Overview

Dataset statistics

Number of variables9
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory84.3 B

Variable types

Categorical1
Text1
Numeric7

Dataset

Description광주환경공단 음식물자원화시설 폐기물 반입 현황에 대한 데이터로, 음식물자원화시설을 구분하여 월별 반입일수, 동구 반입량, 서구 반입량, 남구 반입량, 북구 반입량, 광산구 반입량의 항목을 제공합니다.
Author광주환경공단
URLhttps://www.data.go.kr/data/15029626/fileData.do

Alerts

반입일수 is highly overall correlated with 처리장명High correlation
동구 반입량 is highly overall correlated with 서구 반입량 and 5 other fieldsHigh correlation
서구 반입량 is highly overall correlated with 동구 반입량 and 5 other fieldsHigh correlation
남구 반입량 is highly overall correlated with 동구 반입량 and 5 other fieldsHigh correlation
북구 반입량 is highly overall correlated with 동구 반입량 and 5 other fieldsHigh correlation
광산구 반입량 is highly overall correlated with 동구 반입량 and 5 other fieldsHigh correlation
반입량 합계 is highly overall correlated with 동구 반입량 and 5 other fieldsHigh correlation
처리장명 is highly overall correlated with 반입일수 and 6 other fieldsHigh correlation
동구 반입량 has unique valuesUnique
서구 반입량 has unique valuesUnique
남구 반입량 has unique valuesUnique
북구 반입량 has unique valuesUnique
광산구 반입량 has unique valuesUnique
반입량 합계 has unique valuesUnique

Reproduction

Analysis started2024-03-14 11:08:25.102427
Analysis finished2024-03-14 11:08:37.704425
Duration12.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처리장명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size320.0 B
제1음식물자원화시설
12 
제2음식물자원화시설
12 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제1음식물자원화시설
2nd row제1음식물자원화시설
3rd row제1음식물자원화시설
4th row제1음식물자원화시설
5th row제1음식물자원화시설

Common Values

ValueCountFrequency (%)
제1음식물자원화시설 12
50.0%
제2음식물자원화시설 12
50.0%

Length

2024-03-14T20:08:37.902609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:08:38.197409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제1음식물자원화시설 12
50.0%
제2음식물자원화시설 12
50.0%

구분
Text

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size320.0 B
2024-03-14T20:08:38.878719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01월
2nd row02월
3rd row03월
4th row04월
5th row05월
ValueCountFrequency (%)
01월 2
8.3%
02월 2
8.3%
03월 2
8.3%
04월 2
8.3%
05월 2
8.3%
06월 2
8.3%
07월 2
8.3%
08월 2
8.3%
09월 2
8.3%
10월 2
8.3%
Other values (2) 4
16.7%
2024-03-14T20:08:39.964689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
월 24
33.3%
0 20
27.8%
1 10
13.9%
2 4
 
5.6%
3 2
 
2.8%
4 2
 
2.8%
5 2
 
2.8%
6 2
 
2.8%
7 2
 
2.8%
8 2
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48
66.7%
Other Letter 24
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20
41.7%
1 10
20.8%
2 4
 
8.3%
3 2
 
4.2%
4 2
 
4.2%
5 2
 
4.2%
6 2
 
4.2%
7 2
 
4.2%
8 2
 
4.2%
9 2
 
4.2%
Other Letter
ValueCountFrequency (%)
월 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
66.7%
Hangul 24
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20
41.7%
1 10
20.8%
2 4
 
8.3%
3 2
 
4.2%
4 2
 
4.2%
5 2
 
4.2%
6 2
 
4.2%
7 2
 
4.2%
8 2
 
4.2%
9 2
 
4.2%
Hangul
ValueCountFrequency (%)
월 24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
66.7%
Hangul 24
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
월 24
100.0%
ASCII
ValueCountFrequency (%)
0 20
41.7%
1 10
20.8%
2 4
 
8.3%
3 2
 
4.2%
4 2
 
4.2%
5 2
 
4.2%
6 2
 
4.2%
7 2
 
4.2%
8 2
 
4.2%
9 2
 
4.2%

반입일수
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.208333
Minimum15
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T20:08:40.263319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15.45
Q124.75
median27.5
Q329
95-th percentile31
Maximum31
Range16
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation4.6248776
Coefficient of variation (CV)0.17646592
Kurtosis1.3519809
Mean26.208333
Median Absolute Deviation (MAD)2
Skewness-1.3936665
Sum629
Variance21.389493
MonotonicityNot monotonic
2024-03-14T20:08:40.456148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
29 5
20.8%
31 3
12.5%
26 3
12.5%
30 2
 
