Python：Pandas Dataframe AttributeError: 'numpy.ndarray' 对象没有属性 'fillna'
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Python: Pandas Dataframe AttributeError: 'numpy.ndarray' object has no attribute 'fillna'
提问by jeangelj
Since I am creating a dataframe, I don't understand why I am getting an array error.
由于我正在创建一个数据框，我不明白为什么会出现数组错误。
M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack()
M2 = np.maximum(-1, (M - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores)
M2.head(2)
AttributeError: 'numpy.ndarray' object has no attribute 'fillna'
采纳答案by piRSquared
(M - 3)is getting interpreted as a numpy.ndarray. This implies that Mis defined somewhere as a numpy.ndarray. Test it out by running:
(M - 3)被解释为numpy.ndarray. 这意味着在M某处定义为 a numpy.ndarray。通过运行来测试它：
print type(M)
回答by tmthydvnprt
Your code is not complete at the moment, so it is hard to pin point why Mis causing an error. There could be a couple reasons:
您的代码目前不完整，因此很难确定M导致错误的原因。可能有几个原因：
You have a typo and
(M - 3)should be(M2 - 3)M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack() M2 = np.maximum(-1, (M2 - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores) M2.head(2)You need to define/convert
Maspandas.DataFramesomewhere else in your code# With out seeing this part of the code, no one can really help you M = pd.DataFrame(...) # ... # ... M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack() M2 = np.maximum(-1, (M - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores) M2.head(2)You could convert it to a
pandas.DataFrameright before you use it.M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack() M2 = np.maximum(-1, (pd.DataFrame(M) - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores) M2.head(2)
你有一个错字，
(M - 3)应该是(M2 - 3)M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack() M2 = np.maximum(-1, (M2 - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores) M2.head(2)您需要定义/转换
M为pandas.DataFrame代码中的其他地方# With out seeing this part of the code, no one can really help you M = pd.DataFrame(...) # ... # ... M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack() M2 = np.maximum(-1, (M - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores) M2.head(2)您可以
pandas.DataFrame在使用之前将其转换为权利。M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack() M2 = np.maximum(-1, (pd.DataFrame(M) - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores) M2.head(2)
