Pandas Profiling Report

Dataset statistics

Number of variables	5
Number of observations	200
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	7.9 KiB
Average record size in memory	40.6 B

Variable types

Numeric	4
Categorical	1

Alerts

`CustomerID` is highly correlated with `Annual Income (k$)`	High correlation
`Annual Income (k$)` is highly correlated with `CustomerID`	High correlation
`CustomerID` is highly correlated with `Annual Income (k$)`	High correlation
`Annual Income (k$)` is highly correlated with `CustomerID`	High correlation
`CustomerID` is highly correlated with `Annual Income (k$)`	High correlation
`Annual Income (k$)` is highly correlated with `CustomerID`	High correlation
`CustomerID` is highly correlated with `Age` and 2 other fields	High correlation
`Age` is highly correlated with `CustomerID` and 1 other fields	High correlation
`Annual Income (k$)` is highly correlated with `CustomerID` and 1 other fields	High correlation
`Spending Score (1-100)` is highly correlated with `CustomerID` and 2 other fields	High correlation
`CustomerID` is uniformly distributed	Uniform
`CustomerID` has unique values	Unique

Reproduction

Analysis started	2023-03-27 15:14:17.825636
Analysis finished	2023-03-27 15:14:22.032250
Duration	4.21 seconds
Software version	pandas-profiling v3.2.0
Download configuration	config.json

CustomerID
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct	200
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	100.5

Minimum	1
Maximum	200
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.7 KiB

Quantile statistics

Minimum	1
5-th percentile	10.95
Q1	50.75
median	100.5
Q3	150.25
95-th percentile	190.05
Maximum	200
Range	199
Interquartile range (IQR)	99.5

Descriptive statistics

Standard deviation	57.87918451
Coefficient of variation (CV)	0.5759122837
Kurtosis	-1.2
Mean	100.5
Median Absolute Deviation (MAD)	50
Skewness	0
Sum	20100
Variance	3350
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	0.5%
138	1	0.5%
128	1	0.5%
129	1	0.5%
130	1	0.5%
131	1	0.5%
132	1	0.5%
133	1	0.5%
134	1	0.5%
135	1	0.5%
Other values (190)	190	95.0%

Value	Count	Frequency (%)
1	1	0.5%
2	1	0.5%
3	1	0.5%
4	1	0.5%
5	1	0.5%
6	1	0.5%
7	1	0.5%
8	1	0.5%
9	1	0.5%
10	1	0.5%

Value	Count	Frequency (%)
200	1	0.5%
199	1	0.5%
198	1	0.5%
197	1	0.5%
196	1	0.5%
195	1	0.5%
194	1	0.5%
193	1	0.5%
192	1	0.5%
191	1	0.5%

Gender
Categorical

Distinct	2
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	1.7 KiB

Female	112
Male	88

Length

Max length	6
Median length	6
Mean length	5.12
Min length	4

Characters and Unicode

Total characters	1024
Distinct characters	6
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	0 ?
Unique (%)	0.0%

Sample

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

Common Values

Value	Count	Frequency (%)
Female	112	56.0%
Male	88	44.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
female	112	56.0%
male	88	44.0%

Most occurring characters

Value	Count	Frequency (%)
e	312	30.5%
a	200	19.5%
l	200	19.5%
F	112	10.9%
m	112	10.9%
M	88	8.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	824	80.5%
Uppercase Letter	200	19.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	312	37.9%
a	200	24.3%
l	200	24.3%
m	112	13.6%

Uppercase Letter

Value	Count	Frequency (%)
F	112	56.0%
M	88	44.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1024	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	312	30.5%
a	200	19.5%
l	200	19.5%
F	112	10.9%
m	112	10.9%
M	88	8.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1024	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	312	30.5%
a	200	19.5%
l	200	19.5%
F	112	10.9%
m	112	10.9%
M	88	8.6%

Age
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	51
Distinct (%)	25.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	38.85

Minimum	18
Maximum	70
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.7 KiB

Quantile statistics

Minimum	18
5-th percentile	19
Q1	28.75
median	36
Q3	49
95-th percentile	66.05
Maximum	70
Range	52
Interquartile range (IQR)	20.25

