Advanced Frequency Analysis of Smart Contract Risks

Welcome to the Advanced Frequency Analysis of Smart Contract Risks repository. This project focuses on conducting a frequency analysis on risk tags associated with smart contracts using Python. The analysis includes loading data, performing calculations, and creating dynamic visualizations to gain insights into the dataset.

Objective

The primary goal of this project is to:

•	Analyze the frequency of risk tags related to smart contracts.
•	Load a dataset, perform data manipulation, and visualize the results using popular Python libraries such as Pandas, Matplotlib, and Seaborn.

This project is intended for individuals with basic programming knowledge and familiarity with Python.

Requirements

To run this analysis, you will need the following Python libraries:

•	Pandas
•	Seaborn
•	Matplotlib
•	Google Colab (or any Python environment that supports Jupyter notebooks)

Before You Start

This repository assumes you have some knowledge of basic Python programming. If you are new to Python, I recommend reviewing Python basics before diving into the notebook.

Getting Started

Follow these steps to set up the environment and perform the analysis.

Step 1: Clone the Repository

git clone https://github.com/yourusername/your-repo-name.git cd your-repo-name

Step 2: Set Up Your Environment

Install the necessary libraries:

pip install pandas seaborn matplotlib

Alternatively, if you’re using Google Colab, the required libraries are pre-installed.

Step 3: Download the Dataset

Download the dataset used in this tutorial. You can run the following command in Colab or manually download the file and load it locally:

!gdown 1NYR65tYzMe4Rwc0kFyTEj2nerJJFRf6n

Step 4: Load the Data

You can load the dataset into your Python environment using the following command:

df = pd.read_excel('/content/compiled_risk_data.xlsx') df.head() # View the first 5 rows of the dataset

Step 5: Perform Frequency Analysis

Now that the dataset is loaded, you can run frequency analysis on specific risk tags. For example, analyzing the is_airdrop_scam column:

df['is_airdrop_scam'].value_counts()

Step 6: Advanced Analysis of Multiple Risk Tags

To analyze the frequency of multiple risk tags, define the risk columns and apply the following analysis:

risk_columns = ['Is_closed_source', 'hidden_owner', 'anti_whale_modifiable', ...] frequencies = df[risk_columns].apply(lambda x: x.value_counts()).loc[True]

Step 7: Visualize the Results

You can create a bar chart to visualize the frequencies of the risk tags using Seaborn:

sns.set_style("whitegrid") plt.figure(figsize=(12, 8)) sns.barplot(x=frequencies.index, y=frequencies.values, palette='viridis') plt.title('Frequency of True Values for Each Risk Tag') plt.xlabel('Risk Tags') plt.ylabel('Frequency') plt.xticks(rotation=45) plt.show()

Step 8: Analyze False Values

Similarly, you can analyze the frequency of ‘False’ values for each risk tag:

frequencies_false = df[risk_columns].apply(lambda x: x.value_counts()).loc[False] print(frequencies_false)

Conclusion

You have successfully completed a frequency analysis of risk tags in smart contracts using Python. You’ve learned how to:

•	Load data,
•	Perform calculations, and
•	Visualize the results using powerful Python libraries.

Next Steps

•	Modify the charts or calculations to explore other aspects of the data.
•	Analyze the frequency of False values or other specific conditions.
•	Use this notebook as a template for analyzing other datasets.

Further Learning Resources

•	Explore the Pandas Documentation for more data manipulation techniques.
•	Learn more about visualization options in Matplotlib and Seaborn.
•	Participate in online forums and communities to enhance your learning and connect with other data scientists.

License

This project is licensed under the MIT License - see the LICENSE file for details.