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# Import Libraries
from PIL import Image
from PIL.ExifTags import TAGS

# Open Image
img=Image.open('img.jpg')

#Get EXIF Data
exif_table={}
for k, v in img.getexif().items():

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def photo2pixelart(image, i_size, o_size):
    """
    image: path to image file
    i_size: size of the small image eg:(8,8)
    o_size: output size eg:(10000,10000)
    """
    #read file
    img=Image.open(image)

    #convert to small image

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def img2sketch(photo, k_size):
    #Read Image
    img=cv2.imread(photo)
    
    # Convert to Grey Image
    grey_img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Invert Image
    invert_img=cv2.bitwise_not(grey_img)
    #invert_img=255-grey_img

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import yfinance as yf

aapl=yf.Ticker('aapl')

aapl.quarterly_balance_sheet
#aapl.balancesheet

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import matplotlib.pyplot as plt
a=[1,2,3,4]
b=[4,8,7,3]

def plotter(s):
    plt.figure()
    plt.style.use(s)
    plt.plot(a,b)
    plt.title(s)
    plt.show()    

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plt.figure(figsize=(12,8))

#Scatterplot
sns.scatterplot(data=hdi_df, x='gni_pc', y='life_ex')

#Title 
plt.title(f"G 20 Countries : {abbr['gni_pc']} vs {abbr['life_ex']}")

# x and y axis labels
plt.xlabel(abbr['gni_pc'])

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currancy_dict={'USD':'Dollar',
             'EUR':'Euro',
             'GBP':'Pound',
              'INR':'Rupee'}

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df_conf.drop(['Province/State','Lat', 'Long'],axis=1, inplace=True)
df_c=pd.DataFrame(columns=df_conf.columns)
df_c['Country/Region']=country_list


for col_name in df_c.keys()[1:]:
    for ctry in country_list:
        x=df_conf[col_name][df_conf["Country/Region"]==ctry].sum()
        df_c[col_name][df_c['Country/Region']==ctry]=x