Difficulty level 2 of 5: Easy
Very nice interactive 3D plots with Plotly. import plotly.graph_objects as go import numpy as np import pandas as pd # Read data from a csv Z = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv').values # Actually not necessary to provide X and Y… X = np.linspace(0,…
For the last five years, I have been fascinated with machine learning techniques, but that fascination is slowly running out. I increasingly consider ML a tool in my tool box among others, not a panacea to all problems. In particular,…
To see who is hosting a website (e.g. netflix.com), you will need the IP address of the website and the WHOIS record for that IP address. You can obtain both from the command-line. Obtain the IP address for netflix.com: #…
In our home we have a mix of Mac and Linux computers. My children have a Raspberry Pi in their room and I have a Mac at my desk. Today we chatted with each other through the Terminal running on…
There are several ways to check what devices are currently logged on to your network (e.g. WiFi at home). The first option is to use arp, which I have found works the best. There will be some cases where it…
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]}) The dates have gaps: dt x 0 2018-11-19…
I teach children how to programm and do other things with technology in an organisation called Coding Pirates in Denmark, which aims to be a kind of scout movement for geeks. A best seller among the kids is learning how…
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…
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(”) df.to_sql(‘mytable’, engine) Read more: pandas.DataFrame.to_sql sqlalchemy engines