如何在一次分配中向 Pandas 数据框添加多列? [英] How to add multiple columns to pandas dataframe in one assignment?

问题描述

我是 Pandas 的新手，并试图弄清楚如何同时向 Pandas 添加多个列.任何帮助在这里表示赞赏.理想情况下，我希望在一个步骤中完成此操作，而不是多个重复步骤...

I'm new to pandas and trying to figure out how to add multiple columns to pandas simultaneously. Any help here is appreciated. Ideally I would like to do this in one step rather than multiple repeated steps...

import pandas as pd

df = {'col_1': [0, 1, 2, 3],
        'col_2': [4, 5, 6, 7]}
df = pd.DataFrame(df)

df[[ 'column_new_1', 'column_new_2','column_new_3']] = [np.nan, 'dogs',3]  #thought this would work here...

推荐答案

我希望你的语法也能正常工作.问题出现是因为当您使用列列表语法 (df[[new1, new2]] = ...) 创建新列时，pandas 要求右侧是一个 DataFrame(注意DataFrame 的列是否与您正在创建的列具有相同的名称实际上并不重要).

I would have expected your syntax to work too. The problem arises because when you create new columns with the column-list syntax (df[[new1, new2]] = ...), pandas requires that the right hand side be a DataFrame (note that it doesn't actually matter if the columns of the DataFrame have the same names as the columns you are creating).

您的语法适用于将标量值分配给现有列，并且 Pandas 也很乐意使用单列语法将标量值分配给新列 (df[new1] =...).因此，解决方案要么将其转换为多个单列分配，要么为右侧创建一个合适的 DataFrame.

Your syntax works fine for assigning scalar values to existing columns, and pandas is also happy to assign scalar values to a new column using the single-column syntax (df[new1] = ...). So the solution is either to convert this into several single-column assignments, or create a suitable DataFrame for the right-hand side.

这里有几种行之有效的方法:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'col_1': [0, 1, 2, 3],
    'col_2': [4, 5, 6, 7]
})

然后是以下之一:

df['column_new_1'], df['column_new_2'], df['column_new_3'] = [np.nan, 'dogs', 3]

2) DataFrame 方便地扩展单行以匹配索引，因此您可以这样做:

2) DataFrame conveniently expands a single row to match the index, so you can do this:

df[['column_new_1', 'column_new_2', 'column_new_3']] = pd.DataFrame([[np.nan, 'dogs', 3]], index=df.index)

3) 用新列创建一个临时数据框，然后与原始数据框合并:

df = pd.concat(
    [
        df,
        pd.DataFrame(
            [[np.nan, 'dogs', 3]], 
            index=df.index, 
            columns=['column_new_1', 'column_new_2', 'column_new_3']
        )
    ], axis=1
)

4) 与前面类似，但使用 join 而不是 concat(可能效率较低):

4) Similar to the previous, but using join instead of concat (may be less efficient):

df = df.join(pd.DataFrame(
    [[np.nan, 'dogs', 3]], 
    index=df.index, 
    columns=['column_new_1', 'column_new_2', 'column_new_3']
))

5) 与前两种相比，使用 dict 是一种更自然"的方式来创建新数据框，但新列将按字母顺序排序(至少 Python 3.6 或 3.7 之前):

df = df.join(pd.DataFrame(
    {
        'column_new_1': np.nan,
        'column_new_2': 'dogs',
        'column_new_3': 3
    }, index=df.index
))

6) 将 .assign() 与多个列参数一起使用.

我非常喜欢@zero 的答案中的这个变体，但与前一个一样，新列将始终按字母顺序排序，至少在 Python 的早期版本中是这样:

6) Use .assign() with multiple column arguments.

I like this variant on @zero's answer a lot, but like the previous one, the new columns will always be sorted alphabetically, at least with early versions of Python:

df = df.assign(column_new_1=np.nan, column_new_2='dogs', column_new_3=3)

7) 这很有趣(基于 https://stackoverflow.com/a/44951376/3830997)，但我不知道什么时候值得麻烦:

7) This is interesting (based on https://stackoverflow.com/a/44951376/3830997), but I don't know when it would be worth the trouble:

new_cols = ['column_new_1', 'column_new_2', 'column_new_3']
new_vals = [np.nan, 'dogs', 3]
df = df.reindex(columns=df.columns.tolist() + new_cols)   # add empty cols
df[new_cols] = new_vals  # multi-column assignment works for existing cols

8) 最后很难通过三个独立的任务:

df['column_new_1'] = np.nan
df['column_new_2'] = 'dogs'
df['column_new_3'] = 3

注意:其他答案中已经涵盖了其中的许多选项:将多个列添加到 DataFrame 并将它们设置为等于现有列, 是否可以一次向 Pandas DataFrame 添加多列?, 将多个空列添加到pandas DataFrame

Note: many of these options have already been covered in other answers: Add multiple columns to DataFrame and set them equal to an existing column, Is it possible to add several columns at once to a pandas DataFrame?, Add multiple empty columns to pandas DataFrame

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