Setting up The Raspberry Pi Owncloud Server

Source -> http://pimylifeup.com/raspberry-pi-owncloud/

Firstly, you will need to have a Raspberry Pi with Raspbian installed. If you haven’t installed Raspbian then check out our guide on how to install Raspbian via NOOBS (New Out of the Box Software).
There are quite a few ways you’re able to install Owncloud onto your Raspberry Pi. In this particular tutorial we’re going to be downloading a web server (nginx) and Owncloud.

1. Firstly, in either The Pi’s command line or via SSH we will need to update the Raspberry Pi and its packages, do this by entering:

   sudo apt-get update
   sudo apt-get upgrade

2. Now we need to open up the Raspi Config Tool to change a few settings.

   sudo raspi-config

3. In here we will need to change a few settings.
   • Change Locale to en_US.UTF8 in internationalisation options -> change local.
   • Change memory split to 16m in Advanced options -> Memory split.
   • Change overclock to medium.
4. Add the www-data user to the www-data group.

   sudo usermod -a -G www-data www-data

5. Now we need to install all the required packages.

sudo apt-get install nginx openssl ssl-cert php5-cli php5-sqlite php5-gd php5-common php5-cgi sqlite3 php-pear php-apc curl libapr1 libtool curl libcurl4-openssl-dev php-xml-parser php5 php5-dev php5-curl php5-gd php5-fpm memcached php5-memcache varnish

6. Now we need to create an SSL certificate you can do this by running the following command:

sudo openssl req $@ -new -x509 -days 730 -nodes -out /etc/nginx/cert.pem -keyout /etc/nginx/cert.key

Simply just enter the relevant data for each of the questions it asks you.

7. Now we need to chmod the two cert files we just generated.

sudo chmod 600 /etc/nginx/cert.pem
sudo chmod 600 /etc/nginx/cert.key

8. Let’s clear the server config file since we will be copying and pasting our own version in it.

sudo sh -c "echo '' > /etc/nginx/sites-available/default"

9. Now let’s configure the web server configuration so that it runs Owncloud correctly.

sudo nano /etc/nginx/sites-available/default

10. Now simply copy and paste the following code into the file. Replace my IP (192.168.1.116) at server_name (There is 2 of them) with your Raspberry Pi’s IP.

upstream php-handler {
    server 127.0.0.1:9000;
    #server unix:/var/run/php5-fpm.sock;
}
server {
    listen 80;
    server_name 192.168.1.116;
    return 301 https://$server_name$request_uri;  # enforce https
}

server {
    listen 443 ssl;
    server_name 192.168.1.116;
    ssl_certificate /etc/nginx/cert.pem;
    ssl_certificate_key /etc/nginx/cert.key;
    # Path to the root of your installation
    root /var/www/owncloud;
    client_max_body_size 1000M; # set max upload size
    fastcgi_buffers 64 4K;
    rewrite ^/caldav(.*)$ /remote.php/caldav$1 redirect;
    rewrite ^/carddav(.*)$ /remote.php/carddav$1 redirect;
    rewrite ^/webdav(.*)$ /remote.php/webdav$1 redirect;
    index index.php;
    error_page 403 /core/templates/403.php;
    error_page 404 /core/templates/404.php;
    location = /robots.txt {
        allow all;
        log_not_found off;
        access_log off;
    }
    location ~ ^/(?:\.htaccess|data|config|db_structure\.xml|README) {
        deny all;
    }
    location / {
        # The following 2 rules are only needed with webfinger
        rewrite ^/.well-known/host-meta /public.php?service=host-meta last;
        rewrite ^/.well-known/host-meta.json /public.php?service=host-meta-json last;
        rewrite ^/.well-known/carddav /remote.php/carddav/ redirect;
        rewrite ^/.well-known/caldav /remote.php/caldav/ redirect;
        rewrite ^(/core/doc/[^\/]+/)$ $1/index.html;
        try_files $uri $uri/ index.php;
    }
    location ~ \.php(?:$|/) {
        fastcgi_split_path_info ^(.+\.php)(/.+)$;
        include fastcgi_params;
        fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
        fastcgi_param PATH_INFO $fastcgi_path_info;
        fastcgi_param HTTPS on;
        fastcgi_pass php-handler;
   }
   # Optional: set long EXPIRES header on static assets
   location ~* \.(?:jpg|jpeg|gif|bmp|ico|png|css|js|swf)$ {
        expires 30d;
        # Optional: Don't log access to assets
        access_log off;
   }
}

