<p>将<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html" rel="nofollow noreferrer">^{<cd1>}</a>与<a href="https://pandas.pydata.org/pandas-docs/version/0.22.0/generated/pandas.core.groupby.DataFrameGroupBy.agg.html" rel="nofollow noreferrer">^{<cd2>}</a>一起使用:</p>
<pre><code>In [1476]: df.groupby(['email','Name','Date']).agg('sum')
Out[1476]:
RegPushupCount EasyPushupCount DifficultPushupCount
email Name Date
a@b.com Jane 2020-05-01 0 0 5
2020-05-02 5 0 0
John 2020-05-01 10 0 0
2020-05-02 0 5 0
b@a.com Bill 2020-05-01 0 0 5
2020-05-02 0 5 0
</code></pre>
<h3>OP评论后:</h3>
<pre><code>In [1566]: res = df.groupby(['email','Name','Date'], as_index=False).agg('sum')
</code></pre>
<p>您可以像这样获取<code>a@b.com</code>的所有记录:</p>
<pre><code>In [1568]: res[res['email'].eq('a@b.com')]
Out[1568]:
email Name Date RegPushupCount EasyPushupCount DifficultPushupCount
0 a@b.com Jane 2020-05-01 0 0 5
1 a@b.com Jane 2020-05-02 5 0 0
2 a@b.com John 2020-05-01 10 0 0
3 a@b.com John 2020-05-02 0 5 0
</code></pre>
<p>你可以得到你的所有记录b@a.com像这样:</p>
<pre><code>In [1569]: res[res['email'].eq('b@a.com')]
Out[1569]:
email Name Date RegPushupCount EasyPushupCount DifficultPushupCount
4 b@a.com Bill 2020-05-01 0 0 5
5 b@a.com Bill 2020-05-02 0 5 0
</code></pre>