将Pandas DataFrame保存到Django模型

时间:2021-07-28 23:44:11

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)

我有一个存储在pandas DataFrame中的股票价格数据,如下所示(实际上它在面板中,但我将其转换为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):

我想将它保存在Django模型中,它看起来像这样(匹配列名):

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:

到目前为止我最好的用它来保存它,其中df是我的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?

有没有更好的方法来保存这个?

I've looked at django-pandas, but looks like it just reads from the DB.

我看过django-pandas,但看起来它只是从数据库读取。

1 个解决方案

#1


14  

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:

使用to_sql()和引擎的适当连接参数是最有效的,并在Django应用程序中运行它,而不是迭代DataFrame并一次保存一个模型实例:

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)

#1


14  

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:

使用to_sql()和引擎的适当连接参数是最有效的,并在Django应用程序中运行它,而不是迭代DataFrame并一次保存一个模型实例:

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)