Python excel cell value

Основные атрибуты и методы объекта ячейки Cell.

Объект ячейки Cell управляет отдельными ячейками электронной таблицы, а также описывает ее свойства. Класс Cell() должен знать свое значение и тип, параметры отображения и любые другие функции ячейки Excel.

Не создавайте объекты ячейки самостоятельно. Доступ к произвольной ячейке можно получить, обратившись к объекту рабочего листа Worksheet как к словарю, при этом ключом будет служить строка с именем ячейки, например ws['A1']. Это вернет ячейку на 'A4' или создаст ее, если она еще не существует. Значения могут быть присвоены напрямую:

>>> from openpyxl import Workbook
>>> wb = Workbook()
>>> ws = wb.active
>>> ws['A4'] = 5
>>> ws['A4'].value
# 5

# присваивание значений
>>> ws['A4'].value = ws['A4'].value * 2
>>> ws['A4'].value
# 10

Для доступа к ячейке с использованием индексов столбцов и строк, можно использовать метод Worksheet.cell().

Пример работы с объектом ячейки через переменную:

# доступ к объекту ячейки
>>> cell = ws.cell(row=4, column=2)
# доступ с присваиваем значения ячейки
>>> cell = ws.cell(row=4, column=2, value=10)
>>> cell.value
# 10

# присваивание значений
>>> cell.value *= 2
>>> cell.value
# 20

Содержание:

• Cell.base_date базовый класс обработки даты,
• Cell.check_error() проверяет правильность кодировка строки,
• Cell.col_idx числовой индекс столбца,
• Cell.column номер столбца этой ячейки,
• Cell.column_letter возвращает букву столбца ячейки,
• Cell.comment возвращает/назначает объект комментария,
• Cell.coordinate возвращает координаты этой ячейки,
• Cell.encoding возвращает кодировку этой ячейки,
• Cell.hyperlink устанавливает гиперссылку,
• Cell.is_date проверяет значение ячейки на дату,
• Cell.offset() смещенный объект ячейки,
• Cell.parent объект рабочего листа,
• Cell.row номер строки этой ячейки,
• Cell.value возвращает/устанавливает значение в ячейку,
• Cell.alignment выравнивает значения в ячейке,
• Cell.font стиль шрифта ячейки,
• Cell.border стиль оформления границ ячейки,
• Cell.fill цвет заливки ячейки.

Cell.base_date:

Свойство Cell.base_date содержит базовый класс обработки даты. По умолчанию это datetime.

Cell.check_string(value):

Метод Cell.check_string() проверяет правильность кодировка строки, а также ее допустимой длины и символ разрыва строки.

Cell.col_idx:

Атрибут Cell.col_idx возвращает числовой индекс столбца. Нумерация столбцов в модуле openpyxl начинается с 1, а не с 0 как в списке.

Cell.column:

Атрибут Cell.column возвращает номер столбца этой ячейки. Нумерация столбцов в модуле openpyxl начинается с 1, а не с 0 как в списке.

Cell.column_letter:

Атрибут Cell.column_letter возвращает букву столбца ячейки.

>>> ws['C4'].column_letter
# 'C'

Cell.comment:

Атрибут Cell.comment возвращает/назначает объект комментария Comment, связанный с этой ячейкой. Объект комментария содержит текстовый атрибут и атрибут автора, которые должны быть установлены.

Если назначить один и тот же комментарий нескольким ячейкам, то openpyxl автоматически создаст копии.

>>> from openpyxl import Workbook
>>> from openpyxl.comments import Comment
>>> wb=Workbook()
>>> ws=wb.active
>>> comment = Comment("Text", "Author")
>>> ws["A1"].comment = comment
>>> ws["A1"].comment.text
# 'Text'
>>> ws["A1"].comment.author
# 'Author'

# присвоим этот же комментарий другой ячейке
>>> ws["B2"].comment = comment
>>> ws["A1"].comment is comment
# True
>>> ws["B2"].comment is comment
# False

Cell.coordinate:

Атрибут Cell.coordinate возвращает координаты этой ячейки.

>>> ws['C4'].coordinate
# 'C4'

Cell.encoding:

Атрибут Cell.encoding возвращает кодировку этой ячейки.

>>> ws['C4'].encoding
# 'utf-8'

Cell.hyperlink:

Свойство Cell.hyperlink устанавливает целевую гиперссылку.

ws['A1'].hyperlink = 'https://docs-python.ru/'
ws['A1'].value = 'Справочник Python'
ws['A1'].style = 'Hyperlink'

Некоторые говорят что код, представленный выше — не работает. В таком случае установить ссылку в ячейку можно используя встроенную в Excel функцию ‘=HYPERLINK(link, linkName)`, например:

>>> link = 'https://docs-python.ru/'
>>> link_name = 'Справочник Python'
>>> ws.cell(row=2, column=1).value = f'=HYPERLINK("{link}", "{link_name}")'

Cell.is_date:

Свойство Cell.is_date возвращает True, если значение отформатировано как дата.

>>> ws['C4'].value = 10
>>> ws['C4'].is_date
# False

>>> import datetime
>>> now = datetime.datetime.now()
>>> ws['C4'].value = now
>>> ws['C4'].is_date
# True

Cell.offset(row=0, column=0):

Метод Cell.offset() возвращает объект ячейки, смещенный на кол-во строк row и столбцов column относительно этой ячейки.

Аргументы:

• row — количество смещения строк.
• column — количество смещения столбцов.

>>> ws['C4'].offset(row=1, column=0)
# <Cell 'Sheet'.C5>

Cell.parent:

Свойство Cell.parent возвращает объект рабочего листа, на котором расположена эта ячейка.

>>> ws['C4'].parent
# <Worksheet "Sheet">

Cell.row:

Свойство Cell.row возвращает номер строки этой ячейки. Нумерация строк в модуле openpyxl начинается с 1, а не с 0 как в списке.

Cell.value:

Свойство Cell.value возвращает или устанавливает значение, хранящееся в ячейке.

Ячейка может хранить следующие значения: str, float, int или datetime.datetime.

>>> ws['C4'].value = 100500
>>> ws['C4'].value
# 100500

Cell.alignment:

Свойство Cell.alignment отвечает за выравнивание значения в ячейке. Значение должно быть объектом Alignment().

