Unique2 ?
Unique (%)2.6%

Sample

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

Common Values

ValueCountFrequency (%)
강화군 22
28.6%
중구 19
24.7%
옹진군 8
 
10.4%
연수구 8
 
10.4%
부평구 6
 
7.8%
남동구 5
 
6.5%
동구 4
 
5.2%
서구 3
 
3.9%
미추홀구 1
 
1.3%
계양구 1
 
1.3%

Length

2023-12-12T16:02:29.255099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:02:29.392372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강화군 22
28.6%
중구 19
24.7%
옹진군 8
 
10.4%
연수구 8
 
10.4%
부평구 6
 
7.8%
남동구 5
 
6.5%
동구 4
 
5.2%
서구 3
 
3.9%
미추홀구 1
 
1.3%
계양구 1
 
1.3%
Distinct55
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size748.0 B
2023-12-12T16:02:29.667670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length6.9350649
Min length3

Characters and Unicode

Total characters534
Distinct characters151
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)42.9%

Sample

1st row강화 농경문화관
2nd row강화갯벌센터
3rd row강화갯벌센터
4th row강화나들길(1코스~20코스)
5th row강화석모도 미네랄 온천
ValueCountFrequency (%)
유람선 4
 
4.3%
수도국산달동네박물관 2
 
2.2%
소래역사관 2
 
2.2%
인천도시역사관 2
 
2.2%
근대건축전시관 2
 
2.2%
부평아트센터 2
 
2.2%
강화갯벌센터 2
 
2.2%
월미도 2
 
2.2%
영흥에너지파크 2
 
2.2%
가천박물관 2
 
2.2%
Other values (57) 71
76.3%
2023-12-12T16:02:30.071383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43
 
8.1%
24
 
4.5%
17
 
3.2%
17
 
3.2%
17
 
3.2%
16
 
3.0%
15
 
2.8%
15
 
2.8%
11
 
2.1%
11
 
2.1%
Other values (141) 348
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 509
95.3%
Space Separator 17
 
3.2%
Decimal Number 5
 
0.9%
Open Punctuation 1
 
0.2%
Math Symbol 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
43
 
8.4%
24
 
4.7%
17
 
3.3%
17
 
3.3%
16
 
3.1%
15
 
2.9%
15
 
2.9%
11
 
2.2%
11
 
2.2%
10
 
2.0%
Other values (133) 330
64.8%
Decimal Number
ValueCountFrequency (%)
5 2
40.0%
1 1
20.0%
2 1
20.0%
0 1
20.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 509
95.3%
Common 25
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
43
 
8.4%
24
 
4.7%
17
 
3.3%
17
 
3.3%
16
 
3.1%
15
 
2.9%
15
 
2.9%
11
 
2.2%
11
 
2.2%
10
 
2.0%
Other values (133) 330
64.8%
Common
ValueCountFrequency (%)
17
68.0%
5 2
 
8.0%
( 1
 
4.0%
1 1
 
4.0%
~ 1
 
4.0%
2 1
 
4.0%
0 1
 
4.0%
) 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 509
95.3%
ASCII 25
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
43
 
8.4%
24
 
4.7%
17
 
3.3%
17
 
3.3%
16
 
3.1%
15
 
2.9%
15
 
2.9%
11
 
2.2%
11
 
2.2%
10
 
2.0%
Other values (133) 330
64.8%
ASCII
ValueCountFrequency (%)
17
68.0%
5 2
 
8.0%
( 1
 
4.0%
1 1
 
4.0%
~ 1
 
4.0%
2 1
 
4.0%
0 1
 
4.0%
) 1
 
4.0%

내외국인
Categorical

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size748.0 B
내국인
55 
외국인
22 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row내국인
2nd row내국인
3rd row외국인
4th row내국인
5th row내국인

Common Values

ValueCountFrequency (%)
내국인 55
71.4%
외국인 22
 
28.6%

Length

2023-12-12T16:02:30.229288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:02:30.330936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내국인 55
71.4%
외국인 22
 
28.6%

2022-01
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3684.0649
Minimum0
Maximum45590
Zeros21
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:30.441283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median823
Q33175
95-th percentile17483.2
Maximum45590
Range45590
Interquartile range (IQR)3175

