dativatools 2.8.5

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

# Dativa Tools

Provides useful libraries for processing large data sets. Developed by the team at [www.dativa.com](https://www.dativa.com) as we find them useful in our projects.

Any questions, please email hello AT dativa.com

## Installation

```
pip install dativatools
```

## Description

The library includes two modules:
* dativatools - which contains the legacy classes
* dativa.tools - which contains the more recent classes.

Over time it is expected that we will migrate all classes over to the dativa.tools module

### dativa.tools.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

```
ac = AthenaClient(aws_region, db_name)
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

```
ac = AthenaClient(aws_region, db_name)
ac.add_query(sql=query,
name="From field {0}".format(experian_columns[i]),
output_location=s3_bucket + 'experian-processed')

i = i + number_fields + 1

ac.wait_for_completion()
```

### dativa.tools.pandas.CSVHandler

A wrapper for pandas CSV handling to read and write DataFrames
that is provided in pandas with consistent CSV parameters and
sniffing the CSV parameters automatically.
Includes reading a CSV into a DataFrame, and writing it out to a string.

### 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

### Legacy classes

#### dativatools.CommonUtility
Supports various common activities including getting detailed descriptions about exceptions, logging activity into a CSV file or database table
and sending email reports of failures.

#### dativatools.DataValidation
Class containing methods to validate file sizes, dates, counts, names and extensions at a specified location.

#### dativatools.DatabaseManagement
Generic database management operations including data insertion, table deletion, backup, rename, drop and create as well as query execution.

#### dativatools.RsyncLib
Class to perform file transfer using Rsync.

#### dativatools.SFTPLib
Class to perform file transfer using SFTP.

#### dativatools.ArchiveManager
Class to manage archiving and unarchiving of files to and from specific locations.

#### dativatools.TextToCsvConverter
Class containing methods required to convert a text file to CSV and change certain parameters like headers, separators etc.

#### dativatools.S3Lib
Supports connecting to and getting and putting data to and from AWS S3 buckets.