muokicaleb

version: '3.7'

volumes:
    prometheus_data: {}

services:

  prometheus:
    image: prom/prometheus
    volumes:

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version: '3.7'

services:
  node_exporter:
    image: quay.io/prometheus/node-exporter:latest
    command:
      - '--path.rootfs=/host'
      - '--web.config=/node_exporter_conf.yaml'
    network_mode: host
    pid: host

muokicaleb

version: '3.7'

services:
  node_exporter:
    image: quay.io/prometheus/node-exporter:latest
    command:
      - '--path.rootfs=/host'
    network_mode: host
    pid: host
    restart: unless-stopped

muokicaleb

basic_auth_users:
  prometheus: $2y$12$UR3iyrDxfWsahCIg0ikKquNBjV3xwdoJZiuz.vhHgpvQrjLhmqmE6s

muokicaleb

#!/usr/bin/env python
# encoding: utf-8

import tweepy #https://github.com/tweepy/tweepy
import csv

#Twitter API credentials
consumer_key = ""
consumer_secret = ""
access_key = ""

muokicaleb

# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048

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# get sum of all unique values in all columns
df.apply(lambda x: len(x.unique()))

# SUm null values
df.isnull().sum()

#apply to rows based on a row

df['Name_entity'] = df.apply (lambda df: name_entity_recognition(df['clean_tweet']), axis=1)

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'''
Make the assumption that weight value is missing in the df but 
 Looking at the columns we'll notice that there is Item_Identifier. Similar items have the same identifier.
 We can have a dictionary of the weigts and identifier. Then look up the missing values for each.
'''
# first create a pivot table 

item_avg_weight = df.pivot_table(values='Item_Weight', index='Item_Identifier')

'''

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git clone https://github.com/fastai/fastai

cd fastai

conda create -n fastai python=3.6 anaconda

conda env update

activate fastai
i

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# a script that automatically commits when the file is saved
# you need to install inotify-tools
inotifywait -q -m -e CLOSE_WRITE --format="git commit -m 'autocommit ' %w" *.* | sh