>>> from openpyxl import Workbook
>>> from openpyxl.styles import Alignment
>>> wb = Workbook()
>>> ws = wb.active
>>> ws['A1'].value = 1500
>>> ws['A2'].value = 1500
>>> ws['A3'].value = 1500
# выравниваем текст в ячейках стилями
>>> ws['A1'].alignment = Alignment(horizontal='left')
>>> ws['A2'].alignment = Alignment(horizontal='center')
>>> ws['A3'].alignment = Alignment(horizontal='right')
# сохраняем и смотрим что получилось
>>> wb.save('test.xlsx')

Cell.font:

Свойство Cell.font задает стиль шрифта в ячейке (имя, высота, цвет и т.д.). Значение должно быть объектом Font().

>>> from openpyxl import Workbook
>>> from openpyxl.styles import Font
>>> wb = Workbook()
>>> ws = wb.active
>>> a1 = ws['A1']
>>> a1.value = 'Ячейка `A1`'
>>> a1.font = Font(size=12)
# можно напрямую
>>> ws['A2'].value = 'Ячейка `A2`'
>>> ws['A2'].font = Font(name= 'Arial', size=12, bold=True, color='00FF00')
# сохраняем и смотрим что получилось
>>> wb.save('test.xlsx')

Cell.border:

Свойство Cell.border задает стиль оформления границ ячейки. Значение должно быть объектом Border().

Пример стилизации границ одной ячейки:

>>> from openpyxl import Workbook
>>> from openpyxl.styles import Border, Side
>>> wb = Workbook()
>>> ws = wb.active
>>> cell = ws['B2']
# установить высоту строки
>>> ws.row_dimensions[2].height = 30
# установить ширину столбца
>>> ws.column_dimensions['B'].width = 40
# определим стили сторон
>>> thins = Side(border_style="medium", color="0000ff")
>>> double = Side(border_style="dashDot", color="ff0000")
# рисуем границы
>>> cell.border = Border(top=double, bottom=double, left=thins, right=thins) 
>>> wb.save("test.xlsx")

Cell.fill:

Свойство Cell.fill задает цвет заливки ячейки. Значение должно быть объектом PatternFill().

>>> from openpyxl import Workbook
>>> from openpyxl.styles import PatternFill
>>> wb = Workbook()
>>> ws = wb.active
>>> a1 = ws['A1']
>>> a1.value = 'Ячейка `A1`'
>>> a1.fill = PatternFill('solid', fgColor="ff0000")
>>> wb.save("test.xlsx")

Faced the same problem. Needed to read cell values whatever those cells are: scalars, formulae with precomputed values or formulae without them, with fail-tolerance preferred over correctness.

The strategy is pretty straightforward:

1. if a cell doesn’t contain formula, return cell’s value;
2. if it’s a formula, try to get its precomputed value;
3. if couldn’t, try to evaluate it using pycel;
4. if failed (due to pycel‘s limited support of formulae or with some error), warn and return None.

I made a class which hides all this machinery and provides simple interface for reading cell values.

It’s easy to modify the class so that it will raise an exception on step 4, if correctness is preferred over fail-tolerance.

Hope it will help someone.

from traceback import format_exc
from pathlib import Path
from openpyxl import load_workbook
from pycel.excelcompiler import ExcelCompiler
import logging


class MESSAGES:
    CANT_EVALUATE_CELL = ("Couldn't evaluate cell {address}."
                          " Try to load and save xlsx file.")


class XLSXReader:
    """
    Provides (almost) universal interface to read xlsx file cell values.

    For formulae, tries to get their precomputed values or, if none,
    to evaluate them.
    """

    # Interface.

    def __init__(self, path: Path):
        self.__path = path
        self.__book = load_workbook(self.__path, data_only=False)

    def get_cell_value(self, address: str, sheet: str = None):
        # If no sheet given, work with active one.
        if sheet is None:
            sheet = self.__book.active.title

        # If cell doesn't contain a formula, return cell value.
        if not self.__cell_contains_formula(address, sheet):
            return self.__get_as_is(address, sheet)

        # If cell contains formula:
        # If there's precomputed value of the cell, return it.
        precomputed_value = self.__get_precomputed(address, sheet)
        if precomputed_value is not None:
            return precomputed_value

        # If not, try to compute its value from the formula and return it.
        # If failed, report an error and return empty value.
        try:
            computed_value = self.__compute(address, sheet)
        except:
            logging.warning(MESSAGES.CANT_EVALUATE_CELL
                            .format(address=address))
            logging.debug(format_exc())
            return None
        return computed_value                

    # Private part.

    def __cell_contains_formula(self, address, sheet):
        cell = self.__book[sheet][address]
        return cell.data_type is cell.TYPE_FORMULA

    def __get_as_is(self, address, sheet):
        # Return cell value.
        return self.__book[sheet][address].value

    def __get_precomputed(self, address, sheet):
        # If the sheet is not loaded yet, load it.
        if not hasattr(self, '__book_with_precomputed_values'):
            self.__book_with_precomputed_values = load_workbook(
                self.__path, data_only=True)
        # Return precomputed value.
        return self.__book_with_precomputed_values[sheet][address].value

    def __compute(self, address, sheet):
        # If the computation engine is not created yet, create it.
        if not hasattr(self, '__formulae_calculator'):
            self.__formulae_calculator = ExcelCompiler(self.__path)
        # Compute cell value.
        computation_graph = self.__formulae_calculator.gen_graph(
            address, sheet=sheet)
        return computation_graph.evaluate(f"{sheet}!{address}")

Introduction

In previous article, “Use openpyxl — create a new Worksheet, change sheet property in Python”, I introduced how to create a new Worksheet and change Worksheet properties.

In this article I show how to read and write Cell in Excel with Python.

Enter values to Cell

Worksheet object has a position of Cell as property. That is dictionary type and consists of row and column number.
Using them, access to Cell and edit the values. A sample code is as below.

 1from openpyxl import Workbook
 2from openpyxl.compat import range
 3from openpyxl.utils import get_column_letter
 4
 5wb = Workbook()
 6ws = wb.active
 7
 8# Enter `hogehogehoge` in column of `B` and row of `2`
 9ws['B2'] = 'hogehogehoge'
10# Enter `fugafugaufga` in column of `F` and row of `5`
11ws['F5'] = 'fugafugaufga'
12
13wb.save(filename = 'sample_book.xlsx')

Or, using cell function can do the same. However, cell function must be given arguments column number and line number.

1ws.cell(row=2, column=2, value='hogehogehoge')
2ws.cell(row=5, column=6, value='fugafugaufga')

Read values in Cell

To read values in Cell, access to value property in Cell object.

Or, using cell function can do the same when reading.

1b2 = ws.cell(column=2, row=2).value

Processing for each row

The iter_rows function can get instances for each row.
For specifying to range of extracting data, min_row, max_row, min_col and max_col options exist.
In addition, if max_row or max_col options are not given in arguments, it is the processing target up to the position where the data is entered.

