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How to Extract Tabular Data from Doc files Using Python?

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This article was published as a part of the Data Science Blogathon

Introduction

Data is present everywhere. Any action we perform generates some or the other form of data. But this data might not be present in a structured form. A beginner starting with the data field is often trained for datasets in standard formats like CSV, TSV, or some text files. CSV files are the most preferred files as they can be loaded into a pandas dataframe and manipulated more easily. The text files can be loaded using naive Python file handling modules.

But in the real world, any type of document can have the data needed for analysis. While I was applying for an internship position in a company, my assignment was to draw analysis out of the data present in the Doc file. In this article, I will explain the ETL process for a Doc file, the difference between Doc and Docx extensions, conversion of Doc to Docx, and at the end, I will show you how I created some interactive plots from that data.

Extract tabular data from doc
Image by Author, Made in Canva

Difference Between Doc and Docx

While dealing with doc files, you will come across these two extensions: ‘.doc’ and ‘.docx’. Both the extensions are used for Microsoft word documents that can be created using Microsoft Word or any other word processing tool. The difference lies in the fact that till word 2007, the “doc” extension was used extensively.

After this version, Microsoft introduced a new extension, “Docx”, which is a Microsft Word Open XML Format Document. This extension allowed files to be smaller, easy to store, and less corrupted. It also opened doors to online tools like Google Sheets which can easily manage these Docx files.

Conversion of Doc to Docx in Python

Today, all the files are by default created with the extension Docx but there are still many old files with Doc extension. A Docx file is a better solution to store and share data but we can’t neglect the data stored in Doc files. It might be of great value. Therefore, to retrieve data from Doc files, we need to convert the Doc file to Docx format. Depending on the platform, Windows or Linux, we have different ways for this conversion.

For Windows

Manually, for a word file to be saved as Docx, you simply need to save the file with the extension “.docx”

doc to docx conversion

We will perform this task using Python. Window’s Component Object Model (COM) allows Windows applications to be controlled by other applications. pywin32 is the Python wrapper module that can interact with this COM and automate any windows application using Python. Therefore, the implementation code goes like this:

from win32com import client as wc
w = wc.Dispatch('Word.Application')
doc = w.Documents.Open("file_name.doc")
doc.SaveAs("file_name.docx", 16)

Breakdown of the code:

  1. First, we are importing the client from the win32com package which is preinstalled module during Python installation.
  2. Next, we are creating a Dispatch object for the Word Application.
  3. Then, we are opening this document and saving it with the Docx extension.

For Linux

We can directly use LibreOffice in-build converter:

lowriter --convert-to docx testdoc.doc

Reading Docx files in Python

Python has a module for reading and manipulating Docx files. It’s called “python-docx”. Here, all the essential functions have been already implemented. You can install this module via pip:

pip install python-docx

I won’t go into detail about how a Docx document is structured but on an abstract level, it has 3 parts: Run, paragraph, and Document objects. For this tutorial, we will be dealing with paragraph and Document objects. Before moving to the actual code implementation, let us see the data will be extracting:

extracting data | Extract tabular data doc

Data in new Docx file

The new Docx file contains the glucose level of a patient after several intervals. Each data row has an Id, Timestamp, type, and glucose level reading. To maintain anonymity, I have blurred out the Patient’s name. Procedure to extract this data:

1. Import the module

import docx

2. Create a Docx file document object and pass the path to the Docx file.

Text = docx.Document('file_name.docx')

3. Create an empty data dictionary

data = {}

4. Create a paragraph object out of the document object. This object can access all the paragraphs of the document

paragraphs = Text.paragraphs

5. Now, we will iterate over all the paragraphs, access the text, and save them into a data dictionary

for i in range(2, len(Text.paragraphs)): data[i] = tuple(Text.paragraphs[i].text.split('t'))

Here I had to split the text at “t” as if you look at one of the rows, it had the tab separator.

6. Access the values of the dictionary

data_values = list(data.values())

Now, these values are transformed as a list and we can pass them into a pandas dataframe. According to my use case, I had to follow some additional steps such as dropping unnecessary columns and timestamp conversion. Here is the final pandas dataframe I got from the initial Doc file:

data | Extract tabular data doc

There are a lot of things that can be done using the python-docx module. Apart from loading the file, one can create a Docx file using this module. You can add headings, paragraphs, make text bold, italics, add images, tables, and much more! Here is the link to the full documentation of the module.

Bonus Step: Plot using Plotly

The main aim of this article was to show you how to extract tabular data from a doc file into a pandas dataframe. Let’s complete the ELT cycle and transform this data into beautiful visualizations using the Plotly library! If you don’t know, Plotly is an amazing visualization library that helps in creating interactive plots.

These plots don’t require much effort as most of the things can be customized. There are many articles on Analytics Vidhya describing the usage of this library. For my use case, here is the configuration for the plot:

import plotly.graph_objects as go fig = go.Figure() fig.add_trace(go.Scatter(x=doc_data.index, y=doc_data['Historic Glucose (mg/dL)'].rolling(5).mean(), mode='lines', marker=dict( size=20, line_width=2, colorscale='Rainbow', showscale=True, ), name = 'Historic Glucose (mg/dL)' )) fig.update_layout(xaxis_tickangle=-45, font=dict(size=15), yaxis={'visible': True}, xaxis_title='Dates', yaxis_title='Glucose', template='plotly_dark', title='Glucose Level Over Time' ) fig.update_layout(hovermode="x")

plot
Image by Author

Conclusion

In this article, I explained what are doc files, the difference between Doc and Docx file extensions, conversion of Doc files into Docx files, loading and manipulation of Docx files, and finally how to load this tabular data into a pandas dataframe.

