df是一个csv文件，其中包含ship_date，order_date和cumulative_ordered。 cumulative_ordered是直到ship_date为止每天增加的订单总数。每个ship_date之前有30天，而这些天只算作一个ship_date。在ship_date 2018-07-01之后，然后下一个ship_date将是2018-08-01，采用相同的程序。
我的问题是，当我计算前30天中的每一天的cumulative_ordered百分比平均值时，我没有剩余的天数（请参阅下面的最后代码输出）。
我有以下代码，它为我提供了一个csv文件中的days_remaining，其中有几个不同的ship_date和order_date递减到每个单独的ship_date。
df['days_remaining'] = pd.to_datetime(df['ship_date']).sub\
(pd.to_datetime(df['order_date'])).dt.days
df['difference'] = df['ship_date'] - df['order_date']
df.head()
输出：
ship_date Order_date cumulative_ordered days_remaining difference
2018-07-01 2018-06-01 7 30 30 days
2018-07-01 2018-06-02 10 29 29 days
2018-07-01 2018-06-03 15 28 28 days
2018-07-01 2018-06-04 30 28 27 days
2018-07-01 2018-06-05 41 28 26 days
然后我尝试查找ship_date之前每天订购的总数
m = df.groupby("difference").mean()
m.head()
这给了我这个输出：
cumulative ordered days_remaining
difference
0 days 352.458124 0.0
1 days 291.234747 1.0
2 days 244.122137 2.0
3 days 201.178765 3.0
4 days 190.153641 4.0
当我尝试通过运行以下代码来尝试计算从每天0天以上的cumulative_ordered输出中填充的百分比得出的每天累积订购的平均值时，遇到了一个问题：
v = m/m[m.index.days == 0].iloc[0]
v.head()
cumulative_ordered days_remaining
difference
0 days 1.000000 NaN
1 days 0.891324 inf
2 days 0.812534 inf
3 days 0.752339 inf
4 days 0.673745 inf
days_remaining更改为NaN和inf。如何保留它，以便它仍为我提供整数？
答案 0 :(得分：1)
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</html>和NaN除以inf所得。
似乎您仅尝试将操作应用于0.0列，因此您应该在最后的代码块中运行此操作：
cumulative_ordered