提问人：rocket 提问时间：11/14/2023 最后编辑：Reinderienrocket 更新时间：11/14/2023 访问量：34
如何编写一个循环函数，一次性制作单独的散点图？另外，如何添加最适合的行？
How do I write a loop function that will make separate scatterplots graphs all in one go? Also how do I add a line of best fit?
问：
我知道如何制作单独的散点图，但我希望它们都放在一个视图中，并排放置。我还想为此视图中的每个图形添加一行最佳拟合线。我将所有这些股票行情与 SPY 指数进行比较，并显示回归。谢谢！
我尝试将东西放入数组中并创建子图，但这不起作用，所以现在我被卡住了。
import yfinance as yf, matplotlib.pyplot as plt, numpy as np
import pandas_datareader.data as reader
import statsmodels.api as sm
import seaborn as sns
import pandas as pd
from scipy import stats
from dateutil.relativedelta import *
import getFamaFrenchFactors as gff
import datetime
from sklearn.linear_model import LinearRegression
import pandas as pd
from xbbg import blp
tickers = ['JPM', 'C', 'BAC', 'MS', 'GS', 'WFC', 'BCS']
weights = np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
today1= datetime.date.today()
olddate1 = today1 - datetime.timedelta(weeks=26.5)
df_spy = yf.download('SPY', olddate1, today1)['Adj Close']
today= datetime.date.today()
olddate = today - datetime.timedelta(weeks=26.5)
df = yf.download(tickers, olddate, today)['Adj Close']
fig,ax=plt.subplots(figsize=(10,10), nrows=2, ncols=1, sharex=True)
Scatter1=ax[0].scatter(x=df_spy.pct_change(), y = df[tickers[0]].pct_change(), c=)
plt.rcParams['axes.grid'] = False
plt.scatter(df_spy.pct_change(),df[tickers[0]].pct_change(), alpha =.6, color='blue', s=55)
plt.scatter(df_spy.pct_change(),df[tickers[1]].pct_change(), alpha =.6, color='blue', s=55)
plt.scatter(df_spy.pct_change(),df[tickers[2]].pct_change(), alpha =.6, color='blue', s=55)
plt.scatter(df_spy.pct_change(),df[tickers[3]].pct_change(), alpha =.6, color='blue', s=55)
plt.scatter(df_spy.pct_change(),df[tickers[4]].pct_change(), alpha =.6, color='blue', s=55)
plt.scatter(df_spy.pct_change(),df[tickers[5]].pct_change(), alpha =.6, color='blue', s=55)
plt.scatter(df_spy.pct_change(),df[tickers[6]].pct_change(), alpha =.6, color='blue', s=55)
答：
0赞
furas
11/14/2023
#1
使用当前代码，您可以使用更多位置放置绘图，然后使用 、 、 将绘图并排放置plt.subplots(..., nrows=2, ncols=7, ...)ax[0][0].scatter()ax[0][1].scatter()ax[1][6].scatter()
完整的工作代码：
import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import yfinance as yf
tickers = ['JPM', 'C', 'BAC', 'MS', 'GS', 'WFC', 'BCS']
weights = np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
today = datetime.date.today()
olddate = today - datetime.timedelta(weeks=26.5)
df_spy = yf.download('SPY', olddate, today)['Adj Close']
df = yf.download(tickers, olddate, today)['Adj Close']
fig, ax = plt.subplots(figsize=(10,10), nrows=2, ncols=7, sharex=True)
ax[0][0].scatter(x=df_spy.pct_change(), y=df[tickers[0]].pct_change())
ax[0][0].set_title('SPY')
plt.rcParams['axes.grid'] = False
for index, item in enumerate(tickers):
ax[1][index].scatter(df_spy.pct_change(), df[item].pct_change(), alpha=.6, color='blue', s=55)
ax[1][index].set_title(item)
#plt.tight_layout()
plt.show()
但这给了空旷的地方。
使用您可以更好地控制绘图并创建一个更大的绘图（上图）和许多小绘图（下图）。plt.subplot(rows, cols, index)
import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import yfinance as yf
tickers = ['JPM', 'C', 'BAC', 'MS', 'GS', 'WFC', 'BCS']
weights = np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
today = datetime.date.today()
olddate = today - datetime.timedelta(weeks=26.5)
df_spy = yf.download('SPY', olddate, today)['Adj Close']
df = yf.download(tickers, olddate, today)['Adj Close']
fig = plt.figure(figsize=(10,10))
ax = plt.subplot(2,1,1) # (rows, cols, index)
ax.scatter(x=df_spy.pct_change(), y=df[tickers[0]].pct_change())
ax.set_title('SPY')
plt.rcParams['axes.grid'] = False
for index, item in enumerate(tickers):
ax = plt.subplot(2, 7, index+1+7) # (rows, cols, index)
ax.scatter(df_spy.pct_change(), df[item].pct_change(), alpha=.6, color='blue', s=55)
ax.set_title(item)
#plt.tight_layout()
plt.show()
评论
axplt.scatter( ax=ax[0], ..)plt.scatter( ax=ax[1], ..)plt.subplots(.., nrows=2, ncols=1, ..)