i38e-utils 1.0.8

Various utilities for IBIS applications in data science and engineering

i38e-utils

i38e-utils is a collection of utility functions and classes that I use in my projects. It is a work in progress and will be updated as I add more functionality.

Currently, it includes the following:

1. DfHelper: A class designed to facilitate data handling and operations within a Django project, particularly focusing on loading data from both parquet files and a database, and potentially saving data to parquet format.
2. GeoPyHelper: A class that provides a set of utility functions for working with GeoPy.
3. OsmxHelper: A class that provides a set of utility functions for working with Osmnx.
4. data_utils: A set of utility functions/classes for working with data.
5. date_utils: A set of utility functions for working with dates.
6. df_utils: A set of utility functions for working with pandas DataFrames.
7. file_utils: A set of utility functions for working with files.
8. log_utils: A set of utility functions for working with logs.

Installation

To install this project, follow these steps:

pip install i38e-utils

Usage

DfHelper: Dataframe Helper Class

Scenarios:

• Connect to a database table using a Django's ORM connection, query, transform and convert the data to a pandas DataFrame.

import pandas as pd
import numpy as np
from i38e_utils.df_helper import DfHelper

phone_mobile_gps_fields = {
    'id_tracking': 'id',
    'id_producto': 'product_id',
    'pk_empleado': 'associate_id',
    'latitud': 'latitude',
    'longitud': 'longitude',
    'fecha_hora_servidor': 'server_dt',
    'fecha_hora': 'date_time',
    'accion': 'action',
    'descripcion': 'description',
    'imei': 'imei'
}


class GpsCube(DfHelper):
    df: pd.DataFrame = None
    live: bool = False
    save_parquet = True
    
    config={
        'connection_name': 'replica',
        'table': 'asm_tracking_movil_gps',
        'field_map': phone_mobile_gps_fields,
        'legacy_filters': True,
    }

    def __init__(self, **opts):
        config = {**self.config, **opts}
        super().__init__(**config)
        
    def load(self, **kwargs):
        self.df = super().load(**kwargs)
        self.fix_data()
        return self.df

    def fix_data(self):
        self.df['latitude'] = self.df['latitude'].astype(np.float64)
        self.df['longitude'] = self.df['longitude'].astype(np.float64)```python

gps_cube=GpsCube(live=True, debug=False)
df=gps_cube.load(date_time__date='2023-03-04')
# to save to a parquet file
gps_cube.save_to_parquet(df, parquet_full_path='gpscube.parquet')

• Use a parquet storage file or folder structure to load data and perform some transformations.

import pandas as pd
from i38e_utils.df_helper import DfHelper

class GpsParquetCube(DfHelper):
    df: pd.DataFrame = None
    
    config={
        'use_parquet': True,
        'df_as_dask': True,
        'parquet_storage_path': '/storage/data/parquet/gps',
        'parquet_start_date': '2024-01-01',
        'parquet_end_date': '2024-03-31',
    }

    def __init__(self, **opts):
        config = {**self.config, **opts}
        super().__init__(**config)
        
    def load(self, **kwargs):
        self.df = super().load(**kwargs)
        return self.df


# The following example would load all the parquet files in the folder structure described in parquet_storage_path matching the date range and return a single dask dataframe for associate_id 27 for the month of March.
# The class converts Django style filters to dask compatible filters.
# The class also converts the parquet files to a dask dataframe for faster processing.

params = {
    'associate_id': 27,
    'date_time__date__range': ['2024-03-01','2024-03-31']
}

dask_df = GpsParquetCube().load(**params)
# to convert to a pandas dataframe
df = dask_df.compute()