This article demonstrates how you can use the Python client for Glassnode API for on-chain data analytics.

You will see how to call various Glassnode API endpoints to retrieve useful blockchain metrics, e.g., market insights, blockchain information, fees, supply, transaction insights, etc.

You will create Python scripts that fetch Glassnode API data using glassnode-api-python-client and the Python request method.

What Is Glassnode?

Glassnode is an on-chain data analytics platform founded by Rafael Schultze Kraft in 2007 to provide insights into complex crypto data. Glassnode ingests blockchain data and employs AI techniques to provide insights into the various networks.

How Do You Use Glassnode Data?

Glassnode insights can help you make crucial investment decisions. For example, Glassnode can help you identify cryptocurrencies with the highest trading volumes. You may want to invest in such cryptocurrencies instead of dormant currencies.

How do I Download Data from Glassnode?

Two main ways to download data from Glassnode:

Glassnode Studio
Glassnode REST API (which you will see in this tutorial)

Is Glassnode Free?

Glassnode offers four pricing plans:

Standard: Free
Advanced: $29
Professional: $799
Institutional: Tailored plan, price varies.

Glassnode API metrics are categorized into three tiers. Access to functionalities within a specific tier depends upon your subscription. You can only access tier 1 metrics with a standard (free) account.

Is Glassnode Reliable?

Glassnode doesn’t generate its data; it extracts information from mostly public blockchain data. Some users report that Glassnode insights are often not based on the latest data.

Why Should I Use Glassnode?

Glassnode offers a free standard account.
Glassnode Studio provides plenty of options to navigate intelligent on-chain data metrics.
Glassnode Academy is an excellent resource learning resource for beginners.

Why Should I Not Use Glassnode?

Some users complain that Glassnode insights are outdated
Not very user-friendly for beginners. Though, you can get help from Glassnode Academy.

What Can You Do with the Glassnode API?

With Glassnode API, you can develop customized software applications that fetch intelligent data insights from Glassnode.

Glassnode API pricing

Glassnode doesn’t have a different pricing plan for its API. The access to Glassnode API depends upon your default Glassnode subscription plan.

Getting Started with Glassnode API in Python

Access to Glassnode API requires an API you can get by signing up with Glassnode.

Signing up for a Glassnode Account

Go to glassnode.com and click the “Sign Up” button at the top right corner of the page.

Fill out the signup form by entering your first name, last name, email, and password. Click the “Sign Up” button.

You will receive an email for account activation. Click the activation link in your email. Once your account is activated, you will see the following message.

Get your API key from Glassnode Studio

Go to the API settings page and click the “Generate Key” button to generate your Glassnode API Key.

You can view the API documentation by clicking the “View API Documentation” button.

Simple Example of Fetching Data from Glassnode API in Python

You can use the Glassnode API Python Client library or the Python request function to make calls to Glassnode API.

Glassnode API Python Client Library Example

The following command installs the glassnode-api-python-client library.

pip install glassnode

The first step is to Import the GlassnodeClient class from the glassnode module and pass your API key to the class constructor.

You can use the get() method from the GlassnodeClient class to make a Glassnode API call. You must pass the endpoint and API call names to the get() method. You also need to pass a Python dictionary containing the API call parameters.

For example, the script below makes the following API call.

https://api.glassnode.com/v1/metrics/addresses/active_count

Here, the endpoint name is highlighted in green, while the metric inside the endpoint is highlighted in yellow.

The call above returns the number of active bitcoin accounts on the blockchain, as we pass “BTC” as the symbol value to the parameter “a,” which corresponds to the asset symbol.

The response from the API call is a list of dictionaries. Each dictionary contains two items: t and v, where t corresponds to the period, and v corresponds to the active account values.

from glassnode import GlassnodeClient
import os
import pandas as pd

api_key = os.environ['GN-Key']

## behind the scene API call
## https://api.glassnode.com/v1/metrics/addresses/active_count

client = GlassnodeClient(api_key)
data = client.get("addresses", "active_count", {"a": "BTC"})
print(type(data))
print(data[0].keys())

Output:

<class 'list'>
dict_keys(['t', 'v'])

For a better view, you can convert the list of dictionaries into a Pandas dataframe using the following script.

