Python pandas DataFrame：用列的平均值替换 nan 值
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pandas DataFrame: replace nan values with average of columns
提问by piokuc
I've got a pandas DataFrame filled mostly with real numbers, but there is a few nanvalues in it as well.
我有一个主要填充实数的 Pandas DataFrame，但其中也有一些nan值。
How can I replace the nans with averages of columns where they are?
如何nan用它们所在的列的平均值替换s？
This question is very similar to this one: numpy array: replace nan values with average of columnsbut, unfortunately, the solution given there doesn't work for a pandas DataFrame.
这个问题与这个问题非常相似：numpy array:replace nan values with average of columns但不幸的是，那里给出的解决方案不适用于pandas DataFrame。
采纳答案by bmu
You can simply use DataFrame.fillnato fill the nan's directly:
您可以简单地使用直接DataFrame.fillna填充nan's ：
In [27]: df
Out[27]:
A B C
0 -0.166919 0.979728 -0.632955
1 -0.297953 -0.912674 -1.365463
2 -0.120211 -0.540679 -0.680481
3 NaN -2.027325 1.533582
4 NaN NaN 0.461821
5 -0.788073 NaN NaN
6 -0.916080 -0.612343 NaN
7 -0.887858 1.033826 NaN
8 1.948430 1.025011 -2.982224
9 0.019698 -0.795876 -0.046431
In [28]: df.mean()
Out[28]:
A -0.151121
B -0.231291
C -0.530307
dtype: float64
In [29]: df.fillna(df.mean())
Out[29]:
A B C
0 -0.166919 0.979728 -0.632955
1 -0.297953 -0.912674 -1.365463
2 -0.120211 -0.540679 -0.680481
3 -0.151121 -2.027325 1.533582
4 -0.151121 -0.231291 0.461821
5 -0.788073 -0.231291 -0.530307
6 -0.916080 -0.612343 -0.530307
7 -0.887858 1.033826 -0.530307
8 1.948430 1.025011 -2.982224
9 0.019698 -0.795876 -0.046431
The docstring of fillnasays that valueshould be a scalar or a dict, however, it seems to work with a Seriesas well. If you want to pass a dict, you could use df.mean().to_dict().
的文档字符串fillna表示value应该是标量或字典，但是，它似乎也适用于 a Series。如果你想传递一个字典，你可以使用df.mean().to_dict().
回答by Jeff
In [16]: df = DataFrame(np.random.randn(10,3))
In [17]: df.iloc[3:5,0] = np.nan
In [18]: df.iloc[4:6,1] = np.nan
In [19]: df.iloc[5:8,2] = np.nan
In [20]: df
Out[20]:
0 1 2
0 1.148272 0.227366 -2.368136
1 -0.820823 1.071471 -0.784713
2 0.157913 0.602857 0.665034
3 NaN -0.985188 -0.324136
4 NaN NaN 0.238512
5 0.769657 NaN NaN
6 0.141951 0.326064 NaN
7 -1.694475 -0.523440 NaN
8 0.352556 -0.551487 -1.639298
9 -2.067324 -0.492617 -1.675794
In [22]: df.mean()
Out[22]:
0 -0.251534
1 -0.040622
2 -0.841219
dtype: float64
Apply per-column the mean of that columns and fill
每列应用该列的平均值并填充
In [23]: df.apply(lambda x: x.fillna(x.mean()),axis=0)
Out[23]:
0 1 2
0 1.148272 0.227366 -2.368136
1 -0.820823 1.071471 -0.784713
2 0.157913 0.602857 0.665034
3 -0.251534 -0.985188 -0.324136
4 -0.251534 -0.040622 0.238512
5 0.769657 -0.040622 -0.841219
6 0.141951 0.326064 -0.841219
7 -1.694475 -0.523440 -0.841219
8 0.352556 -0.551487 -1.639298
9 -2.067324 -0.492617 -1.675794
回答by Ammar Shigri
Try:
尝试：
sub2['income'].fillna((sub2['income'].mean()), inplace=True)
回答by guibor
Another option besides those above is:
除了上述选项之外的另一个选项是：
df = df.groupby(df.columns, axis = 1).transform(lambda x: x.fillna(x.mean()))
It's less elegant than previous responses for mean, but it could be shorter if you desire to replace nulls by some other column function.
它不如之前的均值响应优雅，但如果您希望用其他列函数替换空值，它可能会更短。
回答by Pranay Aryal
If you want to impute missing values with mean and you want to go column by column, then this will only impute with the mean of that column. This might be a little more readable.
如果您想用均值来估算缺失值并且想要逐列进行，那么这只会用该列的均值来估算。这可能更具可读性。
sub2['income'] = sub2['income'].fillna((sub2['income'].mean()))
回答by Roshan jha
# To read data from csv file
Dataset = pd.read_csv('Data.csv')
X = Dataset.iloc[:, :-1].values
# To calculate mean use imputer class
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])
回答by Sunny Barnwal
Directly use df.fillna(df.mean())to fill all the null value with mean
直接用df.fillna(df.mean())均值填充所有空值
If you want to fill null value with mean of that column then you can use this
如果你想用该列的平均值填充空值，那么你可以使用这个
suppose x=df['Item_Weight']here Item_Weightis column name
假设x=df['Item_Weight']这里Item_Weight是列名
here we are assigning (fill null values of x with mean of x into x)
在这里我们分配（用 x 的平均值填充 x 的空值到 x 中）
df['Item_Weight'] = df['Item_Weight'].fillna((df['Item_Weight'].mean()))
If you want to fill null value with some string then use
如果你想用一些字符串填充空值，然后使用
here Outlet_sizeis column name
这Outlet_size是列名
df.Outlet_Size = df.Outlet_Size.fillna('Missing')
回答by pink.slash
Pandas: How to replace NaN (nan) values with the average (mean), median or other statistics of one column
Pandas：如何用nan一列的平均值（mean）、中位数或其他统计数据替换 NaN ( ) 值
Say your DataFrame is dfand you have one column called nr_items. This is: df['nr_items']
假设您的 DataFrame 是，df并且您有一列名为nr_items. 这是： df['nr_items']
If you want to replacethe NaNvalues of your column df['nr_items']with the mean of the column:
如果您想用NaN列df['nr_items']的平均值替换列的值：
Use method .fillna():
使用方法.fillna()：
mean_value=df['nr_items].mean()
df['nr_item_ave']=df['nr_items'].fillna(mean_value)
mean_value=df['nr_items].mean()
df['nr_item_ave']=df['nr_items'].fillna(mean_value)
I have created a new dfcolumn called nr_item_aveto store the new column with the NaNvalues replaced by the meanvalue of the column.
我创建了一个新df列，nr_item_ave用于存储新列，其中的NaN值替换mean为列的值。
You should be careful when using the mean. If you have outliersis more recommendable to use the median
使用mean. 如果您有异常值更推荐使用median
回答by Shrikant Chaudhari
using sklearn library preprocessing class
使用sklearn库预处理类
from sklearn.impute import SimpleImputer
missingvalues = SimpleImputer(missing_values = np.nan, strategy = 'mean', axis = 0)
missingvalues = missingvalues.fit(x[:,1:3])
x[:,1:3] = missingvalues.transform(x[:,1:3])
Note: In the recent version parameter missing_valuesvalue change to np.nanfrom NaN
注意：在最近的版本中参数missing_values值更改为np.nanfromNaN
