pandasnet-netstandard 0.7.2

Description:

pandasnetnetstandard 0.7.2

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.