Pivot Table/Groupby/Sum question

[patrick.waldo]

Hi all,

I tried reading http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/334695
on the same subject, but it didn't work for me. I'm trying to learn
how to make pivot tables from some excel sheets and I am trying to
abstract this into a simple sort of example. Essentially I want to
take input data like this:

Name Time of day Amount
Bob Morn 240
Bob Aft 300
Joe Morn 70
Joe Aft 80
Jil Morn 100
Jil Aft 150

And output it as:

Name Total Morning Afternoon
Bob 540 240 300
Joe 150 70 80
Jil 250 100 150
Total 940 410 530

The writing the output part is the easy part. However, I have a
couple problems. 1) Grouping by name seems to work perfectly, but
working by time does not. ie

I will get:
Bob
240
300
Joe
70
80
Jil
100
150
which is great but...
Morn
240
Aft
300
Morn
70
Aft
80
Morn
100
Aft
150
And not
Morn
240
70
100
Aft
300
80
150

2) I can't figure out how to sum these values because of the
iteration. I always get an error like: TypeError: iteration over non-
sequence

Here's the code:

from itertools import groupby

data = [['Bob', 'Morn', 240],['Bob', 'Aft', 300],['Joe', 'Morn', 70],
['Joe', 'Aft', 80],\
['Jil', 'Morn', 100],['Jil', 'Aft', 150]]

NAME, TIME, AMOUNT=range(3)
for k, g in groupby(data, key=lambda r: r[NAME]):
print k
for record in g:
print "\t", record[AMOUNT]
for k, g in groupby(data, key=lambda r: r[TIME]):
print k
for record in g:
print "\t", record[AMOUNT]

Thanks for any comments

 

[John Machin]

from itertools import groupby

You seem to have overlooked this important sentence in the
documentation: "Generally, the iterable needs to already be sorted on
the same key function"

 

[patrick.waldo]

You seem to have overlooked this important sentence in the
documentation: "Generally, the iterable needs to already be sorted on
the same key function"

Yes, but I imagine this shouldn't prevent me from using and
manipulating the data. It also doesn't explain why the names get
sorted correctly and the time does not.

I was trying to do this:

count_tot = []
for k, g in groupby(data, key=lambda r: r[NAME]):
for record in g:
count_tot.append((k,record[SALARY]))
for i in count_tot:
here I want to say add all the numbers for each person, but I'm
missing something.

If you have any ideas about how to solve this pivot table issue, which
seems to be scant on Google, I'd much appreciate it. I know I can do
this in Excel easily with the automated wizard, but I want to know how
to do it myself and format it to my needs.

 

[John Machin]

Yes, but I imagine this shouldn't prevent me from using and
manipulating the data.

You imagine correctly (and pointlessly) in general; however in
particular it prevents you using itertools.groupby simplistically to
manipulate the data in the way you want to manipulate it.

It also doesn't explain why the names get
sorted correctly and the time does not.

The names in your example were NOT sorted, "correctly" or otherwise.
The output order is the same as the input order: Bob, Joe, Jil.

seq = ['Bob', 'Joe', 'Jil']
sorted(seq) ['Bob', 'Jil', 'Joe']
seq == sorted(seq) False

I was trying to do this:

count_tot = []
for k, g in groupby(data, key=lambda r: r[NAME]):
for record in g:
count_tot.append((k,record[SALARY]))
for i in count_tot:
here I want to say add all the numbers for each person, but I'm
missing something.

If you have any ideas about how to solve this pivot table issue, which
seems to be scant on Google, I'd much appreciate it. I know I can do
this in Excel easily with the automated wizard, but I want to know how
to do it myself and format it to my needs.

Watch this space.

 

[John Machin]

Watch this space.