Descriptive statistics

Standard deviation7930.5912
Coefficient of variation (CV)2.1526741
Kurtosis13.871648
Mean3684.0649
Median Absolute Deviation (MAD)823
Skewness3.5259527
Sum283673
Variance62894277
MonotonicityNot monotonic
2023-12-12T16:02:30.563721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
27.3%
84 1
 
1.3%
22 1
 
1.3%
4473 1
 
1.3%
69 1
 
1.3%
1998 1
 
1.3%
13 1
 
1.3%
3654 1
 
1.3%
325 1
 
1.3%
410 1
 
1.3%
Other values (47) 47
61.0%
ValueCountFrequency (%)
0 21
27.3%
1 1
 
1.3%
3 1
 
1.3%
10 1
 
1.3%
13 1
 
1.3%
22 1
 
1.3%
28 1
 
1.3%
69 1
 
1.3%
84 1
 
1.3%
136 1
 
1.3%
ValueCountFrequency (%)
45590 1
1.3%
37217 1
1.3%
25088 1
1.3%
20588 1
1.3%
16707 1
1.3%
15837 1
1.3%
15732 1
1.3%
9427 1
1.3%
9049 1
1.3%
8160 1
1.3%

2022-02
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3392.6364
Minimum0
Maximum39625
Zeros21
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:30.698850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median574
Q32587
95-th percentile13418.4
Maximum39625
Range39625
Interquartile range (IQR)2587

Descriptive statistics

Standard deviation7302.8654
Coefficient of variation (CV)2.1525635
Kurtosis13.991318
Mean3392.6364
Median Absolute Deviation (MAD)574
Skewness3.5627775
Sum261233
Variance53331842
MonotonicityNot monotonic
2023-12-12T16:02:30.833708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
27.3%
158 1
 
1.3%
23 1
 
1.3%
4119 1
 
1.3%
34 1
 
1.3%
2329 1
 
1.3%
17 1
 
1.3%
4051 1
 
1.3%
404 1
 
1.3%
388 1
 
1.3%
Other values (47) 47
61.0%
ValueCountFrequency (%)
0 21
27.3%
3 1
 
1.3%
6 1
 
1.3%
17 1
 
1.3%
23 1
 
1.3%
34 1
 
1.3%
48 1
 
1.3%
55 1
 
1.3%
67 1
 
1.3%
81 1
 
1.3%
ValueCountFrequency (%)
39625 1
1.3%
37729 1
1.3%
25853 1
1.3%
13484 1
1.3%
13402 1
1.3%
13174 1
1.3%
13019 1
1.3%
11373 1
1.3%
10538 1
1.3%
7698 1
1.3%

2022-03
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3829.0909
Minimum0
Maximum48535
Zeros21
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:31.268030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median685
Q33261
95-th percentile19836.8
Maximum48535
Range48535
Interquartile range (IQR)3261

Descriptive statistics

Standard deviation8969.6725
Coefficient of variation (CV)2.3425071
Kurtosis14.393994
Mean3829.0909
Median Absolute Deviation (MAD)685
Skewness3.7121935
Sum294840
Variance80455025
MonotonicityNot monotonic
2023-12-12T16:02:31.432389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
27.3%
107 1
 
1.3%
48535 1
 
1.3%
2446 1
 
1.3%
10 1
 
1.3%
3261 1
 
1.3%
273 1
 
1.3%
142 1
 
1.3%
310 1
 
1.3%
1816 1
 
1.3%
Other values (47) 47
61.0%
ValueCountFrequency (%)
0 21
27.3%
2 1
 
1.3%
10 1
 
1.3%
11 1
 
1.3%
12 1
 
1.3%
15 1
 
1.3%
40 1
 
1.3%
71 1
 
1.3%
107 1
 
1.3%
142 1
 
1.3%
ValueCountFrequency (%)
48535 1
1.3%
45426 1
1.3%
31273 1
1.3%
28116 1
1.3%
17767 1
1.3%
13226 1
1.3%
12138 1
1.3%
7895 1
1.3%
7749 1
1.3%
6325 1
1.3%

