Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells100
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.8 KiB
Average record size in memory131.3 B

Variable types

Numeric4
Categorical8
DateTime1
Unsupported1
Boolean1

Alerts

ap_addr has constant value ""Constant
year has constant value ""Constant
month has constant value ""Constant
enc_yn has constant value ""Constant
weekofyear is highly overall correlated with idx and 4 other fieldsHigh correlation
in_time is highly overall correlated with idx and 6 other fieldsHigh correlation
distance is highly overall correlated with day and 4 other fieldsHigh correlation
hour is highly overall correlated with day and 4 other fieldsHigh correlation
mac_addr is highly overall correlated with idx and 6 other fieldsHigh correlation
idx is highly overall correlated with mac_addr and 2 other fieldsHigh correlation
day is highly overall correlated with week and 5 other fieldsHigh correlation
week is highly overall correlated with day and 5 other fieldsHigh correlation
manuufacturer has 100 (100.0%) missing valuesMissing
idx has unique valuesUnique
collect_date has unique valuesUnique
manuufacturer is an unsupported type, check if it needs cleaning or further analysisUnsupported
week has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:39:26.618613
Analysis finished2023-12-10 09:39:30.910002
Duration4.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

idx
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81015050
Minimum81000001
Maximum81500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:31.098533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81000001
5-th percentile81000007
Q181000028
median81000054
Q381000078
95-th percentile81000098
Maximum81500000
Range499999
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation85714.286
Coefficient of variation (CV)0.0010580045
Kurtosis29.897768
Mean81015050
Median Absolute Deviation (MAD)25.5
Skewness5.5946485
Sum8.101505 × 109
Variance7.3469388 × 109
MonotonicityNot monotonic
2023-12-10T18:39:31.393061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81000001 1
 
1.0%
81000065 1
 
1.0%
81000075 1
 
1.0%
81000074 1
 
1.0%
81000073 1
 
1.0%
81000072 1
 
1.0%
81000071 1
 
1.0%
81000070 1
 
1.0%
81000069 1
 
1.0%
81000068 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
81000001 1
1.0%
81000003 1
1.0%
81000004 1
1.0%
81000005 1
1.0%
81000006 1
1.0%
81000007 1
1.0%
81000009 1
1.0%
81000010 1
1.0%
81000011 1
1.0%
81000012 1
1.0%
ValueCountFrequency (%)
81500000 1
1.0%
81499999 1
1.0%
81499998 1
1.0%
81000100 1
1.0%
81000099 1
1.0%
81000098 1
1.0%
81000097 1
1.0%
81000096 1
1.0%
81000095 1
1.0%
81000094 1
1.0%

ap_addr
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
78:A3:51:63:23:B4
100 

Length

Max length17
Median length17
Mean length17
Min length17

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row78:A3:51:63:23:B4
2nd row78:A3:51:63:23:B4
3rd row78:A3:51:63:23:B4
4th row78:A3:51:63:23:B4
5th row78:A3:51:63:23:B4

Common Values

ValueCountFrequency (%)
78:A3:51:63:23:B4 100
100.0%

Length

2023-12-10T18:39:31.616781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:31.917210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
78:a3:51:63:23:b4 100
100.0%

mac_addr
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D2319
19 
634B2D54A24BB6A8AD6CF209DFA2C866CB1B700C2B3579BDDE3CD81B409CFF56FAC7482D0050790B7471A72F59E1F5FF0DF7A03D0EFCA92F218FB63DF87BD36BD4AA01DC13FC4570F6AC19AA1A0D2319
18 
634B39013CF3F7EF384959A77D375AE032E99C04FC5D3EC11A46B3F4CE998F2F263934EEBFBA9D979E029BA26FE38A16FBC29FC5FA6685D9248CC22C0B5FC857D4AA01DC13FC4570F6AC19AA1A0D2319
18 
634B0F79BC58348E32625822FEFCAB9164A9BA1B9BE77CCC64BC9D9FA58EA7E7EE9C47282751AA20F5DE4BE6BFBEAFA5C6D783F2E3BBD3AA8FBFEB164CED9058D4AA01DC13FC4570F6AC19AA1A0D2319
16 
634E9959A764FE51712421EA0A13D80D3A10F9D13120A20167D5F4CC8E1963C76D00037DC937AE793E7A63E81806EABC6D0DC470799A4780A860A9EF097F0FCCD4AA01DC13FC4570F6AC19AA1A0D2319
15 
Other values (2)
14 

