A Guide to Excel Spreadsheets in Python With openpyxl

Practical Use Cases

First things first, when would you need to use a package like openpyxl in a real-world scenario? You’ll see a few examples below, but really, there are hundreds of possible scenarios where this knowledge could come in handy.

Learning Some Basic Excel Terminology

Here’s a quick list of basic terms you’ll see when you’re working with Excel spreadsheets:

Getting Started With openpyxl

Now that you’re aware of the benefits of a tool like openpyxl, let’s get down to it and start by installing the package. For this tutorial, you should use Python 3.7 and openpyxl 2.6.2. To install the package, you can do the following:

$ pip install openpyxl

After you install the package, you should be able to create a super simple spreadsheet with the following code:

from openpyxl import Workbook

workbook = Workbook()
sheet = workbook.active

sheet["A1"] = "hello"
sheet["B1"] = "world!"

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

The code above should create a file called hello_world.xlsx in the folder you are using to run the code. If you open that file with Excel you should see something like this:

Woohoo, your first spreadsheet created!

Dataset for This Tutorial

Before you dive deep into some code examples, you should download this sample dataset and store it somewhere as sample.xlsx:

This is one of the datasets you’ll be using throughout this tutorial, and it’s a spreadsheet with a sample of real data from Amazon’s online product reviews. This dataset is only a tiny fraction of what Amazon provides, but for testing purposes, it’s more than enough.

A Simple Approach to Reading an Excel Spreadsheet

Finally, let’s start reading some spreadsheets! To begin with, open our sample spreadsheet:

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

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

>>> sheet.title
'Sheet 1'

In the code above, you first open the spreadsheet sample.xlsx using load_workbook(), and then you can use workbook.sheetnames to see all the sheets you have available to work with. After that, workbook.active selects the first available sheet and, in this case, you can see that it selects Sheet 1 automatically. Using these methods is the default way of opening a spreadsheet, and you’ll see it many times during this tutorial.

Now, after opening a spreadsheet, you can easily retrieve data from it like this:

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

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

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

To return the actual value of a cell, you need to do .value. Otherwise, you’ll get the main Cell object. You can also use the method .cell() to retrieve a cell using index notation. Remember to add .value to get the actual value and not a Cell object:

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

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

You can see that the results returned are the same, no matter which way you decide to go with. However, in this tutorial, you’ll be mostly using the first approach: ["A1"].

The above shows you the quickest way to open a spreadsheet. However, you can pass additional parameters to change the way a spreadsheet is loaded.

Importing Data From a Spreadsheet

Now that you’ve learned the basics about loading a spreadsheet, it’s about time you get to the fun part: the iteration and actual usage of the values within the spreadsheet.

This section is where you’ll learn all the different ways you can iterate through the data, but also how to convert that data into something usable and, more importantly, how to do it in a Pythonic way.

>>> sheet["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>))

>>> # Get all cells from column A
>>> sheet["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
>>> sheet["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
>>> sheet[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
>>> sheet[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>))

>>> for row in sheet.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 sheet.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>)

>>> for value in sheet.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')

>>> for row in sheet.rows:
...     print(row)
(<Cell 'Sheet 1'.A1>, <Cell 'Sheet 1'.B1>, <Cell 'Sheet 1'.C1>
...
<Cell 'Sheet 1'.M100>, <Cell 'Sheet 1'.N100>, <Cell 'Sheet 1'.O100>)

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

>>> for value in sheet.iter_rows(min_row=2,
...                              min_col=4,
...                              max_col=7,
...                              values_only=True):
...     print(value)
('B00FALQ1ZC', 937001370, 'Invicta Women\'s 15150 "Angel" 18k Yellow...)
('B00D3RGO20', 484010722, "Kenneth Cole New York Women's KC4944...)
...

import json
from openpyxl import load_workbook

workbook = load_workbook(filename="sample.xlsx")
sheet = workbook.active

products = {}

# Using the values_only because you want to return the cells' values
for row in sheet.iter_rows(min_row=2,
                           min_col=4,
                           max_col=7,
                           values_only=True):
    product_id = row[0]
    product = {
        "parent": row[1],
        "title": row[2],
        "category": row[3]
    }
    products[product_id] = product

# Using json here to be able to format the output for displaying later
print(json.dumps(products))

{
  "B00FALQ1ZC": {
    "parent": 937001370,
    "title": "Invicta Women's 15150 ...",
    "category": "Watches"
  },
  "B00D3RGO20": {
    "parent": 484010722,
    "title": "Kenneth Cole New York ...",
    "category": "Watches"
  }
}

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

>>> for value in sheet.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
...

