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A selection of tools for easier processing of data using Pandas and AWS

Project description

Dativa Tools

Provides useful libraries for processing large data sets. Developed by the team at www.dativa.com as we find them useful in our projects.

The key libraries included here are:

  • dativa.tools.aws.S3Csv2Parquet - an AWS Glue based tool to transform CSV files to Parquet files
  • dativa.tools.aws.AthenaClient - provide a simple wrapper to execute Athena queries and create tables. When combined with the S3Csv2Parquet handler can automatically change Athena outputs to Parquet format
  • dativa.tools.aws.S3Client - a wrapper for AWS's boto library for S2 enabling easier iteration over S3 files and multiple deletions, as well as uploading multiple files
  • dativa.tools.SQLClient - a wrapper for any PEP249 compliant database client with logging and splitting of queries
  • dativa.tools.pandas.CSVHandler - improved CSV handling for Pandas
  • dativa.tools.pandas.ParquetHandler - improved Parquet handling for pandas

There are also some useful support functions for Pandas date and time handling.

Installation

pip install dativatools

Description

dativa.tools.aws.AthenaClient

An easy to use client for AWS Athena that will create tables from S3 buckets (using AWS Glue) and run queries against these tables. It support full customisation of SerDe and column names on table creation.

Examples:

Creating tables

The library creates a temporary Glue crawler which is deleted after use, and will also create the database if it does not exist.

from dativa.tools.aws import AthenaClient
ac = AthenaClient("us-east-1", "my_athena_db")
ac.create_table(table_name='my_first_table',
                crawler_target={'S3Targets': [
                    {'Path': 's3://my-bucket/table-data'}]}
                )

# Create a table with a custom SerDe and column names, typical for CSV files
ac.create_table(table_name='comcast_visio_match',
                crawler_target={'S3Targets': [
                    {'Path': 's3://my-bucket/table-data-2', 'Exclusions': ['**._manifest']}]},
                serde='org.apache.hadoop.hive.serde2.OpenCSVSerde',
                columns=[{'Name': 'id', 'Type': 'string'}, {
                    'Name': 'device_id', 'Type': 'string'}, {'Name': 'subscriber_id', 'Type': 'string'}]
                )

Running queries

from dativa.tools.aws import AthenaClient

ac = AthenaClient("us-east-1", "my_athena_db")
ac.add_query(sql="select * from table",
                 name="My first query",
                 output_location= "s3://my-bucket/query-location/")

ac.wait_for_completion()

Fetch results of query

from dativa.tools.aws import AthenaClient

ac = AthenaClient("us-east-1", "my_athena_db")
query = ac.add_query(sql="select * from table",
                     name="My first query",
                     output_location= "s3://my-bucket/query-location/")

ac.wait_for_completion()
ac.get_query_result(query)

Running queries with the output in Parquet and create an Athena table

from dativa.tools.aws import AthenaClient, S3Csv2Parquet

scp = S3Csv2Parquet(region="us-east-1",
                    template_location="s3://my-bucket/glue-template-path/")
ac = AthenaClient("us-east-1", "my_athena_db", s3_parquet=scp)
ac.add_query(sql="select * from table",
                 name="my query that outputs Parquet",
                 output_location="s3://my-bucket/query-location/",
                 parquet=True)

ac.wait_for_completion()

ac.create_table({'S3Targets': [{'Path': "s3://my-bucket/query-location/"}]},
                                        table_name="query_location")

dativa.tools.aws.S3Client

An easy to use client for AWS S3 that copies data to S3. Examples:

Batch deleting of files on S3

from dativa.tools.aws import S3Client

# Delete all files in a folder
s3 = S3Client()
s3.delete_files(bucket="bucket_name", prefix="/delete-this-folder/")

# Delete only .csv.metadata files in a folder
s3 = S3Client()
s3.delete_files(bucket="bucket_name", prefix="/delete-this-folder/", suffix=".csv.metadata")

Copy files from folder in local filesystem to s3 bucket

from dativa.tools.aws import S3Client

s3 = S3Client()
s3.put_folder(source="/home/user/my_folder", bucket="bucket_name", destination="backup/files")

# Copy all csv files from folder to s3
s3.put_folder(source="/home/user/my_folder", bucket="bucket_name", destination="backup/files", file_format="*.csv")

dativa.tools.SQLClient

A SQL client that wraps any PEP249 compliant connection object and provides detailed logging and simple query execution. In provides the following methods:

execute_query

Runs a query and ignores any output

Parameters:

  • query - the query to run, either a SQL file or a SQL query
  • parameters - a dict of parameters to substitute in the query
  • replace - a dict or items to be replaced in the SQL text
  • first_to_run - the index of the first query in a mult-command query to be executed

execute_query_to_df

Runs a query and returns the output of the final statement in a DataFrame.

