A simple data analysis of Bitcoin provided by CoinMetrics in Python


This time, I will change my mind a little and introduce an example of simple data analysis (visualization) using Python. CoinMetrics provides data of each cryptocurrency (including stable coins) in csv format, so I used that to visualize the transaction volume and price (USD) of Bitcoin.

Since Python has abundant libraries for data analysis, it is nice to have only a few lines to dozens of lines for simple visualization. (In this case, it is possible to visualize somehow with a very simple code just by using pandas and matplotlib. When trying to realize the same thing with MS-Excel ...)

** ** The data provided by CoinMetrics can be downloaded from the following (Data from major cryptocurrencies can be used) https://coinmetrics.io/community-network-data/

[Python code]


%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd

data = pd.read_csv('xxx\\btc.csv')

data['date'] = pd.to_datetime(data['date'])
data.set_index('date', inplace=True)

** **



** <Visualize price (USD) transitions by year> **



** <Representing the annual transition status of transaction volume and price (USD) in a composite graph> **

fig, ax1 = plt.subplots()
plt.plot(data.TxCnt, color='darkblue', label='TxCnt')

ax2 = ax1.twinx()
plt.plot(data.PriceUSD, color='darkorange', label='PriceUSD')

h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
ax1.legend(h1+h2, l1+l2, loc='upper left')



** <By the way, it is possible to narrow down to a specific year (example: 2014)> **

Excerpt only for changes

plt.plot(df_data['2014'].TxCnt, color='darkblue', label='TxCnt')
plt.plot(df_data['2014'].PriceUSD, color='darkorange', label='PriceUSD')


** **



that's all

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