将数据从Excel传输到Python 3中的Word

Question

I'm attempting to write a script that allows me to read data from an input excel file (saved in .csv format because someone told me to do it that way), and write selected portions of this data to a word document.

It is a script to create personalised delivery notes for participants' meal choices (the choices are cumulatively added to an input spreadsheet).

So far I have created a dummy input spreadsheet, and saved a blank dummy output word file (dummy.csv and dummy.txt, respectively).

The code I have written so far reads the spreadsheet into the terminal, with some formatting to tidy it up.

import csv
f = open("dummy.csv")
csv_f = csv.reader(f)
for row in csv_f:
    print('{:<15}  {:<15}  {:<20} {:<25}'.format(*row))

The output looks like this: (Dummy meal choices kept the same for ease)

Participant ID   Breakfasts       Lunches/dinners      Snacks
1111             Full english     Risotto              Granola
1111             Full english     Risotto              Granola
1111             Full english     Risotto              Granola
1111             Full english     Risotto              Granola
1111             Full english     Risotto              Granola
1111             Full english     Risotto              Granola
1111             Full english     Risotto              Granola
1111                              Risotto              Granola
1111                              Risotto              Granola
1111                              Risotto              Granola
1111                              Risotto              Granola
1111                              Risotto              Granola
1111                              Risotto              Granola
1111                              Risotto              Granola
2222             Avocado toast    Bean chilli          Apple
2222             Avocado toast    Bean chilli          Apple
2222             Avocado toast    Bean chilli          Apple
2222             Avocado toast    Bean chilli          Apple
2222             Avocado toast    Bean chilli          Apple
2222             Avocado toast    Bean chilli          Apple
2222             Avocado toast    Bean chilli          Apple
2222                              Bean chilli          Apple
2222                              Bean chilli          Apple
2222                              Bean chilli          Apple
2222                              Bean chilli          Apple
2222                              Bean chilli          Apple
2222                              Bean chilli          Apple
2222                              Bean chilli          Apple

My next challenge is to somehow write this data to a word file for participant 1111, another for participant 2222, and so on. I don't want the script to necessarily write the exact data from these rows to the word file, but rather whatever data could be on these rows should the food choices in the input file be different.

It would be good to keep the meals split into Breakfasts, Lunches/dinners, and Snacks on the output delivery note.

I can tidy up font etc later, I just want the meal selections to be there for now. I'll also want to have it say "7 x Full english", rather than "Full english, Full english, Full english etc."

Thank you for reading, any help would be hugely appreciated!

Kieran

Answer 1

Just to show where you exemplary could go using pandas :

import pandas as pd

df = pd.read_csv('whereverfilemayroam/filename')

    Participant ID     Breakfasts Lunches/dinners   Snacks
0             1111   Full english         Risotto  Granola
1             1111   Full english         Risotto  Granola
2             1111   Full english         Risotto  Granola
3             1111   Full english         Risotto  Granola
4             1111   Full english         Risotto  Granola
5             1111   Full english         Risotto  Granola
6             1111   Full english         Risotto  Granola
7             1111           None         Risotto  Granola
8             1111           None         Risotto  Granola
9             1111           None         Risotto  Granola
10            1111           None         Risotto  Granola
11            1111           None         Risotto  Granola
12            1111           None         Risotto  Granola
13            1111           None         Risotto  Granola
14            2222  Avocado toast     Bean chilli    Apple
15            2222  Avocado toast     Bean chilli    Apple
16            2222  Avocado toast     Bean chilli    Apple
17            2222  Avocado toast     Bean chilli    Apple
18            2222  Avocado toast     Bean chilli    Apple
19            2222  Avocado toast     Bean chilli    Apple
20            2222  Avocado toast     Bean chilli    Apple
21            2222           None     Bean chilli    Apple
22            2222           None     Bean chilli    Apple
23            2222           None     Bean chilli    Apple
24            2222           None     Bean chilli    Apple
25            2222           None     Bean chilli    Apple
26            2222           None     Bean chilli    Apple
27            2222           None     Bean chilli    Apple

That's your file in a pandas dataframe, the standard container in pandas, if you like. And now you can make a plenty of statistical things with it... Just dig a little through the docs
Examples:

df.groupby(['Participant ID', 'Breakfasts']).Breakfasts.count()

Participant ID  Breakfasts   
1111            Full english     7
2222            Avocado toast    7
Name: Breakfasts, dtype: int64

df.groupby(['Participant ID', 'Lunches/dinners'])['Lunches/dinners'].count()

Participant ID  Lunches/dinners
1111            Risotto            14
2222            Bean chilli        14
Name: Lunches/dinners, dtype: int64

Of course you can separate by Participant ID:

oneoneoneone = df[df['Participant ID'] == 1111]

oneoneoneone

    Participant ID    Breakfasts Lunches/dinners   Snacks
0             1111  Full english         Risotto  Granola
1             1111  Full english         Risotto  Granola
2             1111  Full english         Risotto  Granola
3             1111  Full english         Risotto  Granola
4             1111  Full english         Risotto  Granola
5             1111  Full english         Risotto  Granola
6             1111  Full english         Risotto  Granola
7             1111          None         Risotto  Granola
8             1111          None         Risotto  Granola
9             1111          None         Risotto  Granola
10            1111          None         Risotto  Granola
11            1111          None         Risotto  Granola
12            1111          None         Risotto  Granola
13            1111          None         Risotto  Granola


oneoneoneone.to_csv('target_file')

and so would perhaps

twotwotwotwo.to_csv('another_target_file')

One can also iterate over groups, and then apply always the same operations on each group.
eg:

for name, group in df.groupby('Participant ID'):
    print(name)
    print(group.groupby('Breakfasts').Breakfasts.count().to_string())
    print(group.groupby('Lunches/dinners')['Lunches/dinners'].count().to_string())
    print(group.groupby('Snacks').Snacks.count().to_string(), '\n')

returns:

1111
Breakfasts
Full english    7
Lunches/dinners
Risotto    14
Snacks
Granola    14 

2222
Breakfasts
Avocado toast    7
Lunches/dinners
Bean chilli    14
Snacks
Apple    14