Overview

Dataset statistics

Number of variables13
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows26
Duplicate rows (%)13.1%
Total size in memory22.9 KiB
Average record size in memory117.7 B

Variable types

Categorical5
Numeric8

Alerts

2021 has constant value ""Constant
Dataset has 26 (13.1%) duplicate rowsDuplicates
9 is highly overall correlated with 2 and 9 other fieldsHigh correlation
2 is highly overall correlated with 9 and 9 other fieldsHigh correlation
1116074020008 is highly overall correlated with 3High correlation
0.3 is highly overall correlated with 9 and 9 other fieldsHigh correlation
0.4 is highly overall correlated with 9 and 9 other fieldsHigh correlation
0.5 is highly overall correlated with 9 and 9 other fieldsHigh correlation
5 is highly overall correlated with 9 and 9 other fieldsHigh correlation
8 is highly overall correlated with 9 and 9 other fieldsHigh correlation
3 is highly overall correlated with 9 and 10 other fieldsHigh correlation
0 is highly overall correlated with 9 and 9 other fieldsHigh correlation
0.1 is highly overall correlated with 9 and 9 other fieldsHigh correlation
0.2 is highly overall correlated with 9 and 9 other fieldsHigh correlation
0 is highly imbalanced (73.3%)Imbalance
0.1 is highly imbalanced (81.8%)Imbalance
0.2 is highly imbalanced (59.2%)Imbalance
9 has 15 (7.5%) zerosZeros
2 has 28 (14.1%) zerosZeros
0.3 has 129 (64.8%) zerosZeros
0.4 has 86 (43.2%) zerosZeros
0.5 has 48 (24.1%) zerosZeros
5 has 31 (15.6%) zerosZeros
8 has 22 (11.1%) zerosZeros

Reproduction

Analysis started2023-12-10 06:35:18.247453
Analysis finished2023-12-10 06:35:30.308110
Duration12.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

3
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2
74 
4
48 
1
42 
3
34 
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row3
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 74
37.2%
4 48
24.1%
1 42
21.1%
3 34
17.1%
6 1
 
0.5%

Length

2023-12-10T15:35:30.409018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:30.599539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 74
37.2%
4 48
24.1%
1 42
21.1%
3 34
17.1%
6 1
 
0.5%

2021
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2021
199 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 199
100.0%

Length

2023-12-10T15:35:30.801679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:31.042785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 199
100.0%

9
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.42211
Minimum0
Maximum5783
Zeros15
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:31.251719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median15
Q3153
95-th percentile527.1
Maximum5783
Range5783
Interquartile range (IQR)150.5

Descriptive statistics

Standard deviation440.41436
Coefficient of variation (CV)3.1141832
Kurtosis137.22116
Mean141.42211
Median Absolute Deviation (MAD)14
Skewness10.803265
Sum28143
Variance193964.81
MonotonicityNot monotonic
2023-12-10T15:35:31.525974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
9.0%
2 17
 
8.5%
0 15
 
7.5%
3 7
 
3.5%
15 7
 
3.5%
5 7
 
3.5%
254 5
 
2.5%
546 5
 
2.5%
472 5
 
2.5%
12 5
 
2.5%
Other values (55) 108
54.3%
ValueCountFrequency (%)
0 15
7.5%
1 18
9.0%
2 17
8.5%
3 7
 
3.5%
4 4
 
2.0%
5 7
 
3.5%
6 3
 
1.5%
7 3
 
1.5%
8 3
 
1.5%
9 1
 
0.5%
ValueCountFrequency (%)
5783 1
 
0.5%
652 1
 
0.5%
644 3
1.5%
546 5
2.5%
525 2
 
1.0%
514 3
1.5%
507 3
1.5%
490 2
 
1.0%
472 5
2.5%
452 1
 
0.5%

2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.34673
Minimum0
Maximum3966
Zeros28
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:31.772964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q3100
95-th percentile457
Maximum3966
Range3966
Interquartile range (IQR)98