11. Now simply save and exit.
12. Now that is done there is a few more configurations we will need to update, first open up the PHP config file by entering.

    sudo nano /etc/php5/fpm/php.ini

13. In this file we want to find and update the following lines. (Ctrl + w allows you to search)

    upload_max_filezise = 2000M
    
    post_max_size = 2000M

14. Once done save and exit. Now we need to edit the conf file by entering the following:

    sudo nano /etc/php5/fpm/pool.d/www.conf

15. Update the listen line to the following:

    listen = 127.0.0.1:9000

16. Once done save and then exit. Now we ne also need to edit the dphys-swapfile. To do this open up the file by entering:

    sudo nano /etc/dphys-swapfile

17. Now update the conf_swapsize line to the following:

    CONF_SWAPSIZE = 512

18. Restart the Pi by entering:

    sudo reboot

19. Once the Pi has restarted you will need to install Owncloud onto the Raspberry Pi. Do this by entering the following commands:

sudo mkdir -p /var/www/owncloud
sudo wget https://download.owncloud.org/community/owncloud-8.1.1.tar.bz2
sudo tar xvf owncloud-8.1.1.tar.bz2
sudo mv owncloud/ /var/www/
sudo chown -R www-data:www-data /var/www
rm -rf owncloud owncloud-8.1.1.tar.bz2

20. We also need to make some changes to .htaccess file and the .user.ini file over in the Owncloud folder. Enter the following command to change directory and open up the .htaccess file.

    cd /var/www/owncloud
    sudo nano .htaccess

21. In here set the following values to 2000M

    php_value_upload_max_filesize 2000M
    
    php_value_post_max_size 2000M
    
    php_value_memory_limit 2000M

22. Save and exit, Open up the .user.ini file

sudo nano .user.ini

23. In here update the following values so they are 2000M:

    upload_max_filesize=2000M
    
    post_max_size=2000M
    
    memory_limit=2000M

24. Now that is done we should be able to connect to Owncloud at your PI’s IP address.

Before you set up the admin account you might want to mount an external drive so you have lots of disk space for your Raspberry Pi Owncloud Server. Simply follow the instructions in the next section.

Mounting & Setting up a drive

Setting up an external drive whilst should be relatively straight forward but sometimes things don’t work as perfectly as they should.
These instructions are for mounting and allowing Owncloud to store files onto an external hard drive.

1. Firstly if you have a NTFS drive we will need to install a NFTS package by entering the following:

   sudo apt-get install ntfs-3g

2. Now let’s make a directory we can mount to.

   sudo mkdir /media/ownclouddrive

3. Now we need to get the gid, uid and the uuid as we will need to use them soon. Enter the following command for the gid:

   id -g www-data

4. Now for the uid enter the following command:

   id -u www-data

5. Also if we get the UUID of the hard drive the Pi will remember this drive even if you plug it into a different USB port.

   ls -l /dev/disk/by-uuid

   

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   Copy the light blue letters and numbers of the last entry (Should have something like -> ../../sda1 at the end of it).
6. Now let’s add your drive into the fstab file so it is booted with the correct permissions.

   sudo nano /etc/fstab

7. Now add the following line to the bottom of the file, updating uid, guid and the UUID with the values we got above. (The following should all be on a single line)

   UUID=DC72-0315 /media/ownclouddrive auto uid=33,gid=33,umask=0027,dmask=0027, noatime 0 0

8. Reboot the Raspberry Pi and the drives should automatically be mounted. If they are mounted we’re all good to go.

Basic First Setup

I will briefly go through the basics of setting up Owncloud Raspberry Pi here. If you want more information I highly recommend checkout out the manuals on their website, you can find them here.

1. In your browser enter your Pi’s IP address in my case it is 192.168.1.116.
2. Once you go to the IP you’re like to get a certificate error, simply add this to your exception list as it will be safe to proceed.
3. When you first open up ownCloud you should be presented with a simple setup screen and no errors.
4. Enter your desired username and password.
5. Click on storage & database and enter your external drive /media/ownclouddrive (Skip this step if you didn’t setup an external drive).
6. Click finish setup.

