autompgcsv1 Report

Dataset statistics

Number of variables	9
Number of observations	398
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	28.1 KiB
Average record size in memory	72.3 B

Variable types

Numeric	5
Categorical	4

Alerts

`horsepower` has a high cardinality: 94 distinct values	High cardinality
`car name` has a high cardinality: 305 distinct values	High cardinality
`mpg` is highly correlated with `cylinders` and 4 other fields	High correlation
`cylinders` is highly correlated with `mpg` and 3 other fields	High correlation
`displacement` is highly correlated with `mpg` and 3 other fields	High correlation
`weight` is highly correlated with `mpg` and 3 other fields	High correlation
`model year` is highly correlated with `mpg`	High correlation
`origin` is highly correlated with `mpg` and 3 other fields	High correlation
`mpg` is highly correlated with `cylinders` and 4 other fields	High correlation
`cylinders` is highly correlated with `mpg` and 4 other fields	High correlation
`displacement` is highly correlated with `mpg` and 4 other fields	High correlation
`weight` is highly correlated with `mpg` and 3 other fields	High correlation
`acceleration` is highly correlated with `cylinders` and 1 other fields	High correlation
`model year` is highly correlated with `mpg`	High correlation
`origin` is highly correlated with `mpg` and 3 other fields	High correlation
`mpg` is highly correlated with `cylinders` and 2 other fields	High correlation
`cylinders` is highly correlated with `mpg` and 3 other fields	High correlation
`displacement` is highly correlated with `mpg` and 3 other fields	High correlation
`weight` is highly correlated with `mpg` and 2 other fields	High correlation
`origin` is highly correlated with `cylinders` and 1 other fields	High correlation
`cylinders` is highly correlated with `horsepower`	High correlation
`horsepower` is highly correlated with `cylinders` and 1 other fields	High correlation
`origin` is highly correlated with `horsepower`	High correlation
`mpg` is highly correlated with `cylinders` and 6 other fields	High correlation
`cylinders` is highly correlated with `mpg` and 5 other fields	High correlation
`displacement` is highly correlated with `mpg` and 5 other fields	High correlation
`horsepower` is highly correlated with `mpg` and 6 other fields	High correlation
`weight` is highly correlated with `mpg` and 5 other fields	High correlation
`acceleration` is highly correlated with `mpg` and 4 other fields	High correlation
`model year` is highly correlated with `mpg` and 1 other fields	High correlation
`origin` is highly correlated with `mpg` and 4 other fields	High correlation
`car name` is uniformly distributed	Uniform

Reproduction

Analysis started	2022-06-09 10:43:57.316610
Analysis finished	2022-06-09 10:44:03.612143
Duration	6.3 seconds
Software version	pandas-profiling v3.2.0
Download configuration	config.json

mpg
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	129
Distinct (%)	32.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	23.51457286

Minimum	9
Maximum	46.6
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.2 KiB

Quantile statistics

Minimum	9
5-th percentile	13
Q1	17.5
median	23
Q3	29
95-th percentile	37.03
Maximum	46.6
Range	37.6
Interquartile range (IQR)	11.5

Descriptive statistics

Standard deviation	7.815984313
Coefficient of variation (CV)	0.3323889555
Kurtosis	-0.5107812652
Mean	23.51457286
Median Absolute Deviation (MAD)	6
Skewness	0.457066344
Sum	9358.8
Variance	61.08961077
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
13	20	5.0%
14	19	4.8%
18	17	4.3%
15	16	4.0%
26	14	3.5%
16	13	3.3%
19	12	3.0%
25	11	2.8%
24	11	2.8%
22	10	2.5%
Other values (119)	255	64.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
9	1	0.3%
10	2	0.5%
11	4	1.0%
12	6	1.5%
13	20	5.0%
14	19	4.8%
14.5	1	0.3%
15	16	4.0%
15.5	5	1.3%
16	13	3.3%

