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A python package to query data via amazon athena and bring it into a pandas df using aws-wrangler.

Project description


A package that is used to run SQL queries speficially configured for the Analytical Platform. This packages uses AWS Wrangler's Athena module but adds additional functionality (like Jinja templating, creating temporary tables) and alters some configuration to our specification.


Requires a pip release above 20.

## To install from pypi
pip install pydbtools

## Or install from git with a specific release
pip install "pydbtools @ git+"

Quickstart guide

The examples directory contains more detailed notebooks demonstrating the use of this library, many of which are borrowed from the mojap-aws-tools-demo repo.

Read an SQL Athena query into a pandas dataframe

import pydbtools as pydb
df = pydb.read_sql_query("SELECT * from a_database.table LIMIT 10")

Run a query in Athena

response = pydb.start_query_execution_and_wait("CREATE DATABASE IF NOT EXISTS my_test_database")

Create a temporary table to do further separate SQL queries on later

pydb.create_temp_table("SELECT a_col, count(*) as n FROM a_database.table GROUP BY a_col", table_name="temp_table_1")
df = pydb.read_sql_query("SELECT * FROM __temp__.temp_table_1 WHERE n < 10")

pydb.dataframe_to_temp_table(my_dataframe, "my_table")
df = pydb.read_sql_query("select * from __temp__.my_table where year = 2022")


This package is a wrapper for awswrangler that which presets/defines some of the input parameters to the athena module functions to align with our platform setup. See the awswrangler API reference documentation for Athena to see what functions you can call from pydbtools.

The function parameters that are locked down / altered by pydbtools are:

  • boto3_session: This is auto generated by pydbtools (in order to grab the user credentials from the sts client - this is needed for the R version of this package which calls this package under the hood. In short forcing refreshed credentials are needed in R as boto3 credentials timeout and do not refresh when using reticulate, though this does not apply to the latest version of the platform currently being rolled out.)
  • s3_output: The S3 path where database queries are written to. This is defined by pydbtools based on the IAM user/role calling the query (ensures that each role can only read/write to a S3 path only they can access).
  • database: Will either be set to None or __temp__ depending on other user parameters (if ctas_approach=True). __temp__ is an alias to an autogenerated temp database name which is generated from pydbtools again based on the IAM user/role. References to this temporary database can be referenced by the keyword __temp__ in SQL queries see additional functionality to awswrangler section.
  • sql: We allows reference to the database name __temp__ which is an alias to a user specific temporary database. When a function call has an SQL parameter the SQL is checked with an SQL parser and then any reference to __temp__ as a database is replaced with the actual database name which is autogenerated. This replacement only occurs for SELECT queries.
  • pyarrow_additional_kwargs: This is set to {"coerce_int96_timestamp_unit": "ms", "timestamp_as_object": True} by default. Doing this solves this awswrangler issue)

Additional Functionality

As well as acting as a wrapper function for awswrangler this package also allows you to do the following:

Run query and wait for a response

This function essentially calls two functions from awswrangler.athena. First start_query_execution followed by wait_query.

import pydbtools as pydb

response = pydb.start_query_execution_and_wait("SELECT * from a_database.table LIMIT 10")

Create Temporary Tables

You can use the create_temp_table function to write SQL to create a store a temporary table that sits in your __temp__ database.

import pydbtools as pydb

pydb.create_temp_table("SELECT * from a_database.table LIMIT 10", table_name="temp_table_1")
df = pydb.read_sql_query("SELECT * from __temp__.temp_table_1")

See the example notebook for a more detailed example.

Run SQL from a string of statements or a file

It wil often be more convenient to write your SQL in an editor with language support rather than as a Python string. You can create temporary tables within SQL using the syntax below.

import pydbtools as pydb

sql = """
create temp table A as (
    select * from database.table1
    where year = 2021

create temp table B as (
    select * from database.table2
    where amount > 10

select * from __temp__.A
left join __temp__.B
on =;

with open("queries.sql", "w") as f:
with open("queries.sql", "r") as f:
    df = pydb.read_sql_queries(

Multiple SELECT queries can be returned as a generator of dataframes using read_sql_queries_gen.

See the notebook on creating temporary tables with SQL and the notebook on database administration with SQL for more detailed examples.

Additionally you can use Jinja templating to inject arguments into your SQL.

sql_template = """
FROM {{ db_name }}.{{ table }}
sql = pydb.render_sql_template(sql_template, {"db_name": db_name, "table": "department"})

with open("tempfile.sql", "w") as f:
    f.write("SELECT * FROM {{ db_name }}.{{ table_name }}")
sql = pydb.get_sql_from_file("tempfile.sql", jinja_args={"db_name": db_name, "table_name": "department"})

See the notebook on SQL templating for more details.

Delete databases, tables and partitions together with the data on S3

import pydbtools as pydb

pydb.delete_partitions_and_data(database='my_database', table='my_table', expression='year = 2020 or year = 2021')
pydb.delete_table_and_data(database='my_database', table='my_table')

# These can be used for temporary databases and tables.
pydb.delete_table_and_data(database='__temp__', table='my_temp_table')

For more details see the notebook on deletions.

