afr-dt

• 0.1. References
• 0.2. Other cheatsheets
• 0.3. Manage multiple gcloud config configurations
  • 0.3.1. Switch gcloud context with gcloud config
• 0.4. Credentials
• 0.5. info
• 0.6. projects
• 0.7. zones & regions
• 0.8. Organization
• 0.9. billing

afr-dt

Problem

If you're getting this kind of error when running Node.js programs with binary dependencies that don't support M1 yet, e.g.:

$ yarn test
dyld[51175]: missing symbol called
dyld[51176]: missing symbol called

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# syntax=docker/dockerfile:1

FROM python:3.8-slim-buster

WORKDIR /app

ENV ACCEPT_EULA=Y
RUN apt-get update -y && apt-get update \
  && apt-get install -y --no-install-recommends curl gcc g++ gnupg unixodbc-dev

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Tabs and Windows

Function	Shortcut
New Tab	⌘ + T
Close Tab or Window	⌘ + W (same as many mac apps)
Go to Tab	⌘ + Number Key (ie: ⌘2 is 2nd tab)
Go to Split Pane by Direction	⌘ + Option + Arrow Key
Cycle iTerm Windows	⌘ + backtick (true of all mac apps and works with desktops/mission control)

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def dataframe_difference(df1, df2, which=None):
    """Find rows which are different."""
    comparison_df = df1.merge(df2,
                              indicator=True,
                              how='outer')
    if which is None:
        diff_df = comparison_df[comparison_df['_merge'] != 'both']
    else:
        diff_df = comparison_df[comparison_df['_merge'] == which]
    diff_df.to_csv('data/diff.csv')

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Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

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Default

Powerlevel10k

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Oh my zsh.

Install with curl

sh -c "$(curl -fsSL https://raw.githubusercontent.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"

Enabling Plugins (zsh-autosuggestions & zsh-syntax-highlighting)

• Download zsh-autosuggestions by

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This small subclass of the Pandas sqlalchemy-based SQL support for reading/storing tables uses the Postgres-specific "COPY FROM" method to insert large amounts of data to the database. It is much faster that using INSERT. To acheive this, the table is created in the normal way using sqlalchemy but no data is inserted. Instead the data is saved to a temporary CSV file (using Pandas' mature CSV support) then read back to Postgres using Psychopg2 support for COPY FROM STDIN.

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#!/usr/bin/env python
import sys
import pandas as pd
import pymongo
import json



def import_content(filepath):
    mng_client = pymongo.MongoClient('localhost', 27017)