Girls Who Code Saturday Morning Oct 10th 2021

Dockerfile

# This uses the latest Docker image built from the samples repository,
# defined by the Dockerfile in Build/images/samples.
FROM mcr.microsoft.com/quantum/samples:latest

# Mark that this Dockerfile is used with the samples repository.
ENV IQSHARP_HOSTING_ENV=SAMPLES_HOSTED

# Make sure the contents of our repo are in ${HOME}.
# These steps are required for use on mybinder.org.
USER root
COPY . ${HOME}
RUN chown -R ${USER} ${HOME}

# Finish by dropping back to the notebook user.
USER ${USER}

GWC data science.ipynb

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Pillow-8.3.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n\u001b[K     |████████████████████████████████| 3.0 MB 51.2 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: pyparsing>=2.2.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib) (2.4.7)\nCollecting numpy>=1.16\n  Downloading numpy-1.21.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)\n\u001b[K     |████████████████████████████████| 15.7 MB 48.4 MB/s eta 0:00:01\n\u001b[?25hCollecting cycler>=0.10\n  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\nRequirement already satisfied: python-dateutil>=2.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib) (2.8.2)\nCollecting kiwisolver>=1.0.1\n  Downloading kiwisolver-1.3.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.1 MB)\n\u001b[K     |████████████████████████████████| 1.1 MB 74.4 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: six in /srv/conda/envs/notebook/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.16.0)\nInstalling collected packages: pillow, numpy, kiwisolver, cycler, matplotlib\nSuccessfully installed cycler-0.10.0 kiwisolver-1.3.2 matplotlib-3.4.3 numpy-1.21.2 pillow-8.3.2\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}],"id":"6ba03564-4011-4de8-b22e-f823d0f33d75"},{"cell_type":"code","source":"import pylab as pl","metadata":{"trusted":true},"execution_count":5,"outputs":[],"id":"625d2bea-0da4-4963-9c1a-cf8d62f9cb36"},{"cell_type":"code","source":"%matplotlib inline","metadata":{"trusted":true},"execution_count":6,"outputs":[],"id":"cbed35ef-d3ee-468f-a37d-97ed5f2b011d"},{"cell_type":"code","source":"pl.plot([1, 2, 3], [1, 2, 3])","metadata":{"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"[<matplotlib.lines.Line2D at 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\n"},"metadata":{"needs_background":"light"}}],"id":"77370a02-f025-456f-aa26-d9e84b0aba9e"},{"cell_type":"code","source":"def hello_world(name):\n    print(\"Hello \" + name + \"!\")","metadata":{"trusted":true},"execution_count":10,"outputs":[],"id":"aec79fff-9943-4c2a-9e58-15b356855408"},{"cell_type":"code","source":"hello_world(\"Andrey\")","metadata":{"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Hello Andrey!\n","output_type":"stream"}],"id":"0463fb4f-f183-4876-895f-9aab9110f564"},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[],"id":"bc2c6799-7b4c-472c-a8ab-9d7e318dd581"}]}

GWC quantum computing.ipynb