Progress bar for pandas.DataFrame.to_sql

I wanted to share a variant of the solution posted by miraculixx - that I had to alter for SQLAlchemy:

#these need to be customized - myDataFrame, myDBEngine, myDBTable

df=myDataFrame

def chunker(seq, size):
    return (seq[pos:pos + size] for pos in range(0, len(seq), size))

def insert_with_progress(df):
    con = myDBEngine.connect()
    chunksize = int(len(df) / 10)
    with tqdm(total=len(df)) as pbar:
        for i, cdf in enumerate(chunker(df, chunksize)):
            replace = "replace" if i == 0 else "append"
            cdf.to_sql(name="myDBTable", con=conn, if_exists=replace, index=False) 
            pbar.update(chunksize)
            tqdm._instances.clear()

insert_with_progress(df)

Unfortuantely DataFrame.to_sql does not provide a chunk-by-chunk callback, which is needed by tqdm to update its status. However, you can process the dataframe chunk by chunk:

import sqlite3
import pandas as pd
from tqdm import tqdm

DB_FILENAME='/tmp/test.sqlite'

def chunker(seq, size):
    # from http://stackoverflow.com/a/434328
    return (seq[pos:pos + size] for pos in range(0, len(seq), size))

def insert_with_progress(df, dbfile):
    con = sqlite3.connect(dbfile)
    chunksize = int(len(df) / 10) # 10%
    with tqdm(total=len(df)) as pbar:
        for i, cdf in enumerate(chunker(df, chunksize)):
            replace = "replace" if i == 0 else "append"
            cdf.to_sql(con=con, name="MLS", if_exists=replace, index=False)
            pbar.update(chunksize)
            
df = pd.DataFrame({'a': range(0,100000)})
insert_with_progress(df, DB_FILENAME)

Note I'm generating the DataFrame inline here for the sake of having a complete workable example without dependency.

The result is quite stunning: