# 4.4. Imputation of missing valuesΒΆ

For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs or other placeholders. Such datasets however are incompatible with scikit-learn estimators which assume that all values in an array are numerical, and that all have and hold meaning. A basic strategy to use incomplete datasets is to discard entire rows and/or columns containing missing values. However, this comes at the price of losing data which may be valuable (even though incomplete). A better strategy is to impute the missing values, i.e., to infer them from the known part of the data.

The `SimpleImputer`

class provides basic strategies for imputing missing
values, either using the mean, the median or the most frequent value of
the row or column in which the missing values are located. This class
also allows for different missing values encodings.

The following snippet demonstrates how to replace missing values,
encoded as `np.nan`

, using the mean value of the columns (axis 0)
that contain the missing values:

```
>>> import numpy as np
>>> from sklearn.impute import SimpleImputer
>>> imp = SimpleImputer(missing_values='NaN', strategy='mean', axis=0)
>>> imp.fit([[1, 2], [np.nan, 3], [7, 6]])
SimpleImputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)
>>> X = [[np.nan, 2], [6, np.nan], [7, 6]]
>>> print(imp.transform(X))
[[ 4. 2. ]
[ 6. 3.666...]
[ 7. 6. ]]
```

The `SimpleImputer`

class also supports sparse matrices:

```
>>> import scipy.sparse as sp
>>> X = sp.csc_matrix([[1, 2], [0, 3], [7, 6]])
>>> imp = SimpleImputer(missing_values=0, strategy='mean', axis=0)
>>> imp.fit(X)
SimpleImputer(axis=0, copy=True, missing_values=0, strategy='mean', verbose=0)
>>> X_test = sp.csc_matrix([[0, 2], [6, 0], [7, 6]])
>>> print(imp.transform(X_test))
[[ 4. 2. ]
[ 6. 3.666...]
[ 7. 6. ]]
```

Note that, here, missing values are encoded by 0 and are thus implicitly stored in the matrix. This format is thus suitable when there are many more missing values than observed values.

`SimpleImputer`

can be used in a Pipeline as a way to build a composite
estimator that supports imputation. See Imputing missing values before building an estimator.