# Product fields
PRODUCT_ID = 3
PRODUCT_PARENT = 4
PRODUCT_TITLE = 5
PRODUCT_CATEGORY = 6

# Review fields
REVIEW_ID = 2
REVIEW_CUSTOMER = 1
REVIEW_STARS = 7
REVIEW_HEADLINE = 12
REVIEW_BODY = 13
REVIEW_DATE = 14

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="sample.xlsx", read_only=True)
sheet = workbook.active

products = []
reviews = []

# Using the values_only because you just want to return the cell value
for row in sheet.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])

Product(id='B00FALQ1ZC', parent=937001370, ...)
Review(id='R3O9SGZBVQBV76', customer_id=3653882, ...)

Appending New Data

Before you start creating very complex spreadsheets, have a quick look at an example of how to append data to an existing spreadsheet.

Go back to the first example spreadsheet you created (hello_world.xlsx) and try opening it and appending some data to it, like this:

from openpyxl import load_workbook

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

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

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

Et voilà, if you open the new hello_world_append.xlsx spreadsheet, you’ll see the following change:

Notice the additional writing ;) on cell C1.

Creating a Simple Spreadsheet

Previously, you saw a very quick example of how to write “Hello world!” into a spreadsheet, so you can start with that:

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

The highlighted lines in the code above are the most important ones for writing. In the code, you can see that:

• Line 5 shows you how to create a new empty workbook.
• Lines 8 and 9 show you how to add data to specific cells.
• Line 11 shows you how to save the spreadsheet when you’re done.

Even though these lines above can be straightforward, it’s still good to know them well for when things get a bit more complicated.

One thing you can do to help with coming code examples is add the following method to your Python file or console:

>>> def print_rows():
...     for row in sheet.iter_rows(values_only=True):
...         print(row)

It makes it easier to print all of your spreadsheet values by just calling print_rows().

Basic Spreadsheet Operations

Before you get into the more advanced topics, it’s good for you to know how to manage the most simple elements of a spreadsheet.

>>> sheet["A1"] = "value"

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

>>> cell.value
'hello'

>>> cell.value = "hey"
>>> cell.value
'hey'

>>> # Before, our spreadsheet has only 1 row
>>> print_rows()
('hello', 'world!')

>>> # Try adding a value to row 10
>>> sheet["B10"] = "test"
>>> print_rows()
('hello', 'world!')
(None, None)
(None, None)
(None, None)
(None, None)
(None, None)
(None, None)
(None, None)
(None, None)
(None, 'test')

>>> print_rows()
('hello', 'world!')

>>> # Insert a column before the existing column 1 ("A")
>>> sheet.insert_cols(idx=1)
>>> print_rows()
(None, 'hello', 'world!')

>>> # Insert 5 columns between column 2 ("B") and 3 ("C")
>>> sheet.insert_cols(idx=3, amount=5)
>>> print_rows()
(None, 'hello', None, None, None, None, None, 'world!')

>>> # Delete the created columns
>>> sheet.delete_cols(idx=3, amount=5)
>>> sheet.delete_cols(idx=1)
>>> print_rows()
('hello', 'world!')

>>> # Insert a new row in the beginning
>>> sheet.insert_rows(idx=1)
>>> print_rows()
(None, None)
('hello', 'world!')

>>> # Insert 3 new rows in the beginning
>>> sheet.insert_rows(idx=1, amount=3)
>>> print_rows()
(None, None)
(None, None)
(None, None)
(None, None)
('hello', 'world!')

