34 open source библиотеки python (2019)

Chart formatting: Solid Fill

The solid fill format is used to specify filled areas of chart objects such as the interior of a column or the background of the chart itself.

The following properties can be set for formats in a chart:


none
color
transparency

The property is used to turn the property off (it is generally on by default):

chart.add_series({
    'values' '=Sheet1!$A$1:$A$6',
    'fill'   {'none' True},
    'border' {'color' 'black'}
})

The property sets the color of the area:

chart.add_series({
    'values' '=Sheet1!$A$1:$A$6',
    'fill'   {'color' 'red'}
})

The available colors are shown in the main XlsxWriter documentation. It is also possible to set the color of a fill with a Html style string or a limited number of named colors, see :

chart.add_series({
    'values' '=Sheet1!$A$1:$A$6',
    'fill'   {'color' '#FF9900'}
})

The property sets the transparency of the solid fill color in the integer range 1 — 100. The color must be set for transparency to work, it doesn’t work with an automatic/default color:

chart.set_chartarea({'fill' {'color' 'yellow', 'transparency' 50}})

The format is generally used in conjunction with a format which has the same properties as a format:

chart.add_series({
    'values' '=Sheet1!$A$1:$A$6',
    'fill'   {'color' 'red'},
    'border' {'color' 'black'}
})

Object Positioning with Cell Moving and Sizing

Excel supports three options for “Object Positioning” within a worksheet:

Image, chart and textbox objects in XlsxWriter emulate these options using the parameter:

worksheet.insert_image('B3', 'python.png', {'object_position' 1})

Where has one of the following allowable values:

  1. Move and size with cells.
  2. Move but don’t size with cells.
  3. Don’t move or size with cells.
  4. Same as Option 1 to “move and size with cells” except XlsxWriter applies hidden cells after the object is inserted.

Option 4 appears in Excel as Option 1. However, the worksheet object is sized to take hidden rows or columns into account. This allows the user to hide an image in a cell, possibly as part of an autofilter. For example:

import xlsxwriter

workbook = xlsxwriter.Workbook('image.xlsx')
worksheet = workbook.add_worksheet()

worksheet.insert_image('B2', 'logo.png')
worksheet.insert_image('B9', 'logo.png', {'object_position' 4})

# Hide some rows.
for row in range(1, 13):
    worksheet.set_row(row, None, None, {'hidden' True})

workbook.close()

Chart formatting: Pattern Fill

The pattern fill format is used to specify pattern filled areas of chart objects such as the interior of a column or the background of the chart itself.

The following properties can be set for fill formats in a chart:

pattern   the pattern to be applied (required)
fg_color  the foreground color of the pattern (required)
bg_color  the background color (optional, defaults to white)

For example:

chart.set_plotarea({
    'pattern' {
        'pattern' 'percent_5',
        'fg_color' 'red',
        'bg_color' 'yellow',
    }
})

The following patterns can be applied:

The foreground color, , is a required parameter and can be a Html style string or a limited number of named colors, see .

The background color, , is optional and defaults to white.

format.set_num_format()

(format_string)

Set the number format for a cell.

Parameters: format_string (string) – The cell number format.

This method is used to define the numerical format of a number in Excel. It controls whether a number is displayed as an integer, a floating point number, a date, a currency value or some other user defined format.

The numerical format of a cell can be specified by using a format string or an index to one of Excel’s built-in formats:

cell_format1 = workbook.add_format()
cell_format2 = workbook.add_format()

cell_format1.set_num_format('d mmm yyyy')  # Format string.
cell_format2.set_num_format(0x0F)          # Format index.

Format strings can control any aspect of number formatting allowed by Excel:

cell_format01.set_num_format('0.000')
worksheet.write(1, , 3.1415926, cell_format01)       # -> 3.142

cell_format02.set_num_format('#,##0')
worksheet.write(2, , 1234.56, cell_format02)         # -> 1,235

cell_format03.set_num_format('#,##0.00')
worksheet.write(3, , 1234.56, cell_format03)         # -> 1,234.56

cell_format04.set_num_format('0.00')
worksheet.write(4, , 49.99, cell_format04)           # -> 49.99

cell_format05.set_num_format('mm/dd/yy')
worksheet.write(5, , 36892.521, cell_format05)       # -> 01/01/01

cell_format06.set_num_format('mmm d yyyy')
worksheet.write(6, , 36892.521, cell_format06)       # -> Jan 1 2001

cell_format07.set_num_format('d mmmm yyyy')
worksheet.write(7, , 36892.521, cell_format07)       # -> 1 January 2001

cell_format08.set_num_format('dd/mm/yyyy hh:mm AM/PM')
worksheet.write(8, , 36892.521, cell_format08)       # -> 01/01/2001 12:30 AM

cell_format09.set_num_format('0 "dollar and" .00 "cents"')
worksheet.write(9, , 1.87, cell_format09)            # -> 1 dollar and .87 cents

