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

Reproduction

Analysis started2023-01-25 14:18:46.229622
Analysis finished2023-01-25 14:18:46.554725
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
2023-01-25T14:18:46.640185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T14:18:46.792123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PCOALAUUSDM
Real number (ℝ)

Distinct275
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.06971
Minimum24
Maximum195.18634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-01-25T14:18:46.938695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile26.1
Q133.6
median52.433036
Q385.561735
95-th percentile125.08588
Maximum195.18634
Range171.18634
Interquartile range (IQR)51.961735

Descriptive statistics

Standard deviation33.601432
Coefficient of variation (CV)0.55021438
Kurtosis0.4179963
Mean61.06971
Median Absolute Deviation (MAD)21.433036
Skewness1.0029845
Sum22046.165
Variance1129.0563
MonotonicityNot monotonic
2023-01-25T14:18:47.082965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.5 19
 
5.3%
31 13
 
3.6%
26.1 11
 
3.0%
40.5 10
 
2.8%
25.1 6
 
1.7%
33.1 5
 
1.4%
25.6 5
 
1.4%
38 4
 
1.1%
35 4
 
1.1%
31.4 3
 
0.8%
Other values (265) 281
77.8%
ValueCountFrequency (%)
24 1
 
0.3%
24.45 1
 
0.3%
24.9 1
 
0.3%
24.96428571 1
 
0.3%
25.1 6
1.7%
25.125 1
 
0.3%
25.6 5
1.4%
25.82142857 1
 
0.3%
26.08928571 1
 
0.3%
26.1 11
3.0%
ValueCountFrequency (%)
195.1863354 1
0.3%
173.3035714 1
0.3%
166.9897959 1
0.3%
164.4983766 1
0.3%
143.0758929 1
0.3%
141.8877551 1
0.3%
140.9935714 1
0.3%
137.7631579 1
0.3%
135.9712733 1
0.3%
134.6244643 1
0.3%

Interactions

2023-01-25T14:18:46.270693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-01-25T14:18:46.430322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-25T14:18:46.512232image/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

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
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