How to Work with Excel Spreadsheets using Python

Read it in 16 Mins

Last updated on
01st Dec, 2022
Published
31st Oct, 2019
Views
17,663
How to Work with Excel Spreadsheets using Python

Excel is considered one of the most popular and widely used spreadsheet applications developed by Microsoft. You can organize, analyze and store your data into tabular sheets with the help of Excel. From analysts and sales managers to CEOs, professionals from every field use Excel for creating quick statistics and for data crunching. These features make excel one of the most popular data sources in Python programs.

Check out the use of Self in Python with examples here

Spreadsheets are commonly used in the present world because of their intuitive nature and the ability to handle large datasets. Most importantly, they can work without any prior technical background.

Finding different ways to work with Excel using code is essential since working with data and in Python has some serious advantages in comparison with Excel’s UI. Developers of Python have implemented ways to read, write and manipulate Excel documents. An instructor-led python programming course is a good way to get hands-on experience of using excel within Python.

You can check the quality of your spreadsheet application by going over the checklist below:

  • Is the spreadsheet able to represent static data?
  • Is the spreadsheet able to mix data, calculations, and reports?
  • Is the data in your spreadsheet complete and consistent in nature?
  • Does the spreadsheet have an organized worksheet structure?

This checklist will help you in verifying the qualitative nature of the spreadsheet application you’re going to work on.

Practical Applications  

In this article, we would be using openpyxl to work on data. With the help of this module, you can extract data from a database into an Excel spreadsheet or you can also convert an Excel spreadsheet into a programmatic format. There can be a lot of possible situations where you might feel the need to use a package like openpyxl. Let us discuss a few of them to get a comprehensive overview of it.

Importing New Products Into a Database 

Consider yourself working in an online store company. When they want to add new products to the online store, they make an Excel spreadsheet with a few hundred rows along with the name of the product, description, price, and a few more basic information and then they give it to you. 

Now, if you want to import this particular data, you need to iterate over each row of the spreadsheet and then add each of the products into the database of the online store.

Exporting Database Data Into a Spreadsheet

Consider you have a Database table. In this particular table, you have collected information of all your users which includes their name, contact number, email address, and so forth. Now, the Marketing Team is willing to collectively contact all the users and promote a new product of the company. However, neither do they have access to the Database nor do they have any idea about using SQL to extract the information. 

In this situation, openpyxl comes to play. You can use it effectively to iterate over each User record and transform the required information into an Excel spreadsheet.    

Appending Information to an Existing Spreadsheet

Consider the same online store example we discussed above. You have an Excel spreadsheet with a list of users and your job is to append to each row the total amount they have spent in your store.

In order to perform this, you have to read the spreadsheet first and then iterate through each row and fetch the total amount spent from the Database. Finally, you need to write it back to the spreadsheet.

Starting openpyxl

You can install the openpyxl package using pip. Open your terminal and write the following command: 

$ pip install openpyxl

After you have installed the spreadsheet, you can make up your own simple spreadsheet: 

from openpyxl import Workbook

workbook = Workbook()
spreadsheet = workbook.active

spreadsheet["A1"] = "Hello"
spreadsheet["B1"] = "World!"

workbook.save(filename="HelloWorld.xlsx")

How to Read Excel Spreadsheets with openpyxl 

Let us start with the most important thing that you can do with a spreadsheet,i.e. read it. We will be using a Watch Sample Dataset which contains a list of 100 watches with information like product name, product ID, review, and so forth.  

A Simple Way to Read an Excel Spreadsheet 

Let us start with opening our sample spreadsheet:

>>> from openpyxl import load_workbook
>>> workbook = load_workbook(filename="sample.xlsx")
>>> workbook.sheetnames
['Sheet 1']

>>> spreadsheet = workbook.active
>>> spreadsheet
<Worksheet "Sheet 1">

>>> spreadsheet.title

In the example code above, we open the spreadsheet using load_workbook and then we check all the sheets that are available to work with using workbook.sheetnames. Then Sheet 1 is automatically selected using workbook.active since it is the first sheet available. This is the most common way of opening a spreadsheet.  

Now, let us see the code to retrieve data from the spreadsheet: 

>>> spreadsheet["A1"]
<Cell 'Sheet 1'.A1>

>>> spreadsheet["A1"].value
'marketplace'

>>> spreadsheet["F10"].value
"G-Shock Men's Grey Sport Watch"

You can retrieve the actual value and the cell value both. To get the actual value, use .value and to get the cell, you can use .cell():

>>> spreadsheet.cell(row=10, column=6)
<Cell 'Sheet 1'.F10>

>>> spreadsheet.cell(row=10, column=6).value
"G-Shock Men's Grey Sport Watch"

Importing Data from a Spreadsheet 

In this section, we will discuss how to iterate through the data, and about conversion into a more useful format using Python.