# Conditional numerical formatting.
cell_format10.set_num_format('General;-General;General')
worksheet.write(10, , 123, cell_format10)  # > 0 Green
worksheet.write(11, , -45, cell_format10)  # < 0 Red
worksheet.write(12, ,   , cell_format10)  # = 0 Default color

# Zip code.
cell_format11.set_num_format('00000')
worksheet.write(13, , 1209, cell_format11)

The number system used for dates is described in .

The color format should have one of the following values:

Black Blue Cyan Green Magenta Red White Yellow

For more information refer to the Microsoft documentation on cell formats.

For information on how to get a number format to show up as one of the number format categories such as Currency, Accounting, Date, Time, Percentage, Fraction, Scientific or Text, see , above.

For backwards compatibility XlsxWriter also supports Excel’s built-in formats which are set via an index number, rather than a string:

cell_format.set_num_format(3)  # Same as #,##0

The format indexes and the equivalent strings are shown in the following table:

Index Format String
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
37
38
39
40
41
42
43
44
45
46
47
48
49

Numeric formats 23 to 36 are not documented by Microsoft and may differ in international versions. The listed date and currency formats may also vary depending on system settings.

Number Formats in different locales

As shown in the previous section the method is used to set the number format for Xlsxwriter formats. A common use case is to set a number format with a “grouping/thousands” separator and a “decimal” point:

import xlsxwriter

workbook = xlsxwriter.Workbook('number_format.xlsx')
worksheet = workbook.add_worksheet()

number_format = workbook.add_format({'num_format' '#,##0.00'})
worksheet.write('A1', 1234.56, number_format)

workbook.close()

In the US locale (and some others) where the number “grouping/thousands” separator is “,” and the “decimal” point is “.” this would be shown in Excel as:

In other locales these values may be reversed or different. They are generally set in the “Region” settings of Windows or Mac OS. Excel handles this by storing the number format in the file format in the US locale, in this case , but renders it according to the regional settings of the host OS. For example, here is the same, unmodified, output file shown above in a German locale:

And here is the same file in a Russian locale. Note the use of a space as the “grouping/thousands” separator:

Special-casing conditional statements

One of the most popular use-cases is if and while statements. Instead of a more general solution, this proposal enhances the syntax of these two statements to add a means of capturing the compared value:

if re.search(pat, text) as match:
    print("Found:", match.group(0))

This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return '' when done), and completely useless in more complicated cases (e.g. where the condition is f(x) < 0 and you want to capture the value of f(x)). It also has no benefit to list comprehensions.

Добавление формул

Формулы, начинающиеся со знака равенства, позволяют устанавливать для ячеек значения, рассчитанные на основе значений в других ячейках.

sheet'B9' = '=SUM(B1:B8)'

Эта инструкция сохранит в качестве значения в ячейке . Тем самым для ячейки задается формула, которая суммирует значения, хранящиеся в ячейках от до .

Формула Excel — это математическое выражение, которое создается для вычисления результата и которое может зависеть от содержимого других ячеек. Формула в ячейке Excel может содержать данные, ссылки на другие ячейки, а также обозначение действий, которые необходимо выполнить.

Использование ссылок на ячейки позволяет пересчитывать результат по формулам, когда происходят изменения содержимого ячеек, включенных в формулы. Формулы Excel начинаются со знака . Скобки могут использоваться для определения порядка математических операции.

Примеры формул Excel: =27+36, =А1+А2-АЗ, =SUM(А1:А5), =MAX(АЗ:А5), =(А1+А2)/АЗ.

Хранящуюся в ячейке формулу можно читать, как любое другое значение. Однако, если нужно получить результат расчета по формуле, а не саму формулу, то при вызове функции ей следует передать именованный аргумент со значением .

data

The parameter can be used to specify the data in the cells of the table:

data = 
    'Apples', 10000, 5000, 8000, 6000],
    'Pears',   2000, 3000, 4000, 5000],
    'Bananas', 6000, 6000, 6500, 6000],
    'Oranges',  500,  300,  200,  700],



worksheet.add_table('B3:F7', {'data' data})

Table data can also be written separately, as an array or individual cells:

# These statements are the same as the single statement above.
worksheet.add_table('B3:F7')
worksheet.write_row('B4', data])
worksheet.write_row('B5', data1])
worksheet.write_row('B6', data2])
worksheet.write_row('B7', data3])

Writing the cell data separately is occasionally required when you need to control the methods used to populate the cells or if you wish to modify individual cell formatting.

