Can you use python with excel

You all must have worked with Excel at some time in your life and must have felt the need for automating some repetitive or tedious task. Don’t worry in this tutorial we are going to learn about how to work with Excel using Python, or automating Excel using Python. We will be covering this with the help of the Openpyxl module.

Getting Started

Openpyxl is a Python library that provides various methods to interact with Excel Files using Python. It allows operations like reading, writing, arithmetic operations, plotting graphs, etc.

This module does not come in-built with Python. To install this type the below command in the terminal.

pip install openpyxl

Reading from Spreadsheets

To read an Excel file you have to open the spreadsheet using the load_workbook() method. After that, you can use the active to select the first sheet available and the cell attribute to select the cell by passing the row and column parameter. The value attribute prints the value of the particular cell. See the below example to get a better understanding. 

Note: The first row or column integer is 1, not 0.

Dataset Used: It can be downloaded from here.

Example:

Output:

Name

Reading from Multiple Cells

There can be two ways of reading from multiple cells. 

Method 1: We can get the count of the total rows and columns using the max_row and max_column respectively. We can use these values inside the for loop to get the value of the desired row or column or any cell depending upon the situation. Let’s see how to get the value of the first column and first row.

Example:

Output:

Total Rows: 6
Total Columns: 4

Value of first column
Name
Ankit
Rahul
Priya
Nikhil
Nisha

Value of first row
Ankit  B.Tech CSE 4 

Method 2: We can also read from multiple cells using the cell name. This can be seen as the list slicing of Python.

Output:

Name Course
Ankit  B.Tech
Rahul M.Tech
Priya MBA
Nikhil B.Tech
Nisha B.Tech

Refer to the below article to get detailed information about reading excel files using openpyxl.

• Reading an excel file using Python openpyxl module

Writing to Spreadsheets

First, let’s create a new spreadsheet, and then we will write some data to the newly created file. An empty spreadsheet can be created using the Workbook() method. Let’s see the below example.

Example:

Output:

After creating an empty file, let’s see how to add some data to it using Python. To add data first we need to select the active sheet and then using the cell() method we can select any particular cell by passing the row and column number as its parameter. We can also write using cell names. See the below example for a better understanding.

Example:

Output:

Refer to the below article to get detailed information about writing to excel.

• Writing to an excel file using openpyxl module

Appending to the Spreadsheet

In the above example, you will see that every time you try to write to a spreadsheet the existing data gets overwritten, and the file is saved as a new file. This happens because the Workbook() method always creates a new workbook file object. To write to an existing workbook you must open the file with the load_workbook() method. We will use the above-created workbook.

Example:

Output:

We can also use the append() method to append multiple data at the end of the sheet.

Example:

Output:

Arithmetic Operation on Spreadsheet

Arithmetic operations can be performed by typing the formula in a particular cell of the spreadsheet. For example, if we want to find the sum then =Sum() formula of the excel file is used.

Example:

Output:

Refer to the below article to get detailed information about the Arithmetic operations on Spreadsheet.

• Arithmetic operations in excel file using openpyxl

Adjusting Rows and Column

Worksheet objects have row_dimensions and column_dimensions attributes that control row heights and column widths. A sheet’s row_dimensions and column_dimensions are dictionary-like values; row_dimensions contains RowDimension objects and column_dimensions contains ColumnDimension objects. In row_dimensions, one can access one of the objects using the number of the row (in this case, 1 or 2). In column_dimensions, one can access one of the objects using the letter of the column (in this case, A or B).

Example:

Output:

Merging Cells

A rectangular area of cells can be merged into a single cell with the merge_cells() sheet method. The argument to merge_cells() is a single string of the top-left and bottom-right cells of the rectangular area to be merged.

Example:

Output:

Unmerging Cells

To unmerge cells, call the unmerge_cells() sheet method.

Example:

Output:

Setting Font Style

To customize font styles in cells, important, import the Font() function from the openpyxl.styles module.

