How to impute missing values with means in Python?
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# How to impute missing values with means in Python?

This recipe helps you impute missing values with means in Python

0

## Recipe Objective

Some times we find few missing values in various features in a dataset. Our model can not work efficiently on nun values and in few cases removing the rows having null values can not be considered as an option because it leads to loss of data of other features.

So this is the recipe on How we can impute missing values with means in Python

## Step 1 - Import the library

``` import pandas as pd import numpy as np from sklearn.preprocessing import Imputer ```

We have imported pandas, numpy and Imputer from sklearn.preprocessing.

## Step 2 - Setting up the Data

We have created a empty DataFrame first then made columns C0 and C1 with the values. Clearly we can see that in column C1 three elements are nun. ``` df = pd.DataFrame() df['C0'] = [0.2601,0.2358,0.1429,0.1259,0.7526, 0.7341,0.4546,0.1426,0.1490,0.2500] df['C1'] = [0.7154,np.nan,0.2615,0.5846,np.nan, 0.8308,0.4962,np.nan,0.5340,0.6731] print(df) ```

## Step 3 - Using Imputer to fill the nun values with the Mean

We know that we have few nun values in column C1 so we have to fill it with the mean of remaining values of the column. So for this we will be using Imputer function, so let us first look into the parameters.

• missing_values : In this we have to place the missing values and in pandas it is 'NaN'.
• strategy : In this we have to pass the strategy that we need to follow to impute in missing value it can be mean, median, most_frequent or constant. By default it is mean.
• fill_value : By default it is set as none. It is used when the strategy is set to constant then we have to pass the value that we want to fill as a constant in all the nun places.
• axis : In this we have to pass 0 for columns and 1 for rows.
So we have created an object and called Imputer with the desired parameters. Then we have fit our dataframe and transformed its nun values with the mean and stored it in imputed_df. Then we have printed the final dataframe. ``` miss_mean_imputer = Imputer(missing_values='NaN', strategy='mean', axis=0) miss_mean_imputer = miss_mean_imputer.fit(df) imputed_df = miss_mean_imputer.transform(df.values) print(imputed_df) ``` Output as a dataset is given below, we can see that all the nun values have been filled by the mean of the columns.

```       C0      C1
0  0.2601  0.7154
1  0.2358     NaN
2  0.1429  0.2615
3  0.1259  0.5846
4  0.7526     NaN
5  0.7341  0.8308
6  0.4546  0.4962
7  0.1426     NaN
8  0.1490  0.5340
9  0.2500  0.6731

[[0.2601     0.7154    ]
[0.2358     0.58508571]
[0.1429     0.2615    ]
[0.1259     0.5846    ]
[0.7526     0.58508571]
[0.7341     0.8308    ]
[0.4546     0.4962    ]
[0.1426     0.58508571]
[0.149      0.534     ]
[0.25       0.6731    ]]
```

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