How to create a large pandas dataframe from an sql query without running out of memory?

As mentioned in a comment, starting from pandas 0.15, you have a chunksize option in read_sql to read and process the query chunk by chunk:

sql = "SELECT * FROM My_Table"
for chunk in pd.read_sql_query(sql , engine, chunksize=5):
    print(chunk)

Reference: http://pandas.pydata.org/pandas-docs/version/0.15.2/io.html#querying

Update: Make sure to check out the answer below, as Pandas now has built-in support for chunked loading.

You could simply try to read the input table chunk-wise and assemble your full dataframe from the individual pieces afterwards, like this:

import pandas as pd
import pandas.io.sql as psql
chunk_size = 10000
offset = 0
dfs = []
while True:
  sql = "SELECT * FROM MyTable limit %d offset %d order by ID" % (chunk_size,offset) 
  dfs.append(psql.read_frame(sql, cnxn))
  offset += chunk_size
  if len(dfs[-1]) < chunk_size:
    break
full_df = pd.concat(dfs)

It might also be possible that the whole dataframe is simply too large to fit in memory, in that case you will have no other option than to restrict the number of rows or columns you're selecting.

Code solution and remarks.

# Create empty list
dfl = []  

# Create empty dataframe
dfs = pd.DataFrame()  

# Start Chunking
for chunk in pd.read_sql(query, con=conct, ,chunksize=10000000):

    # Start Appending Data Chunks from SQL Result set into List
    dfl.append(chunk)

# Start appending data from list to dataframe
dfs = pd.concat(dfl, ignore_index=True)

However, my memory analysis tells me that even though the memory is released after each chunk is extracted, the list is growing bigger and bigger and occupying that memory resulting in a net net no gain on free RAM.

Would love to hear what the author / others have to say.