Overview

Dataset statistics

Number of variables6
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory54.3 B

Variable types

Categorical2
Numeric4

Alerts

colct_de is highly overall correlated with avrg_dynmc_popltn_co and 3 other fieldsHigh correlation
avrg_dynmc_popltn_co is highly overall correlated with colct_de and 3 other fieldsHigh correlation
avrg_stay_pd is highly overall correlated with colct_de and 3 other fieldsHigh correlation
mxmm_stay_pd is highly overall correlated with avrg_dynmc_popltn_co and 1 other fieldsHigh correlation
macadrs_nm is highly overall correlated with colct_deHigh correlation
mumm_stay_pd is highly overall correlated with colct_de and 2 other fieldsHigh correlation
macadrs_nm is highly imbalanced (80.6%)Imbalance
colct_de has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:03:56.097545
Analysis finished2023-12-10 10:04:00.059737
Duration3.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

macadrs_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
78:A3:51:63:16:5C
97 
78:A3:51:63:24:B8
 
3

Length

Max length17
Median length17
Mean length17
Min length17

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row78:A3:51:63:16:5C
2nd row78:A3:51:63:24:B8
3rd row78:A3:51:63:16:5C
4th row78:A3:51:63:16:5C
5th row78:A3:51:63:16:5C

Common Values

ValueCountFrequency (%)
78:A3:51:63:16:5C 97
97.0%
78:A3:51:63:24:B8 3
 
3.0%

Length

2023-12-10T19:04:00.166696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:00.331699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
78:a3:51:63:16:5c 97
97.0%
78:a3:51:63:24:b8 3
 
3.0%

colct_de
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210269
Minimum20210105
Maximum20210630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:00.522314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210105
5-th percentile20210111
Q120210184
median20210302
Q320210327
95-th percentile20210416
Maximum20210630
Range525
Interquartile range (IQR)143.75

Descriptive statistics

Standard deviation121.20828
Coefficient of variation (CV)5.9973609 × 10-6
Kurtosis0.42396044
Mean20210269
Median Absolute Deviation (MAD)97.5
Skewness0.59517197
Sum2.0210269 × 109
Variance14691.447
MonotonicityNot monotonic
2023-12-10T19:04:00.826192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210105 1
 
1.0%
20210314 1
 
1.0%
20210324 1
 
1.0%
20210323 1
 
1.0%
20210322 1
 
1.0%
20210321 1
 
1.0%
20210320 1
 
1.0%
20210319 1
 
1.0%
20210318 1
 
1.0%
20210317 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
20210105 1
1.0%
20210107 1
1.0%
20210108 1
1.0%
20210109 1
1.0%
20210110 1
1.0%
20210111 1
1.0%
20210113 1
1.0%
20210114 1
1.0%
20210115 1
1.0%
20210116 1
1.0%
ValueCountFrequency (%)
20210630 1
1.0%
20210629 1
1.0%
20210628 1
1.0%
20210418 1
1.0%
20210417 1
1.0%
20210416 1
1.0%
20210415 1
1.0%
20210414 1
1.0%
20210413 1
1.0%
20210412 1
1.0%

avrg_dynmc_popltn_co
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.90684
Minimum1
Maximum984.4286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:01.086008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.7125
Q165.79795
median118.83425
Q3177.04892
95-th percentile295.63082
Maximum984.4286
Range983.4286
Interquartile range (IQR)111.25097

Descriptive statistics

Standard deviation135.93512
Coefficient of variation (CV)0.99290233
Kurtosis16.539816
Mean136.90684
Median Absolute Deviation (MAD)57.4479
Skewness3.3269699
Sum13690.684
Variance18478.358
MonotonicityNot monotonic
2023-12-10T19:04:01.341116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0 2
 
2.0%
1.3333 2
 
2.0%
71.4545 1
 
1.0%
140.0 1
 
1.0%
261.0833 1
 
1.0%
136.5652 1
 
1.0%
127.087 1
 
1.0%
56.8182 1
 
1.0%
176.5 1
 
1.0%
186.6667 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
1.0 1
1.0%
1.3333 2
2.0%
1.5 1
1.0%
2.0 1
1.0%
2.75 1
1.0%
3.0 1
1.0%
5.5 1
1.0%
6.6667 1
1.0%
8.0 2
2.0%
9.2 1
1.0%
ValueCountFrequency (%)
984.4286 1
1.0%
643.6087 1
1.0%
543.5417 1
1.0%
403.4286 1
1.0%
376.1818 1
1.0%
291.3913 1
1.0%
271.7143 1
1.0%
261.0833 1
1.0%
254.8182 1
1.0%
234.7917 1
1.0%

avrg_stay_pd
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.05
Minimum1
Maximum1263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:01.615049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.95
Q1112
median203.5
Q3280.5
95-th percentile436.55
Maximum1263
Range1262
Interquartile range (IQR)168.5

Descriptive statistics

Standard deviation184.62818
Coefficient of variation (CV)0.85853604
Kurtosis11.32596
Mean215.05
Median Absolute Deviation (MAD)87.5
Skewness2.5767125
Sum21505
Variance34087.563
MonotonicityNot monotonic
2023-12-10T19:04:01.889591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
218 2
 
