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_SLNTY and 4 other fieldsHigh correlation
MIM_SLNTY is highly overall correlated with GRID_CENTR_LA and 4 other fieldsHigh correlation
MXM_SLNTY is highly overall correlated with GRID_CENTR_LA and 3 other fieldsHigh correlation
AVG_SLNTY is highly overall correlated with GRID_CENTR_LA and 4 other fieldsHigh correlation
SLNTY_STDDEV_VAL is highly overall correlated with GRID_CENTR_LA and 3 other fieldsHigh correlation
SLNTY_MDN is highly overall correlated with GRID_CENTR_LA and 4 other fieldsHigh correlation

Reproduction

Analysis started2024-03-13 12:38:38.063990
Analysis finished2024-03-13 12:38:47.773210
Duration9.71 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
2024-03-13T21:38:47.848957image/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
2024-03-13T21:38:48.008798image/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
2024-03-13T21:38:48.193029image/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
2024-03-13T21:38:48.339413image/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%

SLNTY_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
2024-03-13T21:38:48.483058image/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
2024-03-13T21:38:48.668407image/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_SLNTY
Real number (ℝ)

HIGH CORRELATION 

Distinct124
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.799833
Minimum32.637
Maximum32.984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-13T21:38:48.934220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.637
5-th percentile32.6483
Q132.71475
median32.802
Q332.87325
95-th percentile32.953
Maximum32.984
Range0.347
Interquartile range (IQR)0.1585

Descriptive statistics

Standard deviation0.099477586
Coefficient of variation (CV)0.0030328687
Kurtosis-1.1051564
Mean32.799833
Median Absolute Deviation (MAD)0.082
Skewness0.026287631
Sum4723.176
Variance0.0098957902
MonotonicityNot monotonic
2024-03-13T21:38:49.137290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.644 3
 
2.1%
32.87 3
 
2.1%
32.831 2
 
1.4%
32.833 2
 
1.4%
32.953 2
 
1.4%
32.804 2
 
1.4%
32.798 2
 
1.4%
32.913 2
 
1.4%
32.707 2
 
1.4%
32.757 2
 
1.4%
Other values (114) 122
84.7%
ValueCountFrequency (%)
32.637 1
 
0.7%
32.639 1
 
0.7%
32.642 1
 
0.7%
32.644 3
2.1%
32.645 1
 
0.7%
32.648 1
 
0.7%
32.65 1
 
0.7%
32.653 1
 
0.7%
32.655 1
 
0.7%
32.661 2
1.4%
ValueCountFrequency (%)
32.984 1
0.7%
32.978 2
1.4%
32.977 1
0.7%
32.968 1
0.7%
32.967 1
0.7%
32.956 1
0.7%
32.953 2
1.4%
32.952 1
0.7%
32.949 1
0.7%
32.948 1
0.7%

MXM_SLNTY
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.94591
Minimum32.757
Maximum33.177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-13T21:38:49.361557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.757
5-th percentile32.81715
Q132.8835
median32.9515
Q333.00825
95-th percentile33.062
Maximum33.177
Range0.42
Interquartile range (IQR)0.12475

Descriptive statistics

Standard deviation0.080106827
Coefficient of variation (CV)0.002431465
Kurtosis-0.48232398
Mean32.94591
Median Absolute Deviation (MAD)0.0605
Skewness-0.095099695
Sum4744.211
Variance0.0064171037
MonotonicityNot monotonic
2024-03-13T21:38:49.605824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.995 3
 
2.1%
33.033 3
 
2.1%
32.934 3
 
2.1%
33.043 3
 
2.1%
32.893 3
 
2.1%
32.931 2
 
1.4%
32.921 2
 
1.4%
32.85 2
 
1.4%
33.009 2
 
1.4%
33.014 2
 
1.4%
Other values (104) 119
82.6%
ValueCountFrequency (%)
32.757 1
0.7%
32.775 1
0.7%
32.783 1
0.7%
32.784 1
0.7%
32.788 1
0.7%
32.803 1
0.7%
32.814 1
0.7%
32.817 1
0.7%
32.818 1
0.7%
32.823 1
0.7%
ValueCountFrequency (%)
33.177 1
0.7%
33.095 1
0.7%
33.074 1
0.7%
33.073 1
0.7%
33.072 1
0.7%
33.066 1
0.7%
33.063 1
0.7%
33.062 2
1.4%
33.061 2
1.4%
33.055 1
0.7%

AVG_SLNTY
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.868201
Minimum32.709
Maximum33.024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-13T21:38:49.823211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.709
5-th percentile32.7263
Q132.8045
median32.8685
Q332.94525
95-th percentile33.00385
Maximum33.024
Range0.315
Interquartile range (IQR)0.14075

