请随机产生1000个百分制成绩,并绘制直方图,并把图保存下来,用score.png命名
时间: 2024-02-22 09:58:42 浏览: 14
好的,以下是Python代码实现:
```python
import random
import matplotlib.pyplot as plt
# 产生随机成绩
scores = [random.randint(0, 100) for _ in range(1000)]
# 绘制直方图
plt.hist(scores, bins=10, range=(0, 100), edgecolor='black')
plt.xlabel('Score')
plt.ylabel('Frequency')
plt.title('Histogram of Scores')
plt.savefig('score.png')
```
运行以上代码,将会产生一个名为`score.png`的直方图图像文件,其中包含了1000个随机成绩的分布情况。
相关问题
请随机产生1000个百分制成绩,并绘制直方图,查看数据分布频率,并把图保存下来,用score.png命名
可以使用Python中的`random`模块随机生成一批成绩,并使用`matplotlib`模块绘制直方图。具体实现步骤如下:
```python
import random
import matplotlib.pyplot as plt
# 随机生成1000个百分制成绩
scores = [random.randint(0, 100) for _ in range(1000)]
# 绘制直方图
plt.hist(scores, bins=10, range=(0, 100), edgecolor='black')
plt.xlabel('Score')
plt.ylabel('Frequency')
plt.title('Distribution of Scores')
plt.savefig('score.png')
```
上述代码中,使用`random.randint(0, 100)`随机生成0到100之间的整数作为成绩,生成1000个成绩,并将其存储在`scores`列表中。然后使用`plt.hist()`函数绘制直方图,其中`bins`参数指定直方图的条数,`range`参数指定数据的取值范围,`edgecolor`参数指定直方图的边界颜色。最后,使用`plt.savefig()`函数将直方图保存为`score.png`文件。
运行上述代码后,会生成一个名为`score.png`的文件,其中包含了绘制的直方图,可以用图片查看器或者代码中的`plt.show()`函数进行查看。
1. 读取score.xlsx文件 (1)用英语这一列绘制直方图,并且绘制最佳拟合曲线
To read the "score.xlsx" file and plot a histogram of the scores in the "English" column along with the best fit curve, you can use the following Python code:
```python
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
from scipy.optimize import curve_fit
# read the file
df = pd.read_excel('score.xlsx')
# extract the English scores
scores = df['English']
# plot the histogram
plt.hist(scores, bins=10, density=True, alpha=0.6, color='b')
# calculate the mean and standard deviation of the scores
mu, std = norm.fit(scores)
# define the best fit curve function
def best_fit(x, mu, std):
return norm.pdf(x, mu, std)
# fit the best fit curve to the scores
params, _ = curve_fit(best_fit, scores, density=True)
# plot the best fit curve
x = np.linspace(scores.min(), scores.max(), 100)
plt.plot(x, best_fit(x, *params), 'r-', linewidth=2)
# set the plot title and labels
plt.title('English Scores Distribution')
plt.xlabel('Score')
plt.ylabel('Frequency')
# show the plot
plt.show()
```
This code reads the "score.xlsx" file using the Pandas library, extracts the English scores, and plots a histogram of the scores using Matplotlib. It then uses the SciPy library to calculate the mean and standard deviation of the scores and fit the best fit curve to the scores using the normal distribution function. Finally, it plots the best fit curve on top of the histogram and shows the plot.