2022-04
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6560.6883
Minimum0
Maximum69426
Zeros17
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:31.570176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median1800
Q36179
95-th percentile41250.4
Maximum69426
Range69426
Interquartile range (IQR)6170

Descriptive statistics

Standard deviation13584.074
Coefficient of variation (CV)2.0705258
Kurtosis10.782363
Mean6560.6883
Median Absolute Deviation (MAD)1800
Skewness3.2556095
Sum505173
Variance1.8452707 × 108
MonotonicityNot monotonic
2023-12-12T16:02:31.714202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
22.1%
171 1
 
1.3%
132 1
 
1.3%
6179 1
 
1.3%
43 1
 
1.3%
4586 1
 
1.3%
50 1
 
1.3%
4404 1
 
1.3%
347 1
 
1.3%
402 1
 
1.3%
Other values (51) 51
66.2%
ValueCountFrequency (%)
0 17
22.1%
1 1
 
1.3%
3 1
 
1.3%
9 1
 
1.3%
43 1
 
1.3%
50 1
 
1.3%
132 1
 
1.3%
146 1
 
1.3%
161 1
 
1.3%
171 1
 
1.3%
ValueCountFrequency (%)
69426 1
1.3%
63972 1
1.3%
47411 1
1.3%
46680 1
1.3%
39893 1
1.3%
22271 1
1.3%
17440 1
1.3%
17402 1
1.3%
13092 1
1.3%
11947 1
1.3%

2022-05
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9039.6364
Minimum0
Maximum111958
Zeros14
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:31.854142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148
median2579
Q39659
95-th percentile47324.2
Maximum111958
Range111958
Interquartile range (IQR)9611

Descriptive statistics

Standard deviation17540.392
Coefficient of variation (CV)1.9403869
Kurtosis16.210808
Mean9039.6364
Median Absolute Deviation (MAD)2579
Skewness3.618825
Sum696052
Variance3.0766535 × 108
MonotonicityNot monotonic
2023-12-12T16:02:31.988222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
18.2%
397 1
 
1.3%
444 1
 
1.3%
13607 1
 
1.3%
904 1
 
1.3%
2 1
 
1.3%
5574 1
 
1.3%
1282 1
 
1.3%
348 1
 
1.3%
5830 1
 
1.3%
Other values (54) 54
70.1%
ValueCountFrequency (%)
0 14
18.2%
2 1
 
1.3%
10 1
 
1.3%
12 1
 
1.3%
17 1
 
1.3%
34 1
 
1.3%
48 1
 
1.3%
53 1
 
1.3%
162 1
 
1.3%
261 1
 
1.3%
ValueCountFrequency (%)
111958 1
1.3%
58232 1
1.3%
51406 1
1.3%
50413 1
1.3%
46552 1
1.3%
46440 1
1.3%
27765 1
1.3%
18462 1
1.3%
18430 1
1.3%
18139 1
1.3%

2022-06
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6768.2468
Minimum0
Maximum52399
Zeros15
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:32.121404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138
median2388
Q37842
95-th percentile39468
Maximum52399
Range52399
Interquartile range (IQR)7804

Descriptive statistics

Standard deviation11423.906
Coefficient of variation (CV)1.6878678
Kurtosis6.2039849
Mean6768.2468
Median Absolute Deviation (MAD)2388
Skewness2.5362825
Sum521155
Variance1.3050562 × 108
MonotonicityNot monotonic
2023-12-12T16:02:32.271426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
19.5%
239 1
 
1.3%
6990 1
 
1.3%
7842 1
 
1.3%
119 1
 
1.3%
5423 1
 
1.3%
38 1
 
1.3%
5512 1
 
1.3%
658 1
 
1.3%
371 1
 
1.3%
Other values (53) 53
68.8%
ValueCountFrequency (%)
0 15
19.5%
3 1
 
1.3%
4 1
 
1.3%
13 1
 
1.3%
16 1
 
1.3%
38 1
 
1.3%
57 1
 
1.3%
119 1
 
1.3%
234 1
 
1.3%
239 1
 
1.3%
ValueCountFrequency (%)
52399 1
1.3%
44110 1
1.3%
44081 1
1.3%
42656 1
1.3%
38671 1
1.3%
23749 1
1.3%
20726 1
1.3%
18524 1
1.3%
18304 1
1.3%
16846 1
1.3%