2.0%
3 2
 
2.0%
125 2
 
2.0%
16 2
 
2.0%
216 2
 
2.0%
138 2
 
2.0%
4 2
 
2.0%
2 2
 
2.0%
112 2
 
2.0%
67 2
 
2.0%
Other values (79) 80
80.0%
ValueCountFrequency (%)
1 1
1.0%
2 2
2.0%
3 2
2.0%
4 2
2.0%
7 1
1.0%
10 1
1.0%
12 1
1.0%
15 1
1.0%
16 2
2.0%
18 1
1.0%
ValueCountFrequency (%)
1263 1
1.0%
908 1
1.0%
713 1
1.0%
562 1
1.0%
504 1
1.0%
433 1
1.0%
420 1
1.0%
399 1
1.0%
396 1
1.0%
395 1
1.0%

mxmm_stay_pd
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.13
Minimum1
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:02.168806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q133
median52.5
Q364.25
95-th percentile91.35
Maximum122
Range121
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation26.69493
Coefficient of variation (CV)0.54335294
Kurtosis0.15225375
Mean49.13
Median Absolute Deviation (MAD)16.5
Skewness0.11987182
Sum4913
Variance712.61929
MonotonicityNot monotonic
2023-12-10T19:04:02.434456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
6.0%
33 4
 
4.0%
69 4
 
4.0%
58 3
 
3.0%
54 3
 
3.0%
62 3
 
3.0%
44 3
 
3.0%
35 3
 
3.0%
84 2
 
2.0%
13 2
 
2.0%
Other values (51) 67
67.0%
ValueCountFrequency (%)
1 6
6.0%
2 1
 
1.0%
3 2
 
2.0%
6 1
 
1.0%
8 1
 
1.0%
9 1
 
1.0%
13 2
 
2.0%
17 1
 
1.0%
24 1
 
1.0%
27 2
 
2.0%
ValueCountFrequency (%)
122 1
1.0%
117 1
1.0%
112 1
1.0%
103 1
1.0%
98 1
1.0%
91 1
1.0%
84 2
2.0%
83 1
1.0%
79 1
1.0%
78 2
2.0%

mumm_stay_pd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
53 
1
47 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 53
53.0%
1 47
47.0%

Length

2023-12-10T19:04:02.697321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:02.888461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 53
53.0%
1 47
47.0%

Interactions

2023-12-10T19:03:58.395248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:56.492043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:57.070868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:57.742560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:58.996006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:56.644963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:57.237196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:57.909238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:59.323232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:56.792537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:57.387640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:58.075386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:59.513599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:56.933700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:57.559002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:58.242803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:04:03.044277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
macadrs_nmcolct_deavrg_dynmc_popltn_coavrg_stay_pdmxmm_stay_pdmumm_stay_pd
macadrs_nm1.0001.0000.0000.0000.0000.060
colct_de1.0001.0000.3870.4480.5760.919
avrg_dynmc_popltn_co0.0000.3871.0000.9920.5490.767
avrg_stay_pd0.0000.4480.9921.0000.6100.763
mxmm_stay_pd0.0000.5760.5490.6101.0000.644
mumm_stay_pd0.0600.9190.7670.7630.6441.000
2023-12-10T19:04:03.503972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
mumm_stay_pdmacadrs_nm
mumm_stay_pd1.0000.037
macadrs_nm0.0371.000
2023-12-10T19:04:03.724004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
colct_deavrg_dynmc_popltn_coavrg_stay_pdmxmm_stay_pdmacadrs_nmmumm_stay_pd
colct_de1.0000.5390.5390.4440.9740.955
avrg_dynmc_popltn_co0.5391.0000.9970.6980.0000.572
avrg_stay_pd0.5390.9971.0000.7210.0000.569
mxmm_stay_pd0.4440.6980.7211.0000.0000.478
macadrs_nm0.9740.0000.0000.0001.0000.037
mumm_stay_pd0.9550.5720.5690.4780.0371.000

Missing values

2023-12-10T19:03:59.766014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:03:59.986613image/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

macadrs_nmcolct_deavrg_dynmc_popltn_coavrg_stay_pdmxmm_stay_pdmumm_stay_pd
078:A3:51:63:16:5C2021010571.4545120291
178:A3:51:63:24:B820210628122.75203430
278:A3:51:63:16:5C2021010758.956598331
378:A3:51:63:16:5C2021010841.869667331
478:A3:51:63:16:5C2021010961.4091108541
578:A3:51:63:16:5C2021011061.3636116621
678:A3:51:63:16:5C2021011181.3913142421
778:A3:51:63:24:B820210629119.625211700
878:A3:51:63:16:5C2021011382.2083135291
978:A3:51:63:16:5C20210114178.6957291521
macadrs_nmcolct_deavrg_dynmc_popltn_coavrg_stay_pdmxmm_stay_pdmumm_stay_pd
9078:A3:51:63:16:5C20210409376.1818504590
9178:A3:51:63:16:5C20210410213.1739333680
9278:A3:51:63:16:5C20210411254.8182396830
9378:A3:51:63:16:5C2021041276.4545125400
9478:A3:51:63:16:5C20210413181.9565304770
9578:A3:51:63:16:5C20210414129.1304217620
9678:A3:51:63:16:5C20210415145.6957252760
9778:A3:51:63:16:5C20210416543.5417713600
9878:A3:51:63:16:5C20210417403.4286562630
9978:A3:51:63:16:5C20210418271.7143420660