8.3%
28 2
 
8.3%
15 2
 
8.3%
24 2
 
8.3%
27 2
 
8.3%
18 1
 
4.2%
25 1
 
4.2%
ValueCountFrequency (%)
15 2
 
8.3%
18 1
 
4.2%
22 1
 
4.2%
24 2
 
8.3%
25 1
 
4.2%
26 3
12.5%
27 2
 
8.3%
28 2
 
8.3%
29 5
20.8%
30 2
 
8.3%
ValueCountFrequency (%)
31 3
12.5%
30 2
 
8.3%
29 5
20.8%
28 2
 
8.3%
27 2
 
8.3%
26 3
12.5%
25 1
 
4.2%
24 2
 
8.3%
22 1
 
4.2%
18 1
 
4.2%

동구 반입량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.72333
Minimum130.26
Maximum571.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T20:08:40.786430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum130.26
5-th percentile159.9345
Q1254.305
median293.845
Q3469.045
95-th percentile538.928
Maximum571.35
Range441.09
Interquartile range (IQR)214.74

Descriptive statistics

Standard deviation130.54035
Coefficient of variation (CV)0.37649717
Kurtosis-1.1967107
Mean346.72333
Median Absolute Deviation (MAD)65.45
Skewness0.25514996
Sum8321.36
Variance17040.784
MonotonicityNot monotonic
2024-03-14T20:08:41.150076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
264.45 1
 
4.2%
360.73 1
 
4.2%
529.51 1
 
4.2%
327.65 1
 
4.2%
571.35 1
 
4.2%
467.24 1
 
4.2%
507.08 1
 
4.2%
540.59 1
 
4.2%
498.54 1
 
4.2%
299.35 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
130.26 1
4.2%
147.6 1
4.2%
229.83 1
4.2%
231.04 1
4.2%
242.95 1
4.2%
244.36 1
4.2%
257.62 1
4.2%
264.45 1
4.2%
269.03 1
4.2%
271.37 1
4.2%
ValueCountFrequency (%)
571.35 1
4.2%
540.59 1
4.2%
529.51 1
4.2%
507.08 1
4.2%
498.54 1
4.2%
474.46 1
4.2%
467.24 1
4.2%
456.3 1
4.2%
430.93 1
4.2%
360.73 1
4.2%

서구 반입량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean935.44708
Minimum313.4
Maximum1555.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T20:08:41.501456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum313.4
5-th percentile409.1485
Q1648.71
median787.735
Q31261.8625
95-th percentile1527.011
Maximum1555.11
Range1241.71
Interquartile range (IQR)613.1525

Descriptive statistics

Standard deviation384.34364
Coefficient of variation (CV)0.41086626
Kurtosis-1.3287129
Mean935.44708
Median Absolute Deviation (MAD)280.995
Skewness0.22205693
Sum22450.73
Variance147720.04
MonotonicityNot monotonic
2024-03-14T20:08:41.882671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
646.73 1
 
4.2%
1049.23 1
 
4.2%
1397.62 1
 
4.2%
1088.23 1
 
4.2%
1537.61 1
 
4.2%
1202.65 1
 
4.2%
1340.14 1
 
4.2%
1466.95 1
 
4.2%
1555.11 1
 
4.2%
780.19 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
313.4 1
4.2%
384.91 1
4.2%
546.5 1
4.2%
598.64 1
4.2%
606.11 1
4.2%
646.73 1
4.2%
649.37 1
4.2%
654.49 1
4.2%
654.97 1
4.2%
695.13 1
4.2%
ValueCountFrequency (%)
1555.11 1
4.2%
1537.61 1
4.2%
1466.95 1
4.2%
1397.62 1
4.2%
1396.07 1
4.2%
1340.14 1
4.2%
1235.77 1
4.2%
1202.65 1
4.2%
1143.93 1
4.2%
1088.23 1
4.2%

남구 반입량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean711.7675
Minimum209.39
Maximum1251.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T20:08:42.258715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum209.39
5-th percentile279.3195
Q1440.57
median564.21
Q31012.5325
95-th percentile1185.035
Maximum1251.74
Range1042.35
Interquartile range (IQR)571.9625

Descriptive statistics

Standard deviation335.8387
Coefficient of variation (CV)0.47183764
Kurtosis-1.5349584
Mean711.7675
Median Absolute Deviation (MAD)278.685
Skewness0.23434095
Sum17082.42
Variance112787.63
MonotonicityNot monotonic
2024-03-14T20:08:42.640217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
485.35 1
 
4.2%
825.03 1
 
4.2%
1129.09 1
 
4.2%
860.76 1
 
4.2%
1185.71 1
 
4.2%
983.46 1
 
4.2%
1092.34 1
 
4.2%
1181.21 1
 
4.2%
1251.74 1
 
4.2%
636.9 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
209.39 1
4.2%
260.37 1
4.2%
386.7 1
4.2%
408.37 1
4.2%
411.12 1
4.2%
415.61 1
4.2%
448.89 1
4.2%
465.32 1
4.2%
476.96 1
4.2%
485.35 1
4.2%
ValueCountFrequency (%)
1251.74 1
4.2%
1185.71 1
4.2%
1181.21 1
4.2%
1129.09 1
4.2%
1092.34 1
4.2%
1084.24 1
4.2%
988.63 1
4.2%
983.46 1
4.2%
916.11 1
4.2%
860.76 1
4.2%