Value	Count	Frequency (%)
46.6	1	0.3%
44.6	1	0.3%
44.3	1	0.3%
44	1	0.3%
43.4	1	0.3%
43.1	1	0.3%
41.5	1	0.3%
40.9	1	0.3%
40.8	1	0.3%
39.4	1	0.3%

cylinders
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	5
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Memory size	3.2 KiB

4	204
8	103
6	84
3	4
5	3

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	398
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	8
2nd row	8
3rd row	8
4th row	8
5th row	8

Common Values

Value	Count	Frequency (%)
4	204	51.3%
8	103	25.9%
6	84	21.1%
3	4	1.0%
5	3	0.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
4	204	51.3%
8	103	25.9%
6	84	21.1%
3	4	1.0%
5	3	0.8%

Most occurring characters

Value	Count	Frequency (%)
4	204	51.3%
8	103	25.9%
6	84	21.1%
3	4	1.0%
5	3	0.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	398	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
4	204	51.3%
8	103	25.9%
6	84	21.1%
3	4	1.0%
5	3	0.8%

Most occurring scripts

Value	Count	Frequency (%)
Common	398	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
4	204	51.3%
8	103	25.9%
6	84	21.1%
3	4	1.0%
5	3	0.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	398	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
4	204	51.3%
8	103	25.9%
6	84	21.1%
3	4	1.0%
5	3	0.8%

displacement
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	82
Distinct (%)	20.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	193.4258794

Minimum	68
Maximum	455
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.2 KiB

Quantile statistics

Minimum	68
5-th percentile	85
Q1	104.25
median	148.5
Q3	262
95-th percentile	400
Maximum	455
Range	387
Interquartile range (IQR)	157.75

Descriptive statistics

Standard deviation	104.2698382
Coefficient of variation (CV)	0.5390687042
Kurtosis	-0.7465966296
Mean	193.4258794
Median Absolute Deviation (MAD)	58.5
Skewness	0.7196451643
Sum	76983.5
Variance	10872.19915
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
97	21	5.3%
98	18	4.5%
350	18	4.5%
318	17	4.3%
250	17	4.3%
140	16	4.0%
400	13	3.3%
225	13	3.3%
91	12	3.0%
232	11	2.8%
Other values (72)	242	60.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
68	1	0.3%
70	3	0.8%
71	2	0.5%
72	1	0.3%
76	1	0.3%
78	1	0.3%
79	6	1.5%
80	1	0.3%
81	1	0.3%
83	1	0.3%

Value	Count	Frequency (%)
455	3	0.8%
454	1	0.3%
440	2	0.5%
429	3	0.8%
400	13	3.3%
390	1	0.3%
383	2	0.5%
360	4	1.0%
351	8	2.0%
350	18	4.5%

horsepower
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct	94
Distinct (%)	23.6%
Missing	0
Missing (%)	0.0%
Memory size	3.2 KiB

150	22
90	20
88	19
110	18
100	17
Other values (89)	302

Length

Max length	3
Median length	2
Mean length	2.404522613
Min length	1

Characters and Unicode

Total characters	957
Distinct characters	11
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	35 ?
Unique (%)	8.8%

Sample

1st row	130
2nd row	165
3rd row	150
4th row	150
5th row	140

Common Values

Value	Count	Frequency (%)
150	22	5.5%
90	20	5.0%
88	19	4.8%
110	18	4.5%
100	17	4.3%
75	14	3.5%
95	14	3.5%
105	12	3.0%
70	12	3.0%
67	12	3.0%
Other values (84)	238	59.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
150	22	5.5%
90	20	5.0%
88	19	4.8%
110	18	4.5%
100	17	4.3%
75	14	3.5%
95	14	3.5%
105	12	3.0%
70	12	3.0%
67	12	3.0%
Other values (84)	238	59.8%

