Overview

Dataset statistics

Number of variables17
Number of observations435
Missing cells0
Missing cells (%)0.0%
Duplicate rows93
Duplicate rows (%)21.4%
Total size in memory57.9 KiB
Average record size in memory136.3 B

Variable types

CAT16
BOOL1

Reproduction

Analysis started2020-08-25 01:25:37.300069
Analysis finished2020-08-25 01:25:39.800895
Duration2.5 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 93 (21.4%) duplicate rows Duplicates

Variables

Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
1
236
2
187
0
 
12
ValueCountFrequency (%) 
123654.3%
 
218743.0%
 
0122.8%
 
2020-08-25T01:25:39.867239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
123654.3%
 
218743.0%
 
0122.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
123654.3%
 
218743.0%
 
0122.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
123654.3%
 
218743.0%
 
0122.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
123654.3%
 
218743.0%
 
0122.8%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
195
1
192
0
48
ValueCountFrequency (%) 
219544.8%
 
119244.1%
 
04811.0%
 
2020-08-25T01:25:39.991560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
219544.8%
 
119244.1%
 
04811.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
219544.8%
 
119244.1%
 
04811.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
219544.8%
 
119244.1%
 
04811.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
219544.8%
 
119244.1%
 
04811.0%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
253
1
171
0
 
11
ValueCountFrequency (%) 
225358.2%
 
117139.3%
 
0112.5%
 
2020-08-25T01:25:40.117220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
225358.2%
 
117139.3%
 
0112.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
225358.2%
 
117139.3%
 
0112.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
225358.2%
 
117139.3%
 
0112.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
225358.2%
 
117139.3%
 
0112.5%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
1
247
2
177
0
 
11
ValueCountFrequency (%) 
124756.8%
 
217740.7%
 
0112.5%
 
2020-08-25T01:25:40.243106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
124756.8%
 
217740.7%
 
0112.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
124756.8%
 
217740.7%
 
0112.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
124756.8%
 
217740.7%
 
0112.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
124756.8%
 
217740.7%
 
0112.5%
 

el-salvador-adi
Categorical

Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
212
1
208
0
 
15
ValueCountFrequency (%) 
221248.7%
 
120847.8%
 
0153.4%
 
2020-08-25T01:25:40.366772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
221248.7%
 
120847.8%
 
0153.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
221248.7%
 
120847.8%
 
0153.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
221248.7%
 
120847.8%
 
0153.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
221248.7%
 
120847.8%
 
0153.4%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
272
1
152
0
 
11
ValueCountFrequency (%) 
227262.5%
 
115234.9%
 
0112.5%
 
2020-08-25T01:25:40.491973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
227262.5%
 
115234.9%
 
0112.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
227262.5%
 
115234.9%
 
0112.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
227262.5%
 
115234.9%
 
0112.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
227262.5%
 
115234.9%
 
0112.5%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
239
1
182
0
 
14
ValueCountFrequency (%) 
223954.9%
 
118241.8%
 
0143.2%
 
2020-08-25T01:25:40.621041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
223954.9%
 
118241.8%
 
0143.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
223954.9%
 
118241.8%
 
0143.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
223954.9%
 
118241.8%
 
0143.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
223954.9%
 
118241.8%
 
0143.2%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
242
1
178
0
 
15
ValueCountFrequency (%) 
224255.6%
 
117840.9%
 
0153.4%
 
2020-08-25T01:25:40.749034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
224255.6%
 
117840.9%
 
0153.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
224255.6%
 
117840.9%
 
0153.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
224255.6%
 
117840.9%
 
0153.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
224255.6%
 
117840.9%
 
0153.4%
 

mx-missile
Categorical

Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
207
1
206
0
 
22
ValueCountFrequency (%) 
220747.6%
 
120647.4%
 
0225.1%
 
2020-08-25T01:25:40.874573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
220747.6%
 