>>> # Delete the first 4 rows
>>> sheet.delete_rows(idx=1, amount=4)
>>> print_rows()
('hello', 'world!')

sheet = workbook.active

>>> # Let's say you have two sheets: "Products" and "Company Sales"
>>> workbook.sheetnames
['Products', 'Company Sales']

>>> # You can select a sheet using its title
>>> products_sheet = workbook["Products"]
>>> sales_sheet = workbook["Company Sales"]

>>> workbook.sheetnames
['Products', 'Company Sales']

>>> products_sheet = workbook["Products"]
>>> products_sheet.title = "New Products"

>>> workbook.sheetnames
['New Products', 'Company Sales']

>>> workbook.sheetnames
['Products', 'Company Sales']

>>> operations_sheet = workbook.create_sheet("Operations")
>>> workbook.sheetnames
['Products', 'Company Sales', 'Operations']

>>> # You can also define the position to create the sheet at
>>> hr_sheet = workbook.create_sheet("HR", 0)
>>> workbook.sheetnames
['HR', 'Products', 'Company Sales', 'Operations']

>>> # To remove them, just pass the sheet as an argument to the .remove()
>>> workbook.remove(operations_sheet)
>>> workbook.sheetnames
['HR', 'Products', 'Company Sales']

>>> workbook.remove(hr_sheet)
>>> workbook.sheetnames
['Products', 'Company Sales']

>>> workbook.sheetnames
['Products', 'Company Sales']

>>> products_sheet = workbook["Products"]
>>> workbook.copy_worksheet(products_sheet)
<Worksheet "Products Copy">

>>> workbook.sheetnames
['Products', 'Company Sales', 'Products Copy']

Freezing Rows and Columns

Something that you might want to do when working with big spreadsheets is to freeze a few rows or columns, so they remain visible when you scroll right or down.

Freezing data allows you to keep an eye on important rows or columns, regardless of where you scroll in the spreadsheet.

Again, openpyxl also has a way to accomplish this by using the worksheet freeze_panes attribute. For this example, go back to our sample.xlsx spreadsheet and try doing the following:

Python

>>> workbook = load_workbook(filename="sample.xlsx")
>>> sheet = workbook.active
>>> sheet.freeze_panes = "C2"
>>> workbook.save("sample_frozen.xlsx")

If you open the sample_frozen.xlsx spreadsheet in your favorite spreadsheet editor, you’ll notice that row 1 and columns A and B are frozen and are always visible no matter where you navigate within the spreadsheet.

This feature is handy, for example, to keep headers within sight, so you always know what each column represents.

Here’s how it looks in the editor:

Notice how you’re at the end of the spreadsheet, and yet, you can see both row 1 and columns A and B.

Adding Filters

You can use openpyxl to add filters and sorts to your spreadsheet. However, when you open the spreadsheet, the data won’t be rearranged according to these sorts and filters.

At first, this might seem like a pretty useless feature, but when you’re programmatically creating a spreadsheet that is going to be sent and used by somebody else, it’s still nice to at least create the filters and allow people to use it afterward.

The code below is an example of how you would add some filters to our existing sample.xlsx spreadsheet:

Python

>>> # Check the used spreadsheet space using the attribute "dimensions"
>>> sheet.dimensions
'A1:O100'

>>> sheet.auto_filter.ref = "A1:O100"
>>> workbook.save(filename="sample_with_filters.xlsx")

You should now see the filters created when opening the spreadsheet in your editor:

You don’t have to use sheet.dimensions if you know precisely which part of the spreadsheet you want to apply filters to.

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Adding Formulas

Formulas (or formulae) are one of the most powerful features of spreadsheets.

They gives you the power to apply specific mathematical equations to a range of cells. Using formulas with openpyxl is as simple as editing the value of a cell.

You can see the list of formulas supported by openpyxl:

>>> from openpyxl.utils import FORMULAE
>>> FORMULAE
frozenset({'ABS',
           'ACCRINT',
           'ACCRINTM',
           'ACOS',
           'ACOSH',
           'AMORDEGRC',
           'AMORLINC',
           'AND',
           ...
           'YEARFRAC',
           'YIELD',
           'YIELDDISC',
           'YIELDMAT',
           'ZTEST'})

Let’s add some formulas to our sample.xlsx spreadsheet.

Starting with something easy, let’s check the average star rating for the 99 reviews within the spreadsheet:

>>> # Star rating is column "H"
>>> sheet["P2"] = "=AVERAGE(H2:H100)"
>>> workbook.save(filename="sample_formulas.xlsx")

If you open the spreadsheet now and go to cell P2, you should see that its value is: 4.18181818181818. Have a look in the editor:

You can use the same methodology to add any formulas to your spreadsheet. For example, let’s count the number of reviews that had helpful votes:

>>> # The helpful votes are counted on column "I"
>>> sheet["P3"] = '=COUNTIF(I2:I100, ">0")'
>>> workbook.save(filename="sample_formulas.xlsx")

You should get the number 21 on your P3 spreadsheet cell like so:

You’ll have to make sure that the strings within a formula are always in double quotes, so you either have to use single quotes around the formula like in the example above or you’ll have to escape the double quotes inside the formula: "=COUNTIF(I2:I100, \">0\")".