## create a pandas dataframe using the list of dictionaries
df = pd.DataFrame.from_dict(data)
print(df.shape)
df.tail()

Output:

By default, the time values are in UNIX format. The following script can convert the time values to ISO date format.

import datetime
def convert_date(unix_date):
    return datetime.datetime.fromtimestamp(int(unix_date)).strftime('%Y-%m-%d') 
df["t"] = df["t"].apply(convert_date)
df.tail()

Output:

from glassnode import GlassnodeClient
import os
import pandas as pd

api_key = os.environ['GN-Key']

## behind the scene API call
## https://api.glassnode.com/v1/metrics/addresses/active_count

client = GlassnodeClient(api_key)

since = 1232668800 # 2009-01-23
until = 1233532800 # 2009-02-03
resolution = "24h"

params = {"a": "BTC", "s": since, "u": until, "i": resolution}
data = client.get("addresses", "active_count", params)
df = pd.DataFrame.from_dict(data)
df["t"] = df["t"].apply(convert_date)
print(df.shape)
df.tail(20)

Output:

Glassnode API Parameters Info — Glassnode API Request Function Example

The ***glassnode-api-python-client*** library can be a litter slower as it adds a programming layer over the Python ***requests*** library.

If speed is a concern, you can directly make calls to the Glassnode API via the Python ***request*** function, as demonstrated in the following script.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

# make API request
res = requests.get('https://api.glassnode.com/v1/metrics/addresses/active_count',
    params={'a': 'BTC', 'api_key': api_key})

# convert to pandas dataframe
df = pd.read_json(res.text, convert_dates=['t'])
df.head()

Output:

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']


since = 1232668800 # 2009-01-23
until = 1233532800 # 2009-02-03
resolution = "24h"

# make API request
res = requests.get('https://api.glassnode.com/v1/metrics/addresses/active_count',
    params={"a": "BTC", "s": since, "u": until, "i": resolution, 'api_key': api_key})


# convert to pandas dataframe
df = pd.read_json(res.text, convert_dates=['t'])
df.tail(20

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']


# make API request
res = requests.get('https://api.glassnode.com/v2/metrics/endpoints',
    params={'api_key': api_key})

# convert to pandas dataframe
df = pd.read_json(res.text, convert_dates=['t'])
df.head()

Output:

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']


# make API request
res = requests.get('https://api.glassnode.com/v1/metrics/assets',
    params={'api_key': api_key})

# convert to pandas dataframe
df = pd.read_json(res.text, convert_dates=['t'])
df.head()

Output:

What are Glassnode API Endpoints?

Glassnode API offers 17 endpoints listed below:

Out of these endpoints, metrics from Lightning, DeFi, Derivates, Distribution, Entities, and Mempool endpoints are not available with the standard subscription.

The following sections will show you how to call the metrics available with the standard subscriptions. All of these metrics belong to the tier 1 metrics category.

Addresses Endpoint

The addresses API endpoint returns information related to the addresses involved in a blockchain transaction.

The following address metrics are available with the standard subscription.

	Description	Path
Active Addresses	The number of unique addresses active in a successful transaction	https://api.glassnode.com/v1/metrics/addresses/active_count
Sending Addresses	The number of unique sending addresses active in a successful transaction	https://api.glassnode.com/v1/metrics/addresses/sending_count
Receiving Addresses	The number of unique receiving addresses active in a successful transaction	https://api.glassnode.com/v1/metrics/addresses/receiving_count
New Addresses	The number of unique addresses appearing for the first time in a successful transaction	https://api.glassnode.com/v1/metrics/addresses/new_non_zero_count
Total Addresses	The number of all unique addresses involved in a transaction of the native coin in a network	https://api.glassnode.com/v1/metrics/addresses/count

Refer to the complete list of addressee metrics to know more about other metrics.

The following script demonstrates how to call the metrics mentioned above.

We iterate through the list of API calls using a for loop. In each iteration, we convert the response from the API call to a Pandas dataframe, which we append to a Python list. Finally, we concatenate the list of Pandas dataframe.

The dataframe columns in the output dataset correspond to metric names appearing in API calls.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/addresses/active_count", # Active Addresses 
             "https://api.glassnode.com/v1/metrics/addresses/sending_count", # Sending Addresses 
             "https://api.glassnode.com/v1/metrics/addresses/receiving_count", # Receiving Addresses 
             "https://api.glassnode.com/v1/metrics/addresses/new_non_zero_count", # New Addresses  
             "https://api.glassnode.com/v1/metrics/addresses/count" # Total Addresses
            ]

results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC", 'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
    # set Time as the index column
    df.set_index("Time", inplace = True)
    # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.tail()

Output:

Metric	Description	Path
Block Height	The sum of blocks created in the main blockchain	https://api.glassnode.com/v1/metrics/blockchain/block_height
Blocks Mined	The total number of blocks created in the main blockchain in a specific period.	https://api.glassnode.com/v1/metrics/blockchain/block_count
Block Interval (Mean)	The average time between mined blocks (in seconds)	https://api.glassnode.com/v1/metrics/blockchain/block_interval_mean
Block Interval (Median)	The median time between mined blocks (in seconds)	https://api.glassnode.com/v1/metrics/blockchain/block_interval_median
Block Size (Total)	The total size (in bytes) of all blocks mined in a specific period.	https://api.glassnode.com/v1/metrics/blockchain/block_size_sum
Block Size (Mean)	The average size (in bytes) of all blocks mined in a specific period.	https://api.glassnode.com/v1/metrics/blockchain/block_size_mean
UTXOs Created	The number of UTXOs created in a specific period	https://api.glassnode.com/v1/metrics/blockchain/utxo_created_count
UTXOs Spent	The number of spent UTXOs	https://api.glassnode.com/v1/metrics/blockchain/utxo_spent_count
UTXOs (Total)	The total number of UTXOs in the network.	https://api.glassnode.com/v1/metrics/blockchain/utxo_count
UTXO Value Created (Total)	The total amount of coins in a newly created UTXO	https://api.glassnode.com/v1/metrics/blockchain/utxo_created_value_sum
UTXO Value Created (Mean)	The mean amount of coins in a newly created UTXO	https://api.glassnode.com/v1/metrics/blockchain/utxo_created_value_mean
UTXO Value Created (Median)	The median of coins in a newly created UTXO	https://api.glassnode.com/v1/metrics/blockchain/utxo_created_value_median
UTXO Value Spent (Total)	The total amount of coins in spent UTXOs	https://api.glassnode.com/v1/metrics/blockchain/utxo_spent_value_sum
UTXO Value Spent (Mean)	The mean amount of coins in spent UTXOs	https://api.glassnode.com/v1/metrics/blockchain/utxo_spent_value_mean
UTXO Value Spent (Median)	The median amount of coins in spent UTXOs	https://api.glassnode.com/v1/metrics/blockchain/utxo_spent_value_median

The following code shows an example of how you can call some of the standard metrics from the blockchain endpoint.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/blockchain/block_height", # Block Height
             "https://api.glassnode.com/v1/metrics/blockchain/block_count", # Blocks Mined 
             "https://api.glassnode.com/v1/metrics/blockchain/block_interval_mean", # Block Interval (Mean) 
             "https://api.glassnode.com/v1/metrics/blockchain/block_interval_median", # Block Interval (Median) 
             "https://api.glassnode.com/v1/metrics/blockchain/block_size_sum", # Block Size (Total)
             "https://api.glassnode.com/v1/metrics/blockchain/block_size_mean" # Block Size (Mean)
             
            ]

results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC", 'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
    # set Time as the index column
    df.set_index("Time", inplace = True)
    # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.tail()

Output:

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/blockchain/utxo_created_count", # UTXOs Created
             "https://api.glassnode.com/v1/metrics/blockchain/utxo_spent_count", # UTXOs Spent
             #"https://api.glassnode.com/v1/metrics/blockchain/utxo_count", # UTXOs (Total)
             "https://api.glassnode.com/v1/metrics/blockchain/utxo_created_value_sum", # UTXO Value Created (Total)
             "https://api.glassnode.com/v1/metrics/blockchain/utxo_created_value_mean", # UTXO Value Created (Mean)
             "https://api.glassnode.com/v1/metrics/blockchain/utxo_created_value_median", # UTXO Value Created (Median)
             "https://api.glassnode.com/v1/metrics/blockchain/utxo_spent_value_sum", # UTXO Value Spent (Total)
             "https://api.glassnode.com/v1/metrics/blockchain/utxo_spent_value_mean", # UTXO Value Spent (Mean)
             "https://api.glassnode.com/v1/metrics/blockchain/utxo_spent_value_median", # UTXO Value Spent (Median)
            ]


results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC", 'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
    # set Time as the index column
    df.set_index("Time", inplace = True)
    # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.tail()

Output:

You can find the list of all blockchain metrics at the official documentation link.

ETH 2.0 Endpoint

ETH 2.0 endpoint metrics return information about ETH 2.0, e.g., transactions and transfers involving ETH 2.0, total ETH 2.0 volume, etc.

The following table contains the ETH 2.0 endpoint metrics accessible from the standard account.