Parameters:

  • query - the query to run, either a SQL file or a SQL query
  • parameters - a dict of parameters to substitute in the query
  • replace - a dict or items to be replaced in the SQL text

def execute_query_to_csv

Runs a query and writes the output of the final statement to a CSV file.

Parameters:

  • query - the query to run, either a SQL file or a SQL query
  • csvfile - the file name to save the query results to
  • parameters - a dict of parameters to substitute in the query
  • replace - a dict or items to be replaced in the SQL text

Example code

import os
import psycopg2
from dativa.tools import SqlClient

# set up the SQL client from environment variables
sql = SqlClient(psycopg2.connect(
    database=os.environ["DB_NAME"],
    user=os.environ["USER"],
    password=os.environ["PASSWORD"],
    host=os.environ["HOST"],
    port=os.environ["PORT"],
    client_encoding="UTF-8",
    connect_timeout=10))

# create the full schedule table
df = sql.execute_query_to_df(query="sql/my_query.sql",
                             parameters={"start_date": "2018-01-01",
                                         "end_date": "2018-02-01"})

dativa.tools.log_to_stdout

A convenience function to redirect a specific logger and its children to stdout

import logging
from dativa.tools import log_to_stdout

log_to_stdout("dativa.tools", logging.DEBUG)

dativa.tools.pandas.CSVHandler

A wrapper for pandas CSV handling to read and write DataFrames with consistent CSV parameters by sniffing the parameters automatically. Includes reading a CSV into a DataFrame, and writing it out to a string. Files can be read/written from/to local file system or AWS S3.

For S3 access suitable credentials should be available in '~/.aws/credentials' or the AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY environment variables.

CSVHandler

  • base_path - the base path for any CSV file read, defaults to ""
  • detect_parameters - whether the encoding of the CSV file should be automatically detected, defaults to False
  • csv_encoding - the encoding of the CSV files, defaults to UTF-8
  • csv_delimiter - the delimeter used in the CSV, defaults to ','
  • csv_header - the index of the header row, or -1 if there is no header
  • csv_skiprows - the number of rows at the beginning of file to skip
  • csv_quotechar - the quoting character to use, defaults to "

load_df

Opens a CSV file using the specified configuration for the class and raises an exception if the encoding is unparseable. Detects if base_path is an S3 location and loads data from there if required.

Parameters:

  • file - File path. Should begin with 's3://' to load from S3 location.
  • force_dtype - Force data type for data or columns, defaults to None

Returns:

  • dataframe

save_df

Writes a formatted string from a dataframe using the specified configuration for the class the file. Detects if base_path is an S3 location and saves data there if required.

Parameters:

  • df - Dataframe to save
  • file - File path. Should begin with 's3://' to save to an S3 location.

df_to_string

Returns a formatted string from a dataframe using the specified configuration for the class.

Parameters:

  • df - Dataframe to convert to string

Returns:

  • string

Example code

from dativa.tools.pandas import CSVHandler

# Create the CSV handler
csv = CSVHandler(base_path='s3://my-bucket-name/')

# Load a file
df = csv.load_df('my-file-name.csv')

# Create a string
str_df = csv.df_to_string(df)

# Save a file
csv.save_df(df, 'another-path/another-file-name.csv')

Support functions for Pandas

  • dativa.tools.pandas.is_numeric - a function to check whether a series or string is numeric
  • dativa.tools.pandas.string_to_datetime - a function to convert a string, or series of strings to a datetime, with a strptime date format that supports nanoseconds
  • dativa.tools.pandas.datetime_to_string - a function to convert a datetime, or a series of datetimes to a string, with a strptime date format that supports nanoseconds
  • dativa.tools.pandas.format_string_is_valid - a function to confirm whether a strptime format string returns a date
  • dativa.tools.pandas.get_column_name - a function to return the name of a column from a passed column name or index.
  • dativa.tools.pandas.get_unique_column_name - a function to return a unique column name when adding new columns to a DataFrame

dativa.tools.aws import S3Csv2Parquet

An easy to use module for converting csv files on s3 to praquet using aws glue jobs. For S3 access and glue access suitable credentials should be available in '~/.aws/credentials' or the AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY environment variables.

