Overview

Dataset statistics

Number of variables18
Number of observations16
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory152.0 B

Variable types

NUM13
CAT5

Warnings

Aktualisierung has constant value "16" Constant
LAN_ew_AGS is highly correlated with OBJECTID_1 and 1 other fieldsHigh correlation
OBJECTID_1 is highly correlated with LAN_ew_AGS and 1 other fieldsHigh correlation
Fallzahl is highly correlated with LAN_ew_EWZ and 3 other fieldsHigh correlation
LAN_ew_EWZ is highly correlated with Fallzahl and 3 other fieldsHigh correlation
AGS_TXT is highly correlated with OBJECTID_1 and 1 other fieldsHigh correlation
Death is highly correlated with LAN_ew_EWZ and 3 other fieldsHigh correlation
cases7_bl is highly correlated with LAN_ew_EWZ and 3 other fieldsHigh correlation
death7_bl is highly correlated with LAN_ew_EWZ and 3 other fieldsHigh correlation
OBJECTID_1 has unique values Unique
LAN_ew_AGS has unique values Unique
LAN_ew_GEN has unique values Unique
LAN_ew_EWZ has unique values Unique
OBJECTID has unique values Unique
Fallzahl has unique values Unique
AGS_TXT has unique values Unique
GlobalID has unique values Unique
faelle_100000_EW has unique values Unique
Death has unique values Unique
cases7_bl_per_100k has unique values Unique
cases7_bl has unique values Unique
death7_bl has unique values Unique
cases7_bl_per_100k_txt has unique values Unique
SHAPE_Length has unique values Unique
SHAPE_Area has unique values Unique

Reproduction

Analysis started2020-12-29 12:33:35.935308
Analysis finished2020-12-29 12:33:55.752317
Duration19.82 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

OBJECTID_1
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:55.804365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.75
Q14.75
median8.5
Q312.25
95-th percentile15.25
Maximum16
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.760952286
Coefficient of variation (CV)0.5601120336
Kurtosis-1.2
Mean8.5
Median Absolute Deviation (MAD)4
Skewness0
Sum136
Variance22.66666667
MonotocityStrictly increasing
2020-12-29T13:33:56.128660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
1616.2%
 
1516.2%
 
1416.2%
 
1316.2%
 
1216.2%
 
1116.2%
 
1016.2%
 
916.2%
 
816.2%
 
716.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
116.2%
 
216.2%
 
316.2%
 
416.2%
 
516.2%
 
ValueCountFrequency (%) 
1616.2%
 
1516.2%
 
1416.2%
 
1316.2%
 
1216.2%
 

LAN_ew_AGS
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:56.220743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.75
Q14.75
median8.5
Q312.25
95-th percentile15.25
Maximum16
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.760952286
Coefficient of variation (CV)0.5601120336
Kurtosis-1.2
Mean8.5
Median Absolute Deviation (MAD)4
Skewness0
Sum136
Variance22.66666667
MonotocityStrictly increasing
2020-12-29T13:33:56.300816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
1616.2%
 
1516.2%
 
1416.2%
 
1316.2%
 
1216.2%
 
1116.2%
 
1016.2%
 
916.2%
 
816.2%
 
716.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
116.2%
 
216.2%
 
316.2%
 
416.2%
 
516.2%
 
ValueCountFrequency (%) 
1616.2%
 
1516.2%
 
1416.2%
 
1316.2%
 
1216.2%
 

LAN_ew_GEN
Categorical

UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size128.0 B
Mecklenburg-Vorpommern
 
1
Thüringen
 
1
Bremen
 
1
Baden-Württemberg
 
1
Saarland
 
1
Other values (11)
11 
ValueCountFrequency (%) 
Mecklenburg-Vorpommern16.2%
 
Thüringen16.2%
 
Bremen16.2%
 
Baden-Württemberg16.2%
 
Saarland16.2%
 
Sachsen16.2%
 
Hamburg16.2%
 
Schleswig-Holstein16.2%
 
Bayern16.2%
 
Sachsen-Anhalt16.2%
 
Other values (6)637.5%
 
2020-12-29T13:33:56.413919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique16 ?
Unique (%)100.0%
2020-12-29T13:33:56.526021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length10
Mean length11.5
Min length6

LAN_ew_BEZ
Categorical

Distinct4
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size128.0 B
Land
11 
Freistaat
Freie und Hansestadt
 
