Yes, this is clearly nonsense. Sorting is not a hard problem and standard algorithms such as quicksort and mergesort have O(x^2) and O(n log(n)) complexity. But let me scratch this itch of sorting numbers using an evolutionary algorithm, specifically Covariance matrix adaptation evolution strategy (CMA-ES). Technically, we will use what I think is the original …
How to sort numbers with an evolutionary algorithm (CMA-ES) Read More »
Create a pandas dataframe with a date column: import pandas as pd import datetime TODAY = datetime.date.today() ONE_WEEK = datetime.timedelta(days=7) ONE_DAY = datetime.timedelta(days=1) df = pd.DataFrame({’dt’: [TODAY-ONE_WEEK, TODAY-3*ONE_DAY, TODAY], ‘x’: [42, 45,127]})import pandas as pd import datetime TODAY = datetime.date.today() ONE_WEEK = datetime.timedelta(days=7) ONE_DAY = datetime.timedelta(days=1) df = pd.DataFrame({‘dt’: [TODAY-ONE_WEEK, TODAY-3*ONE_DAY, TODAY], ‘x’: …
Cosine similarity is the normalised dot product between two vectors. I guess it is called “cosine” similarity because the dot product is the product of Euclidean magnitudes of the two vectors and the cosine of the angle between them. If you want, read more about cosine similarity and dot products on Wikipedia. Here is how …
Here is the code: %matplotlib inline import geopandas as gpd import matplotlib as mpl # make rcParams available (optional) mpl.rcParams[’figure.dpi’]= 144 # increase dpi (optional) world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")) world = world[world.name != ‘Antarctica’] # remove Antarctica (optional) world[’gdp_per_person’] = world.gdp_md_est / world.pop_est g = world.plot(column=’gdp_per_person’, cmap=’OrRd’, scheme=’quantiles’) g.set_facecolor(’#A8C5DD’) # make the ocean blue (optional)%matplotlib …
How to display a Choropleth map in Jupyter Notebook Read More »
Step one: brew install glpk pip install pulpbrew install glpk pip install pulp Step two: from pulp import * prob = LpProblem("test1", LpMinimize) # Variables x = LpVariable("x", 0, 4, cat="Integer") y = LpVariable("y", -1, 1, cat="Integer") z = LpVariable("z", 0, cat="Integer") # Objective prob += x + 4*y + 9*z …
Assume that you have a file with some locations as text with one location per line. For example, here are some school names in Copenhagen, Denmark, stored in schools.csv: Hyltebjerg Skole Heibergskolen Ellebjerg Skole Katrinedals Skole Peder Lykke Skolen Amager Fælled Skole Tingbjerg Heldagsskole Øster Farimagsgades Skole Sankt Annæ Gymnasiums Grundskole Lykkebo Skole Randersgades Skole …
Python 3.x, and in particular Python 3.5, natively supports asynchronous programming. While asynchronous code can be harder to read than synchronous code, there are many use cases were the added complexity is worthwhile. One such examples is to execute a batch of HTTP requests in parallel, which I will explore in this post. Additionally, the …
Easy parallel HTTP requests with Python and asyncio Read More »
Whenever two things have a directional relationship to each other, then you can compute the pagerank of those things. For example, you can observe a directional relationships between web pages that link to each other, scientists that cite each other, and chess players that beat each other. The relationship is directional because it matters in …
I like to code in Python. I also like the concept of asynchronous workers to build loosely coupled applications. Luckily, RabbitMQ and Celery can help me do exactly that. This post is based on a very nice YouTube video by Max Mautner (the one below). For easy repeatability, I have transcribed the video in this …
Pandas and SQLAlchemy offer powerful conversions between CSV files and tables in databases. Here is a small example: import pandas as pd from sqlalchemy import create_engine df = pd.read_csv(’mydata.csv’) engine = create_engine(’sqlite:///mydata.db’) df.to_sql(’mytable’, engine)import pandas as pd from sqlalchemy import create_engine df = pd.read_csv(‘mydata.csv’) engine = create_engine(‘sqlite:///mydata.db’) df.to_sql(‘mytable’, engine) Read more: pandas.DataFrame.to_sql sqlalchemy …