minghao51

class Config:
  
  # MODE
  PROD = True
  IS_SANDBOX_ENV = True
  TRAINING = False # Prepare data for training/inference if True/False. No Training or Inference executed. Use DO_TRAIN or DO_SERVE.
  DO_TRAIN = False # Run model training if True
  DO_SERVE = True # Run model predict if True
  GENERATE_DATA = True # generate, features, targets if True
  PRINT_CHECKS = True

minghao51

[<999950]0.0,"K";[<999950000]0.0,,"M";0.0,,,"B"

# Convert 
# 7000 -> 7K 
# 2000000 -> 2M

minghao51

import pandas as pd
import numpy as np

def reduce(df):
    cols = df.dtypes.index.tolist()
    types = df.dtypes.values.tolist()
    for i,t in enumerate(types):
        if 'int' in str(t):
            if df[cols[i]].min() > np.iinfo(np.int8).min and df[cols[i]].max() < np.iinfo(np.int8).max:
                df[cols[i]] = df[cols[i]].astype(np.int8)

minghao51

# python number formatter into string with unit
def human_format(num, precision=2, suffixes=['', 'K', 'M', 'G', 'T', 'P']):
    m = sum([abs(num/1000.0**x) >= 1 for x in range(1, len(suffixes))])
    return f'{num/1000.0**m:.{precision}f}{suffixes[m]}'

print('the answer is %s' % human_format(7454538))  

# prints 'the answer is 7.45M'

minghao51

sudo hwclock -s

minghao51

### README
# This Python scripts exports all the OneNote notebooks linked to your Microsoft account to HTML files.

## Output
# The notebooks will each become a subdirectory of the `output` folder, with further subdirectories 
# for the sections within each notebook and the pages within each section. Each page is a directory 
# containing the HTML file `main.html` and two directories `images` and `attachments` (if necessary) 
# for the images and attachments. Any sub-pages will be subdirectories within this one.

## Setup

minghao51

name: ScriptLab Search Wiki
description: Search Wikipedia based on the selected text.
host: POWERPOINT
api_set: {}
script:
  content: |
    $("#insert").click(run);

    Office.onReady((info) => {
      console.log(info.host);