2022-07
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6200.1948
Minimum0
Maximum53629
Zeros15
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:32.437198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1121
median2335
Q37600
95-th percentile27940
Maximum53629
Range53629
Interquartile range (IQR)7479

Descriptive statistics

Standard deviation10113.518
Coefficient of variation (CV)1.6311613
Kurtosis7.759209
Mean6200.1948
Median Absolute Deviation (MAD)2335
Skewness2.6537935
Sum477415
Variance1.0228324 × 108
MonotonicityNot monotonic
2023-12-12T16:02:32.589748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
19.5%
5 2
 
2.6%
42 2
 
2.6%
2728 1
 
1.3%
1454 1
 
1.3%
10313 1
 
1.3%
14011 1
 
1.3%
7289 1
 
1.3%
447 1
 
1.3%
4376 1
 
1.3%
Other values (51) 51
66.2%
ValueCountFrequency (%)
0 15
19.5%
5 2
 
2.6%
42 2
 
2.6%
121 1
 
1.3%
159 1
 
1.3%
258 1
 
1.3%
265 1
 
1.3%
297 1
 
1.3%
320 1
 
1.3%
324 1
 
1.3%
ValueCountFrequency (%)
53629 1
1.3%
38832 1
1.3%
37229 1
1.3%
31268 1
1.3%
27108 1
1.3%
25153 1
1.3%
23332 1
1.3%
17053 1
1.3%
15072 1
1.3%
14011 1
1.3%

2022-08
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6974.8831
Minimum0
Maximum70148
Zeros14
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:32.725055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q134
median2370
Q39251
95-th percentile30871
Maximum70148
Range70148
Interquartile range (IQR)9217

Descriptive statistics

Standard deviation12059.996
Coefficient of variation (CV)1.7290607
Kurtosis11.818459
Mean6974.8831
Median Absolute Deviation (MAD)2370
Skewness3.1408099
Sum537066
Variance1.4544351 × 108
MonotonicityNot monotonic
2023-12-12T16:02:32.883559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
18.2%
752 2
 
2.6%
282 1
 
1.3%
5393 1
 
1.3%
17531 1
 
1.3%
9251 1
 
1.3%
452 1
 
1.3%
4800 1
 
1.3%
34 1
 
1.3%
6360 1
 
1.3%
Other values (53) 53
68.8%
ValueCountFrequency (%)
0 14
18.2%
1 1
 
1.3%
3 1
 
1.3%
18 1
 
1.3%
24 1
 
1.3%
26 1
 
1.3%
34 1
 
1.3%
160 1
 
1.3%
273 1
 
1.3%
282 1
 
1.3%
ValueCountFrequency (%)
70148 1
1.3%
52521 1
1.3%
36835 1
1.3%
35883 1
1.3%
29618 1
1.3%
24018 1
1.3%
18086 1
1.3%
17531 1
1.3%
17048 1
1.3%
15802 1
1.3%

2022-09
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7795.8831
Minimum0
Maximum120865
Zeros14
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:33.130652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1120
median2419
Q37770
95-th percentile26960.2
Maximum120865
Range120865
Interquartile range (IQR)7650

Descriptive statistics

Standard deviation15993.992
Coefficient of variation (CV)2.0515946
Kurtosis33.244497
Mean7795.8831
Median Absolute Deviation (MAD)2419
Skewness5.1080667
Sum600283
Variance2.5580777 × 108
MonotonicityNot monotonic
2023-12-12T16:02:33.323187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
18.2%
163 1
 
1.3%
631 1
 
1.3%
12137 1
 
1.3%
686 1
 
1.3%
26 1
 
1.3%
2388 1
 
1.3%
1314 1
 
1.3%
327 1
 
1.3%
6000 1
 
1.3%
Other values (54) 54
70.1%
ValueCountFrequency (%)
0 14
18.2%
3 1
 
1.3%
26 1
 
1.3%
58 1
 
1.3%
59 1
 
1.3%
69 1
 
1.3%
120 1
 
1.3%
163 1
 
1.3%
166 1
 
1.3%
211 1
 
1.3%
ValueCountFrequency (%)
120865 1
1.3%
44951 1
1.3%
38199 1
1.3%
35237 1
1.3%
24891 1
1.3%
23500 1
1.3%
21407 1
1.3%
18717 1
1.3%
18442 1
1.3%
18437 1
1.3%

