esenthil2018

import tabula
pdf_path = "https://github.com/chezou/tabula-py/raw/master/tests/resources/data.pdf"

dfs = tabula.read_pdf(pdf_path, stream=True)
# read_pdf returns list of DataFrames
print(len(dfs))
dfs[0]

esenthil2018

import calendar

print(calendar.month(2020, 10))

esenthil2018

#Save the model using pickle
import pickle
# save the model to disk
pickle.dump(model, open(model_file_path, 'wb'))

#Load the model 
model = pickle.load(open(model_file_path, 'rb'))

#Saving a Keras model
# Calling `save('my_model')` creates a SavedModel folder `my_model`.

esenthil2018

import pandas as pd
import numpy as np
df = pd.DataFrame({'city':['Sunnyvale','Palo Alto','SanFrancisco'],
                   'day1':[73,80,70],
                   'day2':[80,75,65],
                   'day3':[88,76,60],
                   'day4':[85,80,65],
                   'day5':[80,75,60]})
df
df.melt(id_vars=['city'])

esenthil2018

from datetime import datetime
import pandas_datareader.data as wb

stocks = ['AAPL','GOOG','FB','AMZN','TSLA']

start = datetime(2020,9,1)
end = datetime(2020,9,30)

p = wb.DataReader(stocks, 'yahoo',start,end)

esenthil2018

import requests

TWITTER_KEY = 'Your twitter key'
TWITTER_SECRET_KEY = 'Your secret key'

# Authenticate
auth = tweepy.AppAuthHandler(TWITTER_KEY, TWITTER_SECRET_KEY)

api = tweepy.API(auth, wait_on_rate_limit=True,
				   wait_on_rate_limit_notify=True)

esenthil2018

# Flickr API - to extract the pictures containing giraffe and geo location
# insert your own key and secret
api_key = 'bb442da9177b0f782255e227f2f2cee4'
secret_api_key ='4cf09aea38529025'

fr = flickrapi.FlickrAPI(api_key, secret_api_key)
pset_id = '72157673330147052'

for photo in fr.walk_set(photoset_id=pset_id, extras="geo"):
    print (photo.attrib['id'])

esenthil2018

import tensorflow as tf

# Call back class
class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('accuracy')>0.8):
      print("\nReached 80% accuracy so cancelling training!")
      self.model.stop_training = True

mnist = tf.keras.datasets.fashion_mnist

esenthil2018

import feedparser
import pandas as pd

class WhizRssAggregator():
    feedurl = ""
    #global df
    #df = pd.DataFrame(columns=['title', 'link', 'decription','published', 'content'])
    

    def __init__(self, paramrssurl):

esenthil2018

#Sample code to use the google API 
from google.cloud import vision
from google.cloud.vision import types

import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "keyFile.json"

def vision(url):
    image = types.Image()
    image.source.image_uri = url