prelab12

import pandas as pd

def import_and_merge(circ_file, price_file):
    '''
    This function should read in the CSVs whose names are given by circ_file and price_file
    into separate Pandas dataframes.
    The circ_file and price_file are described by the circulation data and price data descriptions
    in the instructions.
    You should then merge those dataframes, and
    return a Pandas dataframe that is a combination of these two dataframes, i.e. each element (row)
    in its should be a 3-column (3-tuple) containing date, circulation, and price information.
    '''
    circ_df = pd.read_csv(circ_file)
    price_df = pd.read_csv(price_file)
    arranged_df = pd.merge(circ_df,price_df)
    return arranged_df
    pass


def calculate_market_cap(data):
    '''
    This function should accept a Pandas dataframe containing date, circulation and price information,
    and should return a new column (a pd.series object) containing the market cap calculated using
    the formula in the instructions.
    '''
    x = data.Circulation * data.Price
    return x
    pass


def get_max_row(data):
    '''
    This function should accept a Pandas dataframe containing date, circulation, price,
    and market cap information, with column names 'Date', 'Circulation', 'Price', and 'Market Cap',
    and should return the single row of that dataframe corresponding to the highest market cap
    (the day where the Bitcoin market capitalization was at its maximum).
    '''
    max_val_market_cap = data['Market Cap'].max()
    y = data.loc[data['Market Cap'] == max_val_market_cap]
    return y
    pass


def format_row(row):
    '''
    This function should expect one row (with column headers),
    which is actually a 4-tuple/column dataframe
    (as described in the instructions for get_max_row())
    and should return a string that displays that row in the format presented in the grader output.
    '''
    my_tuple = row.iloc[0]
    a = str(my_tuple[0])
    b = str(my_tuple[1])
    c = str(my_tuple[2])
    d = str(my_tuple[3])
    command = 'On %s, circulation was %s and the exchange rate was %s, meaning the market cap was %s'%(a,b,c,d)
    return command
    pass


if __name__ == '__main__':
    '''
    Your main function should import the two CSVs described in the instructions,
    merge them using your import_and_merge,
    then calculate the market cap for the data, and
    finally print out the row of the combined dataframe using you display_row function
    '''
    # First import the two CSVs described in the instructions that contain circulation
    # and price data using import_and_merge
    combined_df = import_and_merge('total_bitcoins.csv','bitcoin_price.csv')
    # Then use the merged dataframe to calculate the market cap for each date (data point/row)
    # using calculate_market_cap
    # Store the result in a new column (called 'Market Cap') of the existing dataframe
    new_df = calculate_market_cap(combined_df)
    new_df.columns = ['Market Cap']
    complete_df = pd.concat([combined_df,new_df], axis=1)
    complete_df.columns =['Date','Cirulation','Price','Market Cap']
    # Then find the row of the dataframe with the highest market cap using get_max_row
    max_row = get_max_row(complete_df)
    # Finally, print the max_row by printing the string returned by display_row
    print(format_row(max_row))