Python client for cryptocurrency data from Santiment API. This library provides utilities for accessing the GraphQL Santiment API endpoint and convert the result to pandas dataframe.
More documentation regarding the API and definitions of metrics can be found on Santiment Academy
- sanpy
- Table of contents
- Installation
- Upgrade to latest version
- Install extra packages
- Restricted metrics
- Configuration
- Getting the data
- Execute SQL queries and get the result
- Available metrics
- Available Metrics for Slug
- Fetch timeseries metric
- Fetching metadata for a metric
- Batching multiple queries
- Rate Limit Tools
- Metric Complexity
- Include Incomplete Data Flag
- Metric/Asset pair available cince
- Transform the result
- Available projects
- Non-standard metrics
- Extras
- Development
- Running tests
- Running integration tests
To install the latest sanpy from PyPI:
pip install sanpypip install --upgrade sanpyThere are few scripts under extras directory related to backtesting and event studies. To install their dependencies use:
pip install sanpy[extras]In order to access real-time data or historical data for some of the metrics, you'll need to set the API key, generated from an account with a paid API plan.
You can provide an API key which gives access to the restricted metrics in two different ways:
During loading of the san module, if the SANPY_APIKEY exists, its content
is read and set as the API key.
export SANPY_APIKEY="my_apikey"import san
>>> san.ApiConfig.api_key
'my_apikey'import san
san.ApiConfig.api_key = "my_apikey"To obtain an API key you should log in to sanbase
and go to the Account page - https://app.santiment.net/account.
There is an API Keys section and a Generate new api key button.
The library provides the get and get_many functions that are used to fetch data.
get is used to fetch timeseries data for a single metric/asset pair.
get_many is used to fetch timeseries data for a single metric, but many assets. This is counted as 1 API call.
The first argument to the functions is the metric name.
The rest of the parameters are::
slug- (forget) The project identificator, as seen in the Available projects sectionslugs- (forget_many) A list of projects' identificators, as seen in the Available projects sectionselector- Allow for more flexible selection of the target. Some metrics are computed on blockchain addresses, for others you can provide a list of slugs, labels, amount of top holders. etc.from_date- A date or datetime in ISO8601 format specifying the start of the queried period. Defaults todatetime.utcnow() - 365 daysto_date- A date or datetime in ISO86091 format specifying the end of the queried period. Defaults todatetime.utcnow()interval- The interval between the data points in the timeseries. Defaults to'1d'It is represented in two different ways:- a fixed range: an integer followed by one of:
s,m,h,dorw - a function, providing some semantic or a dynamic range:
toStartOfMonth,toStartOfDay,toStartOfWeek,toMonday..
- a fixed range: an integer followed by one of:
The returned result for time-series data is transformed into pandas DataFrame and is indexed by datetime.
For get, the value column is named value.
For get_many, there is one column per asset queried. The asset slugs are used for the column names.
For backwards compatibility, fetching the metric by providing "metric/slug" as
the first instead of using a separate 'slug'/'selector' continues to work,
but it is not the recommended approach.
For non-metric related data like getting the list of available assets, the data
is fetched by providing a string in the format query/argument and additional
parameters.
The examples below contain some of the described scenarios.