Let us first start with iterating through the data. There are a number of iterating methods that depend solely on the user.

You can slice the data with a combination of rows and columns:

>>> spreadsheet["A1:C2"]
((<Cell 'Sheet 1'.A1>, <Cell 'Sheet 1'.B1>, <Cell 'Sheet 1'.C1>), 
 (<Cell 'Sheet 1'.A2>, <Cell 'Sheet 1'.B2>, <Cell 'Sheet 1'.C2>)) 

You can also iterate through the dataset by ranging between rows and columns: 

>>> # Get all cells from column A 
>>> spreadsheet["A"]
(<Cell 'Sheet 1'.A1>, 
 <Cell 'Sheet 1'.A2>, 
 ... 
 <Cell 'Sheet 1'.A99>, 
 <Cell 'Sheet 1'.A100>) 

>>> # Get all cells for a range of columns 
>>> spreadsheet["A:B"] 
((<Cell 'Sheet 1'.A1>, 
  <Cell 'Sheet 1'.A2>, 
  ... 
  <Cell 'Sheet 1'.A99>, 
  <Cell 'Sheet 1'.A100>), 
 (<Cell 'Sheet 1'.B1>, 
  <Cell 'Sheet 1'.B2>, 
  ... 
  <Cell 'Sheet 1'.B99>, 
  <Cell 'Sheet 1'.B100>)) 

>>> # Get all cells from row 5 
>>> spreadsheet[5]
(<Cell 'Sheet 1'.A5>,
 <Cell 'Sheet 1'.B5>,
 ... 
 <Cell 'Sheet 1'.N5>,
 <Cell 'Sheet 1'.O5>)

>>> # Get all cells for a range of rows
>>> spreadsheet[5:6]
((<Cell 'Sheet 1'.A5>, 
  <Cell 'Sheet 1'.B5>, 
  ... 
  <Cell 'Sheet 1'.N5>, 
  <Cell 'Sheet 1'.O5>), 
 (<Cell 'Sheet 1'.A6>, 
  <Cell 'Sheet 1'.B6>, 
  ... 
  <Cell 'Sheet 1'.N6>, 
  <Cell 'Sheet 1'.O6>)) 

Python offers arguments by which you can set limits to the iteration with the help of Python generators like .iter_rows() and .iter_cols()

>>> for row in spreadsheet.iter_rows(min_row=1,
...                           max_row=2,
...                           min_col=1,
...                           max_col=3):
...    print(row)
(<Cell 'Sheet 1'.A1>, <Cell 'Sheet 1'.B1>, <Cell 'Sheet 1'.C1>) 
(<Cell 'Sheet 1'.A2>, <Cell 'Sheet 1'.B2>, <Cell 'Sheet 1'.C2>)

>>> for column in spreadsheet.iter_cols(min_row=1...                              max_row=2,
...                              min_col=1,
...                              max_col=3):
... print(column)
(<Cell 'Sheet 1'.A1>, <Cell 'Sheet 1'.A2>) 
(<Cell 'Sheet 1'.B1>, <Cell 'Sheet 1'.B2>) 
(<Cell 'Sheet 1'.C1>, <Cell 'Sheet 1'.C2>) 

You can also add Boolean values_only in the above example and set it to True to get the values of cell: 

>>> for value in spreadsheet.iter_rows(min_row=1...                          max_row=2...                          min_col=1...                          max_col=3...                          values_only=True):
...    print(value)
('marketplace', 'customer_id', 'review_id') 
('US', 3653882, 'R3O9SGZBVQBV76')

Since we are now done with iterating the data, let us now manipulate data using Python’s primitive data structures. 

Consider a situation where you want to extract information about a product from the sample spreadsheet and then store it in the dictionary. The key to the dictionary would be the product ID.   

Convert Data into Python classes

To convert data into Python data classes, let us first decide what we want to store and how to store it.  