Length

Max length160
Median length160
Mean length160
Min length160

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D2319
2nd row912B1272C504549E41A505EDB1B1BD566FF71B238F2E3E52F9614E5837F446A76E5218DA76076F33CC17AF59837F8F552A818623340520628CDEF4B8BAC0FE93D4AA01DC13FC4570F6AC19AA1A0D2319
3rd row634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D2319
4th row634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D2319
5th row634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D2319

Common Values

ValueCountFrequency (%)
634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D2319 19
19.0%
634B2D54A24BB6A8AD6CF209DFA2C866CB1B700C2B3579BDDE3CD81B409CFF56FAC7482D0050790B7471A72F59E1F5FF0DF7A03D0EFCA92F218FB63DF87BD36BD4AA01DC13FC4570F6AC19AA1A0D2319 18
18.0%
634B39013CF3F7EF384959A77D375AE032E99C04FC5D3EC11A46B3F4CE998F2F263934EEBFBA9D979E029BA26FE38A16FBC29FC5FA6685D9248CC22C0B5FC857D4AA01DC13FC4570F6AC19AA1A0D2319 18
18.0%
634B0F79BC58348E32625822FEFCAB9164A9BA1B9BE77CCC64BC9D9FA58EA7E7EE9C47282751AA20F5DE4BE6BFBEAFA5C6D783F2E3BBD3AA8FBFEB164CED9058D4AA01DC13FC4570F6AC19AA1A0D2319 16
16.0%
634E9959A764FE51712421EA0A13D80D3A10F9D13120A20167D5F4CC8E1963C76D00037DC937AE793E7A63E81806EABC6D0DC470799A4780A860A9EF097F0FCCD4AA01DC13FC4570F6AC19AA1A0D2319 15
15.0%
634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D2319 11
11.0%
912B1272C504549E41A505EDB1B1BD566FF71B238F2E3E52F9614E5837F446A76E5218DA76076F33CC17AF59837F8F552A818623340520628CDEF4B8BAC0FE93D4AA01DC13FC4570F6AC19AA1A0D2319 3
 
3.0%

Length

2023-12-10T18:39:32.231681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:32.495836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
634b058c3b7579977bddcbb7f8c4cef46d72aeefecddd6a385a7bfebcc91ab0a65fdaa88bcb6eb74d6996758594f08bc69d7492dd0dc1f54053b221c8e9cdb37d4aa01dc13fc4570f6ac19aa1a0d2319 19
19.0%
634b2d54a24bb6a8ad6cf209dfa2c866cb1b700c2b3579bdde3cd81b409cff56fac7482d0050790b7471a72f59e1f5ff0df7a03d0efca92f218fb63df87bd36bd4aa01dc13fc4570f6ac19aa1a0d2319 18
18.0%
634b39013cf3f7ef384959a77d375ae032e99c04fc5d3ec11a46b3f4ce998f2f263934eebfba9d979e029ba26fe38a16fbc29fc5fa6685d9248cc22c0b5fc857d4aa01dc13fc4570f6ac19aa1a0d2319 18
18.0%
634b0f79bc58348e32625822fefcab9164a9ba1b9be77ccc64bc9d9fa58ea7e7ee9c47282751aa20f5de4be6bfbeafa5c6d783f2e3bbd3aa8fbfeb164ced9058d4aa01dc13fc4570f6ac19aa1a0d2319 16
16.0%
634e9959a764fe51712421ea0a13d80d3a10f9d13120a20167d5f4cc8e1963c76d00037dc937ae793e7a63e81806eabc6d0dc470799a4780a860a9ef097f0fccd4aa01dc13fc4570f6ac19aa1a0d2319 15
15.0%
634eff2d9fabebb56cf8c7f37f35ac6059b701428003121c6d8c9b1f3dc42bd2e931c2110130e74f28df19d30a03cb5a488c19cf9fd3e01f717d857805f4e0c9d4aa01dc13fc4570f6ac19aa1a0d2319 11
11.0%
912b1272c504549e41a505edb1b1bd566ff71b238f2e3e52f9614e5837f446a76e5218da76076f33cc17af59837f8f552a818623340520628cdef4b8bac0fe93d4aa01dc13fc4570f6ac19aa1a0d2319 3
 