Descriptive statistics

Standard deviation0.089982095
Coefficient of variation (CV)0.0027376641
Kurtosis-1.0871275
Mean32.868201
Median Absolute Deviation (MAD)0.072
Skewness-0.058495092
Sum4733.021
Variance0.0080967773
MonotonicityNot monotonic
2024-03-13T21:38:50.038829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.003 3
 
2.1%
32.729 3
 
2.1%
32.968 3
 
2.1%
32.881 2
 
1.4%
32.905 2
 
1.4%
32.883 2
 
1.4%
32.814 2
 
1.4%
32.754 2
 
1.4%
32.999 2
 
1.4%
32.982 2
 
1.4%
Other values (108) 121
84.0%
ValueCountFrequency (%)
32.709 1
 
0.7%
32.715 1
 
0.7%
32.717 1
 
0.7%
32.719 1
 
0.7%
32.721 1
 
0.7%
32.722 1
 
0.7%
32.724 1
 
0.7%
32.726 1
 
0.7%
32.728 1
 
0.7%
32.729 3
2.1%
ValueCountFrequency (%)
33.024 1
 
0.7%
33.023 1
 
0.7%
33.019 1
 
0.7%
33.016 1
 
0.7%
33.014 1
 
0.7%
33.012 2
1.4%
33.004 1
 
0.7%
33.003 3
2.1%
32.999 2
1.4%
32.998 1
 
0.7%

SLNTY_STDDEV_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041340278
Minimum0.02
Maximum0.108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-13T21:38:50.235265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.023
Q10.034
median0.0395
Q30.048
95-th percentile0.06085
Maximum0.108
Range0.088
Interquartile range (IQR)0.014

Descriptive statistics

Standard deviation0.013059393
Coefficient of variation (CV)0.31589997
Kurtosis5.6735713
Mean0.041340278
Median Absolute Deviation (MAD)0.0065
Skewness1.618301
Sum5.953
Variance0.00017054774
MonotonicityNot monotonic
2024-03-13T21:38:50.583187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.038 9
 
6.2%
0.04 9
 
6.2%
0.039 8
 
5.6%
0.036 7
 
4.9%
0.041 7
 
4.9%
0.037 6
 
4.2%
0.046 5
 
3.5%
0.032 5
 
3.5%
0.033 5
 
3.5%
0.05 5
 
3.5%
Other values (36) 78
54.2%
ValueCountFrequency (%)
0.02 2
1.4%
0.022 4
2.8%
0.023 4
2.8%
0.024 2
1.4%
0.025 2
1.4%
0.026 2
1.4%
0.027 1
 
0.7%
0.028 4
2.8%
0.03 3
2.1%
0.031 1
 
0.7%
ValueCountFrequency (%)
0.108 1
0.7%
0.091 1
0.7%
0.087 1
0.7%
0.071 1
0.7%
0.066 1
0.7%
0.064 1
0.7%
0.061 2
1.4%
0.06 1
0.7%
0.059 2
1.4%
0.058 1
0.7%

SLNTY_MDN
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.866479
Minimum32.696
Maximum33.023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-13T21:38:50.933330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.696
5-th percentile32.7133
Q132.80275
median32.8655
Q332.9475
95-th percentile33.00485
Maximum33.023
Range0.327
Interquartile range (IQR)0.14475

Descriptive statistics

Standard deviation0.092156371
Coefficient of variation (CV)0.0028039624
Kurtosis-1.0365963
Mean32.866479
Median Absolute Deviation (MAD)0.072
Skewness-0.10277438
Sum4732.773
Variance0.0084927968
MonotonicityNot monotonic
2024-03-13T21:38:51.227883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.805 5
 
3.5%
32.799 3
 
2.1%
32.817 2
 
1.4%
33.004 2
 
1.4%
32.822 2
 
1.4%
32.84 2
 
1.4%
32.849 2
 
1.4%
32.856 2
 
1.4%
32.843 2
 
1.4%
32.723 2
 
1.4%
Other values (104) 120
83.3%
ValueCountFrequency (%)
32.696 1
0.7%
32.7 1
0.7%
32.703 1
0.7%
32.708 1
0.7%
32.709 1
0.7%
32.71 1
0.7%
32.712 1
0.7%
32.713 1
0.7%
32.715 1
0.7%
32.717 2
1.4%
ValueCountFrequency (%)
33.023 1
0.7%
33.022 1
0.7%
33.018 1
0.7%
33.014 2
1.4%
33.01 1
0.7%
33.008 1
0.7%
33.005 1
0.7%
33.004 2
1.4%
33.002 2
1.4%
32.999 1
0.7%