The two essential elements that can be extracted from the data are as follows:

Convert Data into Python classes

                                                     1. Products                                             2. Review

                                                          • ID                                                         • ID
                                                          • Title                                                     • Customers ID
                                                          • Parent                                                 • Headline
                                                          • Category                                            • Body
                                                                                                                         • Date

Let us implement the two elements: 

import datetime
from dataclasses import dataclass

@dataclass
class Product:
    id: str
    parent: str
    title: str
    category: str
@dataclass
class Review:
    id: str
    customer_id: str
    stars: int
    headline: str
    body: str 
    date: datetime.datetime 

The next step is to create a mapping between columns and the required fields: 

>>> for value in spreadsheet.iter_rows(min_row=1,
...                             max_row=1,
...                             values_only=True):
...    print(value)
('marketplace', 'customer_id', 'review_id', 'product_id', ...)

>>> # Or an alternative
>>> for cell in sheet[1]:
...  print(cell.value)
marketplace
Customer_ID
Review_ID
Product_ID
Product_Parent
...

Finally, let us convert the data into new structures which will parse the data in spreadsheet into a list of products and review objects: 

from datetime import datetime 
from openpyxl import load_workbook 
from classes import Product,Review 
from mapping import PRODUCT_ID,PRODUCT_PARENT,PRODUCT_TITLE, \
    PRODUCT_CATEGORY,REVIEW_DATE,REVIEW_ID,REVIEW_CUSTOMER, \
    REVIEW_STARS,REVIEW_HEADLINE,REVIEW_BODY

# Using the read_only method since you're not gonna be editing the spreadsheet 
workbook = load_workbook(filename="watch_sample.xlsx",read_only=True) 
spreadsheet = workbook.active

products = []
reviews = []

# Using the values_only because you just want to return the cell value
for row in spreadsheet .iter_rows(min_row=2, values_only=True): 
    product = Product(id=row[PRODUCT_ID], 
                      parent=row[PRODUCT_PARENT], 
                      title=row[PRODUCT_TITLE], 
                      category=row[PRODUCT_CATEGORY]) 
    products.append(product)
# You need to parse the date from the spreadsheet into a datetime format
spread_date = row[REVIEW_DATE] 
parsed_date = datetime.strptime(spread_date,"%Y-%m-%d")

review = Review(id=row[REVIEW_ID],
              Customer_ID=row[REVIEW_CUSTOMER],
              stars=row[REVIEW_STARS],
              headline=row[REVIEW_HEADLINE],
              body=row[REVIEW_BODY],
              date=parsed_date)
reviews.append(review)

print(products[0])
print(reviews[0])

After you execute the code, you will get an output that looks like this:

Product(id='A90FALZ1ZC',parent=937111370,...)
Review(id='D3O9OGZVVQBV76',customer_id=3903882,...)

Appending Data 

To understand how to append data, let us hover back to the first sample spreadsheet. We will open the document and append some data to it: 

from openpyxl import load_workbook

# Start by opening the spreadsheet and selecting the main sheet 
workbook = load_workbook(filename="hello_world.xlsx")
spreadsheet = workbook.active

# Write what you want into a specific cell
spreadsheet["C1"]="Manipulating_Data ;)"

# Save the spreadsheet
workbook.save(filename="hello_world_append.xlsx"

If you open your Excel file, you will notice the additional Manipulating_Data being added to an adjacent cell. 

Writing Excel Spreadsheets With openpyxl 

A spreadsheet is a file that helps to store data in specific rows and columns. We can calculate and store numerical data and also perform computation using formulas. 

So, let’s begin with some simple Spreadsheets and understand what each line means. 

Creating our first simple Spreadsheet

 1 from openpyxl import Workbook
 2  
 3 filename = "first_program.xlsx"
 4  
 5 workbook = Workbook()
 6 spreadsheet = workbook.active
 7  
 8 sheet["A1"] = "first"
 9 sheet["B1"] = "program!"
10  
11 workbook.save(filename=filename)

Line 5: In order to make a Spreadsheet, at first,  we have to create an Empty workbook to perform further operations. 

Lines 8 and 9: We can add data to a specific cell as per our requirement. In this example, we can see that two values “first” and “program” have been added to specific cells in the sheet. 

Line 11: The line shows how to save data after all the operations we have done. 

Basic Spreadsheet Operations 

Before going to the difficult coding part, where a python advanced certification might be required to land a job, you need to strengthen the building blocks like how to add and update values, how to manage rows and columns, adding filters, styles, or formulas in a Spreadsheet. 

We have already explained the following code by which we can add values to a Spreadsheet: 

>>> spreadsheet["A1"] = "the_value_we_want_to_add"

There is another way that we can add values to the Spreadsheet: 

>>> cell = sheet["A1"]
>>> cell
<Cell 'Sheet'.A1>

>>> cell.value
'hello'

>>> cell.value = "hey"
Profile

KnowledgeHut

Author
KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.