chriddyp

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
import pandas as pd

app = dash.Dash()

df = pd.read_csv(
    'https://gist.githubusercontent.com/chriddyp/'

anandology

"""Reactive expressions for Dash!

Dash[1] is an interesting project build reactive web applications in Python.
While the ideas are exciting, the syntax for specifying custom code is way
too complicated that it should be.

Here is a sample code to display sum of two input values.

@app.callback(
    [Input(component_id='x', component_property='value')]

alysivji

# standard library
import os

# dash libs
import dash
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
import plotly.figure_factory as ff
import plotly.graph_objs as go

yhilpisch

Workshop PyConf Hyderabad

Time Series Analysis with Python

With Applications of Machine Learning Algorithms

Session by Dr. Yves J. Hilpisch | The Python Quants GmbH

Hyderabad, 07. October 2017

bshishov

import numpy as np

EPSILON = 1e-10


def _error(actual: np.ndarray, predicted: np.ndarray):
    """ Simple error """
    return actual - predicted

tuanchauict

@import url('https://fonts.googleapis.com/css?family=Goudy+Bookletter+1911|Inconsolata|Lora|Slabo+27px|Song+Myung|Source+Serif+Pro|Roboto Mono');

* {
    -webkit-box-sizing: border-box;
    box-sizing: border-box;
}

body {
    font-family: 'Source Serif Pro', Lora, 'Slabo 27px', 'Goudy Bookletter 1911', serif;
    line-height: 1.4em;

josef-pkt

roarkemc

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

josef-pkt

jasonjkras

def coskew(df):
    
    # Number of stocks
    num = len(df.columns)
    
    # Two dimionsal matrix for tensor product 
    mtx = np.zeros(shape = (len(df), num**2))
    
    v = df.values
    means = v.mean(0,keepdims=True)