This is the official implementation of the DataFrame specification provided by Raven Computing.
This library is available on PyPI.
Install via:
pip install raven-pydf
For more information see pypi.org.
After installation you can use the entire DataFrame API by importing one class:
from raven.struct.dataframe import DataFrame
# read a DataFrame file into memory
df = DataFrame.read("/path/to/myFile.df")
# show the first 10 rows on stdout
print(df.head(10))
Alternatively, you can import all concrete Column types directly, for example:
from raven.struct.dataframe import (DefaultDataFrame,
IntColumn,
DoubleColumn,
StringColumn)
# create a DataFrame with 3 columns and 3 rows
df = DefaultDataFrame(
IntColumn("A", [1, 2, 3]),
DoubleColumn("B", [4.4, 5.5, 6.6]),
StringColumn("C", ["cat", "dog", "horse"]))
print(df)
# _| A B C
# 0| 1 4.4 cat
# 1| 2 5.5 dog
# 2| 3 6.6 horse
This library requires Python3.7 or higher.
Internally, this library uses Numpy for array operations. The minimum required version is v1.19.0
The unified documentation is available here.
Additional features implemented in Python are documented in the Wiki.
If you want to change code of this library or if you want to include it manually as a dependency without installing via PIP, you can do so by cloning this repository.
We are using virtual environments and the virtualenvwrapper utilities for all of our Python projects. If you are running on Linux then you can set up your development environment by sourcing the setup.sh script. This will create a virtual environment pydf for you and install all dependencies:
source setup.sh
Execute all unit tests via:
python -m unittest
Run pylint to perform static code analysis of the source code via:
pylint raven
This library is licensed under the Apache License Version 2 - see the LICENSE for details.