如何将数据从mongodb导入到熊猫？

Question

我在mongodb的集合中有大量数据需要分析.如何将数据导入熊猫？

我是熊猫和numpy的新手.

编辑:mongodb集合包含标记有日期和时间的传感器值.传感器值为float数据类型.

样本数据:

{
"_cls" : "SensorReport",
"_id" : ObjectId("515a963b78f6a035d9fa531b"),
"_types" : [
    "SensorReport"
],
"Readings" : [
    {
        "a" : 0.958069536790466,
        "_types" : [
            "Reading"
        ],
        "ReadingUpdatedDate" : ISODate("2013-04-02T08:26:35.297Z"),
        "b" : 6.296118156595,
        "_cls" : "Reading"
    },
    {
        "a" : 0.95574014778624,
        "_types" : [
            "Reading"
        ],
        "ReadingUpdatedDate" : ISODate("2013-04-02T08:27:09.963Z"),
        "b" : 6.29651468650064,
        "_cls" : "Reading"
    },
    {
        "a" : 0.953648289182713,
        "_types" : [
            "Reading"
        ],
        "ReadingUpdatedDate" : ISODate("2013-04-02T08:27:37.545Z"),
        "b" : 7.29679823731148,
        "_cls" : "Reading"
    },
    {
        "a" : 0.955931884300997,
        "_types" : [
            "Reading"
        ],
        "ReadingUpdatedDate" : ISODate("2013-04-02T08:28:21.369Z"),
        "b" : 6.29642922525632,
        "_cls" : "Reading"
    },
    {
        "a" : 0.95821381,
        "_types" : [
            "Reading"
        ],
        "ReadingUpdatedDate" : ISODate("2013-04-02T08:41:20.801Z"),
        "b" : 7.28956613,
        "_cls" : "Reading"
    },
    {
        "a" : 4.95821335,
        "_types" : [
            "Reading"
        ],
        "ReadingUpdatedDate" : ISODate("2013-04-02T08:41:36.931Z"),
        "b" : 6.28956574,
        "_cls" : "Reading"
    },
    {
        "a" : 9.95821341,
        "_types" : [
            "Reading"
        ],
        "ReadingUpdatedDate" : ISODate("2013-04-02T08:42:09.971Z"),
        "b" : 0.28956488,
        "_cls" : "Reading"
    },
    {
        "a" : 1.95667927,
        "_types" : [
            "Reading"
        ],
        "ReadingUpdatedDate" : ISODate("2013-04-02T08:43:55.463Z"),
        "b" : 0.29115237,
        "_cls" : "Reading"
    }
],
"latestReportTime" : ISODate("2013-04-02T08:43:55.463Z"),
"sensorName" : "56847890-0",
"reportCount" : 8
}

Answer 1

pymongo 可能会帮助你,以下是我正在使用的一些代码:

import pandas as pd
from pymongo import MongoClient


def _connect_mongo(host, port, username, password, db):
    """ A util for making a connection to mongo """

    if username and password:
        mongo_uri = 'mongodb://%s:%s@%s:%s/%s' % (username, password, host, port, db)
        conn = MongoClient(mongo_uri)
    else:
        conn = MongoClient(host, port)


    return conn[db]


def read_mongo(db, collection, query={}, host='localhost', port=27017, username=None, password=None, no_id=True):
    """ Read from Mongo and Store into DataFrame """

    # Connect to MongoDB
    db = _connect_mongo(host=host, port=port, username=username, password=password, db=db)

    # Make a query to the specific DB and Collection
    cursor = db[collection].find(query)

    # Expand the cursor and construct the DataFrame
    df =  pd.DataFrame(list(cursor))

    # Delete the _id
    if no_id:
        del df['_id']

    return df

Answer 2

您可以使用此代码将mongodb数据加载到pandas DataFrame.这个对我有用.希望也适合你.

import pymongo
import pandas as pd
from pymongo import MongoClient
client = MongoClient()
db = client.database_name
collection = db.collection_name
data = pd.DataFrame(list(collection.find()))

Answer 3

Monary就是这样,而且速度非常快.(另一个链接)

看到这个很酷的帖子,其中包括快速教程和一些时间.

Answer 4

我发现另一个非常有用的选项是：

from pandas.io.json import json_normalize

cursor = my_collection.find()
df = json_normalize(cursor)

（或者json_normalize(list(cursor))，取决于您的 python/pandas 版本）。

通过这种方式，您可以免费展开嵌套的 mongodb 文档。

Answer 5

根据PEP,简单比复杂更好:

import pandas as pd
df = pd.DataFrame.from_records(db.<database_name>.<collection_name>.find())

您可以像使用常规mongoDB数据库一样包含条件,甚至可以使用find_one()从数据库中获取一个元素,等等.

瞧!

Answer 6

import pandas as pd
from odo import odo

data = odo('mongodb://localhost/db::collection', pd.DataFrame)

Answer 7

为了有效地处理核外(不适合RAM)数据(即并行执行),您可以尝试使用Python Blaze生态系统:Blaze/Dask/Odo.

Blaze(和Odo)具有处理MongoDB的开箱即用功能.

一些有用的文章开始:

• 介绍Blaze Expessions(使用MongoDB查询示例)
• ReproduceIt:Reddit字数
• Dask数组和Blaze之间的区别

还有一篇文章展示了Blaze堆栈可能带来的惊人之处:使用Blaze和Impala分析17亿Reddit评论(基本上,在几秒钟内查询975 Gb的Reddit评论).

PS我不隶属于任何这些技术.

Answer 8

您还可以使用pymongoarrow —— 它是 MongoDB 提供的官方库，用于将 mongodb 数据导出到 pandas、numPy、parquet 文件等。

Answer 9

使用

pandas.DataFrame(list(...))

如果迭代器/生成器结果很大，将消耗大量内存

最好生成小块并在最后连接

def iterator2dataframes(iterator, chunk_size: int):
  """Turn an iterator into multiple small pandas.DataFrame

  This is a balance between memory and efficiency
  """
  records = []
  frames = []
  for i, record in enumerate(iterator):
    records.append(record)
    if i % chunk_size == chunk_size - 1:
      frames.append(pd.DataFrame(records))
      records = []
  if records:
    frames.append(pd.DataFrame(records))
  return pd.concat(frames)