pdmongo

======== Overview

This package allows you to read/write pandas dataframes in MongoDB in the simplest way possible.

• Free software: MIT license

=========== Quick Start

Install pdmongo::

pip install pdmongo

Write a pandas DataFrame to a MongoDB collection::

import pandas as pd
import pdmongo as pdm

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df.to_mongo("MyCollection", "mongodb://localhost:27017/mydb")

Read a MongoDB collection into a pandas DataFrame::

import pdmongo as pdm

df = pdm.read_mongo("MyCollection", [], "mongodb://localhost:27017/mydb")
print(df)

==================== Examples / use cases

Reading a MongoDB collection into a pandas data frame (aggregation query)

You can use an aggregation query to filter/transform data in MongoDB before fetching them into a data frame. This allows you to delegate the slow operation to MongoDB.

Reading a collection from MongoDB into a pandas DataFrame by using an aggregation query::

import pdmongo as pdm
import pandas as pd

# First generate some data and write them to MongoDB
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df.to_mongo(df, 'MyCollection', "mongodb://localhost:27017/mydb")

# Filter with an aggregate query and parse results into a data frame.
query = [{"$match": {'A': 1} }]
df = pdm.read_mongo("MyCollection", query, "mongodb://localhost:27017/mydb")
print(df) # Only values where A > 1 is returned

The query accepts the same arguments as the aggregate method of pymongo package.

Write MongoDB to a PostgreSQL table

You can write a MongoDB collection to a PostgreSQL table::

import numpy as np
import pandas as pd
import pdmongo as pdm
from sqlalchemy import create_engine

# Generate some data and write them to MongoDB
df = pd.DataFrame({'A': [1, 2, 3]})
df.to_mongo("MyCollection", "mongodb://localhost:27017/mydb")

# Read data from MongoDB and write them to PostgreSQL
new_df = pdm.read_mongo("MyCollection", [], "mongodb://localhost:27017/mydb")
engine = create_engine('postgres://postgres:postgres@localhost:5432', echo=False)
new_df[["A"]].to_sql("APostgresTable", engine)

Plot data retrieved from a MongoDB Collection

You can plot a collection retrieved from MongoDB

::

import numpy as np
import pandas as pd
import pdmongo as pdm
import matplotlib.pyplot as plt

# Generate data and write them to MongoDB
df = pd.DataFrame({'Value': np.random.randn(1000)})
df.to_mongo('TimeSeries', 'mongodb://localhost:27017/mydb')

# Read collection from MongoDB and plot data
new_df = pdm.read_mongo("TimeSeries", [], "mongodb://localhost:27017/mydb")
new_df.plot()
plt.show()

============ Installation

::

pip install pdmongo

You can also install the in-development version with::

pip install https://github.com/pakallis/python-pandas-mongo/archive/master.zip

============= Documentation

You can find the documentation at::

https://python-pandas-mongo.readthedocs.io/

=========== Development

To run the all tests run::

tox

Note, to combine the coverage data from all the tox environments run:

.. list-table:: :widths: 10 90 :stub-columns: 1

- - Windows
  - ::

        set PYTEST_ADDOPTS=--cov-append
        tox

- - Other
  - ::

        PYTEST_ADDOPTS=--cov-append tox

Changelog

0.3.4 (2022-11-17)

• Support for python3.7-3.10
• Fix wrong version of Python in CI

0.3.3 (2022-11-17)

• Restrict pandas to >=0.20,<1.6
• Restrict pymongo to >=13,<4.4
• Remove hypothesis
• Run tests with tox in CI
• Add flake8 checks in CI

0.2.3 (2022-11-12)

• Add prepare release script

0.2.2 (2022-11-12)

• Fix lint offenses

0.2.1 (2022-11-12)

• Minor changes

0.2.0 (2022-11-12)

• Add compatibility for pymongo 4+

0.1.0 (2020-05-05)

• Added static typing
• Added mypy to travis CI
• Removed unecessary params

0.0.2 (2020-05-04)

• Dropped support for pypy3

0.0.1 (2020-04-30)

• Added read_mongo and basic support for reading MongoDB collections into pandas dataframes
• Added to_mongo and basic support for writing pandas dataframes in MongoDB collections

0.0.0 (2020-03-22)

• First release on PyPI.