# Creating Custom Network Data Metrics Using ATLAS

![](https://5264302.fs1.hubspotusercontent-na1.net/hubfs/5264302/Demo%20Asset%20Resources/CM-Demo-metric_workbench-Cover.png)

While Coin Metrics **Network Data Pro** offers users the ability to analyze aggregated on-chain metrics for a variety of cryptoassets, some power users may require additional customization and granularity. Coin Metrics **ATLAS search engine** equips users with the on-chain analytical toolset they need to workshop their own custom metrics.

### Resources

This notebook demonstrates basic functionality offered by the Coin Metrics Python API Client, ATLAS, and Network Data Pro.

Coin Metrics offers a vast assortment of data for hundreds of cryptoassets. The Python API Client allows for easy access to this data using Python without needing to create your own wrappers using `requests` and other such libraries.

To understand the data that Coin Metrics offers, feel free to peruse the resources below.

* The [Coin Metrics API v4](https://docs.coinmetrics.io/api/v4) website contains the full set of endpoints and data offered by Coin Metrics.
* The [Coin Metrics Product Documentation](https://docs.coinmetrics.io/info) gives detailed, conceptual explanations of the data that Coin Metrics offers.
* The [API Spec](https://docs.coinmetrics.io/python-api-client/reference) contains a full list of functions.

### File Download

Download the entire notebook as either a jupyter notebook to run yourself or as a pdf from the two links below

{% file src="/files/u1HcajhaAgqlAVgO4w4a" %}

{% file src="/files/MtJo3GRqp5fV1S1Pqwrg" %}

### Notebook Setup

```python
from os import environ
import pandas as pd
import logging
from datetime import date, datetime, timedelta
from coinmetrics.api_client import CoinMetricsClient
import json
import logging
import seaborn as sns
import matplotlib.pyplot as plt
# %matplotlib inline
from IPython.display import Markdown as md
```

```python
sns.set_theme()
sns.set(rc={'figure.figsize':(8,6)})
sns.set_style("whitegrid",{'axes.grid' : False,'grid.linestyle': '--', 'grid.color': 'black','axes.edgecolor': 'white','font.family': ['sans-serif']})
```

```python
logging.basicConfig(
    format='%(asctime)s %(levelname)-8s %(message)s',
    level=logging.INFO,
    datefmt='%Y-%m-%d %H:%M:%S'
)
```

```python
# We recommend privately storing your API key in your local environment.
try:
    api_key = environ["CM_API_KEY"]
    logging.info("Using API key found in environment")
except KeyError:
    api_key = ""
    logging.info("API key not found. Using community client")

client = CoinMetricsClient(api_key)
```

```
2024-09-16 14:50:24 INFO     Using API key found in environment
```

## Block-by-Block Metrics

With **Network Data Pro**, block-by-block metrics are offered for both BTC and ETH. With **ATLAS**, however, we have the ability to retrieve block-by-block metrics for any asset in ATLAS coverage.

In this example, we quantify the total amount of USDC and USDT transferred on a block-by-block basis.

```python
hours = 2
end = datetime.now()
start = end - timedelta(hours=hours)
```

```python
# Retrieving data for both USDC and USDT (ETH chain)
asset_list = ['usdc','usdt_eth']
```

```python
for asset in asset_list:
    print('Fetching ' + asset.upper() + ' transactions...')
    
    # ATLAS 'Get List of Transactions' function
    vars()[asset + '_tx'] = client.get_list_of_transactions_v2(
            asset=asset,
            start_time=start,
            end_time=end
    ).to_dataframe().drop_duplicates(subset=['txid'],keep='last')
```

```
Fetching USDC transactions...
Fetching USDT_ETH transactions...
```

#### USDC Transactions

```python
usdc_tx.tail()
```

|      | txid                                              | consensus\_time           | tx\_position      | n\_balance\_updates | amount       | block\_hash                                       | height   | miner\_time               | min\_chain\_sequence\_number | max\_chain\_sequence\_number | fee |
| ---- | ------------------------------------------------- | ------------------------- | ----------------- | ------------------- | ------------ | ------------------------------------------------- | -------- | ------------------------- | ---------------------------- | ---------------------------- | --- |
| 4839 | e4349a2d37cb71ea25068fdae029f99903585d3c221281... | 2024-09-16 14:50:23+00:00 | 89180189833035782 | 2                   | 1047.039959  | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035790            | 89180189833035791            | 0   |
| 4840 | 9c224f0fd2e8a82561351ace87d0b4db0e382d948ec8fe... | 2024-09-16 14:50:23+00:00 | 89180189833035783 | 2                   | 560.0        | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035792            | 89180189833035793            | 0   |
| 4841 | 559ba39db4ccd441c3a22ac6de2536567b7e9c78280925... | 2024-09-16 14:50:23+00:00 | 89180189833035784 | 2                   | 82426.032272 | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035794            | 89180189833035795            | 0   |
| 4842 | fe0d66654af63c8c31f2f012daae75efaff9d91f85be58... | 2024-09-16 14:50:23+00:00 | 89180189833035785 | 2                   | 1455.127734  | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035796            | 89180189833035797            | 0   |
| 4843 | 8651c23909accf78b1ad45cadc85eb74aeb4afe2031f50... | 2024-09-16 14:50:23+00:00 | 89180189833035786 | 2                   | 30.0         | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035798            | 89180189833035799            | 0   |

```python
# Transform balance updates into block-by-block transfer metrics with one line of code
usdc_tx_bbb = pd.DataFrame(usdc_tx.groupby('height')['amount'].sum())
usdc_tx_bbb.head()
```

