Read excel table python

Хотя многие Data Scientist’ы больше привыкли работать с CSV-файлами, на практике очень часто приходится сталкиваться с обычными Excel-таблицами. Поэтому сегодня мы расскажем, как читать Excel-файлы в Pandas, а также рассмотрим основные возможности Python-библиотеки OpenPyXL для чтения метаданных ячеек.

Дополнительные зависимости для возможности чтения Excel таблиц

Для чтения таблиц Excel в Pandas требуются дополнительные зависимости:

• xlrd поддерживает старые и новые форматы MS Excel [1];
• OpenPyXL поддерживает новые форматы MS Excel (.xlsx) [2];
• ODFpy поддерживает свободные форматы OpenDocument (.odf, .ods и .odt) [3];
• pyxlsb поддерживает бинарные MS Excel файлы (формат .xlsb) [4].

Мы рекомендуем установить только OpenPyXL, поскольку он нам пригодится в дальнейшем. Для этого в командной строке прописывается следующая операция:

pip install openpyxl

Затем в Pandas нужно указать путь к Excel-файлу и одну из установленных зависимостей. Python-код выглядит следующим образом:

import pandas as pd
pd.read_excel(io='temp1.xlsx', engine='openpyxl')
#
     Name  Age  Weight
0    Alex   35      87
1   Lesha   57      72
2  Nastya   21      64

Читаем несколько листов

Excel-файл может содержать несколько листов. В Pandas, чтобы прочитать конкретный лист, в аргументе нужно указать sheet_name. Можно указать список названий листов, тогда Pandas вернет словарь (dict) с объектами DataFrame:

dfs = pd.read_excel(io='temp1.xlsx',
                    engine='openpyxl',
                    sheet_name=['Sheet1', 'Sheet2'])
dfs
#
{'Sheet1':      Name  Age  Weight
 0    Alex   35      87
 1   Lesha   57      72
 2  Nastya   21      64,
 'Sheet2':     Name  Age  Weight
 0  Gosha   43      95
 1   Anna   24      65
 2   Lena   22      78}

Если таблицы в словаре имеют одинаковые атрибуты, то их можно объединить в один DataFrame. В Python это выглядит так:

pd.concat(dfs).reset_index(drop=True)
     Name  Age  Weight
0    Alex   35      87
1   Lesha   57      72
2  Nastya   21      64
3   Gosha   43      95
4    Anna   24      65
5    Lena   22      78

Указание диапазонов

Таблицы могут размещаться не в самом начале, а как, например, на рисунке ниже. Как видим, таблица располагается в диапазоне A:F.

Чтобы прочитать такую таблицу, нужно указать диапазон в аргументе usecols. Также дополнительно можно добавить header — номер заголовка таблицы, а также nrows — количество строк, которые нужно прочитать. В аргументе header всегда передается номер строки на единицу меньше, чем в Excel-файле, поскольку в Python индексация начинается с 0 (на рисунке это номер 5, тогда указываем 4):

pd.read_excel(io='temp1.xlsx',
              engine='openpyxl',
              usecols='D:F',
              header=4, # в excel это №5
              nrows=3)
#
    Name  Age  Weight
0  Gosha   43      95
1   Anna   24      65
2   Lena   22      78

Читаем таблицы в OpenPyXL

Pandas прочитывает только содержимое таблицы, но игнорирует метаданные: цвет заливки ячеек, примечания, стили таблицы и т.д. В таком случае пригодится библиотека OpenPyXL. Загрузка файлов осуществляется через функцию load_workbook, а к листам обращаться можно через квадратные скобки:

from openpyxl import load_workbook
wb = load_workbook('temp2.xlsx')
ws = wb['Лист1']
type(ws)
# openpyxl.worksheet.worksheet.Worksheet

Допустим, имеется Excel-файл с несколькими таблицами на листе (см. рисунок выше). Если бы мы использовали Pandas, то он бы выдал следующий результат:

pd.read_excel(io='temp2.xlsx',
              engine='openpyxl')
#
     Name  Age  Weight  Unnamed: 3 Name.1  Age.1  Weight.1
0    Alex   35      87         NaN  Tanya     25        66
1   Lesha   57      72         NaN  Gosha     43        77
2  Nastya   21      64         NaN  Tolya     32        54

Можно, конечно, заняться обработкой и привести таблицы в нормальный вид, а можно воспользоваться OpenPyXL, который хранит таблицу и его диапазон в словаре. Чтобы посмотреть этот словарь, нужно вызвать ws.tables.items. Вот так выглядит Python-код:

ws.tables.items()
wb = load_workbook('temp2.xlsx')
ws = wb['Лист1']
ws.tables.items()
#
[('Таблица1', 'A1:C4'), ('Таблица13', 'E1:G4')]

Обращаясь к каждому диапазону, можно проходить по каждой строке или столбцу, а внутри них – по каждой ячейке. Например, следующий код на Python таблицы объединяет строки в список, где первая строка уходит на заголовок, а затем преобразует их в DataFrame:

dfs = []
for table_name, value in ws.tables.items():
    table = ws[value]
    header, *body = [[cell.value for cell in row]
                      for row in table]
    df = pd.DataFrame(body, columns=header)
    dfs.append(df)

Если таблицы имеют одинаковые атрибуты, то их можно соединить в одну:

pd.concat(dfs)
#
     Name  Age  Weight
0    Alex   35      87
1   Lesha   57      72
2  Nastya   21      64
0   Tanya   25      66
1   Gosha   43      77
2   Tolya   32      54

Сохраняем метаданные таблицы

Как указано в коде выше, у ячейки OpenPyXL есть атрибут value, который хранит ее значение. Помимо value, можно получить тип ячейки (data_type), цвет заливки (fill), примечание (comment) и др.

Например, требуется сохранить данные о цвете ячеек. Для этого мы каждую ячейку с числами перезапишем в виде <значение,RGB>, где RGB — значение цвета в формате RGB (red, green, blue). Python-код выглядит следующим образом:

# _TYPES = {int:'n', float:'n', str:'s', bool:'b'}
data = []
for row in ws.rows:
    row_cells = []
    for cell in row:
        cell_value = cell.value
        if cell.data_type == 'n':
            cell_value = f"{cell_value},{cell.fill.fgColor.rgb}"
        row_cells.append(cell_value)
    data.append(row_cells)

Первым элементом списка является строка-заголовок, а все остальное уже значения таблицы:

pd.DataFrame(data[1:], columns=data[0])
#
     Name          Age       Weight
0    Alex  35,00000000  87,00000000
1   Lesha  57,00000000  72,FFFF0000
2  Nastya  21,FF00A933  64,00000000

Теперь представим атрибуты в виде индексов с помощью метода stack, а после разобьём все записи на значение и цвет методом str.split:

(pd.DataFrame(data[1:], columns=data[0])
 .set_index('Name')
 .stack()
 .str.split(',', expand=True)
)
#
                0         1
Name                       
Alex   Age     35  00000000
       Weight  87  00000000
Lesha  Age     57  00000000
       Weight  72  FFFF0000
Nastya Age     21  FF00A933
       Weight  64  0000000

Осталось только переименовать 0 и 1 на Value и Color, а также добавить атрибут Variable, который обозначит Вес и Возраст. Полный код на Python выглядит следующим образом:

(pd.DataFrame(data[1:], columns=data[0])
 .set_index('Name')
 .stack()
 .str.split(',', expand=True)
 .set_axis(['Value', 'Color'], axis=1)
 .rename_axis(index=['Name', 'Variable'])
 .reset_index()
)
#
     Name Variable Value     Color
0    Alex      Age    35  00000000
1    Alex   Weight    87  00000000
2   Lesha      Age    57  00000000
3   Lesha   Weight    72  FFFF0000
4  Nastya      Age    21  FF00A933
5  Nastya   Weight    64  00000000

Ещё больше подробностей о работе с таблицами в Pandas, а также их обработке на реальных примерах Data Science задач, вы узнаете на наших курсах по Python в лицензированном учебном центре обучения и повышения квалификации IT-специалистов в Москве.

Источники

1. https://xlrd.readthedocs.io/en/latest/
2. https://openpyxl.readthedocs.io/en/latest/
3. https://github.com/eea/odfpy
4. https://github.com/willtrnr/pyxlsb

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.

Pandas можно использовать для чтения и записи файлов Excel с помощью Python. Это работает по аналогии с другими форматами. В этом материале рассмотрим, как это делается с помощью DataFrame.

Помимо чтения и записи рассмотрим, как записывать несколько DataFrame в Excel-файл, как считывать определенные строки и колонки из таблицы и как задавать имена для одной или нескольких таблиц в файле.

Установка Pandas

Для начала Pandas нужно установить. Проще всего это сделать с помощью pip.

Если у вас Windows, Linux или macOS:

pip install pandas # или pip3

В процессе можно столкнуться с ошибками ModuleNotFoundError или ImportError при попытке запустить этот код. Например:

ModuleNotFoundError: No module named 'openpyxl'

В таком случае нужно установить недостающие модули:

pip install openpyxl xlsxwriter xlrd  # или pip3

Будем хранить информацию, которую нужно записать в файл Excel, в DataFrame. А с помощью встроенной функции to_excel() ее можно будет записать в Excel.

