# 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('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 market_cap_raw.count()

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

#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.market_cap_perc.plot.bar(title=TOP_CAP_TITLE) # Annotating the y axis with the label defined above ax.set_ylabel(TOP_CAP_YLABEL)

# 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_CAP_TITLE,logy=True,color=COLORS) # Annotating the y axis with 'USD' ax.set_ylabel('USD') # Final touch! Removing the xlabel as it is not very informative ax.set_xlabel('')

# 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') # Checking the first few rows volatility.head()

#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",ax=axes[0]) # Setting the figure's main title to the text passed as parameter fig.suptitle(title) # Setting the ylabel to '% change' ax.set_ylabel('%change') # Same as above, but for the top 10 winners ax = volatility_series[-10:].plot.bar(color="darkblue",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,DTITLE)

# Sorting 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)

# Selecting everything bigger than 10 billion largecaps = cap.query("market_cap_usd>1E+10") # Printing out largecaps print(largecaps)

# Making a nice function for counting different marketcaps from the # "cap" DataFrame. Returns an int. # INSTRUCTORS NOTE: Since you made it to the end, consider it a gift :D def capcount(query_string): return cap.query(query_string).count().id # Labels for the plot LABELS = ["biggish", "micro", "nano"] # Using capcount count the biggish cryptos biggish = capcount("market_cap_usd>3E+8") # Same as above for micro ... micro = capcount("market_cap_usd>=5E+7 & market_cap_usd<3E+8") # ... and for nano nano = capcount("market_cap_usd<5E+7") # Making a list with the 3 counts values = [biggish,micro,nano] # Plotting them with matplotlib plt.bar(range(len(values)), values, tick_label=LABELS)