Create Sample

The base object is a Sample, so let’s create one

import pandas as pd
from elphick.geomet.utils.data import sample_data
from elphick.geomet import Sample

Load Data

First, let’s load some toy data. For demonstration this toy data has mixed case column names.

df: pd.DataFrame = sample_data(include_moisture=False)
df

	wet_mass	mass_dry	FE	SIO2	al2o3	LOI	group
index
0	100.0	90.0	57.0	5.2	3.0	5.0	grp_1
1	90.0	80.0	59.0	3.1	1.7	4.0	grp_1
2	110.0	90.0	61.0	2.2	0.9	3.0	grp_2

Create Sample

The Sample object has a data attribute that is a pandas DataFrame. Where column names are recognised as components the case is converted to the represent the chemical symbols.

sample: Sample = Sample(data=df, name='sample')
sample.data

2025-03-03 00:31:30,286 - DEBUG - Data has been set.
2025-03-03 00:31:30,286 - INFO - Elapsed time for data: 0:00:00.206286
2025-03-03 00:31:30,340 - INFO - Elapsed time for data: 0:00:00.001855
2025-03-03 00:31:30,345 - INFO - Elapsed time for data: 0:00:00.001833

	wet_mass	mass_dry	H2O	Fe	SiO2	Al2O3	LOI	group
index
0	100.0	90.0	10.000000	57.0	5.2	3.0	5.0	grp_1
1	90.0	80.0	11.111111	59.0	3.1	1.7	4.0	grp_1
2	110.0	90.0	18.181818	61.0	2.2	0.9	3.0	grp_2

Aggregation

The aggregate of the mass-composition is available via the aggregate property of the Sample object.

sample.aggregate

	wet_mass	mass_dry	H2O	Fe	SiO2	Al2O3	LOI
0	300.0	260.0	13.333333	59.0	3.515385	1.873077	4.0

For aggregation/weight-average by a grouping variable we can use the weight_average method.

sample.weight_average(group_by='group')

	wet_mass	mass_dry	H2O	Fe	SiO2	Al2O3	LOI
group
grp_1	190.0	170.0	10.526316	57.941176	4.211765	2.388235	4.529412
grp_2	110.0	90.0	18.181818	61.000000	2.200000	0.900000	3.000000