Actually, it’s rarely to read datas from column A of the first row when handling files.

In the following example, processing in units of one line with the second line as the starting line.

1for row in ws.iter_rows(min_row=2):
2    for cell in row:
3        print(f"col {cell.col_idx}：{cell.value}")
4    print('------------------------------------------')

The output result is as follows.

 1col 1：None
 2col 2：hogehogehoge
 3col 3：None
 4col 4：None
 5col 5：None
 6col 6：None
 7------------------------------------------
 8col 1：None
 9col 2：None
10col 3：None
11col 4：None
12col 5：None
13col 6：None
14------------------------------------------
15col 1：None
16col 2：None
17col 3：None
18col 4：None
19col 5：None
20col 6：None
21------------------------------------------
22col 1：None
23col 2：None
24col 3：None
25col 4：None
26col 5：None
27col 6：fugafugaufga
28------------------------------------------

Processing for each column

The iter_cols function can get instances for each column.
Usage is similar to iter_rows function.

In the following example, processing in units of one column with the second column as the starting column.

1for col in ws.iter_cols(min_row=2):
2    for cell in col:
3        print(f"row {cell.row}：{cell.value}")
4    print('------------------------------------------')

The output result is as follows.

 1row 2：None
 2row 3：None
 3row 4：None
 4row 5：None
 5------------------------------------------
 6row 2：hogehogehoge
 7row 3：None
 8row 4：None
 9row 5：None
10------------------------------------------
11row 2：None
12row 3：None
13row 4：None
14row 5：None
15------------------------------------------
16row 2：None
17row 3：None
18row 4：None
19row 5：None
20------------------------------------------
21row 2：None
22row 3：None
23row 4：None
24row 5：None
25------------------------------------------
26row 2：None
27row 3：None
28row 4：None
29row 5：fugafugaufga
30------------------------------------------

Enter values for a row

The append function in Worksheet instance can enter data for one row.
By giving list type data as an argument, data goes into the sheet left-aligned.

 1wb = Workbook()
 2ws = wb.active
 3
 4data = [
 5        ['A', 100, 1.0],
 6        ['B', 200, 2.0],
 7        ['C', 300, 3.0],    
 8        ['D', 400, 4.0],        
 9]
10
11for row in data:
12    ws.append(row)
13
14wb.save(filename = 'sample_book.xlsx')

Conclusion

It is available to

• Enter or read values to Cell with Cell instance property or cell function
• Process data row by row or colum
• Enter a value for a row with append function

Время на прочтение
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Первая часть статьи была опубликована тут.

Как читать и редактировать Excel файлы при помощи openpyxl

ПЕРЕВОД
Оригинал статьи — www.datacamp.com/community/tutorials/python-excel-tutorial
Автор — Karlijn Willems

Эта библиотека пригодится, если вы хотите читать и редактировать файлы .xlsx, xlsm, xltx и xltm.

Установите openpyxl using pip. Общие рекомендации по установке этой библиотеки — сделать это в виртуальной среде Python без системных библиотек. Вы можете использовать виртуальную среду для создания изолированных сред Python: она создает папку, содержащую все необходимые файлы, для использования библиотек, которые потребуются для Python.

Перейдите в директорию, в которой находится ваш проект, и повторно активируйте виртуальную среду venv. Затем перейдите к установке openpyxl с помощью pip, чтобы убедиться, что вы можете читать и записывать с ним файлы:

# Activate virtualenv
$ source activate venv

# Install `openpyxl` in `venv`
$ pip install openpyxl

Теперь, когда вы установили openpyxl, вы можете начать загрузку данных. Но что именно это за данные? Например, в книге с данными, которые вы пытаетесь получить на Python, есть следующие листы:

Функция load_workbook () принимает имя файла в качестве аргумента и возвращает объект рабочей книги, который представляет файл. Это можно проверить запуском type (wb). Не забудьте убедиться, что вы находитесь в правильной директории, где расположена электронная таблица. В противном случае вы получите сообщение об ошибке при импорте.

# Import `load_workbook` module from `openpyxl`
from openpyxl import load_workbook

# Load in the workbook
wb = load_workbook('./test.xlsx')

# Get sheet names
print(wb.get_sheet_names())

Помните, вы можете изменить рабочий каталог с помощью os.chdir (). Фрагмент кода выше возвращает имена листов книги, загруженной в Python. Вы можете использовать эту информацию для получения отдельных листов книги. Также вы можете проверить, какой лист активен в настоящий момент с помощью wb.active. В приведенном ниже коде, вы также можете использовать его для загрузки данных на другом листе книги:

# Get a sheet by name 
sheet = wb.get_sheet_by_name('Sheet3')

# Print the sheet title 
sheet.title

# Get currently active sheet
anotherSheet = wb.active

# Check `anotherSheet` 
anotherSheet

На первый взгляд, с этими объектами Worksheet мало что можно сделать. Однако, можно извлекать значения из определенных ячеек на листе книги, используя квадратные скобки [], к которым нужно передавать точную ячейку, из которой вы хотите получить значение.

Обратите внимание, это похоже на выбор, получение и индексирование массивов NumPy и Pandas DataFrames, но это еще не все, что нужно сделать, чтобы получить значение. Нужно еще добавить значение атрибута:

# Retrieve the value of a certain cell
sheet['A1'].value

# Select element 'B2' of your sheet 
c = sheet['B2']

# Retrieve the row number of your element
c.row

# Retrieve the column letter of your element
c.column

# Retrieve the coordinates of the cell 
c.coordinate

Помимо value, есть и другие атрибуты, которые можно использовать для проверки ячейки, а именно row, column и coordinate:

Атрибут row вернет 2;
Добавление атрибута column к “С” даст вам «B»;
coordinate вернет «B2».

Вы также можете получить значения ячеек с помощью функции cell (). Передайте аргументы row и column, добавьте значения к этим аргументам, которые соответствуют значениям ячейки, которые вы хотите получить, и, конечно же, не забудьте добавить атрибут value:

# Retrieve cell value 
sheet.cell(row=1, column=2).value

# Print out values in column 2 
for i in range(1, 4):
     print(i, sheet.cell(row=i, column=2).value)

Обратите внимание: если вы не укажете значение атрибута value, вы получите <Cell Sheet3.B1>, который ничего не говорит о значении, которое содержится в этой конкретной ячейке.

Вы используете цикл с помощью функции range (), чтобы помочь вам вывести значения строк, которые имеют значения в столбце 2. Если эти конкретные ячейки пусты, вы получите None.
Более того, существуют специальные функции, которые вы можете вызвать, чтобы получить другие значения, например get_column_letter () и column_index_from_string.

