is it possible to set up tables for Mysql in Python?
Here's my problem, I have bunch of .txt files which I want to load into Mysql database. Instead of creating tables in phpmyadmin manually, is it possible to do the following things all in Python?
Create table, including data type definition.
Load many files one by one. I only know this LOAD DATA LOCAL INFILE command to load one file.
Many thanks
Yes, it is possible, you'll need to read the data from the CSV files using CSV module.
http://docs.python.org/library/csv.html
And the inject the data using Python MySQL binding. Here is a good starter tutorial:
http://zetcode.com/databases/mysqlpythontutorial/
If you already know python it will be easy
It is. Typically what you want to do is use an Object-Retlational Mapping library.
Probably the most widely used in the python ecosystem is SQLAlchemy, but there is a lot of magic going on in it, so if you want to keep a tighter control on your DB schema, or if you are learning about relational DB's and want to follow along what the code does, you might be better off with something lighter like Canonical's storm.
EDIT: Just thought to add. The reason to use ORM's is that they provide a very handy way to manipulate data / interface to the DB. But if all you will ever want to do is to do a script to convert textual data to MySQL tables, than you might get along with something even easier. Check the tutorial linked from the official MySQL website, for example.
HTH!
Related
All explained above is in the context of an ETL process. I have a git repository full of sql files. I need to put all those sql files (once pulled) into a sql table with 2 columns: name and query, so that I can access each file later on using a SQL query instead of loading them from the file path. How can I make this? I am free to use the tool I want to, but I just know python and Pentaho.
Maybe the assumption that this method would require less computation time than simply accessing to the pull file located in the hard drive is wrong. In that case let me know.
You can first define the table you're interested in using something along the lines of (you did not mention the database you are using):
CREATE TABLE queries (
name TEXT PRIMARY KEY,
query TEXT
);
After creating the table, you can use perhaps os.walk to iterate through the files in your repository, and insert both the contents (e.g. file.read()) and the name of the file into the table you created previously.
It sounds like you're trying to solve a different problem though. It seems like you're interested in speeding up some process, because you asked about whether accessing queries using a table would be faster than opening a file on disk. To investigate that (separate!) question further, see this.
I would recommend that you profile the existing process you are trying to speed up using profiling tools. After that, you can see whether IO is your bottleneck. Otherwise, you may do all of this work without any benefit.
As a side note, if you are looking up queries in this way, it may indicate that you need to rearchitect your application. Please consider that possibility as well.
I'm kinda new to Python and webscraping but I'm currently at a point where I need to extract data to a database. Can someone tell me the pros and cons by using sqlite, excel or xml?
I've read that sqlite should be the fastest, so I may go for that database structure, but can someone then tell me what IDE you use to handle sqlite data after I've extracted it from python?
Edit: I hope my post makes sense. I'm currently trying to use a web scraper from here: https://github.com/gingeleski/odds-portal-scraper
Thanks in advance.
For the short term, Excel is a good way to examine your data and prototype analysis and visualizations. It gets old using it for very large datasets, or multiple similar datasets. Basically as soon as you start doing the same thing more than twice or writing VB code you should switch to the pandas/matplotlib solution.
It looks like the scraper you are using already puts the results in an SQLITE database, but if you have your data in a list or dictionary, I'd suggest using pandas to do calculations and matplotlib for visualizations, as that will give you a robust, extensible solution over the long term. It is very easy to read and write data between an SQLITE database and pandas.
A good way of viewing the data in the DB is a must. I'm currently using SQLiteStudio.
When you say IDE, I'm assuming you're looking for a way to view the SQLite data? If so, DBeaver is a free, open source SQL client. You could use this to view the data quite easily.
I am seeking a way to take SDMX files (like here: http://www12.statcan.gc.ca/datasets/Alternative.cfm?PID=105929&EXT=SDMX) and process them into a Postgresql table.
I can use rsdmx (https://cran.r-project.org/web/packages/rsdmx/index.html) for smaller datasets but for large ones we reach a number of limitations in R.
PandaSDMX (https://pandasdmx.readthedocs.io/en/latest/) appears to resolve some of these issues, but I am not experienced in Python and can't seem to get the syntax to work. I'm able to use Response.get() to load a local file as a response object but am not sure where to go to from there.