|          | amount        |
| -------- | ------------- |
| height   |               |
| 20763288 | 113663.822406 |
| 20763289 | 99292.054782  |
| 20763290 | 7651.824353   |
| 20763291 | 1018865.93048 |
| 20763292 | 384271.09176  |

```python
ax = sns.lineplot(
    data=usdc_tx_bbb,
    y=usdc_tx_bbb['amount'],
    x=usdc_tx_bbb.index
)
ax.set_xlabel("Block Height", fontsize = 12)
ax.set_ylabel("Total Transfer \nAmount", fontsize = 12)
plt.setp(ax.get_xticklabels(), rotation=45)
ax.xaxis.set_ticks(plt.gca().get_xticks())
plt.gca().set_xticklabels(['{:.0f}'.format(x) for x in plt.gca().get_xticks()])
ax.yaxis.set_ticks(plt.gca().get_yticks())
plt.gca().set_yticklabels(['${:,.1f}M'.format(y/1000000) for y in plt.gca().get_yticks()])
plt.ylim([usdc_tx_bbb['amount'].min(), usdc_tx_bbb['amount'].max()*1.1])
plt.xlim([usdc_tx_bbb.index[0], usdc_tx_bbb.index[-1]])
plt.annotate(
    'Source: Coin Metrics ATLAS',
    xy=(1, -0.195),
    xycoords='axes fraction',
    color='black',xytext=(-8, 0),
    textcoords='offset pixels',
    horizontalalignment='right',
    verticalalignment='bottom'
)
ax.set_title('\nUSDC Transactions (Block-by-Block) \n', fontsize = 16)
plt.show()
```

<figure><img src="/files/FhXLmI5ENvU5nTBRi48f" alt=""><figcaption></figcaption></figure>

```
largest_tx = usdc_tx.loc[usdc_tx['amount'].idxmax()]
```

```python
largest_tx
```

```
txid                         ca9d6c9c2e86a71c5482845abf9a8821d743bd2923fb73...
consensus_time                                       2024-09-16 14:43:11+00:00
tx_position                                                  89180035214213128
n_balance_updates                                                           10
amount                                                          50893857.89172
block_hash                   19e68e57ad114250502478a0e337ea9ee681f5d0730496...
height                                                                20763845
miner_time                                           2024-09-16 14:43:11+00:00
min_chain_sequence_number                                    89180035214213140
max_chain_sequence_number                                    89180035214213149
fee                                                                          0
Name: 4549, dtype: object
```

```python
md('<br><font size="3.5">Transaction info can also be viewed in the **ATLAS** graphical user interface:<br><br>**Largest Transaction:** <br>https://atlas.coinmetrics.io/transaction-details?asset=usdc&tx_hash=' + str(largest_tx.txid) )
```

\
Transaction info can also be viewed in the **ATLAS** graphical user interface:\
\
**Largest Transaction:**\
<https://atlas.coinmetrics.io/transaction-details?asset=usdc\\&tx\\_hash=ca9d6c9c2e86a71c5482845abf9a8821d743bd2923fb73cb87177e6e83d86d9e>