북구 반입량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1494.1779
Minimum500.88
Maximum2527.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T20:08:43.015008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500.88
5-th percentile713.885
Q11160.055
median1311.375
Q31867.6825
95-th percentile2415.3365
Maximum2527.36
Range2026.48
Interquartile range (IQR)707.6275

Descriptive statistics

Standard deviation528.90935
Coefficient of variation (CV)0.35398016
Kurtosis-0.46943793
Mean1494.1779
Median Absolute Deviation (MAD)300.78
Skewness0.2829486
Sum35860.27
Variance279745.1
MonotonicityNot monotonic
2024-03-14T20:08:43.417538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1191.3 1
 
4.2%
1594.51 1
 
4.2%
2062.81 1
 
4.2%
1659.7 1
 
4.2%
2459.72 1
 
4.2%
1825.36 1
 
4.2%
1994.65 1
 
4.2%
2163.83 1
 
4.2%
2527.36 1
 
4.2%
1185.48 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
500.88 1
4.2%
659.03 1
4.2%
1024.73 1
4.2%
1096.63 1
4.2%
1146.21 1
4.2%
1159.44 1
4.2%
1160.26 1
4.2%
1161.18 1
4.2%
1185.48 1
4.2%
1191.3 1
4.2%
ValueCountFrequency (%)
2527.36 1
4.2%
2459.72 1
4.2%
2163.83 1
4.2%
2062.81 1
4.2%
2060.28 1
4.2%
1994.65 1
4.2%
1825.36 1
4.2%
1765.69 1
4.2%
1659.7 1
4.2%
1626.29 1
4.2%

광산구 반입량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1257.8058
Minimum495.37
Maximum2058.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T20:08:43.796907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum495.37
5-th percentile627.4325
Q1889.7525
median1054.24
Q31710.0075
95-th percentile1962.005
Maximum2058.99
Range1563.62
Interquartile range (IQR)820.255

Descriptive statistics

Standard deviation475.33732
Coefficient of variation (CV)0.37790993
Kurtosis-1.3521499
Mean1257.8058
Median Absolute Deviation (MAD)382.915
Skewness0.21606971
Sum30187.34
Variance225945.57
MonotonicityNot monotonic
2024-03-14T20:08:44.188431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
889.55 1
 
4.2%
1422.19 1
 
4.2%
1817.54 1
 
4.2%
1469.46 1
 
4.2%
1878.96 1
 
4.2%
1452.12 1
 
4.2%
1814.83 1
 
4.2%
2058.99 1
 
4.2%
1976.66 1
 
4.2%
1043.75 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
495.37 1
4.2%
606.86 1
4.2%
744.01 1
4.2%
795.69 1
4.2%
826.58 1
4.2%
889.55 1
4.2%
889.82 1
4.2%
892.32 1
4.2%
932.75 1
4.2%
1027.03 1
4.2%
ValueCountFrequency (%)
2058.99 1
4.2%
1976.66 1
4.2%
1878.96 1
4.2%
1817.54 1
4.2%
1814.83 1
4.2%
1768.95 1
4.2%
1690.36 1
4.2%
1587.52 1
4.2%
1469.46 1
4.2%
1452.12 1
4.2%

반입량 합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4745.9217
Minimum1649.3
Maximum7809.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T20:08:44.559696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1649.3
5-th percentile2216.3225
Q13374.7775
median3943.74
Q36289.8225
95-th percentile7600.083
Maximum7809.41
Range6160.11
Interquartile range (IQR)2915.045

Descriptive statistics

Standard deviation1837.9352
Coefficient of variation (CV)0.38726622
Kurtosis-1.2040629
Mean4745.9217
Median Absolute Deviation (MAD)1385.005
Skewness0.23212965
Sum113902.12
Variance3378005.7
MonotonicityNot monotonic
2024-03-14T20:08:44.935229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3477.38 1
 
4.2%
5251.69 1
 
4.2%
6936.57 1
 
4.2%
5405.8 1
 
4.2%
7633.35 1
 
4.2%
5930.83 1
 
4.2%
6749.04 1
 
4.2%
7411.57 1
 
4.2%
7809.41 1
 
4.2%
3945.67 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1649.3 1
4.2%
2058.77 1
4.2%
3109.12 1
4.2%
3112.86 1
4.2%
3216.09 1
4.2%
3259.39 1
4.2%
3413.24 1
4.2%
3477.38 1
4.2%
3626.5 1
4.2%
3641.3 1
4.2%
ValueCountFrequency (%)
7809.41 1
4.2%
7633.35 1
4.2%
7411.57 1
4.2%
6936.57 1
4.2%
6784.0 1
4.2%
6749.04 1
4.2%
6136.75 1
4.2%
5930.83 1
4.2%
5704.78 1
4.2%
5405.8 1
4.2%