2022-10
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9266.8052
Minimum0
Maximum86049
Zeros15
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:33.470801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1136
median4035
Q311038
95-th percentile38078
Maximum86049
Range86049
Interquartile range (IQR)10902

Descriptive statistics

Standard deviation14806.983
Coefficient of variation (CV)1.597852
Kurtosis9.918269
Mean9266.8052
Median Absolute Deviation (MAD)4035
Skewness2.8197775
Sum713544
Variance2.1924674 × 108
MonotonicityNot monotonic
2023-12-12T16:02:33.607705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
19.5%
4035 2
 
2.6%
20 2
 
2.6%
298 1
 
1.3%
5957 1
 
1.3%
13622 1
 
1.3%
943 1
 
1.3%
8424 1
 
1.3%
101 1
 
1.3%
6629 1
 
1.3%
Other values (51) 51
66.2%
ValueCountFrequency (%)
0 15
19.5%
6 1
 
1.3%
20 2
 
2.6%
101 1
 
1.3%
136 1
 
1.3%
156 1
 
1.3%
298 1
 
1.3%
314 1
 
1.3%
404 1
 
1.3%
603 1
 
1.3%
ValueCountFrequency (%)
86049 1
1.3%
55381 1
1.3%
46449 1
1.3%
40050 1
1.3%
37585 1
1.3%
36747 1
1.3%
29114 1
1.3%
29006 1
1.3%
28222 1
1.3%
22680 1
1.3%

2022-11
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6657.2727
Minimum0
Maximum82046
Zeros15
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:33.749349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1162
median2627
Q37193
95-th percentile24259.4
Maximum82046
Range82046
Interquartile range (IQR)7031

Descriptive statistics

Standard deviation12063.356
Coefficient of variation (CV)1.8120567
Kurtosis20.665164
Mean6657.2727
Median Absolute Deviation (MAD)2627
Skewness3.9817854
Sum512610
Variance1.4552455 × 108
MonotonicityNot monotonic
2023-12-12T16:02:33.895062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
19.5%
250 1
 
1.3%
5397 1
 
1.3%
3323 1
 
1.3%
9254 1
 
1.3%
1774 1
 
1.3%
4941 1
 
1.3%
122 1
 
1.3%
5525 1
 
1.3%
743 1
 
1.3%
Other values (53) 53
68.8%
ValueCountFrequency (%)
0 15
19.5%
8 1
 
1.3%
32 1
 
1.3%
52 1
 
1.3%
122 1
 
1.3%
162 1
 
1.3%
173 1
 
1.3%
181 1
 
1.3%
250 1
 
1.3%
384 1
 
1.3%
ValueCountFrequency (%)
82046 1
1.3%
45336 1
1.3%
31812 1
1.3%
25433 1
1.3%
23966 1
1.3%
21446 1
1.3%
20980 1
1.3%
19874 1
1.3%
19682 1
1.3%
15781 1
1.3%

2022-12
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4464.7403
Minimum0
Maximum48950
Zeros14
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size825.0 B
2023-12-12T16:02:34.033452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142
median1784
Q35823
95-th percentile20129.2
Maximum48950
Range48950
Interquartile range (IQR)5781

Descriptive statistics

Standard deviation7929.3277
Coefficient of variation (CV)1.7759886
Kurtosis13.87165
Mean4464.7403
Median Absolute Deviation (MAD)1758
Skewness3.3508545
Sum343785
Variance62874238
MonotonicityNot monotonic
2023-12-12T16:02:34.208532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
18.2%
26 2
 