Tested as much as you see:

8<---
class SimplePivotTable(object):

def __init__(
self,
row_order=None, col_order=None, # see example
missing=0, # what to return for an empty cell. Alternatives:
'', 0.0, None, 'NULL'
):
self.row_order = row_order
self.col_order = col_order
self.missing = missing
self.cell_dict = {}
self.row_total = {}
self.col_total = {}
self.grand_total = 0
self.headings_OK = False

def add_item(self, row_key, col_key, value):
self.grand_total += value
try:
self.col_total[col_key] += value
except KeyError:
self.col_total[col_key] = value
try:
self.cell_dict[row_key][col_key] += value
self.row_total[row_key] += value
except KeyError:
try:
self.cell_dict[row_key][col_key] = value
self.row_total[row_key] += value
except KeyError:
self.cell_dict[row_key] = {col_key: value}
self.row_total[row_key] = value

def _process_headings(self):
if self.headings_OK:
return
self.row_headings = self.row_order or
list(sorted(self.row_total.keys()))
self.col_headings = self.col_order or
list(sorted(self.col_total.keys()))
self.headings_OK = True

def get_col_headings(self):
self._process_headings()
return self.col_headings

def generate_row_info(self):
self._process_headings()
for row_key in self.row_headings:
row_dict = self.cell_dict[row_key]
row_vals = [row_dict.get(col_key, self.missing) for
col_key in self.col_headings]
yield row_key, self.row_total[row_key], row_vals

def get_col_totals(self):
self._process_headings()
row_dict = self.col_total
row_vals = [row_dict.get(col_key, self.missing) for col_key in
self.col_headings]
return self.grand_total, row_vals

if __name__ == "__main__":

data = [
['Bob', 'Morn', 240],
['Bob', 'Aft', 300],
['Joe', 'Morn', 70],
['Joe', 'Aft', 80],
['Jil', 'Morn', 100],
['Jil', 'Aft', 150],
['Bob', 'Aft', 40],
['Bob', 'Aft', 5],
['Dozy', 'Aft', 1], # Dozy doesn't show up till lunch-time
]
NAME, TIME, AMOUNT = range(3)

print
ptab = SimplePivotTable(
col_order=['Morn', 'Aft'],
missing='uh-oh',
)
for s in data:
ptab.add_item(row_key=s[NAME], col_key=s[TIME],
value=s[AMOUNT])
print ptab.get_col_headings()
for x in ptab.generate_row_info():
print x
print 'Tots', ptab.get_col_totals()
8<---

 

[patrick.waldo]

Wow, I did not realize it would be this complicated! I'm fairly new
to Python and somehow I thought I could find a simpler solution. I'll
have to mull over this to fully understand how it works for a bit.

Thanks a lot!

On Dec 28, 10:05 am, (e-mail address removed) wrote:

Watch this space.

Tested as much as you see:

8<---
class SimplePivotTable(object):

def __init__(
self,
row_order=None, col_order=None, # see example
missing=0, # what to return for an empty cell. Alternatives:
'', 0.0, None, 'NULL'
):
self.row_order = row_order
self.col_order = col_order
self.missing = missing
self.cell_dict = {}
self.row_total = {}
self.col_total = {}
self.grand_total = 0
self.headings_OK = False

def add_item(self, row_key, col_key, value):
self.grand_total += value
try:
self.col_total[col_key] += value
except KeyError:
self.col_total[col_key] = value
try:
self.cell_dict[row_key][col_key] += value
self.row_total[row_key] += value
except KeyError:
try:
self.cell_dict[row_key][col_key] = value
self.row_total[row_key] += value
except KeyError:
self.cell_dict[row_key] = {col_key: value}
self.row_total[row_key] = value

def _process_headings(self):
if self.headings_OK:
return
self.row_headings = self.row_order or
list(sorted(self.row_total.keys()))
self.col_headings = self.col_order or
list(sorted(self.col_total.keys()))
self.headings_OK = True

def get_col_headings(self):
self._process_headings()
return self.col_headings

def generate_row_info(self):
self._process_headings()
for row_key in self.row_headings:
row_dict = self.cell_dict[row_key]
row_vals = [row_dict.get(col_key, self.missing) for
col_key in self.col_headings]
yield row_key, self.row_total[row_key], row_vals

def get_col_totals(self):
self._process_headings()
row_dict = self.col_total
row_vals = [row_dict.get(col_key, self.missing) for col_key in
self.col_headings]
return self.grand_total, row_vals

if __name__ == "__main__":

data = [
['Bob', 'Morn', 240],
['Bob', 'Aft', 300],
['Joe', 'Morn', 70],
['Joe', 'Aft', 80],
['Jil', 'Morn', 100],
['Jil', 'Aft', 150],
['Bob', 'Aft', 40],
['Bob', 'Aft', 5],
['Dozy', 'Aft', 1], # Dozy doesn't show up till lunch-time
]
NAME, TIME, AMOUNT = range(3)

print
ptab = SimplePivotTable(
col_order=['Morn', 'Aft'],
missing='uh-oh',
)
for s in data:
ptab.add_item(row_key=s[NAME], col_key=s[TIME],
value=s[AMOUNT])
print ptab.get_col_headings()
for x in ptab.generate_row_info():
print x
print 'Tots', ptab.get_col_totals()
8<---