Сначала импортируем модуль pandas. Потом используем словарь для заполнения DataFrame:

import pandas as pd


df = pd.DataFrame({'Name': ['Manchester City', 'Real Madrid', 'Liverpool',

                            'FC Bayern München', 'FC Barcelona', 'Juventus'],

                   'League': ['English Premier League (1)', 'Spain Primera Division (1)',

                              'English Premier League (1)', 'German 1. Bundesliga (1)',

                              'Spain Primera Division (1)', 'Italian Serie A (1)'],

                   'TransferBudget': [176000000, 188500000, 90000000,

                                      100000000, 180500000, 105000000]})

Ключи в словаре — это названия колонок. А значения станут строками с информацией.

Теперь можно использовать функцию to_excel() для записи содержимого в файл. Единственный аргумент — это путь к файлу:

df.to_excel('./teams.xlsx')

А вот и созданный файл Excel:

Стоит обратить внимание на то, что в этом примере не использовались параметры. Таким образом название листа в файле останется по умолчанию — «Sheet1». В файле может быть и дополнительная колонка с числами. Эти числа представляют собой индексы, которые взяты напрямую из DataFrame.

Поменять название листа можно, добавив параметр sheet_name в вызов to_excel():

df.to_excel('./teams.xlsx', sheet_name='Budgets', index=False)

Также можно добавили параметр index со значением False, чтобы избавиться от колонки с индексами. Теперь файл Excel будет выглядеть следующим образом:

Запись нескольких DataFrame в файл Excel

Также есть возможность записать несколько DataFrame в файл Excel. Для этого можно указать отдельный лист для каждого объекта:

salaries1 = pd.DataFrame({'Name': ['L. Messi', 'Cristiano Ronaldo', 'J. Oblak'],

                                        'Salary': [560000, 220000, 125000]})


salaries2 = pd.DataFrame({'Name': ['K. De Bruyne', 'Neymar Jr', 'R. Lewandowski'],

                                        'Salary': [370000, 270000, 240000]})


salaries3 = pd.DataFrame({'Name': ['Alisson', 'M. ter Stegen', 'M. Salah'],

                                        'Salary': [160000, 260000, 250000]})


salary_sheets = {'Group1': salaries1, 'Group2': salaries2, 'Group3': salaries3}

writer = pd.ExcelWriter('./salaries.xlsx', engine='xlsxwriter')


for sheet_name in salary_sheets.keys():

    salary_sheets[sheet_name].to_excel(writer, sheet_name=sheet_name, index=False)


writer.save()



  


                       
  

Здесь создаются 3 разных DataFrame с разными названиями, которые включают имена сотрудников, а также размер их зарплаты. Каждый объект заполняется соответствующим словарем.

Объединим все три в переменной salary_sheets, где каждый ключ будет названием листа, а значение — объектом DataFrame.

Дальше используем движок xlsxwriter для создания объекта writer. Он и передается функции to_excel().

Перед записью пройдемся по ключам salary_sheets и для каждого ключа запишем содержимое в лист с соответствующим именем. Вот сгенерированный файл:

Можно увидеть, что в этом файле Excel есть три листа: Group1, Group2 и Group3. Каждый из этих листов содержит имена сотрудников и их зарплаты в соответствии с данными в трех DataFrame из кода.

Параметр движка в функции to_excel() используется для определения модуля, который задействуется библиотекой Pandas для создания файла Excel. В этом случае использовался xslswriter, который нужен для работы с классом ExcelWriter. Разные движка можно определять в соответствии с их функциями.

В зависимости от установленных в системе модулей Python другими параметрами для движка могут быть openpyxl (для xlsx или xlsm) и xlwt (для xls). Подробности о модуле xlswriter можно найти в официальной документации.

Наконец, в коде была строка writer.save(), которая нужна для сохранения файла на диске.

Чтение файлов Excel с python

По аналогии с записью объектов DataFrame в файл Excel, эти файлы можно и читать, сохраняя данные в объект DataFrame. Для этого достаточно воспользоваться функцией read_excel():

top_players = pd.read_excel('./top_players.xlsx')

top_players.head()

Содержимое финального объекта можно посмотреть с помощью функции head().

Примечание:

Этот способ самый простой, но он и способен прочесть лишь содержимое первого листа.

Посмотрим на вывод функции head():

Pandas присваивает метку строки или числовой индекс объекту DataFrame по умолчанию при использовании функции read_excel().

Это поведение можно переписать, передав одну из колонок из файла в качестве параметра index_col:

top_players = pd.read_excel('./top_players.xlsx', index_col='Name')

top_players.head()

Результат будет следующим:

В этом примере индекс по умолчанию был заменен на колонку «Name» из файла. Однако этот способ стоит использовать только при наличии колонки со значениями, которые могут стать заменой для индексов.

Чтение определенных колонок из файла Excel

Иногда удобно прочитать содержимое файла целиком, но бывают случаи, когда требуется получить доступ к определенному элементу. Например, нужно считать значение элемента и присвоить его полю объекта.

Это делается с помощью функции read_excel() и параметра usecols. Например, можно ограничить функцию, чтобы она читала только определенные колонки. Добавим параметр, чтобы он читал колонки, которые соответствуют значениям «Name», «Overall» и «Potential».

Для этого укажем числовой индекс каждой колонки:

cols = [0, 2, 3]


top_players = pd.read_excel('./top_players.xlsx', usecols=cols)

top_players.head()

Вот что выдаст этот код:

Таким образом возвращаются лишь колонки из списка cols.

В DataFrame много встроенных возможностей. Легко изменять, добавлять и агрегировать данные. Даже можно строить сводные таблицы. И все это сохраняется в Excel одной строкой кода.

Рекомендую изучить DataFrame в моих уроках по Pandas.

Выводы

В этом материале были рассмотрены функции read_excel() и to_excel() из библиотеки Pandas. С их помощью можно считывать данные из файлов Excel и выполнять запись в них. С помощью различных параметров есть возможность менять поведение функций, создавая нужные файлы, не просто копируя содержимое из объекта DataFrame.

When you use Python to process data, you often need to handle data in Excel. Nowadays, you basically use Pandas to read data from Excel, but there are some Python packages other than Pandas that can satisfy the need to read Excel data.

Before we begin, learn the concepts involved in Excel.

• workbook : In various libraries, a workbook is actually an excel file, which can be regarded as a database.
• sheet : In an excel file, there may be more than one sheet, a sheet can be regarded as a table in a database
• row : row is actually a row in a table, normally represented by the numbers 1, 2, 3, 4
• column : column is a column in a table, normally represented by the letters A, B, C, D
• cell : cell is a cell in a table, you can use the combination of row + column to represent, for example: A3

Differences between the file formats commonly used in Excel.

• XLS : The file format used before Excel version 2003, the binary way of saving files. xls files support a maximum of 65536 rows. xlsx supports a maximum of 1048576 rows. xls supports a maximum of 256 columns, xlsx is 16384 columns, this is the limit of the number of rows and columns is not from Excel version but the version of the file type.
• XLSX: XLSX is actually a ZIP file, that is, if you change the file name of XLSX to zip, and then you can use the unzip software to open the zip file directly, you open it to see the words, you will be able to see a lot of xml files inside.

Python Excel read/write package of xlrd, xlwt

xlrd, xlwt, xlutils is developed by Simplistix, the original website content is basically emptied, the project migrated to http://www.python-excel.org and open source in GitHub, see https://github.com/python-excel. On the website is also currently Very much not recommended for the above tools, the official currently also do not recommend the continued use of the main reasons.

• xlrd module: can read .xls, .xlsx tables
• xlwt module: can write .xls tables (can not write .xlsx files!!!)
• xlutils is not required, but additionally provides some tool functions to simplify the operation.

xlrd

Read file functionality is provided by the xlrd package. xlrd implements the xlrd.book.Book (hereafter referred to as Book), xlrd.sheet.Sheet (hereafter referred to as Sheet) and xlrd.sheet.Cell (hereafter referred to as Cell) types, which correspond to the workbook, sheet and cell concepts in Excel, where the cell is the minimum operational granularity.

xlrd load form files on a function open_workbook, commonly used parameters on two.

• filename, specify the path to open the Excel file
• on_demand, if it is True, then load the workbook on-demand form, if it is False, then directly load all forms, the default is False, in order to save resources is generally set to True, which is more obvious when the performance of large files.

After reading the Excel file to get the Workbook, the next step is to locate the Sheet. the Book class object has several important properties and methods for indexing Sheets.

• nsheets property, which indicates the number of Sheet objects contained
• sheet_names method, which returns the names of all sheets
• sheet_by_index, sheet_by_name methods, which index the sheets using the serial number and name, respectively
• sheets method, which returns a list of all Sheet objects

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wb = xlrd.open_workbook('读取表.xls')
print(type(wb))
print(wb.nsheets)
print(wb.sheet_names())
print(wb.sheet_by_index(0))
print(wb.sheet_by_name('第一个 sheet'))
for sh in wb.sheets():
    print(sh.name, sh)

After getting the Sheet object, the next step is to index the rows/columns/cells and get the data of the rows/columns/cells. the Sheet class object has several important properties and methods to support the subsequent operations.

