Skip to content

Instantly share code, notes, and snippets.

View leriomaggio's full-sized avatar
🧙

Valerio Maggio leriomaggio

🧙
View GitHub Profile
@leriomaggio
leriomaggio / django_admin_mpld3_view.py
Last active June 30, 2020 13:47
Interactive Matplotlib (Bar) Chart with mpld3 in Django Custom Admin View
#----------------------------------------------------------------------------------------
# model.py
#----------------------------------------------------------------------------------------
from django.db import models
from numpy.random import random_sample
class CustomModel(models.Model):
# Put your fields here
def get_data(self):
@leriomaggio
leriomaggio / pydata_program.md
Last active August 29, 2015 14:04
PyData 2014 Berlin Program

#PyData Berlin Program, 2014

Friday, July 25

Time Beginner/Intermediate Tutorial Intermediate/Advanced Tutorial
12:00-12:45 Registration Registration
-- -- -- -- -- -- -- -- -- --
12:45-14:05 Frank Kaufer (bakdata) Bryan Van De Ven (Continuum Analytics)
Python and Big Data Framework Interactive Plots using Bokeh
@leriomaggio
leriomaggio / pycon7_conference_talk_rankings.md
Last active February 2, 2016 11:20
Community Voting Results and Rankings of Talks proposed at PyCon Sette: http://pycon.it #PyDataIt #DjangoVillage
import pandas as pd
import numpy as np
import pandas as pd
import numpy as np
@leriomaggio
leriomaggio / MSCX-Tutorial.md
Last active August 31, 2018 22:39
NetworkX Tutorial @ MSCX - Setup Instructions

Network Analysis Tutorial using Python & networkx

MSCX Ph.D. Summer School, Salina (ME) - Italy

Author: Valerio Maggio

PostDoc Data Scientist @ FBK/MPBA

@leriomaggio
leriomaggio / pyconx_conference_talks_ranking.md
Last active February 10, 2019 05:37
PyConX Conference Talks Ranking
import pandas as pd
import numpy as np
from IPython.display import HTML 
@leriomaggio
leriomaggio / kale-euroscipy.md
Last active September 4, 2019 13:53
Kubeflow Kale: from Jupyter Notebook to Complex Pipelines

Kubeflow Kale: from Jupyter Notebook to Complex Pipelines

Abstract

In this talk I will present a new solution to automatically scale Jupyter notebooks to complex and reproducibility pipelines based on Kubernetes and KubeFlow.

Description

Nowadays, most of the High Performance Computing (HPC) tasks are carried out in the Cloud, and this is as much

import umap
import numpy as np
import scipy as sp
print('umap: ', umap.__version__)
print('Numpy: ', np.__version__)
print('Scipy: ', sp.__version__)
# umap: 0.4.0
# Numpy: 1.17.5
@leriomaggio
leriomaggio / borda.py
Last active December 16, 2022 16:42
Numpy based implementation of the Borda Selection Algorithm for Rankings
"""Python/Numpy implementation of the Borda Count"""
import numpy as np
from nptyping import NDArray
from typing import Tuple, Union, List, Any
RankingsList = Union[NDArray[(Any, Any), np.int], List[List[int]]]
Ranking = NDArray[(Any,), np.int]
Counts = NDArray[(Any,), np.int]
AveragePositions = NDArray[(Any,), np.float]
@leriomaggio
leriomaggio / pytrousse_protocol_data_pipeline_with_validation.py
Last active October 23, 2020 10:47
PyTrousse Protocol-based Data Pipeline with Simple Validation Algorithm
from typing import Tuple, List, Optional, Union, NewType
from typing import Protocol, runtime_checkable
from abc import ABC, abstractmethod
import pandas as pd
try:
import torch as t
from torch import Tensor as Tensor
except ImportError: # fallback to Numpy, if torch not available - this is just as a POP!
import numpy as t
from numpy import ndarray as Tensor