Connecting to MSSQL from Fedora (and then Python 2.7)

source: http://blog.mdda.net/oss/2015/02/11/mssql-from-fedora-with-python/

With a heavy heart, I find myself having to talk to an MSSQL database. Fortunately, I can do this from a Linux (Fedora) VPS, so all is not lost.
For the following write-up (which are really just notes to myself), these links were helpful :

• ububtu instructions - clear but not directly applicable
• Helpful hints from Stackoverflow
• Fedora notes - wrong target language, though

Step 1 : Check there’s no firewall in the way

Try the port to check that the response differs from one with no server sitting on it :

telnet 192.168.x.y 1433

Step 2 : Install FreeTDS

The FreeTDS package is the one that Fedora uses :

yum install freetds freetds-devel unixODBC unixODBC-devel

Step 3 : Check that simple queries run (command line - direct)

Using the tsql utility (from FreeTDS), test that the basic connection works (each SQL command needs to be followed by ‘go’ on a separate line to get it to execute) :

tsql -H hostname.of.the.server -p 1433 -U username-for-db
#(enter password here not on command line to avoid it appearing in history, or in process list)

When the tsql prompt comes up,

# Do a fully-qualified query 
select count(*) from DATABASENAME.dbo.TABLENAME

# Do the same, drilled down
use DATABASENAME
select count(*) from TABLENAME

# Get a listing of the tables available
SELECT * FROM information_schema.tables where TABLE_TYPE = 'BASE TABLE'

Step 4 : Check that simple queries run (command line - named server)

In order to connect to the database by ‘name’, add it as an entry into /etc/freetds.conf :

[arbitrary-tds-server-title]
  host = hostname.of.the.server
  port = 1433
  tds version = 7.0

Then queries can be run using the given name (which will then be able to pick out the appropriate hostname and port from the configuration file) :

tsql -S arbitrary-tds-server-title -U username-for-db
#(enter password, not-in-history)

Step 5 : Set up ODBC configurations

Firstly, find the driver locations (on disk!) to put into /etc/odbcinst.ini :

find / -iname 'libtds*.so'

Then, create a suitable entry in /etc/odbcinst.ini, so that ODBC know to talk to the FreeTDS drivers :

[FreeTDS]
Description = MS SQL database access with Free TDS
Driver64    = /usr/lib64/libtdsodbc.so
Setup64     = /usr/lib64/libtdsS.so
FileUsage   = 1

Then, create an entry in /etc/odbc.ini (which may have to be created) :

[sqlserverdatasource-name-is-arbitrary]
Driver      = FreeTDS
Description = ODBC connection via FreeTDS
Trace       = No
Servername  = arbitrary-tds-server-title
#Database    = <name of your database - may be useful to restrict usage>

Step 6 : Set up Python connection to ODBC

yum install pyodbc

And then one can use it in the Python shell :

python
>>> import pyodbc
>>> dsn='sqlserverdatasource-name-is-arbitrary'
>>> user='username-for-db'
>>> password='XXXXXXXX'
>>> database='DATABASENAME'
>>> con_string='DSN=%s;UID=%s;PWD=%s;DATABASE=%s;' % (dsn, user, password, database)
>>> cnxn = pyodbc.connect(con_string)
>>> cursor = cnxn.cursor()
>>> cursor.execute("select count(*) from TABLENAME")
<pyodbc.Cursor object at 0x7fe4fd2a0b10>
>>> row=cursor.fetchone()
>>> row
(249619, )

Step 7 : Read up on ODBC databases in Python

For more, see the PyODBC getting started guide.

https://azure.microsoft.com/en-us/documentation/articles/sql-database-develop-python-simple-windows/

This topic presents a code sample written in Python. The sample runs on a Windows computer. The sample and connects to Azure SQL Database by using the pymssql driver.

Requirements

• Python 2.7.6

Install the required modules

Install pymssql.
Make sure you choose the correct whl file.
For example : If you are using Python 2.7 on a 64 bit machine choose : pymssql‑2.1.1‑cp27‑none‑win_amd64.whl. Once you download the .whl file place it in the the C:/Python27 folder.
Now install the pymssql driver using pip from command line. cd into C:/Python27 and run the following

pip install pymssql‑2.1.1‑cp27‑none‑win_amd64.whl

Instructions to enable the use pip can be found here

Create a database and retrieve your connection string

See the Getting Started Topic to learn how to create a sample database and retrieve your connection string. It is important you follow the guide to create an AdventureWorks database template. The samples shown below only work with the AdventureWorks schema.