• the name property, which is the name of the form.
• nrows, ncols properties, indicating the maximum number of rows and columns read into the form. Since cells only support row number indexing, these two properties are necessary to check for out-of-bounds content.
• cell method, accepts 2 parameters, i.e. row and column serial numbers, returns Cell object, note that xlrd only supports indexing cells by row serial number, row serial number starts from 0.
• cell_value method, similar to the cell method, except that the value returned is the value in the cell, not the Cell object.
• cell_type method, returns the type of cell
• row, col method, returns a list of Cell objects composed of 1 whole row (column).
• row_types, col_types, return the type of cells in a number of columns (rows) within the specified row (column).
• row_values, col_values, returns the value of the cell in the specified row (column) of a number of columns (rows).
• row_slice, col_slice, return to the specified row (column) within a number of columns (rows) of cells, is a combination of types and values.

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wb = xlrd.open_workbook('读取表.xls')
sh = wb.sheet_by_index(0)
print(sh.nrows, sh.ncols)
print(sh.cell(1, 2))
print(sh.cell_value(1, 2))
print(sh.row_values(1))
print(sh.col_values(1))
print(sh.cell_type(1, 2))
print(sh.col_types(2, 1))  # 第2列，第1行起始
print(sh.row_slice(1, 0, 2)) # 第1行，第0列起始)

Note that xlrd reads excel workbooks with row and column indexes starting from 0.

• row = ws.row_values(i, ca, cb) # read the contents of the [ca, cb) column in row i, return list. note that the cb column is not included
• col = ws.col_values(i, ra, rb) # read the contents of the [ra, rb) row in column i, return to list. note that the rb row is not included
• cell= ws.cell_value(r, c) # read the contents of the cell in column j of row i

For predefined constants of data types

The date data type is read as a floating point number and needs to be manually converted to time format, such as a cell date of 2020-2-5, xlrd module reads the value: 43866.0, there are two ways to convert a floating point number to the correct time format.

• xldate_as_tuple(xdate,datemode): returns a meta ancestor consisting of (year,month,day,hours,minutes,seconds), datemode parameter has 2 values, 0 means 1900 as the base timestamp (common), 1 means 1904 as the base timestamp. Dates before 1900-3-1 cannot be converted to tuples.
• xldate_as_datetime(xdate,datemode) (need to introduce datetime module first), return a datetime object directly, xlrd.xldate_as_datetime(xdate,datemode).strftime( ‘%Y-%m-%d %H:%M:%S’)

For indexing purposes, the cellname, cellnameabs, and colname functions of the xlrd package convert the row and column serial numbers to Excel-style cell addresses; the rowcol_to_cell and rowcol_pair_to_cellrange functions of the xlwt.Utils module can also convert the row and column serial numbers to Excel-style cell address; and col_by_name, cell_to_rowcol, cell_to_rowcol2, cellrange_to_rowcol_pair functions, the Excel-style cell address converted to row number.

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print(xlrd.cellname(2, 10))
print(xlrd.cellnameabs(2, 10))  # 结果为绝对引用地址
print(xlwt.Utils.col_by_name('K'))  # 注意列名称必须大写
print(xlwt.Utils.cell_to_rowcol('K3'))  # 行列均无绝对引用
print(xlwt.Utils.cell_to_rowcol('K$3'))  # 行绝对引用
print(xlwt.Utils.cell_to_rowcol2('K$3'))  # 与上一个函数的区别是忽略绝对引用符号

The row number and cell address conversions are summarized in the following figure.

To iterate through all the cells in a sheet, usually by row and column order to get the cell by cell, and then read out the cell value to save for subsequent processing. You can also directly get a whole row (column), the whole row (column) to deal with the data.

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wb = xlrd.open_workbook('读取表.xls')
sh = wb.sheet_by_index(0)

# 1、逐单元格处理
for rx in range(sh.nrows):
    for cx in range(sh.ncols):
        c = sh.cell(rx, cx)
        # 对单元格的进一步处理
        print(c.ctype, c.value)

# 2、整行处理
for rx in range(sh.nrows):
    row = sh.row(rx)
    # 对行的进一步处理
    print(len(row))

# 3、整列处理
for cx in range(sh.ncols):
    col = sh.col(cx)
    # 对列的进一步处理
    print(len(col))

xlwt

xlrd package can only read out the data in the form, can not do anything to rewrite the data, rewrite the data and save it to a file, by xlwt package. xlwt implements a set of xlwt.Workbook. Worksheet (hereinafter referred to as Worksheet) types, but unfortunately there is no inheritance relationship with the xlrd package, which results in the Book and Sheet objects read out of the xlrd package can not be used directly to create Workbook and Worksheet objects, but only to store the data temporarily for subsequent writing back, making the process very cumbersome.

The types, methods, functions and parameters exposed to the public by the xlwt package are also very concise and fit closely with the process of rewriting data and saving it to a file.

• Call the Workbook function of the Workbook module to create a Workbook object, the first parameter is encoding
• call the Workbook object’s add_sheet method to add Worksheet objects to Workbook, the first parameter sheetname specifies the name of the form, the second parameter cell_overwrite_ok determines whether to allow cell overwriting, it is recommended to set to True, to avoid the program may write data to the cell multiple times and throw an error.
• call the Worksheet object write method, to the Worksheet row / column / cell write data, the data used here in most cases from the xlrd package from the Excel file to read the results, the first two parameters for the row number, the third parameter is the value to be written, the fourth parameter is the cell style, such as no special needs default can be; * call the Workbook object write method, to write data to the Worksheet row / column / cell.
• Call the save method of the Workbook object to save the Workbook object to a file, with the parameters of the file name or file stream object.

Other properties, methods, functions are generally used less.

xlwt mainly involves three classes: Workbook corresponds to the workbook file, Worksheet corresponds to the worksheet, XFStyle object used to control the cell format (XF record).

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# 创建Workbook对象
workbook = xlwt.Workbook.Workbook(encoding='ascii', style_compression=0)
# style_compression表示是否对格式进行压缩 默认为0不压缩 =1表示压缩字体信息 =2表示压缩字体和XF record
# 对于名为workbook的Workbook对象 可以有以下操作
# 添加工作表 并返回添加的工作表
workbook.add_sheet(sheet_name, cell_overwrite_ok=False)
# 获取指定名称的工作表
workbook.get_sheet(Sheet_name)
# 保存xls文件
workbook.save(file_name)
# 对于名为worksheet的Worksheet对象 有以下操作
# 写入内容指定单元格的内容与格式
worksheet.write(rowx, colx, cell_value, style)
worksheet.row(rowx).write(colx, cell_value, style)
worksheet.col(colx).write(rows, cell_value, style)
# 将完成编辑的行flush，flush之后的行不可再编辑
worksheet.flush_row_data()

Example.

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rwb = xlrd.open_workbook('读取表.xls')
rsh = rwb.sheet_by_index(0)

wbk = xlwt.Workbook()
wsh = wbk.add_sheet("Sheet1", cell_overwrite_ok=True)
for rx in range(rsh.nrows):
    for cx in range(rsh.ncols):
        wsh.write(rx, cx, rsh.cell_value(rx, cx))

wsh.write(0, 0, '新数据A1')
wsh.write(0, 1, 3.14159)
wsh.write(0, 6, False)
wsh.write(4 + 1, 0 + 1, False)
wsh.write(3 + 1, xlwt.Utils.col_by_name('D'), '列D')

wbk.save('data2.xls')
wbk.save('data-second.xlsx')  # 可以多次保存, 本质还是xls格式，与后缀无关。需要改成xls后才能使用Excel正常打开

There are two things to remember about saving.

• All Python libraries involving Excel operations do not support “edit and save in place”, and xlwt is no exception. “Save” is actually “Save As”, except that if you specify to save to the original file, the original file is overwritten.
• Even if you specify the extension .xlsx, the file format itself is still xls format.

Note that the date read from data.xls is essentially a numeric value, copied and written or numeric, you need to set the cell to date format in Excel to display as a date form.

xlwt also supports writing formulas, but more limited.

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wsh.write(2, 4, xlwt.Formula('sum(A3:D3)'))

In addition, xlwr supports writing the contents of merged cells across rows or columns (rowx and colx starting from 0):

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write_merge(start_rowx, end_rowx, start_colx, end_colx,content='', sytle)

Setting excel cell styles

Set cell data formatting.

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mf = xlwt.XFStyle() #返回用于设定单元格格式的实例
mf.num_format_str = 'yyyy/mm/dd' #将数字转换为日期格式
mf.font = '宋体' #设置字体

The style instance needs to be specified in ws.write() to take effect.

Example.

