midwest=pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv")# Prepare Data # Createasmany colorsasthere are unique midwest['category']categories=np.unique(midwest['category'])colors=[plt.cm.tab10(i/float(len(categories)-1))foriinrange(len(categories))...
6))point_numbers=list(range(random_walk.num_points))plt.scatter(random_walk.x_values,random_walk.y_values,c=point_numbers,cmap=plt.cm.Blues,edgecolor="none",s=1)# 突出起点和终点
plt.hist(values, bins=20, edgecolor='black') # 参数bins表示直方图的柱子数量,edgecolor表示柱子的边框颜色 plt.title('Histogram of column_name') #将'column_name'替换为你要绘制直方图的数据列名 plt.xlabel('Values') # 添加x轴标签 plt.ylabel('Frequency') # 添加y轴标签 plt.show() 这段代码首先...
# white wine - wine qualityax2 = fig.add_subplot(1,2,2)ax2.set_title("White Wine")ax2.set_xlabel("Quality")ax2.set_ylabel("Frequency")ww_q = white_wine['quality'].value_counts()ww_q = (list(ww_q.index), list(ww_q.values)...
def get_pixels_hu(slices):image = np.stack([s.pixel_array for s in slices])# Convert to int16 (from sometimes int16),# should be possible as values should always be low enough (<32k)image = image.astype(np.int16)# Set outside-of-scan pixels to 0# The intercept is usually -102...
tupleList = [("python", "6"), ("JavaScript", "9"), ("C", "2")] print("The list of tuples before conversion is : " + str(tupleList)) # Converting integer values in list of tuples to float conTupList = [] for tup in tupleList: convColl = [] for ele in tup: if ele...
import numpy as np import pandas as pd import pickle import matplotlib.pylab as plt import rpy2.robjects as robjects robjects.r('library(forecast)') #定义R时序对象的调用设置 robjects.r(''' setRTS<-function(tsdata,tsstart){ return(ts(tsdata,start=tsstart,frequency=4)) } ''') #定义...
def get_fft_values(y_values, T, N, f_s): N2 = 2 ** (int(np.log2(N)) + 1) # round up to next highest power of 2 f_values = np.linspace(0.0, 1.0/(2.0*T), N2//2) fft_values_ = fft(y_values) fft_values = 2.0/N2 * np.abs(fft_values_[0:N2//2]) return f_val...
通过实现特殊方法__len__和__getitem__,我们的FrenchDeck表现得像一个标准的 Python 序列,允许它从核心语言特性(例如迭代和切片)和标准库中受益,如使用random.choice、reversed和sorted的示例所示。得益于组合,__len__和__getitem__实现可以将所有工作委托给一个list对象self._cards。
prices = data.history(context.asset,'price', bar_count=context.long_window, frequency='1d') short_mean = prices[-context.short_window:].mean() long_mean = prices.mean() ifshort_mean>long_mean and not context.portfolio.positions[context.asset].amount: ...