prafuld3

import pandas as pd
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow,Flow
from google.auth.transport.requests import Request
import os
import pickle

SCOPES = ['https://www.googleapis.com/auth/spreadsheets']

# here enter the id of your google sheet

prafuld3

df_gold=df[(df['Medal']=='Gold') & (df['Sport']=='Gymnastics')]

#change this by your sheet ID
SAMPLE_SPREADSHEET_ID_input = '1cvZswLiDo3LfhnA7RcS8vFqacx73RGor-OZ_FtvyLE8'

#change the range if needed
SAMPLE_RANGE_NAME = 'A1:AA1000'

def Create_Service(client_secret_file, api_service_name, api_version, *scopes):
    global service

prafuld3

from keras.models import Sequential

from keras.layers.convolutional import Convolution2D

from keras.layers.convolutional import MaxPooling2D

from keras.layers.core import Activation

from keras.layers.core import Flatten

prafuld3

#https://stackoverflow.com/questions/10138085/python-pylab-plot-normal-distribution
import numpy as np
import matplotlib.pyplot as plt

mean = 0; variance = 1
x = np.arange(-5,5,.01)
y = np.exp(-np.square(x-mean)/2*variance)/(np.sqrt(2*np.pi*variance))

plt.plot(x,y)
plt.ylabel('gaussian distribution')