<p>我的解决方案并不优雅,但它是有效的。看一看。你知道吗</p>
<p>两个问题的相同部分是:</p>
<pre><code>In [1]: import pandas as pd
df = pd.DataFrame(
{
'month1': [1, 2, 'NA', 1, 4, 'NA', 'NA'],
'month2': ['NA', 5, 1, 2, 'NA', 1, 3],
'amount': [10, 20, 40, 50, 60, 70, 100],
}
)
def make_sum_amount(row, amount_sum):
if row['month1'] == 'NA':
if row['month2'] == 'NA':
return 0
return amount_sum.get(row['month2'], 0)
return amount_sum.get(row['month1'], 0)
</code></pre>
<p>第一个问题的解决方案:</p>
<pre><code>In [2]: grouped_df = df[df['month1']!='NA'].groupby('month1').sum().reset_index()
amount_sum = {k: v for k, v in zip(grouped_df['month1'], grouped_df['amount'])}
df['sum_amount'] = df.apply(lambda row: make_sum_amount(row, amount_sum), axis=1)
df
Out [2]: month1 month2 amount sum_amount
0 1.0 NA 10 60
1 2.0 5.0 20 20
2 NA 1.0 40 60
3 1.0 2.0 50 60
4 4.0 NA 60 60
5 NA 1.0 70 60
6 NA 3.0 100 0
</code></pre>
<p>第二个问题的解决方案:</p>
<pre><code>In [3]: grouped_df = df[df['month2']!='NA'].groupby('month2').sum().reset_index()
amount_sum = {k: v for k, v in zip(grouped_df['month2'], grouped_df['amount'])}
df['sum_amount'] = df.apply(lambda row: make_sum_amount(row, amount_sum), axis=1)
df
Out [3]: month1 month2 amount sum_amount
0 1.0 NA 10 110
1 2.0 5.0 20 50
2 NA 1.0 40 110
3 1.0 2.0 50 110
4 4.0 NA 60 0
5 NA 1.0 70 110
6 NA 3.0 100 100
</code></pre>