Working with Python Dataframes in Odoo
by Hafiz Junaid
When you are working with large numbers of
records in Odoo, it can be helpful to use Python dataframes to process and
analyse the data more efficiently. Here are some tips for working with
dataframes in Odoo:
1. Use the read_group method
The read_group method can be used to retrieve aggregated data from Odoo models in a single query. This can be especially useful when dealing with large numbers of records. You can then convert the result into pandas dataframe using the pd.DataFrame method.
2. Use the search method with chunking
If you need to retrieve a large number of
records, you can use the search method to retrieve the records in chunks
instead of all at once. This can help to reduce memory usage and improve
performance. You can do this by specifying the offset and limit parameters to
retrieve the records in batches.
3. Use the drop_duplicates method
If you have duplicate records in your data, you
can use the drop_duplicates method to remove them. This can help to reduce the
size of your dataframe and make it easier to work with.
4.
Use the apply method
The apply method can be used to apply a function
to each row or column of a dataframe. This can be useful for performing
calculations or transformations on your data.
5.
Use the merge method
The merge method can be used to combine data from multiple dataframes based on a common column. This can be useful for combining data from different models in Odoo.
By using these tips and techniques, you can work
more efficiently with large numbers of records in Odoo using Python dataframes.
Have a Look at the Example Script
Here's an example script for working with large
numbers of records in Odoo using Python dataframes:
from odoo import models, fields, api
import pandas as pd
class MyModel(models.Model):
_name = 'my.model'
@api.model
def process_data(self):
# Use read_group to retrieve aggregated data
data = self.read_group([], ['field1', 'field2', 'field3'], ['field1', 'field2'])
# Convert result into a pandas dataframe
df = pd.DataFrame(data)
# Use search with chunking to retrieve records
records = self.search([], offset=0, limit=1000)
while records:
# Process the records
df = df.append(records.read(), ignore_index=True)
# Get the next batch of records
offset += 1000
records = self.search([], offset=offset, limit=1000)
# Use drop_duplicates to remove duplicates
df = df.drop_duplicates()
# Use apply to perform calculations on data
df['field4'] = df.apply(lambda row: row['field1'] + row['field2'], axis=1)
# Use merge to combine data from multiple dataframes
other_data = pd.DataFrame(self.env['other.model'].search([]).read())
merged_data = pd.merge(df, other_data, on='common_field')
# Use write to save the data back to Odoo
for index, row in merged_data.iterrows():
record = self.search([('field1', '=', row['field1'])])
record.write({'field2': row['field2'], 'field3': row['field3'], 'field4': row['field4']})
In the above script, the process_data method uses the read_group method to retrieve aggregated data from the model. It then uses the search method with chunking to retrieve the records in batches and process them. The drop_duplicates method is used to remove duplicates, and the apply method is used to perform calculations on the data. The merge method is used to combine data from multiple dataframes, and the write method is used to save the data back to Odoo.
Note that this is just an example and may need to be modified to fit your specific use case. Additionally, you may want to consider using other libraries or techniques such as lazy loading or database indexing to further optimise your code for large data sets.