import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') # 插入列 for col_num in range(4, 9): df.insert(loc=col_num, column=f'列{col_num-3}', value=None) # 保存修改后的DataFrame到新的Excel文件 df.to_excel('结果.xlsx', index=False) test() 4...
df['New Column'] = ‘指定的值’ 在示例数据中,我们可以新增加“总成绩”这列 df['总成绩'] = df['数学']+df['英语']+df['语文'] 17、重命名 DataFrame 中的列 df.columns = ['Column 1', 'Column 2', 'Column 3'] 18、沿轴按标签对系列进行排序 按索引标签对系列进行排序,如果 inplace ...
Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. DataFrame.isin(values) 是否包含数据框中的元素 DataFrame.where(cond[, other, inplace, …]) 条件筛选 DataFrame.mask(cond[, other, inplace, axis, …]) Return an object of same shape as self and whose corresponding...
import pandas as pd # 创建一个示例DataFrame data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Age': [25, 30, 35, 40]} df = pd.DataFrame(data) # 选择需要添加列值的行 rows_to_add = [1, 3] # 添加新的列值 df.loc[rows_to_add, 'New Column'] = ['Value 1', '...
Add new categories. remove_categories Remove the specified categories. remove_unused_categories Remove categories which are not used. set_categories Set the categories to the specified ones. None of them seem to do what I need to do. So it seems the way to go would be: ...
importpandasaspdimportnumpyasnp# 假设df是一个DataFramedf['new_column'] = np.log(df['existing_column']) apply方法:除了基本的向量化操作外,apply方法允许我们应用自定义函数到DataFrame的行或列上。这对于复杂的数据转换非常有用: defcustom_function(row):returnrow['column1'] + row['column2'] *2df[...
选择某个元素,不输出索引:finished.iloc[1, 1]; 选择第2行和第2列交叉的那个元素——这里加上="new_value",就可以修改某个cell的取值了; 检索符合某个条件值的多行:df = df[ df['ID'].isin(['102', '301', '402']) ]; 删除符合条件的多行:df.loc[~df['column_name'].isin(some_values)]...
ser=pd.Series(mydict)#series转换为dataframedf =ser.to_frame()#索引列转换为dataframe的列df.reset_index(inplace=True)print(df.head())#> index 00 a 01 b 1 2 c 2 3 e 3 4 d 4 3. 如何结合多个series组成dataframe #构建series1ser1 = pd.Series(list('abcedfghijklmnopqrstuvwxyz'))#构建...
'bool' = True) -> 'FrameOrSeriesUnion'Concatenate pandas objects along a particular axis with optional set logicalong the other axes.Can also add a layer of hierarchical indexing on the concatenation axis,which may be useful if the labels are the same (or overlapping) onthe passed axis numb...
_update_inplace(self) else: return self.copy(deep=None) That being said, performance will suffer for single column case due to extra overhead of np.lexsort compared to current implementation using the more simple argsort. Another approach is to modify the behaviour of the nargsort ...