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I have a dataframe with the dtypes shown below and I want to insert the dataframe into a postgres DB but it fails due to error can't adapt type 'numpy.int64'

id_code               int64
sector              object
created_date         float64
updated_date    float64

How can I convert these types to native python types such as from int64 (which is essentially 'numpy.int64') to a classic int that would then be acceptable to postgres via the psycopg2 client.

data['id_code'].astype(np.int)  defaults to int64

It is nonetheless possible to convert from one numpy type to another (e.g from int to float)

data['id_code'].astype(float)

changes to

dtype: float64

The bottomline is that psycopg2 doesn't seem to understand numpy datatypes if any one has ideas how to convert them to classic types that would be helpful.

Updated: Insertion to DB

def insert_many():
    """Add data to the table."""
    sql_query = """INSERT INTO classification(
                id_code, sector, created_date, updated_date)
                VALUES (%s, %s, %s, %s);"""
    data = pd.read_excel(fh, sheet_name=sheetname)
    data_list = list(data.to_records())

    conn = None
    try:
        conn = psycopg2.connect(db)
        cur = conn.cursor()
        cur.executemany(sql_query, data_list)
        conn.commit()
        cur.close()
    except(Exception, psycopg2.DatabaseError) as error:
        print(error)
    finally:
        if conn is not None:
            conn.close()

Answers

I'm not sure why your data_list contains NumPy data types, but the same thing happens to me when I run your code. Here is an alternative way to construct data_list that so that integers and floats end up as their native python types:

data_list = [list(row) for row in data.itertuples(index=False)] 

Alternate approach

I think you could accomplish the same thing in fewer lines of code by using pandas to_sql:

import sqlalchemy
import pandas as pd
data = pd.read_excel(fh, sheet_name=sheetname)
engine = sqlalchemy.create_engine("postgresql://username@hostname/dbname")
data.to_sql(engine, 'classification', if_exists='append', index=False)