Welcome to indexed.xyz Discussions!

[radiofreejohn]

👋 Welcome!

We’re using Discussions as a place to connect with other members of our community. We hope that you:

• Ask questions you’re wondering about.
• Share ideas for things we can build together.
• Share how you're using the indexed.xyz data.
• Engage with other community members.
• Welcome others and are open-minded. Remember that this is a community we
  build together 💪.

To get started, comment below with an introduction of yourself and tell us about what you do with this community.

[radiofreejohn]

Hey everyone! I'm John, the resident support person here at Goldsky. I'm here to help you get up and running with the Indexed.xyz dataset and various tools.

I like memes, turtles, and my cat Molly.

If you have a cat, please post cat pictures here as well, that's allowed, until it isn't.

[salieflewis]

Hi John!

[mseidlx]

Hey John, I think adding a simple Python step-by-step guide would be super helpful for many data analysts out there that aren't super familiar with S3 APIs :)

Also - I'm not sure if I fully understood the schema. E.g. is there an easy way to load all Ethereum transactions for the last 7d ?

-- unfortunately no cat pics :/

[hemaaanth]

Hey! We’ve added a python-based tutorial to our backlog! What do you think would be better, a notebook or a streamlit app?

[mseidlx]

A notebook sounds great!

[hemaaanth]

Hey! So sorry for the very late response here, lost this to-do in my backlog. Quick sample below.

Once you have the Goldsky CLI installed, getting a sample Base Goerli set of decoded logs:

goldsky indexed sync decoded-logs --network=base-goerli --contract-address=0xac6564f3718837caadd42eed742d75c12b90a052

Once this is downloaded and available as a bunch of parquet files in a folder on your machine, can set up a Python notebook with some of the base dependencies:

import pandas as pd
import os
import pyarrow.parquet as pq

And with this code (replace the directory structure as appropriate for your machine, you'll have the data available in a pandas dataframe! Can do the usual python magic from there.

directory = '<directory>' # Define your directory here

# Getting all the parquet files
file_list = [f for f in os.listdir(directory) if f.endswith('.parquet')]

# Load into a bunch of dataframes
dataframes = []
for file_name in file_list:
    file_path = os.path.join(directory, file_name)
    dataframe = pq.read_table(file_path).to_pandas()
    dataframes.append(dataframe)

# Concatenating into a single one
combined_dataframe = pd.concat(dataframes, ignore_index=True)

This is heavily not-optimized, and is best for single-contract exploration vs chain-level exploration, but should be good enough for small exploratory research. Let me know what you think and if it meets the needs / how you might want to improve it.