From junior to geek the road is sleek
We have a string: “and a[x, 152] > 44.127895465113006 because 1368.1 > 1247.0” We need to extract 152 from it. This python code will do the thing: import re r = re.search(‘and\ a\[x,\ (.+?)\]’, l) print(r.group(1)) So if you run the code it prints “152”.
There’s a number of different ways to turn off Python warnings. There’s a lot of annoying future deprecation warnings that might flood your console space like crazy. So in order to suppress unwanted warnings in Python, the easiest way is the following: import warnings warnings.filterwarnings(‘ignore’) if you would like disable some specific type of warnings, … Read more
The easiest and pythonic way to do this is the following: for idx, item in enumerate(my_list): print(idx) print(item)
Here’s my example: params = { ‘param1’: [[25, 30, 35],[-25, -30, -35]], ‘param2’: [[20, 25, 30],[-20, -25, -30]] } for key, value in paramsNEW.items(): print( key, value[0], value[1] ) for v in value: print(v) for key, value in paramsNEW.items(): print(key, [(v1,v2) for v1 in value[0] for v2 in value[1]])
Here’s a good example to understand quantiles in python: import numpy as np d = [1, 1.2, 1.5, 2, 6, 7, 22, 3] q = 0.99 qr = np.quantile(d, q) print(f”{q*100}% less than {qr}”)
import pandas as pd from datetime import datetime import plotly import plotly.graph_objects as go df = pd.read_csv(‘https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv’) fig = go.Figure(data=[go.Candlestick(x=df[‘Date’], open=df[‘AAPL.Open’], high=df[‘AAPL.High’], low=df[‘AAPL.Low’], close=df[‘AAPL.Close’])]) # fig.show() plotly.offline.plot(fig)
list = [a,b,c,d] for i, val in enumerate(list): print(i, val)
combs = [] list1 = [1,2,3] list2 = [3,1,4] for x in list1: for y in list2: if x != y: combs.append((x, y)) print(combs) print(combs[2][0]) print(combs[2][1]) results in: [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)] 2 3 But there’s also a more elegant solution using list comprehensions: list1 … Read more
Also, imagine we have list that we need to disassemble a list and split it into 4 different lists like these: [1, 5, 9] [2, 6, 10] [3, 7, 11] [4, 8, 12] Then we should do the following params = [0.25, 0.5, 0.75, 1] a_list = [1, 2, 3, 4, 5, 6, 7, 8, … Read more
I’ve been looking for means of parallel execution in Python for quite a while. There’s a bunch of various libraries like Dask and Joblib Parallel. However using multiple threads won’t give you better performance due to the global interpreter lock called GIL. At the moment the best solution I’ve found is multiprocessing. It uses subprocesses … Read more