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## 初始化样式
style = xlwt.XFStyle()  # 样式类实例

## 创建字体
font = xlwt.Font() # 字体类实例
font.name = 'Times New Roman' # 字体名称
font.bold = True # 加粗
font.italic =True # 倾斜
font.height = 300 # 字号 200 为 10 points
font.colour_index=3 # 颜色编码

## 创建边框
borders= xlwt.Borders() # 边框类实例
borders.left= 6
borders.right= 6
borders.top= 6
borders.bottom= 6

## 创建对齐
alignment = xlwt.Alignment() # 对齐类实例
#alignment.horz = xlwt.Alignment.HORZ_LEFT    # 水平左对齐
#alignment.horz = xlwt.Alignment.HORZ_RIGHT     # 水平右对齐
alignment.horz = xlwt.Alignment.HORZ_CENTER      # 水平居中
#alignment.vert = xlwt.Alignment.VERT_TOP  # 垂直靠上
#alignment.vert = xlwt.Alignment.VERT_BOTTOM  # 垂直靠下
alignment.vert = xlwt.Alignment.VERT_CENTER      # 垂直居中
alignment.wrap = 1      # 自动换行

## 创建模式
pattern = xlwt.Pattern() # 模式类实例
pattern.pattern = xlwt.Pattern.SOLID_PATTERN # 固定的样式
pattern.pattern_fore_colour = xlwt.Style.colour_map['yellow'] # 背景颜色

## 应用样式
style.font = font
style.borders = borders
style.num_format_str = '#,##0.0000' # 内容格式
style.alignment = alignment
style.pattern=pattern

## 合并单元格(A,B,C,D) 表示合并左上角[A,C]和右下角[B,D]单元格坐标(均在合并单元格内部)
wsh.write_merge(3, 5, 3, 5, ' Merge ',style) # ' Merge ' 为写入内容，应用 style 样式
wsh.write(0, 0, 1234567.890123,style) # 向[0,0]坐标单元格写入数据，应用style样式

style.num_format_str = '#,##0.000%' # 内容格式
wsh.write(6, 0, 67.8123456,style) # 整数部分用逗号分隔，小数部分保留3位小数并以百分数表示

style.num_format_str = '###%' # 内容格式
wsh.write(6, 5, 0.128,style)

style.num_format_str = '###.##%' # 内容格式
wsh.write(6, 4, 0.128,style)

style.num_format_str = '000.00%' # 内容格式
wsh.write(6, 3, 0.128,style)

Or.

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def set_style(font_name, font_color, font_height, font_bold=False):
    style = xlwt.XFStyle()
    font = xlwt.Font()
    font.name = font_name
    font.colour_index = font_color
    font.bold = font_bold
    font.height = 20 * font_height
    style.font = font
    return style

ws.write(r, c, label=sheet.cell_value(r, c), style=set_style('黑体', 3, 30, True))

XFStyle is used to specify the cell content format, use the easyxf function to get an XFStyle object.

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xlwt.Style.easyxf(strg_to_parse='', num_format_str=None, field_sep=', ', line_sep=';', intro_sep=':', esc_char='\', debug=False)

strg_to_parse is a string that defines the format, and can control the formatting properties including font (font), alignment (align), border form (border), color style (pattern) and cell protection (protection), etc. The specific formatting properties are listed in detail at the end of the article.

String strg_to_parse syntax format is as follows.

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(<element>:(<attribute> <value>,)+;)+

For example.

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'font: bold on; align: wrap on, vert centre, horiz center'  # 字体加粗 对齐方式 允许换行 垂直居中 水平居中

The parameter string num_format_str is used to specify the format of the number, e.g.

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"#,##0.00"
"dd/mm/yyyy"

The following are some use cases for xlwt.Style.easyxf.

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style1 = easyxf('font: name Times New Roman')
style2 = easyxf('font: underline single')
style3 = easyxf('border: left thick, top thick')
style4 = easyxf('pattern: pattern solid, fore_colour red;')
style5 = xlwt.easyxf(num_format_str='yyyy-mm-dd hh:mm:ss')
style6 = xlwt.easyxf('font: name Times New Roman, color-index red, bold on', num_format_str='#,##0.00')

xlutils

xlutils depends on xlrd and xlwt and contains the following modules.

• copy: copy xlrd.Book object to xlwt.Workbook object
• display: to display information about xlrd related objects in a more friendly and secure way
• filter: a small framework for splitting and filtering existing Excel files to new Excel files
• margins: get how much useful information is contained in the Excel file
• Book object into an Excel file
• styles: a tool for formatting information in Excel files
• view: use the view information of the worksheet in workbook

Here we mainly introduce the use of two functions, the first xlutils.copy.copy(wb). From the above steps, if you are only generating a brand new Excel file, you can use the xlwt package. If you are “editing” some data in the Excel file, you must use xlrd to load the original file and make a copy of the original table, and then use xlwt to handle the cells that need to be edited, which is a cumbersome process. xlutils package copy is created to simplify this process, and can convert xlrd’s Book object to xlwt’s Workbook object.

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import xlutils.copy  # 导入模块

rbk = xlrd.open_workbook('读取表.xls')
wbk = xlutils.copy.copy(rbk)
sh = wbk.get_sheet(0)  # 索引到Sheet1
sh.write(0, 6, 'COPIED')
wbk.add_sheet('表单2')  # 新增表单
wbk.save('data-copy.xls')

The other is the function xlutils.filter.process(reader, *chain) in xlutils.filter.

The module xlutils.filter contains some built-in modules reader, writer and filter, and the function process() for stringing them together, with the main function of filtering and splitting Excel files.

• The reader is used to fetch data from the data source and convert it into a series of Book objects, which will then call the first filter-related method. There are some basic reader classes provided within the module.
• filterThe user gets the results needed for a specific task. Some specific methods have to be defined in the filter. The implementation of these methods can be filled with any functionality as needed, but will usually end with a call to the corresponding method of the next filter.
• writer handles the specific method in the last filter in the parameter chain. writer is usually used to copy information from the data source and write it to the output file. Since there is a lot of work involved in the writer and usually only writing binary data to the target location is slightly different, some basic writer classes are provided within the module.
• process(reader, *chain) can execute built-in or custom readers, writers and filters in tandem.

XFStyle format

format attributes

• font
  • bold: boolean value, default is False
  • charset: see next section for optional values, default is sys_default
  • color (or color_index, color_index, color): see the next section for optional values, default is automatic
  • escapement: optional value is none, superscript or subscript, default value is none
  • family: a string containing the font family of the font, the default value is none
  • height: the height value obtained by multiplying point size by 20, the default is 200, corresponding to 10pt
  • italic: boolean value, default is False
  • name: a string containing the name of the font, default is Arial
  • outline: Boolean value, default is False
  • shadow: Boolean value, default is False
  • struck_out: Boolean value, default is False
  • underline: boolean value or one of none, single, single_acc, double, double_acc. The default value is none
• alignment (or align)
  • direction (or dire): one of the general, lr, rl, default general
  • horizontal (or horiz, horz): one of the following: general, left, center|centre, right, filled, justified, center|centre_across_selection, distributed one of the following, the default value is general
  • indent (or inde): indent value 0 to 15, default value 0
  • rotation (or rota): integer value between -90 and +90 or stacked, one of none, default is none
  • shrink_to_fit (or shri, shrink): boolean value, default is False
  • vertical (or vert): one of top, center|centre, bottom, justified, distributed, default is bottom
  • wrap: Boolean value, default is False
• borders (or borders)
  • left: border style, see the next section for details
  • right: border style, see next section
  • top: border style, see next section
  • bottom: border style, see next section
  • diag: the border style, see the next section
  • left_colour (or left_color): color value, see next section, default is automatic
  • right_colour (or right_color): color value, see next section, default is automatic
  • top_colour (or top_color): color value, see next section, default is automatic
  • bottom_colour (or bottom_color): color value, see next section, default is automatic
  • diag_colour (or diag_color): color value, see next section, default is automatic
  • need_diag_1: Boolean value, default is False
  • need_diag_2: Boolean value, default is False
• pattern
  • back_colour (or back_color, pattern_back_colour, pattern_back_color): color value, see the next section, default is automatic
  • fore_colour (or fore_color, pattern_fore_colour, pattern_fore_color): color value, see next section for details, default is automatic
  • pattern: no_fill, none, solid, solid_fill, solid_pattern, fine_dots, alt_bars, sparse_dots, thick_horz_bands, thick_vert_bands, thick_ backward_diag, thick_forward_diag, big_spots, bricks, thin_horz_bands, thin_vert_bands, thin_backward_diag, thin_forward_diag, squares, and diamonds one of them, default is none
• protection
  • cell_locked: Boolean value, default is True
  • formula_hidden: Boolean value, default is False

Description of the values taken

Boolean

• True can be represented as 1, yes, true, or on.
• False can be 0, no, false, or off.

charset

The optional values for the character set are as follows.

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ansi_latin, sys_default, symbol, apple_roman, ansi_jap_shift_jis, ansi_kor_hangul, ansi_kor_johab, ansi_chinese_gbk, ansi_chinese_big5, ansi_greek, ansi_turkish, ansi_vietnamese, ansi_hebrew, ansi_arabic, ansi_baltic, ansi_cyrillic, ansi_thai, ansi_latin_ii, oem_latin_i

color

The available values for color are as follows.

borderline

Can be an integer value from 0 to 13, or one of the following values.

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no_line, thin, medium, dashed, dotted, thick, double, hair, medium_dashed, thin_dash_dotted, medium_dash_dotted, thin_dash_dot_dotted, medium_dash_dot_dotted, slanted_medium_dash_dotted

Reference link.

• https://github.com/python-excel
• http://xlrd.readthedocs.io/en/latest/
• http://xlwt.readthedocs.io/en/latest/api.html
• http://xlutils.readthedocs.io/en/latest/

XlsxWriter is a Python module for writing documents in Excel 2007+ XLSX file format.

xlsxwriter can be used to write text, numbers, formulas and hyperlinks to multiple worksheets, supports formatting and more, and includes.

• 100% compatible with Excel XLSX files.
• Full formatting.
• Merge cells.
• Defined names.
• Charting.
• Automatic filtering.
• Data validation and drop-down lists.
• Conditional formatting.
• Worksheet png/jpeg/bmp/wmf/emf images.
• Rich multi-format strings.
• Cell annotation.
• Integration with Pandas.
• Text boxes.
• Support for adding macros.
• Memory-optimized mode for writing large files.