S3Csv2Parquet

Parameters:

  • region - str, AWS region in which glue job is to be run
  • template_location - str, S3 bucket Folder in which template scripts are located or need to be copied. format s3://bucketname/folder/file.csv
  • glue_role - str, Name of the glue role which need to be assigned to the Glue Job.
  • max_jobs - int, default 5 Maximum number of jobs the can run concurrently in the queue
  • retry_limit - int, default 3 Maximum number of retries allowed per job on failure

convert

Parameters:

  • csv_path - str or list of str for multiple files, s3 location of the csv file format s3://bucketname/folder/file.csv Pass a list for multiple files
  • output_folder - str, default set to folder where csv files are located s3 location at which paraquet file should be copied format s3://bucketname/folder
  • schema - list of tuples, If not specified scema is inferred from the file format [(column1, datatype), (column2, datatype)] Supported datatypes are boolean, double, float, integer, long, null, short, string
  • name - str, default 'parquet_csv_convert' Name to be assigned to glue job
  • allocated_capacity - int, default 2 The number of AWS Glue data processing units (DPUs) to allocate to this Job. From 2 to 100 DPUs can be allocated
  • delete_csv - boolean, default False If set source csv files are deleted post successful completion of job
  • separator - character, default ',' Delimiter character in csv files
  • withHeader- int, default 1 Specifies whether to treat the first line as a header Can take values 0 or 1
  • compression - str, default None If not specified compression is not applied. Can take values snappy, gzip, and lzo
  • partition_by - list of str, default None List containing columns to partition data by
  • mode - str, default append Options include: overwrite: will remove data from output_folder before writing out converted file. append: Will write out to output_folder without deleting existing data. ignore: Silently ignore this operation if data already exists.

####Example

from dativa.tools.aws import S3Csv2Parquet

# Initial setup
csv2parquet_obj = S3Csv2Parquet("us-east-1", "s3://my-bucket/templatefolder")

# Create/update a glue job to convert csv files and execute it
csv2parquet_obj.convert("s3://my-bucket/file_to_be_converted_1.csv")
csv2parquet_obj.convert("s3://my-bucket/file_to_be_converted_2.csv")

# Wait for completion of jobs 
csv2parquet_obj.wait_for_completion()

dativa.tools.pandas.ParquetHandler

ParquetHandler class, specify path of parquet file, and get pandas dataframe for analysis and modification.

  • param base_path : The base location where the parquet_files are stored.
  • type base_path : str
  • param row_group_size : The size of the row groups while writing out the parquet file.
  • type row_group_size : int
  • param use_dictionary : Specify whether to use boolean encoding or not
  • type use_dictionary : bool
  • param use_deprecated_int96_timestamps : Write nanosecond resolution timestamps to INT96 Parquet format.
  • type use_deprecated_int96_timestamps : bool
  • param coerce_timestamps : Cast timestamps a particular resolution. Valid values: {None, 'ms', 'us'}
  • type coerce_timestamps : str
  • param compression : Specify the compression codec.
  • type compression : str
from dativa.tools.pandas import CSVHandler, ParquetHandler

# Read a parquet file
pq_obj = ParquetHandler()
df_parquet = pq_obj.load_df('data.parquet')

# save a csv_file to parquet
csv = CSVHandler(csv_delimiter=",")
df = csv.load_df('emails.csv')
pq_obj = ParquetHandler()
pq_obj.save_df(df, 'emails.parquet')

dativa.toos.aws.PipelineClient

PipelineClient class, provide api key, source s3 location, destination s3 location, rules, and get source file cleaned and posted to destination. Refer https://www.dativa.com/tools/dativatools/aws-api/ for more details. :param api_key : The individual key provided by the pipeline api :type api_key : str :param source_s3_url : The s3 source where the csv files are present :type source_s3_url : str :param destination_s3_url : The destination where the files are to be posted after cleansing :type destination_s3_url : str :param rules : Rules by which to clean the file :type rules : list, str specifying location of the rules file :param url : The url of the pipeline api, defaults to https://pipeline-api.dativa.com/clean :type url : str :param status_url : the url to query for to check status of the api call, defaults to https://pipeline-api.dativa.com/status/{0} :type status_url : url :param source_delimiter : the delimiter of the source file, defaults to , :type source_delimiter : str :param destination_delimiter : the delimiter of the destination file, defaults to , :type destination_delimiter : str :param source_encoding : the encoding of the source file, defaults to utf-8 :type source_encoding : str :param destination_encoding : the encoding of the destination file, defaults to utf-8 :type destination_encoding : str

from dativa.tools.aws import PipelineClient

obj = PipelineClient(api_key=api_key,
                     rules=rules,
                     source_s3_url="https://s3-us-west-2.amazonaws.com/{0}/source_key".format(bucket),
                     destination_s3_url="https://s3-us-west-2.amazonaws.com/{0}/dest_key".format(bucket),
                     url="https://pipeline-api.dativa.com/clean",
                     status_url="https://pipeline-api.dativa.com/status/{0}",
                     )
obj.run_job()

Legacy classes

The modules in the dativatools namespace are legacy only and will be deprecated in future.

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