3.0%

in_time
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-05-21 08:17:28.0
19 
2021-05-19 08:51:59.0
18 
2021-05-29 08:02:26.0
18 
2021-05-18 05:28:51.0
16 
2021-05-18 09:00:46.0
15 
Other values (2)
14 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-05-21 08:17:28.0
2nd row2021-05-31 02:44:55.0
3rd row2021-05-21 08:17:28.0
4th row2021-05-21 08:17:28.0
5th row2021-05-21 08:17:28.0

Common Values

ValueCountFrequency (%)
2021-05-21 08:17:28.0 19
19.0%
2021-05-19 08:51:59.0 18
18.0%
2021-05-29 08:02:26.0 18
18.0%
2021-05-18 05:28:51.0 16
16.0%
2021-05-18 09:00:46.0 15
15.0%
2021-05-20 02:02:02.0 11
11.0%
2021-05-31 02:44:55.0 3
 
3.0%

Length

2023-12-10T18:39:32.806881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:32.974348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-05-18 31
15.5%
2021-05-21 19
9.5%
08:17:28.0 19
9.5%
2021-05-19 18
9.0%
08:51:59.0 18
9.0%
2021-05-29 18
9.0%
08:02:26.0 18
9.0%
05:28:51.0 16
8.0%
09:00:46.0 15
7.5%
2021-05-20 11
 
5.5%
Other values (3) 17
8.5%

duration
Real number (ℝ)

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.97
Minimum2
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:33.174438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11.9
Q126
median38.5
Q352.25
95-th percentile67.15
Maximum76
Range74
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation17.894446
Coefficient of variation (CV)0.45918518
Kurtosis-0.71084145
Mean38.97
Median Absolute Deviation (MAD)12.5
Skewness0.049701116
Sum3897
Variance320.21121
MonotonicityNot monotonic
2023-12-10T18:39:33.396725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 4
 
4.0%
39 4
 
4.0%
26 4
 
4.0%
38 4
 
4.0%
40 3
 
3.0%
49 3
 
3.0%
43 3
 
3.0%
36 3
 
3.0%
34 3
 
3.0%
33 3
 
3.0%
Other values (48) 66
66.0%
ValueCountFrequency (%)
2 1
 
1.0%
4 1
 
1.0%
7 1
 
1.0%
9 1
 
1.0%
10 1
 
1.0%
12 1
 
1.0%
13 2
2.0%
14 4
4.0%
15 2
2.0%
17 1
 
1.0%
ValueCountFrequency (%)
76 1
 
1.0%
75 1
 
1.0%
73 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
67 1
 
1.0%
66 3
3.0%
65 2
2.0%
62 1
 
1.0%
61 2
2.0%

distance
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
34 
5
30 
0
21 
1
15 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row0
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 34
34.0%
5 30
30.0%
0 21
21.0%
1 15
15.0%

Length

2023-12-10T18:39:33.633435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:33.810097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 34
34.0%
5 30
30.0%
0 21
21.0%
1 15
15.0%

collect_date
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2021-05-18 05:28:55
Maximum2021-05-31 02:45:33
2023-12-10T18:39:33.981587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:34.184202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021
100 

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 100
100.0%

Length

2023-12-10T18:39:34.375291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:34.553215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 100
100.0%

month
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
5
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 100
100.0%

Length

2023-12-10T18:39:34.830775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:35.018350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 100
100.0%

day
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.34
Minimum18
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:35.216018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q118
median20
Q321
95-th percentile29
Maximum31
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.2669034
Coefficient of variation (CV)0.19994861
Kurtosis-0.097331303
Mean21.34
Median Absolute Deviation (MAD)2
Skewness1.2447333
Sum2134
Variance18.206465
MonotonicityNot monotonic
2023-12-10T18:39:35.423706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
18 31
31.0%
21 19
19.0%
19 18
18.0%
29 18
18.0%
20 11
 