Fetch metric by providing metric as first argument and slug as named parameter:
import san
san.get(
"price_usd",
slug="bitcoin",
from_date="2022-01-01",
to_date="2022-01-05",
interval="1d"
)datetime value
2022-01-01 00:00:00+00:00 47686.811509
2022-01-02 00:00:00+00:00 47345.220564
2022-01-03 00:00:00+00:00 46458.116959
2022-01-04 00:00:00+00:00 45928.661063
2022-01-05 00:00:00+00:00 43569.003348
Fetch prices for multiple assets:
import san
san.get_many(
"price_usd",
slugs=["bitcoin", "ethereum", "tether"],
from_date="2022-01-01",
to_date="2022-01-05",
interval="1d"
)datetime bitcoin ethereum tether
2022-01-01 00:00:00+00:00 47686.811509 3769.696916 1.000500
2022-01-02 00:00:00+00:00 47345.220564 3829.565045 1.000460
2022-01-03 00:00:00+00:00 46458.116959 3761.380274 1.000165
2022-01-04 00:00:00+00:00 45928.661063 3795.890130 1.000208
2022-01-05 00:00:00+00:00 43569.003348 3550.386882 1.000122
Fetch development activity of a specific Github organization:
import san
san.get(
"dev_activity",
selector={"organization": "google"},
from_date="2022-01-01",
to_date="2022-01-05",
interval="1d"
)datetime value
2022-01-01 00:00:00+00:00 176.0
2022-01-02 00:00:00+00:00 129.0
2022-01-03 00:00:00+00:00 562.0
2022-01-04 00:00:00+00:00 1381.0
2022-01-05 00:00:00+00:00 1334.0
Fetch a metric for a contract address, not a slug:
import san
san.get(
"contract_transactions_count",
selector={"contractAddress": "0x00000000219ab540356cBB839Cbe05303d7705Fa"},
from_date="2022-01-01",
to_date="2022-01-05",
interval="1d"
)datetime value
2022-01-01 00:00:00+00:00 90.0
2022-01-02 00:00:00+00:00 339.0
2022-01-03 00:00:00+00:00 486.0
2022-01-04 00:00:00+00:00 314.0
2022-01-05 00:00:00+00:00 328.0
Fetch top holders metric and specify the number of top holders to be counted:
import san
san.get(
"amount_in_top_holders",
selector={"slug": "santiment", "holdersCount": 10},
from_date="2022-01-01",
to_date="2022-01-05",
interval="1d"
)datetime value
2022-01-01 00:00:00+00:00 7.391186e+07
2022-01-02 00:00:00+00:00 7.391438e+07
2022-01-03 00:00:00+00:00 7.391984e+07
2022-01-04 00:00:00+00:00 7.391984e+07
2022-01-05 00:00:00+00:00 7.391984e+07
Fetch trade volume of a given DEX for a given slug
import san
# This requires Santiment API PRO apikey configured
san.get(
"total_trade_volume_by_dex",
selector={"slug": "ethereum", "label": "decentralized_exchange", "owner": "UniswapV2"},
from_date="2022-01-01",
to_date="2022-01-05",
interval="1d"
)datetime value
2022-01-01 00:00:00+00:00 96882.176846
2022-01-02 00:00:00+00:00 85184.970249
2022-01-03 00:00:00+00:00 107489.846163
2022-01-04 00:00:00+00:00 105204.677503
2022-01-05 00:00:00+00:00 174178.848916
Fetch metric by providing metric/slug as first argument and no slug as named parameter:
import san
san.get(
"daily_active_addresses/bitcoin",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)datetime value
2018-06-01 00:00:00+00:00 692508.0
2018-06-02 00:00:00+00:00 521887.0
2018-06-03 00:00:00+00:00 531464.0
2018-06-04 00:00:00+00:00 702902.0
2018-06-05 00:00:00+00:00 655695.0
Fetch non-timeseries data:
import san
san.get("projects/all") name slug ticker totalSupply
0 0chain 0chain ZCN 400000000
1 0x 0x ZRX 1000000000
2 0xBitcoin 0xbtc 0xBTC 20999984
...
Some of the available queries in the Santiment API do not have a dedicated sanpy function. Alternatively, if the returned format needs to be parsed differently, this approach can be used, too. They can be fetched by providing the raw GraphQL query.
Fetching data for many slugs at the same time. Note that this is also available as san.get_many
import san
import pandas as pd
result = san.graphql.execute_gql("""
{
getMetric(metric: "price_usd") {
timeseriesDataPerSlug(
selector: {slugs: ["ethereum", "bitcoin"]}
from: "2022-05-05T00:00:00Z"
to: "2022-05-08T00:00:00Z"
interval: "1d") {
datetime
data{
value
slug
}
}
}
}
""")
data = result['getMetric']['timeseriesDataPerSlug']
rows = []
for datetime_point in data:
row = {'datetime': datetime_point['datetime']}
for slug_data in datetime_point['data']:
row[slug_data['slug']] = slug_data['value']
rows.append(row)
df = pd.DataFrame(rows)
df.set_index('datetime', inplace=True)datetime bitcoin ethereum
2022-05-05T00:00:00Z 36575.142133 2749.213042
2022-05-06T00:00:00Z 36040.922350 2694.979684
2022-05-07T00:00:00Z 35501.954144 2636.092958
Fetching a specific set of fields for a project:
import san
import pandas as pd
result = san.graphql.execute_gql("""{
projectBySlug(slug: "santiment") {
slug
name
ticker
infrastructure
mainContractAddress
twitterLink
}
}""")
pd.DataFrame(result["projectBySlug"], index=[0]) infrastructure mainContractAddress name slug ticker twitterLink
0 ETH 0x7c5a0ce9267ed19b22f8cae653f198e3e8daf098 Santiment santiment SAN https://twitter.com/santimentfeed
One of the Santiment products is Santiment Queries. It allows you to execute SQL queries on a database hosted by Santiment. Explore the documentation in order to get familiar with the available data and how to write SQL queries.
In order to execute a query you need to provide your API key.
Executing a query and getting the result as a pandas DataFrame:
import san
san.execute_sql(query="SELECT * FROM daily_metrics_v2 LIMIT 5") metric_id asset_id dt value computed_at
0 10 1369 2015-07-17T00:00:00Z 0.0 2020-10-21T08:48:42Z
1 10 1369 2015-07-18T00:00:00Z 0.0 2020-10-21T08:48:42Z
2 10 1369 2015-07-19T00:00:00Z 0.0 2020-10-21T08:48:42Z
3 10 1369 2015-07-20T00:00:00Z 0.0 2020-10-21T08:48:42Z
4 10 1369 2015-07-21T00:00:00Z 0.0 2020-10-21T08:48:42Z
In order to change the index to one of the columns, provide the set_index parameter:
import san
san.execute_sql(query="SELECT * FROM daily_metrics_v2 LIMIT 5", set_index="dt")dt metric_id asset_id value computed_at
2015-07-17T00:00:00Z 10 1369 0.0 2020-10-21T08:48:42Z
2015-07-18T00:00:00Z 10 1369 0.0 2020-10-21T08:48:42Z
2015-07-19T00:00:00Z 10 1369 0.0 2020-10-21T08:48:42Z
2015-07-20T00:00:00Z 10 1369 0.0 2020-10-21T08:48:42Z
2015-07-21T00:00:00Z 10 1369 0.0 2020-10-21T08:48:42Z
The queries can be parametrized. In the query the parameters are named parameters,
surrounded by two curly brackets {{key}}. The parameters is a dictionary. The query
can be a multiline string:
san.execute_sql(query="""
SELECT
get_metric_name(metric_id) AS metric,
get_asset_name(asset_id) AS asset,
dt,
argMax(value, computed_at)
FROM daily_metrics_v2
WHERE
asset_id = get_asset_id({{slug}}) AND
metric_id = get_metric_id({{metric}}) AND
dt >= now() - INTERVAL {{last_n_days}} DAY
GROUP BY dt, metric_id, asset_id
ORDER BY dt ASC
""",
parameters={'slug': 'bitcoin', 'metric': 'daily_active_addresses', 'last_n_days': 7},
set_index="dt")dt metric asset value
2023-03-22T00:00:00Z daily_active_addresses bitcoin 941446.0
2023-03-23T00:00:00Z daily_active_addresses bitcoin 913215.0
2023-03-24T00:00:00Z daily_active_addresses bitcoin 884271.0
2023-03-25T00:00:00Z daily_active_addresses bitcoin 906851.0
2023-03-26T00:00:00Z daily_active_addresses bitcoin 835596.0
2023-03-27T00:00:00Z daily_active_addresses bitcoin 1052637.0
2023-03-28T00:00:00Z daily_active_addresses bitcoin 311566.0
Getting all of the metrics as a list is done using the following code:
san.available_metrics()Getting all of the metrics for a given slug is achieved with the following code:
san.available_metrics_for_slug("santiment")import san
san.get(
"daily_active_addresses",
slug="santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)Using the defaults params (last 1 year of data with 1 day interval):
san.get("daily_active_addresses", slug="santiment")
san.get("price_usd", slug="santiment")Fetching the metadata for an on-chain metric.
san.metadata(
"nvt",
arr=["availableSlugs", "defaultAggregation", "humanReadableName", "isAccessible", "isRestricted", "restrictedFrom", "restrictedTo"]
)Example result:
{"availableSlugs": ["0chain", "0x", "0xbtc", "0xcert", "1sg", ...],
"defaultAggregation": "AVG", "humanReadableName": "NVT (Using Circulation)", "isAccessible": True, "isRestricted": True, "restrictedFrom": "2020-03-21T08:44:14Z", "restrictedTo": "2020-06-17T08:44:14Z"}availableSlugs- A list of all slugs available for this metric.defaultAggregation- If big interval are queried, all values that fall into this interval will be aggregated with this aggregation.humanReadableName- A name of the metric suitable for showing to users.isAccessible-Trueif the metric is accessible. If API key is configured, c hecks the API plan subscriptions.Falseif the metric is not accessible. For examplecirculation_1drequiresPROplan subscription in order to be accessible at all.isRestricted-Trueif time restrictions apply to the metric and your current plan (Freeif no API key is configured). CheckrestrictedFromandrestrictedTo.restrictedFrom- The first datetime available of that metric for your current plan.restrictedTo- The last datetime available of that metric and your current plan.
Multiple queries can be executed in a batch to speed up the performance.
There are two batch classes provided - Batch and AsyncBatch.
Note: Batching improves the performance and the developer experience, but every query put inside the batch is still counted as one separate API call. To fetch a metric for multiple assets at a time take a look at
san.get_many
-
AsyncBatchis the recommended batch class. It executes all the queries in separate HTTP requests. The benefit of usingAsyncBatchover looping and executing every API call is that the queries can be executed concurrently. Putting multiple API calls in separate HTTP calls also allows to fetch more data, otherwise you might run into Complexity issues. The concurrency is controlled by themax_workersoptional parameter to theexecutefunction. By default themax_workersvalue is 10. It also supportsget_manyfunction to fetch data for many assets. -
Batchcombines all the provided queries in a single GraphQL document and executes them in a single HTTP request. This batching technique should be used when lightweight queries that don't fetch a lot of data are used. The reason is that the complexity of each query is accumulated and the batch can be rejected.
Note: If you have been using Batch() and want to switch to the newer AsyncBatch() you only need to
change the batch initialization. The functions for adding queries and executing the batch, as well as the
format of the response, are the same.
from san import Batch
batch = Batch()
batch.get(
"daily_active_addresses",
slug="santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
batch.get(
"transaction_volume",
slug="santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
[daa, trx_volume] = batch.execute()from san import AsyncBatch
batch = AsyncBatch()
batch.get(
"daily_active_addresses",
slug="santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
batch.get_many(
"daily_active_addresses",
slugs=["bitcoin", "ethereum"],
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
[daa, daa_many] = batch.execute(max_workers=10)There are two functions, which can help you in handling the rate limits:
is_rate_limit_exception- Returns whether the exception caught is because of rate limitationrate_limit_time_left- Returns the time left before the rate limit expiresapi_calls_made- Returns the API calls for each day in which it was usedapi_calls_remaining- Returns the API calls remaining for the month, hour and minute
Example:
import time
import san
try:
san.get(
"price_usd",
slug="santiment",
from_date="utc_now-30d",
to_date="utc_now",
interval="1d"
)
except Exception as e:
if san.is_rate_limit_exception(e):
rate_limit_seconds = san.rate_limit_time_left(e)
print(f"Will sleep for {rate_limit_seconds}")
time.sleep(rate_limit_seconds)
...
calls_by_day = san.api_calls_made()
calls_remaining = san.api_calls_remaining()Fetch the complexity of a metric. The complexity depends on the from/to/interval parameters, as well as the metric and the subscription plan. A request might have a maximum complexity of 50000. If a request has a higher complexity there are a few ways to solve the issue:
- Break down the request into multiple requests with smaller from-to ranges.
- Upgrade to a higher subscription plan.
