Database Conversion (MS access to Sqlite3)
Used mdbtools (linux) to convert MS access (mdb) to Sqlite3
Shell script
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#!/bin/bash
# Inspired by
# https://www.codeenigma.com/community/blog/using-mdbtools-nix-convert-microsoft-access-mysql
# USAGE
# Rename your MDB file to migration-export.mdb
# run ./mdb2sqlite.sh migration-export.mdb
# wait and wait a bit longer...
now=$(date +%s)
sqlite=sqlite3
fname=$1
sql=${fname/mdb/sqlite}
schema=${fname/mdb/schema}
dir=${fname/.mdb/}-$now
mkdir $dir
mdb-schema $fname sqlite > $dir/$schema
for i in $( mdb-tables $fname ); do
echo $i
mdb-export -D "%Y-%m-%d %H:%M:%S" -H -I sqlite $fname $i > $dir/$i.sql
done
< $dir/$schema $sqlite $sql
for f in $dir/*.sql ; do
echo $f
(echo 'BEGIN;'; cat $f; echo 'COMMIT;') | $sqlite $sql
done
echo "Using $dir" |
Overview website: http://ilrdss.isws.illinois.edu/fox/
To acquire the CSV file from MS access we first converted the .mdb database into SQLite. Once we had access to the .sqlite3 file we created a python script to access it and , execute SQL statements. Our python script generates , and generate a CSV file from the result resulting table.
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- Data location: gltg-source-data/Fox River Watershed Database
- CSV for latest sqlite3 file: selected_final_foxdb_20171116.csv
- Lastest MS access db: FoxDB_20171116.mdb
- Converted sqlite3 file: foxdb_20171116.sqlite3 (please use this one)
- Database design document:
- All documents: http://ilrdss.sws.uiuc.edu/fox/fox_report_phase1.asp?ws=3
- General description http://ilrdss.sws.uiuc.edu/fox/downloads/Fox_Chapter_4.pdf
- Section 4.4.1 shows a good example of SQL query
- Presentation at Nutrient Monitoring Council that explains what kinds of query Jong Lee did to extract the data from the database.
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- SQL statement:
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SELECT tblsample.station_id, tblstation_information.latitude, tblstation_information.longitude, tblstation_information.place_name_description, tblsample.sample_code, tblsample.idloc, tblparameter_codes.full_name, tblparameter_codes.short_name, tblsample.start_date, tblresults.result_value, tblparameter_group.parameter_group FROM (((( (tblresults INNER JOIN tblsample ON ( tblresults.sample_code = tblsample.sample_code ) AND ( tblresults.idloc = tblsample.idloc )) INNER JOIN tblparameter_codes ON tblresults.parameter_code = tblparameter_codes.parameter_code) INNER JOIN tblqappgroups ON tblparameter_codes.parameter_code = tblqappgroups.parameter_code) INNER JOIN tblqapp_group_codes ON tblqappgroups.qappcode = tblqapp_group_codes.qappcode) INNER JOIN tblparameter_group ON tblqapp_group_codes.parameter_group = tblparameter_group.parameter_group) LEFT JOIN tblstation_information ON tblstation_information.station_id = tblsample.station_id WHERE (( ( tblparameter_group.parameter_group ) = 9 OR ( tblparameter_group.parameter_group ) = 10 )) |
- Python3 Python 3 code:
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""" Access a sqlite3 file, executes a SQL statement, and generates a CSV file for parsing Variables ---------- data_loc : str The location of the database (for example, ./foxdb/foxdb_20171116.sqlite3) query : str The SQL statement """ import sqlite3 import pandas as pd data_loc = "" query = "" conn = sqlite3.connect(data_loc) df = pd.read_sql_query(query, conn) df['Start_Date'] = pd.to_datetime(df['Start_Date'], errors='coerce') #use this to format date and time to only date df.to_csv("output.csv", encoding='utf-8', index=False, date_format='%Y-%m-%d') conn.close() |
Resources:
- Presentation at Nutrient Monitoring Council that explains what kinds of query Jong Lee did to extract the data from the database.
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- Python sqlite3 module
- Python Data Analysis Library