11.0%
31 3
 
3.0%
ValueCountFrequency (%)
18 31
31.0%
19 18
18.0%
20 11
 
11.0%
21 19
19.0%
29 18
18.0%
31 3
 
3.0%
ValueCountFrequency (%)
31 3
 
3.0%
29 18
18.0%
21 19
19.0%
20 11
 
11.0%
19 18
18.0%
18 31
31.0%

hour
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20
55 
17
16 
21
15 
14
14 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row14
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 55
55.0%
17 16
 
16.0%
21 15
 
15.0%
14 14
 
14.0%

Length

2023-12-10T18:39:35.619521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:35.797643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 55
55.0%
17 16
 
16.0%
21 15
 
15.0%
14 14
 
14.0%

week
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.66
Minimum0
Maximum5
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:35.971449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5841386
Coefficient of variation (CV)0.59554081
Kurtosis-1.4058117
Mean2.66
Median Absolute Deviation (MAD)1
Skewness0.1901496
Sum266
Variance2.5094949
MonotonicityNot monotonic
2023-12-10T18:39:36.215492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 31
31.0%
4 19
19.0%
2 18
18.0%
5 18
18.0%
3 11
 
11.0%
0 3
 
3.0%
ValueCountFrequency (%)
0 3
 
3.0%
1 31
31.0%
2 18
18.0%
3 11
 
11.0%
4 19
19.0%
5 18
18.0%
ValueCountFrequency (%)
5 18
18.0%
4 19
19.0%
3 11
 
11.0%
2 18
18.0%
1 31
31.0%
0 3
 
3.0%

weekofyear
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20
79 
21
18 
22
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row22
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 79
79.0%
21 18
 
18.0%
22 3
 
3.0%

Length

2023-12-10T18:39:36.757151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:36.910708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 79
79.0%
21 18
 
18.0%
22 3
 
3.0%

manuufacturer
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

enc_yn
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
100 
ValueCountFrequency (%)
True 100
100.0%
2023-12-10T18:39:37.067087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-10T18:39:29.740263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:27.680498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.284988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:29.158700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:29.863852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:27.889773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.504900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:29.316941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:30.023138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.025918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.772439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:29.470487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:30.173423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.161360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:29.019537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:29.609647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:39:37.168540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idxmac_addrin_timedurationdistancecollect_datedayhourweekweekofyear
idx1.0001.0001.0000.0000.4421.0001.0000.5891.0001.000
mac_addr1.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
in_time1.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
duration0.0000.0000.0001.0000.0001.0000.0000.0000.0000.000
distance0.4421.0001.0000.0001.0001.0000.7360.9450.9540.495
collect_date1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
day1.0001.0001.0000.0000.7361.0001.0000.7321.0001.000
hour0.5891.0001.0000.0000.9451.0000.7321.0000.9170.401
week1.0001.0001.0000.0000.9541.0001.0000.9171.0001.000
weekofyear1.0001.0001.0000.0000.4951.0001.0000.4011.0001.000
2023-12-10T18:39:37.375352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
weekofyearin_timedistancehourmac_addr
weekofyear1.0000.9790.4910.3900.979
in_time0.9791.0000.9840.9841.000
distance0.4910.9841.0000.6860.984
hour0.3900.9840.6861.0000.984
mac_addr0.9791.0000.9840.9841.000
2023-12-10T18:39:37.584195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idxdurationdayweekmac_addrin_timedistancehourweekofyear
idx1.0000.0190.017-0.1630.9740.9740.2950.4020.995
duration0.0191.0000.1020.1420.0000.0000.0000.0000.000
day0.0170.1021.0000.8160.9890.9890.6750.6700.990
week-0.1630.1420.8161.0000.9950.9950.8510.7970.984
mac_addr0.9740.0000.9890.9951.0001.0000.9840.9840.979
in_time0.9740.0000.9890.9951.0001.0000.9840.9840.979
distance0.2950.0000.6750.8510.9840.9841.0000.6860.491
hour0.4020.0000.6700.7970.9840.9840.6861.0000.390
weekofyear0.9950.0000.9900.9840.9790.9790.4910.3901.000