В двух функциях уже более или менее указано, что вы можете получить, используя их. Но лучше всего сделать их явными: пока вы можете получить букву прежнего столбца, можно сделать обратное или получить индекс столбца, перебирая букву за буквой. Как это работает:

# Import relevant modules from `openpyxl.utils`
from openpyxl.utils import get_column_letter, column_index_from_string

# Return 'A'
get_column_letter(1)

# Return '1'
column_index_from_string('A')

Вы уже получили значения для строк, которые имеют значения в определенном столбце, но что нужно сделать, если нужно вывести строки файла, не сосредотачиваясь только на одном столбце?

Конечно, использовать другой цикл.

Например, вы хотите сосредоточиться на области, находящейся между «A1» и «C3», где первый указывает левый верхний угол, а второй — правый нижний угол области, на которой вы хотите сфокусироваться. Эта область будет так называемой cellObj, которую вы видите в первой строке кода ниже. Затем вы указываете, что для каждой ячейки, которая находится в этой области, вы хотите вывести координату и значение, которое содержится в этой ячейке. После окончания каждой строки вы хотите выводить сообщение-сигнал о том, что строка этой области cellObj была выведена.

# Print row per row
for cellObj in sheet['A1':'C3']:
      for cell in cellObj:
              print(cells.coordinate, cells.value)
      print('--- END ---')

Обратите внимание, что выбор области очень похож на выбор, получение и индексирование списка и элементы NumPy, где вы также используете квадратные скобки и двоеточие чтобы указать область, из которой вы хотите получить значения. Кроме того, вышеприведенный цикл также хорошо использует атрибуты ячейки!

Чтобы визуализировать описанное выше, возможно, вы захотите проверить результат, который вернет вам завершенный цикл:

('A1', u'M')
('B1', u'N')
('C1', u'O')
--- END ---
('A2', 10L)
('B2', 11L)
('C2', 12L)
--- END ---
('A3', 14L)
('B3', 15L)
('C3', 16L)
--- END ---

Наконец, есть некоторые атрибуты, которые вы можете использовать для проверки результата импорта, а именно max_row и max_column. Эти атрибуты, конечно, являются общими способами обеспечения правильной загрузки данных, но тем не менее в данном случае они могут и будут полезны.

# Retrieve the maximum amount of rows 
sheet.max_row

# Retrieve the maximum amount of columns
sheet.max_column

Это все очень классно, но мы почти слышим, что вы сейчас думаете, что это ужасно трудный способ работать с файлами, особенно если нужно еще и управлять данными.
Должно быть что-то проще, не так ли? Всё так!

Openpyxl имеет поддержку Pandas DataFrames. И можно использовать функцию DataFrame () из пакета Pandas, чтобы поместить значения листа в DataFrame:

# Import `pandas` 
import pandas as pd

# Convert Sheet to DataFrame
df = pd.DataFrame(sheet.values)
Если вы хотите указать заголовки и индексы, вам нужно добавить немного больше кода:
# Put the sheet values in `data`
data = sheet.values

# Indicate the columns in the sheet values
cols = next(data)[1:]

# Convert your data to a list
data = list(data)

# Read in the data at index 0 for the indices
idx = [r[0] for r in data]

# Slice the data at index 1 
data = (islice(r, 1, None) for r in data)

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

Затем вы можете начать управлять данными при помощи всех функций, которые есть в Pandas. Но помните, что вы находитесь в виртуальной среде, поэтому, если библиотека еще не подключена, вам нужно будет установить ее снова через pip.

Чтобы записать Pandas DataFrames обратно в файл Excel, можно использовать функцию dataframe_to_rows () из модуля utils:

# Import `dataframe_to_rows`
from openpyxl.utils.dataframe import dataframe_to_rows

# Initialize a workbook 
wb = Workbook()

# Get the worksheet in the active workbook
ws = wb.active

# Append the rows of the DataFrame to your worksheet
for r in dataframe_to_rows(df, index=True, header=True):
    ws.append(r)

Но это определенно не все! Библиотека openpyxl предлагает вам высокую гибкость в отношении того, как вы записываете свои данные в файлы Excel, изменяете стили ячеек или используете режим только для записи. Это делает ее одной из тех библиотек, которую вам точно необходимо знать, если вы часто работаете с электронными таблицами.

И не забудьте деактивировать виртуальную среду, когда закончите работу с данными!

Теперь давайте рассмотрим некоторые другие библиотеки, которые вы можете использовать для получения данных в электронной таблице на Python.

Готовы узнать больше?

Чтение и форматирование Excel файлов xlrd
Эта библиотека идеальна, если вы хотите читать данные и форматировать данные в файлах с расширением .xls или .xlsx.

# Import `xlrd`
import xlrd

# Open a workbook 
workbook = xlrd.open_workbook('example.xls')

# Loads only current sheets to memory
workbook = xlrd.open_workbook('example.xls', on_demand = True)

Если вы не хотите рассматривать всю книгу, можно использовать такие функции, как sheet_by_name () или sheet_by_index (), чтобы извлекать листы, которые необходимо использовать в анализе.

# Load a specific sheet by name
worksheet = workbook.sheet_by_name('Sheet1')

# Load a specific sheet by index 
worksheet = workbook.sheet_by_index(0)

# Retrieve the value from cell at indices (0,0) 
sheet.cell(0, 0).value

Наконец, можно получить значения по определенным координатам, обозначенным индексами.
О том, как xlwt и xlutils, соотносятся с xlrd расскажем дальше.

Запись данных в Excel файл при помощи xlrd

Если нужно создать электронные таблицы, в которых есть данные, кроме библиотеки XlsxWriter можно использовать библиотеки xlwt. Xlwt идеально подходит для записи и форматирования данных в файлы с расширением .xls.

Когда вы вручную хотите записать в файл, это будет выглядеть так:

# Import `xlwt` 
import xlwt

# Initialize a workbook 
book = xlwt.Workbook(encoding="utf-8")

# Add a sheet to the workbook 
sheet1 = book.add_sheet("Python Sheet 1") 

# Write to the sheet of the workbook 
sheet1.write(0, 0, "This is the First Cell of the First Sheet") 

# Save the workbook 
book.save("spreadsheet.xls")

Если нужно записать данные в файл, то для минимизации ручного труда можно прибегнуть к циклу for. Это позволит немного автоматизировать процесс. Делаем скрипт, в котором создается книга, в которую добавляется лист. Далее указываем список со столбцами и со значениями, которые будут перенесены на рабочий лист.

