comments | difficulty | edit_url | tags | |
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true |
简单 |
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DataFramedf1
+-------------+--------+ | Column Name | Type | +-------------+--------+ | student_id | int | | name | object | | age | int | +-------------+--------+ DataFramedf2
+-------------+--------+ | Column Name | Type | +-------------+--------+ | student_id | int | | name | object | | age | int | +-------------+--------+
编写一个解决方案,将两个 DataFrames 垂直 连接成一个 DataFrame。
结果格式如下示例所示。
示例 1:
输入: df1 +------------+---------+-----+ | student_id | name | age | +------------+---------+-----+ | 1 | Mason | 8 | | 2 | Ava | 6 | | 3 | Taylor | 15 | | 4 | Georgia | 17 | +------------+---------+-----+ df2 +------------+------+-----+ | student_id | name | age | +------------+------+-----+ | 5 | Leo | 7 | | 6 | Alex | 7 | +------------+------+-----+ 输出: +------------+---------+-----+ | student_id | name | age | +------------+---------+-----+ | 1 | Mason | 8 | | 2 | Ava | 6 | | 3 | Taylor | 15 | | 4 | Georgia | 17 | | 5 | Leo | 7 | | 6 | Alex | 7 | +------------+---------+-----+ 解释: 两个 DataFrame 被垂直堆叠,它们的行被合并。
import pandas as pd
def concatenateTables(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:
return pd.concat([df1, df2], ignore_index=True)