Pros.

• More powerful: Relatively speaking, this is the most powerful tool other than Excel itself. Font settings, foreground color background color, border settings, view zoom (zoom), cell merge, autofilter, freeze panes, formulas, data validation, cell comments, row height and column width settings, etc.
• Support for large file writes: If the amount of data is very large, you can enable constant memory mode, which is a sequential write mode that writes a row of data as soon as you get it, without keeping all the data in memory.

Disadvantages.

• No read and modify support: The author did not intend to make an XlsxReader to provide read operations. If you can’t read, you can’t modify. It can only be used to create new files. When you write data in a cell, there is still no way to read the information that has been written unless you have saved the relevant content yourself.
• XLS files are not supported: XLS is the format used in Office 2013 or earlier and is a binary format file. XLSX is a compressed package made up of a series of XML files (the final X stands for XML). If you have to create a lower version of XLS file, please go to xlwt.
• Pivot Table is not supported at this time.

xlsxwriter easy to use

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# -*- coding: utf-8 -*-
import xlsxwriter

data = [
    ['年度', '数量', '剩余数量'],
    ['2016', '100', '30'],
    ['2017', '150', '50'],
    ['2018', '170', '40'],
    ['2019', '190', '15'],
    ['2020', '200', '100'],
]
wb = xlsxwriter.Workbook('test.xlsx')  # 创建一个新的excel表格
sheet = wb.add_worksheet('sheet1')  # 创建一个新的sheet
# 将data数组的数据插入到excel表格中
for row, item in enumerate(data):
    for column, value in enumerate(item):
        sheet.write(row, column, value)
wb.close()

We can also set the style to the excel table, set the style to the table using the add_format method.

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# -*- coding: utf-8 -*-
import xlsxwriter

data = [
    ['年度', '数量', '剩余数量'],
    ['2016', '100', '30'],
    ['2017', '150', '50'],
    ['2018', '170', '40'],
    ['2019', '190', '15'],
    ['2020', '200', '100'],
]

wb = xlsxwriter.Workbook('test.xlsx')  # 创建一个新的excel表格
sheet = wb.add_worksheet('sheet1')  # 创建一个新的sheet
# 将data数组的数据插入到excel表格中

# 增加样式配置
style = wb.add_format({
    'bold': True,  # 字体加粗
    'border': 1,  # 单元格边框宽度
    'align': 'left',  # 水平对齐方式
    'valign': 'vcenter',  # 垂直对齐方式
    'fg_color': 'yellow',  # 单元格背景颜色
    'text_wrap': True,  # 是否自动换行
    'font_color': 'red',  # 文字颜色
})

for row, item in enumerate(data):
    for column, value in enumerate(item):
        sheet.write(row, column, value, style)
wb.close()

The xlsxwriter package allows us to insert data by row and column, using the following methods.

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# -*- coding: utf-8 -*-
import xlsxwriter

data1 = ['年份', '数量', '剩余数量']
data2 = ['2013', '100', '50']
wb = xlsxwriter.Workbook('test.xlsx')
sheet = wb.add_worksheet('sheet1')
sheet.write_row('A1', data1)
sheet.write_row('A2', data2)
sheet = wb.add_worksheet('sheet2')
sheet.write_column('A1', data1)
sheet.write_column('B1', data2)
wb.close()

xlsxwriter包中我们可以给excel插入图表，简单梳理如下：
# -*- coding: utf-8 -*-
import xlsxwriter

wb = xlsxwriter.Workbook('test.xlsx')  # 创建新的excel
sheet = wb.add_worksheet('sheet1')  # 创建新的sheet
# 向excel文件中插入数据
data1 = ['年份', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']
sheet.write_column('A1', data1)
data2 = ['数量', 100, 200, 500, 400, 500, 600, 150, 300]
sheet.write_column('B1', data2)
# 设置图表类型 ,type常见参数有:area：面积图,bar：条形图,column：直方图,doughnut：环状图,line：折线图,pie：饼状图,scatter：散点图,radar：雷达图,stock:箱线图
chart = wb.add_chart({'type': 'line'})
# 给图表设置信息
chart.add_series(
    {
        'name': '发展趋势',  # 设置折线名称
        'categories': '=sheet1!$A$2:$A$9',  # 设置x轴信息
        'values': '=sheet1!$B$2:$B$9',  # 设置y轴信息
        'line': {'color': 'red'}  # 给折线设置样式
    }
)
chart.set_title({'name': '测试'})  # 设置表头标题
chart.set_x_axis({'name': "x轴"})  # 设置x轴名称
chart.set_y_axis({'name': 'y轴'})  # 设置y轴名称
chart.set_style(1)
sheet.insert_chart('A10', chart, {'x_offset': 25, 'y_offset': 10})  # 放置图表位置
wb.close()

Common functions of xlsxwriter module

Set cell formatting

Set the formatting directly by means of a dictionary.

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workfomat = workbook.add_format({
    'bold': True,  # 字体加粗
    'border': 1,  # 单元格边框宽度
    'align': 'center',  # 对齐方式
    'valign': 'vcenter',  # 字体对齐方式
    'fg_color': '#F4B084',  # 单元格背景颜色
    'text_wrap': True,  # 是否自动换行
})

Set the cell format by means of the format object.

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workfomat = workbook.add_format()
workfomat.set_bold(1)  # 设置边框宽度
workfomat.set_num_format('0.00')  # 格式化数据格式为小数点后两位
workfomat.set_align('center')  # 设置对齐方式
workfomat.set_fg_color('blue')  # 设置单元格背景颜色
workfomat.set_bg_color('red')  # 设置单元格背景颜色 (经测试和上边的功能一样)

There are many more operations like this for some cell tables, so you can study them according to your needs.

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worksheet.merge_range('D1:D7', '合并单元格')  # 合并单元格
worksheet.set_tab_color('red')  # 设置sheet标签颜色
worksheet.set_column('A:D', 25)  # 设置A到D列的列宽为25
worksheet.write_formula('E2', '=B2/C2')  # 设置表格中的计算，‘E2’是计算结果，'=B2/C2'是计算公式

# 写入单个单元格数据
# row:行， col：列， data:要写入的数据, bold:单元格的样式
worksheet1.write(row, col, data, bold)

# 写入一整行， A1:从A1单元格开始插入数据，按行插入， data:要写入的数据（格式为一个列表), bold:单元格的样式
worksheet1.write_row(“A1”, data, bold)

# 写入一整列 ， A1:从A1单元格开始插入数据，按列插入， data:要写入的数据（格式为一个列表), bold:单元格的样式
worksheet1.write_column(“A1”, data, bold)

# 插入图片， 第一个参数是插入的起始单元格，第二个参数是图片你文件的绝对路径
worksheet1.insert_image('A1', 'f:.jpg')

# 写入超链接
worksheet1.write_url(row, col, "internal:%s!A1" % ("要关联的工作表表名"), string="超链接显示的名字")

# 插入图表 
""" 参数中的type指的是图表类型，图表类型示例如下：[area：面积图,bar：条形图,column：直方图,
doughnut：环状图,line：折线图,pie：饼状图,scatter：散点图,radar：雷达图,stock:箱线图] """
workbook.add_chartsheet(type = "")

# 获得当前excel文件的所有工作表
"""
workbook.worksheets() 用于获得当前工作簿中的所有工作表，
这个函数的存在便利了对于工作表的循环操作，
如果你想在当前工作簿的所有工作表的A1单元格中输入一个字符创‘Hello xlsxwriter’，
那么这个命令就派上用场了。
"""
workbook.worksheets()

# 关闭excel文件
"""
这个命令是使用xlsxwriter操作Excel的最后一条命令，一定要记得关闭文件。
"""
workbook.close()

Common chart types.

• area: Creates an Area (solid line) style sheet.
• bar: Creates a bar style (transposed histogram) chart.
• column: Creates a column style (histogram) chart.
• line: Creates a line chart.
• pie: Creates a pie-style chart.
• doughnut: Creates a doughnut style chart.
• scatter: Creates a scatter chart style chart.
• stock: Creates a stock style chart.
• radar: Creates a radar style sheet.