There are a ton of other formulas you can add to your spreadsheet using the same procedure you tried above. Give it a go yourself!

Adding Styles

Even though styling a spreadsheet might not be something you would do every day, it’s still good to know how to do it.

Using openpyxl, you can apply multiple styling options to your spreadsheet, including fonts, borders, colors, and so on. Have a look at the openpyxl documentation to learn more.

You can also choose to either apply a style directly to a cell or create a template and reuse it to apply styles to multiple cells.

Let’s start by having a look at simple cell styling, using our sample.xlsx again as the base spreadsheet:

>>> # Import necessary style classes
>>> from openpyxl.styles import Font, Color, Alignment, Border, Side

>>> # Create a few styles
>>> bold_font = Font(bold=True)
>>> big_red_text = Font(color="00FF0000", size=20)
>>> center_aligned_text = Alignment(horizontal="center")
>>> double_border_side = Side(border_style="double")
>>> square_border = Border(top=double_border_side,
...                        right=double_border_side,
...                        bottom=double_border_side,
...                        left=double_border_side)

>>> # Style some cells!
>>> sheet["A2"].font = bold_font
>>> sheet["A3"].font = big_red_text
>>> sheet["A4"].alignment = center_aligned_text
>>> sheet["A5"].border = square_border
>>> workbook.save(filename="sample_styles.xlsx")

If you open your spreadsheet now, you should see quite a few different styles on the first 5 cells of column A:

There you go. You got:

• A2 with the text in bold
• A3 with the text in red and bigger font size
• A4 with the text centered
• A5 with a square border around the text

You can also combine styles by simply adding them to the cell at the same time:

>>> # Reusing the same styles from the example above
>>> sheet["A6"].alignment = center_aligned_text
>>> sheet["A6"].font = big_red_text
>>> sheet["A6"].border = square_border
>>> workbook.save(filename="sample_styles.xlsx")

Have a look at cell A6 here:

When you want to apply multiple styles to one or several cells, you can use a NamedStyle class instead, which is like a style template that you can use over and over again. Have a look at the example below:

>>> from openpyxl.styles import NamedStyle

>>> # Let's create a style template for the header row
>>> header = NamedStyle(name="header")
>>> header.font = Font(bold=True)
>>> header.border = Border(bottom=Side(border_style="thin"))
>>> header.alignment = Alignment(horizontal="center", vertical="center")

>>> # Now let's apply this to all first row (header) cells
>>> header_row = sheet[1]
>>> for cell in header_row:
...     cell.style = header

>>> workbook.save(filename="sample_styles.xlsx")

If you open the spreadsheet now, you should see that its first row is bold, the text is aligned to the center, and there’s a small bottom border! Have a look below:

As you saw above, there are many options when it comes to styling, and it depends on the use case, so feel free to check openpyxl documentation and see what other things you can do.

Conditional Formatting

This feature is one of my personal favorites when it comes to adding styles to a spreadsheet.

It’s a much more powerful approach to styling because it dynamically applies styles according to how the data in the spreadsheet changes.

In a nutshell, conditional formatting allows you to specify a list of styles to apply to a cell (or cell range) according to specific conditions.

For example, a widespread use case is to have a balance sheet where all the negative totals are in red, and the positive ones are in green. This formatting makes it much more efficient to spot good vs bad periods.

Without further ado, let’s pick our favorite spreadsheet—sample.xlsx—and add some conditional formatting.

You can start by adding a simple one that adds a red background to all reviews with less than 3 stars:

>>> from openpyxl.styles import PatternFill
>>> from openpyxl.styles.differential import DifferentialStyle
>>> from openpyxl.formatting.rule import Rule

>>> red_background = PatternFill(fgColor="00FF0000")
>>> diff_style = DifferentialStyle(fill=red_background)
>>> rule = Rule(type="expression", dxf=diff_style)
>>> rule.formula = ["$H1<3"]
>>> sheet.conditional_formatting.add("A1:O100", rule)
>>> workbook.save("sample_conditional_formatting.xlsx")

Now you’ll see all the reviews with a star rating below 3 marked with a red background:

Code-wise, the only things that are new here are the objects DifferentialStyle and Rule:

• DifferentialStyle is quite similar to NamedStyle, which you already saw above, and it’s used to aggregate multiple styles such as fonts, borders, alignment, and so forth.
• Rule is responsible for selecting the cells and applying the styles if the cells match the rule’s logic.

