Box画法笔记
import pandas as pd normal_sample = np.random.normal(loc=0.0, scale=1.0, size=10000) random_sample = np.random.random(size=10000) gamma_sample = np.random.gamma(2, size=10000) df = pd.DataFrame({\'normal\': normal_sample, \'random\': random_sample, \'gamma\': gamma_sample})
df.describe()
2.作图
plt.figure() # create a boxplot of the normal data, assign the output to a variable to supress output _ = plt.boxplot(df[\'normal\'], whis=\'range\')
3. 显示三列
# clear the current figure plt.clf() # plot boxplots for all three of df\'s columns _ = plt.boxplot([ df[\'normal\'], df[\'random\'], df[\'gamma\'] ], whis=\'range\')
4.
import mpl_toolkits.axes_grid1.inset_locator as mpl_il plt.figure() plt.boxplot([ df[\'normal\'], df[\'random\'], df[\'gamma\'] ], whis=\'range\') # overlay axis on top of another ax2 = mpl_il.inset_axes(plt.gca(), width=\'60%\', height=\'40%\', loc=2) ax2.hist(df[\'gamma\'], bins=100) ax2.margins(x=0.5)
5. y坐标标记转换位置
# switch the y axis ticks for ax2 to the right side ax2.yaxis.tick_right()
6. whis的妙用
# if `whis` argument isn\'t passed, boxplot defaults to showing 1.5*interquartile (IQR) whiskers with outliers plt.figure() _ = plt.boxplot([ df[\'normal\'], df[\'random\'], df[\'gamma\'] ] )