我有一个包含重复 ID 的数据框，但数据在多个区域中部分完成。
df = pd.DataFrame([[1234, 'Customer A', '123 Street', np.nan, np.nan],
[1234, 'Customer A', np.nan, '333 Street', np.nan],
[1234, 'Customer A', '12345 Street', np.nan, np.nan],
[1234, 'Customer A', np.nan, np.nan, np.nan],
[1233, 'Customer B', '444 Street', '3335 Street', np.nan],
[1233, 'Customer B', '555 Street', '666 Street', np.nan],
[1233, 'Customer B', '553 Street', '666 Street', '<a href="https://stackoverflow.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="a8c9cacbe8cdc5c9c1c486cbc7c5" rel="noreferrer noopener nofollow">[email protected]</a>'],
[1235, 'Customer C', '1553 Street', '644 Street', '<a href="https://stackoverflow.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="e3828180a3868e828a8fcd808c8e" rel="noreferrer noopener nofollow">[email protected]</a>'],
[1235, 'Customer C', '2553 Street', '644 Street', '<a href="https://stackoverflow.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="1d7c7f7e5d78707c7471337e7270" rel="noreferrer noopener nofollow">[email protected]</a>']],
columns=['ID', 'Customer', 'Billing Address', 'Shipping Address', 'Contact'])
df
ID Customer Billing Address Shipping Address Contact
0 1234 Customer A 123 Street NaN NaN
1 1234 Customer A NaN 333 Street NaN
2 1234 Customer A 12345 Street NaN NaN
3 1234 Customer A NaN NaN NaN
4 1233 Customer B 444 Street 3335 Street NaN
5 1233 Customer B 555 Street 666 Street NaN
6 1233 Customer B 553 Street 666 Street <a href="https://stackoverflow.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="84e5e6e7c4e1e9e5ede8aae7ebe9" rel="noreferrer noopener nofollow">[email protected]</a>
7 1235 Customer C 1553 Street 644 Street <a href="https://stackoverflow.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="aacbc8c9eacfc7cbc3c684c9c5c7" rel="noreferrer noopener nofollow">[email protected]</a>
8 1235 Customer C 2553 Street 644 Street <a href="https://stackoverflow.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="caaba8a98aafa7aba3a6e4a9a5a7" rel="noreferrer noopener nofollow">[email protected]</a>
我想保留所有数据，以便在数据存在时创建新列，使其看起来像下面的数据框:
我尝试了以下操作，但它删除了我想要保留的数据。
df.drop_duplicates(subset=['ID'], inplace=True)
df
ID Customer Billing Address Shipping Address Contact
0 1234 Customer A 123 Street NaN NaN
4 1233 Customer B 444 Street 3335 Street NaN
7 1235 Customer C 1553 Street 644 Street <a href="https://stackoverflow.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="e3828180a3868e828a8fcd808c8e" rel="noreferrer noopener nofollow">[email protected]</a>
编辑:我添加了更多数据，因为从原始帖子中不清楚可以存在多行 ID。
最佳答案
这是一种使用 apply 并创建新列的方法，使用 dict 创建 pd.Series
In [1057]: cols = ['Billing Address', 'Shipping Address']
In [1058]: (df.groupby(['ID', 'Customer'])
.apply(lambda g: pd.Series({'%s %s' % (x, i+1): v[x]
for i, v in enumerate(g[cols].to_dict('r'))
for x in v})))
Out[1058]:
Billing Address 1 Billing Address 2 Shipping Address 1 \
ID Customer
1233 Customer B 444 Street 555 Street 333 Street
1234 Customer A 123 Street NaN NaN
Shipping Address 2
ID Customer
1233 Customer B 666 Street
1234 Customer A 333 Street
关于python - Pandas 删除每行中部分完成数据的重复项并合并数据，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/45842932/