Using a Rule object, you can create numerous conditional formatting scenarios.

However, for simplicity sake, the openpyxl package offers 3 built-in formats that make it easier to create a few common conditional formatting patterns. These built-ins are:

• ColorScale
• IconSet
• DataBar

The ColorScale gives you the ability to create color gradients:

>>> from openpyxl.formatting.rule import ColorScaleRule
>>> color_scale_rule = ColorScaleRule(start_type="min",
...                                   start_color="00FF0000",  # Red
...                                   end_type="max",
...                                   end_color="0000FF00")  # Green

>>> # Again, let's add this gradient to the star ratings, column "H"
>>> sheet.conditional_formatting.add("H2:H100", color_scale_rule)
>>> workbook.save(filename="sample_conditional_formatting_color_scale.xlsx")

Now you should see a color gradient on column H, from red to green, according to the star rating:

You can also add a third color and make two gradients instead:

>>> from openpyxl.formatting.rule import ColorScaleRule
>>> color_scale_rule = ColorScaleRule(start_type="num",
...                                   start_value=1,
...                                   start_color="00FF0000",  # Red
...                                   mid_type="num",
...                                   mid_value=3,
...                                   mid_color="00FFFF00",  # Yellow
...                                   end_type="num",
...                                   end_value=5,
...                                   end_color="0000FF00")  # Green

>>> # Again, let's add this gradient to the star ratings, column "H"
>>> sheet.conditional_formatting.add("H2:H100", color_scale_rule)
>>> workbook.save(filename="sample_conditional_formatting_color_scale_3.xlsx")

This time, you’ll notice that star ratings between 1 and 3 have a gradient from red to yellow, and star ratings between 3 and 5 have a gradient from yellow to green:

The IconSet allows you to add an icon to the cell according to its value:

>>> from openpyxl.formatting.rule import IconSetRule

>>> icon_set_rule = IconSetRule("5Arrows", "num", [1, 2, 3, 4, 5])
>>> sheet.conditional_formatting.add("H2:H100", icon_set_rule)
>>> workbook.save("sample_conditional_formatting_icon_set.xlsx")

You’ll see a colored arrow next to the star rating. This arrow is red and points down when the value of the cell is 1 and, as the rating gets better, the arrow starts pointing up and becomes green:

The openpyxl package has a full list of other icons you can use, besides the arrow.

Finally, the DataBar allows you to create progress bars:

>>> from openpyxl.formatting.rule import DataBarRule

>>> data_bar_rule = DataBarRule(start_type="num",
...                             start_value=1,
...                             end_type="num",
...                             end_value="5",
...                             color="0000FF00")  # Green
>>> sheet.conditional_formatting.add("H2:H100", data_bar_rule)
>>> workbook.save("sample_conditional_formatting_data_bar.xlsx")

You’ll now see a green progress bar that gets fuller the closer the star rating is to the number 5:

As you can see, there are a lot of cool things you can do with conditional formatting.

Here, you saw only a few examples of what you can achieve with it, but check the openpyxl documentation to see a bunch of other options.

Adding Images

Even though images are not something that you’ll often see in a spreadsheet, it’s quite cool to be able to add them. Maybe you can use it for branding purposes or to make spreadsheets more personal.

To be able to load images to a spreadsheet using openpyxl, you’ll have to install Pillow:

$ pip install Pillow

Apart from that, you’ll also need an image. For this example, you can grab the Real Python logo below and convert it from .webp to .png using an online converter such as cloudconvert.com, save the final file as logo.png, and copy it to the root folder where you’re running your examples:

Afterward, this is the code you need to import that image into the hello_word.xlsx spreadsheet:

from openpyxl import load_workbook
from openpyxl.drawing.image import Image

# Let's use the hello_world spreadsheet since it has less data
workbook = load_workbook(filename="hello_world.xlsx")
sheet = workbook.active

logo = Image("logo.png")

# A bit of resizing to not fill the whole spreadsheet with the logo
logo.height = 150
logo.width = 150

sheet.add_image(logo, "A3")
workbook.save(filename="hello_world_logo.xlsx")

You have an image on your spreadsheet! Here it is:

The image’s left top corner is on the cell you chose, in this case, A3.

