Connect Soda to Dask and Pandas (Experimental)
Last modified on 31-May-23
For use with programmatic Soda scans, only.
Define a programmatic scan for the data in the DataFrames. Refer to the following example.
Install package: soda-core-pandas-dask
import dask.datasets
import pandas as pd
from soda.scan import Scan
# Create a Soda scan object
scan = Scan()
scan.set_scan_definition_name("test")
scan.set_data_source_name("dask")
# Load timeseries data from dask datasets
df_timeseries = dask.datasets.timeseries().reset_index()
df_timeseries["email"] = "a@soda.io"
# Create an artificial pandas dataframe
df_employee = pd.DataFrame({"email": ["a@soda.io", "b@soda.io", "c@soda.io"]})
# Add Dask dataframe to scan and assign a dataset name to refer from checks yaml
scan.add_dask_dataframe(dataset_name="timeseries", dask_df=df_timeseries)
# Add Pandas dataframe to scan and assign a dataset name to refer from checks yaml
scan.add_pandas_dataframe(dataset_name="employee", pandas_df=df_employee)
# Define checks in yaml format
# Alternatively, you can refer to a yaml file using scan.add_sodacl_yaml_file(<filepath>)
checks = """
for each dataset T:
datasets:
- include %
checks:
- row_count > 0
profile columns:
columns:
- employee.%
checks for employee:
- values in (email) must exist in timeseries (email) # Error expected
- row_count same as timeseries # Error expected
checks for timeseries:
- avg_x_minus_y between -1 and 1:
avg_x_minus_y expression: AVG(x - y)
- failed rows:
samples limit: 50
fail condition: x >= 3
- schema:
name: Confirm that required columns are present
warn:
when required column missing: [x]
when forbidden column present: [email]
when wrong column type:
email: varchar
fail:
when required column missing:
- y
- invalid_count(email) = 0:
valid format: email
- valid_count(email) > 0:
valid format: email
"""
scan.add_sodacl_yaml_str(checks)
scan.set_verbose(True)
scan.execute()
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Last modified on 31-May-23