在向 pandas dataframe 添加新列时替换现有列名

Question

假设我有一个 dataframe 如下所示：

df = pd.DataFrame({0:['Hello World!']}) # here df could have more than one column of data as shown below
df = pd.DataFrame({0:['Hello World!'], 1:['Hello Mars!']}) # or df could have more than one row of data as shown below
df = pd.DataFrame({0:['Hello World!', 'Hello Mars!']})

我还有一个列名列表，如下所示：

new_col_names = ['a','b','c','d'] # here, len(new_col_names) might vary like below
new_col_names = ['a','b','c','d','e'] # but we can always be sure that the len(new_col_names) >= len(df.columns)

鉴于此，我如何替换df中的列名，使其结果如下：

df = pd.DataFrame({0:['Hello World!']})
new_col_names = ['a','b','c','d']
# result would be like this
a               b               c               d
Hello World!    (empty string)  (empty string)  (empty string)


df = pd.DataFrame({0:['Hello World!'], 1:['Hello Mars!']}) 
new_col_names = ['a','b','c','d']
# result would be like this
a               b               c               d
Hello World!    Hello Mars!     (empty string)  (empty string)


df = pd.DataFrame({0:['Hello World!', 'Hello Mars!']})
new_col_names = ['a','b','c','d','e']
a               b               c               d               e
Hello World!    (empty string)  (empty string)  (empty string)  (empty string)
Hellow Mars!    (empty string)  (empty string)  (empty string)  (empty string)

通过阅读诸如此类的 StackOverflow答案，我有一个模糊的想法，它可能如下所示：

df[new_col_names] = '' # but this returns KeyError
# or this
df.columns=new_col_names # but this returns ValueError: Length mismatch (of course)

如果有人可以向我展示一种覆盖现有 dataframe 列名并同时在行中添加具有空字符串值的新数据列的方法，我将非常感谢您的帮助。

Answer 1

想法是通过zip的现有列名创建字典，仅重命名现有列，然后通过DataFrame.reindex添加所有新列：

df = pd.DataFrame({0:['Hello World!', 'Hello Mars!']})
new_col_names = ['a','b','c','d','e']

df1 = (df.rename(columns=dict(zip(df.columns, new_col_names)))
        .reindex(new_col_names, axis=1, fill_value=''))
print (df1)
              a b c d e
0  Hello World!        
1   Hello Mars!      


df1 = (df.rename(columns=dict(zip(df.columns, new_col_names)))
         .reindex(new_col_names, axis=1))
print (df1)
              a   b   c   d   e
0  Hello World! NaN NaN NaN NaN
1   Hello Mars! NaN NaN NaN NaN  

Answer 2

这是一个 function 可以满足您的需求

• 我找不到 1-liner，但jezrael找到了： 他的回答

import pandas as pd

# function
def rename_add_col(df: pd.DataFrame, cols: list) -> pd.DataFrame:
    c_len = len(df.columns)
    if c_len == len(cols):
        df.columns = cols
    else:
        df.columns = cols[:c_len]
        df = pd.concat([df, pd.DataFrame(columns=cols[c_len:])]) 
    return df

# create dataframe
t1 = pd.DataFrame({'a': ['1', '2', '3'], 'b': ['4', '5', '6'], 'c': ['7', '8', '9']})

    a   b   c
0   1   4   7
1   2   5   8
2   3   6   9

# call function
cols = ['d', 'e', 'f']
t1 = rename_add_col(t1, cols)

    d   e   f
0   1   4   7
1   2   5   8
2   3   6   9

# call function
cols = ['g', 'h', 'i', 'new1', 'new2']
t1 = rename_add_col(t1, cols)


    g   h   i   new1    new2
0   1   4   7    NaN     NaN
1   2   5   8    NaN     NaN
2   3   6   9    NaN     NaN

Answer 3

这可能会帮助您一次完成所有操作

使用旧的 Dataframe 使用 pd.DataFrame() 方法重新创建另一个 dataframe ，然后通过列表添加在列参数中添加新列。

注意：这将根据索引长度添加新列，但使用 NaN 值，解决方法是执行df.fillna(' ')

pd.DataFrame(df.to_dict(), columns = list(df.columns)+['b','c'])

希望这可以帮助！ 干杯！