"������������������(���������) ������.csv"의 파일명이 "공중이용시설(공연장) 현황.csv"으로 변경 됨.

Overview

Dataset statistics

Number of variables12
Number of observations24
Missing cells7
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory105.3 B

Variable types

Categorical7
Numeric4
Boolean1

Dataset

Description공중이용시설(공연장) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=GCJHPFN1ABDLV192J7KJ13727403&infSeq=1

Alerts

다중이용업소여부 has constant value "False" Constant
위생업종명 has constant value "공중이용시설" Constant
위생업태명 has constant value "공연장" Constant
다중이용업소여부 is highly correlated with 소재지도로명주소 and 6 other fieldsHigh correlation
소재지도로명주소 is highly correlated with 다중이용업소여부 and 6 other fieldsHigh correlation
시군명 is highly correlated with 다중이용업소여부 and 5 other fieldsHigh correlation
위생업태명 is highly correlated with 다중이용업소여부 and 6 other fieldsHigh correlation
영업상태명 is highly correlated with 다중이용업소여부 and 5 other fieldsHigh correlation
사업장명 is highly correlated with 다중이용업소여부 and 6 other fieldsHigh correlation
소재지지번주소 is highly correlated with 다중이용업소여부 and 6 other fieldsHigh correlation
위생업종명 is highly correlated with 다중이용업소여부 and 6 other fieldsHigh correlation
다중이용업소여부 has 1 (4.2%) missing values Missing
위생업종명 has 1 (4.2%) missing values Missing
위생업태명 has 1 (4.2%) missing values Missing
소재지도로명주소 has 2 (8.3%) missing values Missing
WGS84위도 has 1 (4.2%) missing values Missing
WGS84경도 has 1 (4.2%) missing values Missing
사업장명 has unique values Unique
소재지지번주소 has unique values Unique

Reproduction

Analysis started2023-03-18 03:27:15.519796
Analysis finished2023-03-18 03:27:17.399954
Duration1.88 second
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size320.0 B
의정부시
안산시
성남시
수원시
과천시
Other values (8)

Length

Max length4
Median length3
Mean length3.333333333
Min length3

Unique

Unique9 ?
Unique (%)37.5%

Sample

1st row과천시
2nd row광주시
3rd row구리시
4th row군포시
5th row부천시

Common Values

ValueCountFrequency (%)
의정부시8
33.3%
안산시3
 
12.5%
성남시2
 
8.3%
수원시2
 
8.3%
과천시1
 
4.2%
광주시1
 
4.2%
구리시1
 
4.2%
군포시1
 
4.2%
부천시1
 
4.2%
안양시1
 
4.2%
Other values (3)3
 
12.5%

Length

2023-03-18T12:27:17.433977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
의정부시8
33.3%
안산시3
 
12.5%
성남시2
 
8.3%
수원시2
 
8.3%
과천시1
 
4.2%
광주시1
 
4.2%
구리시1
 
4.2%
군포시1
 
4.2%
부천시1
 
4.2%
안양시1
 
4.2%
Other values (3)3
 
12.5%

사업장명
Categorical

HIGH CORRELATION
UNIQUE

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size320.0 B
과천시민회관
 
1
광주시 문화스포츠센터
 
1
롯데씨네(구리시네마)
 
1
군포문화예술회관
 
1
부천시민회관
 
1
Other values (19)
19 

Length

Max length11
Median length9
Mean length7.25
Min length4

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row과천시민회관
2nd row광주시 문화스포츠센터
3rd row롯데씨네(구리시네마)
4th row군포문화예술회관
5th row부천시민회관

Common Values

ValueCountFrequency (%)
과천시민회관1
 
4.2%
광주시 문화스포츠센터1
 
4.2%
롯데씨네(구리시네마)1
 
4.2%
군포문화예술회관1
 
4.2%
부천시민회관1
 
4.2%
성남시민회관1
 
4.2%
성남아트센터 대극장1
 
4.2%
영통키넥스51
 
4.2%
경기도문화의전당1
 
4.2%
안산문화예술의전당1
 
4.2%
Other values (14)14
58.3%

Length

2023-03-18T12:27:17.509294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
과천시민회관1
 