1
Freie Hansestadt
 
1
ValueCountFrequency (%) 
Land1168.8%
 
Freistaat318.8%
 
Freie und Hansestadt16.2%
 
Freie Hansestadt16.2%
 
2020-12-29T13:33:56.633118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)12.5%
2020-12-29T13:33:56.697176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:56.801271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length4
Mean length6.6875
Min length4

LAN_ew_EWZ
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5197919.438
Minimum681202
Maximum17947221
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:56.896357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum681202
5-th percentile910465.75
Q12061846.75
median3286632
Q36714462
95-th percentile14330358
Maximum17947221
Range17266019
Interquartile range (IQR)4652615.25

Descriptive statistics

Standard deviation4947234.003
Coefficient of variation (CV)0.9517719662
Kurtosis1.76745119
Mean5197919.438
Median Absolute Deviation (MAD)1558936.5
Skewness1.544805277
Sum83166711
Variance2.447512428e+13
MonotocityNot monotonic
2020-12-29T13:33:56.988441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
219478216.2%
 
290377316.2%
 
160813816.2%
 
1794722116.2%
 
184725316.2%
 
366949116.2%
 
68120216.2%
 
628808016.2%
 
409390316.2%
 
1110039416.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
68120216.2%
 
98688716.2%
 
160813816.2%
 
184725316.2%
 
213337816.2%
 
ValueCountFrequency (%) 
1794722116.2%
 
1312473716.2%
 
1110039416.2%
 
799360816.2%
 
628808016.2%
 

OBJECTID
Real number (ℝ≥0)

UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:57.092536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.75
Q14.75
median8.5
Q312.25
95-th percentile15.25
Maximum16
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.760952286
Coefficient of variation (CV)0.5601120336
Kurtosis-1.2
Mean8.5
Median Absolute Deviation (MAD)4
Skewness0
Sum136
Variance22.66666667
MonotocityNot monotonic
2020-12-29T13:33:57.172609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
1616.2%
 
1516.2%
 
1416.2%
 
1316.2%
 
1216.2%
 
1116.2%
 
1016.2%
 
916.2%
 
816.2%
 
716.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
116.2%
 
216.2%
 
316.2%
 
416.2%
 
516.2%
 
ValueCountFrequency (%) 
1616.2%
 
1516.2%
 
1416.2%
 
1316.2%
 
1216.2%
 

Fallzahl
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104045.375
Minimum11168
Maximum383008
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:57.268695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum11168
5-th percentile12736.25
Q126761.5
median54835.5
Q3127866.75
95-th percentile331952.5
Maximum383008
Range371840
Interquartile range (IQR)101105.25

Descriptive statistics

Standard deviation112744.1674
Coefficient of variation (CV)1.083605758
Kurtosis1.612359809
Mean104045.375
Median Absolute Deviation (MAD)40591.5
Skewness1.550408578
Sum1664726
Variance1.271124729e+10
MonotocityNot monotonic
2020-12-29T13:33:57.356776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
3964716.2%
 
3782016.2%
 
12642116.2%
 
23168616.2%
 
31493416.2%
 
1116816.2%
 
3548416.2%
 
1934116.2%
 
13220416.2%
 
1325916.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
1116816.2%
 
1325916.2%
 
1934116.2%
 
2378416.2%
 
2775416.2%
 
ValueCountFrequency (%) 
38300816.2%
 
31493416.2%
 
23168616.2%
 
13220416.2%
 
12642116.2%
 

Aktualisierung
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size128.0 B
2020/12/28 23:00:00
16 
ValueCountFrequency (%) 
2020/12/28 23:00:0016100.0%
 
2020-12-29T13:33:57.461871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-29T13:33:57.520925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:57.584983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

AGS_TXT
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:57.670060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.75
Q14.75
median8.5
Q312.25
95-th percentile15.25
Maximum16
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.760952286
Coefficient of variation (CV)0.5601120336
Kurtosis-1.2
Mean8.5
Median Absolute Deviation (MAD)4
Skewness0
Sum136
Variance22.66666667
MonotocityStrictly increasing
2020-12-29T13:33:57.750133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
1616.2%
 
1516.2%
 
1416.2%
 
1316.2%
 
1216.2%
 
1116.2%
 
1016.2%
 
916.2%
 
816.2%
 
716.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
116.2%
 
216.2%
 
316.2%
 
416.2%
 
516.2%
 
ValueCountFrequency (%) 
1616.2%
 
1516.2%
 
1416.2%
 
1316.2%
 
1216.2%
 

GlobalID
Categorical

UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size128.0 B
{3A3E2817-BB19-4F8C-8BCA-D8FF591140D4}
 