2.6%
114 1
 
1.3%
216 1
 
1.3%
10749 1
 
1.3%
665 1
 
1.3%
52 1
 
1.3%
1435 1
 
1.3%
767 1
 
1.3%
4605 1
 
1.3%
Other values (53) 53
68.8%
ValueCountFrequency (%)
0 14
18.2%
7 1
 
1.3%
13 1
 
1.3%
26 2
 
2.6%
34 1
 
1.3%
42 1
 
1.3%
52 1
 
1.3%
63 1
 
1.3%
114 1
 
1.3%
155 1
 
1.3%
ValueCountFrequency (%)
48950 1
1.3%
28942 1
1.3%
25047 1
1.3%
24866 1
1.3%
18945 1
1.3%
15873 1
1.3%
14024 1
1.3%
13221 1
1.3%
10749 1
1.3%
9213 1
1.3%

Interactions

2023-12-12T16:02:27.401838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:13.918394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.971898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.382250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.504148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:19.064796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.349867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.551888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.665091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.637526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.115215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.232777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:27.498167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:13.986671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:15.073776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.479032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.579606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:19.180282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.435486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.637121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.742778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.706012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.182747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.327568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:27.602933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.064463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:15.203938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.587337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.681288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:19.304111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.546120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.753087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.830163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.792289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.275365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.422649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:27.702445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.144334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:15.321322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.672220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.786964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:19.420122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.642491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.861102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.907313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.874117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.357513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.509407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:27.811751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.233310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:15.436983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.752949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.886800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:19.534833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.782228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.951171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.991512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.997372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.450891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.625127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:27.912689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.324939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:15.527199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.859373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:18.332361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:19.649163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.903175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.056476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.081016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:24.114281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.529663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.731559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:28.002786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.406676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:15.635913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.953099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:18.446802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:19.739675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.988398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.142637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.160935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:24.208433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.615556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.821946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:28.089870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.482235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:15.767994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.037195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:18.570167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:19.839659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.096395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.232002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.252757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:24.300406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.719139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.922977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:28.187266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.571267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:15.917283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.125070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:18.669434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:19.955192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.205830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.337800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.337537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:24.390950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.831718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:27.029330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:28.288481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.660973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.037284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.215407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:18.780132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.063678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.294303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.418766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.414840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:24.844812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.957780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:27.139504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:28.375403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.752665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.144168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.341245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:18.872515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.161661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.386292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.497133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.490729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:24.924227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.046932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:27.233875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:28.464813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:14.861029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:16.263424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:17.416970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:18.974813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:20.261686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:21.462532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:22.578462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:23.562581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:25.008981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:26.142628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:27.315544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:02:34.325679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
군구관광지내외국인2022-012022-022022-032022-042022-052022-062022-072022-082022-092022-102022-112022-12
군구1.0001.0000.0000.0000.0000.3510.0000.0000.0000.1650.5310.0000.0000.0000.115
관광지1.0001.0000.0000.9520.7710.9090.9500.9720.8650.9720.9710.9710.9020.9220.919
내외국인0.0000.0001.0000.1070.3160.0560.1830.3160.4680.3340.3670.1930.3670.3690.394
2022-010.0000.9520.1071.0000.9640.9520.8820.8750.9430.8100.9480.8590.9740.9050.787
2022-020.0000.7710.3160.9641.0000.9240.8450.8940.8110.8390.8130.7320.9050.9370.896
2022-030.3510.9090.0560.9520.9241.0000.9890.8180.8470.8390.8690.8000.8740.8180.812
2022-040.0000.9500.1830.8820.8450.9891.0000.8670.8590.8540.8630.7760.8520.8020.824
2022-050.0000.9720.3160.8750.8940.8180.8671.0000.9310.9350.9470.8190.8920.9780.951
2022-060.0000.8650.4680.9430.8110.8470.8590.9311.0000.8600.9710.8160.9510.8510.829
2022-070.1650.9720.3340.8100.8390.8390.8540.9350.8601.0000.9450.8920.8400.9230.886
2022-080.5310.9710.3670.9480.8130.8690.8630.9470.9710.9451.0000.8380.9490.8990.893
2022-090.0000.9710.1930.8590.7320.8000.7760.8190.8160.8920.8381.0000.8920.8580.623
2022-100.0000.9020.3670.9740.9050.8740.8520.8920.9510.8400.9490.8921.0000.9150.795
2022-110.0000.9220.3690.9050.9370.8180.8020.9780.8510.9230.8990.8580.9151.0000.944
2022-120.1150.9190.3940.7870.8960.8120.8240.9510.8290.8860.8930.6230.7950.9441.000
2023-12-12T16:02:34.493879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
군구내외국인
군구1.0000.000
내외국인0.0001.000
2023-12-12T16:02:34.599743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2022-012022-022022-032022-042022-052022-062022-072022-082022-092022-102022-112022-12군구내외국인
2022-011.0000.9600.9620.8900.8610.7960.8000.8310.8630.8320.8290.7990.0000.069
2022-020.9601.0000.9570.8860.8760.8010.8190.8290.8670.8460.8550.8480.0000.219
2022-030.9620.9571.0000.8860.8380.7680.7860.8010.8370.8160.8100.8020.1770.048
2022-040.8900.8860.8861.0000.9710.9120.9020.9000.9320.9350.9470.8960.0000.187
2022-050.8610.8760.8380.9711.0000.9470.9250.9240.9550.9650.9730.9280.0000.219
2022-060.7960.8010.7680.9120.9471.0000.9480.9360.9020.9150.9280.8750.0000.337
2022-070.8000.8190.7860.9020.9250.9481.0000.9840.9370.9010.9150.8760.0620.316
2022-080.8310.8290.8010.9000.9240.9360.9841.0000.9470.9130.9130.8800.2810.262
2022-090.8630.8670.8370.9320.9550.9020.9370.9471.0000.9570.9500.9140.0000.230
2022-100.8320.8460.8160.9350.9650.9150.9010.9130.9571.0000.9670.9290.0000.262
2022-110.8290.8550.8100.9470.9730.9280.9150.9130.9500.9671.0000.9680.0000.257
2022-120.7990.8480.8020.8960.9280.8750.8760.8800.9140.9290.9681.0000.0430.275
군구0.0000.0000.1770.0000.0000.0000.0620.2810.0000.0000.0000.0431.0000.000
내외국인0.0690.2190.0480.1870.2190.3370.3160.2620.2300.2620.2570.2750.0001.000

