blocks|key|4110352|text|您对执行时间的测量太粗糙了。以下内容允许您测量模拟所需的时间，而不同于打印所需的时间：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4110353|它正在使用numpy。|4110354|import+matplotlib.pyplot+as+plt
import+numpy+as+np
import+time


def+run_sims(num_sims,+num_flips):
++++start+=+time.time()
++++sims+=+[np.random.choice(coins,+num_flips).cumsum()+for+_+in+range(num_sims)]
++++end+=+time.time()
++++print(f"sim+time+=+{end-start}")
++++return+sims


def+plot_sims(sims):
++++start+=+time.time()
++++for+line+in+sims:
++++++++plt.plot(line)
++++end+=+time.time()
++++print(f"plotting+time+=+{end-start}")
++++plt.show()


if+__name__+==+'__main__':

++++start_time+=+time.time()
++++num_sims+=+2000
++++num_flips+=+2000
++++coins+=+np.array([150,+-100])

++++plot_sims(run_sims(num_sims,+num_flips))|code-block|syntax|javascript|4110355|结果：|4110356|sim+time+=+0.13962197303771973
plotting+time+=+6.621474981307983|4110357|正如您所看到的，sim时间大大减少了(在我2011年的笔记本电脑上大约是7秒)；绘图时间取决于matplotlib。|4110358|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|R|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|S|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|T|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|U|8|@]|9|@]|A|$G|H]]|$1|M|3|N|5|6|7|V|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

Your measure of execution time is too rough. The following allows you to measure the time needed for the simulation, separate from the time needed for plotting: 

It is using numpy.

<pre><code>import matplotlib.pyplot as plt
import numpy as np
import time


def run_sims(num_sims, num_flips):
 start = time.time()
 sims = [np.random.choice(coins, num_flips).cumsum() for _ in range(num_sims)]
 end = time.time()
 print(f"sim time = {end-start}")
 return sims


def plot_sims(sims):
 start = time.time()
 for line in sims:
 plt.plot(line)
 end = time.time()
 print(f"plotting time = {end-start}")
 plt.show()


if __name__ == '__main__':

 start_time = time.time()
 num_sims = 2000
 num_flips = 2000
 coins = np.array([150, -100])

 plot_sims(run_sims(num_sims, num_flips))
</code></pre>

<h3>result:</h3>

<pre><code>sim time = 0.13962197303771973
plotting time = 6.621474981307983
</code></pre>

As you can see, the sim time is greatly reduced (it was on the order of 7 seconds on my 2011 laptop); The plotting time is matplotlib dependent.

blocks|key|4110416|text|为了更好地优化您的代码，我总是尝试使用numpy的矢量化来替换循环，或者，根据我的特定需求，在引擎盖下使用numpy的其他库。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4110417|在这种情况下，您可以这样计算和绘制您的利润：|4110418|import+matplotlib.pyplot+as+plt
import+time
import+numpy+as+np

start_time+=+time.time()
num_of_graphs+=+2000
tries+=+2000
coins+=+[150,+-100]

#+Create+a+2-D+array+with+random+choices
#+rows+for+tries,+columns+for+individual+runs+(graphs).
coin_tosses+=+np.random.choice(coins,+(tries,+num_of_graphs))

#+Caculate+2-D+array+of+profits+by+summing+
#+cumulatively+over+rows+(trials).
profits+=+coin_tosses.cumsum(axis=0)

#+Plot+everything+in+one+shot.
plt.plot(profits)
plt.show()

print("---+%25s+seconds+---"+%25+(time.time()+-+start_time))|code-block|syntax|javascript|4110419|在我的配置中，这段代码使用了aprox。6.3秒(6.2绘图)，而您的代码几乎花了15秒。|4110420|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|O|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|P|8|@]|9|@]|A|$]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

In order to better optimize your code, I would always try to replace loops by vectorization using numpy or, depending on my specific needs, other libraries that use numpy under the hood.

In this case, you could calculate and plot your profits this way:

<pre><code>import matplotlib.pyplot as plt
import time
import numpy as np

start_time = time.time()
num_of_graphs = 2000
tries = 2000
coins = [150, -100]

# Create a 2-D array with random choices
# rows for tries, columns for individual runs (graphs).
coin_tosses = np.random.choice(coins, (tries, num_of_graphs))

# Caculate 2-D array of profits by summing 
# cumulatively over rows (trials).
profits = coin_tosses.cumsum(axis=0)

# Plot everything in one shot.
plt.plot(profits)
plt.show()

print("--- %s seconds ---" % (time.time() - start_time))
</code></pre>

In my configuration, this code took aprox. 6.3 seconds (6.2 plotting) to run, while your code took almost 15 seconds.

I'm trying so simulate coin tosses and profits and plot the graph in matplotlib:
<pre><code>from random import choice
import matplotlib.pyplot as plt
import time

start_time = time.time()
num_of_graphs = 2000
tries = 2000
coins = [150, -100]
last_loss = 0


for a in range(num_of_graphs):
 profit = 0
 line = []
 for i in range(tries):
 profit = profit + choice(coins)
 if (profit &lt; 0 and last_loss &lt; i):
 last_loss = i
 line.append(profit)
 plt.plot(line)
plt.show()

print(&quot;--- %s seconds ---&quot; % (time.time() - start_time))
print(&quot;No losses after &quot; + str(last_loss) + &quot; iterations&quot;)
</code></pre>
The end result is
<pre><code>--- 9.30498194695 seconds ---
No losses after 310 iterations
</code></pre>
Why is it taking so long to run this script? If I change <code>num_of_graphs</code> to 10000, the scripts never finishes.
How would you optimize this?
<img src="https://i.stack.imgur.com/YEbCO.png" alt="Screenshot of the script running in Jypiter" />

Why is this Python script with Matplotlib so slow?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在尝试模拟抛硬币和利润，并在matplotlib中绘制图表：from random import choiceimport matplotlib.pyplot as pltimport timestart_time = time.time()num_of_graphs = 2000tries = 2000coins...

问为什么这个带有Matplotlib的Python脚本这么慢？
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问为什么这个带有Matplotlib的Python脚本这么慢？
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