Question

我将两组不同的数据框垂直附加到一个系列中。我想在使用ControlTemplate拟合模型后绘制图表并绘制实际值和预测值。我正在考虑的两个数据集可以在this link

中找到

我的预测解决方案和尝试使用这些值的图表如下所示

RandomForestRegressor

当我只使用单个数据集（例如：import glob import os import pandas as pd import numpy as np from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from math import sqrt from sklearn.cross_validation import train_test_split import matplotlib.pyplot as plt import seaborn as sns df = pd.concat(map(pd.read_csv, glob.glob(os.path.join('', "cubic*.csv"))),ignore_index=True) #df = pd.read_csv('cubic31.csv') #df.sort_values(['time'], inplace=True) df['time'] = pd.Series(["{0:.10f}".format(val * 100) for val in df['time']], index = df.index) for i in range(1,3): df['X_t'+str(i)] = df['X'].shift(i) print(df) df.dropna(inplace=True) X =np.array (pd.DataFrame({ 'X_%d'%i : df['X'].shift(i) for i in range(3)}).apply(np.nan_to_num, axis=0).values) X = df.drop('Y', axis=1) y = df['Y'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40) X_train = X_train.drop('time', axis=1) X_test = X_test.drop('time', axis=1) print(X.shape) print(df['Y'].shape) print() print("Size of X_train:",(len(X_train))) print("Size of Y_train:",(len(X_train))) print("Size of X_test:",(len(X_test))) print("Size of Y_test:",(len(y_test))) print(X_train.shape) print(y_train.shape) print() ####### to add the trendline fig, ax = plt.subplots() #df.plot(x='time', y='Y', ax=ax) ax.plot(df['time'].values, df['Y'].values) fig, ax = plt.subplots() plt.annotate('annote test!', xy=(len(modelPred_test), modelPred_test[-1]), xycoords='data', xytext=(-30,30), textcoords='offset points', arrowprops=dict(arrowstyle="->")) index_values=range(0,len(y_test)) y_test.sort_index(inplace=True) X_test.sort_index(inplace=True) modelPred_test = reg.predict(X_test) ax.plot(pd.Series(index_values), y_test.values) plotsInOne=pd.DataFrame(pd.concat([pd.Series(modelPred_test), pd.Series(y_test.values)], axis=1)) plt.figure(); plotsInOne.plot(); plt.legend(loc='best')）时 cubic31.csv并应用绘图命令

df = pd.read_csv('cubic31.csv')

这是我得到的情节。

当我们将第二个数据集fig, ax = plt.subplots() ax.plot(df['time'].values, df['Y'].values) fig, ax = plt.subplots()）作为

时

cubic32.csv并应用绘图命令

df = pd.read_csv('testdata2.csv')

这是我得到的情节

但是，如果我将两个数据集合并为

fig, ax = plt.subplots() ax.plot(df['time'].values, df['Y'].values) fig, ax = plt.subplots()，这是我得到的情节

我想在每个情节结束时加上一个标记（如图中的红色箭头所示）。我已尝试使用以下内容，但它仅指向第二个绘图的结尾但不是第一个绘图，如下所示

df = pd.concat(map(pd.read_csv, glob.glob(os.path.join('', "cubic*.csv"))))

我们如何对绘图命令执行此操作，以便我们可以自动注释和标记（例如plt.annotate('annote test!', xy=(len(modelPred_test), modelPred_test[-1]), xycoords='data', xytext=(-30,30), textcoords='offset points', arrowprops=dict(arrowstyle="->"))，this is where cubic31.csv ends，...等）图，如下所示？

Answer 1

考虑在annotate的xy坐标中使用df.ix[]，您可以通过df.shape检索索引（行数和列数的元组）。下面用随机数据进行演示（种子可重复）。第二个数据帧是第一个数据帧的一半。下面还显示了水平和垂直合并注释图：

数据

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# DATAFRAME 1
np.random.seed(33)
df1 = pd.DataFrame({'Y': [np.random.randint(0,1000) for i in range(50)],
                   'time': range(50)})

# DATAFRAME 2
np.random.seed(64)
df2 = pd.DataFrame({'Y': [np.random.randint(0,1000) for i in range(25)],
                   'time': range(25)})

个人注释

def runplot(df, title):   
    rows, cols = df.shape

    fig, ax = plt.subplots()
    ax.plot(df['time'].values, df['Y'].values)

    plt.title(title)
    plt.annotate('annote test!', 
                 xy=(rows, df.ix[rows-1,'Y']),  
                 xycoords='data',
                 xytext=(-30,30),
                 textcoords='offset points',
                 arrowprops=dict(arrowstyle="->"))


runplot(df1, 'Dataframe 1')
runplot(df2, 'Dataframe 2')

水平合并/垂直附加注释

def runplot_merge(left_df, right_df, df, title):               
    fig, ax = plt.subplots()
    ax.plot(df['time'].values, df['Y'].values)

    rows, cols = left_df.shape

    plt.title(title)
    plt.annotate('annote test 1!', 
                 xy=(rows, left_df.ix[rows-1,'Y']),  
                 xycoords='data',
                 xytext=(-30,30),
                 textcoords='offset points',
                 arrowprops=dict(arrowstyle="->"))

    rows, cols = right_df.shape

    plt.annotate('annote test 2!', 
                 xy=(rows, right_df.ix[rows-1,'Y']),  
                 xycoords='data',
                 xytext=(-30,30),
                 textcoords='offset points',
                 arrowprops=dict(arrowstyle="->"))        


df_wide = pd.concat([df1, df2], axis=1)
runplot_merge(df1, df2, df_wide, 'Horizontal Merge')

df_long = pd.concat([df1, df2]).reset_index(drop=True).sort_values('time')
runplot_merge(df1, df2, df_long, 'Vertical Append')

plt.show()
plt.clf()
plt.close()

我们如何使用matplotlib正确地在同一图表中注释多个图？

1 个答案: