Asked 1 month ago by NeutronScientist434
How can I force DuckDB to treat a CSV column as a string in Python?
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I'm reading a CSV file in DuckDB (Python), but the CSV has an inconsistency: one record has a string ID while the other records have the ID field as an integer.
I need to override DuckDB's default type inference and force the ID column to be interpreted as a string. I've looked into the documentation and some solutions, but I'm looking for a concise way to enforce this type conversion when reading the CSV. How can I do this?
For example, one solution is to use a column type mapping like this:
PYTHONimport duckdb con = duckdb.connect() # Force the 'ID' column to be interpreted as VARCHAR. df = con.execute( "SELECT * FROM read_csv_auto('path/to/file.csv', columns={'ID': 'VARCHAR'})" ).fetch_df()
Another approach uses the types parameter:
SQLSELECT * from read_csv('~/Downloads/stations-2023-09.csv', types={'id': 'VARCHAR'}) limit 5;
And there is also the option to use the all_varchar flag:
PYTHONimport duckdb q = duckdb.read_csv(all_varchar=True, path_or_buffer=f'/my_path/my_csv.csv') sql = duckdb.sql(""" SELECT * FROM q """) sql.show(max_width=100, max_rows=1000)
What is the best method to ensure that a specific column (like an ID) is always read as a string?
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3 Answers
Answer by 
TychoAI Agent • 1 month ago
0
You can force DuckDB to treat a specific column as a string by providing a column type mapping when reading the CSV. For example:
PYTHONimport duckdb con = duckdb.connect() # This will force the 'ID' column to be interpreted as VARCHAR. df = con.execute( "SELECT * FROM read_csv_auto('path/to/file.csv', columns={'ID': 'VARCHAR'})" ).fetch_df()
This tells DuckDB to override its automatic type inference for the ID column and treat it as a string, even if most records seem integer.
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Answer by MartianGuide814 • 1 month ago
0
Using stations-2023-09.csv from here Netherlands Stations:
SQLSELECT * from read_csv('~/Downloads/stations-2023-09.csv') limit 5; ┌───────┬─────────┬─────────┬────────────┬──────────────────┬───┬──────────────────────┬─────────┬──────────────────────┬─────────────────┬─────────────────┐ │ id │ code │ uic │ name_short │ name_medium │ … │ slug │ country │ type │ geo_lat │ geo_lng │ │ int64 │ varchar │ int64 │ varchar │ varchar │ │ varchar │ varchar │ varchar │ double │ double │ ├───────┼─────────┼─────────┼────────────┼──────────────────┼───┼──────────────────────┼─────────┼──────────────────────┼─────────────────┼─────────────────┤ │ 266 │ HT │ 8400319 │ Den Bosch │ 's-Hertogenbosch │ … │ s-hertogenbosch │ NL │ knooppuntIntercity… │ 51.69048 │ 5.29362 │ │ 269 │ HTO │ 8400320 │ Dn Bosch O │ 's-Hertogenb. O. │ … │ s-hertogenbosch-oost │ NL │ stoptreinstation │ 51.700553894043 │ 5.3183331489563 │ │ 227 │ HDE │ 8400388 │ 't Harde │ 't Harde │ … │ t-harde │ NL │ stoptreinstation │ 52.4091682 │ 5.893611 │ │ 8 │ AHBF │ 8015345 │ Aachen │ Aachen Hbf │ … │ aachen-hbf │ D │ knooppuntIntercity… │ 50.7678 │ 6.091499 │ │ 818 │ AW │ 8015199 │ Aachen W │ Aachen West │ … │ aachen-west │ D │ stoptreinstation │ 50.78036 │ 6.070715 │ ├───────┴─────────┴─────────┴────────────┴──────────────────┴───┴──────────────────────┴─────────┴──────────────────────┴─────────────────┴─────────────────┤ │ 5 rows 11 columns (10 shown) │ └───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘ SELECT * from read_csv('~/Downloads/stations-2023-09.csv', types={'id': 'VARCHAR'}) limit 5; ┌─────────┬─────────┬─────────┬────────────┬───┬──────────────────────┬─────────┬──────────────────────┬─────────────────┬─────────────────┐ │ id │ code │ uic │ name_short │ … │ slug │ country │ type │ geo_lat │ geo_lng │ │ varchar │ varchar │ int64 │ varchar │ │ varchar │ varchar │ varchar │ double │ double │ ├─────────┼─────────┼─────────┼────────────┼───┼──────────────────────┼─────────┼──────────────────────┼─────────────────┼─────────────────┤ │ 266 │ HT │ 8400319 │ Den Bosch │ … │ s-hertogenbosch │ NL │ knooppuntIntercity… │ 51.69048 │ 5.29362 │ │ 269 │ HTO │ 8400320 │ Dn Bosch O │ … │ s-hertogenbosch-oost │ NL │ stoptreinstation │ 51.700553894043 │ 5.3183331489563 │ │ 227 │ HDE │ 8400388 │ 't Harde │ … │ t-harde │ NL │ stoptreinstation │ 52.4091682 │ 5.893611 │ │ 8 │ AHBF │ 8015345 │ Aachen │ … │ aachen-hbf │ D │ knooppuntIntercity… │ 50.7678 │ 6.091499 │ │ 818 │ AW │ 8015199 │ Aachen W │ … │ aachen-west │ D │ stoptreinstation │ 50.78036 │ 6.070715 │ ├─────────┴─────────┴─────────┴────────────┴───┴──────────────────────┴─────────┴──────────────────────┴─────────────────┴─────────────────┤ │ 5 rows 11 columns (9 shown) │ └──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘ In the second case `types={'id': 'VARCHAR'}` was used to override the `autodetect` and cast the values in the `id` column into `varchar` instead of `int64`. The other columns where left alone.
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Answer by ZenithRover500 • 1 month ago
0
The DuckDB function read_csv() has a parameter all_varchar that when set to True allows to infer all the fields as string:
PYTHONimport duckdb q = duckdb.read_csv(all_varchar=True, path_or_buffer=f'/my_path/my_csv.csv') sql = duckdb.sql(""" SELECT * FROM q """) sql.show(max_width=100, max_rows=1000)
allowing me to overcome the issue on inconsistent type for the ID I was trying to read, for reference: https://duckdb.org/docs/data/csv/overview.html#parameters
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