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    """
    Generate PDF reports from data included in several Pandas DataFrames
    From pbpython.com
    """
    from __future__ import print_function
    import pandas as pd
    import numpy as np
    import argparse
    from jinja2 import Environment, FileSystemLoader
    from weasyprint import HTML

PureMath86

# List unique values in a DataFrame column
# h/t @makmanalp for the updated syntax!
df['Column Name'].unique()

# Convert Series datatype to numeric (will error if column has non-numeric values)
# h/t @makmanalp
pd.to_numeric(df['Column Name'])

# Convert Series datatype to numeric, changing non-numeric values to NaN
# h/t @makmanalp for the updated syntax!

PureMath86

import sys

with open(sys.argv[1],"rb") as file:
    file.seek(0)
    pdf = file.read()

startmark = b"\xff\xd8"
startfix = 0
endmark = b"\xff\xd9"
endfix = 2

PureMath86

import cv2
import numpy as np

img = cv2.imread('path/to/img', 0) # 0 = grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(img,100,200)

lines = cv2.HoughLines(edges,1,np.pi/180,200)
for rho, theta in lines[0]:
    a = np.cos(theta)

PureMath86

# import the necessary packages
from PIL import Image # get image from disk in PIL format
import pytesseract # python "wrapper" of Google's Tesseract binaries
import argparse # build command-line interfaces
import cv2 # opencv-python
import os # operating system


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()

PureMath86

# import packages
from PIL import Image
import os
import argparse # build command-line interfaces


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d",
                "--directory",

PureMath86

API workthough

1. Open a browser

   # start an instance of firefox with selenium-webdriver
   driver = Selenium::WebDriver.for :firefox
   # :chrome -> chrome
   # :ie     -> iexplore
   

• Go to a specified URL

PureMath86

import pandas as pd
import pyperclip
import io


def frame_clipboard():
    s = pyperclip.paste()
    clip_df = pd.read_table(io.StringIO(s))
    return clip_df

PureMath86

This configuration worked for me, hope it helps

It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b

and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support

Hardware

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import pyautogui

def mouse_now():
    print('Press Ctrl-C to quit.')
    
    try:
        while True:
            # Get and print the mouse coordinates.
            x, y = pyautogui.position()
            positionStr = 'X: ' + str(x).rjust(4) + ' Y: ' + str(y).rjust(4)