I'm gonna use data from a .csv to train a model to predict user activity on google ads (impressions, clicks) in relation to the weather for a given day. And I have a .csv that contains 6000+ recordings of this info and want to parse it into a database using Python.
I tried making a df in pandas but for some reason the whole table isn't shown. The middle columns (there's about 7 columns I think) and rows (numbered over 6000 as I mentioned) are replaced with '...' when I print the table so I'm not sure if the entirety of the information is being stored and if this will be usable.
My next attempt will possible be SQLite but since it's local memory, will this interfere with someone else making requests to my API endpoint if I don't have the db actively open at all times?
Thanks in advance.
If you used pd.read_csv() i can assure you all of the info is there, it's just not displaying it.
You can check by doing something like print(df['Column_name_you_are_interested_in'].tolist()) just to make sure though. You can also use the various count type methods in pandas to make sure all of your lines are there.
Panadas is pretty versatile so it shouldn't have trouble with 6000 lines
Related
I have 1000's of scanned field books as PDF. Each has a unique filename. In a spreadsheet I have metadata for each, where each row has:
index number, filename, info1, info2, info3, info4, etc.
filename is the exact file name of the PDF. info1 is just an example of a metadata field, such as 'Year' or whatever. There are only about 8 fields or so, not ever PDF is relevant to all of them.
I assume there should be a reasonable way to create a database, mysql, or other, reading the spreadsheet (which I can just saves as .csv or .txt or something). This part I am sure I can handle.
I want to be able to lookup/search for a pdf file based on entering in various search items based on the metadata, and get a list of results. In a web interface, or a custom window, and be able to click on the results and open the file. Basically a typical search window with predefined fields you can enter and get results - like at an old school library terminal.
I have decent coding skills in python, mostly math, but some file skills as well. Looking for guidance on what tools and approach I should take to this. My short term goal is to be able to query and find files and open whatever results. Long term want to be able to share this with the public so they can search and find stuff.
After trying to figure out what to search for online, I am obviously at a loss. How do you suggest I do this and what tools or libraries should I use. I cannot find an example of this online. Not sure how to word it.
The actual data stuff could be done with Pandas:
read the excel file into Pandas
perform the search on the Pandas dataframe, e.g. using df.query()
But this does not give you a GUI. For that you could go for a web app, using Flask or Django framework. That, however, one does not master over night :)
This is a good course to learn that kind of stuff: https://www.edx.org/course/cs50s-web-programming-with-python-and-javascript?index=product&queryID=01efddd992de28a8b1b27d136111a2a8&position=3
So I'm building a data entry app for shop floor operators (manufacturing) to use so they can log inventory tags that are entered into our main inventory system. Users enter the requested data and the program appends the data to a CSV file. The CSV file is connected to Excel to provide the supervisor with a real-time view of tags entered. However, I also want to provide a view of entered tags so that the user has a list built within the app. To do this, I'm using Pandas to re-import the CSV into the app and display the X number of tags entered. If I print the variable with the pandas dataframe assigned, the data is nicely organized into columns and rows (with header) in my VS Code terminal.
The problem: even though the data displays in my app, the data isn't really organized (aligned) in nice even columns. Is there something I'm not doing or am doing wrong? If needed, I can provide code...but one should be familiar with Flet.
Thank you!!
visualizing a pandas dataframe usually works better with the display built-in function rather than print, but without any code it's hard to see if that would help here. Another option might be to get it created as an image first and then displayed.
I am trying to parse through an Excel sheet that has columns for the website name (column A), the number of visitors (F), a contact at that website's first name (B), one for last name (C), for email (E), and date it was last modified (L).
I want to write a python script that goes through the sheet and looks at sites that have been modified in the last 3 months and prints out the name of the website and an email.
It is pretty straightforward to do this. I think a little bit of googling can help you a lot. But in short, you need to use a library called Pandas which is a really powerful tool for handling spreadsheets, datasets, and table-based files.
Pandas documentation is very well written. You can use the tutorials provided within the documentation to work your way through the problem easily. However, I'll give you a brief overview of what you should do.
First open the spreadsheet (excel file) inside python using Pandas and load it into a data frame (read the docs and you'll understand).
Second Using one of the methods provided by pandas called where (actually there are a couple of methods) you can easily set a condition (like if date is older than some data) and get the masked data frame (which represents your spreadsheet) back from the method.
#Background
I am currently playing with some web scraping project as I am learning python.
I have a project which scrapes products with information about price etc using Selenium.
Than I add every record to pandas DF, do some additional data manipulation and than store data in csv and upload to google drive. This runs every night
#Question itself
I would like to watch price changes, new products etc. Would you recommend, how to store data with date key, so there is option to flag new products etc?
My idea is to store every load in one csv and add one column with "date_of_load"... But this seems noob_like... Maybe store data in PostrgreDB? I would like to start learning SQL, so I would try making my own DB.
Thanks for your ideas
As for me better to use NoSQL (Mongo) for this task. You can create JSON (data of prices) with keys are date.
This can help you:
https://www.mongodb.com/blog/post/getting-started-with-python-and-mongodb
https://www.mongodb.com/python
https://realpython.com/introduction-to-mongodb-and-python/
https://www.google.com/search?&q=python+mongo
That is cool! I would suggest sqlite3 (https://docs.python.org/3/library/sqlite3.html) just to get a feeling with SQL. As you can see, it says "It’s also possible to prototype an application using SQLite and then port the code to a larger database such as PostgreSQL or Oracle", which is sort of what you suggested(?), so it could be a nice place to start.
However, CSV might do just fine. As long as there is not too much data (it takes forever to load(and process) all your necessary data), it doesn't matter much how you store it as long as you manage to apply it as you desire.
Problem
I was trying to implement an web API(based on Flask), which would be used to query the database given some specific conditions, reconstruct the data and finally export the result to a .csv file.
Since the amount of data is really really huge, I can not construct the whole dataset and generate the .csv file all at once(e.g. create a DataFrame using pandas and finally call df.to_csv()), because that would cause a slow query and maybe the http connection would end up timeout.
So I create a generator which query the database 500 records per time and yield the result one by one, like:
def __generator(q):
[...] # some code here
while True:
if records == None:
break
records = q[offset:offset+limit] # q means a sqlalchemy query object
[...] # omit some reconstruct code
for record in records:
yield record
and finally construct a Response object, and send .csv to client side:
return Response(__generate(q), mimetype='text/csv') # Flask
The generator works well and all data are encoded by 'uft-8', but when I try to open the .csv file using Microsoft Excel, it appears to be messy code.
Measures Already Tried
add a BOM header to the export file, doesn't work;
using some other encode like 'gb18030', and 'cp936', most of the messy code disappear, some still remained, and some part of the table structure become weird.
My Question Is
How can I make my code compatible to Microsoft Excel? That means at least two conditions should be satisfied:
no messy code, well displayed;
well structured table;
I would be really appreciated for your answer!
How are you importing the csv file to excel? Have you tried importing the csv as a text file?
By reading as text format for each column, it wont modify columns that it reads as different types like dates. Your code may be correct, and excel may just be modifying the data when it parses it as a csv - by importing as text format, it wont modify anything.
I would recommend you look into xlutils. It's been around for quite some time, and our company has used it both for reading configuration files to run automated test and for generating reports of test results.