使用 PyMongo 将 Pandas 数据框插入 mongodb

Question

使用 PyMongo 将 pandas DataFrame 插入 mongodb 的最快方法是什么？

尝试

db.myCollection.insert(df.to_dict())

报错

InvalidDocument: documents must have only string keys, the key was Timestamp('2013-11-23 13:31:00', tz=None)

---

 db.myCollection.insert(df.to_json())

报错

TypeError: 'str' object does not support item assignment

---

 db.myCollection.insert({id: df.to_json()})

报错

InvalidDocument: documents must have only string a keys, key was <built-in function id>

---

df

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 150 entries, 2013-11-23 13:31:26 to 2013-11-23 13:24:07
Data columns (total 3 columns):
amount    150  non-null values
price     150  non-null values
tid       150  non-null values
dtypes: float64(2), int64(1)

Answer 1

这个怎么样：

db.myCollection.insert({id: df.to_json()})

id 将是该 df 的唯一字符串

Answer 2

我怀疑是否存在 quickest 和 simple 两种方法。如果你不担心数据转换，你可以这样做

>>> import json
>>> df = pd.DataFrame.from_dict({'A': {1: datetime.datetime.now()}})
>>> df
                           A
1 2013-11-23 21:14:34.118531

>>> records = json.loads(df.T.to_json()).values()
>>> db.myCollection.insert(records)

但如果你尝试load data back，你会得到：

>>> df = read_mongo(db, 'myCollection')
>>> df
                     A
0  1385241274118531000
>>> df.dtypes
A    int64
dtype: object

因此您必须将“A”列转换回datetimes，以及DataFrame 中所有非int、float 或str 字段。对于这个例子：

>>> df['A'] = pd.to_datetime(df['A'])
>>> df
                           A
0 2013-11-23 21:14:34.118531

Answer 3

这里有最快捷的方法。使用 pymongo 3 中的 insert_many 方法和 to_dict 方法的“记录”参数。

db.collection.insert_many(df.to_dict('records'))

Answer 4

odo 可以使用

odo(df, db.myCollection)

Answer 5

如果您的数据框缺少数据（即 None、nan）并且您不希望文档中有 null 键值：

db.insert_many(df.to_dict("records")) 将插入具有空值的键。如果您不希望文档中出现空键值，您可以使用修改版的 pandas .to_dict("records") 代码如下：

from pandas.core.common import _maybe_box_datetimelike
my_list = [dict((k, _maybe_box_datetimelike(v)) for k, v in zip(df.columns, row) if v != None and v == v) for row in df.values]
db.insert_many(my_list)

if v != None and v == v 我添加了检查以确保在将其放入行字典之前该值不是None 或nan。现在您的 .insert_many 将只包含文档中具有值的键（并且没有 null 数据类型）。

Answer 6

我认为这个问题有很酷的想法。就我而言，我一直在花时间更多地处理大型数据帧的移动。在这种情况下，pandas 倾向于允许您选择 chunksize（例如 pandas.DataFrame.to_sql 中的示例）。所以我想我可以通过添加我在这个方向上使用的功能来做出贡献。

def write_df_to_mongoDB(  my_df,\
                          database_name = 'mydatabasename' ,\
                          collection_name = 'mycollectionname',
                          server = 'localhost',\
                          mongodb_port = 27017,\
                          chunk_size = 100):
    #"""
    #This function take a list and create a collection in MongoDB (you should
    #provide the database name, collection, port to connect to the remoete database,
    #server of the remote database, local port to tunnel to the other machine)
    #
    #---------------------------------------------------------------------------
    #Parameters / Input
    #    my_list: the list to send to MongoDB
    #    database_name:  database name
    #
    #    collection_name: collection name (to create)
    #    server: the server of where the MongoDB database is hosted
    #        Example: server = 'XXX.XXX.XX.XX'
    #    this_machine_port: local machine port.
    #        For example: this_machine_port = '27017'
    #    remote_port: the port where the database is operating
    #        For example: remote_port = '27017'
    #    chunk_size: The number of items of the list that will be send at the
    #        some time to the database. Default is 100.
    #
    #Output
    #    When finished will print "Done"
    #----------------------------------------------------------------------------
    #FUTURE modifications.
    #1. Write to SQL
    #2. Write to csv
    #----------------------------------------------------------------------------
    #30/11/2017: Rafael Valero-Fernandez. Documentation
    #"""



    #To connect
    # import os
    # import pandas as pd
    # import pymongo
    # from pymongo import MongoClient

    client = MongoClient('localhost',int(mongodb_port))
    db = client[database_name]
    collection = db[collection_name]
    # To write
    collection.delete_many({})  # Destroy the collection
    #aux_df=aux_df.drop_duplicates(subset=None, keep='last') # To avoid repetitions
    my_list = my_df.to_dict('records')
    l =  len(my_list)
    ran = range(l)
    steps=ran[chunk_size::chunk_size]
    steps.extend([l])

    # Inser chunks of the dataframe
    i = 0
    for j in steps:
        print j
        collection.insert_many(my_list[i:j]) # fill de collection
        i = j

    print('Done')
    return

Answer 7

只需制作字符串键！

import json
dfData = json.dumps(df.to_dict('records'))
savaData = {'_id': 'a8e42ed79f9dae1cefe8781760231ec0', 'df': dfData}
res = client.insert_one(savaData)

##### load dfData
data = client.find_one({'_id': 'a8e42ed79f9dae1cefe8781760231ec0'}).get('df')
dfData = json.loads(data)
df = pd.DataFrame.from_dict(dfData)

Answer 8

如果您想一次发送多个：

db.myCollection.insert_many(df.apply(lambda x: x.to_dict(), axis=1).to_list())

Answer 9

如果您想确保不会引发 InvalidDocument 错误，那么类似以下内容是个好主意。这是因为 mongo 无法识别 np.int64、np.float64 等类型。

from pymongo import MongoClient
client = MongoClient()
db = client.test 
col = db.col


def createDocsFromDF(df, collection = None, insertToDB=False):
    docs = [] 
    fields = [col for col in df.columns]
    for i in range(len(df)):
        doc = {col:df[col][i] for col in df.columns if col != 'index'}
        for key, val in doc.items():
            # we have to do this, because mongo does not recognize these np. types
            if type(val) == np.int64:
                doc[key] = int(val)
            if type(val) == np.float64:
                doc[key] = float(val)
            if type(val) == np.bool_:
                doc[key] = bool(val)
        docs.append(doc) 
    if insertToDB and collection:
        db.collection.insert_many(docs)
    return docs 

Answer 10

我使用以下部分将数据框插入到数据库中的集合中。

df.reset_index(inplace=True)
data_dict = df.to_dict("records")
myCollection.insert_many(data_dict)