Descriptive statistics

Standard deviation	13.96900733
Coefficient of variation (CV)	0.3595626083
Kurtosis	-0.6715728616
Mean	38.85
Median Absolute Deviation (MAD)	11
Skewness	0.485568851
Sum	7770
Variance	195.1331658
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
32	11	5.5%
35	9	4.5%
19	8	4.0%
31	8	4.0%
30	7	3.5%
49	7	3.5%
40	6	3.0%
38	6	3.0%
47	6	3.0%
27	6	3.0%
Other values (41)	126	63.0%

Value	Count	Frequency (%)
18	4	2.0%
19	8	4.0%
20	5	2.5%
21	5	2.5%
22	3	1.5%
23	6	3.0%
24	4	2.0%
25	3	1.5%
26	2	1.0%
27	6	3.0%

Value	Count	Frequency (%)
70	2	1.0%
69	1	0.5%
68	3	1.5%
67	4	2.0%
66	2	1.0%
65	2	1.0%
64	1	0.5%
63	2	1.0%
60	3	1.5%
59	4	2.0%

Annual Income (k$)
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	64
Distinct (%)	32.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	60.56

Minimum	15
Maximum	137
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.7 KiB

Quantile statistics

Minimum	15
5-th percentile	19
Q1	41.5
median	61.5
Q3	78
95-th percentile	103
Maximum	137
Range	122
Interquartile range (IQR)	36.5

Descriptive statistics

Standard deviation	26.26472117
Coefficient of variation (CV)	0.4336975093
Kurtosis	-0.09848708653
Mean	60.56
Median Absolute Deviation (MAD)	16.5
Skewness	0.3218425499
Sum	12112
Variance	689.8355779
Monotonicity	Increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
54	12	6.0%
78	12	6.0%
48	6	3.0%
71	6	3.0%
63	6	3.0%
62	6	3.0%
87	6	3.0%
60	6	3.0%
88	4	2.0%
77	4	2.0%
Other values (54)	132	66.0%

Value	Count	Frequency (%)
15	2	1.0%
16	2	1.0%
17	2	1.0%
18	2	1.0%
19	4	2.0%
20	4	2.0%
21	2	1.0%
23	2	1.0%
24	2	1.0%
25	2	1.0%

Value	Count	Frequency (%)
137	2	1.0%
126	2	1.0%
120	2	1.0%
113	2	1.0%
103	4	2.0%
101	2	1.0%
99	2	1.0%
98	2	1.0%
97	2	1.0%
93	2	1.0%

Spending Score (1-100)
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	84
Distinct (%)	42.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	50.2

Minimum	1
Maximum	99
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.7 KiB

Quantile statistics

Minimum	1
5-th percentile	6
Q1	34.75
median	50
Q3	73
95-th percentile	92
Maximum	99
Range	98
Interquartile range (IQR)	38.25

Descriptive statistics

Standard deviation	25.82352167
Coefficient of variation (CV)	0.5144127822
Kurtosis	-0.8266291062
Mean	50.2
Median Absolute Deviation (MAD)	20
Skewness	-0.04722020137
Sum	10040
Variance	666.8542714
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
42	8	4.0%
55	7	3.5%
46	6	3.0%
73	6	3.0%
35	5	2.5%
52	5	2.5%
59	5	2.5%
48	5	2.5%
75	5	2.5%
50	5	2.5%
Other values (74)	143	71.5%

Value	Count	Frequency (%)
1	2	1.0%
3	1	0.5%
4	2	1.0%
5	4	2.0%
6	2	1.0%
7	1	0.5%
8	1	0.5%
9	1	0.5%
10	2	1.0%
11	1	0.5%

Value	Count	Frequency (%)
99	1	0.5%
98	1	0.5%
97	2	1.0%
95	2	1.0%
94	1	0.5%
93	2	1.0%
92	3	1.5%
91	2	1.0%
90	2	1.0%
89	1	0.5%

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

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6
3	4	Female	23	16	77
4	5	Female	31	17	40
5	6	Female	22	17	76
6	7	Female	35	18	6
7	8	Female	23	18	94
8	9	Male	64	19	3
9	10	Female	30	19	72

Last rows

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
190	191	Female	34	103	23
191	192	Female	32	103	69
192	193	Male	33	113	8
193	194	Female	38	113	91
194	195	Female	47	120	16
195	196	Female	35	120	79
196	197	Female	45	126	28
197	198	Male	32	126	74
198	199	Male	32	137	18
199	200	Male	30	137	83

Overview

Variables

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Phik (φk)

Missing values

Sample

First rows

Last rows