Develop Reference

Python script to automate tasks with Excel

First of all I am very sorry if this is a common questions, however I am still very new in the programming world, and cannot figure out the keywords for what I'm actually looking for.
Basically I have an Excel spreadsheet with data Picture of Excel Sheet - For each identifier I have a 0 or a 1. My task is to copy the number in the identifier column, paste it into a webpage from my workplace and click a button to exclude the customer from billing. Once I have done this, I will go back to the Excel spreadsheet and change the number from 0 to 1. Since there is a lot to go through, I thought this would be a fun project for me to start learning a basic script. I did not make it very far though!
import pandas as pd
Migreringer = pd.read_excel('Migreringer.xls')
ExludeBilling = Migreringer[["Exclude Billing", "Identifier"]]
IsExcludedBilling = Migreringer["Exclude Billing"] > 0
I was hoping that someone was able to give me a good idea as to how to move on from here, My idea is that it would check each row for the True/False statement from IsExcludedBilling, and then as a start copy the identifier if the statement is False and paste it into a word document or something similar to test this out? I just cannot seem to figure out how to make Python go through each row and validate the statements, and then make it copy something from a different column in the same row to a different document?
I know from experience that I am more motivated in learning with a project in mind, so that is why I've chosen to start here. Maybe I should take a couple of steps back before engaging with a project like this?

do you wan this ?
df = pd.DataFrame(
{
'Exclude Billing': [1,0,0,1,0],
'Identifier': [ 41823,41835,41819,41798,41812],
}
)
df_zero=df.loc[df['Exclude Billing']==0,:]
df_zero.to_csv('data_with_zero.csv',index=False)

How to create pdfs from rows in a dataset and save them

Very general question here. Need ideas. I have a dataset with about 20 rows. I want to use python or R to automatically take each of these rows and create 1 pdf per row. The pdf is formatted in a particular way that I need to be able to play around with.
Imagine each row is a student's name, and I need to make a pdf "Report Card" for each student. The report card will have a designated spot that says "Math Grade" and then the value will come from the dataset.
I want to be able to hit run, and have all 20 of the pdfs save to a folder on my machine. Eventually, I may try to have this run on a server or something so it is fully automatic. The pdfs ultimately get emailed out to a distribution list.
I am very pretty familiar with R, and mildly familiar with Python. I have no experience in HTML, but is that what I need here?
Any tutorials, ideas, explanations of the process I should use would be appreciated.
I thought about using plot.ly.dash. But I think that is mostly for viewing in a web browser. I want pdfs, so I don't know if that will work.

Take data from an xls sheet and add them into python commands

I've been asked to create a Python script to automate a server deployment for 80 retail stores.
As part of this script, I have a secondary script that I call to change multiple values in 9 XML files, however, the values are unique for each store, so this script needs to be changed each time, but after I am gone, this is going to be done by semi / non-technical people, so we don't want them to change the Python scripts directly for fear of breaking them.
This in mind, I would like to have these people input the store details into an XLS sheet, and a python file read this sheet and put the data it finds into the existing python script with the data to be changed.
The file will be 2 columns, with the required data in the 2nd one.
I'm sorry if this is a long explanation, but that is the gist of it. I'm using python 2.6. Does anyone have a clue about how I can do this? Or which language might be better for this. I also know Bash and Javascript.
Thanks in advance

Depending on the complexity and the volume of your data
for small Openpyxl,
for large pandas

Parsing a CSV into a database for an API using Python?

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

Having a trouble with table reading on pandas

I'm really new to programming and I've been trying to emulate the 'pandas.read_table' code from Python for Data Analysis book(the chapter on MovieLens 1M Data Set, pg.23ish). Below is the link to the file used for database and the images of jupyter notebook on which I've typed the codes. As you'll see there, I'm having a trouble with the data values not reading properly as it should, and I can't seem to figure out why. Your help will be much appreciated!
Trouble screen
Database file

If you are reading data from a .csv file, use pd.read_csv.
If you want to use pd.read_table, you have to specify the delimiter as the comma with the argument sep=','. What is happening is that pd.read_table is trying to separate your input information at every ::, but it looks like your data is separated by commas instead.
More information here:
http://pandas.pydata.org/pandas-docs/stable/io.html
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_table.html