Example:

Output:

Refer to the below article to get detailed information about adjusting rows and columns.

• Adjusting rows and columns of an excel file using openpyxl module

Plotting Charts

Charts are composed of at least one series of one or more data points. Series themselves are comprised of references to cell ranges. For plotting the charts on an excel sheet, firstly, create chart objects of specific chart class( i.e BarChart, LineChart, etc.). After creating chart objects, insert data in it, and lastly, add that chart object in the sheet object.

Example 1:

Output:

Example 2:

Output:

Refer to the below articles to get detailed information about plotting in excel using Python.

• Plotting charts in excel sheet using openpyxl module | Set  1
• Plotting charts in excel sheet using openpyxl module | Set  2
• Plotting charts in excel sheet using openpyxl module | Set 3

Adding Images

For the purpose of importing images inside our worksheet, we would be using openpyxl.drawing.image.Image. The method is a wrapper over PIL.Image method found in PIL (pillow) library. Due to which it is necessary for the PIL (pillow) library to be installed in order to use this method.

Image Used:

Example:

Output:

Refer to the below article to get detailed information about adding images.

• Openpyxl – Adding Image

Some More Functionality of Excel using Python

• How to delete one or more rows in excel using Openpyxl?
• Trigonometric operations in excel file using openpyxl
• How to copy data from one excel sheet to another
• How to Automate an Excel Sheet in Python?

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Добрый день, уважаемые читатели.

В сегодняшней статье я хотел бы, как можно подробнее, рассмотреть интеграцию приложений Python и MS Excel. Данные вопрос может возникнуть, например, при создании какой-либо системы онлайн отчетности, которая должна выгружать результаты в общепринятый формат ну или какие-либо другие задачи. Также в статье я покажу и обратную интеграцию, т.е. как использовать функцию написанную на python в Excel, что также может быть полезно для автоматизации отчетов.

Работаем с файлами MS Excel на Python

Для работы с Excel файлами из Python мне известны 2 варианта:

1. Использование библиотек, таких как xlrd, xlwt, xlutils или openpyxl
2. Работа с com-объектом

Рассмотрим работу с этими способами подробнее. В качестве примера будем использовать готовый файл excel из которого мы сначала считаем данные из первой ячейки, а затем запишем их во вторую. Таких простых примеров будет достаточно для первого ознакомления.

Использование библиотек

Итак, первый метод довольно простой и хорошо описан. Например, есть отличная статья для описания работы c xlrd, xlwt, xlutils. Поэтому в данном материале я приведу небольшой кусок кода с их использованием.

Для начала загрузим нужные библиотеки и откроем файл xls на чтение и выберем
нужный лист с данными:

import xlrd, xlwt
#открываем файл
rb = xlrd.open_workbook('../ArticleScripts/ExcelPython/xl.xls',formatting_info=True)

#выбираем активный лист
sheet = rb.sheet_by_index(0)

Теперь давайте посмотрим, как считать значения из нужных ячеек:

#получаем значение первой ячейки A1
val = sheet.row_values(0)[0]

#получаем список значений из всех записей
vals = [sheet.row_values(rownum) for rownum in range(sheet.nrows)]

Как видно чтение данных не составляет труда. Теперь запишем их в другой файл. Для этого создам новый excel файл с новой рабочей книгой:

wb = xlwt.Workbook()
ws = wb.add_sheet('Test')

Запишем в новый файл полученные ранее данные и сохраним изменения:

#в A1 записываем значение из ячейки A1 прошлого файла
ws.write(0, 0, val[0])

#в столбец B запишем нашу последовательность из столбца A исходного файла
i = 0
for rec in vals:
    ws.write(i,1,rec[0])
    i =+ i

#сохраняем рабочую книгу
wb.save('../ArticleScripts/ExcelPython/xl_rec.xls')

Из примера выше видно, что библиотека xlrd отвечает за чтение данных, а xlwt — за запись, поэтому нет возможности внести изменения в уже созданную книгу без ее копирования в новую. Кроме этого указанные библиотеки работают только с файлами формата xls (Excel 2003) и у них нет поддержки нового формата xlsx (Excel 2007 и выше).