Most occurring characters

Value	Count	Frequency (%)
1	197	20.6%
0	171	17.9%
5	129	13.5%
8	106	11.1%
7	90	9.4%
9	75	7.8%
6	67	7.0%
2	52	5.4%
4	35	3.7%
3	29	3.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	951	99.4%
Other Punctuation	6	0.6%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	197	20.7%
0	171	18.0%
5	129	13.6%
8	106	11.1%
7	90	9.5%
9	75	7.9%
6	67	7.0%
2	52	5.5%
4	35	3.7%
3	29	3.0%

Other Punctuation

Value	Count	Frequency (%)
?	6	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	957	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	197	20.6%
0	171	17.9%
5	129	13.5%
8	106	11.1%
7	90	9.4%
9	75	7.8%
6	67	7.0%
2	52	5.4%
4	35	3.7%
3	29	3.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	957	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	197	20.6%
0	171	17.9%
5	129	13.5%
8	106	11.1%
7	90	9.4%
9	75	7.8%
6	67	7.0%
2	52	5.4%
4	35	3.7%
3	29	3.0%

weight
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	351
Distinct (%)	88.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2970.424623

Minimum	1613
Maximum	5140
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.2 KiB

Quantile statistics

Minimum	1613
5-th percentile	1923.5
Q1	2223.75
median	2803.5
Q3	3608
95-th percentile	4464
Maximum	5140
Range	3527
Interquartile range (IQR)	1384.25

Descriptive statistics

Standard deviation	846.8417742
Coefficient of variation (CV)	0.2850911508
Kurtosis	-0.7855289051
Mean	2970.424623
Median Absolute Deviation (MAD)	637.5
Skewness	0.5310625126
Sum	1182229
Variance	717140.9905
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1985	4	1.0%
2130	4	1.0%
2125	3	0.8%
2945	3	0.8%
2265	3	0.8%
2300	3	0.8%
2155	3	0.8%
2720	3	0.8%
1800	2	0.5%
2408	2	0.5%
Other values (341)	368	92.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1613	1	0.3%
1649	1	0.3%
1755	1	0.3%
1760	1	0.3%
1773	1	0.3%
1795	2	0.5%
1800	2	0.5%
1825	2	0.5%
1834	1	0.3%
1835	2	0.5%

Value	Count	Frequency (%)
5140	1	0.3%
4997	1	0.3%
4955	1	0.3%
4952	1	0.3%
4951	1	0.3%
4906	1	0.3%
4746	1	0.3%
4735	1	0.3%
4732	1	0.3%
4699	1	0.3%

acceleration
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	95
Distinct (%)	23.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	15.56809045

Minimum	8
Maximum	24.8
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.2 KiB

Quantile statistics

Minimum	8
5-th percentile	11.285
Q1	13.825
median	15.5
Q3	17.175
95-th percentile	20.415
Maximum	24.8
Range	16.8
Interquartile range (IQR)	3.35

Descriptive statistics

Standard deviation	2.75768893
Coefficient of variation (CV)	0.1771372628
Kurtosis	0.419496883
Mean	15.56809045
Median Absolute Deviation (MAD)	1.7
Skewness	0.2787768446
Sum	6196.1
Variance	7.604848234
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
14.5	23	5.8%
15.5	21	5.3%
14	16	4.0%
16	16	4.0%
13.5	15	3.8%
17	14	3.5%
15	14	3.5%
16.5	13	3.3%
19	12	3.0%
13	12	3.0%
Other values (85)	242	60.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
8	1	0.3%
8.5	2	0.5%
9	1	0.3%
9.5	2	0.5%
10	4	1.0%
10.5	1	0.3%
11	7	1.8%
11.1	1	0.3%
11.2	1	0.3%
11.3	1	0.3%

Value	Count	Frequency (%)
24.8	1	0.3%
24.6	1	0.3%
23.7	1	0.3%
23.5	1	0.3%
22.2	2	0.5%
22.1	1	0.3%
21.9	1	0.3%
21.8	1	0.3%
21.7	1	0.3%
21.5	1	0.3%

model year
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	13
Distinct (%)	3.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	76.01005025

