Overview

Dataset statistics

Number of variables2
Number of observations361
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory16.4 B

Variable types

DateTime1
Numeric1

Warnings

DATE has unique values Unique
PORANGUSDM has unique values Unique

Reproduction

Analysis started2021-05-11 22:15:37.509086
Analysis finished2021-05-11 22:15:38.119172
Duration0.61 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

DATE
Date

UNIQUE

Distinct361
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Minimum1990-01-01 00:00:00
Maximum2020-01-01 00:00:00
2021-05-11T22:15:38.214147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-11T22:15:38.405532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PORANGUSDM
Real number (ℝ≥0)

UNIQUE

Distinct361
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.194204671
Minimum0.56105
Maximum2.172547619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2021-05-11T22:15:38.580640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.56105
5-th percentile0.7371818182
Q10.9244318182
median1.15725
Q31.428714286
95-th percentile1.86452381
Maximum2.172547619
Range1.611497619
Interquartile range (IQR)0.5042824675

Descriptive statistics

Standard deviation0.3473087284
Coefficient of variation (CV)0.2908284793
Kurtosis-0.400216634
Mean1.194204671
Median Absolute Deviation (MAD)0.2507738095
Skewness0.5405907172
Sum431.1078862
Variance0.1206233528
MonotonicityNot monotonic
2021-05-11T22:15:38.754548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1861
 
0.3%
0.94121739131
 
0.3%
1.2960217391
 
0.3%
1.4803181821
 
0.3%
0.8348260871
 
0.3%
1.9998181821
 
0.3%
1.4521904761
 
0.3%
1.4287142861
 
0.3%
1.3296190481
 
0.3%
1.1254130431
 
0.3%
Other values (351)351
97.2%
ValueCountFrequency (%)
0.561051
0.3%
0.57661904761
0.3%
0.5945476191
0.3%
0.60968421051
0.3%
0.61213043481
0.3%
0.6284251
0.3%
0.66740476191
0.3%
0.66920454551
0.3%
0.67265909091
0.3%
0.69107894741
0.3%
ValueCountFrequency (%)
2.1725476191
0.3%
2.036023811
0.3%
2.0126904761
0.3%
2.0122751
0.3%
2.003251
0.3%
1.9998181821
0.3%
1.9852619051
0.3%
1.98061
0.3%
1.9782368421
0.3%
1.9766251
0.3%

Interactions

2021-05-11T22:15:37.564863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-05-11T22:15:38.896478image/svg+xmlMatplotlib v3.4.2, 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.
2021-05-11T22:15:39.035563image/svg+xmlMatplotlib v3.4.2, 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.
2021-05-11T22:15:39.172017image/svg+xmlMatplotlib v3.4.2, 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.

Missing values

2021-05-11T22:15:37.836281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-11T22:15:38.068575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DATEPORANGUSDM
01990-01-011.913636
11990-02-011.940289
21990-03-011.922636
31990-04-011.960125
41990-05-011.949477
51990-06-011.864524
61990-07-011.833381
71990-08-011.724717
81990-09-011.445579
91990-10-011.230826

Last rows

DATEPORANGUSDM
3512019-04-011.084357
3522019-05-010.986045
3532019-06-011.027250
3542019-07-011.014955
3552019-08-010.993318
3562019-09-011.006300
3572019-10-010.984370
3582019-11-010.981600
3592019-12-010.975905
3602020-01-010.969095