pdmongo 0.3.4

Description:

pdmongo 0.3.4

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:






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.