Insert 10gb csv files into sql table via python

[mg24]

Hi Team,

I want to insert CSV Records into sql table via python,
my csv file size is 10 gb,
I found this code on google, Can you suggest better way if someone has done this.

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import pyodbc import pandas as pd import csv import time   # Define the SQL Server login and password sql_login = 'XXXX\\XXX-XXX-XXX' sql_password = 'xxxxxxx'   # Connect to the SQL Server database connection = pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server};SERVER=DESKTOP-GQK64O6;DATABASE=Customer;UID=' + sql_login + ';PWD=' + sql_password + ';') cursor = connection.cursor()   # Define the file path and chunk size file_path = 'abc.csv' chunk_size = 1000   # Start the timer start_time = time.time()   # Create a pandas dataframe iterator to read the CSV file in chunks csv_iterator = pd.read_csv(file_path, chunksize=chunk_size, sep="|")   # Loop through the dataframe iterator for chunk in csv_iterator:     # Get the field names for the chunk     field_names = list(chunk.columns)       # Concatenate the field names into a string     field_names_concatenated = ",".join(field_names)       # Create a list of values for the chunk     values = chunk.values.tolist()       # Create a string of percents based on the number of values in each row     percents_concatenated = ",".join(len(field_names) * ["?"])       # Create the SQL statement     sql = f"INSERT INTO employee ({field_names_concatenated}) VALUES ({percents_concatenated})"       # Execute the SQL statement for the chunk     cursor.executemany(sql, values)     connection.commit()       print(cursor.rowcount, "details inserted")   # Calculate the elapsed time elapsed_time = time.time() - start_time print(f"Elapsed time: {elapsed_time:.2f} seconds")

[Larz60+]

You can load CSV file from pandas directly to the database.
the csv data may need to be 'normalized', see https://python-forum.io/thread-38263-pos...#pid161978
This uses sqllite model, which is constructed like example here: https://python-forum.io/thread-24127.html
search for 'Database Model'

Once you have a model you can load table something like this:

1 2	df = pd.read_csv(fn) df.to_sql(tablename, con=self.Model.engine, if_exists='replace')

Above code was taken from tutorial mentioned below:
DB model can easily be constructed by hand, or if you want to get fancy, the tutorial below shows how to automate everythng from model generation to database load.

see tutorial here if interested.

[snippsat]

my csv file size is 10 gb,

For data this big is Dask or Polars better.
Dask DataFrame copies the pandas DataFrame API,so it will work the same as Pandas

Example with timing.

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# pip install "dask[complete]"   import time from dask import dataframe as dd   start = time.time() df = dd.read_csv('large.csv') end = time.time() print(f"Total Time: {(end-start)} sec")

Just bye doing this so will Dask do a lot,eg a medium size .csv 230 mb,so dos Dask read it in 0.01-sec and Pandas read it in 6.2 sec.
Dask utilizes multiple CPU cores by internally chunking dataframe and process in parallel.
Example want to import 10 GB data in your eg 6 GB RAM or more RAM.
This can’t be achieved via Pandas since whole data in a single shot doesn’t fit into memory(without chunking up),but Dask can.
Dask instead of computing first, create a graph of tasks which says about how to perform that task.
It's lazy computation which means that Dask’s task scheduler creating a graph at first followed by computing that graph when requested.