pandasnet 0.3

Extensions of pythonnet package to support pandas DataFrame conversions

pandasnet

pandasnet si a python package build on top of pythonnet. It provides additional data conversions for pandas, numpy and datetime

Prerequisites

• python 3.6 or higher. (Note: Python 3.9 not yet fully supported by pythonnet)
• dotnet.

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

Installation

pip install pandasnet

Features

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

import pandasnet

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 Now you can load it in your python environmanet then using it.

import clr
import pandasnet # 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 you can found 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