import pandas as pd # 读取CSV文件 df = pd.read_csv('data.csv') # 按列名选择 selected_columns_by_name = df[['Name', 'Salary']] print(selected_columns_by_name) # 按索引选择(假设'Name'是第一列,'Salary'是第四列) selected_columns_by_index = df.iloc[:, [0, 3]] print(selected_...
DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False 意思是第二列出现类型混乱,原因如下 pandas读取csv文件默认是按块读取的,即不一次性全部读取; 另外pandas对数据的类型是完全靠猜的,所以pandas每读取一块数据就对csv字段的数据类型进行猜一次,所以有可能pandas...
pd.read_csv("stock_day2.csv", names=["open","high","close","low","volume","price_change","p_change","ma5","ma10","ma20","v_ma5","v_ma10","v_ma20","turnover"]) 2.写入CSV文件:datafram.tocsv() DataFrame.to_csv(path_or_buf=None,sep=',',columns=None,header=True,in...
'column2']filtered_df=df[selected_columns]在上面的例子中，我们分别根据条件、索引和列名对数据进行了...
9.df.to_csv() # 将DataFrame存为csv格式。 DataFrame.to_csv(path_or_buf=None,sep=',',na_rep='',float_format=None,columns=None,header=True,index=True,index_label=None,mode='w',encoding=None,compression='infer',quoting=None,quotechar='"',line_terminator=None,chunksize=None,date_format=No...
Using the read_csv() function, you can select only the columns you need after loading the file, but this means you must know what columns you need prior to loading the data if you wish to perform this operation from within the read_csv() function. If you do know the columns you need...
可以传入列的名字或者位置; 评论 1.2导入.csv数据¶ 评论 pandas.read_csv():用于读取CSV数据。在使用上与pandas.read_excel()类似,但专门针对CSV文件格式。函数签名 pandas.read_csv(filepath_or_buffer,sep=',',delimiter=None,header='infer',index_col=None,usecols=None,dtype=None,parse_dates=False,...
In this version, you select the columns first, then take the first five rows. The result is the same – the order of the functions (and the execution) is different. One more thing. What happens if you replace the ‘article_read’ value with the originalread_csv()function: ...
pandas.read_csv(filepath_or_buffer, sep =',', usecols ) filepath_or_buffer:文件路径 sep :分隔符,默认用","隔开 usecols:指定读取的列名,列表形式 举例:读取之前的股票的数据: 5.1.2 to_csv DataFrame.to_csv(path_or_buf=None, sep=', ’, columns=None, header=True, index=True, mode='w'...
columns='Salary_Level', aggfunc='count') # 时间序列处理 df['Join_Date'] = pd.date_range('2020-01-01', periods=4) df.set_index('Join_Date', inplace=True) monthly_salary = df['Salary'].resample('M').mean() 1. 2. 3.