Minimum	70
Maximum	82
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.2 KiB

Quantile statistics

Minimum	70
5-th percentile	70
Q1	73
median	76
Q3	79
95-th percentile	82
Maximum	82
Range	12
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	3.697626647
Coefficient of variation (CV)	0.04864654917
Kurtosis	-1.181231743
Mean	76.01005025
Median Absolute Deviation (MAD)	3
Skewness	0.01153459402
Sum	30252
Variance	13.67244282
Monotonicity	Increasing

Histogram with fixed size bins (bins=13)

Value	Count	Frequency (%)
73	40	10.1%
78	36	9.0%
76	34	8.5%
82	31	7.8%
75	30	7.5%
70	29	7.3%
79	29	7.3%
80	29	7.3%
81	29	7.3%
71	28	7.0%
Other values (3)	83	20.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
70	29	7.3%
71	28	7.0%
72	28	7.0%
73	40	10.1%
74	27	6.8%
75	30	7.5%
76	34	8.5%
77	28	7.0%
78	36	9.0%
79	29	7.3%

Value	Count	Frequency (%)
82	31	7.8%
81	29	7.3%
80	29	7.3%
79	29	7.3%
78	36	9.0%
77	28	7.0%
76	34	8.5%
75	30	7.5%
74	27	6.8%
73	40	10.1%

origin
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	3.2 KiB

1	249
3	79
2	70

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	398
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	249	62.6%
3	79	19.8%
2	70	17.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
1	249	62.6%
3	79	19.8%
2	70	17.6%

Most occurring characters

Value	Count	Frequency (%)
1	249	62.6%
3	79	19.8%
2	70	17.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	398	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	249	62.6%
3	79	19.8%
2	70	17.6%

Most occurring scripts

Value	Count	Frequency (%)
Common	398	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	249	62.6%
3	79	19.8%
2	70	17.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	398	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	249	62.6%
3	79	19.8%
2	70	17.6%

car name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	305
Distinct (%)	76.6%
Missing	0
Missing (%)	0.0%
Memory size	3.2 KiB

ford pinto	6
toyota corolla	5
amc matador	5
ford maverick	5
chevrolet chevette	4
Other values (300)	373

Length

Max length	36
Median length	28
Mean length	16.09547739
Min length	6

Characters and Unicode

Total characters	6406
Distinct characters	45
Distinct categories	8 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	249 ?
Unique (%)	62.6%

Sample

1st row	chevrolet chevelle malibu
2nd row	buick skylark 320
3rd row	plymouth satellite
4th row	amc rebel sst
5th row	ford torino

Common Values

Value	Count	Frequency (%)
ford pinto	6	1.5%
toyota corolla	5	1.3%
amc matador	5	1.3%
ford maverick	5	1.3%
chevrolet chevette	4	1.0%
amc gremlin	4	1.0%
chevrolet impala	4	1.0%
peugeot 504	4	1.0%
amc hornet	4	1.0%
toyota corona	4	1.0%
Other values (295)	353	88.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
ford	51	4.9%
chevrolet	43	4.1%
plymouth	31	3.0%
sw	28	2.7%
amc	28	2.7%
dodge	28	2.7%
toyota	25	2.4%
datsun	23	2.2%
custom	18	1.7%
buick	17	1.6%
Other values (305)	748	71.9%

Most occurring characters

Value	Count	Frequency (%)
	642	10.0%
o	530	8.3%
a	501	7.8%
e	419	6.5%
r	390	6.1%
t	383	6.0%
c	352	5.5%
l	332	5.2%
d	267	4.2%
i	255	4.0%
Other values (35)	2335	36.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5363	83.7%
Space Separator	642	10.0%
Decimal Number	309	4.8%
Open Punctuation	36	0.6%
Close Punctuation	36	0.6%
Dash Punctuation	10	0.2%
Other Punctuation	8	0.1%
Math Symbol	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	530	9.9%
a	501	9.3%
e	419	7.8%
r	390	7.3%
t	383	7.1%
c	352	6.6%
l	332	6.2%
d	267	5.0%
i	255	4.8%
s	247	4.6%
Other values (16)	1687	31.5%