Descriptive statistics

Standard deviation309.72842
Coefficient of variation (CV)2.9969831
Kurtosis123.09002
Mean103.34673
Median Absolute Deviation (MAD)11
Skewness10.042103
Sum20566
Variance95931.692
MonotonicityNot monotonic
2023-12-10T15:35:31.994478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
14.1%
1 19
 
9.5%
3 13
 
6.5%
5 10
 
5.0%
2 7
 
3.5%
4 6
 
3.0%
91 5
 
2.5%
199 5
 
2.5%
6 5
 
2.5%
323 5
 
2.5%
Other values (55) 96
48.2%
ValueCountFrequency (%)
0 28
14.1%
1 19
9.5%
2 7
 
3.5%
3 13
6.5%
4 6
 
3.0%
5 10
 
5.0%
6 5
 
2.5%
7 2
 
1.0%
8 2
 
1.0%
9 2
 
1.0%
ValueCountFrequency (%)
3966 1
 
0.5%
572 1
 
0.5%
546 1
 
0.5%
523 3
1.5%
489 3
1.5%
457 2
1.0%
439 2
1.0%
401 2
1.0%
393 2
1.0%
360 3
1.5%

0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
179 
1
 
12
2
 
5
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 179
89.9%
1 12
 
6.0%
2 5
 
2.5%
3 2
 
1.0%
4 1
 
0.5%

Length

2023-12-10T15:35:32.241697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:32.409650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 179
89.9%
1 12
 
6.0%
2 5
 
2.5%
3 2
 
1.0%
4 1
 
0.5%

1116074020008
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9082957 × 1011
Minimum1048
Maximum1.123077 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:32.645953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1048
5-th percentile1195.5
Q18571
median11305608
Q311680635
95-th percentile1.1230631 × 1012
Maximum1.123077 × 1012
Range1.123077 × 1012
Interquartile range (IQR)11672064

Descriptive statistics

Standard deviation4.2144159 × 1011
Coefficient of variation (CV)2.2084711
Kurtosis1.1175723
Mean1.9082957 × 1011
Median Absolute Deviation (MAD)11276571
Skewness1.7624435
Sum3.7975085 × 1013
Variance1.7761301 × 1023
MonotonicityNot monotonic
2023-12-10T15:35:32.906538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11680545 5
 
2.5%
11680640 5
 
2.5%
11680531 5
 
2.5%
11500540 3
 
1.5%
11305534 3
 
1.5%
11500620 3
 
1.5%
11500640 3
 
1.5%
1123076010004 3
 
1.5%
7595 2
 
1.0%
11500591 2
 
1.0%
Other values (149) 165
82.9%
ValueCountFrequency (%)
1048 1
0.5%
1078 1
0.5%
1079 1
0.5%
1094 2
1.0%
1095 1
0.5%
1116 1
0.5%
1132 1
0.5%
1168 1
0.5%
1173 1
0.5%
1198 1
0.5%
ValueCountFrequency (%)
1123077040004 1
 
0.5%
1123076020001 2
1.0%
1123076010004 3
1.5%
1123067030004 1
 
0.5%
1123064040003 1
 
0.5%
1123064030002 1
 
0.5%
1123064020005 1
 
0.5%
1123063020008 1
 
0.5%
1123063020004 1
 
0.5%
1123059030005 1
 
0.5%

0.1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
190 
1
 
8
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 190
95.5%
1 8
 
4.0%
4 1
 
0.5%

Length

2023-12-10T15:35:33.247747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:33.418578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 190
95.5%
1 8
 
4.0%
4 1
 
0.5%

0.2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
165 
1
26 
2
 
7
14
 
1

Length

Max length2
Median length1
Mean length1.0050251
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 165
82.9%
1 26
 