3.8%
광주시1
 
3.8%
숭문상가1
 
3.8%
성혼예식장1
 
3.8%
삼천리탄업(주1
 
3.8%
의장부기독교청년회1
 
3.8%
행복예식장1
 
3.8%
허니문예식장1
 
3.8%
동원웨딩홀1
 
3.8%
경기도북부여성회관31
 
3.8%
Other values (16)16
61.5%

인허가일자
Real number (ℝ≥0)

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20011757.58
Minimum19910408
Maximum20130911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-03-18T12:27:17.578370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19910408
5-th percentile19950331
Q119957707
median19975460
Q320070335.5
95-th percentile20109488.8
Maximum20130911
Range220503
Interquartile range (IQR)112628.5

Descriptive statistics

Standard deviation66295.94672
Coefficient of variation (CV)0.003312849781
Kurtosis-1.507490205
Mean20011757.58
Median Absolute Deviation (MAD)45090.5
Skewness0.2948417378
Sum480282182
Variance4395152551
MonotonicityNot monotonic
2023-03-18T12:27:17.653583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
199503313
 
12.5%
199607032
 
8.3%
199104081
 
4.2%
201003021
 
4.2%
200702161
 
4.2%
199802191
 
4.2%
199604151
 
4.2%
200509201
 
4.2%
200710291
 
4.2%
200703061
 
4.2%
Other values (11)11
45.8%
ValueCountFrequency (%)
199104081
 
4.2%
199503313
12.5%
199504081
 
4.2%
199505041
 
4.2%
199601081
 
4.2%
199604151
 
4.2%
199607032
8.3%
199607041
 
4.2%
199707011
 
4.2%
199802191
 
4.2%
ValueCountFrequency (%)
201309111
4.2%
201111101
4.2%
201003021
4.2%
200801021
4.2%
200710291
4.2%
200704061
4.2%
200703121
4.2%
200703061
4.2%
200702161
4.2%
200509201
4.2%

영업상태명
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size320.0 B
운영중
23 
폐업 등
 
1

Length

Max length4
Median length3
Mean length3.041666667
Min length3

Unique

Unique1 ?
Unique (%)4.2%

Sample

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

Common Values

ValueCountFrequency (%)
운영중23
95.8%
폐업 등1
 
4.2%

Length

2023-03-18T12:27:17.861579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-18T12:27:17.921947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
운영중23
92.0%
폐업1
 
4.0%
1
 
4.0%

다중이용업소여부
Boolean

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)4.3%
Missing1
Missing (%)4.2%
Memory size176.0 B
False
23 
(Missing)
 
1
ValueCountFrequency (%)
False23
95.8%
(Missing)1
 
4.2%
2023-03-18T12:27:17.970280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

위생업종명
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)4.3%
Missing1
Missing (%)4.2%
Memory size320.0 B
공중이용시설
23 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공중이용시설23
95.8%
(Missing)1
 
4.2%

Length

2023-03-18T12:27:18.017850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-18T12:27:18.083157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
공중이용시설23
100.0%

위생업태명
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)4.3%
Missing1
Missing (%)4.2%
Memory size320.0 B
공연장
23 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공연장23
95.8%
(Missing)1
 
4.2%

Length

2023-03-18T12:27:18.135732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-18T12:27:18.193674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
공연장23
100.0%

소재지도로명주소
Categorical

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)100.0%
Missing2
Missing (%)8.3%
Memory size320.0 B
경기도 안산시 상록구 광덕1로 385
 
1
경기도 구리시 경춘로 243 (인창동)
 
1
경기도 군포시 고산로 599 (산본동, 군포문화예술회관)
 
1
경기도 부천시 부일로 365 (중동)
 
1
경기도 성남시 수정구 수정로153번길 3 (태평동)
 
1
Other values (17)
17 

Length

Max length32
Median length28.5
Mean length24.45454545
Min length19

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row경기도 과천시 통영로 5 (중앙동)
2nd row경기도 광주시 회안대로 891 (송정동)
3rd row경기도 구리시 경춘로 243 (인창동)
4th row경기도 군포시 고산로 599 (산본동, 군포문화예술회관)
5th row경기도 부천시 부일로 365 (중동)

