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I have stock price data that is stored in a pandas DataFrame as shown below (actually it was in a panel, but I converted it to a DataFrame)
date ticker close tsr
0 2013-03-28 abc 22.81 1.000439
1 2013-03-28 def 94.21 1.006947
2 2013-03-28 ghi 95.84 1.014180
3 2013-03-28 jkl 31.80 1.000000
4 2013-03-28 mno 32.10 1.003125
...many more rows
I want to save this in a Django model, which looks like this (matches the column names):
class HistoricalPrices(models.Model):
ticker = models.CharField(max_length=10)
date = models.DateField()
tsr = models.DecimalField()
close = models.DecimalField()
The best I've come up so far is using this to save it, where df is my DataFrame:
entries = []
for e in df.T.to_dict().values():
entries.append(HistoricalPrices(**e))
HistoricalPrices.objects.bulk_create(entries)
Is there a better way to save this?
I've looked at django-pandas, but looks like it just reads from the DB.
974
Django (version > 1.4 ) provides bulk_create as an object manager method which takes as input an array of objects created using the class constructor. See my example code below: import pandas as pd df=pd. read_csv('test_csv.
Creating objects To create an object, instantiate it using keyword arguments to the model class, then call save() to save it to the database. This performs an INSERT SQL statement behind the scenes. Django doesn't hit the database until you explicitly call save() . The save() method has no return value.
dataframe_image has the ability to export both normal and styled DataFrames as images from within a Python script. Pass your normal or styled DataFrame to the export function along with a file location to save it as an image. You may also export directly from the DataFrame or styled DataFrame using the dfi.
It would be most efficient to use to_sql() with appropriate connection parameters for the engine, and run this inside your Django app rather than iterating through the DataFrame and saving one model instance at a time:
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(
user=user,
password=password,
database_name=database_name,
)
engine = create_engine(database_url, echo=False)
df.to_sql(HistoricalPrices, con=engine)
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