Чтобы успешно работать с форматом xlsx, понадобится библиотека openpyxl. Для демонстрации ее работы проделаем действия, которые были показаны для предыдущих библиотек.

Для начала загрузим библиотеку и выберем нужную книгу и рабочий лист:

import openpyxl
wb = openpyxl.load_workbook(filename = '../ArticleScripts/ExcelPython/openpyxl.xlsx')
sheet = wb['test']

Как видно из вышеприведенного листинга сделать это не сложно. Теперь посмотрим как можно считать данные:

#считываем значение определенной ячейки
val = sheet['A1'].value

#считываем заданный диапазон
vals = [v[0].value for v in sheet.range('A1:A2')]

Отличие от прошлых библиотек в том, что openpyxl дает возможность отображаться к ячейкам и последовательностям через их имена, что довольно удобно и понятно при чтении программы.

Теперь посмотрим как нам произвести запись и сохранить данные:

#записываем значение в определенную ячейку
sheet['B1'] = val

#записываем последовательность
i = 0
for rec in vals:
    sheet.cell(row=i, column=2).value = rec
    i =+ 1

# сохраняем данные
wb.save('../ArticleScripts/ExcelPython/openpyxl.xlsx')

Из примера видно, что запись, тоже производится довольно легко. Кроме того, в коде выше, можно заметить, что openpyxl кроме имен ячеек может работать и с их индексами.

К недостаткам данной библиотеки можно отнести, то что, как и в предыдущем примере, нет возможности сохранить изменения без создания новой книги.

Как было показано выше, для более менее полноценной работы с excel файлами, в данном случае, нужно 4 библиотеки, и это не всегда удобно. Кроме этого, возможно нужен будет доступ к VBA (допустим для какой-либо последующей обработки) и с помощью этих библиотек его не получить.

Однако, работа с этими библиотеками достаточно проста и удобна для быстрого создания Excel файлов их форматирования, но если Вам надо больше возможностей, то следующий подпункт для Вас.

Работа с com-объектом

В своих отчетах я предпочитаю использовать второй способ, а именно использование файла Excel через com-объект с использованием библиотеки win32com. Его преимуществом, является то, что вы можете выполнять с файлом все операции, которые позволяет делать обычный Excel с использованием VBA.

Проиллюстрируем это на той же задаче, что и предыдущие примеры.

Для начала загрузим нужную библиотеку и создадим COM объект.

import win32com.client
Excel = win32com.client.Dispatch("Excel.Application")

Теперь мы можем работать с помощью объекта Excel мы можем получить доступ ко всем возможностям VBA. Давайте, для начала, откроем любую книгу и выберем активный лист. Это можно сделать так:

wb = Excel.Workbooks.Open(u'D:\Scripts\DataScience\ArticleScripts\ExcelPython\xl.xls')
sheet = wb.ActiveSheet

Давайте получим значение первой ячейки и последовательности:

#получаем значение первой ячейки
val = sheet.Cells(1,1).value

#получаем значения цепочки A1:A2
vals = [r[0].value for r in sheet.Range("A1:A2")]

Как можно заметить, мы оперируем здесь функциями чистого VBA. Это очень удобно если у вас есть написанные макросы и вы хотите использовать их при работе с Python при минимальных затратах на переделку кода.

Посмотрим, как можно произвести запись полученных значений:

#записываем значение в определенную ячейку
sheet.Cells(1,2).value = val

#записываем последовательность
i = 1
for rec in vals:
    sheet.Cells(i,3).value = rec
    i = i + 1

#сохраняем рабочую книгу
wb.Save()

#закрываем ее
wb.Close()

#закрываем COM объект
Excel.Quit()

Из примера видно, что данные операции тоже довольно просто реализовываются. Кроме этого, можно заметить, что изменения мы сохранили в той же книге, которую открыли для чтения, что достаточно удобно.