Missing values

2023-12-10T18:39:30.406038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:39:30.776135image/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

idxap_addrmac_addrin_timedurationdistancecollect_dateyearmonthdayhourweekweekofyearmanuufacturerenc_yn
08100000178:A3:51:63:23:B4634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D23192021-05-21 08:17:28.01452021-05-21 08:17:42.0202152120420<NA>Y
18149999878:A3:51:63:23:B4912B1272C504549E41A505EDB1B1BD566FF71B238F2E3E52F9614E5837F446A76E5218DA76076F33CC17AF59837F8F552A818623340520628CDEF4B8BAC0FE93D4AA01DC13FC4570F6AC19AA1A0D23192021-05-31 02:44:55.02702021-05-31 02:45:22.0202153114022<NA>Y
28100000378:A3:51:63:23:B4634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D23192021-05-21 08:17:28.02652021-05-21 08:17:54.0202152120420<NA>Y
38100000478:A3:51:63:23:B4634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D23192021-05-21 08:17:28.02752021-05-21 08:17:55.0202152120420<NA>Y
48100000578:A3:51:63:23:B4634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D23192021-05-21 08:17:28.03152021-05-21 08:17:59.0202152120420<NA>Y
58100000678:A3:51:63:23:B4634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D23192021-05-21 08:17:28.03352021-05-21 08:18:01.0202152120420<NA>Y
68100000778:A3:51:63:23:B4634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D23192021-05-21 08:17:28.03452021-05-21 08:18:02.0202152120420<NA>Y
78149999978:A3:51:63:23:B4912B1272C504549E41A505EDB1B1BD566FF71B238F2E3E52F9614E5837F446A76E5218DA76076F33CC17AF59837F8F552A818623340520628CDEF4B8BAC0FE93D4AA01DC13FC4570F6AC19AA1A0D23192021-05-31 02:44:55.03602021-05-31 02:45:31.0202153114022<NA>Y
88100000978:A3:51:63:23:B4634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D23192021-05-21 08:17:28.03952021-05-21 08:18:07.0202152120420<NA>Y
98100001078:A3:51:63:23:B4634B058C3B7579977BDDCBB7F8C4CEF46D72AEEFECDDD6A385A7BFEBCC91AB0A65FDAA88BCB6EB74D6996758594F08BC69D7492DD0DC1F54053B221C8E9CDB37D4AA01DC13FC4570F6AC19AA1A0D23192021-05-21 08:17:28.04352021-05-21 08:18:11.0202152120420<NA>Y
idxap_addrmac_addrin_timedurationdistancecollect_dateyearmonthdayhourweekweekofyearmanuufacturerenc_yn
908100009178:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.02352021-05-20 02:02:25.0202152014320<NA>Y
918100009278:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.02652021-05-20 02:02:28.0202152014320<NA>Y
928100009378:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.02852021-05-20 02:02:30.0202152014320<NA>Y
938100009478:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.03352021-05-20 02:02:35.0202152014320<NA>Y
948100009578:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.03452021-05-20 02:02:36.0202152014320<NA>Y
958100009678:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.03852021-05-20 02:02:40.0202152014320<NA>Y
968100009778:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.03952021-05-20 02:02:41.0202152014320<NA>Y
978100009878:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.04052021-05-20 02:02:42.0202152014320<NA>Y
988100009978:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.05052021-05-20 02:02:52.0202152014320<NA>Y
998100010078:A3:51:63:23:B4634EFF2D9FABEBB56CF8C7F37F35AC6059B701428003121C6D8C9B1F3DC42BD2E931C2110130E74F28DF19D30A03CB5A488C19CF9FD3E01F717D857805F4E0C9D4AA01DC13FC4570F6AC19AA1A0D23192021-05-20 02:02:02.05252021-05-20 02:02:54.0202152014320<NA>Y