Renaming columns in a pandas DataFrame is a common task in data manipulation. You can rename columns using therenamemethod. Here’s a basic example to illustrate how you can rename columns: Suppose you have a D
# header=None, index_col=False 会禁止默认行为 food_info = pandas.read_csv(file_name)# 返回一个DataFrame对象 n_rows = food_info.head(n) #获取前n行数据,返回的依旧是个DataFrame column_names = food_info.columns #获取所有的列名 dimensions = food_info.shape #获取数据的shape 1. 2. 3. 4....
前者是将已有的一列信息设置为标签列,而后者是将原标签列归为数据,并重置为默认数字标签 set_axis,设置标签列,一次只能设置一列信息,与rename功能相近,但接收参数为一个序列更改全部标签列信息(...切片形式访问时按行进行查询,又区分数字切片和标签切片两种情况:当输入数字索引切片时,类似于普通列表切片;...
使用rename()方法:可以使用DataFrame的rename()方法来重命名列名,并传入一个字典来指定新旧列名的对应关系。例如: 代码语言:txt 复制 import pandas as pd df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df = df.rename(columns={'A': 'col1', 'B': 'col2', 'C': 'col3'}...
header=header, **kwargs)if col_mapping:df.rename(columns=col_mapping, inplace=True)return df.t...
df.rename(mapper : dict-like or function,axis=1,inplace=True),其中mapper :Function / dict values must be unique (1-to-1). 第二种方法思想更通用,可以发现rename()还可以对Index进行重命名。mapper+axis== colums=mapper or index=mapper
Set theaxis=1and pass column names you want to rename as a dictionary Example Let’s see how to rename all three columns of a dataframe. importpandasaspd student_dict = {"name": ["Joe","Nat","Harry"],"age": [20,21,19],"marks": [85.10,77.80,91.54]} ...
通常情况下,它是透明的,这就是为什么不能直接写df.City.name = ' city ',而必须写一个不那么明显的df.rename(columns={' A ': ' A '}, inplace=True) Index有一个名称(在MultiIndex的情况下,每个级别都有一个名称)。不幸的是,这个名称在Pandas中没有得到充分使用。一旦你在索引中包含了这一列,就不...
data.rename(columns={'c1': 'c3', 'c3': 'c1'}, inplace=True) print(data) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. c1 c2 c3 0 a 1 0.1 1 a 2 0.3 2 c 3 0.5 3 d 4 0.7 c1 c2 c3 0 0.1 1 a 1 0.3 2 a ...
Example 1 explains how to rename the column names of all variables in a data set.The following Python code uses the columns attribute to create a copy of our DataFrame where the original header is replaced by the new column names col1, col2, and col3.data_new1 = data.copy() # ...