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我有一個數據幀什麼：只填寫一個數據幀丟失的值（熊貓）
email user_name sessions ymo
[email protected] JD 1 2015-03-01
[email protected] JD 2 2015-05-01
我需要什麼：
email user_name sessions ymo
[email protected] JD 0 2015-01-01
[email protected] JD 0 2015-02-01
[email protected] JD 1 2015-03-01
[email protected] JD 0 2015-04-01
[email protected] JD 2 2015-05-01
[email protected] JD 0 2015-06-01
[email protected] JD 0 2015-07-01
[email protected] JD 0 2015-08-01
[email protected] JD 0 2015-09-01
[email protected] JD 0 2015-10-01
[email protected] JD 0 2015-11-01
[email protected] JD 0 2015-12-01
ymo列是pd.Timestamp S：
all_ymo
[Timestamp('2015-01-01 00:00:00'),
Timestamp('2015-02-01 00:00:00'),
Timestamp('2015-03-01 00:00:00'),
Timestamp('2015-04-01 00:00:00'),
Timestamp('2015-05-01 00:00:00'),
Timestamp('2015-06-01 00:00:00'),
Timestamp('2015-07-01 00:00:00'),
Timestamp('2015-08-01 00:00:00'),
Timestamp('2015-09-01 00:00:00'),
Timestamp('2015-10-01 00:00:00'),
Timestamp('2015-11-01 00:00:00'),
Timestamp('2015-12-01 00:00:00')]
不幸的是，這個答案：Adding values for missing data combinations in Pandas不好，因爲它會爲現有的ymo值。
我想這樣的事情，但它是非常緩慢：
for em in all_emails:
existent_ymo = fill_ymo[fill_ymo['email'] == em]['ymo']
existent_ymo = set([pd.Timestamp(datetime.date(t.year, t.month, t.day)) for t in existent_ymo])
missing_ymo = list(existent_ymo - all_ymo)
multi_ind = pd.MultiIndex.from_product([[em], missing_ymo], names=col_names)
fill_ymo = sessions.set_index(col_names).reindex(multi_ind, fill_value=0).reset_index()
如果丟失的條目數量超過填充，然後填充開始用pd.data_range一個新的數據幀。然後在日期匹配的地方添加會話值。如果電子郵件地址和用戶名是1對1，那麼考慮只在數據幀中包含其中一個以節省內存（如果大小是個問題） – dodell