plt.subplot(2, 1, 2) for asset in cumulative_returns.columns: cumulative_returns[asset].plot(label=asset) plt.title('Cumulative Portfolio Returns') plt.xlabel('Date') plt.ylabel('Cumulative Return') plt.legend() plt.grid(True) plt.tight_layout() plt.show() # Print portfolio performance ...
有个瑕疵,某一块儿比例过小时,文字会重叠。 1 def pizza(data,labs,title): 2 import matplotlib 3 import matplotlib.pyplot as plt 4 cols=[col for col in matplotlib.colors.TAB
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All error bars denote mean values ± SD or SEM as indicated in every figure legend; *p < 0.05, **p < 0.01, ***p < 0.001. Samples were not randomized for the experiments. No samples were excluded from the analysis. Code availability Custom-made image analysis scripts ...
for asset in cumulative_returns.columns: cumulative_returns[asset].plot(label=asset) plt.title('Cumulative Portfolio Returns') plt.xlabel('Date') plt.ylabel('Cumulative Return') plt.legend() plt.grid(True) plt.tight_layout() plt.show() ...