Metric	Description	Path
ETH 2.0 New Deposits	The number of transactions depositing 32 ETH to ETH 2 deposit contract	https://api.glassnode.com/v1/metrics/eth2/staking_deposits_count
ETH 2.0 New Value Staked	The total amount of ETH deposited to the ETH2 deposit contract	https://api.glassnode.com/v1/metrics/eth2/staking_volume_sum
ETH 2.0 New Validators	The number of new unique addresses depositing ETH to the ETH2 deposit contract	https://api.glassnode.com/v1/metrics/eth2/staking_validators_count
ETH 2.0 Total Number of Deposits	The total number of transactions in the ETH2 deposit contract	https://api.glassnode.com/v1/metrics/eth2/staking_total_deposits_count
ETH 2.0 Total Value Staked	The total amount of ETH deposited to the ETH2 deposit contract	https://api.glassnode.com/v1/metrics/eth2/staking_total_volume_sum
ETH 2.0 Total Number of Validators	The number of unique addresses depositing ETH to the ETH2 deposit contract	https://api.glassnode.com/v1/metrics/eth2/staking_total_validators_count
ETH 2.0 Phase 0 Staking Goal	The ETH volume divided by 524,288.	https://api.glassnode.com/v1/metrics/eth2/staking_phase_0_goal_percent

The code below shows how to access the ETH 2.0 endpoint in Python.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/eth2/staking_deposits_count", 
             "https://api.glassnode.com/v1/metrics/eth2/staking_volume_sum",
             "https://api.glassnode.com/v1/metrics/eth2/staking_validators_count",
             "https://api.glassnode.com/v1/metrics/eth2/staking_total_deposits_count",
             "https://api.glassnode.com/v1/metrics/eth2/staking_total_volume_sum",
             "https://api.glassnode.com/v1/metrics/eth2/staking_total_validators_count",
             "https://api.glassnode.com/v1/metrics/eth2/staking_phase_0_goal_percent"
            ]



results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "ETH",  'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
    # set Time as the index column
    df.set_index("Time", inplace = True)
    # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.tail()

Output:

Check the official documentation to see the complete list of ETH 2.0 endpoint metrics.

Fees Endpoint

The fees API endpoint provides metrics that return the fee information for various cryptocurrency assets. The table below summarizes the fees metrics available with the standard account.

Metric	Description	Path
Fees (Total)	The total fee paid to miners	https://api.glassnode.com/v1/metrics/fees/volume_sum
Fees (Mean)	The average fee per transaction	https://api.glassnode.com/v1/metrics/fees/volume_mean
Fees (Median)	The median fee per transaction	https://api.glassnode.com/v1/metrics/fees/volume_median
Transaction Gas Limit (Mean)	The average gas limit per transaction	https://api.glassnode.com/v1/metrics/fees/gas_limit_tx_mean
Transaction Gas Limit (Median)	The median gas limit per transaction	https://api.glassnode.com/v1/metrics/fees/gas_limit_tx_median
Gas Price (Mean)	The average gas price per transaction	https://api.glassnode.com/v1/metrics/fees/gas_price_mean
Gas Price (Median)	The median gas price per transaction	https://api.glassnode.com/v1/metrics/fees/gas_price_median
Gas Used (Mean)	The average gas used per transaction	https://api.glassnode.com/v1/metrics/fees/gas_used_mean
Gas Used (Median)	The median amount of gas used per transaction	https://api.glassnode.com/v1/metrics/fees/gas_used_median
Gas Used (Total)	The total gas used per transaction	https://api.glassnode.com/v1/metrics/fees/gas_used_sum

The following script demonstrates how to call some of the aforementioned fees metrics.
Note: I have commented some of the API calls since not all API calls return an equal number of rows which results in an error while concentrating pandas dataframes. You can make API calls individually to see the results.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/fees/volume_sum", # Fees (Total)
              "https://api.glassnode.com/v1/metrics/fees/volume_mean",# Fees (Mean)
              "https://api.glassnode.com/v1/metrics/fees/volume_median", # Fees (Median)
#             "https://api.glassnode.com/v1/metrics/fees/gas_limit_tx_mean", # Transaction Gas Limit (Mean)
#              "https://api.glassnode.com/v1/metrics/fees/gas_limit_tx_median", # Transaction Gas Limit (Median)
#              "https://api.glassnode.com/v1/metrics/fees/gas_price_mean", # Gas Price (Mean)
#              "https://api.glassnode.com/v1/metrics/fees/gas_price_median", # Gas Price (Median)
#              "https://api.glassnode.com/v1/metrics/fees/gas_used_mean", # Gas Used (Mean)
#             "https://api.glassnode.com/v1/metrics/fees/gas_used_median", # Gas Used (Median)
#             "https://api.glassnode.com/v1/metrics/fees/gas_used_sum" # Gas Used (Total)
            ]



results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC",  'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
#     # set Time as the index column
#     df.set_index("Time", inplace = True)
#     # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.tail()

Output:

Check out the official documentation to see the complete list of fees endpoint metrics.