#### USDT Transactions

```python
usdt_eth_tx.tail()
```

|       | txid                                              | consensus\_time           | tx\_position      | n\_balance\_updates | amount     | block\_hash                                       | height   | miner\_time               | min\_chain\_sequence\_number | max\_chain\_sequence\_number | fee |
| ----- | ------------------------------------------------- | ------------------------- | ----------------- | ------------------- | ---------- | ------------------------------------------------- | -------- | ------------------------- | ---------------------------- | ---------------------------- | --- |
| 15151 | 0493f4f094032f2fc01a6a67d1bb7b18cad05d6b3727eb... | 2024-09-16 14:50:23+00:00 | 89180189833035822 | 2                   | 0.111413   | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035890            | 89180189833035891            | 0   |
| 15152 | 616e8f6cbb50ed258575820c0cdac2b4d768bbb7c4cd7a... | 2024-09-16 14:50:23+00:00 | 89180189833035823 | 2                   | 75.0       | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035892            | 89180189833035893            | 0   |
| 15153 | bdb7bee6537d63c72055b0b015f9a07e6129aff9fb2c43... | 2024-09-16 14:50:23+00:00 | 89180189833035824 | 2                   | 5.0        | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035894            | 89180189833035895            | 0   |
| 15154 | 38988bf92c9a968df5d7db4c0307a08a3c857d6879e7c0... | 2024-09-16 14:50:23+00:00 | 89180189833035825 | 2                   | 211.714933 | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035896            | 89180189833035897            | 0   |
| 15155 | 562fc4b718a681f189cf9aa452937d49527fce7a265193... | 2024-09-16 14:50:23+00:00 | 89180189833035826 | 2                   | 300.0      | 0a590f6b2dd842efa8a7f1a0ebce2746cdb20a2c069bb8... | 20763881 | 2024-09-16 14:50:23+00:00 | 89180189833035898            | 89180189833035899            | 0   |

```python
# Transform balance updates into block-by-block transfer metrics with one line of code
usdt_tx_bbb = pd.DataFrame(usdt_eth_tx.groupby('height')['amount'].sum())
usdt_tx_bbb.head()
```

|          | amount         |
| -------- | -------------- |
| height   |                |
| 20763288 | 523600.540279  |
| 20763289 | 149740.436168  |
| 20763290 | 143192.868507  |
| 20763291 | 79358.83167    |
| 20763292 | 1944483.921281 |

```python
ax = sns.lineplot(
    data=usdt_tx_bbb,
    y=usdt_tx_bbb['amount'],
    x=usdt_tx_bbb.index,
    color='green'
)
ax.set_xlabel("Block Height", fontsize = 15)
ax.set_ylabel("Total Transfer \nAmount", fontsize = 15)
plt.setp(ax.get_xticklabels(), rotation=45)
ax.xaxis.set_ticks(plt.gca().get_xticks())
plt.gca().set_xticklabels(['{:.0f}'.format(x) for x in plt.gca().get_xticks()])
ax.yaxis.set_ticks(plt.gca().get_yticks())
plt.gca().set_yticklabels(['${:,.1f}M'.format(y/1000000) for y in plt.gca().get_yticks()])
plt.ylim([usdt_tx_bbb['amount'].min(), usdt_tx_bbb['amount'].max()*1.1])
plt.xlim([usdt_tx_bbb.index[0], usdt_tx_bbb.index[-1]])
plt.annotate(
    'Source: Coin Metrics ATLAS',
    xy=(1, -0.195), 
    xycoords='axes fraction',color='black',
    xytext=(-8, 6), 
    textcoords='offset pixels',
    horizontalalignment='right',
    verticalalignment='bottom'
)
ax.set_title('\nUSDT Transactions (Block-by-Block) \n', fontsize = 17);

```

<figure><img src="/files/PJjfbmfjaBZdi2GQlqYf" alt=""><figcaption></figcaption></figure>

```

largest_usdt_tx = usdt_eth_tx.loc[usdt_eth_tx['amount'].idxmax()]
largest_usdt_tx
```

```
txid                         ec6ca03c17cea5c528378bbfa65ef1fb7471395c74a6e8...
consensus_time                                       2024-09-16 13:01:47+00:00
tx_position                                                  89177883435597842
n_balance_updates                                                            2
amount                                                              65000000.0
block_hash                   20c5ffdb60ac0aecbe1827788f2d448c8f0d8ab72827f3...
height                                                                20763344
miner_time                                           2024-09-16 13:01:47+00:00
min_chain_sequence_number                                    89177883435597866
max_chain_sequence_number                                    89177883435597867
fee                                                                          0
Name: 1462, dtype: object
```

```python
md('<br><font size="3.5">Transaction info can also be viewed in the **ATLAS** graphical user interface:<br><br>**Largest Transaction:** <br>https://atlas.coinmetrics.io/transaction-details?asset=usdt_eth&tx_hash=' + str(largest_usdt_tx.txid) )
```

\
Transaction info can also be viewed in the **ATLAS** graphical user interface:\
\
**Largest Transaction:**\
<https://atlas.coinmetrics.io/transaction-details?asset=usdt\\_eth\\&tx\\_hash=ec6ca03c17cea5c528378bbfa65ef1fb7471395c74a6e89d7344be0932c94a1c>

## Cross-Asset Metrics

With **Network Data Pro**, users have the ability to retrieve aggregated daily transfers for specific assets such as USDC and USDT. With **ATLAS**, however, we have the ability to create more customized, granular metrics.

