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)