13.1%
2 7
 
3.5%
14 1
 
0.5%

Length

2023-12-10T15:35:33.603460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:33.796113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 165
82.9%
1 26
 
13.1%
2 7
 
3.5%
14 1
 
0.5%

0.3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2663317
Minimum0
Maximum146
Zeros129
Zeros (%)64.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:33.983647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7.1
Maximum146
Range146
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.979063
Coefficient of variation (CV)4.8444202
Kurtosis150.42936
Mean2.2663317
Median Absolute Deviation (MAD)0
Skewness11.669332
Sum451
Variance120.53982
MonotonicityNot monotonic
2023-12-10T15:35:34.154311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 129
64.8%
1 18
 
9.0%
4 12
 
6.0%
2 12
 
6.0%
3 9
 
4.5%
6 4
 
2.0%
5 4
 
2.0%
8 4
 
2.0%
29 3
 
1.5%
9 2
 
1.0%
Other values (2) 2
 
1.0%
ValueCountFrequency (%)
0 129
64.8%
1 18
 
9.0%
2 12
 
6.0%
3 9
 
4.5%
4 12
 
6.0%
5 4
 
2.0%
6 4
 
2.0%
7 1
 
0.5%
8 4
 
2.0%
9 2
 
1.0%
ValueCountFrequency (%)
146 1
 
0.5%
29 3
 
1.5%
9 2
 
1.0%
8 4
 
2.0%
7 1
 
0.5%
6 4
 
2.0%
5 4
 
2.0%
4 12
6.0%
3 9
4.5%
2 12
6.0%

0.4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.407035
Minimum0
Maximum891
Zeros86
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:34.697638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile43.6
Maximum891
Range891
Interquartile range (IQR)10

Descriptive statistics

Standard deviation64.71156
Coefficient of variation (CV)4.8266868
Kurtosis173.08805
Mean13.407035
Median Absolute Deviation (MAD)1
Skewness12.757933
Sum2668
Variance4187.586
MonotonicityNot monotonic
2023-12-10T15:35:34.900480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 86
43.2%
1 19
 
9.5%
2 13
 
6.5%
11 7
 
3.5%
4 6
 
3.0%
9 6
 
3.0%
5 6
 
3.0%
3 6
 
3.0%
27 5
 
2.5%
15 5
 
2.5%
Other values (17) 40
20.1%
ValueCountFrequency (%)
0 86
43.2%
1 19
 
9.5%
2 13
 
6.5%
3 6
 
3.0%
4 6
 
3.0%
5 6
 
3.0%
6 1
 
0.5%
7 3
 
1.5%
8 2
 
1.0%
9 6
 
3.0%
ValueCountFrequency (%)
891 1
 
0.5%
93 3
1.5%
50 4
2.0%
49 2
 
1.0%
43 4
2.0%
39 2
 
1.0%
36 5
2.5%
34 1
 
0.5%
30 3
1.5%
27 5
2.5%

0.5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.708543
Minimum0
Maximum2882
Zeros48
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:35.123673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q336.5
95-th percentile157.4
Maximum2882
Range2882
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation208.94368
Coefficient of variation (CV)4.4733504
Kurtosis173.48247
Mean46.708543
Median Absolute Deviation (MAD)5
Skewness12.765367
Sum9295
Variance43657.46
MonotonicityNot monotonic
2023-12-10T15:35:35.373957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 48
24.1%
1 21
 
10.6%
2 14
 
7.0%
5 9
 
4.5%
3 9
 
4.5%
90 7
 
3.5%
7 6
 
3.0%
4 6
 
3.0%
120 5
 
2.5%
57 5
 
2.5%
Other values (37) 69
34.7%
ValueCountFrequency (%)
0 48
24.1%
1 21
10.6%
2 14
 