Common Values

ValueCountFrequency (%)
경기도 안산시 상록구 광덕1로 3851
 
4.2%
경기도 구리시 경춘로 243 (인창동)1
 
4.2%
경기도 군포시 고산로 599 (산본동, 군포문화예술회관)1
 
4.2%
경기도 부천시 부일로 365 (중동)1
 
4.2%
경기도 성남시 수정구 수정로153번길 3 (태평동)1
 
4.2%
경기도 성남시 분당구 성남대로 808 (야탑동)1
 
4.2%
경기도 수원시 영통구 신원로 231 (매탄동)1
 
4.2%
경기도 안산시 단원구 화랑로 312 (고잔동, 817)1
 
4.2%
경기도 안산시 상록구 석호로 226 (사동)1
 
4.2%
경기도 광주시 회안대로 891 (송정동)1
 
4.2%
Other values (12)12
50.0%
(Missing)2
 
8.3%

Length

2023-03-18T12:27:18.251509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도22
 
18.5%
의정부시8
 
6.7%
의정부동4
 
3.4%
안산시3
 
2.5%
162
 
1.7%
가능동2
 
1.7%
성남시2
 
1.7%
태평로2
 
1.7%
상록구2
 
1.7%
죽전동1
 
0.8%
Other values (71)71
59.7%

소재지지번주소
Categorical

HIGH CORRELATION
UNIQUE

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size320.0 B
경기도 과천시 중앙동 6-2번지
 
1
경기도 광주시 송정동 340-1번지 52필지
 
1
경기도 구리시 인창동 676-2번지
 
1
경기도 군포시 산본동 1101번지 군포문화예술회관
 
1
경기도 부천시 중동 788번지
 
1
Other values (19)
19 

Length

Max length29
Median length24
Mean length21.33333333
Min length16

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row경기도 과천시 중앙동 6-2번지
2nd row경기도 광주시 송정동 340-1번지 52필지
3rd row경기도 구리시 인창동 676-2번지
4th row경기도 군포시 산본동 1101번지 군포문화예술회관
5th row경기도 부천시 중동 788번지

Common Values

ValueCountFrequency (%)
경기도 과천시 중앙동 6-2번지1
 
4.2%
경기도 광주시 송정동 340-1번지 52필지1
 
4.2%
경기도 구리시 인창동 676-2번지1
 
4.2%
경기도 군포시 산본동 1101번지 군포문화예술회관1
 
4.2%
경기도 부천시 중동 788번지1
 
4.2%
경기도 성남시 수정구 태평동 3493-1번지1
 
4.2%
경기도 성남시 분당구 야탑동 757번지1
 
4.2%
경기도 수원시 영통구 매탄동 491-10번지1
 
4.2%
경기도 수원시 팔달구 인계동 1117번지1
 
4.2%
경기도 안산시 단원구 고잔동 817번지1
 
4.2%
Other values (14)14
58.3%

Length

2023-03-18T12:27:18.327362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도24
22.0%
의정부시8
 
7.3%
의정부동6
 
5.5%
안산시3
 
2.8%
수원시2
 
1.8%
성남시2
 
1.8%
상록구2
 
1.8%
가능동2
 
1.8%
안양동1
 
0.9%
550번지1
 
0.9%
Other values (58)58
53.2%

소재지우편번호
Real number (ℝ≥0)

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean438652.0833
Minimum14613
Maximum480849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-03-18T12:27:18.397022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14613
5-th percentile426043.7
Q1434556.75
median460817.5
Q3480209.5
95-th percentile480848.85
Maximum480849
Range466236
Interquartile range (IQR)45652.75

Descriptive statistics

Standard deviation92788.95435
Coefficient of variation (CV)0.2115320042
Kurtosis21.21328238
Mean438652.0833
Median Absolute Deviation (MAD)20010
Skewness-4.486669215
Sum10527650
Variance8609790050
MonotonicityNot monotonic
2023-03-18T12:27:18.483232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4800102
 
8.3%
4808492
 
8.3%
4268241
 
4.2%
4598131
 
4.2%
4710101
 
4.2%
4358021
 
4.2%
146131
 
4.2%
4618221
 
4.2%
4638391
 
4.2%
4438031
 
4.2%
Other values (12)12
50.0%
ValueCountFrequency (%)
146131
4.2%
4259061
4.2%
4268241
4.2%
4268631
4.2%
4278051
4.2%
4308211
4.2%
4358021
4.2%
4378021
4.2%
4428351
4.2%
4438031
4.2%
ValueCountFrequency (%)
4808492
8.3%
4808481
4.2%
4808421
4.2%
4808131
4.2%
4808081
4.2%
4800102
8.3%
4710101
4.2%
4649031
4.2%
4638391
4.2%
4618221
4.2%

