Save Pandas DataFrame as Django Model
2 min read

Save Pandas DataFrame as Django Model

Save Pandas DataFrame as Django Model

TL;DR: use bulk_upload coupled with a comprehension to speed up loading.

Initially, I started to convert the data frame to a Model object row by row and save it. While easy, it's also arguably the slowest method to load records, because the save() call performs a commit. Then I looked up how to save pandas data frames and I found out quite a few ways...

Manual commit

The first step would be to tweak the transaction to manually commit like described here:

from django.db import transaction

def save(df):
    for item in df.to_dict('records'):
        entry = Entry(**item)


Other solution would be to use raw SQL. Here, you need to replicate the table fields by tweaking the data frame.

from django.conf import settings

user = settings.DATABASES['default']['USER']
password = settings.DATABASES['default']['PASSWORD']
database_name = settings.DATABASES['default']['NAME']

database_url = 'postgresql://{user}:{password}@localhost:5432/{database_name}'.format(

engine = create_engine(database_url, echo=False)
df.to_sql(model._meta.db_table, con=engine)


  • model._meta.db_table is the table where we want to save
  • database_url is a sql_alchemy database URL (above configured for PostgreSQL)

This option has a few drawbacks:

  1. The URL needs to be manually created. If you have different DB engines for dev/test, you'll need to cater for that
  2. You'll need to add a pk column with incremental values
  3. The code needs to be changed for each migration. Ouch!

Using bulk_create()

More recent versions of django have added bulk_create() where you can pass an array of model objects and they get created at once. The code would be something like:

    Model(**vals) for vals in df.to_dict('records')

The operation works like this:

  1. Convert the dataframe in a list of dicts
  2. Create a Model object for each item in the list
  3. Perform the bulk_create() on the list

This approach has some disadvantages too:

  1. It creates objects (not updates) so you can end up with duplicates if you're not careful
  2. You need to make sure the dict generated by a row can be used to create the model object
  3. It's slower than the SQL method above
  4. You might end up with lots of memory allocated if the dataframe is very large

I find the above disadvantages are minor compared to the simplicity of the execution.

Adapting DataFrame to Match

My approach to making sure the dict generated from a dataframe's row can be used to create a Model object is:

  • Note the model's fields which are not nullable

  • Change the existing column names to match the field names

  • Pay attention and convert timestamp values to datetime values:

    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')

    Create any missing columns