Indicators Endpoint

The indicators endpoint metrics provide information about various blockchain indicators, e.g., the extent of bitcoin mining difficulty, spent output profit ratio, stock-to-flow ratio, etc.

The following indicators metrics are accessible with the standard account.

Metric	Description	Path
Pi Cycle Top Indicator	Indicates 111-day moving average and 2x multiple of the 350-day moving average of Bitcoin’s price	https://api.glassnode.com/v1/metrics/indicators/pi_cycle_top
Difficulty Ribbon	Indicates the extent of bitcoin mining difficulty.	https://api.glassnode.com/v1/metrics/indicators/difficulty_ribbon
SOPR	Indicates the spent output profit ratio	https://api.glassnode.com/v1/metrics/indicators/sopr
Stock-to-Flow Ratio	Indicates the stock-to-flow-ratio	https://api.glassnode.com/v1/metrics/indicators/stock_to_flow_ratio

The following code shows how to call the indicators endpoint metrics in a Python script.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/indicators/pi_cycle_top", # Pi Cycle Top Indicator
#             "https://api.glassnode.com/v1/metrics/indicators/difficulty_ribbon", # Difficulty Ribbon - value too big for pandas dataframe
              "https://api.glassnode.com/v1/metrics/indicators/sopr", # SOPR
              "https://api.glassnode.com/v1/metrics/indicators/stock_to_flow_ratio" # Stock-to-Flow Ratio
            ]



results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC",  'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
#     # set Time as the index column
#     df.set_index("Time", inplace = True)
#     # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.head()

Output:

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/institutions/purpose_etf_flows_sum", # Purpose Bitcoin ETF Flows
             "https://api.glassnode.com/v1/metrics/institutions/purpose_etf_holdings_sum" # Purpose Bitcoin ETF Holdings
            ]


results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC",  'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
#     # set Time as the index column
#     df.set_index("Time", inplace = True)
#     # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.tail()

Output:

Check out the official documentation to see the complete list of institutions endpoint metrics.

Market Endpoint

Market endpoint contains metrics that return market information, e.g., the market cap, the opening, high, low, and closing values of an asset in USD, etc.

The following four market endpoints are available with the standard account.

Metric	Description	Path
Market Cap	The product of the current USD price by the current supply of an asset	https://api.glassnode.com/v1/metrics/market/marketcap_usd
Price	The closing price of an asset in USD	https://api.glassnode.com/v1/metrics/market/price_usd_close
Price OHLC	Candlestick chart values for open, high, low, and close values of an asset in USD	https://api.glassnode.com/v1/metrics/market/price_usd_ohlc
Price Drawdown from ATH	The percent decrease of an asset price from the previous all-time high	https://api.glassnode.com/v1/metrics/market/price_drawdown_relative

The following example shows how to access the Glassnode Market endpoint in Python.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/market/marketcap_usd", # Market Cap
             "https://api.glassnode.com/v1/metrics/market/price_usd_close", # Price
             "https://api.glassnode.com/v1/metrics/market/price_usd_ohlc", # Price OHLC
             "https://api.glassnode.com/v1/metrics/market/price_drawdown_relative" # Price Drawdown from ATH
            ]


results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC",  'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
#     # set Time as the index column
#     df.set_index("Time", inplace = True)
#     # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.tail()

Output:

Refer to the official documentation to see the complete list of market endpoint metrics.

Mining Endpoint

This endpoint provides information on blockchain mining statistics, e.g., the number of hashes required to mine a block for an asset.

The following two metrics are accessible with the standard account.

Metric	Description	Path
Difficulty	The estimated number of hashes required to mine a block for an asset	https://api.glassnode.com/v1/metrics/mining/difficulty_latest
Hashrate	The estimated mean value for hashes produced by miners in the network	https://api.glassnode.com/v1/metrics/mining/hash_rate_mean

The script below demonstrates how to call these two mining metrics in Python.

In this case, the output is not in the form of a Pandas dataframe because, sometimes, the values returned by Glassnode API metrics are too big to be stored in a Pandas dataframe.