 

[petr.jakes.tpc]

What about to let SQL to work for you.
HTH

Petr Jakes

Tested on Python 2.5.1
8<----------------------

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sqlite3
con = sqlite3.connect(":memory:")
cur = con.cursor()

inputData=(
('Bob', 'Morn', 240),
('Bob', 'Aft', 300),
('Joe', 'Morn', 70),
('Joe', 'Aft', 80),
('Jil', 'Morn', 100),
('Jil', 'Aft', 150),
('Jil', 'Aft', 150),
('Jil', 'Aft', 150))

def data_generator(dataSet):
for dataSetRow in dataSet:
yield dataSetRow

pivotSelect='''
SELECT
NAME,
sum (AMOUNT) as TOTAL,
sum (case when (TIME_OF_DAY) = 'Morn' then AMOUNT else 0 END) as
MORN,
sum (case when (TIME_OF_DAY) = 'Aft' then AMOUNT else 0 END) as AFT
FROM MY_NAMES
GROUP BY 1'''

cur.execute("create table MY_NAMES(NAME, TIME_OF_DAY, AMOUNT)")
cur.executemany("""insert into MY_NAMES(NAME, TIME_OF_DAY, AMOUNT)
values (?,?,?)""", data_generator(inputData))
cur.execute(pivotSelect)
for row in cur.fetchall():
print row

 

[John Machin]

What about to let SQL to work for you.

The OP is "trying to learn how to make pivot tables from some excel
sheets". You had better give him a clue on how to use ODBC on an
"excel sheet"

[snip]

SELECT
NAME,
sum (AMOUNT) as TOTAL,
sum (case when (TIME_OF_DAY) = 'Morn' then AMOUNT else 0 END) as
MORN,
sum (case when (TIME_OF_DAY) = 'Aft' then AMOUNT else 0 END) as AFT

This technique requires advance knowledge of what the column key
values are (the hard-coded 'Morn' and 'Aft').

<rant>
It is the sort of thing that one sees when %SQL% is the *only*
language used to produce end-user reports. Innocuous when there are
only 2 possible columns, but bletchworthy when there are more than 20
and the conditions are complex and the whole thing is replicated
several times in the %SQL% script because either %SQL% doesn't support
temporary procedures/functions or the BOsFH won't permit their use...
not in front of the newbies, please!
</rant>

 

[patrick.waldo]

Petr, thanks for the SQL suggestion, but I'm having enough trouble in
Python.

John would you mind walking me through your class in normal speak? I
only have a vague idea of why it works and this would help me a lot to
get a grip on classes and this sort of particular problem. The next
step is to imagine if there was another variable, like departments and
add up the information by name, department, and time, and so on...that
will come another day.

Thanks.

What about to let SQL to work for you.

The OP is "trying to learn how to make pivot tables from some excel
sheets". You had better give him a clue on how to use ODBC on an
"excel sheet"

[snip]

SELECT
NAME,
sum (AMOUNT) as TOTAL,
sum (case when (TIME_OF_DAY) = 'Morn' then AMOUNT else 0 END) as
MORN,
sum (case when (TIME_OF_DAY) = 'Aft' then AMOUNT else 0 END) as AFT

This technique requires advance knowledge of what the column key
values are (the hard-coded 'Morn' and 'Aft').

<rant>
It is the sort of thing that one sees when %SQL% is the *only*
language used to produce end-user reports. Innocuous when there are
only 2 possible columns, but bletchworthy when there are more than 20
and the conditions are complex and the whole thing is replicated
several times in the %SQL% script because either %SQL% doesn't support
temporary procedures/functions or the BOsFH won't permit their use...
not in front of the newbies, please!
</rant>

 

[John Machin]

John would you mind walking me through your class in normal speak?
Yes.

