通过执行df['column1'].str.extract(r'(U\s*\d)') == True，可以隐式调用pandas.DataFrame.eq。
获取 Dataframe 和其他元素的等于（二元运算符eq）
返回：**布尔值的 Dataframe **
您可以简单地使用此函数来获取第一个匹配项：

extracted = df['column1'].str.extract(r'(U\s*\d)')

或pandas.Series.str.findall以获取所有匹配项的列表：

extracted = df['column1'].str.findall(r'(U\s*\d)')

#输出：

extract     findall
9370      U 1  [U 1, U 2]
9371      U 1  [U 1, U 3]
9372      U 1  [U 1, U 6]
9373      U 1  [U 1, U 8]
9374      U 1  [U 1, U 9]
...       ...         ...
34032      U9        [U9]
34033      U9        [U9]
34034      U9        [U9]
34035      U9        [U9]
34250      U2        [U2]

[11 rows x 2 columns]

#编辑：

根据评论，您可以根据自己的期望选择以下解决方案之一：

donatation = pd.read_csv(os.path.join("zuwendungen-berlin.csv"))

# --- To create a Series with the extracted value (#Return: Series)
extracted = donatation['Zweck'].str.extract(r'(U\s*\d)', expand=False)
print(extracted)

# --- To create a new DataFrame with a single column containing (if possible) the extracted value (#Return: DataFrame)
extracted = donatation['Zweck'].str.extract(r'(U\s*\d)')
print(extracted)

# --- To create a new Column with the extracted value in the original dataframe (#Return: DataFrame)
extracted = donatation.assingn(New_Zweck= donatation['Zweck'].str.extract(r'(U\s*\d)', expand=False))
print(extracted['New_Zweck'])

# --- To filter the original dataframe without creating a column (#Return: DataFrame)
extracted = donatation.loc[donatation['Zweck'].str.contains(r'(U\s*\d)', na=False)]
print(extracted['Zweck'])

# --- To create a column with the extracted value and remove NaN values/rows (#Return: DataFrame)
donatation['New_Zweck'] = donatation['Zweck'].str.extract(r'(U\s*\d)')
extracted = donatation.loc[donatation['New_Zweck'].notna()]
print(extracted['New_Zweck'])