Connect to your SQL Database

The pymssql.connect function is used to connect to SQL Database.

import pymssql
conn = pymssql.connect(server='yourserver.database.windows.net', user='yourusername@yourserver', password='yourpassword', database='AdventureWorks')

Execute an SQL SELECT statement

The cursor.execute function can be used to retrieve a result set from a query against SQL Database. This function essentially accepts any query and returns a result set which can be iterated over with the use of cursor.fetchone().

import pymssql
conn = pymssql.connect(server='yourserver.database.windows.net', user='yourusername@yourserver', password='yourpassword', database='AdventureWorks')
cursor = conn.cursor()
cursor.execute('SELECT c.CustomerID, c.CompanyName,COUNT(soh.SalesOrderID) AS OrderCount FROM SalesLT.Customer AS c LEFT OUTER JOIN SalesLT.SalesOrderHeader AS soh ON c.CustomerID = soh.CustomerID GROUP BY c.CustomerID, c.CompanyName ORDER BY OrderCount DESC;')
row = cursor.fetchone()
while row:
    print str(row[0]) + " " + str(row[1]) + " " + str(row[2])   
    row = cursor.fetchone()

Insert a row, pass parameters, and retrieve the generated primary key

In SQL Database the IDENTITY property and the SEQUENCE object can be used to auto-generate primary key values.

import pymssql
conn = pymssql.connect(server='yourserver.database.windows.net', user='yourusername@yourserver', password='yourpassword', database='AdventureWorks')
cursor = conn.cursor()
cursor.execute("INSERT SalesLT.Product (Name, ProductNumber, StandardCost, ListPrice, SellStartDate) OUTPUT INSERTED.ProductID VALUES ('SQL Server Express', 'SQLEXPRESS', 0, 0, CURRENT_TIMESTAMP)")
row = cursor.fetchone()
while row:
    print "Inserted Product ID : " +str(row[0])
    row = cursor.fetchone()

Transactions

This code example demonstrates the use of transactions in which you:
-Begin a transaction
-Insert a row of data
-Rollback your transaction to undo the insert

import pymssql
conn = pymssql.connect(server='yourserver.database.windows.net', user='yourusername@yourserver', password='yourpassword', database='AdventureWorks')
cursor = conn.cursor()
cursor.execute("BEGIN TRANSACTION")
cursor.execute("INSERT SalesLT.Product (Name, ProductNumber, StandardCost, ListPrice, SellStartDate) OUTPUT INSERTED.ProductID VALUES ('SQL Server Express New', 'SQLEXPRESS New', 0, 0, CURRENT_TIMESTAMP)")
cnxn.rollback()

Next steps

For more information, see the Python Developer Center.

Example to Read/Write Excel for Python 3.5

$ pip install openpyxl

$ pip install pillow

Write Sample code:

from openpyxl import Workbook
wb = Workbook()

# grab the active worksheet
ws = wb.active

# Data can be assigned directly to cells
ws['A1'] = 42

# Rows can also be appended
ws.append([1, 2, 3])

# Python types will automatically be converted
import datetime
ws['A2'] = datetime.datetime.now()

# Save the file
wb.save("sample.xlsx")

Read Sample code:

from xlrd import open_workbook

book = open_workbook('simple.xls',on_demand=True)
for name in book.sheet_names():
    if name.endswith('2'):
        sheet = book.sheet_by_name(name)

        # Attempt to find a matching row (search the first column for 'john')
        rowIndex = -1
        for cell in sheet.col(0): # 
            if 'john' in cell.value:
                break

        # If we found the row, print it
        if row != -1:
            cells = sheet.row(row)
            for cell in cells:
                print cell.value

        book.unload_sheet(name) 