I
only have a vague idea of why it works and this would help me a lot to
get a grip on classes and this sort of particular problem.

It's about time you got a *concrete* idea of how something works. Grab
a pencil and a large piece of paper, pretend you are python.exe and
follow through what happens when it executes
ptab = SimplePivotTable(.......)
and the ptab.add_item(......) loop with this set of data:
data = [
['Bob', 'Morn', 240],
['Bob', 'Aft', 300],
['Bob', 'Morn', 40],
]
with the goal of being able to say what is in ptab.cell_dict and
understanding how it got there.

Cheers,
John

 

[petr.jakes.tpc]

Patrick,

in your first posting you are writing "... I'm trying to learn how to
make pivot tables from some excel sheets...". Can you be more specific
please? AFIK Excel offers very good support for pivot tables. So why
to read tabular data from the Excel sheet and than transform it to
pivot tabel in Python?

Petr

 

[patrick.waldo]

Patrick,

in your first posting you are writing "... I'm trying to learn how to
make pivot tables from some excel sheets...". Can you be more specific
please? AFIK Excel offers very good support for pivot tables. So why
to read tabular data from the Excel sheet and than transform it to
pivot tabel in Python?

Petr

Yes, I realize Excel has excellent support for pivot tables. However,
I hate how Excel does it and, for my particular excel files, I need
them to be formated in an automated way because I will have a number
of them over time and I'd prefer just to have python do it in a flash
than to do it every time with Excel.

It's about time you got a *concrete* idea of how something works.

Absolutely right. I tend to take on ideas that I'm not ready for, in
the sense that I only started using Python some months ago for some
basic tasks and now I'm trying on some more complicated ones. With
time, though, I will get a concrete idea of what python.exe does, but,
for someone who studied art history and not comp sci, I'm doing my
best to get a handle on all of it. I think a pad of paper might be a
good way to visualize it.

 

[petr.jakes.tpc]

Yes, I realize Excel has excellent support for pivot tables. However,

I hate how Excel does it and, for my particular excel files, I need
them to be formated in an automated way because I will have a number
of them over time and I'd prefer just to have python do it in a flash
than to do it every time with Excel.

Patrick,

Few more questions:
- Where the data come from (I mean: are your data in Excel already
when you get them)?

- If your primary source of data is the Excel file, how do you read
data from the Excel
file to Python (I mean did you solve this part of the task already)?

Petr

 

[patrick.waldo]

Sorry for the delay in my response. New Year's Eve and moving
apartment

- Where the data come from (I mean: are your data in Excel already
when you get them)?
- If your primary source of data is the Excel file, how do you read
data from the Excel file to Python (I mean did you solve this part of the task already)?

Yes, the data comes from Excel and I use xlrd and PyExcelerator to
read and write, respectively.
#open for reading
path_file = "c:\\1\\data.xls"
book = xlrd.open_workbook(path_file)
Counts = book.sheet_by_index(1)
#get data
n=1
data = []
while n<Counts.nrows:
data.append(Counts.row_values(n, start_colx=0, end_colx=None))
n=n+1
#
#Heres the part where I need to do the pivot table
#
#open a new Excel file for writing
wb=pyExcelerator.Workbook()
matrix = wb.add_sheet("matrix")
wb.save('c:\\1\\matrix.xls')


So the data comes in as a long list. I'm dealing with some
information on various countries with 6 pieces of information to
pivot. Just to make it simple it's like a video store database. The
data is like [Country, Category, Sub Category, Film Title, Director,
Number of Copies]. data = [['Italy', 'Horror', '70s', 'Suspiria',
'Dario Argento', 4],['Italy', 'Classics', 'Neo-Realist', 'Otto e
Mezzo', 'Fellini', 3],['Italy', 'Horror', '70s', 'Profondo Rosso',
'Dario Argento', 4],...]. So there are 4 copies of Suspiria and 3 of
8 1/2. What I want is the total number of films for each country,
category and subcategory, ie there are 11 Italian films and 8 Italian
horror films from the 70s, etc...I will then output the data like this
| Horror | Classics ...
Total | 70s Slasher | Neo-Realist Western ...
Total
America 200 20 30 0 10 ...
Argentina 304 1 0 0 0 ...
.....
Italy 11 7 0 3 0 ...