7.0%
3 9
 
4.5%
4 6
 
3.0%
5 9
 
4.5%
6 4
 
2.0%
7 6
 
3.0%
8 3
 
1.5%
9 3
 
1.5%
ValueCountFrequency (%)
2882 1
 
0.5%
234 3
1.5%
174 3
1.5%
170 3
1.5%
156 3
1.5%
143 2
 
1.0%
137 2
 
1.0%
134 1
 
0.5%
124 2
 
1.0%
120 5
2.5%

5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.376884
Minimum0
Maximum6309
Zeros31
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:35.609397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median10
Q372.5
95-th percentile296.4
Maximum6309
Range6309
Interquartile range (IQR)71

Descriptive statistics

Standard deviation453.52581
Coefficient of variation (CV)5.0181616
Kurtosis180.98197
Mean90.376884
Median Absolute Deviation (MAD)10
Skewness13.16102
Sum17985
Variance205685.66
MonotonicityNot monotonic
2023-12-10T15:35:35.819603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
15.6%
1 19
 
9.5%
2 11
 
5.5%
4 10
 
5.0%
6 10
 
5.0%
3 7
 
3.5%
11 5
 
2.5%
82 5
 
2.5%
21 5
 
2.5%
134 5
 
2.5%
Other values (47) 91
45.7%
ValueCountFrequency (%)
0 31
15.6%
1 19
9.5%
2 11
 
5.5%
3 7
 
3.5%
4 10
 
5.0%
5 3
 
1.5%
6 10
 
5.0%
7 2
 
1.0%
8 2
 
1.0%
9 3
 
1.5%
ValueCountFrequency (%)
6309 1
 
0.5%
378 3
1.5%
370 3
1.5%
336 3
1.5%
292 1
 
0.5%
269 2
1.0%
268 1
 
0.5%
266 2
1.0%
235 2
1.0%
216 2
1.0%

8
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.54271
Minimum0
Maximum7992
Zeros22
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:36.035227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median14
Q3125
95-th percentile519.2
Maximum7992
Range7992
Interquartile range (IQR)122

Descriptive statistics

Standard deviation584.06146
Coefficient of variation (CV)4.0974488
Kurtosis166.81208
Mean142.54271
Median Absolute Deviation (MAD)13
Skewness12.40987
Sum28366
Variance341127.79
MonotonicityNot monotonic
2023-12-10T15:35:36.266817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
11.1%
1 13
 
6.5%
2 12
 
6.0%
3 9
 
4.5%
4 6
 
3.0%
7 6
 
3.0%
125 5
 
2.5%
203 5
 
2.5%
24 5
 
2.5%
5 5
 
2.5%
Other values (54) 111
55.8%
ValueCountFrequency (%)
0 22
11.1%
1 13
6.5%
2 12
6.0%
3 9
4.5%
4 6
 