Однако, внимательный читатель, обратит внимание на переменную i, которая инициализируется не 0, как принято python, а 1. Это связано с тем, что мы работаем с индексами ячеек как из VBA, а там нумерация начинается не с 0, а с 1.

На этом закончим разбор способов работы с excel файлами в python и перейдем к обратной задаче.

Вызываем функции Python из MS Excel

Может возникнуть такая ситуация, что у вас уже есть какой-либо функция, которая обрабатывает данные на python, и нужно перенести ее функциональность в Excel. Конечно же можно переписать ее на VBA, но зачем?

Для использования функций python в Excel есть прекрасная надстройка ExcelPython. С ее помощью вы сможете вызывать функции написанные на python прямо из Excel, правда придется еще написать небольшую обертку на VBA, и все это будет показано ниже.

Итак, предположим у нас есть функция, написанная на python, которой мы хотим воспользоваться:

def get_unique(lists):
    sm = 0
    for i in lists:
        sm = sm + int(i.pop()) 
    return sm

На вход ей подается список, состоящий из списков, это одно из условий, которое должно выполняться для работы данной функции в Excel.

Сохраним функцию в файле plugin.py и положим его в ту же директорию, где будет лежать наш excel файл, с которым мы будем работать.

Теперь установим ExcelPython. Установка происходит через запуск exe-файла и не вызывает затруднений.

Когда все приготовления выполнены, открываем тестовый файл excel и вызовем редактор VBA (Alt+F11). Для работы с вышеуказанной надстройкой необходимо ее подключить, через Tools->References, как показано на рисунке:

Ну что же, теперь можно приступить к написанию функции-обертки для нашего Python-модуля plugin.py. Выглядеть она будет следующим образом:

Function sr(lists As Range)
    On Error GoTo do_error
        Set plugin = PyModule("plugin", AddPath:=ThisWorkbook.Path)
        Set result = PyCall(plugin, "get_unique", PyTuple(lists.Value2))
        sr = WorksheetFunction.Transpose(PyVar(result))
        Exit Function
do_error:
        sr = Err.Description
End Function

Итак, что же происходит в данной функции?

Для начала, с помощью PyModule, мы подключаем нужный модуль. Для этого в качестве параметров ей передается имя модуля без расширения, и путь до папки в которой он находится. На выходе работы PyModule мы получаем объект для работы с модулем.

Затем, с помощью PyCall, вызываем нужную нам функцию из указанного модуля. В качестве параметров PyCall получает следующее:

1. Объект модуля, полученный на предыдущем шаге
2. Имя вызываемой функции
3. Параметры, передаваемые функции (передаются в виде списка)

Функция PyTuple, получает на вход какие-либо значения и преобразует их в объект tuple языка Python.
Ну и, соответственно, PyVar выполняет операцию преобразования результата функции python, к типу понятному Excel.

Теперь, чтобы убедиться в работоспособности нашей связки, вызовем нашу свежеиспеченую функцию на листе в Excel:

Как видно из рисунка все отработало правильно.

Надо отметить, что в данном материале используется старая версия ExcelPython, и на GitHub’e автора доступна новая версия.

Заключение

В качестве заключения, надо отметить, примеры в данной статье самые простые и для более глубоко изучения данных методов, я рекомендую обратиться к
документации по нужным пакетам.

Также хочу заметить, что указанные пакеты не являются единственными и в статье опущено рассмотрение, таких пакетов как xlsxwriter для генерации excel файлов или xlwings, который может работать с Excel файлами «на лету», а также же PyXLL, который выполняет аналогичные функции ExcelPython.

Кроме этого в статье я попытался несколько обобщить разборасанный по сети материал, т.к. такие вопросы часто фигурируют на форумах и думаю некоторым будет полезно иметь, такую «шпаргалку» под рукой.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Editing Excel Spreadsheets in Python With openpyxl

Excel spreadsheets are one of those things you might have to deal with at some point. Either it’s because your boss loves them or because marketing needs them, you might have to learn how to work with spreadsheets, and that’s when knowing openpyxl comes in handy!

Spreadsheets are a very intuitive and user-friendly way to manipulate large datasets without any prior technical background. That’s why they’re still so commonly used today.

In this article, you’ll learn how to use openpyxl to:

• Manipulate Excel spreadsheets with confidence
• Extract information from spreadsheets
• Create simple or more complex spreadsheets, including adding styles, charts, and so on

This article is written for intermediate developers who have a pretty good knowledge of Python data structures, such as dicts and lists, but also feel comfortable around OOP and more intermediate level topics.

from openpyxl import Workbook

workbook = Workbook()
sheet = workbook.active

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

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

>>> 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'

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

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

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

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

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

>>> 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, ...)

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")

 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)

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

>>> 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!')

>>> # 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']

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

>>> # 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")

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

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

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

>>> # 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")

>>> # 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")

>>> 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")

>>> 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")

>>> 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")

>>> 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")

>>> 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")

>>> 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")

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")

 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)

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")

 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)

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")

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

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

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

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")

from dataclasses import dataclass
from typing import List

@dataclass
class Sale:
    quantity: int

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

 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)

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)

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")

 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)

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")

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)

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)

>>> 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

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Editing Excel Spreadsheets in Python With openpyxl

Microsoft Excel is one of the most powerful spreadsheet software applications in the world, and it has become critical in all business processes. Companies across the world, both big and small, are using Microsoft Excel to store, organize, analyze, and visualize data.

As a data professional, when you combine Python with Excel, you create a unique data analysis bundle that unlocks the value of the enterprise data.

In this tutorial, we’re going to learn how to read and work with Excel files in Python.

After you finish this tutorial, you’ll understand the following:

• Loading Excel spreadsheets into pandas DataFrames
• Working with an Excel workbook with multiple spreadsheets
• Combining multiple spreadsheets
• Reading Excel files using the xlrd package

In this tutorial, we assume you know the fundamentals of pandas DataFrames. If you aren’t familiar with the pandas library, you might like to try our Pandas and NumPy Fundamentals – Dataquest.

Let’s dive in.

Reading Spreadsheets with Pandas

Technically, multiple packages allow us to work with Excel files in Python. However, in this tutorial, we’ll use pandas and xlrd libraries to interact with Excel workbooks. Essentially, you can think of a pandas DataFrame as a spreadsheet with rows and columns stored in Series objects. Traversability of Series as iterable objects allows us to grab specific data easily. Once we load an Excel workbook into a pandas DataFrame, we can perform any kind of data analysis on the data.

Before we proceed to the next step, let’s first download the following spreadsheet:

Sales Data Excel Workbook — xlsx ver.

The Excel workbook consists of two sheets that contain stationery sales data for 2020 and 2021.

NOTE

Although Excel spreadsheets can contain formula and also support formatting, pandas only imports Excel spreadsheets as flat files, and it doesn’t support spreadsheet formatting.

To import the Excel spreadsheet into a pandas DataFrame, first, we need to import the pandas package and then use the read_excel() method:

import pandas as pd
df = pd.read_excel('sales_data.xlsx')

display(df)

If you want to load only a limited number of rows into the DataFrame, you can specify the number of rows using the nrows argument:

df = pd.read_excel('sales_data.xlsx', nrows=5)
display(df)

Skipping a specific number of rows from the begining of a spreadsheet or skipping over a list of particular rows is available through the skiprows argument, as follows:

df = pd.read_excel('sales_data.xlsx', skiprows=range(5))
display(df)

The code above skips the first five rows and returns the rest of the data. Instead, the following code returns all the rows except for those with the mentioned indices:

df = pd.read_excel('sales_data.xlsx', skiprows=[1, 4,7,10])
display(df)

Another useful argument is usecols, which allows us to select spreadsheet columns with their letters, names, or positional numbers. Let’s see how it works:

df = pd.read_excel('sales_data.xlsx', usecols='A:C,G')
display(df)

In the code above, the string assigned to the usecols argument contains a range of columns with : plus column G separated by a comma. Also, we’re able to provide a list of column names and assign it to the usecols argument, as follows:

df = pd.read_excel('sales_data.xlsx', usecols=['OrderDate', 'Region', 'Rep', 'Total'])
display(df)

The usecols argument accepts a list of column numbers, too. The following code shows how we can pick up specific columns using their indices:

df = pd.read_excel('sales_data.xlsx', usecols=[0, 1, 2, 6])
display(df)

Working with Multiple Spreadsheets

Excel files or workbooks usually contain more than one spreadsheet. The pandas library allows us to load data from a specific sheet or combine multiple spreadsheets into a single DataFrame. In this section, we’ll explore how to use these valuable capabilities.

By default, the read_excel() method reads the first Excel sheet with the index 0. However, we can choose the other sheets by assigning a particular sheet name, sheet index, or even a list of sheet names or indices to the sheet_name argument. Let’s try it:

df = pd.read_excel('sales_data.xlsx', sheet_name='2021')
display(df)

The code above reads the second spreadsheet in the workbook, whose name is 2021. As mentioned before, we also can assign a sheet position number (zero-indexed) to the sheet_name argument. Let’s see how it works:

df = pd.read_excel('sales_data.xlsx', sheet_name=1)
display(df)

As you can see, both statements take in either the actual sheet name or sheet index to return the same result.

Sometimes, we want to import all the spreadsheets stored in an Excel file into pandas DataFrames simultaneously. The good news is that the read_excel() method provides this feature for us. In order to do this, we can assign a list of sheet names or their indices to the sheet_name argument. But there is a much easier way to do the same: to assign None to the sheet_name argument. Let’s try it:

all_sheets = pd.read_excel('sales_data.xlsx', sheet_name=None)

Before exploring the data stored in the all_sheets variable, let’s check its data type:

type(all_sheets)

dict

As you can see, the variable is a dictionary. Now, let’s reveal what is stored in this dictionary:

for key, value in all_sheets.items():
    print(key, type(value))

2020 <class 'pandas.core.frame.DataFrame'>
2021 <class 'pandas.core.frame.DataFrame'>

The code above shows that the dictionary’s keys are the Excel workbook sheet names, and its values are pandas DataFrames for each spreadsheet. To print out the content of the dictionary, we can use the following code:

for key, value in all_sheets.items():
    print(key)
    display(value)

2020

2021

Combining Multiple Excel Spreadsheets into a Single Pandas DataFrame

Having one DataFrame per sheet allows us to have different columns or content in different sheets.

But what if we prefer to store all the spreadsheets’ data in a single DataFrame? In this tutorial, the workbook spreadsheets have the same columns, so we can combine them with the concat() method of pandas.

If you run the code below, you’ll see that the two DataFrames stored in the dictionary are concatenated:

combined_df = pd.concat(all_sheets.values(), ignore_index=True)
display(combined_df)

Now the data stored in the combined_df DataFrame is ready for further processing or visualization. In the following piece of code, we’re going to create a simple bar chart that shows the total sales amount made by each representative. Let’s run it and see the output plot:

total_sales_amount = combined_df.groupby('Rep').Total.sum()
total_sales_amount.plot.bar(figsize=(10, 6))

Reading Excel Files Using xlrd

Although importing data into a pandas DataFrame is much more common, another helpful package for reading Excel files in Python is xlrd. In this section, we’re going to scratch the surface of how to read Excel spreadsheets using this package.

NOTE

The xlrd package doesn’t support xlsx files due to a potential security vulnerability. So, we use the xls version of the sales data. You can download the xls version from the link below:
Sales Data Excel Workbook — xls ver.

Let’s see how it works:

import xlrd
excel_workbook = xlrd.open_workbook('sales_data.xls')

Above, the first line imports the xlrd package, then the open_workbook method reads the sales_data.xls file.

We can also open an individual sheet containing the actual data. There are two ways to do so: opening a sheet by index or by name. Let’s open the first sheet by index and the second one by name:

excel_worksheet_2020 = excel_workbook.sheet_by_index(0)
excel_worksheet_2021 = excel_workbook.sheet_by_name('2021')

Now, let’s see how we can print a cell value. The xlrd package provides a method called cell_value() that takes in two arguments: the cell’s row index and column index. Let’s explore it:

print(excel_worksheet_2020.cell_value(1, 3))

Pencil

We can see that the cell_value function returned the value of the cell at row index 1 (the 2nd row) and column index 3 (the 4th column).

The xlrd package provides two helpful properties: nrows and ncols, returning the number of nonempty spreadsheet’s rows and columns respectively:

print('Columns#:', excel_worksheet_2020.ncols)
print('Rows#:', excel_worksheet_2020.nrows)

Columns#: 8
Rows#: 13

Knowing the number of nonempty rows and columns in a spreadsheet helps us with iterating over the data using nested for loops. This makes all the Excel sheet data accessible via the cell_value() method.

Conclusion

This tutorial discussed how to load Excel spreadsheets into pandas DataFrames, work with multiple Excel sheets, and combine them into a single pandas DataFrame. We also explored the main aspects of the xlrd package as one of the simplest tools for accessing the Excel spreadsheets data.

I believe you have used Microsoft Excel on some occasions. It is very powerful when it comes to working with spreadsheets, tables, charts, etc. But what does Python have to do with that?

Python is a game-changer when it comes to Excel files because it can automate daunting stuff you might encounter in an Excel-related task. For instance, you may be required to look for some information in hundreds of spreadsheets of the company’s budgets. Very daunting, isn’t it? In this tutorial, I will show you how Python can be used easily to work with Excel documents.

Oh, don’t worry if you don’t have Microsoft Excel installed on your machine. You can use other alternatives to walk through this tutorial, such as LibreOffice Calc and OpenOffice Calc.

Let’s get started!

OpenPyXL

OpenPyXL is a library used to read and write Excel 2010 .xlsx/.xlsm/.xltx/.xltm files. This is the library we will be using in this tutorial to work with Excel documents.

The first thing we need to do in order to make use of this library is install OpenPyXL.

Installing OpenPyXL

In order to install OpenPyXL, we will be using pip, which is (based on Wikipedia):

A package management system used to install and manage software packages written in Python. Many packages can be found in the Python Package Index (PyPI).

You can follow the steps mentioned in the Python Packaging User Guide for installing pip, but if you have Python 2.7.9 and higher, or Python 3.4 and higher, you already have pip!

OpenPyXL now can be simply installed by typing the following command (in macOS’s Terminal):

Opening an Excel Document

After installing OpenPyXL, we are ready to start working with Excel documents. The first normal task we would perform on an Excel document is to open that document. Go ahead and download the Excel file sample.xlsx in order to follow along with the tutorial, or you can use whichever Excel file you like.

Before we can use OpenPyXL, we need to import it, as follows:

The method we need in order to open the Excel document is load_workbook(). If you are wondering what is meant by a workbook, it is simply the Excel spreadsheet document. The script that we thus need to open an Excel document is as follows:

1

import openpyxl

2

excel_document = openpyxl.load_workbook('sample.xlsx')

Let’s now see the type returned from the load_workbook() method. This can be done as follows:

1

print type(excel_document)

This will return the following:

1

<class 'openpyxl.workbook.workbook.Workbook'>

As we can see, the object returned is Workbook, of data type workbook. The Workbook object here represents the Excel file.

Sheet Names

Sheets in Excel consist of columns (with letters starting from A, B, C, etc.) and rows (starting from 1, 2, 3, etc.). In order to check what sheets we have in our Excel document, we use the get_sheet_names() method as follows:

1

print(excel_document.sheetnames)

If we print the above command, we get the following:

Thus showing that we have one sheet, called Sheet1.

If you have multiple sheets, you can access a specific sheet by its name using this method: get_sheet_by_name(). For example, to get the current sheet:

1

print(excel_document. get_sheet_by_name('Sheet1'))

The output will be:

Accessing Cells

Now that we have learned how to open an Excel file and get the sheet, let’s see how easy it is to access a cell in that sheet. All you have to do is retrieve that sheet, and then determine the location (coordinate) of the cell. Let’s say that we want to access column A row 2 in the Excel document we have, that is A2. This can be implemented as follows:

1

sheet = excel_document['Sheet1']

2

print(sheet['A2'].value)

In this case, you will have the following value returned:

We can also use a row-column notation. For instance, if we want to access the cell at row 5 and column 2, we type the following:

1

sheet.cell(row = 5, column = 2).value

The output in this case will be programmer.

If we want to see the object type representing the cell, we can type:

1

print(type(sheet['A2']))

In this case, you would get the following output:

1

<class 'openpyxl.cell.cell.Cell'>

which means that the object is of type Cell.

Accessing a Range of Cells

What if you were interested in accessing a range of cells rather than only one cell? Let’s say we want to access the cells from A1 to B3, which look like this in our Excel document?

This can be done using the following script:

1

multiple_cells = sheet['A1':'B3']

2

for row in multiple_cells:

3

    for cell in row:

4

        print(cell.value)

In this case, you will get the following output:

1

Name

2

Profession

3

Abder

4

Student

5

Bob

6

Engineer

Accessing All Rows and Columns

OpenPyXL enables you to access all the rows and columns in your Excel document, using the rows() and columns() methods, respectively.

In order to access all the rows, we can do the following:

1

for row in sheet.rows:

2

    print(row)

The output of the above script would be as follows:

1

(<Cell 'Sheet1'.A1>, <Cell 'Sheet1'.B1>)

2

(<Cell 'Sheet1'.A2>, <Cell 'Sheet1'.B2>)

3

(<Cell 'Sheet1'.A3>, <Cell 'Sheet1'.B3>)

4

(<Cell 'Sheet1'.A4>, <Cell 'Sheet1'.B4>)

5

(<Cell 'Sheet1'.A5>, <Cell 'Sheet1'.B5>)

6

(<Cell 'Sheet1'.A6>, <Cell 'Sheet1'.B6>)

7

(<Cell 'Sheet1'.A7>, <Cell 'Sheet1'.B7>)

On the other hand, if we want to access all the columns, we simply do the following:

1

for column in sheet.columns:

2

    print(column)

In which case, you will get the following output:

1

(<Cell 'Sheet1'.A1>, <Cell 'Sheet1'.A2>, <Cell 'Sheet1'.A3>, <Cell 'Sheet1'.A4>, <Cell 'Sheet1'.A5>, <Cell 'Sheet1'.A6>, <Cell 'Sheet1'.A7>)

2

(<Cell 'Sheet1'.B1>, <Cell 'Sheet1'.B2>, <Cell 'Sheet1'.B3>, <Cell 'Sheet1'.B4>, <Cell 'Sheet1'.B5>, <Cell 'Sheet1'.B6>, <Cell 'Sheet1'.B7>)

There is of course more that you can do with Excel documents, as you can see in the OpenPyXL documentation.

Conclusion

From this tutorial, we have noticed how flexible it can be to work with Excel documents using Python. Remember the scenario mentioned at the beginning of the tutorial? It’s worth trying as a project!

This post has been updated with contributions from Esther Vaati. Esther is a software developer and writer for Envato Tuts+.

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Dr. Aber-Rahman Ali is a researcher who uses machine learning and image processing in medical image analysis.

He also likes writing about Python!