1
{80394DDF-C6A4-4A6E-BE8E-0259A81B22A9}
 
1
{1FF920F4-62CD-4A4F-B8C9-F042F2A3E00A}
 
1
{3FD77024-C29B-4843-9BE8-682AD48E60C9}
 
1
{FC5BA936-C95C-432C-8A33-9EB2F30B660F}
 
1
Other values (11)
11 
ValueCountFrequency (%) 
{3A3E2817-BB19-4F8C-8BCA-D8FF591140D4}16.2%
 
{80394DDF-C6A4-4A6E-BE8E-0259A81B22A9}16.2%
 
{1FF920F4-62CD-4A4F-B8C9-F042F2A3E00A}16.2%
 
{3FD77024-C29B-4843-9BE8-682AD48E60C9}16.2%
 
{FC5BA936-C95C-432C-8A33-9EB2F30B660F}16.2%
 
{93277AC4-E8FC-48C7-8940-028DC2ED66AF}16.2%
 
{4132268B-54DE-4327-AC1E-760E915112F1}16.2%
 
{256D2405-A7C8-4FEC-93DF-29F3C808CD25}16.2%
 
{0F3E860C-5181-4D3F-A421-1D51F50315EA}16.2%
 
{E9B4296F-9BE2-4E53-9A58-CCF1396CB03D}16.2%
 
Other values (6)637.5%
 
2020-12-29T13:33:57.862235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique16 ?
Unique (%)100.0%
2020-12-29T13:33:57.965329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length38
Mean length38
Min length38

faelle_100000_EW
Real number (ℝ≥0)

UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1835.829998
Minimum694.4677633
Maximum3104.663565
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:58.050406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum694.4677633
5-th percentile787.9211581
Q11449.228344
median1933.659356
Q32110.36055
95-th percentile2706.44723
Maximum3104.663565
Range2410.195801
Interquartile range (IQR)661.132206

Descriptive statistics

Standard deviation622.4520244
Coefficient of variation (CV)0.3390575516
Kurtosis0.2574221869
Mean1835.829998
Median Absolute Deviation (MAD)328.6027995
Skewness-0.07302979954
Sum29373.27997
Variance387446.5227
MonotocityNot monotonic
2020-12-29T13:33:58.137485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
2573.70845216.2%
 
1959.79884216.2%
 
1946.41237116.2%
 
1264.54472516.2%
 
1858.41421416.2%
 
819.072289716.2%
 
2087.18717616.2%
 
3104.66356516.2%
 
2134.079716.2%
 
1920.9063416.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
694.467763316.2%
 
819.072289716.2%
 
1264.54472516.2%
 
1297.91203216.2%
 
1499.66711516.2%
 
ValueCountFrequency (%) 
3104.66356516.2%
 
2573.70845216.2%
 
2399.54522516.2%
 
2134.079716.2%
 
2102.45416716.2%
 

Death
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1936.125
Minimum152
Maximum6398
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:58.224564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum152
5-th percentile177.5
Q1502
median1039.5
Q32646
95-th percentile6269.75
Maximum6398
Range6246
Interquartile range (IQR)2144

Descriptive statistics

Standard deviation2074.706594
Coefficient of variation (CV)1.07157678
Kurtosis0.7351050266
Mean1936.125
Median Absolute Deviation (MAD)728.5
Skewness1.372522317
Sum30978
Variance4304407.45
MonotocityNot monotonic
2020-12-29T13:33:58.305638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
639816.2%
 
90716.2%
 
41216.2%
 
185116.2%
 
18616.2%
 
15216.2%
 
53216.2%
 
59816.2%
 
117216.2%
 
622716.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
15216.2%
 
18616.2%
 
39416.2%
 
41216.2%
 
53216.2%
 
ValueCountFrequency (%) 
639816.2%
 
622716.2%
 
458716.2%
 
283816.2%
 
258216.2%
 

cases7_bl_per_100k
Real number (ℝ≥0)

UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.0437824
Minimum77.23217784
Maximum364.6636973
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:58.393718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum77.23217784
5-th percentile83.181839
Q1107.5034273
median137.0907519
Q3150.6432661
95-th percentile296.4389702
Maximum364.6636973
Range287.4315194
Interquartile range (IQR)43.13983885

