Overview

Dataset statistics

Number of variables8
Number of observations144
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory72.9 B

Variable types

Numeric8

Alerts

GRID_CENTR_LA is highly overall correlated with MIM_CCTR and 4 other fieldsHigh correlation
MIM_CCTR is highly overall correlated with GRID_CENTR_LA and 4 other fieldsHigh correlation
MXM_CCTR is highly overall correlated with GRID_CENTR_LA and 4 other fieldsHigh correlation
AVG_CCTR is highly overall correlated with GRID_CENTR_LA and 4 other fieldsHigh correlation
CCTR_STDDEV_VAL is highly overall correlated with GRID_CENTR_LA and 4 other fieldsHigh correlation
CCTR_MDN is highly overall correlated with GRID_CENTR_LA and 4 other fieldsHigh correlation

Reproduction

Analysis started2024-01-14 06:58:34.723395
Analysis finished2024-01-14 06:58:42.169113
Duration7.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GRID_CENTR_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.15
Minimum34.104167
Maximum34.195833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-14T15:58:42.216717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.104167
5-th percentile34.104167
Q134.127083
median34.15
Q334.172917
95-th percentile34.195833
Maximum34.195833
Range0.0916666
Interquartile range (IQR)0.0458333

Descriptive statistics

Standard deviation0.028867507
Coefficient of variation (CV)0.00084531499
Kurtosis-1.2172621
Mean34.15
Median Absolute Deviation (MAD)0.025
Skewness0
Sum4917.6
Variance0.00083333296
MonotonicityDecreasing
2024-01-14T15:58:42.332298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
34.1958333 12
8.3%
34.1875 12
8.3%
34.1791667 12
8.3%
34.1708333 12
8.3%
34.1625 12
8.3%
34.1541667 12
8.3%
34.1458333 12
8.3%
34.1375 12
8.3%
34.1291667 12
8.3%
34.1208333 12
8.3%
Other values (2) 24
16.7%
ValueCountFrequency (%)
34.1041667 12
8.3%
34.1125 12
8.3%
34.1208333 12
8.3%
34.1291667 12
8.3%
34.1375 12
8.3%
34.1458333 12
8.3%
34.1541667 12
8.3%
34.1625 12
8.3%
34.1708333 12
8.3%
34.1791667 12
8.3%
ValueCountFrequency (%)
34.1958333 12
8.3%
34.1875 12
8.3%
34.1791667 12
8.3%
34.1708333 12
8.3%
34.1625 12
8.3%
34.1541667 12
8.3%
34.1458333 12
8.3%
34.1375 12
8.3%
34.1291667 12
8.3%
34.1208333 12
8.3%

GRID_CENTR_LO
Real number (ℝ)

Distinct12
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.3
Minimum126.25417
Maximum126.34583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-14T15:58:42.447040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.25417
5-th percentile126.25417
Q1126.27708
median126.3
Q3126.32292
95-th percentile126.34583
Maximum126.34583
Range0.0916666
Interquartile range (IQR)0.0458333

Descriptive statistics

Standard deviation0.028867507
Coefficient of variation (CV)0.000228563
Kurtosis-1.2172621
Mean126.3
Median Absolute Deviation (MAD)0.025
Skewness0
Sum18187.2
Variance0.00083333296
MonotonicityNot monotonic
2024-01-14T15:58:42.547508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
126.2541667 12
8.3%
126.2625 12
8.3%
126.2708333 12
8.3%
126.2791667 12
8.3%
126.2875 12
8.3%
126.2958333 12
8.3%
126.3041667 12
8.3%
126.3125 12
8.3%
126.3208333 12
8.3%
126.3291667 12
8.3%
Other values (2) 24
16.7%
ValueCountFrequency (%)
126.2541667 12
8.3%
126.2625 12
8.3%
126.2708333 12
8.3%
126.2791667 12
8.3%
126.2875 12
8.3%
126.2958333 12
8.3%
126.3041667 12
8.3%
126.3125 12
8.3%
126.3208333 12
8.3%
126.3291667 12
8.3%
ValueCountFrequency (%)
126.3458333 12
8.3%
126.3375 12
8.3%
126.3291667 12
8.3%
126.3208333 12
8.3%
126.3125 12
8.3%
126.3041667 12
8.3%
126.2958333 12
8.3%
126.2875 12
8.3%
126.2791667 12
8.3%
126.2708333 12
8.3%

CCTR_DATA_CNT
Real number (ℝ)