The easiest way to understand xs is to show an example. I will take a data example from the pivot table article.
First we get the data uploaded into a simple pivot table. Do my standard imports, read in the data and create my pivot table:

import pandas as pd
import numpy as np

df = pd.read_excel("sales-funnel.xlsx")
table = pd.pivot_table(df,index=["Manager","Rep","Product"],
               values=["Price","Quantity"],
               aggfunc=[np.sum,np.mean],fill_value=0)

table

			sum		mean
			Price	Quantity	Price	Quantity
Manager	Rep	Product
Debra Henley	Craig Booker	CPU	65000	2	32500	1.0
		Maintenance	5000	2	5000	2.0
		Software	10000	1	10000	1.0
	Daniel Hilton	CPU	105000	4	52500	2.0
		Software	10000	1	10000	1.0
	John Smith	CPU	35000	1	35000	1.0
		Maintenance	5000	2	5000	2.0
Fred Anderson	Cedric Moss	CPU	95000	3	47500	1.5
		Maintenance	5000	1	5000	1.0
		Software	10000	1	10000	1.0
	Wendy Yule	CPU	165000	7	82500	3.5
		Maintenance	7000	3	7000	3.0
		Monitor	5000	2	5000	2.0

This is fairly straightforward once you understand the pivot_table syntax.
Now, let’s take a look at what xs can do:

table.xs('Debra Henley', level=0)

		sum		mean
		Price	Quantity	Price	Quantity
Rep	Product
Craig Booker	CPU	65000	2	32500	1
	Maintenance	5000	2	5000	2
	Software	10000	1	10000	1
Daniel Hilton	CPU	105000	4	52500	2
	Software	10000	1	10000	1
John Smith	CPU	35000	1	35000	1
	Maintenance	5000	2	5000	2

Ok, this is pretty interesting. xs allows me to drill down to one cross-section of the pivot table. We can drill down multiple levels as well. If we want to just see one rep’s results:

table.xs(('Debra Henley','Craig Booker'), level=0)

	sum		mean
	Price	Quantity	Price	Quantity
Product
CPU	65000	2	32500	1
Maintenance	5000	2	5000	2
Software	10000	1	10000	1

If you’re like me, you just had light bulb go off and realize that a lot of cutting and pasting you have done in Excel can be a thing of the past.
We need the get_level_values to make this work as seamlessly as possible. For example, if we want to see all the Manager values:

table.index.get_level_values(0)

Index([u'Debra Henley', u'Debra Henley', u'Debra Henley', u'Debra Henley', u'Debra Henley', u'Debra Henley', u'Debra Henley', u'Fred Anderson', u'Fred Anderson', u'Fred Anderson', u'Fred Anderson', u'Fred Anderson', u'Fred Anderson'], dtype='object')

If we want to see all the rep values:

table.index.get_level_values(1)

Index([u'Craig Booker', u'Craig Booker', u'Craig Booker', u'Daniel Hilton', u'Daniel Hilton', u'John Smith', u'John Smith', u'Cedric Moss', u'Cedric Moss', u'Cedric Moss', u'Wendy Yule', u'Wendy Yule', u'Wendy Yule'], dtype='object')

To make it a little simpler for iterating, use unique :

table.index.get_level_values(0).unique()

array([u'Debra Henley', u'Fred Anderson'], dtype=object)

Now it should be clear what we’re about to do. I’ll print it out first so you can see.

for manager in table.index.get_level_values(0).unique():
    print(table.xs(manager, level=0))

                              sum            mean
                            Price Quantity  Price Quantity
Rep           Product
Craig Booker  CPU           65000        2  32500        1
              Maintenance    5000        2   5000        2
              Software      10000        1  10000        1
Daniel Hilton CPU          105000        4  52500        2
              Software      10000        1  10000        1
John Smith    CPU           35000        1  35000        1
              Maintenance    5000        2   5000        2
                            sum            mean
                          Price Quantity  Price Quantity
Rep         Product
Cedric Moss CPU           95000        3  47500      1.5
            Maintenance    5000        1   5000      1.0
            Software      10000        1  10000      1.0
Wendy Yule  CPU          165000        7  82500      3.5
            Maintenance    7000        3   7000      3.0
            Monitor        5000        2   5000      2.0

As we pull it all together, it is super simple to create a single Excel sheet with one tab per manager:

writer = pd.ExcelWriter('output.xlsx')

for manager in table.index.get_level_values(0).unique():
    temp_df = table.xs(manager, level=0)
    temp_df.to_excel(writer,manager)

writer.save()

