import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns; sns.set(style="ticks", color_codes=True)
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
from sklearn import preprocessing

pd.set_option('display.float_format', lambda x: '%.4f' % x)
dfBlockchainData = pd.read_csv('../input/bitcoin_dataset.csv')
dfBlockchainData.set_index('Date', inplace=True)
nRow, nCol = dfBlockchainData.shape
minFilled = dfBlockchainData.describe()[(dfBlockchainData.describe().index == 'count')].T.min()['count']
print(f'Dataset contains {nRow} lines (at least {((minFilled/nRow) * 100).round(4)}% completed) and {nCol} columns, described below')
print(dfBlockchainData.info())
"""
## Column Definition 

btc_market_price

btc_total_bitcoins

btc_market_cap

btc_trade_volume

btc_blocks_size

btc_avg_block_size

btc_n_orphaned_blocks

btc_n_transactions_per_block

btc_median_confirmation_time

btc_hash_rate

btc_difficulty

btc_miners_revenue

btc_transaction_fees

btc_cost_per_transaction_percent

btc_cost_per_transaction

btc_n_unique_addresses

btc_n_transactions

btc_n_transactions_total

btc_n_transactions_excluding_popular

btc_n_transactions_excluding_chains_longer_than_100

btc_output_volume

btc_estimated_transaction_volume

btc_estimated_transaction_volume_usd
"""