## List comprehension from python dictionary

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]])

## Numba njit “ZeroDivisionError: division by zero”

In a normal njit function setting error_model=”numpy” does exactly this. There are also significant speedups possible by setting this option. So: @njit(error_model=”numpy”) If you are wandering what Numba is, read more about Numba here. It’s a really cool thing, I highly recommend getting familiar with it.

## Fit Transform dataset for Machine Learning

from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0,1)) a = df.to_numpy() a = scaler.fit_transform(a)

## Quantile / percentile example in python

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}”)

## How to calculate percentile (quantile) for each column in pandas dataframe

Here we calculate 0.9th quantile of each column in our dataframe: q = 0.9 for column in df: qr = df[column].quantile(q) print(f”{q*100}% are lower than {qr}”) Here’s a good example to understand quantiles.