对于python-firebase和to_dict：

postdata = my_df.to_dict()

# Assumes any auth/headers you need are already taken care of.
result = firebase.post('/my_endpoint', postdata, {'print': 'pretty'})
print(result)
# Snapshot info

您可以使用快照信息和端点取回数据，并使用from_dict()重新建立df。您可以将此解决方案调整为SQL和JSON解决方案，pandas也支持这些解决方案。
或者，根据脚本的执行位置，您可以考虑将firebase视为数据库，并使用firebase_admin中的dbapi（查看this）。
至于它是否符合最佳实践，在不了解您的用例的情况下很难说。

如果只想减少代码长度以及创建和删除文件的步骤，可以使用upload_from_string：

import firebase_admin
from firebase_admin import db, storage

cred   = firebase_admin.credentials.Certificate(cert_json)
app    = firebase_admin.initialize_app(cred, config)
bucket = storage.bucket(app=app)

def upload_df(df, data_id):
    """
    Upload a Dataframe as a csv to Firebase Storage
    :return: storage_ref
    """
    storage_ref = data_id + '.csv'
    blob = bucket.blob(storage_ref)
    blob.upload_from_string(df.to_csv())

    return storage_ref

https://googleapis.github.io/google-cloud-python/latest/storage/blobs.html#google.cloud.storage.blob.Blob.upload_from_string

经过几个小时的摸索，下面的解决方案对我来说很有效。你需要把你的csv文件转换成字节，然后上传它。

import pyrebase
import pandas

firebaseConfig = {
   "apiKey": "xxxxx",
   "authDomain": "xxxxx",
   "projectId": "xxxxx",
   "storageBucket": "xxxxx",
   "messagingSenderId": "xxxxx",
   "appId": "xxxxx",
   "databaseURL":"xxxxx"
};

firebase = pyrebase.initialize_app(firebaseConfig)

storage = firebase.storage()

df = pd.read_csv("/content/Future Prices.csv")

# here is the magic. Convert your csv file to bytes and then upload it
df_string = df.to_csv(index=False)
db_bytes = bytes(df_string, 'utf8')

fileName = "Future Prices.csv"

storage.child("predictions/" + fileName).put(db_bytes)

这就是快乐编程！

我发现从非常小的 Dataframe 开始（低于100 KB！），在存储之前压缩它们是值得的。我使用了下面的google cloud和pickle库。你的文件也可以在通常的firebase存储器上使用这种方式，你在内存和速度上都得到了收益，无论是在写还是阅读的时候。

import firebase_admin
from firebase_admin import credentials, initialize_app, storage
from google.cloud import storage
import pickle

cred = credentials.Certificate(json_cert_file)
firebase_admin.initialize_app(cred, {'storageBucket': 'YOUR_storageBucket (without gs://)'})
bucket = storage.bucket()

file_name = data_id + ".pkl"
blob = bucket.blob(file_name)

# write df to storage
blob.upload_from_string(pickle.dumps(df))

# read df from storage
df = pickle.loads(blob.download_as_string())