In this example, we quantify the total size of **transactions where USDT and USDC are transferred simultaneously**, on a block-by-block basis.

```python
usdc_tx['USDC Amount'] = usdc_tx['amount']
usdt_eth_tx['USDT Amount'] = usdt_eth_tx['amount']

merged = pd.merge(usdc_tx[['height','USDC Amount','txid']], usdt_eth_tx[['USDT Amount','txid']], on ='txid')
merged['Total Amount'] = merged['USDT Amount'] + merged['USDC Amount']
```

```python
merged.tail()
```

|     | height   | USDC Amount  | txid                                              | USDT Amount  | Total Amount |
| --- | -------- | ------------ | ------------------------------------------------- | ------------ | ------------ |
| 310 | 20763867 | 149.890998   | 6288646aa12f6e17854e0e567d33e59baadb000fcccaa7... | 225.073709   | 374.964707   |
| 311 | 20763868 | 20.215977    | 33b82dd5137e8610868966f7738467b3cca7aadee525f0... | 40.291364    | 60.507341    |
| 312 | 20763872 | 59998.50869  | 973b87937c485db542909d2c69730a3d5ce41f78cfe01e... | 60000.0      | 119998.50869 |
| 313 | 20763877 | 160.087258   | 97d6b653d389cb1b1a31e26d555e1e9f182633295b0e88... | 2087.301319  | 2247.388577  |
| 314 | 20763878 | 56908.231643 | 8bd1605839db7cf700ac524012a09c71cf9add77d0edec... | 23055.975936 | 79964.207579 |

```python
last_tx = merged.iloc[-1]
txhash = last_tx.txid
```

```python
md('<br><font size="3.5">Transaction info can also be viewed in the **ATLAS** graphical user interface:<br><br>**USDC:** <br>https://atlas.coinmetrics.io/transaction-details?asset=usdc&tx_hash=' + str(txhash) + '<br>**USDT:** <br>https://atlas.coinmetrics.io/transaction-details?asset=usdt_eth&tx_hash=' + str(txhash))
```

\
Transaction info can also be viewed in the **ATLAS** graphical user interface:\
\
**USDC:**\
<https://atlas.coinmetrics.io/transaction-details?asset=usdc\\&tx\\_hash=8bd1605839db7cf700ac524012a09c71cf9add77d0edec1b81a8342285d092db\\>
**USDT:**\
<https://atlas.coinmetrics.io/transaction-details?asset=usdt\\_eth\\&tx\\_hash=8bd1605839db7cf700ac524012a09c71cf9add77d0edec1b81a8342285d092db>

```python
# Determine total amount of USDT + USDC transferred on a block-by-block basis
both_tx_bbb = pd.DataFrame(merged.groupby('height')['Total Amount'].sum())
both_tx_bbb
```

|          | Total Amount  |
| -------- | ------------- |
| height   |               |
| 20763294 | 161872.380601 |
| 20763296 | 195635.133756 |
| 20763306 | 28362.220094  |
| 20763309 | 7325.095764   |
| 20763316 | 796760.645528 |
| ...      | ...           |
| 20763867 | 374.964707    |
| 20763868 | 60.507341     |
| 20763872 | 119998.50869  |
| 20763877 | 2247.388577   |
| 20763878 | 79964.207579  |

240 rows × 1 columns

```python
ax = sns.lineplot(
    data=both_tx_bbb,
    y=both_tx_bbb['Total Amount'],
    x=both_tx_bbb.index,
    color='purple'
)
ax.set_xlabel("Block Height", fontsize = 15)
ax.set_ylabel("Total Transfer \nAmount", fontsize = 15)
plt.setp(ax.get_xticklabels(), rotation=45)
ax.xaxis.set_ticks(plt.gca().get_xticks())
plt.gca().set_xticklabels(['{:.0f}'.format(x) for x in plt.gca().get_xticks()])
ax.yaxis.set_ticks(plt.gca().get_yticks())
plt.gca().set_yticklabels(['${:,.1f}M'.format(y/1000000) for y in plt.gca().get_yticks()])
plt.ylim([both_tx_bbb['Total Amount'].min(), both_tx_bbb['Total Amount'].max()*1.1])
plt.xlim([both_tx_bbb.index[0], both_tx_bbb.index[-1]])
plt.annotate(
    'Source: Coin Metrics ATLAS',
    xy=(1, -0.195),
    xycoords='axes fraction',
    color='black',
    xytext=(-8, 6), 
    textcoords='offset pixels',
    horizontalalignment='right',
    verticalalignment='bottom'
)
ax.set_title('\nUSDC and USDT\nTransferred in Same Transaction\n', fontsize = 17);
```

<figure><img src="/files/zwXNTtEOZlDCKG7c4mhm" alt=""><figcaption></figcaption></figure>

## Entity-Based Metrics

With **ATLAS,** we can derived our own metrics based on externally-sourced tagged addresses.

