Python导入Excel表格并Check the first 10 rows

可以使用 pandas 库来导入 Excel 表格并查看前 10 行数据。以下是示例代码：

import pandas as pd

# 读取 Excel 文件
df = pd.read_excel('your_excel_file.xlsx')

# 查看前 10 行数据
print(df.head(10))

注意，需要将 `your_excel_file.xlsx` 替换为你要导入的 Excel 文件的路径。

相关问题

write python code,please:CS305 is a hard course, but luckly the nice professor provided chances to get bonus points. At the end of the semester, the professor want to check the grades of all the students, and want to pick up top students to invite them become his graduate student. You will be given a dataframe with three columns: student_id, base, and bonus. You should calculate the bonus_percentage column, which contains the percentage of each student's bonus relative to their base grade. The result should be sorted in ascending order by student_id. Additionally, the result should only include students if they have a base that is greater than the average base of all students, and if their bonus is at least the average bonus of all students. Input The first line is a number n, indicating the number of students. The next n lines give the students' information (student_id, base, bonus). Output For each filtered out rows, print the student_id and bonus_percentage (output to 2 decimal places). If there is no row to display, print None.

Here's the Python code to solve the problem:

import pandas as pd

# read input
n = int(input())
data = []
for i in range(n):
    student_id, base, bonus = input().split()
    data.append([student_id, float(base), float(bonus)])
df = pd.DataFrame(data, columns=['student_id', 'base', 'bonus'])

# calculate bonus_percentage
df['bonus_percentage'] = df['bonus'] / df['base'] * 100

# filter out rows
avg_base = df['base'].mean()
avg_bonus = df['bonus'].mean()
filtered_df = df[(df['base'] > avg_base) & (df['bonus'] >= avg_bonus)]

# sort by student_id and print result
if len(filtered_df) == 0:
    print("None")
else:
    filtered_df = filtered_df.sort_values(by='student_id')
    for index, row in filtered_df.iterrows():
        print(row['student_id'], "{:.2f}".format(row['bonus_percentage']))

The code uses the pandas library to read the input and manipulate the data as a dataframe. It calculates the bonus_percentage column by dividing the bonus by the base and multiplying by 100. It then filters out the rows that meet the criteria (base > average_base and bonus >= average_bonus) and sorts the result by student_id. Finally, it prints out the filtered rows, or "None" if there are no rows to display.

用Python代码爬取电影

我可以提供一个示例代码，这样你就可以用Python代码爬取电影了：import requests, lxml.html as lhfrom lxml.cssselect import CSSSelectorimport pandas as pd#URL of the page you want to scrapurl='https://www.imdb.com/chart/top'#Create a handle, page, to handle the contents of the websitepage = requests.get(url)#Store the contents of the website under docdoc = lh.fromstring(page.content)#Parse data that are stored between <tr>..</tr> of HTMLtr_elements = doc.xpath('//tr')#Check the length of the first 12 rows[len(T) for T in tr_elements[:12]]#Create empty listcol=[]i=0#For each row, store each first element (header) and an empty listfor t in tr_elements[0]:i+=1name=t.text_content()print ('%d:"%s"'%(i,name))col.append((name,[]))#Since out first row is the header, data is stored on the second row onwardsfor j in range(1,len(tr_elements)):#T is our j'th rowT=tr_elements[j]#If row is not of size 10, the //tr data is not from our tableif len(T)!=3:break#i is the index of our columni=0#Iterate through each element of the rowfor t in T.iterchildren():data=t.text_content()#Check if row is emptyif i>0:#Convert any numerical value to integerstry:data=int(data)except:pass#Append the data to the empty list of the i'th columncol[i][1].append(data)#Increment i for the next columni+=1[len(C) for (title,C) in col]#Create the dictionary{title:column for (title,column) in col}#Create the dataframedf=pd.DataFrame(dict)#View the top 5 rowsdf.head()