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’: […]

# Category: Analytics

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 […]

https://seaborn.pydata.org/generated/seaborn.heatmap.html

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 […]

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 […]

Here is an analytical query that you (and I) will often need to do if you work in e-commerce, marketing or similar domain. It answers the question, within each group of items (e.g. partitioned by territory, age groups or something else) what are the top-k items for some utility function over the items (e.g. the […]