In this example, we leverage our catalog of Uniswap liquidity pool contract addresses to **estimate USDC inflows and DEX Supply** for major trading pairs.

#### Retrieving DEX Markets

```python
defi_mkts = client.reference_data_markets(
    exchange="uniswap_v3_eth",
    asset="usdc",
    page_size=1000
).to_dataframe()
```

```python
defi_mkts.tail()
```

|     | market                                      | exchange         | base         | quote | pair              | symbol | type | size\_asset | margin\_asset | strike | ... | order\_amount\_min | order\_amount\_max | order\_price\_increment | order\_price\_min | order\_price\_max | order\_size\_min | order\_taker\_fee | order\_maker\_fee | margin\_trading\_enabled | experimental |
| --- | ------------------------------------------- | ---------------- | ------------ | ----- | ----------------- | ------ | ---- | ----------- | ------------- | ------ | --- | ------------------ | ------------------ | ----------------------- | ----------------- | ----------------- | ---------------- | ----------------- | ----------------- | ------------------------ | ------------ |
| 372 | uniswap\_v3\_eth-agg-wbtc-usdc-spot         | uniswap\_v3\_eth | wbtc         | usdc  | wbtc-usdc         | \<NA>  | spot | \<NA>       | \<NA>         | \<NA>  | ... | \<NA>              | \<NA>              | \<NA>                   | \<NA>             | \<NA>             | \<NA>            | \<NA>             | \<NA>             | \<NA>                    | True         |
| 373 | uniswap\_v3\_eth-agg-wsteth-usdc-spot       | uniswap\_v3\_eth | wsteth       | usdc  | wsteth-usdc       | \<NA>  | spot | \<NA>       | \<NA>         | \<NA>  | ... | \<NA>              | \<NA>              | \<NA>                   | \<NA>             | \<NA>             | \<NA>            | \<NA>             | \<NA>             | \<NA>                    | True         |
| 374 | uniswap\_v3\_eth-agg-xaut\_2\_eth-usdc-spot | uniswap\_v3\_eth | xaut\_2\_eth | usdc  | xaut\_2\_eth-usdc | \<NA>  | spot | \<NA>       | \<NA>         | \<NA>  | ... | \<NA>              | \<NA>              | \<NA>                   | \<NA>             | \<NA>             | \<NA>            | \<NA>             | \<NA>             | \<NA>                    | True         |
| 375 | uniswap\_v3\_eth-agg-xsgd\_eth-usdc-spot    | uniswap\_v3\_eth | xsgd\_eth    | usdc  | xsgd\_eth-usdc    | \<NA>  | spot | \<NA>       | \<NA>         | \<NA>  | ... | \<NA>              | \<NA>              | \<NA>                   | \<NA>             | \<NA>             | \<NA>            | \<NA>             | \<NA>             | \<NA>                    | True         |
| 376 | uniswap\_v3\_eth-agg-yfi-usdc-spot          | uniswap\_v3\_eth | yfi          | usdc  | yfi-usdc          | \<NA>  | spot | \<NA>       | \<NA>         | \<NA>  | ... | \<NA>              | \<NA>              | \<NA>                   | \<NA>             | \<NA>             | \<NA>            | \<NA>             | \<NA>             | \<NA>                    | True         |

5 rows × 37 columns

```python
defi_list = defi_mkts.contract_address.dropna().tolist()
defi_list[0:5]
```

```
['2ee7e6e459fffbbc655f09f2e1b3131abf98c397',
 '735a26a57a0a0069dfabd41595a970faf5e1ee8b',
 'bafead7c60ea473758ed6c6021505e8bbd7e8e5d',
 '5e35c4eba72470ee1177dcb14dddf4d9e6d915f4',
 '0eff06710d737c7548c8177ba42e3abdcf3477a7']
```

```python
len(defi_list)
```

```
262
```

```python
day_end = datetime.now()
day_start = day_end - timedelta(days=1)
```

```python
defi_balance = client.get_list_of_balance_updates_v2(
    asset=asset,
    accounts=defi_list,
    start_time=day_start,
    end_time=day_end,
    page_size=10000
).to_dataframe()
```

```python
defi_balance
```