I'm just making up data here, but that's the idea. As I said, Excel
does it in it's own way, but that whole idea here is to organize it
exactly how I want it in an automated way. It's an interesting
problem, one that I think a lot of people might want to use. I'm
surprised that Programming Python, with all it's talk of dictionaries,
doesn't have a section on organizing tabular data like this, or I'm
more likely missing something or not making a connection.

Any ideas, hints, or pointers on where I could learn more about this
would be helpful. Otherwise, I'm off to the drawing board!

 

[petr.jakes.tpc]

So the data comes in as a long list. I'm dealing with some

information on various countries with 6 pieces of information to
pivot. Just to make it simple it's like a video store database. The
data is like [Country, Category, Sub Category, Film Title, Director,
Number of Copies]. data = [['Italy', 'Horror', '70s', 'Suspiria',
'Dario Argento', 4],['Italy', 'Classics', 'Neo-Realist', 'Otto e
Mezzo', 'Fellini', 3],['Italy', 'Horror', '70s', 'Profondo Rosso',
'Dario Argento', 4],...]. So there are 4 copies of Suspiria and 3 of
8 1/2. What I want is the total number of films for each country,
category and subcategory, ie there are 11 Italian films and 8 Italian
horror films from the 70s, etc...I will then output the data like this
| Horror | Classics ...
Total | 70s Slasher | Neo-Realist Western ...
Total
America 200 20 30 0 10 ...
Argentina 304 1 0 0 0 ...
....
Italy 11 7 0 3 0 ...

Did you mean your table has to look like the following?

| Horror | Horror | Classics | Classics
Total | 70s | Slasher | Neo-Realist | Western ...
Total
America 200 20 30 0 10 ...
Argentina 304 1 0 0 0 ...
.....

 

[patrick.waldo]

Yes in the sense that the top part will have merged cells so that
Horror and Classics don't need to be repeated every time, but the
headers aren't the important part. At this point I'm more interested
in organizing the data itself and i can worry about putting it into a
new excel file later.

 

[petr.jakes.tpc]

Yes in the sense that the top part will have merged cells so that
Horror and Classics don't need to be repeated every time, but the
headers aren't the important part. At this point I'm more interested
in organizing the data itself and i can worry about putting it into a
new excel file later.

What I am able to do is to use SQL, I think it is quite simple, about
50 rows of code including sample data and comments. It works for me
and IMHO it is easy tu understand and I think you can use as it is.
Otherwise you can control Excel from Python using win32api, win32com
( http://tinyurl.com/2m3x3v )

HTH

Petr Jakes

#!/usr/bin/env python
# -*- coding: cp1250 -*-

import sqlite3
con = sqlite3.connect(":memory:")
cur = con.cursor()

normalizedInputData=[]
subCategories=[]

rawData = [['Italy', 'Horror', '70s', 'Suspiria','Dario Argento', 4],
['Italy', 'Classics', 'Neo-Realist', 'Otto eMezzo', 'Fellini',
3],
['GB', 'Classics', 'Neo-Humoristic', 'Otto eMezzo', 'Fellini',
3],
['Fr', 'Horror', 'Neo-Realist', 'Otto eMezzo', 'Fellini', 8],
['Fr', 'Classics', 'Neo-Realist', 'Otto eMezzo', 'Fellini',
55],
['GB', 'Horror', 'Neo-Realist', 'Otto eMezzo', 'Fellini', 5],
['Italy', 'Horror', '70s', 'Profondo Rosso','Dario Argento',
4]]

def alphanum(s):
"""only letters, numbers and '_' are acceptable for column names
by SQL"""
filtered=''
for ch in s:
if ch.isalnum() or ch in '_':
filtered+=ch
return filtered

for myRow in rawData :
cat_SubCat = alphanum("_".join(myRow[1:3]))
if cat_SubCat not in subCategories:
subCategories.append(cat_SubCat)
myRow[1:3] = [cat_SubCat]
normalizedInputData.append(myRow)

def data_generator(dataSet):
for dataSetRow in dataSet:
yield dataSetRow

subCategories=sorted(subCategories)

# create SQL table named "MOVIES" with the apropriate fields (the tabe
is store in the memory only)
cur.execute("create table MOVIES(COUNTRY, CATEGORY, TITLE, DIRECTOR,
QUANTITY)")