Descriptive statistics

Standard deviation73.9015617
Coefficient of variation (CV)0.4958379378
Kurtosis4.559253196
Mean149.0437824
Median Absolute Deviation (MAD)24.16977948
Skewness2.044894744
Sum2384.700518
Variance5461.440821
MonotocityNot monotonic
2020-12-29T13:33:58.470788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
135.374498416.2%
 
145.115494216.2%
 
139.05675516.2%
 
161.534665416.2%
 
138.807005316.2%
 
85.1650593916.2%
 
90.4283898216.2%
 
364.663697316.2%
 
77.2321778416.2%
 
131.322025716.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
77.2321778416.2%
 
85.1650593916.2%
 
87.2196885316.2%
 
90.4283898216.2%
 
113.195106516.2%
 
ValueCountFrequency (%) 
364.663697316.2%
 
273.697394516.2%
 
172.181109616.2%
 
161.534665416.2%
 
147.012799716.2%
 

cases7_bl
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7755.9375
Minimum616
Maximum24912
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:58.558868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum616
5-th percentile1085.5
Q12377.5
median5174
Q310270.25
95-th percentile22128.75
Maximum24912
Range24296
Interquartile range (IQR)7892.75

Descriptive statistics

Standard deviation7515.281857
Coefficient of variation (CV)0.9689714309
Kurtosis0.5351574545
Mean7755.9375
Median Absolute Deviation (MAD)3326.5
Skewness1.263587408
Sum124095
Variance56479461.4
MonotocityNot monotonic
2020-12-29T13:33:58.654956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
1631916.2%
 
697216.2%
 
124216.2%
 
341416.2%
 
61616.2%
 
129616.2%
 
2120116.2%
 
2491216.2%
 
583916.2%
 
532516.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
61616.2%
 
124216.2%
 
129616.2%
 
209116.2%
 
247316.2%
 
ValueCountFrequency (%) 
2491216.2%
 
2120116.2%
 
1631916.2%
 
1484916.2%
 
874416.2%
 

death7_bl
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.625
Minimum3
Maximum108
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:58.751043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.5
Q113.25
median33.5
Q350.25
95-th percentile93.75
Maximum108
Range105
Interquartile range (IQR)37

Descriptive statistics

Standard deviation30.35319862
Coefficient of variation (CV)0.8067295315
Kurtosis0.6187512943
Mean37.625
Median Absolute Deviation (MAD)19.5
Skewness1.003663497
Sum602
Variance921.3166667
MonotocityNot monotonic
2020-12-29T13:33:58.843127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
3016.2%
 
2816.2%
 
8916.2%
 
2316.2%
 
5416.2%
 
6716.2%
 
4916.2%
 
1516.2%
 
516.2%
 
10816.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
316.2%
 
516.2%
 
716.2%
 
816.2%
 
1516.2%
 
ValueCountFrequency (%) 
10816.2%
 
8916.2%
 
6716.2%
 
5416.2%
 
4916.2%
 

cases7_bl_per_100k_txt
Categorical

UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size128.0 B
85,2
 
1
138,8
 
1
139,1
 
1
145,1
 
1
135,4
 
1
Other values (11)
11 
ValueCountFrequency (%) 
85,216.2%
 
138,816.2%
 
139,116.2%
 
145,116.2%
 
135,416.2%
 
87,216.2%
 
113,216.2%
 
131,316.2%
 
364,716.2%
 
172,216.2%
 
Other values (6)637.5%
 
2020-12-29T13:33:58.956229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique16 ?
Unique (%)100.0%
2020-12-29T13:33:59.057322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.75
Min length4

SHAPE_Length
Real number (ℝ≥0)

UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.85829039
Minimum2.443409917
Maximum30.04011032
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:59.141398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.443409917
5-th percentile2.621774505
Q110.95251499
median15.58376583
Q320.02354315
95-th percentile29.87173266
Maximum30.04011032
Range27.5967004
Interquartile range (IQR)9.071028162

Descriptive statistics

Standard deviation9.302046724
Coefficient of variation (CV)0.5865731108
Kurtosis-0.7912237374
Mean15.85829039
Median Absolute Deviation (MAD)4.738305095
Skewness-0.01272884394
Sum253.7326462
Variance86.52807325
MonotocityNot monotonic
2020-12-29T13:33:59.218468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
15.2497070416.2%
 
15.0324529616.2%
 
2.44340991716.2%
 
29.8156067716.2%
 
30.0401103216.2%
 
14.2172902916.2%
 
2.68122936716.2%
 
4.22859886616.2%
 
19.7250153816.2%
 
20.9191264616.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
2.44340991716.2%
 