|      | chain\_sequence\_number | account                                  | account\_creation\_height | change        | previous\_balance | new\_balance    | transaction\_sequence\_number | n\_debits | n\_credits | block\_hash                                       | height   | consensus\_time           | credit | total\_received    | total\_sent        | previous\_debit\_height | previous\_credit\_height | previous\_chain\_sequence\_number | txid                                              |
| ---- | ----------------------- | ---------------------------------------- | ------------------------- | ------------- | ----------------- | --------------- | ----------------------------- | --------- | ---------- | ------------------------------------------------- | -------- | ------------------------- | ------ | ------------------ | ------------------ | ----------------------- | ------------------------ | --------------------------------- | ------------------------------------------------- |
| 0    | 89149553831313431       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | 0.496         | 8838821.586622    | 8838822.082622  | 1                             | 449800    | 479578     | 7ed29942135604bafad3c2820bd06b9757c2ef380e1a87... | 20756748 | 2024-09-15 14:55:23+00:00 | True   | 52777858266.775307 | 52769019444.692688 | 20756718                | 20756719                 | 89149429277261831                 | fa419c42e98bb83b53529891a8ba32768709eb8b254fc2... |
| 1    | 89149738514907201       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | 588.944621    | 8838822.082622    | 8839411.027243  | 0                             | 449800    | 479579     | 304588d181e196c16aedf2a9721aa6e27ae16484662d13... | 20756791 | 2024-09-15 15:03:59+00:00 | True   | 52777858855.719925 | 52769019444.692688 | 20756718                | 20756748                 | 89149553831313431                 | 9c6bfbf5f155940e832deb6150585f4022f5b07d65c8c8... |
| 2    | 89149777169612812       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | -287.318755   | 8839411.027243    | 8839123.708488  | 0                             | 449801    | 479579     | 19b757a736ac358fe9f269806a985eb32a8549b01ebfa3... | 20756800 | 2024-09-15 15:05:47+00:00 | False  | 52777858855.719925 | 52769019732.011436 | 20756718                | 20756791                 | 89149738514907201                 | 2095dd79f657e109c82e0589c610a313b873cfa5c5e874... |
| 3    | 89149794349482033       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | 1801.0        | 8839123.708488    | 8840924.708488  | 0                             | 449801    | 479580     | 148424378d89962dee8f9bd2c63de38cb13ff02297ed31... | 20756804 | 2024-09-15 15:06:35+00:00 | True   | 52777860656.719925 | 52769019732.011436 | 20756800                | 20756791                 | 89149777169612812                 | 09498f7b3ec4307371fc300365d99a0d9eba79a5c69b68... |
| 4    | 89149957558239248       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | -399.070421   | 8840924.708488    | 8840525.638067  | 0                             | 449802    | 479580     | c51c6f67d2af4dcb65c712c13ed8bfc6f87051b60e2e50... | 20756842 | 2024-09-15 15:14:11+00:00 | False  | 52777860656.719925 | 52769020131.081856 | 20756800                | 20756804                 | 89149794349482033                 | 3c89ecbd238ff474415db6b9d5b4f1821667ea4cb08061... |
| ...  | ...                     | ...                                      | ...                       | ...           | ...               | ...             | ...                           | ...       | ...        | ...                                               | ...      | ...                       | ...    | ...                | ...                | ...                     | ...                      | ...                               | ...                                               |
| 1031 | 89179966494736406       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | -2.047597     | 11071334.757652   | 11071332.710055 | 0                             | 450269    | 480108     | 9468ed9db151616d84f3206a3c865f15cd1a844d1dabce... | 20763829 | 2024-09-16 14:39:59+00:00 | False  | 52795130311.495941 | 52784058978.785889 | 20763827                | 20763803                 | 89179957904801814                 | b7292f6c1fa6f8e32b9f0934ed1f2ae3564335bee8f755... |
| 1032 | 89179970789703720       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | -57.627626    | 11071332.710055   | 11071275.082429 | 0                             | 450270    | 480108     | 6db5093f808e69f6e0871973efb6ef56e845c595c7b357... | 20763830 | 2024-09-16 14:40:11+00:00 | False  | 52795130311.495941 | 52784059036.413513 | 20763829                | 20763803                 | 89179966494736406                 | 3aaf4aa1784b63c9498315d8d3e6efb39b81bb95cd03c7... |
| 1033 | 89180005149442094       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | -28863.215208 | 11071275.082429   | 11042411.867221 | 1                             | 450271    | 480108     | 1edac3c91bbe0bb36fb8c807ed9e41b8f7ee200f16df59... | 20763838 | 2024-09-16 14:41:47+00:00 | False  | 52795130311.495941 | 52784087899.628723 | 20763830                | 20763803                 | 89179970789703720                 | 4756f0560c8b10673f64b914c1bdc938fd14166a0fbfd6... |
| 1034 | 89180005149442110       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | -29995.561842 | 11042411.867221   | 11012416.305379 | 0                             | 450272    | 480108     | 1edac3c91bbe0bb36fb8c807ed9e41b8f7ee200f16df59... | 20763838 | 2024-09-16 14:41:47+00:00 | False  | 52795130311.495941 | 52784117895.190559 | 20763838                | 20763803                 | 89180005149442094                 | b9ea155e1f0d6e98a1cb085f4fd4b4efcd5e7df9bbdd35... |
| 1035 | 89180056689049603       | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 13609065                  | 4160.547575   | 11012416.305379   | 11016576.852954 | 1                             | 450272    | 480109     | 2b8cab87607901cf45b8739a8ac88ff4c80b3edf3e2bed... | 20763850 | 2024-09-16 14:44:11+00:00 | True   | 52795134472.043518 | 52784117895.190559 | 20763838                | 20763803                 | 89180005149442110                 | 2184156b4af099618f7ce8773c06881027e3ebf711768b... |

