Dify

# Streamlining Customer Data Processing for GreenTech Solutions

## Task: Automate Data Cleaning

To automate the data cleaning process for GreenTech Solutions in the renewable energy sector, we will use Python and the pandas library. Below is a step-by-step guide with code snippets to help you achieve this goal.

### Step 1: Import Required Libraries
```python
import pandas as pd
import numpy as np
```

### Step 2: Load the Dataset
```python
data = pd.read_csv('customer_data.csv')
```

### Step 3: Handle Missing Values
```python
data.fillna({'Age': data['Age'].mean(), 'Income': data['Income'].median()}, inplace=True)
```

### Step 4: Remove Duplicates
```python
data.drop_duplicates(inplace=True)
```

### Step 5: Save the Cleaned Data
```python
data.to_csv('cleaned_customer_data.csv', index=False)
```

By following these steps, you can efficiently clean and prepare your customer data for further analysis.