I know I need to apply the code tables (structure file) but I'm not sure how to do that or make it so I can use odo (http://odo.pydata.org/en/latest/) to send it to postgresql.
Hoping someone can help out or suggest another method to pursue.
For a school project I am to write and organize a set of data to a Microsoft Access database file. I am fairly comfortable with using python to read and write to files but can't find any information online regarding what I am specifically looking for.
I want to know what i would need to do to write to a database in specific Columns and Tables, for example, write the variable "name" in a field called "name" instead of just randomly adding it to the database.
EDIT: I cannot use any additional packages when doing this.
You can use PyODBC. Here is an example.
Edit: "no extra modules" is nice but unless you want to re-write pyodbc from scratch you may as well just use it.
Edit2: if you want to know what that would look like, check out pypyodbc - a pure-python odbc driver in about 3000 lines of Python code.
I am using Flask to make a small webapp to manage a group project, in this website I need to manage attendances, and also meetings reports. I don't have the time to get into SQLAlchemy, so I need to know what might be the bad things about using CSV as a database.
Just don't do it.
The problem with CSV is …
a, concurrency is not possible: What this means is that when two people access your app at the same time, there is no way to make sure that they don't interfere with each other, making changes to each other's data. There is no way to solve this with when using a CSV file as a backend.
b, speed: Whenever you make changes to a CSV file, you need to reload more or less the whole file. Parsing the file is eating up both memory and time.
Databases were made to solve this issues.
I agree however, that you don't need to learn SQLAlchemy for a small app.
There are lightweight alternatives that you should consider.
What you are looking for are ORM - Object-relational mapping - who translate Python code into SQL and manage the SQL databases for you.
PeeweeORM and PonyORM. Both are easy to use and translate all SQL into Python and vice versa. Both are free for personal use, but Pony costs money if you use it for commercial purposes. I highly recommend PeeweeORM. You can start using SQLite as a backend with Peewee, or if your app grows larger, you can plug in MySQL or PostGreSQL easily.
Don't do it, CSV that is.
There are many other possibilities, for instance the sqlite database, python shelve, etc. The available options from the standard library are summarised here.
Given that your application is a webapp, you will need to consider the effect of concurrency on your solution to ensure data integrity. You could also consider a more powerful database such as postgres for which there are a number of python libraries.
I think there's nothing wrong with that as long as you abstract away from it. I.e. make sure you have a clean separation between what you write and how you implement i . That will bloat your code a bit, but it will make sure you can swap your CSV storage in a matter of days.
I.e. pretend that you can persist your data as if you're keeping it in memory. Don't write "openCSVFile" in you flask app. Use initPersistence(). Don't write "csvFile.appendRecord()". Use "persister.saveNewReport()". When and if you actually realise CSV to be a bottleneck, you can just write a new persister plugin.
There are added benefits like you don't have to use a mock library in tests to make them faster. You just provide another persister.
I am absolutely baffled by how many people discourage using CSV as an database storage back-end format.
Concurrency: There is NO reason why CSV can not be used with concurrency. Just like how a database thread can write to one area of a binary file at the same time that another thread writes to another area of the same binary file. Databases can do EXACTLY the same thing with CSV files. Just as a journal is used to maintain the atomic nature of individual transactions, the same exact thing can be done with CSV.
Speed: Why on earth would a database read and write a WHOLE file at a time, when the database can do what it does for ALL other database storage formats, look up the starting byte of a record in an index file and SEEK to it in constant time and overwrite the data and comment out anything left over and record the free space for latter use in a separate index file, just like a database could zero out the bytes of any unneeded areas of a binary "row" and record the free space in a separate index file... I just do not understand this hostility to non-binary formats, when everything that can be done with one format can be done with the other... everything, except perhaps raw binary data compression, depending on the particular CSV syntax in use (special binary comments... etc.).
Emergency access: The added benefit of CSV is that when the database dies, which inevitably happens, you are left with a CSV file that can still be accessed quickly in the case of an emergency... which is the primary reason I do not EVER use binary storage for essential data that should be quickly accessible even when the database breaks due to incompetent programming.
Yes, the CSV file would have to be re-indexed every time you made changes to it in a spread sheet program, but that is no different than having to re-index a binary database after the index/table gets corrupted/deleted/out-of-sync/etc./etc..