Sample Code Explanation

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import xlsxwriter

workbook = xlsxwriter.Workbook('chart_data_table.xlsx')  # 可以生成.xls文件但是会报错
worksheet = workbook.add_worksheet('Sheet1')  # 工作页

# 准备测试数据
bold = workbook.add_format({'bold': 1})
headings = ['Number', 'Batch 1', 'Batch 2']
data = [
    [2, 3, 4, 5, 6, 7],
    [10, 40, 50, 20, 10, 50],
    [30, 60, 70, 50, 40, 30],
]

# 插入数据
worksheet.write_row('A1', headings, bold)  # 行插入操作  注意这里的'A1'
worksheet.write_column('A2', data[0])  # 列插入操作 注意这里的'A2'
worksheet.write_column('B2', data[1])
worksheet.write_column('C2', data[2])

# 插入直方图1
chart1 = workbook.add_chart({'type': 'column'})  # 选择 直方图 'column'
chart1.add_series({
    'name': '=Sheet1!$B$1',
    'categories': '=Sheet1!$A$2:$A$7',  # X轴值（实在不知道怎么叫，就用XY轴表示）
    'values': '=Sheet1!$B$2:$B$7',  # Y轴值
    'data_labels': {'value': True}  # 显示数字，就是直方图上面的数字，默认不显示
})

# 注意上面写法 '=Sheet1!$B$2:$B$7'  Sheet1是指定工作页， $A$2:$A$7是从A2到A7数据，熟悉excel朋友应该一眼就能认得出来

# 插入直方图2
chart1.add_series({
    'name': ['Sheet1', 0, 2],
    'categories': ['Sheet1', 1, 0, 6, 0],
    'values': ['Sheet1', 1, 2, 6, 2],
    'data_labels': {'value': True}
})

chart1.set_title({'name': 'Chart with Data Table'})  # 直方图标题
chart1.set_x_axis({'name': 'Test number'})  # X轴描述
chart1.set_y_axis({'name': 'Sample length (mm)'})  # Y轴描述
chart1.set_table()
chart1.set_style(3)  # 直方图类型

worksheet.insert_chart('D2', chart1, {'x_offset': 25, 'y_offset': 10})  # 直方图插入到 D2位置 
workbook.close()

Types supported by XlsxWriter

Excel often treats different types of input data, such as strings and numbers, differently, though usually transparently to the user. the XlsxWriter view emulates this with the worksheet.write() method, by mapping Python data types to the types supported by Excel.

The write() method serves as a generic alias for several more specific methods.

• write_string()
• write_number()
• write_blank()
• write_formula()
• write_datetime()
• write_boolean()
• write_url()

In the code here, we use some of these methods to handle different types of data.

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worksheet.write_string  (row, col,     item              )
worksheet.write_datetime(row, col + 1, date, date_format )
worksheet.write_number  (row, col + 2, cost, money_format)

This is mainly to show that if you need more control over the data you write to the worksheet, you can use the appropriate methods. In this simple example, the write() method actually works out well.

Date handling is also new to the program.

Dates and times in Excel are floating-point numbers applied in a numeric format to make it easier to display them in the correct format. If the date and time are Python datetime objects, then XlsxWriter will automatically do the required numeric conversion. However, we also need to add numeric formatting to ensure that Excel displays them as dates.

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from datetime import datetime

date_format = workbook.add_format({'num_format': 'mmmm d yyyy'})
...

for item, date_str, cost in (expenses):
    # Convert the date string into a datetime object.
    date = datetime.strptime(date_str, "%Y-%m-%d")
    ...
    worksheet.write_datetime(row, col + 1, date, date_format )
    ...

Finally, set_column() is needed to adjust the width of column B so that the date can be displayed clearly.

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# Adjust the column width.
worksheet.set_column('B:B', 15)

Reference links.

• https://xlsxwriter.readthedocs.io/

Python Excel Reading and Writing with OpenPyXL

And you can make detailed settings for the cells in the Excel file, including cell styles and other content, and even support the insertion of charts, print settings and other content. openpyxl can read and write xltm, xltx, xlsm, xlsx and other types of files.

The general process of using openpyxl is: create/read excel file -> select sheet object -> operate on form/cell -> save excel

Create/read excel files

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from openpyxl import Workbook
from openpyxl import load_workbook

wb = Workbook()  # 新建空白工作簿
wb = load_workbook('1.xlsx')  # 读取excel
wb.save('filename.xlsx')  # 保存excel

sheet form operations

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from openpyxl import load_workbook

wb = load_workbook('读取表.xlsx')  # 读取excel
print(wb.sheetnames)  # 以list方式返回excel文件所有sheet名称（->list[str,str..]）
# 选定需要操作的sheet
ws = wb['第一个 sheet']  # 根据sheet名称选取
ws = wb.active  # 选择当前活动的sheet，默认为第一个
# 创建新的sheet
ws = wb.create_sheet("newsheet_end")  # 默认插入到最后
ws = wb.create_sheet("newsheet_first", 0)  # 插入到最开始的位置(从0开始计算)

# 复制一个sheet对象
source = wb.active
target = wb.copy_worksheet(source)

# 移动工作表
wb.move_sheet(ws, offset=0)

# sheet常见属性
ws = wb['第一个 sheet']  # 根据sheet名称选取
print(ws.title)  # sheet名称
print(ws.max_row)  # 最大行
print(ws.max_column)  # 最大列
rows = ws.rows  # 行生成器, 里面是每一行的cell对象，由一个tuple包裹。
columns = ws.columns  # 列生成器, 里面是每一列的cell对象，由一个tuple包裹。

# 可以使用list(sheet.rows)[0].value 类似方法来获取数据，或
for row in ws.rows:
    for cell in row:
        print(cell.value)

# 删除sheet
del wb['第三个 sheet']
wb.save('output.xlsx')

Cell object

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from openpyxl import load_workbook
from openpyxl.utils import get_column_letter, column_index_from_string

wb = load_workbook('读取表.xlsx')  # 读取excel
ws = wb.active  # 选择当前活动的sheet，默认为第一个

# 根据名称访问
print(ws['A1'])  # A列1行的单元对象 A1
print(ws['a2'])  # 也可以小写 A1

# cell方法访问
print(ws.cell(row=2, column=2))  # B2
print(ws.cell(3, 2))  # B3

# 从cell列表中返回
print(list(ws.rows)[2][1])  # B3
print(list(ws.columns)[1][2])  # B3

# 选择多个单元格
a2_b3 = ws['a2':'b3']  # 切片访问,以行组成tuple返回tuple
print(a2_b3)

# 单独字母与数字返回列与行的所有数据
b = ws['b']  # 返回b列的所有cell对象
print(b)
row1 = ws['1']  # 返回第1行的所有cell
row1 = ws[1]  # 加引号和不加引号效果一样
print(row1)
# 当然也能范围选择
a_e = ws['a:e']  # a-e列的cell对象
print(a_e)

# 单元格属性
cell = ws['A1']
print(cell.column)
print(cell.row)
print(cell.value)  # 注意: 如果单元格是使用的公式，则值是公式而不是计算后的值
print(cell.number_format)  # 返回单元格格式属性,# 默认为General格式
print(cell.font)  # 单元格样式

# 更改单元格值
# 直接赋值
ws['a2'] = 222
ws['a2'] = 'aaa'
ws['b2'] = '=SUM(A1:A17)'  # 使用公式
# value属性赋值
cell.value = 222
# 或
ws.cell(1, 2, value=222)

# 移动单元格
ws.move_range("D4:F10", rows=-1, cols=2)  # 表示单元格D4: F10向上移动一行，右移两列。单元格将覆盖任何现有单元格。
ws.move_range("G4:H10", rows=1, cols=1, translate=True)  # 移动中包含公式的自动转换

# 合并与拆分单元格
ws.merge_cells('A2:D2')  # 合并单元格,以最左上角写入数据或读取数据
ws.unmerge_cells('A2:D2')  # 拆分单元格

# 列字母和坐标数字相互转换
print(get_column_letter(3))  # C # 根据列的数字返回字母
print(column_index_from_string('D'))  # 4 # 根据字母返回列的数字

# 遍历单元格
# 注意
# openpyxl 读取 Excel 的索引是从 1 开始的
# 因为 range 函数是左闭右开，再加上索引是从 1 开始的，所以最大值都要 +1
for i in range(1, ws.max_row + 1):
    for j in range(1, ws.max_column + 1):
        print(ws.cell(i, j).value)

for row in ws.rows:
    for cell in row:
        print(cell.value)

for col in ws.cols:
    for cell in col:
        print(cell.value)

for row in ws.iter_rows(min_row=1, max_col=3, max_row=2):
    for cell in row:
        print(cell)

for col in ws.iter_cols(min_row=1, max_col=3, max_row=2):
    for cell in col:
        print(cell)

cell_range = ws['A1':'C2']
for row in cell_range:
    for cell in row:
        print(cell.value)

for x in tuple(ws.rows):
    for y in x:
        print(y.value)

for x in tuple(ws.cols):
    for y in x:
        print(y.value)

# 多个单元格的操作
# 同一行中，多个单元格同时输入
datas = ["A5追加", "B5追加", "C5追加"]
ws.append(datas)

# 复数行中，多个单元格同时输入
datas = [
    ["A6追加", "B6追加", "C6追加"],
    ["A7追加", "B7追加", "C7追加"],
]
for row_data in datas:
    ws.append(row_data)

Format style setting

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from openpyxl import load_workbook
from openpyxl.styles import Font, colors, Alignment, PatternFill, Border, Side, NamedStyle

wb = load_workbook('读取表.xlsx')  # 读取excel
ws = wb.active  # 选择当前活动的sheet，默认为第一个
# 字体
font = Font(name='Calibri',
            size=11,
            bold=False,
            italic=False,
            vertAlign=None,
            underline='none',
            strike=False,
            color='FF000000')

# 示例：设定字体为等线24号，加粗斜体，字体颜色红色。将字体赋值给A1
ws['A1'].font = Font(name='等线', size=24, italic=True, color=colors.RED, bold=True)

# 对齐方式
alignment = Alignment(horizontal='general',
                      vertical='bottom',
                      text_rotation=0,
                      wrap_text=False,
                      shrink_to_fit=False,
                      indent=0)