If you want to read/explore every article of mine, then head over to my master article list which gets updated every time I publish a new article on any platform!

For any doubts, queries, or potential opportunities, you can reach out to me via:

1. Linkedin — in/kaustubh-gupta/

2. Twitter — @Kaustubh1828

3. GitHub — kaustubhgupta

4. Medium — @kaustubhgupta1828

The media shown in this article on Interactive Dashboard using Bokeh are not owned by Analytics Vidhya and are used at the Author’s discretion.

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Source: https://www.analyticsvidhya.com/blog/2021/09/how-to-extract-tabular-data-from-doc-files-using-python/

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Proximity labeling: an enzymatic tool for spatial biology

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In this Forum, we highlight how cutting-edge, proximity-dependent, enzymatic labeling tools, aided by sequencing technology developments, have enabled the extraction of spatial information of proteomes, transcriptomes, genome organization, and cellular networks. We also discuss the potential applications of proximity labeling in the unexplored field of spatial biology in live systems.

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Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00211-0?rss=yes

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Synthetic biology applications of the yeast mating signal pathway

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Glossary

Central carbon metabolism (CCM)

as the main source of energy, CCM oxidizes carbon through glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle.

Chassis

a cell host or an organism for the production of biochemicals such as enzymes by introducing synthetic modules or devices into the cell.

Circuit

an assembly of biological parts that enables cells to perform logical functions, such as genetic switches, oscillators, and logic gates.

Convolutional neural network

a class of artificial neural networks with multiple building blocks that automatically and adaptively learn spatial hierarchies of features through back-propagation.

Clustered regularly interspaced short palindromic repeats (CRISPR)

a genome-editing tool in which CRISPR-associated nuclease 9 (Cas9)–guide RNA (gRNA) complexes recognize a protospacer adjacent motif through base-pairing and then cleave the target DNA,

CRISPR activation or interference (CRISPRa/i)

a tool that uses dead Cas protein and gRNA to activate or repress genes, resulting in gene upregulation or downregulation, respectively.

Cubic ternary complex model

an equilibrium model that describes the interactions between receptor and ligand. This model simulates the interactions of G proteins and receptors in both their active and inactive conformations.

G proteins

heterotrimeric G protein complexes are composed of α, β and γ subunits. Replacement of GDP by GTP in Gα causes a conformational change that dissociates the Gβγ subunits, leading to the activation of downstream signaling.

G protein-coupled receptor (GPCR)

a generic class of versatile, seven transmembrane-domain proteins that regulate a diverse array of intracellular signaling cascades in response to hormones, neurotransmitters, and other stimuli.

Karyogamy

a cascade of molecular events that finally lead to fusion of the nuclei and the formation of diploid cells.

Metabolic engineering

a new scientific field that combines multi-gene recombination technology with metabolic regulation and biochemical engineering to overproduce desired products.

Mitogen-activated protein kinases (MAPKs)

a family of serine/threonine kinases that convert extracellular signals into a diverse range of cellular responses.

Omics

studies include genomics, transcriptomics, proteomics, and metabolomics that characterize and quantify pools of biological molecules, and together give rise to the field of integrative genetics.

Oscillator

a genetic circuit where oscillation is generated by the inhibition and activation of transcriptional/translational feedback loops.

Pheromone-response element (PRE)

a cis element that is present in multiple copies in the promoters of a variety of pheromone-responsive genes; PREs interact with Ste12 to initiate the transcription of pheromone-induced genes.

Quorum sensing

a cell density-dependent phenomenon in which cells adapt their behavior by synthesizing, secreting, perceiving, and reacting to small diffusible signaling molecules termed autoinducers.

Scaffold protein

proteins that recruit other proteins to form a functional unit, thus enhancing signaling efficiency and fidelity.

Ste5ΔN-CTM

a Ste5 mutant that lacks the Gβγ-binding site because its N-terminus has been truncated; Ste5ΔN-CTM is no longer recruited to the plasma membrane following pheromone treatment.

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Biotechnology of functional proteins and peptides for hair cosmetic formulations

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    Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00213-4?rss=yes

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    VW’s 9-month electric vehicle deliveries to China more than triple

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    FRANKFURT (Reuters) – Volkswagen’s deliveries of battery-powered electric vehicles to China more than tripled in the first nine months of the year, the carmaker said on Friday, less than two months after it flagged the need to change its e-car strategy there.

    Deliveries of battery electric vehicles (BEV) to the world’s largest car market stood at 47,200 in the January-September period, up from 15,700 in the same period last year.

    “As planned, we significantly accelerated the BEV market ramp-up in China in the third quarter, and we are on track to meet our target for the year of delivering 80,000 to 100,000 vehicles of the ID. model family,” Christian Dahlheim, head of group sales, said.

    Volkswagen Chief Executive Herbert Diess in July said the carmaker had to change its approach to how it markets its BEVs in China after first-half deliveries stood at just 18,285.

    (Reporting by Christoph Steitz; Editing by Maria Sheahan)

    Image Credit: Reuters

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    Source: https://datafloq.com/read/vws-9-month-electric-vehicle-deliveries-china-triple/18644

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