So, pandas has a lot going for it. Yes I know. Those who are all about speed use something else. I don’t. I really like pandas, and I can wait a millisecond for my process to complete.

Pandas function, .to_sql, which in combination with SQLAlchemy is really really cool. I had use for something that worked with django, so I whipped up this contraption. No, it’s no where near .to_sql, but it does the job. For me. In this instance.

import pandas as pd
import numpy as np
from django.db import models

def dataframe_to_django(
    df: Series | Any,
    model: type[models.Model],
    dbmapping: dict[str, str | None],
    convmapping: dict[str, Any],
    replace: bool = False,
) -> None:
    """Uses bulk_create to insert data in table.

    KeywordArguments:
      df           --  the dataframe.
      model        --  the django table in to which the dataframe should be inserted.
      dbmapping    --  mapping from dataframe name to db name. Typically {"ProductName": "product_name", "SkuDescription": None, ...}
      convmapping  --  converters for spesific fields. Typically, {"StartDate": pd.to_datetime, ...} .
      replace      --  bool False|True. If set to True it will wipe the table before import.
    """

    if replace:
        model.objects.all().delete()

    df.replace([np.nan], ["N/A"], inplace=True)
    for key in convmapping:
        df[key] = convmapping[key](df[key])

    model_list: list[models.Model] = [
        model(**{dbmapping[k]: v for k, v in x.items() if dbmapping[k] is not None}) for x in df.to_dict("records")
    ]
    model.objects.bulk_create(model_list)

The link to the gist is gist.github.com/roderik333/0343f54af1e27dcb..

until next time..