# fill the table with data
cur.executemany("""insert into MOVIES(COUNTRY, CATEGORY, TITLE,
DIRECTOR, QUANTITY) values (?,?,?,?,?)""",
data_generator(normalizedInputData))

# assemble dynamic SQL SELECT query, which returns PIVOT TABLE
prologue = "select COUNTRY, SUM(QUANTITY) AS TOTAL, "
template = "SUM (CASE CATEGORY WHEN '%s' THEN QUANTITY ELSE 0 END) %s"
epilogue = " FROM MOVIES GROUP BY 1 ORDER BY 1"
pivotSelect = prologue + ", ".join([template % (x, x) for x in
subCategories]) + epilogue

# execute SQL SELECT and return data row by row
cur.execute(pivotSelect)
for row in cur.fetchall():
print row

 

[patrick.waldo]

Petr thanks so much for your input. I'll try to learn SQL, especially
if I'll do a lot of database work.

I tried to do it John's way as en exercise and I'm happy to say I
understand a lot more. Basically I didn't realize I could nest
dictionaries like db = {country:{genre:{sub_genre:3}}} and call them
like db[country][genre][sub_genre]. The Python Cookbook was quite
helpful to figure out why items needed to be added the way they did.
Also using the structure of the dictionary was a conceptually easier
solution than what I found on http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/334695.

So, now I need to work on writing it to Excel. I'll update with the
final code.

Thanks again.

#Movie Store Example
class PivotData:
def __init__(self):
self.total_mov = 0
self.total_cou = {}
self.total_gen = {}
self.total_sub = {}
self.total_cou_gen ={}
self.db = {}
def add_data(self,country,genre,sub_genre,value):
self.total_mov += value
try:
self.total_cou[country] += value
except KeyError:
self.total_cou[country] = value
try:
self.total_gen[genre] += value
except:
self.total_gen[genre] = value
try:
self.total_sub[sub_genre] += value
except:
self.total_sub[sub_genre] = value
try:
self.total_cou_gen[country][genre] += value
except KeyError:
try:
self.total_cou_gen[country][genre] = value
except KeyError:
self.total_cou_gen[country] = {genre:value}
try:
self.db[country][genre][sub_genre] += value
except KeyError:
try:
self.db[country][genre][sub_genre] = value
except KeyError:
try:
self.db[country][genre] = {sub_genre:value}
except:
self.db[country] = {genre:{sub_genre:value}}

data = [['argentina','Horror', 'Slasher',4],
['argentina','Horror', 'Halloween',6],
['argentina','Drama','Romance',5],
['argentina','Drama','Romance',1],
['argentina','Drama','True Life',1],
['japan','Classics','WWII',1],
['japan','Cartoons','Anime',1],
['america','Comedy','Stand-Up',1],
['america','Cartoons','WB',10],
['america','Cartoons','WB',3]]

COUNTRY, GENRE, SUB_GENRE, VALUE =range(4)
x=PivotData()
for s in data:
x.add_data(s[COUNTRY],s[GENRE],s[SUB_GENRE],s[VALUE])
print
print 'Total Movies:\n', x.total_mov
print 'Total for each country\n', x.total_cou
print 'Total Genres\n', x.total_gen
print 'Total Sub Genres\n', x.total_sub
print 'Total Genres for each Country\n', x.total_cou_gen
print
print x.db

 

[petr.jakes.tpc]

Petr thanks so much for your input. I'll try to learnSQL, especially
if I'll do a lot of database work.

I tried to do it John's way as en exercise and I'm happy to say I
understand a lot more. Basically I didn't realize I could nest
dictionaries like db = {country:{genre:{sub_genre:3}}} and call them
like db[country][genre][sub_genre]. The Python Cookbook was quite
helpful to figure out why items needed to be added the way they did.
Also using the structure of the dictionary was a conceptually easier
solution than what I found onhttp://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/334695.

So, now I need to work on writing it to Excel. I'll update with the
final code.

Hi, good to know you have succeded. I think it is matter of taste
which way to go (dictionary or database). My feelig is: for data use
database! If you are trying to work with data, you will need it sooner
or later anyway. Again: database is made for data!

Writing your data to excel?
Just save your numbers separated by commas in the file with the
extension csv (my_data.csv) and you can open it directly in Excel.

Good luck

Petr