# 示例：设置B1中的数据垂直居中和水平居中
ws['B1'].alignment = Alignment(horizontal='center', vertical='center')

# horizontal 的可用样式为：{'left':'左对齐', 'center':'居中对齐', 'right':'右对齐', 'distributed':'分散对齐', 'centerContinuous':'跨列居中', 'justify':'两端对齐', 'fill':'填充', 'general':'常规'}
# vertical 的可用样式为：{'top':'顶端对齐', 'center':'居中对齐', 'bottom':'底端对齐', 'distributed':'分散对齐', 'justify':'两端对齐'}
# wrap_text 为自动换行。

# 填充单元格颜色
fill = PatternFill(fill_type=None,
                   start_color='FFFFFFFF',
                   end_color='FF000000')

ws['A1'].fill = fill
# 可选择的填充样式为：['none', 'solid', 'darkDown', 'darkGray', 'darkGrid', 'darkHorizontal', 'darkTrellis', 'darkUp', 'darkVertical', 'gray0625', 'gray125', 'lightDown', 'lightGray', 'lightGrid', 'lightHorizontal', 'lightTrellis', 'lightUp', 'lightVertical', 'mediumGray']

# 设置行高和列宽
ws.row_dimensions[2].height = 40
ws.column_dimensions['C'].width = 30

# 设置边框
border = Border(left=Side(border_style=None, color='FF000000'),
                right=Side(border_style=None, color='FF000000'),
                top=Side(border_style=None, color='FF000000'),
                bottom=Side(border_style=None, color='FF000000'),
                diagonal=Side(border_style=None, color='FF000000'),
                diagonal_direction=0,
                outline=Side(border_style=None, color='FF000000'),
                vertical=Side(border_style=None, color='FF000000'),
                horizontal=Side(border_style=None, color='FF000000')
                )

# 可选择的边框样式为：['dashDot', 'dashDotDot', 'dashed', 'dotted', 'double', 'hair', 'medium', 'mediumDashDot', 'mediumDashDotDot', 'mediumDashed', 'slantDashDot', 'thick', 'thin']

# 设置工作表标签底色
ws.sheet_properties.tabColor = "1072BA"

# 创建一个样式预设
highlight = NamedStyle(name='highlight')
highlight.font = Font(bold=True, size=20)
bd = Side(style='thick', color='000000')
highlight.border = Border(left=bd, top=bd, right=bd, bottom=bd)
# Once a named style has been created, it can be registered with the workbook:
wb.add_named_style(highlight)
# Named styles will also be registered automatically the first time they are assigned to a cell:
ws['A1'].style = highlight
# Once registered, assign the style using just the name:
ws['D5'].style = 'highlight'

Other

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

wb = load_workbook('读取表.xlsx')  # 读取excel
ws = wb.active  # 选择当前活动的sheet，默认为第一个

# 插入图片
img = Image('logo.png')
img.width, img.height = (180, 80)  # 指定图片尺寸，可省略
ws.add_image(img, 'A1')

# 插入批注
comment = Comment('This is the comment text', 'Comment Author')
ws["A1"].comment = comment

Python Excel manipulation of xlwings

xlwings is a BSD-licensed Python based library. It makes it easy to call each other between Python and Excel:

• Scripting: Automate the processing of Excel data in Python or interact with Excel using VBA-like syntax.
• Macros: Replace VBA macros with powerful and clean Python code.
• UDFs (User Defined Functions): Write User Defined Functions (UDFs) in Python, for windows only.
• REST API: Open Excel workbooks to the outside through the REST API.
• Support for Windows and MacOS

xlwings open source free , can be very easy to read and write data in Excel files , and cell formatting changes . xlwings can also seamlessly connect with matplotlib, Numpy and Pandas , support for reading and writing Numpy, Pandas data types , matplotlib visual charts into excel. The most important thing is that xlwings can call the program written by VBA in Excel file, and also can let VBA call the program written in Python. It supports reading of .xls files and reading and writing of .xlsx files.

The main structure of xlwings.

As you can see, the direct interface with xlwings is the apps, that is, the Excel application, then the workbook books and worksheet sheets, and finally the cell area range, which is quite different from openpyxl, and because of this, xlwings needs to still have the Excel application environment installed.

App common syntax

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import xlwings as xw

# 创建应用app：
# 参数：visible：应用是否可见（True|False）,add_book:是否创建新工作簿(True|False)
app = xw.App(visible=True, add_book=True)
wb = app.books.active  # get新创建的工作簿（刚创建的工作簿为活动工作簿，使用active获取）
# 警告提示（True|False）
app.display_alerts = False
# 屏幕刷新（True|False）
app.screen_updating = False
# 工作表自动计算{'manual':'手动计算','automatic':'自动计算','semiautomatic':'半自动'}
app.calculation = 'manual'
# 应用计算,calculate方法同样适用于工作簿，工作表
app.calculate()
# 退出应用
app.quit()

Book common syntax

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import xlwings as xw

app = xw.App(visible=True, add_book=False)

# 新建工作簿
wb = app.books.add()  # 方法1
wb = xw.Book()  # 方法2
wb = xw.books.add()

# 打开工作簿
file_path = '读取表.xlsx'
wb = app.books.open(file_path)
wb = xw.Book(file_path)

# 工作簿保存
wb.save()
wb.save(path=None)  # 或者指定path参数保存到其他路径，如果没保存为脚本所在路径

# 其他：获取名称、激活、关闭
wb = xw.books['工作簿名称']  # get指定名称的工作簿
wb.activate()  # 激活为当前工作簿
print(wb.fullname)  # 返回工作簿的绝对路径
print(wb.name)  # 工作簿名称
wb.close()  # 关闭工作簿

Sheet common syntax

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import xlwings as xw

# 工作表引用
wb = xw.books['工作簿名字']
sheet = wb.sheets['工作表名字']
sheet = wb.sheets[0]  # 也可以使用数字索引,从0开始，类似于vba的worksheets(1)
sheet = xw.sheets.active  # 当前活动工作表，sheets是工作表集合
sheet = wb.sheets.active

# 新建工作表表
# 参数：name：新建工作表名称；before创建的工作表位置在哪个工作表前面；after：创建位置在哪个工作表后面；
# before和after参数可以传入数字，也可以传入已有的工作表名称，传入数字n表示从左往右第n个sheet位置
# before和after参数不传，创建sheet默认在当前活动工作表左侧
sheet = xw.sheets.add(name=None, before=None, after=None)
wb.sheets.add(name='新工作表4', before='新工作表')

sheet.activate()  # 激活为活动工作簿
sheet.clear()  # 清除工作表的内容和格式
sheet.clear_contents()  # 清除工作表内容，不清除样式
sheet_name = sheet.name  # 工作表名称
sheet.delete()  # 删除工作表
sheet.calculate()  # 工作表计算
sheet.used_range  # 工作表的使用范围，等价与vba的usedrange

# 自动匹配工作表列、行宽度
# 若要自动调整行，请使用以下内容之一：rows或r
# 若要自动装配列，请使用以下内容之一：columns或c
# 若要自动调整行和列，请不提供参数。
sheet.autofit()

Range common syntax

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import xlwings as xw
import datetime

# 单元格引用
rng = xw.books['工作簿名称'].sheets['工作表名称'].range('a1')
# 第一个应用第一个工作簿第一张sheet的第一个单元格
xw.apps[0].books[0].sheets[0].range('a1')

# 引用活动sheet的单元格,直接接xw，Range首字母大写
rng = xw.Range('a1')  # a1
rng = xw.Range(1, 1)  # a1，行列用tuple进行引用，圆括号从1开始
rng = xw.Range((1, 1), (3, 3))  # a1:a3

# 也可以工作表对象接方括号引用单元格
sheet = xw.books['工作簿'].sheets['工作表名称']
rng = sheet['a1']  # a1单元格
rng = sheet['a1:b5']  # a1:b5单元格
rng = sheet[0, 1]  # b1单元格，也可以根据行列索引，从0开始为
rng = sheet[:10, :10]  # a1:j10

# 单元格邻近范围
rng = sheet[0, 0].current_region  # a1单元格邻近区域=vba：currentregion

# 返回excel：ctrl键+方向键跳转单元格对象:上：up，下：down，左：left，右：right
# 等同于vba：end语法：xlup，xldown，xltoleft，xltoright
rng = sheet[0, 0].end('down')

# 数据的读取
# 获取单元格的值,单元格的value属性
val = sheet.range('a1').value
ls = sheet.range("a1:a2").value  # 一维列表
ls = sheet.range("a1:b2").value  # 二维列表

# 单元格值默认读取格式
# 默认情况下，带有数字的单元格被读取为float，带有日期单元格被读取为datetime.datetime, 空单元格转化为None；数据读取可以通过option操作指定格式读取
sheet[1, 1].value = 1
sheet[1, 1].value  # 输出是1.0
sheet[1, 1].options(numbers=int).value  # 输出是1
sheet[2, 1].options(dates=datetime.date).value  # 指定日期格式为datetime.date
sheet[2, 1].options(empty='NA').value  # 指定空单元格为'NA'

