First, you will use the json.loads function to read a JSON string by passing the data variable as a parameter to it. Then, you will use the json_normalize function to flatten the nested JSON data into a table.
[例3]有如下程序段:import pandas as pd data={"姓名":["李刚","沈玉","王红","张明明","陈光耀"],"xb":["男","女","女","女","男"],"入学成绩":[580,615,596,603,588]df1=pd. DataFrame(data)print(df1)程序段执行后,输出的结果为() A. B.0李刚男580李刚 男5801沈玉 女615沈玉 女...
1.安装了多个python版本,默认使用python3.8 2.安装pandas、numpy等模块 3.进入python,import pandas时报如下错误 4.由以上可知,python打开默认的版本为3.8,而pip3安装pandas和numpy模块默认的路径在python3.6下面。解决方法是命令窗口输入python3,此时python版本为3.6,再import pandas等模块就无上...Linux...
To import multiple CSV files (or all CSV files from the specified folder) into Pandas DataFrame, you can useglob.glob()method which takes the path of the folder where all the required files are located. Secondly, it takes the string as a parameter which works as an identification of the ...
import.io.data报错&&pandas_datareader报错 Problem: 利用python进行数据分析书中,import.io.data报错 根据路径,找到io.data文件,发现它迁移到了pandas-datareader 根据提示,https://github.com/pydata/pandas-datareader 中下载文件, 把压缩包解压,并把pandas_datareader_master中的pandas_datareader文件夹拷贝到路...
解析 A 【详解】 本题考查Python程序pandas模块的应用相关内容。题干语句执行完,此时df是,执行语句print(df.groupby("性别",as_index=False).mean())在聚合过程中会因数据类型而报错,,因此不能得到右侧输出结果,其余选项均一致。故本题答案为A选项。反馈 收藏 ...
1. Import Excel Data Write a Pandas program to import given excel data (coalpublic2013.xlsx ) into a Pandas dataframe.Go to Excel data Sample Solution: Python Code : importpandasaspdimportnumpyasnp df=pd.read_excel('E:\coalpublic2013.xlsx')print(df.head) ...
importpandasaspddata={'name':['John','Alice','Bob'],'age':[25,30,35],'salary':[50000,60000,70000]}df=pd.DataFrame(data)df['age']=df['age'].apply(lambdax:x/10)print(df['age'].sum())A2.5B3C3.5D10 相关知识点: 试题来源: 解析 根据代码,'age'列中的每个值都被应用了一...
import pandas as pd #从CSV文件获取数据 data = pd.read_csv('data.csv') # 从数据库获取数据 from sqlalchemy import create_engine engine = create_engine('sqlite:///mydatabase.db') data = pd.read_sql('SELECT * FROM my_table', engine) ...
import pandas as pd import talib # 读取历史数据 data = pd.read_csv('HK2269.csv') data['Date'] = pd.to_datetime(data['Date']) data.set_index('Date', inplace=True) # 计算RSI和KDJ data['RSI'] = talib.RSI(data['Close'], timeperiod=14) ...