WGS84위도
Real number (ℝ≥0)

MISSING

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean37.49138836
Minimum37.06722319
Maximum37.75288059
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-03-18T12:27:18.562942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37.06722319
5-th percentile37.25703997
Q137.32216528
median37.42820745
Q337.73647778
95-th percentile37.7493418
Maximum37.75288059
Range0.6856574034
Interquartile range (IQR)0.4143125

Descriptive statistics

Standard deviation0.2101527799
Coefficient of variation (CV)0.005605361367
Kurtosis-1.190226342
Mean37.49138836
Median Absolute Deviation (MAD)0.1635382892
Skewness-0.01876040937
Sum862.3019322
Variance0.0441641909
MonotonicityNot monotonic
2023-03-18T12:27:18.627443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
37.428207451
 
4.2%
37.384605791
 
4.2%
37.067223191
 
4.2%
37.601794571
 
4.2%
37.365784471
 
4.2%
37.488632931
 
4.2%
37.442787951
 
4.2%
37.402954811
 
4.2%
37.256192281
 
4.2%
37.264669161
 
4.2%
Other values (13)13
54.2%
ValueCountFrequency (%)
37.067223191
4.2%
37.256192281
4.2%
37.264669161
4.2%
37.293357321
4.2%
37.309049061
4.2%
37.318460361
4.2%
37.325870191
4.2%
37.365784471
4.2%
37.384605791
4.2%
37.402954811
4.2%
ValueCountFrequency (%)
37.752880591
4.2%
37.750466741
4.2%
37.73921731
4.2%
37.738444891
4.2%
37.737026551
4.2%
37.736690971
4.2%
37.736264581
4.2%
37.734439291
4.2%
37.601794571
4.2%
37.488632931
4.2%

WGS84경도
Real number (ℝ≥0)

MISSING

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean127.0184678
Minimum126.770656
Maximum127.2547251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-03-18T12:27:18.699044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum126.770656
5-th percentile126.8258818
Q1126.9602212
median127.0417033
Q3127.0625978
95-th percentile127.1415412
Maximum127.2547251
Range0.4840691034
Interquartile range (IQR)0.1023765342

Descriptive statistics

Standard deviation0.1139008465
Coefficient of variation (CV)0.0008967266605
Kurtosis0.3090387353
Mean127.0184678
Median Absolute Deviation (MAD)0.0525937324
Skewness-0.4704493445
Sum2921.424759
Variance0.01297340282
MonotonicityNot monotonic
2023-03-18T12:27:18.894611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
126.98910961
 
4.2%
126.93133291
 
4.2%
127.06541161
 
4.2%
127.14206451
 
4.2%
126.92746591
 
4.2%
126.7706561
 
4.2%
127.13683181
 
4.2%
127.13054991
 
4.2%
127.05978391
 
4.2%
127.03944531
 
4.2%
Other values (13)13
54.2%
ValueCountFrequency (%)
126.7706561
4.2%
126.82300031
4.2%
126.85181531
4.2%
126.85535721
4.2%
126.92746591
4.2%
126.93133291
4.2%
126.98910961
4.2%
127.03258761
4.2%
127.03529311
4.2%
127.03944531
4.2%
ValueCountFrequency (%)
127.25472511
4.2%
127.14206451
4.2%
127.13683181
4.2%
127.13054991
4.2%
127.10590381
4.2%
127.06541161
4.2%
127.05978391
4.2%
127.05205221
4.2%
127.05133251
4.2%
127.04522171
4.2%

Interactions

2023-03-18T12:27:16.752383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:15.791783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.091290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.376829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.810253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:15.897582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.171072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.438574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.868771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:15.957734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.245965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.501076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.938441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.022649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.317533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-03-18T12:27:16.564986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-03-18T12:27:18.955707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-03-18T12:27:19.037706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-03-18T12:27:19.117774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-03-18T12:27:19.218320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/