Distinct16
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.270833
Minimum20
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-14T15:58:42.667481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q131.5
median36
Q345
95-th percentile45
Maximum45
Range25
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation6.9173126
Coefficient of variation (CV)0.19071281
Kurtosis-0.64680726
Mean36.270833
Median Absolute Deviation (MAD)6
Skewness-0.38791147
Sum5223
Variance47.849213
MonotonicityNot monotonic
2024-01-14T15:58:42.812149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
36 55
38.2%
45 37
25.7%
24 14
 
9.7%
30 13
 
9.0%
40 6
 
4.2%
27 3
 
2.1%
42 2
 
1.4%
37 2
 
1.4%
38 2
 
1.4%
25 2
 
1.4%
Other values (6) 8
 
5.6%
ValueCountFrequency (%)
20 2
 
1.4%
24 14
 
9.7%
25 2
 
1.4%
27 3
 
2.1%
29 2
 
1.4%
30 13
 
9.0%
32 1
 
0.7%
33 1
 
0.7%
36 55
38.2%
37 2
 
1.4%
ValueCountFrequency (%)
45 37
25.7%
43 1
 
0.7%
42 2
 
1.4%
41 1
 
0.7%
40 6
 
4.2%
38 2
 
1.4%
37 2
 
1.4%
36 55
38.2%
33 1
 
0.7%
32 1
 
0.7%

MIM_CCTR
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0998542
Minimum0.433
Maximum2.196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-14T15:58:42.947545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.433
5-th percentile0.45715
Q10.7535
median1.068
Q31.4495
95-th percentile1.7803
Maximum2.196
Range1.763
Interquartile range (IQR)0.696

Descriptive statistics

Standard deviation0.44215886
Coefficient of variation (CV)0.4020159
Kurtosis-0.82837579
Mean1.0998542
Median Absolute Deviation (MAD)0.35
Skewness0.31339508
Sum158.379
Variance0.19550446
MonotonicityNot monotonic
2024-01-14T15:58:43.384893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.835 2
 
1.4%
1.543 2
 
1.4%
1.138 2
 
1.4%
1.139 2
 
1.4%
1.09 2
 
1.4%
1.316 2
 
1.4%
0.905 2
 
1.4%
0.844 1
 
0.7%
0.594 1
 
0.7%
0.772 1
 
0.7%
Other values (127) 127
88.2%
ValueCountFrequency (%)
0.433 1
0.7%
0.435 1
0.7%
0.441 1
0.7%
0.443 1
0.7%
0.448 1
0.7%
0.451 1
0.7%
0.454 1
0.7%
0.457 1
0.7%
0.458 1
0.7%
0.479 1
0.7%
ValueCountFrequency (%)
2.196 1
0.7%
2.043 1
0.7%
2.031 1
0.7%
2.014 1
0.7%
2.006 1
0.7%
1.971 1
0.7%
1.826 1
0.7%
1.783 1
0.7%
1.765 1
0.7%
1.761 1
0.7%

MXM_CCTR
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2624306
Minimum0.681
Maximum3.898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-14T15:58:43.608031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.681
5-th percentile0.8209
Q11.535
median2.3355
Q32.94675
95-th percentile3.6007
Maximum3.898
Range3.217
Interquartile range (IQR)1.41175

Descriptive statistics

Standard deviation0.88928632
Coefficient of variation (CV)0.39306679
Kurtosis-1.088515
Mean2.2624306
Median Absolute Deviation (MAD)0.701
Skewness-0.14501464
Sum325.79
Variance0.79083016
MonotonicityNot monotonic
2024-01-14T15:58:43.845445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.657 2
 
1.4%
2.85 2
 
1.4%
2.364 2
 
1.4%
2.731 2
 
1.4%
0.954 1
 
0.7%
1.671 1
 
0.7%
1.076 1
 
0.7%
1.182 1
 
0.7%
1.355 1
 
0.7%
1.518 1
 
0.7%
Other values (130) 130
90.3%
ValueCountFrequency (%)
0.681 1
0.7%
0.688 1
0.7%
0.709 1
0.7%
0.721 1
0.7%
0.732 1
0.7%
0.759 1
0.7%
0.814 1
0.7%
0.82 1
0.7%
0.826 1
0.7%
0.829 1
0.7%
ValueCountFrequency (%)
3.898 1
0.7%
3.89 1
0.7%
3.802 1
0.7%
3.773 1
0.7%
3.739 1
0.7%
3.714 1
0.7%
3.692 1
0.7%
3.607 1
0.7%
3.565 1
0.7%
3.562 1
0.7%

