Python 在 Pandas 中删除 nan 行的更好方法
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better way to drop nan rows in pandas
提问by kilojoules
On my own I found a way to drop nan rows from a pandas dataframe. Given a dataframe datwith column xwhich contains nan values,is there a more elegant way to do drop each row of datwhich has a nan value in the xcolumn?
我自己找到了一种从 Pandas 数据框中删除 nan 行的方法。给定一个包含 nan 值的dat列的数据框x，是否有更优雅的方法来删除列中dat具有 nan 值的每一行x？
dat = dat[np.logical_not(np.isnan(dat.x))]
dat = dat.reset_index(drop=True)
回答by TerminalWitchcraft
Use dropna:
使用dropna：
dat.dropna()
You can pass param howto drop if all labels are nan or any of the labels are nan
how如果所有标签都是 nan 或任何标签都是 nan，您可以将参数传递给 drop
dat.dropna(how='any') #to drop if any value in the row has a nan
dat.dropna(how='all') #to drop if all values in the row are nan
Hope that answers your question!
希望这能回答你的问题！
Edit 1:In case you want to drop rows containing nanvalues only from particular column(s), as suggested by J. Doe in his answer below, you can use the following:
编辑 1：如果您想删除nan仅包含特定列中值的行，如 J. Doe 在下面的回答中所建议的，您可以使用以下内容：
dat.dropna(subset=[col_list]) # col_list is a list of column names to consider for nan values.
回答by J. Doe
To expand Hitesh's answer if you want to drop rows where 'x' specifically is nan, you can use the subset parameter. His answer will drop rows where other columns have nans as well
如果要删除“x”特别是 nan 的行，要扩展 Hitesh 的答案，可以使用子集参数。他的回答将删除其他列也有 nan 的行
dat.dropna(subset=['x'])
回答by hRt
Just in case commands in previous answers doesn't work,
Try this:
dat.dropna(subset=['x'], inplace = True)
以防万一先前答案中的命令不起作用，请尝试以下操作：
dat.dropna(subset=['x'], inplace = True)
回答by Chunxiao Li
bool_series=pd.notnull(dat["x"])
dat=dat[bool_series]
回答by Naveen Gabriel
To remove rows based on Nan value of particular column:
根据特定列的 Nan 值删除行：
d= pd.DataFrame([[2,3],[4,None]]) #creating data frame
d
Output:
0 1
0 2 3.0
1 4 NaN
d = d[np.isfinite(d[1])] #Select rows where value of 1st column is not nan
d
Output:
0 1
0 2 3.0
