rotatelabels=False, #自定义是否按照角度进行调整每块饼的label *, normalize=None, data=None)[source] 例: name = ["Apple", "Banana", "Orange", "Melon"] freq = [27, 18, 32, 11] exp = [0,0,0,0.1] plt.pie(freq,labels=name,explode=exp,autopct="%0.1f%%") plt.show()· 结果: ...
binaryImage[labels == label] = 0 else: break minRotate = 0 minCount = -1 (cX, cY) = (maskW // 2, maskH // 2) points = np.column_stack(np.where(binaryImage > 0))[:pointsNum].astype(np.int16) for rotate in range(-angleThres, angleThres): rotatePoints = rotate_points(poin...
LabelOpts(formatter="{value} /day"),name="Y轴名称"),# 设置Y轴名称、定制化刻度单位xaxis_opts=...
wedgeprops=None, textprops=None, center=(0, 0), frame=False, rotatelabels=False, *, data=None)pie()函数的常用函数参数名称含义 x 传入的数据 explode 默认x的饼图不爆炸。自定义确定哪一块爆炸&爆炸距离。 labels和labeldistance 默认x没有标签,标签位于1.1倍半径处。自定义每块饼的标签,和位置。 auto...
label_z = tk.Label(frame, text="Z轴旋转角度") label_z.pack(side=tk.LEFT) entry_z = tk.Entry(frame) entry_z.pack(side=tk.LEFT) button_rotate = tk.Button(root, text="旋转", command=rotate) button_rotate.pack() root.mainloop() ...
rotatelabels=True, period_label={x:0,y:0}) 07 动态气泡图 multi_index_df = pd.read_csv("data/multi.csv", header=[0,1], index_col=0) multi_index_df.index = pd.to_datetime(multi_index_df.index, dayfirst=True) map_chart = multi_index_df.plot_animated( ...
covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0])covid_df.plot_animated(filename='examples/example-pie-chart.gif', kind="pie",rotatelabels=True, period_label={'x': 0, 'y': 0}) 动态气泡图 multi_index_df = pd.read_csv("data/multi.csv", header=[0...
(self.rect())rect.adjust(0,0,-1,-1)center=rect.center()painter.translate(center)painter.rotate(-90)painter.drawText(rect,Qt.AlignCenter,self.text())if__name__=="__main__":app=QApplication([])widget=QWidget()label=VerticalLabel("竖直显示的文本")widget.resize(200,200)widget.show()...
pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=0, radius=1, counterclock=True, wedgeprops=None, textprops=None, center=0, 0, frame=False, rotatelabels=False, *, normalize=None, data=None) ...
rotatelabels :布尔类型,默认为 False。如果为 True,旋转每个 label 到指定的角度。 2. 检查重复数据,并删除数据集里面的重复数据 *注意:我们想做的是对邮件进行分类,因此重复的数据对我们来说没用。我们只关心某个邮件是否是垃圾邮件 # 这里的思路就是看看删除重复项之前和之后,重复项的数量的变化print(data.dup...