python - 如何放大图表？

我正在使用回归算法预测股票价格。所有代码都在下面。一切正常。我只是想弄清楚如何放大蓝色的预测值。理想情况下，我想使它成为动态过程，而不是硬编码过程。 TIA！

import quandl, math
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn import svm
from sklearn.model_selection import cross_validate
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
import datetime

style.use('ggplot')

df = quandl.get("WIKI/GOOGL")
df = df[['Adj. Open',  'Adj. High',  'Adj. Low',  'Adj. Close', 'Adj. Volume']]
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Close'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0

df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
forecast_col = 'Adj. Close'
df.fillna(value=-99999, inplace=True)
forecast_out = int(math.ceil(0.01 * len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)

X = np.array(df.drop(['label'], 1))
X = preprocessing.scale(X)
X_lately = X[-forecast_out:]
X = X[:-forecast_out]

df.dropna(inplace=True)

y = np.array(df['label'])

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
#X_train, X_test, y_train, y_test = cross_validate.train_test_split(X, y, test_size=0.2)
clf = LinearRegression(n_jobs=-1)
clf.fit(X_train, y_train)
confidence = clf.score(X_test, y_test)

forecast_set = clf.predict(X_lately)
df['Forecast'] = np.nan

last_date = df.iloc[-1].name
last_unix = last_date.timestamp()
one_day = 86400
next_unix = last_unix + one_day

for i in forecast_set:
    next_date = datetime.datetime.fromtimestamp(next_unix)
    next_unix += 86400
    df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]


df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=2)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

最佳答案

根据我的评论，Bokeh非常适合制作交互式绘图，但是如果您只想更改轴设置，则：

# Modified 5th to last line of your code:
ax = df['Forecast'].plot()
# Example with made up date range.
ax.set_xlim(pd.Timestamp('2018-01-01'), pd.Timestamp('2018-04-01'))