3.0%
5 5
 
2.5%
6 4
 
2.0%
7 6
 
3.0%
8 4
 
2.0%
9 5
 
2.5%
ValueCountFrequency (%)
7992 1
 
0.5%
669 3
1.5%
559 1
 
0.5%
536 3
1.5%
521 2
1.0%
519 3
1.5%
467 2
1.0%
456 2
1.0%
440 3
1.5%
431 1
 
0.5%

Interactions

2023-12-10T15:35:28.476910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:19.110016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:20.302572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:21.770828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:23.053161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:24.263556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:25.826355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:27.174430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:28.622483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:19.261994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:20.500071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:21.926846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:23.203828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:24.411185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:25.986974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:27.318246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:28.789892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:19.404008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:20.735697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:22.071860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:23.364384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:24.591814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:26.166691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:27.491387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:28.953509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:19.560936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:20.949023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:22.222790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:23.522597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:24.750498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:26.347832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:27.649205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:29.123073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:19.710561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:21.102052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:22.424590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:23.681784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:24.894724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:26.536155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:27.829177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:29.289924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:19.849357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:21.264273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:22.550503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:23.812115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:25.362302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:26.665095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:27.973341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:29.487208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:19.981121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:21.449433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:22.724957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:23.959484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:25.520733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:26.858444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:28.136030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:29.622559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:20.131590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:21.617941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:22.876977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:24.114509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:25.664132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:27.006506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:28.321344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:35:36.452380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
392011160740200080.10.20.30.40.558
31.0000.7220.7460.8781.0000.7310.6940.7200.7201.0001.0001.000
90.7221.0000.9720.7910.0000.9420.7100.9410.9411.0001.0001.000
20.7460.9721.0000.7310.0500.9440.7490.9420.9421.0001.0001.000
00.8780.7910.7311.0000.0460.7140.7140.7130.7131.0001.0001.000
11160740200081.0000.0000.0500.0461.0000.0000.1840.0000.0000.0000.0000.000
0.10.7310.9420.9440.7140.0001.0000.7330.9420.9421.0001.0001.000
0.20.6940.7100.7490.7140.1840.7331.0000.7010.7011.0001.0001.000
0.30.7200.9410.9420.7130.0000.9420.7011.0001.0001.0001.0001.000
0.40.7200.9410.9420.7130.0000.9420.7011.0001.0001.0001.0001.000
0.51.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.7000.700
51.0001.0001.0001.0000.0001.0001.0001.0001.0000.7001.0000.700
81.0001.0001.0001.0000.0001.0001.0001.0001.0000.7000.7001.000
2023-12-10T15:35:36.651946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
0.20.103
0.21.0000.7760.6510.628
0.10.7761.0000.6980.721
00.6510.6981.0000.529
30.6280.7210.5291.000
2023-12-10T15:35:36.820177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
9211160740200080.30.40.558300.10.2
91.0000.9540.2130.7440.8900.9370.9280.9500.7090.8040.7040.747
20.9541.0000.2120.7380.8930.9300.9380.9520.7410.7210.7110.796
11160740200080.2130.2121.0000.1830.2130.2220.2020.1940.9920.0530.0000.120
0.30.7440.7380.1831.0000.7870.7640.7510.7470.7050.6970.7040.736
0.40.8900.8930.2130.7871.0000.8910.9010.9000.7050.6970.7040.736
0.50.9370.9300.2220.7640.8911.0000.9360.9370.9920.9920.9970.995
50.9280.9380.2020.7510.9010.9361.0000.9400.9920.9920.9970.995
80.9500.9520.1940.7470.9000.9370.9401.0000.9920.9920.9970.995
30.7090.7410.9920.7050.7050.9920.9920.9921.0000.5290.7210.628
00.8040.7210.0530.6970.6970.9920.9920.9920.5291.0000.6980.651
0.10.7040.7110.0000.7040.7040.9970.9970.9970.7210.6981.0000.776
0.20.7470.7960.1200.7360.7360.9950.9950.9950.6280.6510.7761.000

Missing values

2023-12-10T15:35:29.928356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:35:30.210814image/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

3202192011160740200080.10.20.30.40.558
03202162011160590100100000004
12202150752301150064001636170378536
212021000146730001024
312021000303760000003
42202147232321168064000427120215401
54202100076340000001
62202164448911150062001550174370669
712021100148550000010
8420211212061980000129
922021159121111680510121866107117
3202192011160740200080.10.20.30.40.558
189120217402471880000003
1904202118120612700207923
19132021272201109074020004000291621
192420213129076180015131337
193120215202626620000000
1944202120012230000100
1952202154643901130563501436137269521
1963202195011160520200010000046
1973202155011230590300050001345
1984202131060830001267

Duplicate rows

Most frequently occurring

3202192011160740200080.10.20.30.40.558# duplicates
42202114291011680545114957821255
92202125419901168053100215901342035
1322021472323211680640004271202154015
1522021507523011500640016361703785363
162202151424701150054000330941504403
18220215463600113055340129932343365193
2022021644489111500620015501743706693
22320211030112307601000400007673
02202116110115005350003516222
12202120130113056080001217242