Converting namedtuples to XLSX in Python

This Python snippet allows you to convert an iterable of namedtuple instances to an XLSX file using xlsxwriter.

The header is automatically determined from the first element of the iterable. If the iterable is empty, the resulting XLSX file will also be empty.

import xlsxwriter
import itertools
from collections import namedtuple

def xlsx_write_rows(filename, rows):
    Write XLSX rows from an iterable of rows.
    Each row must be an iterable of writeable values.

    Returns the number of rows written
    workbook = xlsxwriter.Workbook(filename)
    worksheet = workbook.add_worksheet()
    # Write values
    nrows = 0
    for i, row in enumerate(rows):
        for j, val in enumerate(row):
            worksheet.write(i, j, val)
        nrows += 1
    # Cleanup
    return nrows

def namedtuples_to_xlsx(filename, values):
    Convert a list or generator of namedtuples to an XLSX file.
    Returns the number of rows written.
        # Ensure its a generator (next() not allowed on lists)
        values = (v for v in values)
        # Use first row to generate header
        peek = next(values)
        header = list(peek.__class__._fields)
        return xlsx_write_rows(filename, itertools.chain([header], [peek], values))
    except StopIteration:  # Empty generator
        # Write empty xlsx
        return xlsx_write_rows(filename, [])

Example Usage:

MyType = namedtuple("MyType", ["ID", "Name", "Value"])
namedtuples_to_xlsx("test.xlsx", [
    MyType(1, "a", "b"),
    MyType(2, "c", "d"),
    MyType(3, "e", "f"),

This example will generate this table:

ID	Name	Value
1	a	b
2	c	d
3	e	f


Fixing TensorFlow cannot open shared object file on Ubuntu


When you run import tensorflow in Python, you get one of the following errors:

ImportError: cannot open shared object file: No such file or directory
ImportError: cannot open shared object file: No such file or directory
ImportError: cannot open shared object file: No such file or directory
ImportError: cannot open shared object file: No such file or directory
ImportError: cannot open shared object file: No such file or directory


Install the required packages using:

apt-get install libcublas8.0 libcusolver8.0 libcudart8.0 libcufft8.0 libcurand8.0

Note that you also need to install cuDNN – see this followup post

Which version on CuDNN should you install for TensorFlow GPU on Ubuntu?

for details on how to do that.

If this method does not work, you can (as a quick workaround) uninstall tensorflow-gpu and install the tensorflow – the version without GPU support:

pip3 uninstall tensorflow-gpu
pip3 install tensorflow

However, this will likely make your applications much slower.

For other solutions see the TensorFlow bugtracker on GitHub.

How to use concurrent.futures map with a tqdm progress bar


You have a concurrent.futures executor, e.g.

import concurrent.futures

executor = concurrent.futures.ThreadPoolExecutor(64)

Using this executor, you want to map a function over an iterable in parallel (e.g. parallel download of HTTP pages).

In order to aid interactive execution, you want to use tqdm to provide a progress bar, showing the fraction of futures


You can use this function:

from tqdm import tqdm
import concurrent.futures

def tqdm_parallel_map(executor, fn, *iterables, **kwargs):
    Equivalent to, *iterables),
    but displays a tqdm-based progress bar.
    Does not support timeout or chunksize as executor.submit is used internally
    **kwargs is passed to tqdm.
    futures_list = []
    for iterable in iterables:
        futures_list += [executor.submit(fn, i) for i in iterable]
    for f in tqdm(concurrent.futures.as_completed(futures_list), total=len(futures_list), **kwargs):
        yield f.result()

Note that internally, executor.submit() is used, not because there is no way of calling concurrent.futures.as_completed() on the iterator returned by

Note: In constract to this function does NOT yield the arguments in the same order as the input.

requests: Download file if it doesn’t exist


You want to download a URL to a file using the requests python library, but you want to skip the download if it doesn’t exist


Use the following functions:

import requests
import os.path

def download_file(filename, url):
    Download an URL to a file
    with open(filename, 'wb') as fout:
        response = requests.get(url, stream=True)
        # Write response data to file
        for block in response.iter_content(4096):

def download_if_not_exists(filename, url):
    Download a URL to a file if the file
    does not exist already.

    True if the file was downloaded,
    False if it already existed
    if not os.path.exists(filename):
        download_file(filename, url)
        return True
    return False


Removing spans/divs with style attributes from HTML

Occasionally I have to clean up some HTML code – mostly because parts of it were pasted into a CMS like WordPress from rich text editor like Word.

I’ve noticed that the formatting I want to remove is mostly based on span and div elements with a style attribute. Therefore, I’ve written a simple Python script based on BeautifulSoup4 which will replace certain tags with their contents if they have a style attribute. While in some cases some other formatting might be destroyed by such a script, it is very useful for some recurring usecases.

Read more

Upload multiple files to the tornado webserver

The following html code can be used to create an html form that allows uploading multiple files at once:

<form enctype="multipart/form-data" method="POST" action="">
  <table style="width: 100%">
      <td>Choose the files to upload:</td>
      <td style="text-align: right"><input type="file" multiple="" id="files" name="files"></td>
      <td><input id="fileUploadButton" type="submit" value="Upload &gt;&gt;"></td>

Read more

Normalizing electronics engineering value notations using Python

In electronics engineering there is a wide variety of notations for values that need to be recognized by intuitive user interfaces. Examples include:

  • 1fA
  • 0.1A
  • 0.00001
  • 1e-6
  • 4,5nA
  • 4,500.123 A
  • 4A5
  • 4k0 A

The wide variety of options, including thousands separators, comma-as-decimal-separator and suffix-as-decimal-separator, optional whitespace and scientific notations makes it difficult to normalize values without using specialized libraries. Read more

Engineering for the super-lazy: Solving equations without activating your brain


In electronics engineering, from time to time you have to use standard formulas to characterize your circuits. To what extent you need to calculate all parameters most often depends on the requirement.

For example, consider the formula for the -3dB cutoff frequency of a 1st order RC lowpass filter:

f_c=\frac{1}{2\pi RC}

Although this equation is fairly simple and most people won’t have any problem solving it for any particular variable in a few seconds, it can serve as a basic example on how to solve an equation symbolically.

One of the easiest ways of performing this task is to use SymPy, a Python library for symbolic mathematics.

Read more