2.68122936716.2%
 
3.10951982816.2%
 
4.22859886616.2%
 
13.1938203616.2%
 
ValueCountFrequency (%) 
30.0401103216.2%
 
29.8156067716.2%
 
29.2832556616.2%
 
20.9191264616.2%
 
19.7250153816.2%
 

SHAPE_Area
Real number (ℝ≥0)

UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.867620854
Minimum0.0540986322
Maximum8.661119201
Zeros0
Zeros (%)0.0%
Memory size128.0 B
2020-12-29T13:33:59.310552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0540986322
5-th percentile0.08865860736
Q11.632337438
median2.583459701
Q34.030020052
95-th percentile6.924384144
Maximum8.661119201
Range8.607020569
Interquartile range (IQR)2.397682614

Descriptive statistics

Standard deviation2.347794837
Coefficient of variation (CV)0.8187256811
Kurtosis1.230216465
Mean2.867620854
Median Absolute Deviation (MAD)1.554624554
Skewness0.9585607475
Sum45.88193367
Variance5.512140597
MonotocityNot monotonic
2020-12-29T13:33:59.394628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
4.41341330916.2%
 
2.68129889416.2%
 
8.66111920116.2%
 
0.118016871716.2%
 
6.34547245916.2%
 
2.48562050816.2%
 
0.054098632216.2%
 
2.36506980616.2%
 
2.07032354616.2%
 
0.31837911416.2%
 
Other values (6)637.5%
 
ValueCountFrequency (%) 
0.054098632216.2%
 
0.100178599116.2%
 
0.118016871716.2%
 
0.31837911416.2%
 
2.07032354616.2%
 
ValueCountFrequency (%) 
8.66111920116.2%
 
6.34547245916.2%
 
4.41341330916.2%
 
4.3542126616.2%
 
3.9219558516.2%
 

Interactions

2020-12-29T13:33:38.222387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:38.331487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:38.433579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:38.539676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:38.640767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:38.751868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:38.854962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:38.957055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.057146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.154234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.253325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.350412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.446499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.537582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.639675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.742768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.848865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:39.950958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:40.540493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:40.643588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:40.737673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:40.837764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:40.934852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.031940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.128027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.224115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.317199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.427299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.533396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.643496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.750593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.862695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:41.967791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.066881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.169975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.270065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.370156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.469247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.569337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.666426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.768518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.869610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:42.974706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.076799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.184897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.290994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.388081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.487171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.583259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.681348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.775433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.870520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:43.961602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:44.071703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:44.179801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:44.291903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:44.399000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:44.512103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:44.620201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:44.873431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:44.978527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:45.080620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:45.183713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:45.284805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:45.387899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:45.486989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:45.592084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:45.695178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:45.803276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:45.906370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.014469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.115561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.208645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.308736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.405824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.502912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.597998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.694086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.785169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.878253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:46.969337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.065424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.161510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.258599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.348681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.429754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.515833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.599909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.684987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.770064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.858144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:47.938217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.037306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.140400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.243494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.343585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.450682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.549773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.638854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.735942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.829026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:48.919108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:49.009190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:49.099272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:49.185350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:49.279436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:49.373521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:49.471610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:49.565696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:49.666787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:49.952047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.037124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.129208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.218288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.306369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.394449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.481528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.565604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.658689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.752774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.849862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:50.943948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.043038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.136123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.220199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.310281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.397360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.483438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.569517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.656595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.738670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.830754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:51.923838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.019926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.117014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.217106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.309189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.392264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.481345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.568424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.655504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.741582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.827660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:52.908734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.001818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.095904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.192992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.287077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.389171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.484257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.570335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.662418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.751499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.839580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:53.928661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.016740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.101818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.190899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.277978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.371063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.459142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.552227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.642309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.722382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.808460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.892536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:54.974611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:55.056685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:55.137760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-29T13:33:59.490715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-29T13:33:59.695902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-29T13:33:59.900087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-29T13:34:00.110279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-29T13:34:00.327475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-29T13:33:55.317923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T13:33:55.627204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

