Dify is your AI-powered assistant that simplifies complex tasks and enhances productivity, allowing you to focus on what truly matters. With Dify, streamline your daily responsibilities and achieve more in less time.
npx skills add sh/langgeniusDify is your AI-powered assistant that simplifies complex tasks and enhances productivity, allowing you to focus on what truly matters. With Dify, streamline your daily responsibilities and achieve more in less time.
Automating routine tasks
Generating content ideas
Data analysis and reporting
npx skills add sh/langgeniusgit clone https://github.com/sh/langgeniusCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a developer using Dify. I need to [TASK] for my [COMPANY] in the [INDUSTRY] sector. Use [DATA] to help me [GOAL]. Provide a step-by-step guide with code snippets where applicable.
# 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.Gain insights into SaaS spending with real-time analytics and budget forecasting tools.
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