More about the complexity can be found on Santiment Academy
san.metric_complexity(
metric="price_usd",
from_date="2020-01-01",
to_date="2020-02-20",
interval="1d"
)Daily metrics have one value per day. For the current day, the latest computed
value will not include a full day of data. For example, computing
daily_active_addresses at 08:00 includes data for one third of the day. To
reduce confusion, the current day value for metrics that have this behaviour is
excluded. To force fetching the current day value, the includeIncompleteData
flag must be used.
san.get(
"daily_active_addresses/bitcoin",
from_date="utc_now-3d",
to_date="utc_now",
interval="1d",
include_incomplete_data=True
)Fetch the first datetime for which a metric is available for a given slug.
san.available_metric_for_slug_since(metric="daily_active_addresses", slug="santiment")Example usage:
san.get(
"price_usd",
slug="santiment",
from_date="2020-06-01",
to_date="2021-06-05",
interval="1d",
transform={"type": "moving_average", "moving_average_base": 100},
aggregation="LAST"
)Where the parameters, that are not mentioned, are optional:
transform - Apply a transformation on the data. The supported transformations are:
- "moving_average" - Replace every value Vi with the average of the last "moving_average_base" values.
- "consecutive_differences" - Replace every value Vi with the value Vi - Vi-1 where i is the position in the list. Automatically fetches some extra data needed in order to compute the first value.
- "percent_change" - Replace every value Vi with the percent change of Vi-1 and Vi ( (Vi / Vi-1 - 1) * 100) where i is the position in the list. Automatically fetches some extra data needed in order to compute the first value.
aggregation - the aggregation which is used for the query results.
Returns a DataFrame with all the projects available in the Santiment API. Not all metrics will be available for each of the projects.
slug is the unique identifier of a project, used in the metrics fetching.
san.get("projects/all")Example result:
name slug ticker totalSupply
0 0chain 0chain ZCN 400000000
1 0x 0x ZRX 1000000000
2 0xBitcoin 0xbtc 0xBTC 20999984
3 0xcert Protocol 0xcert ZXC 500000000
4 1World 1world 1WO 37219453
5 AB-Chain RTB ab-chain-rtb RTB 27857813
6 Abulaba abulaba AAA 397000000
7 AC3 ac3 AC3 80235326.0
...
Here is a list of metrics that are not part of the returned list of metrics found above. This is due to having different response format and semantics.
san.get(
"prices",
slug="santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)Notes:
- This query cannot be batched.
- The format with separate
slug/selectorargument is not supported
san.get(
"ohlcv/santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)Example result:
datetime openPriceUsd closePriceUsd highPriceUsd lowPriceUsd volume marketcap
2018-06-01 00:00:00+00:00 1.24380 1.27668 1.26599 1.19099 852857 7.736268e+07
2018-06-02 00:00:00+00:00 1.26136 1.30779 1.27612 1.20958 1242520 7.864724e+07
2018-06-03 00:00:00+00:00 1.28270 1.28357 1.24625 1.21872 1032910 7.844339e+07
2018-06-04 00:00:00+00:00 1.23276 1.24910 1.18528 1.18010 617451 7.604326e+07Historical balance for erc20 token or eth address. Returns the historical balance for a given address in the given interval.
san.get(
"historical_balance",
slug="santiment",
address="0x1f3df0b8390bb8e9e322972c5e75583e87608ec2",
from_date="2019-04-18",
to_date="2019-04-23",
interval="1d"
)Example result:
datetime balance
2019-04-18 00:00:00+00:00 382338.33
2019-04-19 00:00:00+00:00 382338.33
2019-04-20 00:00:00+00:00 382338.33
2019-04-21 00:00:00+00:00 215664.33
2019-04-22 00:00:00+00:00 215664.33
Top ETH transactions for project's team wallets.
