#Python tools for postgresql full
#Python tools for postgresql software
#Python tools for postgresql code
#Python tools for postgresql zip

Here is what the code looks like: # here is a snippet of code showing how the data was read into R and Nevertheless, R is often our tool of choice and the code worked smoothly. Probably if we were to do this again we would simply use psql. This is definitely not the most efficient approach since the data gets read into R first and then exported to PostgreSQL. The most efficient approach would be to use the command line and psql using code like the following: psql -h localhost -U user -d db -c 'COPY datatable(field1, field2) FROM 'datatable.txt' DELIMITER ',' CSV HEADER'Д«ut we already had R scripts for writing to PostgreSQL from another project. Unzip('temp', exdir='d://junk', junkpaths=TRUE)Д«ringing the data in to PostgreSQL can be done many ways. # download and unzip (an example using 1 - our code loops through all)

#Python tools for postgresql zip

# find the zip URLs using regular expressions # use the XML package to pull out the tables of URLsДat1<-readHTMLTable(url, stringsAsFactors = F)] # Annual summary data The basic download and unzipping uses the following R code: # - data URL For us we needed several pollutants and several aggregation levels (annual, daily) so we wrote scripts to loop through the pollutants, identify the appropriate URL to the zips and then download. The data is in a nice, easy-to-use CSV format. The air pollution data is housed at the US EPA and provided in year/pollutant specific tables for annual and for daily data. Download and unzip the air pollution data We are using PostgreSQL 9.3 and PostGIS 2.1. By using this template you are including the spatial functionality that comes with PostGIS.

In my case, this template is called template_postgis_21. For example, if you're using pgAdmin III you would create a new database and under the вЂњDefinitionвЂќ tab you would choose the template you likely installed when you installed PostGIS. Once you've installed both PostgreSQL and PostGIS creating a spatially-enabled database simply involves using a spatial template. Metropolitan areas are polygons and the monitornig locations are represented by points.

For this project the spatial component is small but we need to be able to assign a metro code to the monitoring sites. In our office we've moved from using MySQL to PostgreSQL primarily because of the latter's spatial functionality through PostGIS. Create a spatially-enabled PostgreSQL database The site table gave us descriptions of the type of location (land use types, urban/suburban etc) along with coordinates and the geographic file of US metro areas allows us to assign metro areas codes to the sites so that we can compute averages by metro area.

In addition to the air pollution data itself the task required information from two additional data components вЂ“ a table with detailed descriptions of the monitoring locations and a geographic file of US metropolitan areas. Ultimately the daily data comprises more than 70 million records and 30 columns вЂ“ totaling approximately 2,100,000,000 data cells. The raw air pollution data all comes from the same source and you can download it yourself from here. The database needed to include data on annual and daily concentrations of ozone and other pollutants 1999-2012. The project required us to assemble a large database of air pollution data form the US Environmental Protection Agency. The project: assemble a database of air pollution and compute metro averages

#Python tools for postgresql full

Note that rather than bog down this post with the full code including things like looping through dozens of text fields etc we've limited to key code snippets. As an example, below we've outlined our workflow on a recent project in which we used R, Python, PostgreSQL/PostGIS (SQL) and GDAL.

#Python tools for postgresql software

Although many data science-related projects can be completed with a single software tool we often find that decisions about what tool to use for a project involve weighing a combination of what tool would be вЂњbestвЂќ for the job, what tools we're most familiar with and whether we already have scripts we can use.