r4sn4

from datetime import datetime
date = '2018-09-13T18:00:00.000Z'
parsed_date = datetime.strptime(time, '%Y-%m-%dT%H:%M:%S.000Z')
print(parsed_date)
# parsed_date is  datetime.datetime(2018, 9, 13, 18, 0)

r4sn4

import mysql.connector
from openpyxl import Workbook

def main():

    # Connect to DB -----------------------------------------------------------
    db = mysql.connector.connect( user='usr', password='pwd', host='hostip')
    cur = db.cursor()

    #  database name

r4sn4

#install virtual environment
pip install virtualenv

#Create the virtual environment
virtualenv myenv

#Activate the virtual environment
#On Mac OS / Linux
source myenv/bin/activate

r4sn4

from __future__ import print_function
import datetime
from googleapiclient.discovery import build
from httplib2 import Http
from oauth2client import file, client, tools

# If modifying these scopes, delete the file token.json.
SCOPES = 'https://www.googleapis.com/auth/calendar'

def set_event_calender(summary, time):

r4sn4

grep -rnw '/path/to/somewhere/' -e 'pattern'

-r or -R is recursive,
-n is line number, and
-w stands for match the whole word.
-l (lower-case L) file name of matching files.

r4sn4

#regex to find url in text
r"(https?:\/\/)(\s)?(www\.)?(\s?)(\w+\.)*([\w\-\s]+\/)*([\w-]+)\/?"

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# importing module
import logging

# Create and configure logger
logging.basicConfig(filename="newfile.log",
                    format='%(asctime)s - %(levelname)s: %(message)s',
                    filemode='w')

# Creating an object
logger = logging.getLogger()

r4sn4

#read csv as data frame
df = pd.read_csv(file_path, header=0)

#write dataframe to csv file
df.to_csv(file_name, sep='\t', encoding='utf-8')

#create zero filled data frame with fixed rows and columns
df_final = pd.DataFrame(0, index=range(5), columns=range(3))

#insert column in dataframe at fixed index

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# read train data from csv
train = pd.read_csv(file_path, header=0, sep='\t')

# drop identifier columns and convert to np.array
X = np.array(train.drop(['identifier_col'], 1).astype(float))
y = np.array(train['identifier_col'])


#apply kmeans

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    #determine k range
    k_range = range(1,6)

    #fit the model for each n clusters = k
    k_means_var = [KMeans(n_clusters=k).fit(train) for k in k_range]

    #pull out the cluster centers for each model
    centroids = [V.cluster_centers_ for V in k_means_var]

    ## calculate euclidean distance from each point to each cluster center