把文件scores.xlsx转换成DataFrame对象 (1)按年份升序排列 (2)获取历年一本、二本文理科最高和最低的分数线及极差 (3)计算2006—2018年分数的各项指标,用describe()方法
时间: 2024-05-20 14:17:21 浏览: 17
import pandas as pd
# 读取文件并转换成DataFrame对象
df = pd.read_excel('scores.xlsx')
# 按年份升序排列
df = df.sort_values(by='年份')
# 获取历年一本、二本文理科最高和最低的分数线及极差
df_max = df.groupby(['年份', '科类', '批次'])['分数'].max().unstack(level=[1, 2])
df_min = df.groupby(['年份', '科类', '批次'])['分数'].min().unstack(level=[1, 2])
df_range = df_max - df_min
# 计算2006—2018年分数的各项指标
df_stats = df[df['年份'].between(2006, 2018)].groupby('年份')['分数'].describe()
print('历年一本、二本文理科最高分数线:')
print(df_max)
print('\n历年一本、二本文理科最低分数线:')
print(df_min)
print('\n历年一本、二本文理科分数线极差:')
print(df_range)
print('\n2006—2018年分数的各项指标:')
print(df_stats)
相关问题
用python读取给定的文件‘scores.xlsx’,根据文件格式自行设置行标签列标签
以下是一个示例代码,用于读取名为'scores.xlsx'的文件,设置行标签为'student_id',列标签为'subject':
```python
import pandas as pd
# 读取文件
df = pd.read_excel('scores.xlsx')
# 设置行标签和列标签
df = df.set_index('student_id')
df = df.set_index('subject', append=True)
# 查看结果
print(df)
```
运行后,输出的结果类似于:
```
score
student_id subject
1 math 80
english 75
2 math 90
english 85
```
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.
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