AVG_CCTR
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5976181
Minimum0.566
Maximum2.764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-14T15:58:44.073610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.566
5-th percentile0.6173
Q11.02025
median1.6675
Q32.09625
95-th percentile2.56075
Maximum2.764
Range2.198
Interquartile range (IQR)1.076

Descriptive statistics

Standard deviation0.61602567
Coefficient of variation (CV)0.38559008
Kurtosis-1.1651986
Mean1.5976181
Median Absolute Deviation (MAD)0.473
Skewness-0.12327639
Sum230.057
Variance0.37948762
MonotonicityNot monotonic
2024-01-14T15:58:44.218219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.048 2
 
1.4%
1.957 2
 
1.4%
1.611 2
 
1.4%
2.165 1
 
0.7%
1.198 1
 
0.7%
1.387 1
 
0.7%
0.778 1
 
0.7%
0.834 1
 
0.7%
0.956 1
 
0.7%
1.018 1
 
0.7%
Other values (131) 131
91.0%
ValueCountFrequency (%)
0.566 1
0.7%
0.568 1
0.7%
0.574 1
0.7%
0.578 1
0.7%
0.586 1
0.7%
0.59 1
0.7%
0.615 1
0.7%
0.617 1
0.7%
0.619 1
0.7%
0.625 1
0.7%
ValueCountFrequency (%)
2.764 1
0.7%
2.711 1
0.7%
2.698 1
0.7%
2.64 1
0.7%
2.616 1
0.7%
2.606 1
0.7%
2.581 1
0.7%
2.572 1
0.7%
2.497 1
0.7%
2.43 1
0.7%

CCTR_STDDEV_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31986111
Minimum0.086
Maximum0.729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-14T15:58:44.355239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.086
5-th percentile0.10215
Q10.2225
median0.3085
Q30.412
95-th percentile0.539
Maximum0.729
Range0.643
Interquartile range (IQR)0.1895

Descriptive statistics

Standard deviation0.14217371
Coefficient of variation (CV)0.44448578
Kurtosis-0.51915984
Mean0.31986111
Median Absolute Deviation (MAD)0.1005
Skewness0.29169033
Sum46.06
Variance0.020213365
MonotonicityNot monotonic
2024-01-14T15:58:44.535159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.267 3
 
2.1%
0.496 3
 
2.1%
0.4 2
 
1.4%
0.353 2
 
1.4%
0.404 2
 
1.4%
0.086 2
 
1.4%
0.499 2
 
1.4%
0.237 2
 
1.4%
0.238 2
 
1.4%
0.368 2
 
1.4%
Other values (117) 122
84.7%
ValueCountFrequency (%)
0.086 2
1.4%
0.09 1
0.7%
0.091 1
0.7%
0.095 1
0.7%
0.096 1
0.7%
0.097 1
0.7%
0.102 1
0.7%
0.103 1
0.7%
0.104 1
0.7%
0.107 1
0.7%
ValueCountFrequency (%)
0.729 1
0.7%
0.675 1
0.7%
0.606 1
0.7%
0.596 1
0.7%
0.565 1
0.7%
0.56 1
0.7%
0.545 1
0.7%
0.539 2
1.4%
0.537 1
0.7%
0.536 1
0.7%

CCTR_MDN
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5741458
Minimum0.558
Maximum2.817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-14T15:58:44.967281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.558
5-th percentile0.6009
Q10.9815
median1.609
Q32.0675
95-th percentile2.54905
Maximum2.817
Range2.259
Interquartile range (IQR)1.086

Descriptive statistics

Standard deviation0.62327126
Coefficient of variation (CV)0.39594251
Kurtosis-1.192676
Mean1.5741458
Median Absolute Deviation (MAD)0.4995
Skewness-0.095443425
Sum226.677
Variance0.38846706
MonotonicityNot monotonic
2024-01-14T15:58:45.105937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.569 2
 
1.4%
1.85 2
 
1.4%
1.439 2
 
1.4%
1.609 2
 
1.4%
1.204 2
 
1.4%
0.561 2
 
1.4%
2.02 2
 
1.4%
0.951 1
 
0.7%
1.411 1
 
0.7%
0.76 1
 
0.7%
Other values (127) 127
88.2%
ValueCountFrequency (%)
0.558 1
0.7%
0.561 2
1.4%
0.567 1
0.7%
0.58 1
0.7%
0.587 1
0.7%
0.593 1
0.7%
0.6 1
0.7%
0.606 1
0.7%
0.608 1
0.7%
0.613 1
0.7%
ValueCountFrequency (%)
2.817 1
0.7%
2.721 1
0.7%
2.637 1
0.7%
2.594 1
0.7%
2.574 1
0.7%
2.569 2
1.4%
2.551 1
0.7%
2.538 1
0.7%
2.472 1
0.7%
2.431 1
0.7%

