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def extract_error_message(value, message = ''):
    for k, v in value.items():
        if isinstance(v,dict):
            message += f'{k} -> '
            message = extract_error_message(v, message)
        else:
            if isinstance(v,list):
                message += f'{k} : {v[0]}, '
            else:
                message += f'{k} : {v}, '

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import string
import re
def clean_text(text):
    text = text.translate(string.punctuation)
    
    ## Convert words to lower case and split them
    text = text.lower().split()
    
    ## Remove stop words
    stops = set(stopwords.words("english"))

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from __future__ import print_function
import json

def find_values(id, json_repr):
    results = []

    def _decode_dict(a_dict):
        try:
            results.append(a_dict[id])
        except KeyError:

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def column_dropper(df, threshold):
  # Takes a dataframe and threshold for missing values. Returns a dataframe.
  total_records = df.count()
  for col in df.columns:
    # Calculate the percentage of missing values
    missing = df.where(df[col].isNull()).count()
    missing_percent = missing / total_records
    # Drop column if percent of missing is more than threshold
    if missing_percent > threshold:
      df = df.drop(col)

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# pip install modin[all]
# import modin.pandas as mpd
import pandas as pd
import math

def handler(df, n_rows, file_name):
    if df.shape[0] >= n_rows:
        split_index_ = int(df.index[0]) + n_rows
        split_index = n_rows
        new_df = df.iloc[:split_index,:]

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[program:celery]
directory = <django_project_directory> example:[/home/ubuntu/prod/api]
command = <virual_environment_directory/bin/celery> <command_to_be_executed> example: [/home/ubuntu/anaconda3/envs/api_env/bin/celery] [-A prodapi worker -l info --without-gossip --without-mingle --without-heartbeat -Ofair --pool=solo] 
stdout_logfile=/var/log/supervisor/celery.log 
stderr_logfile=/var/log/supervisor/celery.log
user=<username> example: [ubuntu]

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from PIL import Image
import requests
from io import BytesIO

response = requests.get(url)
img = Image.open(BytesIO(response.content))

im = Image.open(requests.get(url, stream=True).raw)

import numpy, Image

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Docker Commands, Help & Tips

Show commands & management commands

$ docker

Docker version info

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import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
tf_config = tf.ConfigProto()
tf_config.gpu_options.per_process_gpu_memory_fraction = 0.2
set_session(tf.Session(config=tf_config))

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import requests
import cv2
import numpy as np

url = 'http://172.17.17.118:8080/shot.jpg'

while True:
    img_resp = requests.get(url)
    img_arr = np.array(bytearray(img_resp.content), dtype=np.uint8)
    img = cv2.imdecode(img_arr, -1)