Цикл for будет следить за тем, чтобы все значения попадали в файл: задаем, что с каждым элементом в диапазоне от 0 до 4 (5 не включено) мы собираемся производить действия. Будем заполнять значения строка за строкой. Для этого указываем row элемент, который будет “прыгать” в каждом цикле. А далее у нас следующий for цикл, который пройдется по столбцам листа. Задаем условие, что для каждой строки на листе смотрим на столбец и заполняем значение для каждого столбца в строке. Когда заполнили все столбцы строки значениями, переходим к следующей строке, пока не заполним все имеющиеся строки.

# Initialize a workbook
book = xlwt.Workbook()

# Add a sheet to the workbook
sheet1 = book.add_sheet("Sheet1")

# The data
cols = ["A", "B", "C", "D", "E"]
txt = [0,1,2,3,4]

# Loop over the rows and columns and fill in the values
for num in range(5):
      row = sheet1.row(num)
      for index, col in enumerate(cols):
          value = txt[index] + num
          row.write(index, value)

# Save the result
book.save("test.xls")

В качестве примера скриншот результирующего файла:

Теперь, когда вы видели, как xlrd и xlwt взаимодействуют вместе, пришло время посмотреть на библиотеку, которая тесно связана с этими двумя: xlutils.

Коллекция утилит xlutils

Эта библиотека в основном представляет собой набор утилит, для которых требуются как xlrd, так и xlwt. Включает в себя возможность копировать и изменять/фильтровать существующие файлы. Вообще говоря, оба этих случая подпадают теперь под openpyxl.

Использование pyexcel для чтения файлов .xls или .xlsx

Еще одна библиотека, которую можно использовать для чтения данных таблиц в Python — pyexcel. Это Python Wrapper, который предоставляет один API для чтения, обработки и записи данных в файлах .csv, .ods, .xls, .xlsx и .xlsm.

Чтобы получить данные в массиве, можно использовать функцию get_array (), которая содержится в пакете pyexcel:

# Import `pyexcel`
import pyexcel

# Get an array from the data
my_array = pyexcel.get_array(file_name="test.xls")
 
Также можно получить данные в упорядоченном словаре списков, используя функцию get_dict ():
# Import `OrderedDict` module 
from pyexcel._compact import OrderedDict

# Get your data in an ordered dictionary of lists
my_dict = pyexcel.get_dict(file_name="test.xls", name_columns_by_row=0)

# Get your data in a dictionary of 2D arrays
book_dict = pyexcel.get_book_dict(file_name="test.xls")

Однако, если вы хотите вернуть в словарь двумерные массивы или, иными словами, получить все листы книги в одном словаре, стоит использовать функцию get_book_dict ().

Имейте в виду, что обе упомянутые структуры данных, массивы и словари вашей электронной таблицы, позволяют создавать DataFrames ваших данных с помощью pd.DataFrame (). Это упростит обработку ваших данных!

Наконец, вы можете просто получить записи с pyexcel благодаря функции get_records (). Просто передайте аргумент file_name функции и обратно получите список словарей:

# Retrieve the records of the file
records = pyexcel.get_records(file_name="test.xls")

Записи файлов при помощи pyexcel

Так же, как загрузить данные в массивы с помощью этого пакета, можно также легко экспортировать массивы обратно в электронную таблицу. Для этого используется функция save_as () с передачей массива и имени целевого файла в аргумент dest_file_name:

# Get the data
data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

# Save the array to a file
pyexcel.save_as(array=data, dest_file_name="array_data.xls")

Обратите внимание: если указать разделитель, то можно добавить аргумент dest_delimiter и передать символ, который хотите использовать, в качестве разделителя между “”.

Однако, если у вас есть словарь, нужно будет использовать функцию save_book_as (). Передайте двумерный словарь в bookdict и укажите имя файла, и все ОК:

# The data
2d_array_dictionary = {'Sheet 1': [
                                   ['ID', 'AGE', 'SCORE']
                                   [1, 22, 5],
                                   [2, 15, 6],
                                   [3, 28, 9]
                                  ],
                       'Sheet 2': [
                                    ['X', 'Y', 'Z'],
                                    [1, 2, 3],
                                    [4, 5, 6]
                                    [7, 8, 9]
                                  ],
                       'Sheet 3': [
                                    ['M', 'N', 'O', 'P'],
                                    [10, 11, 12, 13],
                                    [14, 15, 16, 17]
                                    [18, 19, 20, 21]
                                   ]}

# Save the data to a file                        
pyexcel.save_book_as(bookdict=2d_array_dictionary, dest_file_name="2d_array_data.xls")

Помните, что когда используете код, который напечатан в фрагменте кода выше, порядок данных в словаре не будет сохранен!

Чтение и запись .csv файлов

Если вы все еще ищете библиотеки, которые позволяют загружать и записывать данные в CSV-файлы, кроме Pandas, рекомендуем библиотеку csv:

# import `csv`
import csv

# Read in csv file 
for row in csv.reader(open('data.csv'), delimiter=','):
      print(row)
      
# Write csv file
data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
outfile = open('data.csv', 'w')
writer = csv.writer(outfile, delimiter=';', quotechar='"')
writer.writerows(data)
outfile.close()

Обратите внимание, что NumPy имеет функцию genfromtxt (), которая позволяет загружать данные, содержащиеся в CSV-файлах в массивах, которые затем можно помещать в DataFrames.

Финальная проверка данных

Когда данные подготовлены, не забудьте последний шаг: проверьте правильность загрузки данных. Если вы поместили свои данные в DataFrame, вы можете легко и быстро проверить, был ли импорт успешным, выполнив следующие команды:

# Check the first entries of the DataFrame
df1.head()

# Check the last entries of the DataFrame
df1.tail()

Note: Используйте DataCamp Pandas Cheat Sheet, когда вы планируете загружать файлы в виде Pandas DataFrames.

Если данные в массиве, вы можете проверить его, используя следующие атрибуты массива: shape, ndim, dtype и т.д.:

# Inspect the shape 
data.shape

# Inspect the number of dimensions
data.ndim

# Inspect the data type
data.dtype

Что дальше?

Поздравляем, теперь вы знаете, как читать файлы Excel в Python Но импорт данных — это только начало рабочего процесса в области данных. Когда у вас есть данные из электронных таблиц в вашей среде, вы можете сосредоточиться на том, что действительно важно: на анализе данных.

Если вы хотите глубже погрузиться в тему — знакомьтесь с PyXll, которая позволяет записывать функции в Python и вызывать их в Excel.