1036 rows × 19 columns

#### Retrieve DEX Inflows

```python
defi_inflow = defi_balance[defi_balance.change > 0]
```

```python
largest_inflow = defi_balance.loc[defi_balance['change'].idxmax()]
largest_inflow
```

```
chain_sequence_number                                             89166329973571601
account                                    3416cf6c708da44db2624d63ea0aaef7113527c6
account_creation_height                                                    13609065
change                                                               2561701.979951
previous_balance                                                     8861581.886413
new_balance                                                         11423283.866364
transaction_sequence_number                                                       2
n_debits                                                                     450061
n_credits                                                                    479906
block_hash                        bbce1f72a7384588aaafd053e449b38c93cd07c5d8e997...
height                                                                     20760654
consensus_time                                            2024-09-16 03:59:35+00:00
credit                                                                         True
total_received                                                   52791531415.555038
total_sent                                                       52780108131.688683
previous_debit_height                                                      20760654
previous_credit_height                                                     20760654
previous_chain_sequence_number                                    89166329973571599
txid                              efdc8d8347d53459d3a581d9700b291cb75311f63289ea...
Name: 608, dtype: object
```

```python
largest_inflow.txid
md('<br><font size="3.5">Transaction info can also be viewed in the **ATLAS** graphical user interface:<br><br>**Largest Transaction:** <br>https://atlas.coinmetrics.io/transaction-details?asset=usdc&tx_hash=' + str(largest_inflow.txid) )
```

\
Transaction info can also be viewed in the **ATLAS** graphical user interface:\
\
**Largest Transaction:**\
<https://atlas.coinmetrics.io/transaction-details?asset=usdc\\&tx\\_hash=efdc8d8347d53459d3a581d9700b291cb75311f63289ea0d1859b358d91d8b7f>

```python
defi_inflow = pd.DataFrame(defi_inflow[['consensus_time','account','change','height']])
defi_inflow_sum = pd.DataFrame(defi_inflow.groupby('height')['change'].sum())
```

```python
defi_inflow_sum
```

|          | change       |
| -------- | ------------ |
| height   |              |
| 20756748 | 0.496        |
| 20756791 | 588.944621   |
| 20756804 | 1801.0       |
| 20756883 | 20.0         |
| 20756906 | 28.88709     |
| ...      | ...          |
| 20763786 | 90000.0      |
| 20763794 | 1000.0       |
| 20763798 | 33356.45     |
| 20763803 | 25530.595361 |
| 20763850 | 4160.547575  |

510 rows × 1 columns

```python
ax = defi_inflow_sum['change'].plot.area(color='navy')
plt.title('\n ' + asset.upper() + ' Inflows to Uniswap V3\n',fontdict={'fontsize':23})
ax.set_xlabel("\nBlock Height")
ax.yaxis.set_ticks(plt.gca().get_yticks())
plt.setp(ax.get_xticklabels(), rotation=45)
ax.xaxis.set_ticks(plt.gca().get_xticks())
plt.gca().set_xticklabels(['{:.0f}'.format(x) for x in plt.gca().get_xticks()])
plt.xlim([defi_inflow_sum.index[0], defi_inflow_sum.index[-1]])
plt.annotate(
    'Source: Coin Metrics ATLAS',
    xy=(1, -0.195),
    xycoords='axes fraction',
    color='black',
    xytext=(-8, 6),
    textcoords='offset pixels',
    horizontalalignment='right',
    verticalalignment='bottom'
)
plt.gca().set_yticklabels(['${:,.2f}M'.format(x/1000000) for x in plt.gca().get_yticks()]);
```

<figure><img src="/files/sdJwvruFHxEPKXgbWAYJ" alt=""><figcaption></figcaption></figure>

