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toddbirchard

import React from 'react'
import PropTypes from 'prop-types'
import { StaticQuery, graphql } from 'gatsby'
import { FontAwesomeIcon } from '@fortawesome/react-fontawesome'

const TwitterWidget = ({ data }) => {
  const tweets = data.tweets.edges
  const twitterProfile = data.twitterProfile.user

  return (

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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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"""Collects locally stored images listed in report generated from https://www.deadlinkchecker.com/"""
import pandas as pd
import os
from shutil import copyfile


def read_csv_of_images():
    """Read CSV of broken images generated from link checker."""
    links_df = pd.read_csv('data/brokenlinks.csv')
    links_df.dropna(subset=['anchor'], inplace=True, axis=0)

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def timeit(method):
  """Print execution time of decorated function."""
    def timed(*args, **kw):
        ts = time.time()
        result = method(*args, **kw)
        te = time.time()
        if 'log_time' in kw:
            name = kw.get('log_name', method.__name__.upper())
            kw['log_time'][name] = int((te - ts) * 1000)
        else:

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import sys
import pymysql
import logging


class Database:
    """Database connection class."""

    def __init__(self, config):
        self.host = config.db_host

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import os
import json
import glob
import PIL
from PIL import Image


def get_all_images():
    """Create an array of PNGs and JPGs."""
    img_arr = []

toddbirchard

Animated Terminal Session

Animated terminal session with scriptable output.

A Pen by Todd Birchard on CodePen.

License.

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import os
import json
import boto3
from botocore.client import Config
import botocore
from urllib.parse import unquote
import PIL


# Initialize a session using DigitalOcean Spaces.

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from google.cloud import storage
from google.cloud import bigquery
import pprint


bucket_uri = 'gs://your-bucket/'
bucket_name = 'your-bucket'
bucket_target = 'datasets/data_upload.csv'
local_dataset = 'data/test.csv'
bucket_target_uri = bucket_uri + bucket_target