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

Introduction

Excel is ubiquitous. Many software projects originate from some company or process overgrowing the Excel spreadsheets that were being employed to manage it. As application developers, more than once we’ve had to reverse-engineer Excel files and translate them into a software application. Exploring working with Excel in Python is the goal of this article.

Excel is also quite common as an export format for data, and sometimes also as an input format. And while Excel is not by any means a domain-specific language, we can sometimes consider it as part of the “notation” that some domain experts use to formalize their problems. Thus, software that can “speak” Excel is often closer to its users. Even when the long-term objective is to convert them to an application or to a domain-specific language (DSL), it’s often a good idea to provide an automated migration path from Excel, as well as borrowing notation from Excel.

For example, an application could consume existing Excel files that people use to exchange among themselves as part of the project, effectively gaining the ability to “speak the same language” as the users. Or, it could produce a spreadsheet as the output of some query or calculation, because some users may want to further expand on it to compile a report with additional charts and formulas.

In this tutorial, we’ll explore working with Excel spreadsheets in Python, using the openpyxl library and other tools. In particular, we’ll learn how to:

• Process Excel files as input, with various methods to access the data in them;
• Evaluate formulas;
• Write Excel files as output.

Note: here, we’re talking about “modern” (2010) XML-based Excel files only, that have a .xlsx extension. Openpyxl does not support legacy binary Excel files (.xls). There are other solutions for that that we won’t explore in this tutorial.

All the sample code shown in this tutorial is available in our GitHub repository.

Setup

In the following, we assume a UNIX-like environment – Linux, OSX, or WSL, the Windows Substrate for Linux. As the first step, we’ll create a directory to hold our project:

mkdir python-excel
cd python-excel

Alternatively, if we plan to push our code on GitHub, we may create a repository on GitHub first, and clone it on our local machine:

git clone <repo-url> python-excel
cd python-excel

This will give us the option to have GitHub generate a .gitignore file for Python, a README file, and a license file for us.

After we’ve created the project root directory, we can use pip to install openpyxl. We may want to set a virtual environment up so that we don’t pollute our system’s Python installation:

python -m venv .venv
source .venv/bin/activate

Having activated the environment, we should see it reflected in our shell prompt, like this:

(.venv) python-excel (main*) »

We’ll then write a requirements.txt file that lists our dependencies:

echo openpyxl > requirements.txt

Even if we only have a single dependency right now, explicitly listing it in a file that can be interpreted by the machine will make it easier for other developers to work on our project – including a future version of ourselves that has long forgotten about it.

Then, we’ll install the required dependencies with pip:

python -m pip install -r requirements.txt

We can also easily automate these steps in our CI pipeline, such as GitHub actions.

Loading an Excel File

In Openpyxl, an Excel file is called a “workbook” and is represented by an instance of the class openpyxl.workbook.Workbook. Opening one is super easy:

wb = load_workbook(path)

Openpyxl opens workbooks for reading and for writing at the same time unless we specify that we want a read-only workbook with read_only=True as an argument to load_workbook.

When we finish working with an Excel file, we have to close it:

wb.close()

Unfortunately, a Workbook is not a “context manager”, so we cannot use Python’s with statement to automatically close it when we’re done. Instead, we have to manually arrange to close it even in case of exceptions:

wb = load_workbook(path)
try:
    # Use wb...
finally:
    wb.close()

Processing an Excel File – Common Cases

In general, workbooks may contain multiple sheets, so in order to access the data in an Excel file, we first need to choose a sheet to work on.

Later on, we’ll see how to process multiple worksheets, but for now, we’ll assume that the data that we’re interested in is in the active sheet – the one that the user will see when they open the file in their spreadsheet application:

sheet = wb.active

This is quite often the only sheet in the document, actually.

Now that we have a sheet, we can access the data in its cells in several ways.
Iterating over rows. We can process the data one row at a time, using a Pythonic generator:

for row in sheet.rows():
    # Do something with the row

The rows that rows() yields are themselves generators, and we can iterate through them:

for row in sheet.rows():
    for cell in row:
        # Do something with the cell

Or we can access them by index:

for row in sheet.rows():
    header = row[2]

Actually, the sheet itself is iterable in row order, so we can omit rows altogether:

for row in sheet:
    pass

Iterating over columns. Similarly, we can iterate by column using the cols method:

for col in sheet.cols():
    # Use the column

Columns work the same as rows: they are themselves iterable and addressable by index.

Accessing cells by address. If we need a piece of data that is in one specific cell, we don’t need to iterate through the whole sheet until we encounter it; we can use Excel-style coordinates to access the cell:

cell = sheet['C5']

We can also obtain a generator for a row, column, or a range of cells; we’ll show that in a later section.

Processing Cells

In any case, to process the data in a spreadsheet we have to deal with individual cells. In Openpyxl, a cell has a value and a bunch of other information that is mostly interesting only for writing, such as style information.

Conveniently, we work with cell values as Python objects (numbers, dates, strings, and so on) as Openpyxl will translate them to Excel types and back appropriately. So, cell contents are not restricted to be strings. Here, for example, we read the contents of a cell as a number:

tax_percentage = sheet['H16'].value
tax_amount = taxable_amount * tax_percentage

However, we have no guarantee that the user actually put a number in that cell; if it contains the string “bug”, in the lucky case, we’ll get a runtime error when we run the code above:

TypeError: can't multiply sequence by non-int of type 'float'

However, in the not-so-lucky case, i.e. when taxable_amount is an integer – as it should be, since we’re dealing with money in the example – we’ll get a long string of “bug” repeated taxable_amount times. That’s because Python overloads the * operator for strings and integers to mean “repeat the string n times”. This will potentially result in further type errors down the line, or in a memory error when Python cannot allocate such a big string.
Therefore, we should always validate the input to our program, including Excel files. In this particular case, we can check the type of the value of a cell with Python’s isinstance function:

if isinstance(cell.value, numbers.Number):
    # Use the value

Or we can ask the cell for which type of data it contains:

if cell.data_type == TYPE_NUMERIC:
    # Use the numeric value

Advanced Addressing of Cells

So far, we’ve explored the simplest, most straightforward ways of accessing cells. However, that doesn’t cover all the use cases that we may encounter; let’s have a look at more complex access schemes.

Sheets other than the active one. We can obtain sheets by access them by name from the workbook:

sheet = wb['2020 Report']

Then we can access cells in the sheet as we’ve seen earlier.

