本文整理汇总了Python中utility.Utility.normalize_data方法的典型用法代码示例。如果您正苦于以下问题：Python Utility.normalize_data方法的具体用法？Python Utility.normalize_data怎么用？Python Utility.normalize_data使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类utility.Utility的用法示例。
在下文中一共展示了Utility.normalize_data方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: plot_selected
# 需要导入模块: from utility import Utility [as 别名]
# 或者: from utility.Utility import normalize_data [as 别名]
def plot_selected(self, df, columns, start_index, end_index):
"""Plot the desired columns over index values in the given range."""
util = Utility()
df = util.normalize_data(df)
self.plot_data(df.ix[start_index:end_index, columns], "Bitcoin")示例2: test_run
# 需要导入模块: from utility import Utility [as 别名]
# 或者: from utility.Utility import normalize_data [as 别名]
def test_run():
'''function to test all the utlities'''
# Define a date range
dates = pd.date_range('2015-04-02', '2016-04-01')
# Choose feature symbols to read
location = os.path.join(base_dir, "BitcoinData")
symbols = os.listdir(location)
#build dataframe consisting of all features
dfreader = DataReader()
util = Utility()
location = os.path.join(base_dir, "BitcoinData")
df = dfreader.get_data(location, symbols, dates)
df = util.normalize_data(df)
for index in range(len(symbols)):
symbols[index] = symbols[index].strip('.csv')
plotter = DataPlotting()
#plot dataframe in selected range and given features list
plotter.plot_selected(df, symbols, '2015-05-01', '2015-06-01')
#plot dataframe for all given data
plotter.plot_data(df, "Bitcoin")
dates = pd.date_range('2010-01-01', '2016-01-01')
btc_file = "bitcoin-market-price.csv"
location = os.path.join(base_dir, btc_file)
df_btc = dfreader.get_btc(location, btc_file, dates)
stats = Statistics(df)
rmean = stats.get_rolling_mean(df_btc['bitcoin-market-price'], window=20)
rstd = stats.get_rolling_std(df_btc.ix[:, 'bitcoin-market-price'], window=20)
upper_band, lower_band = stats.get_bollinger_bands(rmean, rstd)
# Plot raw values, rolling mean and Bollinger Bands
ax = df_btc['bitcoin-market-price'].plot(title="Bollinger Bands", \
label='bitcoin-market-price')
rmean.plot(label='Rolling mean', ax=ax)
upper_band.plot(label='upper band', ax=ax)
lower_band.plot(label='lower band', ax=ax)
# Add axis labels and legend
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.legend(loc='upper left')
plt.show()
#compute daily returns
daily_returns = stats.compute_daily_returns(df_btc)
plotter.plot_data(daily_returns, title="Daily returns", ylabel="Daily returns")
daily_returns.replace(to_replace=np.inf, value=np.NaN, inplace=True)
# Plot a histogram
daily_returns.hist(bins=21)
# Get mean as standard deviation
mean = daily_returns.mean()
std = daily_returns.std()
#print type(mean)
plt.axvline(mean[0], color='w', linestyle='dashed', linewidth=2)
plt.axvline(std[0], color='r', linestyle='dashed', linewidth=2)
plt.axvline(-std[0], color='r', linestyle='dashed', linewidth=2)
plt.show()
# Scatterplots
df.plot(kind='scatter', x='hash_rate', y='market_cap')
beta_XOM, alpha_XOM = np.polyfit(df['hash_rate'], df['market_cap'], 1) # fit poly degree 1
plt.plot(df['hash_rate'], beta_XOM*df['market_cap'] + alpha_XOM, '-', color='r')
plt.show()
# Calculate correlation coefficient
correlation = df['avg_block_size'].corr(df['n_tx'], method='pearson')
print correlation