How to impute missing values with means in Python?
DATA MUNGING DATA CLEANING PYTHON MACHINE LEARNING RECIPES PANDAS CHEATSHEET     ALL TAGS

# 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    ]]
```

#### Relevant Projects

##### PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

##### Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

##### Predict Census Income using Deep Learning Models
In this project, we are going to work on Deep Learning using H2O to predict Census income.

##### Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

##### Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

##### Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

##### German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

##### Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

##### Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

##### Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.