1 object數據類型是dataframe中特殊的數據類型,當某一列出現數字、字符串、特殊字符和時間格式兩種及以上時,就會出現object類型,即便把不同類型的拆分開,仍然是object類型.

如下replace()函數改變數據類型后,用astype()函數再轉化一次才能將object格式轉化,但有的時候不用.

print(train.info()) train['repay_date'] = train['repay_date'].replace("\\N",'2020-01-01') train['repay_date'] = pd.to_datetime(train['repay_date']) train['repay_amt'] = train['repay_amt'].replace("\\N",0) train['repay_amt'] = train['repay_amt'].astype(float) print(train.info()) # <class 'pandas.core.frame.DataFrame'> # RangeIndex: 1000000 entries, 0 to 999999 # Data columns (total 7 columns): # user_id 1000000 non-null int64 # listing_id 1000000 non-null int64 # due_date 1000000 non-null datetime64[ns] # due_amt 1000000 non-null float64 # repay_date 1000000 non-null object # repay_amt 1000000 non-null object # order_id 1000000 non-null int64 # dtypes: datetime64[ns](1), float64(1), int64(3), object(2) # memory usage: 53.4+ MB # None # <class 'pandas.core.frame.DataFrame'> # RangeIndex: 1000000 entries, 0 to 999999 # Data columns (total 7 columns): # user_id 1000000 non-null int64 # listing_id 1000000 non-null int64 # due_date 1000000 non-null datetime64[ns] # due_amt 1000000 non-null float64 # repay_date 1000000 non-null datetime64[ns] # repay_amt 1000000 non-null float64 # order_id 1000000 non-null int64 # dtypes: datetime64[ns](2), float64(2), int64(3) # memory usage: 53.4 MB # None

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轉載于:https://www.cnblogs.com/xxswkl/p/11002954.html

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