Decimal Number

Value	Count	Frequency (%)
0	100	32.4%
1	56	18.1%
2	48	15.5%
4	26	8.4%
5	25	8.1%
3	15	4.9%
6	13	4.2%
8	11	3.6%
9	11	3.6%
7	4	1.3%

Other Punctuation

Value	Count	Frequency (%)
/	3	37.5%
.	3	37.5%
@	1	12.5%
'	1	12.5%

Space Separator

Value	Count	Frequency (%)
	642	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	36	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	36	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	10	100.0%

Math Symbol

Value	Count	Frequency (%)
+	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	5363	83.7%
Common	1043	16.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	530	9.9%
a	501	9.3%
e	419	7.8%
r	390	7.3%
t	383	7.1%
c	352	6.6%
l	332	6.2%
d	267	5.0%
i	255	4.8%
s	247	4.6%
Other values (16)	1687	31.5%

Common

Value	Count	Frequency (%)
	642	61.6%
0	100	9.6%
1	56	5.4%
2	48	4.6%
(	36	3.5%
)	36	3.5%
4	26	2.5%
5	25	2.4%
3	15	1.4%
6	13	1.2%
Other values (9)	46	4.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6406	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	642	10.0%
o	530	8.3%
a	501	7.8%
e	419	6.5%
r	390	6.1%
t	383	6.0%
c	352	5.5%
l	332	5.2%
d	267	4.2%
i	255	4.0%
Other values (35)	2335	36.5%

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.

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.

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.

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.

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.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	mpg	cylinders	displacement	horsepower	weight	acceleration	model year	origin	car name
0	18.0	8	307.0	130	3504	12.0	70	1	chevrolet chevelle malibu
1	15.0	8	350.0	165	3693	11.5	70	1	buick skylark 320
2	18.0	8	318.0	150	3436	11.0	70	1	plymouth satellite
3	16.0	8	304.0	150	3433	12.0	70	1	amc rebel sst
4	17.0	8	302.0	140	3449	10.5	70	1	ford torino
5	15.0	8	429.0	198	4341	10.0	70	1	ford galaxie 500
6	14.0	8	454.0	220	4354	9.0	70	1	chevrolet impala
7	14.0	8	440.0	215	4312	8.5	70	1	plymouth fury iii
8	14.0	8	455.0	225	4425	10.0	70	1	pontiac catalina
9	15.0	8	390.0	190	3850	8.5	70	1	amc ambassador dpl

Last rows

	mpg	cylinders	displacement	horsepower	weight	acceleration	model year	origin	car name
388	26.0	4	156.0	92	2585	14.5	82	1	chrysler lebaron medallion
389	22.0	6	232.0	112	2835	14.7	82	1	ford granada l
390	32.0	4	144.0	96	2665	13.9	82	3	toyota celica gt
391	36.0	4	135.0	84	2370	13.0	82	1	dodge charger 2.2
392	27.0	4	151.0	90	2950	17.3	82	1	chevrolet camaro
393	27.0	4	140.0	86	2790	15.6	82	1	ford mustang gl
394	44.0	4	97.0	52	2130	24.6	82	2	vw pickup
395	32.0	4	135.0	84	2295	11.6	82	1	dodge rampage
396	28.0	4	120.0	79	2625	18.6	82	1	ford ranger
397	31.0	4	119.0	82	2720	19.4	82	1	chevy s-10

Overview

Variables

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Other Punctuation

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Decimal Number

Other Punctuation

Space Separator

Open Punctuation

Close Punctuation

Dash Punctuation

Math Symbol

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample

First rows

Last rows