Adding Pretty Charts

Another powerful thing you can do with spreadsheets is create an incredible variety of charts.

Charts are a great way to visualize and understand loads of data quickly. There are a lot of different chart types: bar chart, pie chart, line chart, and so on. openpyxl has support for a lot of them.

Here, you’ll see only a couple of examples of charts because the theory behind it is the same for every single chart type:

For any chart you want to build, you’ll need to define the chart type: BarChart, LineChart, and so forth, plus the data to be used for the chart, which is called Reference.

Before you can build your chart, you need to define what data you want to see represented in it. Sometimes, you can use the dataset as is, but other times you need to massage the data a bit to get additional information.

Let’s start by building a new workbook with some sample data:

 1from openpyxl import Workbook
 2from openpyxl.chart import BarChart, Reference
 3
 4workbook = Workbook()
 5sheet = workbook.active
 6
 7# Let's create some sample sales data
 8rows = [
 9    ["Product", "Online", "Store"],
10    [1, 30, 45],
11    [2, 40, 30],
12    [3, 40, 25],
13    [4, 50, 30],
14    [5, 30, 25],
15    [6, 25, 35],
16    [7, 20, 40],
17]
18
19for row in rows:
20    sheet.append(row)

Now you’re going to start by creating a bar chart that displays the total number of sales per product:

22chart = BarChart()
23data = Reference(worksheet=sheet,
24                 min_row=1,
25                 max_row=8,
26                 min_col=2,
27                 max_col=3)
28
29chart.add_data(data, titles_from_data=True)
30sheet.add_chart(chart, "E2")
31
32workbook.save("chart.xlsx")

There you have it. Below, you can see a very straightforward bar chart showing the difference between online product sales online and in-store product sales:

Like with images, the top left corner of the chart is on the cell you added the chart to. In your case, it was on cell E2.

Try creating a line chart instead, changing the data a bit:

 1import random
 2from openpyxl import Workbook
 3from openpyxl.chart import LineChart, Reference
 4
 5workbook = Workbook()
 6sheet = workbook.active
 7
 8# Let's create some sample sales data
 9rows = [
10    ["", "January", "February", "March", "April",
11    "May", "June", "July", "August", "September",
12     "October", "November", "December"],
13    [1, ],
14    [2, ],
15    [3, ],
16]
17
18for row in rows:
19    sheet.append(row)
20
21for row in sheet.iter_rows(min_row=2,
22                           max_row=4,
23                           min_col=2,
24                           max_col=13):
25    for cell in row:
26        cell.value = random.randrange(5, 100)

With the above code, you’ll be able to generate some random data regarding the sales of 3 different products across a whole year.

Once that’s done, you can very easily create a line chart with the following code:

28chart = LineChart()
29data = Reference(worksheet=sheet,
30                 min_row=2,
31                 max_row=4,
32                 min_col=1,
33                 max_col=13)
34
35chart.add_data(data, from_rows=True, titles_from_data=True)
36sheet.add_chart(chart, "C6")
37
38workbook.save("line_chart.xlsx")

Here’s the outcome of the above piece of code:

One thing to keep in mind here is the fact that you’re using from_rows=True when adding the data. This argument makes the chart plot row by row instead of column by column.

In your sample data, you see that each product has a row with 12 values (1 column per month). That’s why you use from_rows. If you don’t pass that argument, by default, the chart tries to plot by column, and you’ll get a month-by-month comparison of sales.

Another difference that has to do with the above argument change is the fact that our Reference now starts from the first column, min_col=1, instead of the second one. This change is needed because the chart now expects the first column to have the titles.

There are a couple of other things you can also change regarding the style of the chart. For example, you can add specific categories to the chart:

cats = Reference(worksheet=sheet,
                 min_row=1,
                 max_row=1,
                 min_col=2,
                 max_col=13)
chart.set_categories(cats)

Add this piece of code before saving the workbook, and you should see the month names appearing instead of numbers:

Code-wise, this is a minimal change. But in terms of the readability of the spreadsheet, this makes it much easier for someone to open the spreadsheet and understand the chart straight away.

