代码: importpandasaspd# List of Tuplesstudents=[('Ankit',22,'A'),('Swapnil',22,'B'),('Priya',22,'B'),('Shivangi',22,'B'),]# Create a DataFrame objectstu_df=pd.DataFrame(students,columns=['Name','Age','Section'],
import pandas as pd # 创建一个示例DataFrame data = { 'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9] } df = pd.DataFrame(data) # 指定要遍历的列名 columns_to_iterate = ['A', 'B'] # 使用apply函数遍历指定列 for column in columns_to_iterate: df[column].apply(lam...
'Amit','Aishwarya','Priyanka'],'Age':[21,19,20,18],'Stream':['Math','Commerce','Arts','Biology'],'Percentage':[88,92,95,70]}# Convert the dictionary into DataFramedf=pd.DataFrame(data,columns=['Name','Age','Stream','Percentage'])print("Given Dataframe...
for row in df[:1].itertuples(): print(row) ## accessing the complete row - index following by columns print(row.Index) ## accessing the index of the row print(row.a) ## accessing the value of column 'a' 使用下面的代码,使用itertuples()遍历DataFrame df。 start = time.time() # ...
# Iterate over two given columns # onlyfromthe dataframeforcolumninstu_df[['Name','Section']]: # Select column contents by column # nameusing[]operatorcolumnSeriesObj=stu_df[column] print('Colunm Name :', column) print('Column Contents :', columnSeriesObj.values) ...
df = pd.DataFrame(data, columns=['Name', 'Age', 'Stream', 'Percentage']) print("Given Dataframe :\n", df) print("\nIterating over rows using loc function :\n") # iterate through each row and select # 'Name' and 'Age' column respectively. ...
for row in df[:1].itertuples(): print(row) ## accessing the complete row - index following by columns print(row.Index) ## accessing the index of the row print(row.a) ## accessing the value of column 'a' 使用下面的代码,使用itertuples()遍历DataFrame...
df= pd.DataFrame(data, columns = ['Name','Age','Stream','Percentage']) print("Given Dataframe :\n", df) print("\nIterating over rows using iterrows() method :\n") # iterate through each row andselect#'Name'and'Age'column respectively.forindex, rowindf.iterrows(): ...
复制 In [17]: df.index.names Out[17]: FrozenList([None, None]) 这个索引可以支持 pandas 对象的任何轴,并且索引的级别数量由你决定: 代码语言:javascript 代码运行次数:0 运行 复制 In [18]: df = pd.DataFrame(np.random.randn(3, 8), index=["A", "B", "C"], columns=index) In [19...
如果我们测量这两个调用的内存使用情况,我们会发现在这种情况下指定columns使用的内存约为 1/10。 使用pandas.read_csv(),您可以指定usecols来限制读入内存的列。并非所有可以被 pandas 读取的文件格式都提供读取子集列的选项。 使用高效的数据类型 默认的 pandas 数据类型并不是最节省内存的。特别是对于具有相对少量...