OBJECTID_1LAN_ew_AGSLAN_ew_GENLAN_ew_BEZLAN_ew_EWZOBJECTIDFallzahlAktualisierungAGS_TXTGlobalIDfaelle_100000_EWDeathcases7_bl_per_100kcases7_bldeath7_blcases7_bl_per_100k_txtSHAPE_LengthSHAPE_Area
011Schleswig-HolsteinLand290377315237842020/12/28 23:00:001{FC5BA936-C95C-432C-8A33-9EB2F30B660F}819.07229039485.16505924731585,220.9191262.159546
122HamburgFreie und Hansestadt18472536354842020/12/28 23:00:002{0F3E860C-5181-4D3F-A421-1D51F50315EA}1920.906340598113.19510620915113,23.1095200.100179
233NiedersachsenLand799360891037502020/12/28 23:00:003{3FD77024-C29B-4843-9BE8-682AD48E60C9}1297.912032185187.21968969722887,229.8156076.345472
344BremenFreie Hansestadt6812025132592020/12/28 23:00:004{4132268B-54DE-4327-AC1E-760E915112F1}1946.41237118690.428390616390,42.4434100.054099
455Nordrhein-WestfalenLand17947221103830082020/12/28 23:00:005{561D658F-3EE5-46E3-BC95-3528C6558AB9}2134.0797006227138.80700524912108138,819.7250154.413413
566HessenLand628808071322042020/12/28 23:00:006{93277AC4-E8FC-48C7-8940-028DC2ED66AF}2102.4541672582139.056755874449139,115.9178252.681299
677Rheinland-PfalzLand409390311700242020/12/28 23:00:007{E9B4296F-9BE2-4E53-9A58-CCF1396CB03D}1710.4459971328122.694651502338122,713.1938202.485621
788Baden-WürttembergLand1110039412316862020/12/28 23:00:008{80394DDF-C6A4-4A6E-BE8E-0259A81B22A9}2087.1871764587147.0128001631967147,018.9961734.354213
899BayernFreistaat1312473723149342020/12/28 23:00:009{1FF920F4-62CD-4A4F-B8C9-F042F2A3E00A}2399.5452256398161.5346652120189161,529.2832568.661119
91010SaarlandLand98688712193412020/12/28 23:00:0010{E3396A6F-8A30-4FDF-8DF7-DEF77DD38BEA}1959.798842412131.32202612967131,34.2285990.318379

Last rows

OBJECTID_1LAN_ew_AGSLAN_ew_GENLAN_ew_BEZLAN_ew_EWZOBJECTIDFallzahlAktualisierungAGS_TXTGlobalIDfaelle_100000_EWDeathcases7_bl_per_100kcases7_bldeath7_blcases7_bl_per_100k_txtSHAPE_LengthSHAPE_Area
677Rheinland-PfalzLand409390311700242020/12/28 23:00:007{E9B4296F-9BE2-4E53-9A58-CCF1396CB03D}1710.4459971328122.694651502338122,713.1938202.485621
788Baden-WürttembergLand1110039412316862020/12/28 23:00:008{80394DDF-C6A4-4A6E-BE8E-0259A81B22A9}2087.1871764587147.0128001631967147,018.9961734.354213
899BayernFreistaat1312473723149342020/12/28 23:00:009{1FF920F4-62CD-4A4F-B8C9-F042F2A3E00A}2399.5452256398161.5346652120189161,529.2832568.661119
91010SaarlandLand98688712193412020/12/28 23:00:0010{E3396A6F-8A30-4FDF-8DF7-DEF77DD38BEA}1959.798842412131.32202612967131,34.2285990.318379
101111BerlinLand36694913944422020/12/28 23:00:0011{C7033073-2BB0-486B-97AF-5B3C639E7219}2573.7084521172145.115494532530145,12.6812290.118017
111212BrandenburgLand25218934378202020/12/28 23:00:0012{B560AF89-5895-4BE4-96F8-0F581CD8A858}1499.667115814135.374498341437135,418.8795053.921956
121313Mecklenburg-VorpommernLand16081388111682020/12/28 23:00:0013{A27F5628-E790-45BE-898D-F0A6841C0F7E}694.46776315277.2321781242877,230.0401103.142812
131414SachsenFreistaat4071971131264212020/12/28 23:00:0014{256D2405-A7C8-4FEC-93DF-29F3C808CD25}3104.6635652838364.6636971484954364,715.2497072.365070
141515Sachsen-AnhaltLand219478214277542020/12/28 23:00:0015{66106BBE-F04B-4F92-80E1-5B5B30DA25B5}1264.544725532172.181110377923172,214.2172902.690416
151616ThüringenFreistaat213337816396472020/12/28 23:00:0016{3A3E2817-BB19-4F8C-8BCA-D8FF591140D4}1858.414214907273.697394583941273,715.0324532.070324