Available transaction types:
- ALL
- IN
- OUT
san.get(
"eth_top_transactions",
slug="santiment",
from_date="2019-04-18",
to_date="2019-04-30",
limit=5,
transaction_type="ALL"
)Example result:
The result is shortened for convenience
datetime fromAddress fromAddressInExchange toAddress toAddressInExchange trxHash trxValue
2019-04-29 21:33:31+00:00 0xe76fe52a251c8f... False 0x45d6275d9496b... False 0x776cd57382456a... 100.00
2019-04-29 21:21:18+00:00 0xe76fe52a251c8f... False 0x468bdccdc334f... False 0x848414fb5c382f... 40.95
2019-04-19 14:14:52+00:00 0x1f3df0b8390bb8... False 0xd69bc0585e05e... False 0x590512e1f1fbcf... 19.48
2019-04-19 14:09:58+00:00 0x1f3df0b8390bb8... False 0x723fb5c14eaff... False 0x78e0720b9e72d1... 15.15
ETH spent for each interval from the project's team wallet and time period
san.get(
"eth_spent_over_time",
slug="santiment",
from_date="2019-04-18",
to_date="2019-04-23",
interval="1d"
)Example result:
datetime ethSpent
2019-04-18 00:00:00+00:00 0.000000
2019-04-19 00:00:00+00:00 34.630284
2019-04-20 00:00:00+00:00 0.000000
2019-04-21 00:00:00+00:00 0.000158
2019-04-22 00:00:00+00:00 0.000000
Top transactions for the token of a given project
san.get(
"token_top_transactions",
slug="santiment",
from_date="2019-04-18",
to_date="2019-04-30",
limit=5
)Example result:
The result is shortened for convenience
datetime fromAddress fromAddressInExchange toAddress toAddressInExchange trxHash trxValue
2019-04-21 13:51:59+00:00 0x1f3df0b8390bb8... False 0x5eaae5e949952... False 0xdbced935b09dd0... 166674.00000
2019-04-28 07:43:38+00:00 0x0a920bfdf7f977... False 0x868074aab18ea... False 0x5f2214d34bcdc3... 33181.82279
2019-04-28 07:53:32+00:00 0x868074aab18ea3... False 0x876eabf441b2e... True 0x90bd286da38a2b... 33181.82279
2019-04-26 14:38:45+00:00 0x876eabf441b2ee... True 0x76af586d041d6... False 0xe45b86f415e930... 28999.64023
2019-04-30 15:17:28+00:00 0x876eabf441b2ee... True 0x1f4a90043cf2d... False 0xc85892b9ef8c64... 20544.42975
Top transfers for the token of a given project, address and transaction_type arguments can be added as well, in the form of a key-value pair. The transaction_type parameter can have one of these three values: ALL, OUT, IN.
san.get(
"top_transfers",
slug="santiment",
from_date="utc_now-30d",
to_date="utc_now",
)The result is shortened for convenience
Example result:
fromAddress toAddress trxHash trxValue
datetime
2021-06-17 00:16:26+00:00 0xa48df... 0x876ea... 0x62a56... 136114.069733
2021-06-17 00:10:05+00:00 0xbd3c2... 0x876ea... 0x732a5... 117339.779890
2021-06-19 21:36:03+00:00 0x59646... 0x0d45b... 0x5de31... 112336.882707
...
san.get(
"top_transfers",
slug="santiment",
address="0x26e068650ae54b6c1b149e1b926634b07e137b9f",
transaction_type="ALL",
from_date="utc_now-30d",
to_date="utc_now",
)Example result:
fromAddress toAddress trxHash trxValue
datetime
2021-06-13 09:14:01+00:00 0x26e06... 0xfd3d... 0x4af6... 69854.528
2021-06-13 09:13:01+00:00 0x876ea... 0x26e0... 0x18c1... 69854.528
2021-06-14 08:54:52+00:00 0x876ea... 0x26e0... 0xdceb... 59920.591
...
Emerging trends for a given period of time.
san.get(
"emerging_trends",
from_date="2019-07-01",
to_date="2019-07-02",
interval="1d",
size=5
)Example result:
datetime score word
2019-07-01 00:00:00+00:00 375.160034 lnbc
2019-07-01 00:00:00+00:00 355.323281 dent
2019-07-01 00:00:00+00:00 268.653820 link
2019-07-01 00:00:00+00:00 231.721809 shorts
2019-07-01 00:00:00+00:00 206.812798 btt
2019-07-02 00:00:00+00:00 209.343752 bounce
2019-07-02 00:00:00+00:00 135.412811 vidt
2019-07-02 00:00:00+00:00 116.842801 bat
2019-07-02 00:00:00+00:00 98.517600 bottom
2019-07-02 00:00:00+00:00 89.309975 haiku
Take a look at the examples folder.
It is recommended to use pipenv for managing your local environment.
Setup project:
pipenv installInstall main dependencies:
pipenv run pip install -e .Install dev dependencies:
pipenv run pip install -e '.[dev]'Install extra dependencies:
pipenv run pip install -e '.[extras]'Running tests:
pipenv run pytestRunning integration tests:
pipenv run pytest -m integrationpytestpytest -m integrationpip install '.[dev]'or just
pip install ruffruff check