120647.4%
 
0225.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
220747.6%
 
120647.4%
 
0225.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
220747.6%
 
120647.4%
 
0225.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
220747.6%
 
120647.4%
 
0225.1%
 

immigration
Categorical

Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
216
1
212
0
 
7
ValueCountFrequency (%) 
221649.7%
 
121248.7%
 
071.6%
 
2020-08-25T01:25:40.999081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
221649.7%
 
121248.7%
 
071.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
221649.7%
 
121248.7%
 
071.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
221649.7%
 
121248.7%
 
071.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
221649.7%
 
121248.7%
 
071.6%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
1
264
2
150
0
 
21
ValueCountFrequency (%) 
126460.7%
 
215034.5%
 
0214.8%
 
2020-08-25T01:25:41.129139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
126460.7%
 
215034.5%
 
0214.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
126460.7%
 
215034.5%
 
0214.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
126460.7%
 
215034.5%
 
0214.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
126460.7%
 
215034.5%
 
0214.8%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
1
233
2
171
0
 
31
ValueCountFrequency (%) 
123353.6%
 
217139.3%
 
0317.1%
 
2020-08-25T01:25:41.258435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
123353.6%
 
217139.3%
 
0317.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
123353.6%
 
217139.3%
 
0317.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
123353.6%
 
217139.3%
 
0317.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
123353.6%
 
217139.3%
 
0317.1%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
209
1
201
0
 
25
ValueCountFrequency (%) 
220948.0%
 
120146.2%
 
0255.7%
 
2020-08-25T01:25:41.382967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
220948.0%
 
120146.2%
 
0255.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
220948.0%
 
120146.2%
 
0255.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
220948.0%
 
120146.2%
 
0255.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
220948.0%
 
120146.2%
 
0255.7%
 

crime
Categorical

Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
248
1
170
0
 
17
ValueCountFrequency (%) 
224857.0%
 
117039.1%
 
0173.9%
 
2020-08-25T01:25:41.506583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
224857.0%
 
117039.1%
 
0173.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
224857.0%
 
117039.1%
 
0173.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
224857.0%
 
117039.1%
 
0173.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
224857.0%
 
117039.1%
 
0173.9%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
1
233
2
174
0
 
28
ValueCountFrequency (%) 
123353.6%
 
217440.0%
 
0286.4%
 
2020-08-25T01:25:41.633835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
123353.6%
 
217440.0%
 
0286.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
123353.6%
 
217440.0%
 
0286.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
123353.6%
 
217440.0%
 
0286.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
123353.6%
 
217440.0%
 
0286.4%
 
Distinct count3
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2
269
0
104
1
62
ValueCountFrequency (%) 
226961.8%
 
010423.9%
 
16214.3%
 
2020-08-25T01:25:41.761693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
226961.8%
 
010423.9%
 
16214.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number435100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
226961.8%
 
010423.9%
 
16214.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common435100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
226961.8%
 
010423.9%
 
16214.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
226961.8%
 
010423.9%
 
16214.3%
 

target
Boolean

Distinct count2
Unique (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
0
267
1
168
ValueCountFrequency (%) 
026761.4%
 
116838.6%
 

Correlations

2020-08-25T01:25:41.895061image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T01:25:42.444877image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T01:25:42.811763image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T01:25:43.173636image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T01:25:43.515364image/svg+xmlMatplotlib v3.3.1, 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

2020-08-25T01:25:39.212359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:39.612222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

handicapped-infantswater-project-cost-sharingadoption-of-the-budget-resolutionphysician-fee-freezeel-salvador-adireligious-groups-in-schoolsanti-satellite-test-banaid-to-nicaraguan-contrasmx-missileimmigrationsynfuels-corporation-cutbackeducation-spendingsuperfund-right-to-suecrimeduty-free-exportsexport-administration-act-south-africatarget
012122211120222121
112122211111222101
202202211112122110
312210211112121120
422212211112022220
512212211111122220
612122211111102220
712122211111122021
812122211111222121
922211122211111000

Last rows

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Duplicate rows

Most frequent

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