Exploring the Bitcoin Cryptocurrency Market

# Importing pandas
import pandas as pd

# Importing matplotlib and setting aesthetics for plotting later.
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'svg' 
plt.style.use('fivethirtyeight')

# Reading datasets/coinmarketcap_06122017.csv into pandas
dec6 = pd.read_csv('datasets/coinmarketcap_06122017.csv')

# Selecting the 'id' and the 'market_cap_usd' columns
market_cap_raw = dec6[['id','market_cap_usd']]

# Counting the number of values
# ... YOUR CODE FOR TASK 2 ...

# Filtering out rows without a market capitalization
cap = market_cap_raw.query('market_cap_usd > 0')
print(cap.count())
# Counting the number of values again
# ... YOUR CODE FOR TASK 3 ...

#Declaring these now for later use in the plots
TOP_CAP_TITLE = 'Top 10 market capitalization'
TOP_CAP_YLABEL = '% of total cap'

# Selecting the first 10 rows and setting the index
cap10 = cap[:10].set_index('id')

# Calculating market_cap_perc
cap10 = cap10.assign(market_cap_perc = lambda x: (x.market_cap_usd / cap.market_cap_usd.sum()) * 100)

# Plotting the barplot with the title defined above 
ax = cap10.plot.bar()
plt.title("Top 10 market capitalization")
ax.set_ylabel("% of total cap")
# Annotating the y axis with the label defined above
# ... YOUR CODE FOR TASK 4 ...

# Colors for the bar plot
COLORS = ['orange', 'green', 'orange', 'cyan', 'cyan', 'blue', 'silver', 'orange', 'red', 'green']

# Plotting market_cap_usd as before but adding the colors and scaling the y-axis  
ax = cap10['market_cap_usd'].plot.bar(title="Top 10 market capitalization",logy=True, color=COLORS)
ax.set_ylabel('USD')
# Annotating the y axis with 'USD'
# ... YOUR CODE FOR TASK 5 ...
ax.set_xlabel("")
# Final touch! Removing the xlabel as it is not very informative
# ... YOUR CODE FOR TASK 5 ...

# Selecting the id, percent_change_24h and percent_change_7d columns
volatility = dec6[['id', 'percent_change_24h', 'percent_change_7d']]

# Setting the index to 'id' and dropping all NaN rows
volatility = volatility.set_index('id').dropna()

# Sorting the DataFrame by percent_change_24h in ascending order
volatility = volatility.sort_values('percent_change_24h',ascending = True)
volatility.head()
# Checking the first few rows
# ... YOUR CODE FOR TASK 6 ...

#Defining a function with 2 parameters, the series to plot and the title
def top10_subplot(volatility_series, title):
    # Making the subplot and the figure for two side by side plots
    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))
    
    # Plotting with pandas the barchart for the top 10 losers
    ax = volatility_series[:10].plot.bar(color='darkred', title='top losers',ax=axes[0])
    
    # Setting the figure's main title to the text passed as parameter
    # ... YOUR CODE FOR TASK 7 ...
    fig.suptitle(title)
    # Setting the ylabel to '% change'
    # ... YOUR CODE FOR TASK 7 ...
    ax.set_ylabel('% change')
    # Same as above, but for the top 10 winners
    ax = volatility_series[-10:].plot.bar(color='darkblue', title='top winners',ax=axes[1])
    
    # Returning this for good practice, might use later
    return fig, ax

DTITLE = "24 hours top losers and winners"

# Calling the function above with the 24 hours period series and title DTITLE  
fig, ax = top10_subplot(volatility.percent_change_24h, title= DTITLE)

# Sorting percent_change_7d in ascending order
volatility7d = volatility.sort_values("percent_change_7d")

WTITLE = "Weekly top losers and winners"

# Calling the top10_subplot function
fig, ax = top10_subplot(volatility7d.percent_change_7d, WTITLE);