You now get an output that looks like this:

Stop and Think

As you sit back and think about this code, just take a second to revel in how much we are doing with 7 lines of code (plus 2 imports):

import pandas as pd
import numpy as np

df = pd.read_excel("sales-funnel.xlsx")
table = pd.pivot_table(df,index=["Manager","Rep","Product"], values=["Price","Quantity"],aggfunc=[np.sum,np.mean],fill_value=0)
writer = pd.ExcelWriter('output.xlsx')
for manager in table.index.get_level_values(0).unique():
    temp_df = table.xs(manager, level=0)
    temp_df.to_excel(writer,manager)
writer.save()

We have just read in an Excel file, created a powerful summary of data, then broken the data up into an output Excel file with separate tabs for each manager. Just by using 9 lines of code!
I think my excitement about this functionality is warranted.

Taking It One Step Further

In some cases, you might want to generate separate files per manager or do some other manipulation. It should be pretty simple to understand how to do so given the examples above.
To close out this discussion, I decided I would wrap things up with a fully functional program that utilizes additional python functions to make this script a truly useful program that utilizes good python programming practices so that you can scale it up for your own needs:

"""
Sample report generation script from pbpython.com

This program takes an input Excel file, reads it and turns it into a
pivot table.

The output is saved in multiple tabs in a new Excel file.
"""

import argparse
import pandas as pd
import numpy as np


def create_pivot(infile, index_list=["Manager", "Rep", "Product"],
                 value_list=["Price", "Quantity"]):
    """
    Read in the Excel file, create a pivot table and return it as a DataFrame
    """
    df = pd.read_excel(infile)
    table = pd.pivot_table(df, index=index_list,
                           values=value_list,
                           aggfunc=[np.sum, np.mean], fill_value=0)
    return table


def save_report(report, outfile):
    """
    Take a report and save it to a single Excel file
    """
    writer = pd.ExcelWriter(outfile)
    for manager in report.index.get_level_values(0).unique():
        temp_df = report.xs(manager, level=0)
        temp_df.to_excel(writer, manager)
    writer.save()

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Script to generate sales report')
    parser.add_argument('infile', type=argparse.FileType('r'),
                        help="report source file in Excel")
    parser.add_argument('outfile', type=argparse.FileType('w'),
                        help="output file in Excel")
    args = parser.parse_args()
    # We need to pass the full file name instead of the file object
    sales_report = create_pivot(args.infile.name)
    save_report(sales_report, args.outfile.name)

The simplest example of reading a CSV file:

import csv
with open('some.csv', newline='') as f:
    reader = csv.reader(f)
    for row in reader:
        print(row)

Reading a file with an alternate format:

import csv
with open('passwd', newline='') as f:
    reader = csv.reader(f, delimiter=':', quoting=csv.QUOTE_NONE)
    for row in reader:
        print(row)

The corresponding simplest possible writing example is:

import csv
with open('some.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerows(someiterable)

Since open() is used to open a CSV file for reading, the file will by default be decoded into unicode using the system default encoding (see locale.getpreferredencoding()). To decode a file using a different encoding, use the encoding argument of open:

import csv
with open('some.csv', newline='', encoding='utf-8') as f:
    reader = csv.reader(f)
    for row in reader:
        print(row)

The same applies to writing in something other than the system default encoding: specify the encoding argument when opening the output file.
Registering a new dialect:

import csv
csv.register_dialect('unixpwd', delimiter=':', quoting=csv.QUOTE_NONE)
with open('passwd', newline='') as f:
    reader = csv.reader(f, 'unixpwd')

A slightly more advanced use of the reader — catching and reporting errors:

import csv, sys
filename = 'some.csv'
with open(filename, newline='') as f:
    reader = csv.reader(f)
    try:
        for row in reader:
            print(row)
    except csv.Error as e:
        sys.exit('file {}, line {}: {}'.format(filename, reader.line_num, e))

And while the module doesn’t directly support parsing strings, it can easily be done:

import csv
for row in csv.reader(['one,two,three']):
    print(row)

Footnotes

[1]

(1, 2) If newline='' is not specified, newlines embedded inside quoted fields will not be interpreted correctly, and on platforms that use \r\n linendings on write an extra \r will be added. It should always be safe to specify newline='', since the csv module does its own (universal) newline handling.