Interactions

2024-03-13T21:38:46.305826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:38.405477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:39.394165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:40.468535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:41.518166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:42.617837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:43.658210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:44.803262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:46.463383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:38.558981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:39.543724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:40.617125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:41.650769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:42.722360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:43.792828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:44.937921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:46.610138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:38.685704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:39.707883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:40.756152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:41.788464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:42.910423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:43.949009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:45.085644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:46.747766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:38.809596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:39.851282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:40.881671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:41.963701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:43.061074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:44.118584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:45.257563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:46.894899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:38.940552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:40.009600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:41.042730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:42.124279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:43.177096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:44.289205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:45.391904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:47.009835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:39.038640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:40.109306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:41.150849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:42.241092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:43.283293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:44.414340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:45.499034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:47.141821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:39.156194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:40.233125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:41.267716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:42.374990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:43.399683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:44.554310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:45.608949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:47.302852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:39.272408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:40.335643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:41.392983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:42.487566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:43.524269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:44.681592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:38:45.730503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:38:51.430113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GRID_CENTR_LAGRID_CENTR_LOSLNTY_DATA_CNTMIM_SLNTYMXM_SLNTYAVG_SLNTYSLNTY_STDDEV_VALSLNTY_MDN
GRID_CENTR_LA1.0000.0000.8640.9070.8320.9180.4610.907
GRID_CENTR_LO0.0001.0000.0000.0000.0000.0000.2470.000
SLNTY_DATA_CNT0.8640.0001.0000.7150.6850.7000.4980.689
MIM_SLNTY0.9070.0000.7151.0000.8850.9660.4780.954
MXM_SLNTY0.8320.0000.6850.8851.0000.9230.7960.916
AVG_SLNTY0.9180.0000.7000.9660.9231.0000.4920.990
SLNTY_STDDEV_VAL0.4610.2470.4980.4780.7960.4921.0000.492
SLNTY_MDN0.9070.0000.6890.9540.9160.9900.4921.000
2024-03-13T21:38:51.654010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GRID_CENTR_LAGRID_CENTR_LOSLNTY_DATA_CNTMIM_SLNTYMXM_SLNTYAVG_SLNTYSLNTY_STDDEV_VALSLNTY_MDN
GRID_CENTR_LA1.0000.0000.230-0.957-0.814-0.9660.579-0.966
GRID_CENTR_LO0.0001.000-0.2030.008-0.099-0.0340.026-0.047
SLNTY_DATA_CNT0.230-0.2031.000-0.271-0.177-0.2250.385-0.228
MIM_SLNTY-0.9570.008-0.2711.0000.8290.982-0.6600.981
MXM_SLNTY-0.814-0.099-0.1770.8291.0000.880-0.3160.857
AVG_SLNTY-0.966-0.034-0.2250.9820.8801.000-0.5700.997
SLNTY_STDDEV_VAL0.5790.0260.385-0.660-0.316-0.5701.000-0.592
SLNTY_MDN-0.966-0.047-0.2280.9810.8570.997-0.5921.000

Missing values

2024-03-13T21:38:47.505634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:38:47.688130image/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_LOSLNTY_DATA_CNTMIM_SLNTYMXM_SLNTYAVG_SLNTYSLNTY_STDDEV_VALSLNTY_MDN
034.195833126.2541672432.64232.78832.7190.04832.723
134.195833126.26252532.65333.17732.7620.10832.748
234.195833126.2708332732.66432.99932.7660.08732.737
334.195833126.2791672732.66332.81732.730.05232.713
434.195833126.28753032.64433.09532.7320.09132.703
534.195833126.2958333032.64432.83632.7150.06132.696
634.195833126.3041673032.65532.81432.7170.0532.7
734.195833126.31253032.6532.77532.7210.0432.733
834.195833126.3208333032.66732.80332.7280.03832.737
934.195833126.3291672732.66232.78432.7290.03232.733
GRID_CENTR_LAGRID_CENTR_LOSLNTY_DATA_CNTMIM_SLNTYMXM_SLNTYAVG_SLNTYSLNTY_STDDEV_VALSLNTY_MDN
13434.104167126.2708333632.96833.06233.0120.03333.008
13534.104167126.2791673632.97833.06133.0140.02833.014
13634.104167126.28753632.97833.06133.0190.02733.018
13734.104167126.2958333632.98433.06633.0230.02833.023
13834.104167126.3041673632.97733.07433.0240.0333.022
13934.104167126.31253632.94833.07333.0160.03733.014
14034.104167126.3208333632.92333.07232.9930.04632.994
14134.104167126.3291673632.92933.04732.9850.04332.986
14234.104167126.33753632.9333.04332.9760.04132.964
14334.104167126.3458333632.92433.03332.9790.03632.984