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

Alerts

DATE has unique valuesUnique
PORANGUSDM has unique valuesUnique

Reproduction

Analysis started2023-01-25 14:18:59.649098
Analysis finished2023-01-25 14:18:59.983087
Duration0.33 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

DATE
Date

Distinct361
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Minimum1990-01-01 00:00:00
Maximum2020-01-01 00:00:00
2023-01-25T14:19:00.061371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T14:19:00.215313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PORANGUSDM
Real number (ℝ)

Distinct361
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1942047
Minimum0.56105
Maximum2.1725476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-01-25T14:19:00.364405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.56105
5-th percentile0.73718182
Q10.92443182
median1.15725
Q31.4287143
95-th percentile1.8645238
Maximum2.1725476
Range1.6114976
Interquartile range (IQR)0.50428247

Descriptive statistics

Standard deviation0.34730873
Coefficient of variation (CV)0.29082848
Kurtosis-0.40021663
Mean1.1942047
Median Absolute Deviation (MAD)0.25077381
Skewness0.54059072
Sum431.10789
Variance0.12062335
MonotonicityNot monotonic
2023-01-25T14:19:00.516078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.913636364 1
 
0.3%
1.502880952 1
 
0.3%
1.435095238 1
 
0.3%
1.410522727 1
 
0.3%
1.40285 1
 
0.3%
1.329619048 1
 
0.3%
1.462978261 1
 
0.3%
1.373815789 1
 
0.3%
1.374657895 1
 
0.3%
1.288340909 1
 
0.3%
Other values (351) 351
97.2%
ValueCountFrequency (%)
0.56105 1
0.3%
0.5766190476 1
0.3%
0.594547619 1
0.3%
0.6096842105 1
0.3%
0.6121304348 1
0.3%
0.628425 1
0.3%
0.6674047619 1
0.3%
0.6692045455 1
0.3%
0.6726590909 1
0.3%
0.6910789474 1
0.3%
ValueCountFrequency (%)
2.172547619 1
0.3%
2.03602381 1
0.3%
2.012690476 1
0.3%
2.012275 1
0.3%
2.00325 1
0.3%
1.999818182 1
0.3%
1.985261905 1
0.3%
1.9806 1
0.3%
1.978236842 1
0.3%
1.976625 1
0.3%

Interactions

2023-01-25T14:18:59.688336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-01-25T14:18:59.856697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-25T14:18:59.940299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

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
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