Interactions

2024-01-14T15:58:41.125395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:34.956042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.732638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.523345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:37.372975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:38.275925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.447684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:40.081853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:41.235927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.038503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.849226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.631555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:37.464708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:38.388702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.528390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:40.199464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:41.350114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.121129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.959228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.737212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:37.552064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:38.495331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.601127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:40.396224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:41.482721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.205764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.066190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.874130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:37.641562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:38.802874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.675852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:40.535143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:41.673326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.299318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.165695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.966398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:37.742115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:38.958930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.766533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:40.667066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:41.755493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.405602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.255708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:37.072728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:37.864002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.165910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.852468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:40.815455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:41.826404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.512142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.349418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:37.171388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:38.013090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.285125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.921816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:40.924553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:41.901471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:35.624085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:36.443034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:37.277468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:38.181524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:39.373427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:40.002299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:58:41.028510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:58:45.205834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GRID_CENTR_LAGRID_CENTR_LOCCTR_DATA_CNTMIM_CCTRMXM_CCTRAVG_CCTRCCTR_STDDEV_VALCCTR_MDN
GRID_CENTR_LA1.0000.0000.8640.8220.8070.8420.7750.833
GRID_CENTR_LO0.0001.0000.0000.2560.0000.0000.2730.305
CCTR_DATA_CNT0.8640.0001.0000.6760.6250.6780.4160.664
MIM_CCTR0.8220.2560.6761.0000.8530.9250.7250.914
MXM_CCTR0.8070.0000.6250.8531.0000.9220.8610.888
AVG_CCTR0.8420.0000.6780.9250.9221.0000.8300.975
CCTR_STDDEV_VAL0.7750.2730.4160.7250.8610.8301.0000.801
CCTR_MDN0.8330.3050.6640.9140.8880.9750.8011.000
2024-01-14T15:58:45.331528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GRID_CENTR_LAGRID_CENTR_LOCCTR_DATA_CNTMIM_CCTRMXM_CCTRAVG_CCTRCCTR_STDDEV_VALCCTR_MDN
GRID_CENTR_LA1.0000.0000.2300.8970.7870.8640.5260.863
GRID_CENTR_LO0.0001.000-0.2030.0770.0750.0780.0790.104
CCTR_DATA_CNT0.230-0.2031.0000.1640.3300.2750.2790.256
MIM_CCTR0.8970.0770.1641.0000.8770.9570.5840.947
MXM_CCTR0.7870.0750.3300.8771.0000.9480.8510.925
AVG_CCTR0.8640.0780.2750.9570.9481.0000.7200.992
CCTR_STDDEV_VAL0.5260.0790.2790.5840.8510.7201.0000.702
CCTR_MDN0.8630.1040.2560.9470.9250.9920.7021.000

Missing values

2024-01-14T15:58:42.007092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:58:42.121903image/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

GRID_CENTR_LAGRID_CENTR_LOCCTR_DATA_CNTMIM_CCTRMXM_CCTRAVG_CCTRCCTR_STDDEV_VALCCTR_MDN
034.195833126.254167241.6023.0222.1650.4482.054
134.195833126.2625251.5082.8521.9340.391.85
234.195833126.270833271.5212.2361.8190.1981.809
334.195833126.279167271.5432.151.8150.1631.78
434.195833126.2875301.3842.271.9240.2561.983
534.195833126.295833301.4142.2251.9810.2632.101
634.195833126.304167301.6792.3532.050.2252.113
734.195833126.3125301.7172.3411.9770.1821.941
834.195833126.320833301.7652.782.140.2672.073
934.195833126.329167272.0313.2942.3520.2962.276
GRID_CENTR_LAGRID_CENTR_LOCCTR_DATA_CNTMIM_CCTRMXM_CCTRAVG_CCTRCCTR_STDDEV_VALCCTR_MDN
13434.104167126.270833360.4350.7090.5860.1040.593
13534.104167126.279167360.4540.6880.5780.0860.567
13634.104167126.2875360.4580.7210.5740.090.561
13734.104167126.295833360.4570.6810.5660.0910.558
13834.104167126.304167360.4330.7320.5680.0960.561
13934.104167126.3125360.4410.9910.6290.150.62
14034.104167126.320833360.4511.1880.7610.2310.675
14134.104167126.329167360.5351.190.830.2330.761
14234.104167126.3375360.5871.1430.8730.2090.898
14334.104167126.345833360.6411.0980.8540.160.82