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

NUM1
DATE1

Warnings

DATE has unique values Unique

Reproduction

Analysis started2020-10-25 20:11:17.663535
Analysis finished2020-10-25 20:11:18.562735
Duration0.9 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

DATE
Date

UNIQUE

Distinct361
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum1990-01-01 00:00:00
Maximum2020-01-01 00:00:00
2020-10-25T20:11:18.657563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:11:18.886752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PCOALAUUSDM
Real number (ℝ≥0)

Distinct275
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.06970979
Minimum24
Maximum195.1863354
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2020-10-25T20:11:19.110860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile26.1
Q133.6
median52.43303571
Q385.56173469
95-th percentile125.0858766
Maximum195.1863354
Range171.1863354
Interquartile range (IQR)51.96173469

Descriptive statistics

Standard deviation33.60143246
Coefficient of variation (CV)0.5502143793
Kurtosis0.4179963001
Mean61.06970979
Median Absolute Deviation (MAD)21.43303571
Skewness1.002984538
Sum22046.16523
Variance1129.056264
MonotocityNot monotonic
2020-10-25T20:11:19.322600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
39.5195.3%
 
31133.6%
 
26.1113.0%
 
40.5102.8%
 
25.161.7%
 
25.651.4%
 
33.151.4%
 
3841.1%
 
3541.1%
 
27.1530.8%
 
Other values (265)28177.8%
 
ValueCountFrequency (%) 
2410.3%
 
24.4510.3%
 
24.910.3%
 
24.9642857110.3%
 
25.161.7%
 
ValueCountFrequency (%) 
195.186335410.3%
 
173.303571410.3%
 
166.989795910.3%
 
164.498376610.3%
 
143.075892910.3%
 

Interactions

2020-10-25T20:11:18.028146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-25T20:11:19.502688image/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.
2020-10-25T20:11:19.670158image/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.
2020-10-25T20:11:19.845285image/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.

Missing values

2020-10-25T20:11:18.324844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:11:18.480934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

DATEPCOALAUUSDM
01990-01-0138.0
11990-02-0138.0
21990-03-0138.0
31990-04-0138.0
41990-05-0140.5
51990-06-0140.5
61990-07-0140.5
71990-08-0140.5
81990-09-0140.5
91990-10-0140.5

Last rows

DATEPCOALAUUSDM
3512019-04-0188.764643
3522019-05-0189.564286
3532019-06-0177.629821
3542019-07-0177.845807
3552019-08-0169.739286
3562019-09-0166.958673
3572019-10-0169.194255
3582019-11-0169.729082
3592019-12-0170.464643
3602020-01-0172.106169