Skip to content
Quickly ingest messy CSV and XLS files. Export to clean pandas, SQL, parquet
Branch: master
Clone or download
d6tdev Merge pull request #8 from brunompacheco/patch-1
pd_to_mssql query correction
Latest commit 9938a28 Mar 5, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
d6tstack pd_to_mssql query correction Feb 6, 2019
docs recommit Feb 2, 2019
tests recommit Feb 2, 2019
LICENSE recommit Feb 2, 2019 recommit Feb 2, 2019
examples-dask.ipynb recommit Feb 2, 2019
examples-excel.ipynb recommit Feb 2, 2019
examples-read-write.ipynb recommit Feb 2, 2019
examples-sql.ipynb recommit Feb 2, 2019
requirements-dev.txt recommit Feb 2, 2019
setup.cfg recommit Feb 2, 2019 recommit Feb 2, 2019

Databolt File Ingest

Quickly ingest raw files. Works for XLS, CSV, TXT which can be exported to CSV, Parquet, SQL and Pandas. d6tstack solves many performance and schema problems typically encountered when ingesting raw files.

Features include

  • Fast pd.to_sql() for postgres and mysql
  • Quickly check columns for consistency across files
  • Fix added/missing columns
  • Fix renamed columns
  • Check Excel tabs for consistency across files
  • Excel to CSV converter (incl multi-sheet support)
  • Out of core functionality to process large files
  • Export to CSV, parquet, SQL, pandas dataframe

Sample Use

import d6tstack

# fast CSV to SQL import - see SQL examples notebook
d6tstack.utils.pd_to_psql(df, 'postgresql+psycopg2://usr:pwd@localhost/db', 'tablename')
d6tstack.utils.pd_to_mysql(df, 'mysql+mysqlconnector://usr:pwd@localhost/db', 'tablename')
d6tstack.utils.pd_to_mssql(df, 'mssql+pymssql://usr:pwd@localhost/db', 'tablename') # experimental

# ingest mutiple CSVs which may have data schema changes - see CSV examples notebook

import glob
>>> c = d6tstack.combine_csv.CombinerCSV(glob.glob('data/*.csv'))

# quick check if all files have consistent columns
>>> c.is_all_equal()

# show which files have missing columns
>>> c.is_col_present()
   filename  cost  date profit profit2 sales
0  feb.csv  True  True   True   False  True
2  mar.csv  True  True   True    True  True

>>> c.combine_preview() # keep all columns
   filename  cost        date profit profit2 sales
0   jan.csv  -80  2011-01-01     20     NaN   100
0   mar.csv  -100  2011-03-01    200     400   300

>>> d6tstack.combine_csv.CombinerCSV(glob.glob('*.csv'), columns_select_common=True).combine_preview() # keep common columns
   filename  cost        date profit sales
0   jan.csv  -80  2011-01-01     20   100
0   mar.csv  -100  2011-03-01    200   300

>>> d6tstack.combine_csv.CombinerCSV(glob.glob('*.csv'), columns_rename={'sales':'revenue'}).combine_preview()
   filename  cost        date profit profit2 revenue
0   jan.csv  -80  2011-01-01     20     NaN   100
0   mar.csv  -100  2011-03-01    200     400   300

# to come: check if columns match database
>>> c.is_columns_match_db('postgresql+psycopg2://usr:pwd@localhost/db', 'tablename')

# export to csv, parquet, sql. Out of core with optimized fast imports for postgres and mysql
>>> c.to_pandas()
>>> c.to_csv_align(output_dir='process/')
>>> c.to_parquet_align(output_dir='process/')
>>> c.to_sql_combine('postgresql+psycopg2://usr:pwd@localhost/db', 'tablename')
>>> c.to_psql_combine('postgresql+psycopg2://usr:pwd@localhost/db', 'tablename') # fast, using COPY FROM
>>> c.to_mysql_combine('mysql+mysqlconnector://usr:pwd@localhost/db', 'tablename') # fast, using LOAD DATA LOCAL INFILE

# read Excel files - see Excel examples notebook for more details
import d6tstack.convert_xls

    sheet_name='Sheet1', header_xls_range="B2:E2")




We recommend using the latest version from github pip install git+

If you cannot install from github, use the latest published version pip install d6tstack. For Excel and parquet support, install d6tstack[xls] and d6tstack[parquet]. Certain database specific function require packages which you will be prompted for as you use them.


Faster Data Engineering

Check out other d6t libraries to solve common data engineering problems, including

  • data ingest, quickly ingest raw data
  • fuzzy joins, quickly join data
  • data pipes, quickly share and distribute data

And we encourage you to join the Databolt blog to get updates and tips+tricks

Collecting Errors Messages and Usage statistics

To help us make this library better, it collects anonymous error messages and usage statistics. It works similar to how websites collect data. See d6tcollect for details including how to disable collection.

It might not catch all errors so if you run into any problems, please raise an issue on github.

You can’t perform that action at this time.