Usage / Examples


import pydbtools as pydb

# Run a query using pydbtools
response = pydb.start_query_execution_and_wait("CREATE DATABASE IF NOT EXISTS my_test_database")

# Read data from an athena query directly into pandas
pydb.read_sql("SELECT * from a_database.table LIMIT 10")

# Create a temp table to do further seperate SQL queries later on
pydb.create_temp_table("SELECT a_col, count(*) as n FROM a_database.table GROUP BY a_col", table_name="temp_table_1")
df = pydb.read_sql_query("SELECT * FROM __temp__.temp_table_1 WHERE n < 10")

More advanced usage

Get the actual name for your temp database, create your temp db then delete it using awswrangler (note: awswrangler will raise an error if the database does not exist)

import awswrangler as wr
import pydbtools as pydb

user_id, out_path = pydb.get_user_id_and_table_dir()
temp_db_name = pydb.get_database_name_from_userid(user_id)



The functions:

  • pydbtools.get_athena_query_response
  • pydbtools.read_sql

Are now deprecated and calls to these functions will raise an warning. They have been replaced by pydbtools.start_query_execution_and_wait and pydbtools.read_sql_query.

Docs for versions below v3.0.0

This is a simple package that let's you query databases using Amazon Athena and get the s3 path to the athena out (as a csv). This is significantly faster than using the the database drivers so might be a good option when pulling in large data. By default, data is converted into a pandas dataframe with equivalent column data types as the Athena table - see "Meta Data" section below.

Note to use this package you need to be added to the StandardDatabaseAccess IAM Policy on the Analytical Platform. Please contact the team if you require access.

To install...

pip install pydbtools

Or from github...

pip install git+git://

package requirements are:

  • pandas (preinstalled)
  • boto3 (preinstalled)
  • numpy (preinstalled)
  • s3fs
  • gluejobutils


Most simple way to use pydbtools. This will return a pandas df reprentation of the data (with matching meta data).

import pydbtools as pydb

# Run SQL query and return as a pandas df
df = pydb.read_sql("SELECT * from database.table limit 10000")

You might want to cast the data yourself or read all the columns as strings.

import pydbtools as pydb

# Run SQL query and return as a pandas df
df = pydb.read_sql("SELECT * from database.table limit 10000", cols_as_str=True)

df.dtypes # all objects

You can also pass additional arguments to the pandas.read_csv that reads the resulting Athena SQL query. Note you cannot pass dtype as this is specified within the read_sql function.

import pydbtools as pydb

# pass nrows parameter to pandas.read_csv function
pydb.read_sql("SELECT * from database.table limit 10000", nrows=20)

If you didn't want to read the data into pandas you can run the SQL query and get the s3 path and meta data of the output using the get_athena_query_response. The data is then read in using boto3, io and csv.

import pydbtools as pydb
import io
import csv
import boto3

response = pydb.get_athena_query_response("SELECT * from database.table limit 10000")

# print out path to athena query output (as a csv)

# print out meta data

# Read the csv into a string in memory
s3_resource = boto3.resource('s3')
bucket, key = response['s3_path'].replace("s3://", "").split('/', 1)
obj = s3_resource.Object(bucket, key)
text = obj.get()['Body'].read().decode('utf-8')

# Use csv reader to print the outputting csv
reader = csv.reader(text.split('\n'), delimiter=',')
for row in reader:

Meta data

The output from get_athena_query_response(...) is a dictionary one of it's keys is meta. The meta key is a list where each element in this list is the name (name) and data type (type) for each column in your athena query output. For example for this table output:

col1 col2
1 2018-01-01
2 2018-01-02

Would have a meta like:

for m in response['meta']:
    print(m['name'], m['type'])


> col1 int
> col1 date

The meta types follow those listed as the generic meta data types used in etl_manager. If you want the actual athena meta data instead you can get them instead of the generic meta data types by setting the return_athena_types input parameter to True e.g.

response = pydb.get_athena_query_response("SELECT * from database.table limit 10000", return_athena_types=True)


If you wish to read your SQL query directly into a pandas dataframe you can use the read_sql function. You can apply *args or **kwargs into this function which are passed down to pd.read_csv().

import pydbtools as pydb

df = pydb.read_sql("SELECT * FROM database.table limit 1000")

Meta data conversion

Below is a table that explains what the conversion is from our data types to a pandas df (using the read_sql function):

data type pandas column type Comment
character object see here
int np.float64 Pandas integers do not allow nulls so using floats
long np.float64 Pandas integers do not allow nulls so using floats
date pandas timestamp
datetime pandas timestamp
boolean np.bool
float np.float64
double np.float64
decimal np.float64

Unit tests

Unit tests run in unittest through Poetry. Run poetry run python -m unittest to activate them. If you've changed any dependencies, run poetry update first.

The tests run against a test Glue database callled dbtools. They use data stored on s3 in alpha-dbtools-test-bucket.


  • Amazon Athena using a flavour of SQL called presto docs can be found here
  • To query a date column in Athena you need to specify that your value is a date e.g. SELECT * FROM db.table WHERE date_col > date '2018-12-31'
  • To query a datetime or timestamp column in Athena you need to specify that your value is a timestamp e.g. SELECT * FROM db.table WHERE datetime_col > timestamp '2018-12-31 23:59:59'
  • Note dates and datetimes formatting used above. See more specifics around date and datetimes here
  • To specify a string in the sql query always use '' not "". Using ""'s means that you are referencing a database, table or col, etc.
  • When data is pulled back into rStudio the column types are either R characters (for any col that was a dates, datetimes, characters) or doubles (for everything else).

See changelog for release changes

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