Ranges of cells. We’re not limited to addressing cells one by one – we can also obtain cell ranges:

• sheet['D'] is a whole row (D in this case)
• sheet[7] is a whole column (7 in this case)
• sheet['B:F'] represents a range of rows
• sheet['4:10'] represents a range of columns
• sheet['C3:H5'] is the most versatile option, representing an arbitrary range of cells.

In any of the above cases, the result is an iterable of all the cells, in row order (except when the range represents one or more columns, in which case, cells are arranged in column order):

for cell in sheet['B2:F10']:
    # B2, B3, ..., F1, F2, ..., F10
for cell in sheet['4:10']:
    # A4, B4, ..., A10, B10, ... 

Cell Iterators

If the above addressing schemes don’t suit our problem, we can resort to the lower-level methods iter_rows and iter_columns, that return generators – respectively by row and by column – over a range of cells.

In particular, both methods take 5 named parameters:

• min_row – the number of the starting row (1 is A, 2 is B, etc.)
• min_col – the starting column
• max_row – the number of the last row
• max_col – the last column
• values_only – if true, the generator will yield only the value of each cell, rather than the entire cell object. So, we won’t have to spell cell.value, just value. On the other hand, we won’t have access to the other properties of the cell, such as its data_type.

So, for example, if we want to iterate over the range B2:F10 by column we’ll write:

for cell in sheet.iter_columns(min_row=2, min_col=2, max_row=6, max_col=10):
    # Use the cell

Writing an Excel File

To write an Excel file, we just call the save method on our workbook:

wb.save('someFile.xlsx')

There’s not much to say about that. It’s more interesting to know how to modify a workbook before saving it. This can be a workbook that has been loaded from a file or a brand new workbook created in Python with the new operator.

Modifying Individual Cells

We can alter the value of a cell simply by assigning to it:

cell.value = 42

Note that this will automatically update the cell’s data type to reflect the new value. Besides the obvious primitive types (integer, float, string), the available types include various classes in the datetime module, as well as NumPy numeric types if NumPy is installed.

Not just values and types, we can set other properties of cells, notably style information (font, color, …), which is useful if we’re set on producing a good-looking report. The documentation of Openpyxl contains a thorough explanation of working with styles so we’ll just refer to that.

Adding and Removing Sheets

So far we’ve seen that we can address some objects – particularly workbooks and worksheets – as if they were dictionaries, to access their components: worksheets, rows, columns, individual cells, cell ranges. We’ll now see how we can add new information to the dictionaries and how to replace existing information. We’ll start with sheets.

To create a worksheet, we use the create_sheet method on a workbook:

new_sheet = wb.create_sheet()

This will add a new sheet to the workbook, after the other sheets, and will return it. We can also give it a title:

new_sheet = wb.create_sheet(title = 'My new sheet')

If we want to place the sheet at another position in the list, we can specify its index (which is zero-based, zero being the first):

# The new sheet will be inserted as the third sheet
new_sheet = wb.create_sheet(index = 2)

To delete a sheet, instead, we have two options. We can delete it by name in accordance with the dictionary abstraction:

del wb['My sheet']

We can check if a sheet with a given name is present in the workbook using the in operator:

name = 'My sheet'
if name in workbook:
    del workbook[name]

Alternatively, we can call the remove method with the sheet as the argument:

wb.remove(sheet)

Adding and Removing Rows, Columns, and Cells

Similarly, we have methods for adding or removing rows, columns, or individual cells in a worksheet. Let’s see some examples.

First and foremost, by simply accessing a cell, we cause the creation of all rows and columns needed to make room for it:

wb = Workbook()
# Initially, an empty worksheet has a single row and column, A and 1
self.assertEqual(wb.active.max_row, 1)
self.assertEqual(wb.active.max_column, 1)
# We set the value of the cell at C3; 
# openpyxl creates rows B, C and columns 2, 3 automatically
wb.active['C3'].value = 12
# Now the sheet has 3 rows and columns
self.assertEqual(wb.active.max_row, 3)
self.assertEqual(wb.active.max_column, 3)
wb.close()

Additionally, we can use the insert_rows and insert_cols methods to add rows or columns in the middle of the sheet. Existing cells are automatically moved after the newly inserted rows/columns:

wb = Workbook()
self.assertEqual(wb.active.max_row, 1)
wb.active['A1'].value = 11
# Insert 3 rows, starting at index 0 (i.e. row 1)
wb.active.insert_rows(0, 3)
self.assertEqual(wb.active.max_row, 4)
# Note how the cell, A1, has automatically moved by 3 rows to A4
self.assertEqual(wb.active['A4'].value, 11)

We have the corresponding delete_rows and delete_cols to remove rows/columns instead:

# Delete 2 columns, starting from index 1, i.e. column B
sheet.delete_columns(1, 2)

Working With Formulas

Spreadsheets are powerful because they support formulas to compute cell values. Calculated cells automatically update their value when other cells change. Let’s see how we can work with formulas in Openpyxl.

First of all, we can ignore formulas altogether if we just want to read an Excel file. In that case, opening it in “data only” mode will hide formulas, presenting all cells with a concrete value – as computed the last time Excel opened the file:

wb = load_workbook(filename, data_only=True)

Only when modifying an Excel file we may want to recalculate formulas. While openpyxl has some support for parsing formulas, that we could use to analyze them (e.g., to check if they’re only calling known functions), it doesn’t know how to evaluate formulas by itself. So, we have to resort to a third-party library if we want to compute formulas.

Enter the library called, well, “formulas”. Let’s add it to our requirements.txt file and install it:

$ cat requirements.txt
openpyxl
formulas
pip install -r requirements.txt

With the formulas library, we have two options:

1. calculating the values of all the formulas in a workbook, recursively following dependencies, the way Excel does it;
2. compiling individual formulas to Python functions that we can then invoke with different arguments.

Calculating the Values of All Formulas

The first use case is not the most interesting in the context of this tutorial because it overlaps with the data_only approach we’ve seen earlier. In fact, in that mode, we cannot load a workbook, modify it, and then recompute the formulas in it. We’d have to:

• save the modified workbook to a file;
• have formulas load the file again;
• calculate formula values with an API call;
• save the file with the calculated values;
• open the file with openpyxl in data_only mode and finally see the computed values.

Not really efficient use of developer and computer time!

That said, this feature of the formulas library does have its value, because it supports some advanced use cases:

• Calculating formulas across multiple workbooks. In Excel, it’s possible to have a formula refer to another file. formulas can load multiple workbooks as part of the same set so as to resolve these cross-file references, which is a pretty rarely used feature of Excel that, e.g., Apple’s Numbers doesn’t support.
• Compiling an entire Excel workbook into a Python function. We can define certain cells as input cells, others as output cells, and obtain a function that, given the inputs, computes the formulas in the workbook and returns the values it finds in the output cells after the calculation.