Missing values

2023-12-12T16:02:28.622727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:02:28.844847image/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

시도군구관광지내외국인2022-012022-022022-032022-042022-052022-062022-072022-082022-092022-102022-112022-12
0인천광역시강화군강화 농경문화관내국인84158107171397239258282163298250114
1인천광역시강화군강화갯벌센터내국인284871146261362324273166136173155
2인천광역시강화군강화갯벌센터외국인000000000000
3인천광역시강화군강화나들길(1코스~20코스)내국인37217396254542669426111958523995362970148120865860494533625047
4인천광역시강화군강화석모도 미네랄 온천내국인5111459838855251119101286773941269613973194581578118945
5인천광역시강화군강화역사박물관내국인5686748553757631107989147969111212106331461098026160
6인천광역시강화군강화역사박물관외국인000000000000
7인천광역시강화군강화자연사박물관내국인582976985522767610540837191551061486331240684415833
8인천광역시강화군강화자연사박물관외국인000000000000
9인천광역시강화군강화전적지5개소내국인250882585328116474115140644110312683588338199553813181214024
시도군구관광지내외국인2022-012022-022022-032022-042022-052022-062022-072022-082022-092022-102022-112022-12
67인천광역시부평구기후변화체험관내국인1135931144129792526260736274762657310731572287
68인천광역시부평구부평아트센터내국인02672668236666263803656012002403508357027063
69인천광역시부평구부평아트센터외국인000000000000
70인천광역시부평구부평역사박물관내국인1035944905195226452721215426694046487433613066
71인천광역시부평구안전체험관내국인49643352278725312868294629092589265628172771
72인천광역시부평구인천나비공원내국인7308598878951740217384644037424166788919521156657418
73인천광역시계양구인천어린이과학관내국인157321301912138106681759720726271082961818717226801987424866
74인천광역시서구검단 선사박물관내국인10978541094141916471815145423701713210325491784
75인천광역시서구녹청자박물관내국인181316582344248025792629272834682528424128652714
76인천광역시서구세어도내국인000000000000