Another thing you can do to improve the chart readability is to add an axis. You can do it using the attributes x_axis and y_axis:

chart.x_axis.title = "Months"
chart.y_axis.title = "Sales (per unit)"

This will generate a spreadsheet like the below one:

As you can see, small changes like the above make reading your chart a much easier and quicker task.

There is also a way to style your chart by using Excel’s default ChartStyle property. In this case, you have to choose a number between 1 and 48. Depending on your choice, the colors of your chart change as well:

# You can play with this by choosing any number between 1 and 48
chart.style = 24

With the style selected above, all lines have some shade of orange:

There is no clear documentation on what each style number looks like, but this spreadsheet has a few examples of the styles available.

import random
from openpyxl import Workbook
from openpyxl.chart import LineChart, Reference

workbook = Workbook()
sheet = workbook.active

# Let's create some sample sales data
rows = [
    ["", "January", "February", "March", "April",
    "May", "June", "July", "August", "September",
     "October", "November", "December"],
    [1, ],
    [2, ],
    [3, ],
]

for row in rows:
    sheet.append(row)

for row in sheet.iter_rows(min_row=2,
                           max_row=4,
                           min_col=2,
                           max_col=13):
    for cell in row:
        cell.value = random.randrange(5, 100)

# Create a LineChart and add the main data
chart = LineChart()
data = Reference(worksheet=sheet,
                           min_row=2,
                           max_row=4,
                           min_col=1,
                           max_col=13)
chart.add_data(data, titles_from_data=True, from_rows=True)

# Add categories to the chart
cats = Reference(worksheet=sheet,
                 min_row=1,
                 max_row=1,
                 min_col=2,
                 max_col=13)
chart.set_categories(cats)

# Rename the X and Y Axis
chart.x_axis.title = "Months"
chart.y_axis.title = "Sales (per unit)"

# Apply a specific Style
chart.style = 24

# Save!
sheet.add_chart(chart, "C6")
workbook.save("line_chart.xlsx")

There are a lot more chart types and customization you can apply, so be sure to check out the package documentation on this if you need some specific formatting.

Convert Python Classes to Excel Spreadsheet

You already saw how to convert an Excel spreadsheet’s data into Python classes, but now let’s do the opposite.

Let’s imagine you have a database and are using some Object-Relational Mapping (ORM) to map DB objects into Python classes. Now, you want to export those same objects into a spreadsheet.

Let’s assume the following data classes to represent the data coming from your database regarding product sales:

from dataclasses import dataclass
from typing import List

@dataclass
class Sale:
    quantity: int

@dataclass
class Product:
    id: str
    name: str
    sales: List[Sale]

Now, let’s generate some random data, assuming the above classes are stored in a db_classes.py file:

 1import random
 2
 3# Ignore these for now. You'll use them in a sec ;)
 4from openpyxl import Workbook
 5from openpyxl.chart import LineChart, Reference
 6
 7from db_classes import Product, Sale
 8
 9products = []
10
11# Let's create 5 products
12for idx in range(1, 6):
13    sales = []
14
15    # Create 5 months of sales
16    for _ in range(5):
17        sale = Sale(quantity=random.randrange(5, 100))
18        sales.append(sale)
19
20    product = Product(id=str(idx),
21                      name="Product %s" % idx,
22                      sales=sales)
23    products.append(product)

By running this piece of code, you should get 5 products with 5 months of sales with a random quantity of sales for each month.

Now, to convert this into a spreadsheet, you need to iterate over the data and append it to the spreadsheet:

25workbook = Workbook()
26sheet = workbook.active
27
28# Append column names first
29sheet.append(["Product ID", "Product Name", "Month 1",
30              "Month 2", "Month 3", "Month 4", "Month 5"])
31
32# Append the data
33for product in products:
34    data = [product.id, product.name]
35    for sale in product.sales:
36        data.append(sale.quantity)
37    sheet.append(data)

That’s it. That should allow you to create a spreadsheet with some data coming from your database.

However, why not use some of that cool knowledge you gained recently to add a chart as well to display that data more visually?