Therefore, the output displays a plain text string you can parse to extract relevant information.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/mining/difficulty_latest" # Difficulty
            #"https://api.glassnode.com/v1/metrics/mining/hash_rate_mean"] # Hashrate


results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC",  'api_key': api_key})

    
    results.append(res.text)
    
print(results[0][:225])

Output:

[{"t":1231459200,"v":4294967296},{"t":1231545600,"v":4294967296},{"t":1231632000,"v":4294967296},{"t":1231718400,"v":4294967296},{"t":1231804800,"v":4294967296},{"t":1231891200,"v":4294967296},{"t":1231977600,"v":4294967296},

Check out the official documentation to see the complete list of mining endpoint metrics.

Protocols Endpoint

The protocols endpoint provides metrics that fetch asset information from Uniswap cryptocurrency exchange. It has three metrics that you can access through a standard subscription.

Metric	Description	Path
Uniswap Liquidity	The latest liquidity of an asset on Uniswap	https://api.glassnode.com/v1/metrics/protocols/uniswap_liquidity_latest
Uniswap Transactions	The total number of assets involved in Uniswap contracts	https://api.glassnode.com/v1/metrics/protocols/uniswap_transaction_count
Uniswap Volume	The total volume of an asset traded on Uniswap	https://api.glassnode.com/v1/metrics/protocols/uniswap_volume_sum

The following code shows an example of how you can call the metrics from the protocol endpoint:

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/protocols/uniswap_liquidity_latest",  # Uniswap Liquidity
             "https://api.glassnode.com/v1/metrics/protocols/uniswap_transaction_count", # Uniswap Transactions
             "https://api.glassnode.com/v1/metrics/protocols/uniswap_volume_sum", # Uniswap Volume
            ]

results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "ETH",  'api_key': api_key})

    
    results.append(res.text)
    
print(results[0][:210])

Output:

[{"t":1588636800,"v":0.00490852351350741},{"t":1588723200,"v":0.0159213628437584},{"t":1588809600,"v":0.0159213628437726},{"t":1588896000,"v":0.0887127385961924},{"t":1588982400,"v":0.0887127385961924},{"t":158

You can find the list of all protocols endpoint metrics at the official documentation link.

Supply Endpoint

The supply endpoint metrics provide information on asset supply. You can only access one metric from this endpoint with the standard account.

Metric	Description	Path
Supply Last Active 1+ Years Ago	The percentage value of immobile asset supply in at least a year	https://api.glassnode.com/v1/metrics/supply/active_more_1y_percent

The following code shows how to call the Supply Last Active 1+ Years Ago metric from the supply endpoint.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/supply/active_more_1y_percent", 
            ]


results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC",  'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
#     # set Time as the index column
#     df.set_index("Time", inplace = True)
#     # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.ta

Output:

Metric	Description	Path
Transaction Count	The total amount of successful transactions for an asset	https://api.glassnode.com/v1/metrics/transactions/count
Transaction Rate	The number of successful transactions per second	https://api.glassnode.com/v1/metrics/transactions/rate
Transaction Size (Total)	The total size of all transactions within a specific period	https://api.glassnode.com/v1/metrics/transactions/size_sum
Transaction Size (Mean)	The average size of all transactions within a specific period	https://api.glassnode.com/v1/metrics/transactions/size_mean
Transfer Volume (Total)	The total amount of coins transferred in successful transfers	https://api.glassnode.com/v1/metrics/transactions/transfers_volume_sum
Transfer Volume (Mean)	The average amount of coins transferred in successful transfers	https://api.glassnode.com/v1/metrics/transactions/transfers_volume_mean
Transfer Volume (Median)	The median amount of coins transferred in successful transfers	https://api.glassnode.com/v1/metrics/transactions/transfers_volume_median

Check the official documentation to see the complete list of transactions metrics.

The script below demonstrates how to call the above API metric and store the response in a Pandas dataframe.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/transactions/count", # Transaction Count
              "https://api.glassnode.com/v1/metrics/transactions/rate", # Transaction Rate
              "https://api.glassnode.com/v1/metrics/transactions/size_sum", # Transaction Size (Total)
              "https://api.glassnode.com/v1/metrics/transactions/size_mean", # Transaction Size (Mean)
              "https://api.glassnode.com/v1/metrics/transactions/transfers_volume_sum", # Transfer Volumne (Total)
              "https://api.glassnode.com/v1/metrics/transactions/transfers_volume_mean", # Transfer Volume (Mean)
              "https://api.glassnode.com/v1/metrics/transactions/transfers_volume_median" # Transfer Volume (Median)
            ]


results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC",  'api_key': api_key})