#### Retrieve DEX Supply

```python
defi_new_bal = pd.DataFrame(defi_balance[['consensus_time','account','new_balance','height']])
defi_new_bal = defi_new_bal.sort_values(by='height')
defi_new_bal = pd.DataFrame(defi_new_bal.drop_duplicates(subset=['account','height'],keep='last'))
```

```python
defi_new_bal
```

|      | consensus\_time           | account                                  | new\_balance    | height   |
| ---- | ------------------------- | ---------------------------------------- | --------------- | -------- |
| 0    | 2024-09-15 14:55:23+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 8838822.082622  | 20756748 |
| 1    | 2024-09-15 15:03:59+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 8839411.027243  | 20756791 |
| 2    | 2024-09-15 15:05:47+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 8839123.708488  | 20756800 |
| 3    | 2024-09-15 15:06:35+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 8840924.708488  | 20756804 |
| 4    | 2024-09-15 15:14:11+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 8840525.638067  | 20756842 |
| ...  | ...                       | ...                                      | ...             | ...      |
| 1030 | 2024-09-16 14:39:35+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 11071334.757652 | 20763827 |
| 1031 | 2024-09-16 14:39:59+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 11071332.710055 | 20763829 |
| 1032 | 2024-09-16 14:40:11+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 11071275.082429 | 20763830 |
| 1033 | 2024-09-16 14:41:47+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 11042411.867221 | 20763838 |
| 1035 | 2024-09-16 14:44:11+00:00 | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 11016576.852954 | 20763850 |

933 rows × 4 columns

```python
bal_updates_pivot = defi_new_bal.pivot(index="height",columns="account",values="new_balance")
bal_updates_pivot = bal_updates_pivot.ffill()
bal_updates_pivot = bal_updates_pivot.bfill()
```

```python
bal_updates_w_zeros = bal_updates_pivot.fillna(0)
```

```python
column_list = list(bal_updates_w_zeros)
bal_updates_w_zeros["sum"] =bal_updates_w_zeros[column_list].sum(axis=1)
```

```python
bal_updates_w_zeros
```

| account  | 3416cf6c708da44db2624d63ea0aaef7113527c6 | 7858e59e0c01ea06df3af3d20ac7b0003275d4bf | ee4cf3b78a74affa38c6a926282bcd8b5952818d | sum          |
| -------- | ---------------------------------------- | ---------------------------------------- | ---------------------------------------- | ------------ |
| height   |                                          |                                          |                                          |              |
| 20756748 | 8838822.082622                           | 1201350.942506                           | 9178.353003                              | 1.004935e+07 |
| 20756791 | 8839411.027243                           | 1201350.942506                           | 9178.353003                              | 1.004994e+07 |
| 20756800 | 8839123.708488                           | 1201350.942506                           | 9178.353003                              | 1.004965e+07 |
| 20756804 | 8840924.708488                           | 1201350.942506                           | 9178.353003                              | 1.005145e+07 |
| 20756842 | 8840525.638067                           | 1201350.942506                           | 9178.353003                              | 1.005105e+07 |
| ...      | ...                                      | ...                                      | ...                                      | ...          |
| 20763827 | 11071334.757652                          | 1250073.026459                           | 9178.353003                              | 1.233059e+07 |
| 20763829 | 11071332.710055                          | 1250073.026459                           | 9178.353003                              | 1.233058e+07 |
| 20763830 | 11071275.082429                          | 1250073.026459                           | 9178.353003                              | 1.233053e+07 |
| 20763838 | 11042411.867221                          | 1250073.026459                           | 9178.353003                              | 1.230166e+07 |
| 20763850 | 11016576.852954                          | 1250073.026459                           | 9178.353003                              | 1.227583e+07 |

926 rows × 4 columns

```python
ax = bal_updates_w_zeros['sum'].plot.area(color='navy')
plt.title('\n ' + asset.upper() + ' Supply on Uniswap V3\n',fontdict={'fontsize':23})
plt.suptitle('\n\n\nTracked Liquidity Pools\n',size=13.5)
ax.set_xlabel("\nBlock Height")
ax.yaxis.set_ticks(plt.gca().get_yticks())
plt.setp(ax.get_xticklabels(), rotation=45)
ax.xaxis.set_ticks(plt.gca().get_xticks())
plt.gca().set_xticklabels(['{:.0f}'.format(x) for x in plt.gca().get_xticks()])
plt.xlim([bal_updates_w_zeros.index[0], bal_updates_w_zeros.index[-1]])
plt.annotate('Source: Coin Metrics ATLAS',xy=(1, -0.195), xycoords='axes fraction',color='black',xytext=(-8, 6), textcoords='offset pixels',horizontalalignment='right',verticalalignment='bottom')
plt.gca().set_yticklabels(['${:,.2f}M'.format(x/1000000) for x in plt.gca().get_yticks()]);
```

<figure><img src="/files/5hsc6hKscIbUAQAazMeh" alt=""><figcaption></figcaption></figure>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://gitbook-docs.coinmetrics.io/tutorials-and-examples/tutorials/atlas_metric_workbench.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.