All right, then you could probably do something like this:

38chart = LineChart()
39data = Reference(worksheet=sheet,
40                 min_row=2,
41                 max_row=6,
42                 min_col=2,
43                 max_col=7)
44
45chart.add_data(data, titles_from_data=True, from_rows=True)
46sheet.add_chart(chart, "B8")
47
48cats = Reference(worksheet=sheet,
49                 min_row=1,
50                 max_row=1,
51                 min_col=3,
52                 max_col=7)
53chart.set_categories(cats)
54
55chart.x_axis.title = "Months"
56chart.y_axis.title = "Sales (per unit)"
57
58workbook.save(filename="oop_sample.xlsx")

Now we’re talking! Here’s a spreadsheet generated from database objects and with a chart and everything:

That’s a great way for you to wrap up your new knowledge of charts!

Bonus: Working With Pandas

Even though you can use Pandas to handle Excel files, there are few things that you either can’t accomplish with Pandas or that you’d be better off just using openpyxl directly.

For example, some of the advantages of using openpyxl are the ability to easily customize your spreadsheet with styles, conditional formatting, and such.

But guess what, you don’t have to worry about picking. In fact, openpyxl has support for both converting data from a Pandas DataFrame into a workbook or the opposite, converting an openpyxl workbook into a Pandas DataFrame.

First things first, remember to install the pandas package:

$ pip install pandas

Then, let’s create a sample DataFrame:

 1import pandas as pd
 2
 3data = {
 4    "Product Name": ["Product 1", "Product 2"],
 5    "Sales Month 1": [10, 20],
 6    "Sales Month 2": [5, 35],
 7}
 8df = pd.DataFrame(data)

Now that you have some data, you can use .dataframe_to_rows() to convert it from a DataFrame into a worksheet:

10from openpyxl import Workbook
11from openpyxl.utils.dataframe import dataframe_to_rows
12
13workbook = Workbook()
14sheet = workbook.active
15
16for row in dataframe_to_rows(df, index=False, header=True):
17    sheet.append(row)
18
19workbook.save("pandas.xlsx")

You should see a spreadsheet that looks like this:

If you want to add the DataFrame’s index, you can change index=True, and it adds each row’s index into your spreadsheet.

On the other hand, if you want to convert a spreadsheet into a DataFrame, you can also do it in a very straightforward way like so:

import pandas as pd
from openpyxl import load_workbook

workbook = load_workbook(filename="sample.xlsx")
sheet = workbook.active

values = sheet.values
df = pd.DataFrame(values)

Alternatively, if you want to add the correct headers and use the review ID as the index, for example, then you can also do it like this instead:

import pandas as pd
from openpyxl import load_workbook
from mapping import REVIEW_ID

workbook = load_workbook(filename="sample.xlsx")
sheet = workbook.active

data = sheet.values

# Set the first row as the columns for the DataFrame
cols = next(data)
data = list(data)

# Set the field "review_id" as the indexes for each row
idx = [row[REVIEW_ID] for row in data]

df = pd.DataFrame(data, index=idx, columns=cols)

Using indexes and columns allows you to access data from your DataFrame easily:

>>> df.columns
Index(['marketplace', 'customer_id', 'review_id', 'product_id',
       'product_parent', 'product_title', 'product_category', 'star_rating',
       'helpful_votes', 'total_votes', 'vine', 'verified_purchase',
       'review_headline', 'review_body', 'review_date'],
      dtype='object')

>>> # Get first 10 reviews' star rating
>>> df["star_rating"][:10]
R3O9SGZBVQBV76    5
RKH8BNC3L5DLF     5
R2HLE8WKZSU3NL    2
R31U3UH5AZ42LL    5
R2SV659OUJ945Y    4
RA51CP8TR5A2L     5
RB2Q7DLDN6TH6     5
R2RHFJV0UYBK3Y    1
R2Z6JOQ94LFHEP    5
RX27XIIWY5JPB     4
Name: star_rating, dtype: int64

>>> # Grab review with id "R2EQL1V1L6E0C9", using the index
>>> df.loc["R2EQL1V1L6E0C9"]
marketplace               US
customer_id         15305006
review_id     R2EQL1V1L6E0C9
product_id        B004LURNO6
product_parent     892860326
review_headline   Five Stars
review_body          Love it
review_date       2015-08-31
Name: R2EQL1V1L6E0C9, dtype: object

There you go, whether you want to use openpyxl to prettify your Pandas dataset or use Pandas to do some hardcore algebra, you now know how to switch between both packages.