However, to keep it simple, we’ll leave those out of this tutorial.

Compiling Individual Formulas as Python Functions

Let’s concentrate on individual formulas, that we can better integrate with our work based on openpyxl. As per formulas’ documentation, the incantation for compiling an Excel formula into a Python function is the following:

func = formulas.Parser().ast(value)[1].compile()

Note the [1] there – for some reason, the ast method returns a tuple of two objects of which the second, the builder, is the most useful. Even though this is documented, apparently it’s a piece of internal API that would need to be wrapped in a more user-friendly interface.

Anyway, when we evaluate the code above, the resulting func will be a function with as many arguments as the inputs of the formula:

func = formulas.Parser().ast('=A1+B1')[1].compile()
func(1, 2) == 3 # True

Handling Dependencies of Formulas

So, we can compile the formula of a single cell into a function. However, what happens when the formula depends on other cells that contain formulas themselves? The formulas library doesn’t help us in that regard; we have to compute all the inputs recursively if they are themselves formulas. Let’s see how we might do that.

First of all, how do we distinguish between a cell with a formula and a cell with a regular value? Openpyxl doesn’t offer a method to do that, so we have to check if the value of the cell starts with an equal character:

def has_formula(cell: Cell)
   return isinstance(cell.value, str) and cell.value.startswith('=')

Therefore, we know how to compute the values of cells that don’t contain formulas:

def compute_cell_value(cell: Cell):
   if not has_formula(cell):
       return cell.value

Now, the interesting thing is how to compute the value when the cell does contain a formula:

func = formulas.Parser().ast(cell.value)[1].compile()
args = []
# TODO: compute function arguments
return func(*args)

We compile the formula into a Python function and then we invoke it on its inputs. Since the inputs are references to cells, we recursively invoke compute_cell_value in order to get their values:

sheet = cell.parent
for key in func.inputs.keys():
   args.append(compute_cell_value(sheet[key]))

We leverage the fact that every cell keeps a reference to its parent, i.e., the sheet that contains it. We also make use of introspection information retained by formulas, that allows us to inspect the inputs of a function – a dictionary of cell references.

Note that this doesn’t support references across sheets or, for that matter, files.

Computing Formulas Over Cell Ranges

So far, our compute_cell_value function successfully computes the values of cells without formulas and with formulas that may depend on other cells. However, what about formulas that depend, not on individual cells, but on cell ranges?

Well, in that case, the input of a function is a range expression, such as A1:Z1 in =SUM(A1:Z1). When we call compute_cell_value recursively, we pass it the following:

sheet[key]

When the key is the address of a single cell, we obtain a cell object; but when it refers to a range of cells, we obtain a tuple with one entry per cell. Our compute_cell_value doesn’t know how to deal with such input, so we have to modify it to handle that case:

if isinstance(input, Tuple):
   return tuple(map(compute_cell_value, input))

Then, the complete version of the function becomes:

def compute_cell_value(input: Union[Cell, Tuple]):
   if isinstance(input, Tuple):
       return tuple(map(compute_cell_value, input))
   if not has_formula(input):
       return input.value
   func = formulas.Parser().ast(input.value)[1].compile()
   args = []
   sheet = input.parent
   for key in func.inputs.keys():
       args.append(compute_cell_value(sheet[key]))
   return func(*args)

Adding New Formula Functions

formulas supports many built-in Excel functions, but not all of them. And of course it doesn’t know about user-defined functions in VBA. However, we can register new Python functions with it so that they can be called in formulas:

def is_number(number):
   ... # This is actually defined in formulas, but strangely not exposed as the Excel function

FUNCTIONS = formulas.get_functions()
FUNCTIONS['ISNUMBER'] = is_number

Simple as that. The inputs to the function are its actual arguments as native Python values, so strings, numbers, dates, etc. – not instances of the Cell class.

Also, compared to a regular Python function, we have to guard against XlError, which represents errors in calculations such as #DIV/0! or #REF! (we typically see those in Excel when we’ve made some mistake in writing a formula):

def is_number(number):
    if isinstance(number, XlError):
        return False
    ...

Conclusions

We can work productively with Excel in Python with the aid of two mature open-source libraries, openpyxl and formulas. Consuming and producing complex Excel files is a valuable capability in applications whose users routinely work with Excel.

In this tutorial, we’ve learned how to read and write Excel files that may contain formulas. We didn’t talk about styling, charts, merging cells, and other possibilities you may want to read about.

All the sample code shown in this tutorial is available in our GitHub repository.

Read more:

To discover more about how to write a python parser, you can read Parsing In Python: Tools And Libraries

In this openpyxl tutorial, we will learn how we can read the data from the cell of an Excel Sheet in Python.

The values that are stored in the cells of an Excel Sheet can be accessed easily using the openpyxl library. To read the cell values, we can use two methods, firstly the value can be accessed by its cell name, and secondly, we can access it by using the cell() function.

Program to read cell value using openpyxl Library in Python

Let’s understand it with an example:

Here, we took an Excel file having some values in its cells. We will access and read these values in our Python program using the openpyxl library.

Step1: Import the openpyxl library to the Python program.

import openpyxl

Step2: Load/Connect the Excel workbook to the program.

wb = openpyxl.load_workbook('filename.xlsx') #give the full path of the file here

Step3: Get the title of the default first worksheet of the Workbook.

sh = wb.active

Step4: Create variables and initialize them with the cell names.

c1 = sh['A1']
c2 = sh['B2']

Or

We can also read the cell value by using the cell() function. The cell() function takes rows and columns as the parameter for the position of the cell.

c3 = sh.cell(row=2,column=1)

Step5: Finally, print the values of the cell to the screen.

print("Value of the Cell 1:",c1.value)
print("Value of the Cell 2:",c2.value)
print("Value of the Cell 3:",c3.value)

Here is the complete program:

import openpyxl

wb = openpyxl.load_workbook("//home//codespeedy//Documents//Book2.xlsx")
sh = wb.active

c1 = sh['A2'] 
c2 = sh['B2']

#Using cell() function
c3 = sh.cell(row=3,column=3)

print("Value of the Cell 1:",c1.value) 
print("Value of the Cell 2:",c2.value) 
print("Value of the Cell 3:",c3.value)

Output:

Value of the Cell 1: Sanam
Value of the Cell 2: 21
Value of the Cell 3: 56000

Hope you have enjoyed this article and learned how to read the value of a cell of an Excel Workbook in Python.

You can also read, How to add color to Excel cells using Python