# 单元格数据写入
sheet.range('a1').value = 1  # 单个值
sheet.range("a1:c1").value = [1, 2, 3]  # 写入一维列表
sheet.range("a1:a3").options(transpose=True).value = [1, 2, 3]  # option:设置transpose参数转置下
sheet.range("a1:a3").value = [1, 2, 3]  # 写入二维列表
sheet.range('A1').options(expand='table').value = [[1, 2], [3, 4]]
sheet.range('A1').value = [[1, 2], [3, 4]]  # 也可以直接这样写
# '''
# 尽量减少与excel交互次数有助于提升写入速度
# sheet.range('A1').value = [[1,2],[3,4]]
# 比sheet.range('A1').value = [1, 2] 和 sheet.range('A2').value = [3, 4]会更快
# '''

# expand: 动态选择Range维度
# 可以通过单元格的expand或者options的expand属性动态获取excel中单元格维度；两者再使用区别是， 使用expand方法，只有在访问范围的值才会计算;
# options方法会随着单元格值范围扩增而相应的范围增大，区别示例如下：
# expand参数值除了’table’, 还可以使用‘right’：向右延伸，‘down’：向下延伸；
sheet = xw.sheets.add(name='工作表名称')
sheet.range('a1').value = [[1, 2], [3, 4]]
rng1 = sheet.range('a1').options(expand='table')  # 使用options方法
rng2 = sheet.range('a1').expand('table')  # 使用expand方法，默认是table，‘table’参数也可以不填 
sheet.range('a3').value = [5, 6]  # 现在新增一行数据
print(rng1.value)  # [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]
print(rng2.value)  # [[1.0, 2.0], [3.0, 4.0]] 使用的expand方法，范围没有扩散
print(sheet.range('a1').options(expand='table').value)  # [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],再次expand方法访问，值范围扩散

Transformer

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import xlwings as xw
import numpy as np
import pandas as pd
sheet = xw.books['工作簿'].sheets['工作表名称']
# 字典转化
sheet.range('a1').value = [['a', 1], ['b', 2]]  # 字典转化可以将excel两列数据读取为字典，如果是两行数据，使用transpose转置下；
sheet.range('a1:b2').options(dict).value  # {'a': 1.0, 'b': 2.0}
sheet.range('a4').value = [['a', 'b'], [1, 2]]
sheet.range('a4:b5').options(dict, transpose=True).value  # {'a': 1.0, 'b': 2.0}
# numpy转化
# 相关参数：ndim = None（维度,：1维也可以设置为2转化成二维array）, dtype = None(可指定数据类型)
sheet = xw.Book().sheets[0]
sheet.range('A1').options(transpose=True).value = np.array([1, 2, 3])
sheet.range('A1:A3').options(np.array, ndim=2).value  # 返回二维数组
# 其他方法
rng = xw.Range('A1') # 引用当前活动工作表的单元格
rng.add_hyperlink(r'https://www.baidu.com', '百度','提示：点击即链接到百度')# 加入超链接
rng.address #取得当前range的地址
rng.get_address() #取得当前range的地址
rng.clear_contents() # 清除range的内容
rng.clear() # 清除格式和内容
rng.color # 取得range的背景色,以元组形式返回RGB值
rng.color = (255, 255, 255) # 设置range的颜色
rng.color = None # 清除range的背景色
rng.column # 获得range的第一列列标
rng.row # range的第一行行标
rng.count # 返回range中单元格的数据
rng.formula = '=SUM(B1:B5)' # 获取公式或者输入公式
rng.formula_array # 数组公式
rng.get_address(row_absolute=True, column_absolute=True, include_sheetname=False, external=False) # 获得单元格的绝对地址
rng.column_width # 获得列宽
rng.width # 返回range的总宽度
rng.hyperlink # 获得range的超链接
rng.last_cel # 获得range中右下角最后一个单元格
rng.offset(row_offset=0, column_offset=0) # range平移
rng.resize(row_size=None, column_size=None) # range进行resize改变range的大小
rng.row_height # 行的高度，所有行一样高返回行高，不一样返回None
rng.height # 返回range的总高度
rng.shape # 返回range的行数和列数
rng.sheet # 返回range所在的sheet
rng.rows # 返回range的所有行
rng.rows[0] # range的第一行
rng.rows.count # range的总行数
rng.columns # 返回range的所有列
rng.columns[0] # 返回range的第一列
rng.columns.count # 返回range的列数
rng.autofit() # 所有range的大小自适应
rng.columns.autofit() # 所有列宽度自适应
rng.rows.autofit() # 所有行宽度自适应
# Pandas
# Series与DataFrame转化器
# 相关参数：ndim = None, index = 1（多列，是否使用第一列为索引）, header = True(表头), dtype = None；
# DataFrame的表头可以设置为1，2，1等价于True，2表示二维表头；index: 0等价与False，1等价于True，第一列设置为索引
# 写入两列数据
sheet.range('a1').values = [['name', 'age'], ['张三', 18], ['李四', 20], ['王五', 35]]
# index=0，第一列不为索引，读取结果为DataFrom
df = sheet.range('a1').options(pd.Series, expand='table', index=0).value
# index=1，第一列设置为索引，输出为Series
s = sheet.range('a1').options(pd.Series, expand='table', index=1).value
# 写入，不需要索引，index设置为False，保留表头，header=True
sheet.range('d1').options(pd.DataFrame, index=False, header=True).value = df
# 读取为DataFrame
df = sheet.range('a1').options(pd.DataFrame, expand='table', index=0).value

Reference link.

• https://github.com/xlwings/xlwings
• https://docs.xlwings.org/zh_CN/latest/index.html

PyExcelerate is claimed to be the best performing Python writing package for Excel xlsx files. It is also relatively easy to use.

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from datetime import datetime
from pyexcelerate import Workbook, Color, Style, Font, Fill, Format

# Writing bulk data
# Fastest
data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] # data is a 2D array
wb = Workbook()
wb.new_sheet("sheet name", data=data)
wb.save("output.xlsx")

# Writing bulk data to a range
# Fastest
wb = Workbook()
ws = wb.new_sheet("test", data=[[1, 2], [3, 4]])
wb.save("output.xlsx")
# Fast
wb = Workbook()
ws = wb.new_sheet("test")
ws.range("B2", "C3").value = [[1, 2], [3, 4]]
wb.save("output.xlsx")

# Writing cell data
# Faster
wb = Workbook()
ws = wb.new_sheet("sheet name")
ws.set_cell_value(1, 1, 15) # a number
ws.set_cell_value(1, 2, 20)
ws.set_cell_value(1, 3, "=SUM(A1,B1)") # a formula
ws.set_cell_value(1, 4, datetime.now()) # a date
wb.save("output.xlsx")

# Selecting cells by name
wb = Workbook()
ws = wb.new_sheet("sheet name")
ws.cell("A1").value = 12
wb.save("output.xlsx")

# Merging cells
wb = Workbook()
ws = wb.new_sheet("sheet name")
ws[1][1].value = 15
ws.range("A1", "B1").merge()
wb.save("output.xlsx")

# Styling cells
wb = Workbook()
ws = wb.new_sheet("sheet name")
ws.set_cell_value(1, 1, 1)
ws.set_cell_style(1, 1, Style(font=Font(bold=True)))
ws.set_cell_style(1, 1, Style(font=Font(italic=True)))
ws.set_cell_style(1, 1, Style(font=Font(underline=True)))
ws.set_cell_style(1, 1, Style(font=Font(strikethrough=True)))
ws.set_cell_style(1, 1, Style(fill=Fill(background=Color(255,0,0,0))))
ws.set_cell_value(1, 2, datetime.now())
ws.set_cell_style(1, 1, Style(format=Format('mm/dd/yy')))
wb.save("output.xlsx")

# Styling ranges
wb = Workbook()
ws = wb.new_sheet("test")
ws.range("A1","C3").value = 1
ws.range("A1","C1").style.font.bold = True
ws.range("A2","C3").style.font.italic = True
ws.range("A3","C3").style.fill.background = Color(255, 0, 0, 0)
ws.range("C1","C3").style.font.strikethrough = True

# Styling rows
wb = Workbook()
ws = wb.new_sheet("sheet name")
ws.set_row_style(1, Style(fill=Fill(background=Color(255,0,0,0))))
wb.save("output.xlsx")

# Styling columns
wb = Workbook()
ws = wb.new_sheet("sheet name")
ws.set_col_style(1, Style(fill=Fill(background=Color(255,0,0,0))))
wb.save("output.xlsx")

# Available style attributes
ws[1][1].style.font.bold = True
ws[1][1].style.font.italic = True
ws[1][1].style.font.underline = True
ws[1][1].style.font.strikethrough = True
ws[1][1].style.font.color = Color(255, 0, 255)
ws[1][1].style.fill.background = Color(0, 255, 0)
ws[1][1].style.alignment.vertical = 'top'
ws[1][1].style.alignment.horizontal = 'right'
ws[1][1].style.alignment.rotation = 90
ws[1][1].style.alignment.wrap_text = True
ws[1][1].style.borders.top.color = Color(255, 0, 0)
ws[1][1].style.borders.right.style = '-.'

# Setting row heights and column widths
wb = Workbook()
ws = wb.new_sheet("sheet name")
ws.set_col_style(2, Style(size=0))
wb.save("output.xlsx")

# Linked styles
wb = Workbook()
ws = wb.new_sheet("sheet name")
ws[1][1].value = 1
font = Font(bold=True, italic=True, underline=True, strikethrough=True)
ws[1][1].style.font = font
wb.save("output.xlsx")

# Pandas DataFrames
ws = wb.new_sheet("sheet name", data=df.values.tolist())