    # convert to pandas dataframe
    df = pd.read_json(res.text, convert_dates=['t'])
    
    # rename dataframe column names
    df.columns = ["Time", endpoint_name]
    
    # set Time as the index column
    df.set_index("Time", inplace = True)
    # adding the result to a list of pandas dataframe
    results.append(df)
    
# concatenating all dataframes horizontally
results_df = pd.concat(results, axis=1)
results_df.tail()

Output:

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = ["https://api.glassnode.com/v1/metrics/transactions/transfers_count", # Transfer Count
             "https://api.glassnode.com/v1/metrics/transactions/transfers_rate" # Transfer Rate
            ]

results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "ETH",  'api_key': api_key})

    results.append(res.text)
    
print(results[1][:200])

Output:

[{"t":1438905600,"v":0.022928240740740742},{"t":1438992000,"v":0.017974537037037035},{"t":1439078400,"v":0.014398148148148148},{"t":1439164800,"v":0.01765046296296296},{"t":1439251200,"v":0.0380324074

Common Errors and Issues Accessing Glassnode API

Depending on the metric tier and the way you want to store information from the Glassnode API, you may face some issues.

In some instances, the values returned by the Glassnode APIs are too big to be stored in Pandas dataframes. In such cases, you should consider other data structures for storing the response.

For instance, the following script returns a “Value is too Big” error.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = [#"https://api.glassnode.com/v1/metrics/mining/difficulty_latest"
             "https://api.glassnode.com/v1/metrics/mining/hash_rate_mean"
            ]


results = []
for call in api_calls:
    
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC",  'api_key': api_key})

    df = pd.read_json(res.text, convert_dates=['t'])
    
    results.append(res.text)

Output:

Another common issue you will likely face is the inability to access some metrics. For instance, the following script returns an error while accessing the difficulty metric from the mining endpoint, although the metric falls in the category of tier 1 metrics.

#"https://api.glassnode.com/v1/metrics/blockchain/utxo_count", # UTXOs (Total)

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

api_calls = [#"https://api.glassnode.com/v1/metrics/mining/difficulty_latest"
             "https://api.glassnode.com/v1/metrics/blockchain/utxo_count"
            ]

results = []
for call in api_calls:
    endpoint_name = call.split("/")[-1]
    
    # make API request
    res = requests.get(call, 
                       params={"a": "BTC",  'api_key': api_key})

    df = pd.read_json(res.text, convert_dates=['t'])
    
    results.append(res.text)

Output:

The error in the above output is not clear. In such cases, you may want to make an API call via the glassnode-api-python-client library, as shown in the script below:

from glassnode import GlassnodeClient
import os
import pandas as pd

api_key = os.environ['GN-Key']

## behind the scene API call
## https://api.glassnode.com/v1/metrics/addresses/active_count

client = GlassnodeClient(api_key)
data = client.get("blockchain", "utxo_count", {"a": "BTC"})
print(type(data))
print(data[0].keys())

Output:

As you can see, the error returned, in this case, is more readable as it says that the asset (‘BTC’) is forbidden for this endpoint.

How do I Extract Glassnode Data to Excel?

To extract Glassnode data to an Excel file, you can store the response from the Glassnode API in a Pandas dataframe; you can then use the to_excel() function from the Pandas dataframe to write the data into an excel file.

The following script demonstrates how to extract Glassnode data to Excel.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']


since = 1232668800 # 2009-01-23
until = 1233532800 # 2009-02-03
resolution = "24h"

# make API request
res = requests.get('https://api.glassnode.com/v1/metrics/addresses/active_count',
    params={"a": "BTC", "s": since, "u": until, "i": resolution, 'api_key': api_key})


# convert to pandas dataframe
df = pd.read_json(res.text, convert_dates=['t'])

df.to_excel("D:\Datasets\glassnode_output.xlsx",
             sheet_name='Sheet_name_1')

Output:

Glassnode API Data to Excel — How do I Pull Glassnode Data into Google Sheets?

To pull data from Glassnode into Google sheets, you require three libraries:

Pandas
gspread
Df2gspread

As a first step, you can import the Glassnode data into a Pandas dataframe.

import json
import requests
import os
import pandas as pd

api_key = os.environ['GN-Key']

since = 1232668800 # 2009-01-23
until = 1233532800 # 2009-02-03
resolution = "24h"

# make API request
res = requests.get('https://api.glassnode.com/v1/metrics/addresses/active_count',
    params={"a": "BTC", "s": since, "u": until, "i": resolution, 'api_key': api_key})


# convert to pandas dataframe
df = pd.read_json(res.text, convert_dates=['t'])

The next step is to connect to Google spreadsheets from your Python application. To do so, you need a JSON file containing an API key that lets you connect to your Google spreadsheet account.