Interactions

2024-03-14T20:08:35.251817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:25.502273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:27.009280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:28.214278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:29.755868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:31.479379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:33.284405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:35.506000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:25.757953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:27.155576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:28.483537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:30.008394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:31.743601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:33.544211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:35.735600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:25.991200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:27.272251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:28.715221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:30.238886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:31.979177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:33.780994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:35.986638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:26.241234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:27.409612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:28.995421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:30.481513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:32.236018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:34.031355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:36.228579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:26.490603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:27.543378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:29.137984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:30.718307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:32.489682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:34.277028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:36.488643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:26.694967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:27.782827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:29.307858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:30.978144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:32.765032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:34.541952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:36.744669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:26.856461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:27.969568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:29.505833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:31.233804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:33.026705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:08:34.998752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:08:45.193097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리장명구분반입일수동구 반입량서구 반입량남구 반입량북구 반입량광산구 반입량반입량 합계
처리장명1.0000.0000.9080.8510.8611.0000.8450.8610.986
구분0.0001.0000.0000.3320.3320.0000.0000.4410.168
반입일수0.9080.0001.0000.8000.7620.8480.7130.7660.901
동구 반입량0.8510.3320.8001.0000.9620.9680.8970.9370.897
서구 반입량0.8610.3320.7620.9621.0000.9920.9760.9670.963
남구 반입량1.0000.0000.8480.9680.9921.0000.9710.9710.956
북구 반입량0.8450.0000.7130.8970.9760.9711.0000.9270.889
광산구 반입량0.8610.4410.7660.9370.9670.9710.9271.0000.970
반입량 합계0.9860.1680.9010.8970.9630.9560.8890.9701.000
2024-03-14T20:08:45.506935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
반입일수동구 반입량서구 반입량남구 반입량북구 반입량광산구 반입량반입량 합계처리장명
반입일수1.000-0.166-0.113-0.143-0.073-0.189-0.1420.625
동구 반입량-0.1661.0000.9670.9820.9610.9650.9790.733
서구 반입량-0.1130.9671.0000.9820.9810.9580.9940.742
남구 반입량-0.1430.9820.9821.0000.9800.9630.9930.826
북구 반입량-0.0730.9610.9810.9801.0000.9320.9830.728
광산구 반입량-0.1890.9650.9580.9630.9321.0000.9690.742
반입량 합계-0.1420.9790.9940.9930.9830.9691.0000.767
처리장명0.6250.7330.7420.8260.7280.7420.7671.000

Missing values

2024-03-14T20:08:37.091492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:08:37.532443image/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

처리장명구분반입일수동구 반입량서구 반입량남구 반입량북구 반입량광산구 반입량반입량 합계
0제1음식물자원화시설01월30264.45646.73485.351191.3889.553477.38
1제1음식물자원화시설02월28231.04546.5408.371096.63826.583109.12
2제1음식물자원화시설03월31242.95649.37411.121160.26795.693259.39
3제1음식물자원화시설04월30229.83606.11386.71146.21744.013112.86
4제1음식물자원화시설05월29271.37795.28491.521342.341041.33941.81
5제1음식물자원화시설06월15130.26313.4209.39500.88495.371649.3
6제1음식물자원화시설07월31288.34695.13487.61280.41889.823641.3
7제1음식물자원화시설08월31257.62654.97448.891159.44892.323413.24
8제1음식물자원화시설09월28244.36598.64415.611024.73932.753216.09
9제1음식물자원화시설10월18147.6384.91260.37659.03606.862058.77
처리장명구분반입일수동구 반입량서구 반입량남구 반입량북구 반입량광산구 반입량반입량 합계
14제2음식물자원화시설03월27456.31235.77988.631765.691690.366136.75
15제2음식물자원화시설04월25430.931143.93916.111626.291587.525704.78
16제2음식물자원화시설05월15299.35780.19636.91185.481043.753945.67
17제2음식물자원화시설06월29498.541555.111251.742527.361976.667809.41
18제2음식물자원화시설07월26540.591466.951181.212163.832058.997411.57
19제2음식물자원화시설08월27507.081340.141092.341994.651814.836749.04
20제2음식물자원화시설09월24467.241202.65983.461825.361452.125930.83
21제2음식물자원화시설10월29571.351537.611185.712459.721878.967633.35
22제2음식물자원화시설11월22327.651088.23860.761659.71469.465405.8
23제2음식물자원화시설12월26529.511397.621129.092062.811817.546936.57