xsam 0.1.1

XSAM assistant

XSAM

XSAM is a Python package designed to assist with management and analysis of financial data.

Installation

To install the package, use the following command:

pip install xsam

If you want to run the package's tests, install dev dependencies for pytest

pip install xsam[dev]
pytest

Usage

Entry Point

Run the main program like so

xsam

Or run the main module

python -m xsam.main

Saving Data

You can save a DataFrame, Series, dictionary, or Figure to a file using the save function:

from pathlib import Path
import pandas as pd
from xsam.output import save

# Create a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Save the DataFrame to a CSV filev
save(df, 'data', 'csv', Path('data'))

Additional Examples

1. Save a Series to a Pickle File:

   series = pd.Series([1, 2, 3, 4, 5])
   save(series, 'series_data', 'pickle', Path('data'))
   

2. Save a Dictionary of DataFrames to an Excel File:

   data_dict = {
       'Sheet1': pd.DataFrame({'A': [1, 2], 'B': [3, 4]}),
       'Sheet2': pd.DataFrame({'C': [5, 6], 'D': [7, 8]})
   }
   save(data_dict, 'data_dict', 'xlsx', Path('data'))
   

3. Save a Matplotlib Figure to a SVG File:

   import matplotlib.pyplot as plt
   
   fig, ax = plt.subplots()
   ax.plot([1, 2, 3], [4, 5, 6])
   save(fig, 'figure', 'svg', Path('data'))
   

Loading Data

You can load a DataFrame, Series, or dictionary from a file using the load function:

from xsam.input import load

# Load the latest file in the log
loaded_df = load(file_name='data')

# Print the loaded DataFrame
print(loaded_df)

Arguments for load

• file_name (str): The name of the file to load. If not provided, the function will search the log for the latest file.
• file_format (str): The format of the file to load. Supported formats are 'csv', 'xlsx', and 'pickle'.
• full_file_path (Path | str): The path to the file. If not provided, the function will search the log for the latest file.
• log_id (str): The unique file ID from the log file. If provided, the function will use this to locate the file.

Examples

1. Load by Name:

   loaded_df = load(file_name='data')
   

2. Load by Name and Format:

   loaded_df = load(file_name='data', file_format='csv')
   

3. Load by Full File Path:

   loaded_df = load(full_file_path='data/data.csv')
   

4. Load by Log ID:

   loaded_df = load(log_id='unique-log-id')
   

Aggregating Fields by Label

The aggregate_fields_by_label function aggregates values in a DataFrame by label, with optional regex matching and multipliers.

Parameters

• df (pd.DataFrame): DataFrame containing the data to aggregate.
• id_column (str): Column containing IDs to be associated with groups.
• weight_column (str): Column to use for weighted averages.
• field_columns (list[str]): List of field columns to be aggregated as weighted average by group.
• label_column (str, optional): Column containing labels to use for grouping. Defaults to None.
• label_regex (dict, optional): Dictionary of regex patterns and multipliers to use for grouping. Defaults to None.

Returns

• pd.DataFrame: DataFrame with aggregated values for each group.

Example

import pandas as pd
from xsam.aggregation import aggregate_fields_by_label

data = {
    "id": ["A0", "B0", "C0", "A1", "B1", "C1", "A2", "B2", "C2"],
    "value1": [10, 20, 30, 40, 50, 60, 70, 80, 90],
    "value2": [15, 25, 35, 45, 55, 65, 75, 85, 95],
    "weight": [1, 2, 3, 4, 5, 6, 7, 8, 9],
}
df = pd.DataFrame(data)
regex_dict = {
    "A_group": {"long": ("A.*", 1), "short": ("A1", -1), "other": ("A1", 1)},
    "B_group": {"long": ("B.*", 1), "short": ("B1", -1)},
    "C_group": {"long": ("C.*", 1), "short": ("C1", -1)},
}
result = aggregate_fields_by_label(df, "id", "weight", ["value1", "value2"], label_regex=regex_dict)
print(result)

License

This project is licensed under the MIT License. See the LICENSE file for details.

Authors

• Stabile Frisur - [e-mail]

Contributing

No need to contribute at this point. Thank you!