pandasnet-netstandard 0.7.2

Extensions of pythonnet package to support pandas DataFrame conversions

pandasnet

pandasnet_netstandard is a python package build on top of pythonnet_netstandard. It provides additional data conversions for pandas, numpy and datetime

Prerequisites

• python 3.6 or higher.
• dotnet.

dotnet also provides scripts to proceed the installation by command line.

Installation

pip install pandasnet_netstandard

Features

To load the converter you need to import the package once in your python environment. If the dotnet clr isn't started yet through the pytonnet package the import will.

import pandasnet_netstandard

We construct a simple C# function to test conversion

using System;
using System.Collections.Generic;

namespace LibForTests
{
    public class PandasNet
    {
        public static Dictionary<string, Array> BasicDataFrame(Dictionary<string, Array> df)
            => df;
    }
}

We build this function into a library named LibForTests.dll. We load this library into our python environment then use it.

import clr
import pandasnet_netstandard # Load the converters
import pandas as pd
from datetime import datetime

# Load your dll
clr.AddReference('LibForTests.dll')
from LibForTests import PandasNet as pdnet

x = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [1.23, 1.24, 1.22],
    'C': ['foo', 'bar', 'other'],
    'D': [datetime(2021, 1, 22), datetime(2021, 1, 23), datetime(2021, 1, 24)]
})
y = pdnet.BasicDataFrame(x)

print(y)

Below an exhausitve list of supported data convertions.

Python -> .Net

Python	.Net
datetime.datetime	DateTime
datetime.date	DateTime
datetime.timedelta	TimeSpan
datetime.time	TimeSpan
numpy.ndarray(dtype=bool_)	bool[]
numpy.ndarray(dtype=int8)	sbyte[]
numpy.ndarray(dtype=int16)	short[]
numpy.ndarray(dtype=int32)	int[]
numpy.ndarray(dtype=int64)	long[]
numpy.ndarray(dtype=uint8)	byte[]
numpy.ndarray(dtype=uint16)	ushort[]
numpy.ndarray(dtype=uint32)	uint[]
numpy.ndarray(dtype=uint64)	ulong[]
numpy.ndarray(dtype=float32)	float[]
numpy.ndarray(dtype=float64)	double[]
numpy.ndarray(dtype=datetime64)	DateTime[]
numpy.ndarray(dtype=timedelta64)	TimeSpan[]
numpy.ndarray(dtype=str)	string[]
pandas._libs.tslibs.timestamps.Timestamp	DateTime
pandas._libs.tslibs.timedeltas.TimeDelta	TimeSpan
pandas.core.series.Series	Array
pandas.core.frame.DataFrame	Dictionary[string, Array]

.Net -> Python

.Net	Python
DateTime	datetime.datetime
TimeSpan	datetime.timedelta
bool[]	numpy.ndarray(dtype=bool_)
sbyte[]	numpy.ndarray(dtype=int8)
short[]	numpy.ndarray(dtype=int16)
int[]	numpy.ndarray(dtype=int32)
long[]	numpy.ndarray(dtype=int64)
byte[]	numpy.ndarray(dtype=uint8)
ushort[]	numpy.ndarray(dtype=uint16)
uint[]	numpy.ndarray(dtype=uint32)
ulong[]	numpy.ndarray(dtype=uint64)
float[]	numpy.ndarray(dtype=float32)
double[]	numpy.ndarray(dtype=float64)
DateTime[]	numpy.ndarray(dtype=datetime64)
TimeSpan[]	numpy.ndarray(dtype=timedelta64)
Dictionary[string, Array]	pandas.core.frame.DataFrame

Contributing

Issue tracker: https://github.com/fdieulle/pandasnet/issues

If you want to checkout the project and propose your own contribution, you will need to setup it following few steps:

Create a virtual environment:

python -m venv venv

Activate your virtual environment:

venv/Scripts/activate

Install package dependencies

pip install -r requirements.txt

License

This project is open source under the MIT license.