You can use the gspread library to connect to Google spreadsheets from your Python application. The process is explained on the official documentation page of gspread library.

Download the API Key, and authenticate your credentials using the following script:

# pip install gspread
import gspread

#pip install df2gspread
from df2gspread import df2gspread

from oauth2client import service_account

scope = ['https://spreadsheets.google.com/feeds',
         'https://www.googleapis.com/auth/drive']

credentials = service_account.ServiceAccountCredentials.from_json_keyfile_name(
    'D:\Datasets\my-project-1470409419661-70e344e255e3.json', scope)

gc = gspread.authorize(credentials)

Finally, you can use the upload() function from the df2gspread module to pull the data from the Pandas dataframe into Google sheets, as shown in the following script.

spreadsheet_key = os.environ['GSS-Key']
sheet_name = 'Sheet1'
df2gspread.upload(df, spreadsheet_key, sheet_name, credentials=credentials, row_names=True)

Glassnode Alternatives

Following are some of the other platforms providing on-chain data analytics services:

Glassnode vs. CryptoQuant

	Glassnode	CryptoQuant
Membership Fees	Standard: Free Advanced: $29 Professional: $799 Institutional: Tailored as per requirement	Standard: Free Advanced: $29 Professional $99 Premium: $799
Monthly Visits (August 2022)	640.4K	605.4 K
Features	Glassnode studioGlassnode AcademyGlass node Insights and ReportingAbility to view multiple charts	Ability to set alertsAbility to view multiple chartsLarge user community and social media presence
Beginner Friendliness	Not very beginner friendly due to the complex interface	Beginner-friendly

There is little to choose between Glassnode and CryptoQuant as both platforms have similar pricing and offer comparable features. However, CryptoQuant might be a good starting point for beginners.

Conclusion

Glassnode is an intelligent data analytics platform that provides insights into blockchain data. In addition to a sophisticated GUI dashboard in Glassnode studio, Glass node offers programmatically accessible REST API endpoints.

This tutorial teaches how to fetch data from Glassnode API in a Python application. You saw how to use the Python request method and the glassnode-api-python-client library to call the Glassnode API. The information in this tutorial can help you develop blockchain data analytics software.

What Is Glassnode?

How Do You Use Glassnode Data?

How do I Download Data from Glassnode?

Is Glassnode Free?

Is Glassnode Reliable?

Why Should I Use Glassnode?

Why Should I Not Use Glassnode?

What Can You Do with the Glassnode API?

Glassnode API pricing

Getting Started with Glassnode API in Python

Signing up for a Glassnode Account

Get your API key from Glassnode Studio

Simple Example of Fetching Data from Glassnode API in Python

Glassnode API Python Client Library Example

What are Glassnode API Endpoints?

Addresses Endpoint

ETH 2.0 Endpoint

Fees Endpoint

Indicators Endpoint

Market Endpoint

Mining Endpoint

Protocols Endpoint

Supply Endpoint

Common Errors and Issues Accessing Glassnode API

How do I Extract Glassnode Data to Excel?

Glassnode Alternatives

Glassnode vs. CryptoQuant

Conclusion

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Glassnode API Python Tutorial (2024)

What Is Glassnode?

How Do You Use Glassnode Data?

How do I Download Data from Glassnode?

Is Glassnode Free?

Is Glassnode Reliable?

Why Should I Use Glassnode?

Why Should I Not Use Glassnode?

What Can You Do with the Glassnode API?

Glassnode API pricing

Getting Started with Glassnode API in Python

Signing up for a Glassnode Account

Get your API key from Glassnode Studio

Simple Example of Fetching Data from Glassnode API in Python

Glassnode API Python Client Library Example

What are Glassnode API Endpoints?

Addresses Endpoint

ETH 2.0 Endpoint

Fees Endpoint

Indicators Endpoint

Market Endpoint

Mining Endpoint

Protocols Endpoint

Supply Endpoint

Common Errors and Issues Accessing Glassnode API

How do I Extract Glassnode Data to Excel?

Glassnode Alternatives

Glassnode vs. CryptoQuant

Conclusion

Leave a Comment Cancel reply

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