One possible solution to this issue is to explicitly define the data types of each column using dtype
parameter in read_sql
query. By doing so, you can avoid any data type inference errors and make sure that the columns are correctly casted to the desired data type.
For example, let's say you have a table named mytable
with two columns id
and name
. You can read this table using the following query:
import polars as pl
import pandas as pd
import sqlalchemy
engine = sqlalchemy.create_engine('postgresql://user:password@host:port/database')
query = 'SELECT * FROM mytable'
df = pd.read_sql_query(query, engine)
pl_df = pl.from_pandas(df, None)
If you face an error related to binary data, you can add a dtype
parameter to the read_sql_query
function to explicitly define the data types of each column, like this:
import polars as pl
import pandas as pd
import sqlalchemy
engine = sqlalchemy.create_engine('postgresql://user:password@host:port/database')
query = 'SELECT * FROM mytable'
dtype = {'id': pl.Int64, 'name': pl.Utf8}
df = pd.read_sql_query(query, engine, dtype=dtype)
pl_df = pl.from_pandas(df, None)
In this example, we have explicitly defined the data types of id
column as Int64
and name
column as Utf8
. This will ensure that the columns are correctly casted and any binary data is avoided.
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Asked: 2023-06-18 22:08:00 +0000
Seen: 15 times
Last updated: Jun 18 '23
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