Настройка строк и столбцов

С помощью модуля OpenPyXL можно задавать высоту строк и ширину столбцов таблицы, закреплять их на месте (чтобы они всегда были видны на экране), полностью скрывать из виду, объединять ячейки.

Настройка высоты строк и ширины столбцов

Объекты имеют атрибуты и , которые управляют высотой строк и шириной столбцов.

sheet'A1' = 'Высокая строка'
sheet'B2' = 'Широкий столбец'

sheet.row_dimensions1.height = 70
sheet.column_dimensions'B'.width = 30

Атрибуты s и представляют собой значения, подобные словарю. Атрибут содержит объекты , а атрибут содержит объекты . Доступ к объектам в осуществляется с использованием номера строки, а доступ к объектам в — с использованием буквы столбца.

Для указания высоты строки разрешено использовать целые или вещественные числа в диапазоне от 0 до 409. Для указания ширины столбца можно использовать целые или вещественные числа в диапазоне от 0 до 255. Столбцы с нулевой шириной и строки с нулевой высотой невидимы для пользователя.

Объединение ячеек

Ячейки, занимающие прямоугольную область, могут быть объединены в одну ячейку с помощью метода рабочего листа:

sheet.merge_cells('A1:D3')
sheet'A1' = 'Объединены двенадцать ячеек'

sheet.merge_cells('C5:E5')
sheet'C5' = 'Объединены три ячейки'

Чтобы отменить слияние ячеек, надо вызвать метод :

sheet.unmerge_cells('A1:D3')
sheet.unmerge_cells('C5:E5')

Закрепление областей

Если размер таблицы настолько велик, что ее нельзя увидеть целиком, можно заблокировать несколько верхних строк или крайних слева столбцов в их позициях на экране. В этом случае пользователь всегда будет видеть заблокированные заголовки столбцов или строк, даже если он прокручивает таблицу на экране.

У объекта имеется атрибут , значением которого может служить объект или строка с координатами ячеек. Все строки и столбцы, расположенные выше и левее, будут заблокированы.

Значение атрибута freeze_panes Заблокированные строки и столбцы
Строка 1
Столбец A
Столбцы A и B
Строка 1 и столбцы A и B
Закрепленные области отсутствуют

Exception: FileCreateError

exception

This exception is raised if there is a file permission, or IO error, when writing the xlsx file to disk. This can be caused by an non-existent directory or (in Windows) if the file is already open in Excel:

import xlsxwriter

workbook = xlsxwriter.Workbook('exception.xlsx')

worksheet = workbook.add_worksheet()

# The file exception.xlsx is already open in Excel.
workbook.close()

Raises:

xlsxwriter.exceptions.FileCreateError
    Errno 13 Permission denied 'exception.xlsx'

This exception can be caught in a block where you can instruct the user to close the open file before overwriting it:

while True
    try
        workbook.close()
    except xlsxwriter.exceptions.FileCreateError as e
        # For Python 3 use input() instead of raw_input().
        decision = raw_input("Exception caught in workbook.close(): %s\n"
                             "Please close the file if it is open in Excel.\n"
                             "Try to write file again? [Y/n]: " % e)
        if decision != 'n'
            continue

    break

workbook.set_custom_property()

(name, value, property_type)

Set a custom document property.

Parameters:
  • name (string) – The name of the custom property.
  • value – The value of the custom property (various types).
  • property_type (string) – The type of the property. Optional.

The method can be used to set one or more custom document properties not covered by the standard properties in the method above.

For example:

date = datetime.strptime('2016-12-12', '%Y-%m-%d')

workbook.set_custom_property('Checked by',       'Eve')
workbook.set_custom_property('Date completed',   date)
workbook.set_custom_property('Document number',  12345)
workbook.set_custom_property('Reference number', 1.2345)
workbook.set_custom_property('Has review',       True)
workbook.set_custom_property('Signed off',       False)

Date parameters should be objects.

The optional parameter can be used to set an explicit type for the custom property, just like in Excel. The available types are:

text
date
number
bool

However, in almost all cases the type will be inferred correctly from the Python type, like in the example above.

Object scaling due to automatic row height adjustment

The scaling of a image may be affected if is crosses a row that has its default height changed due to a font that is larger than the default font size or that has text wrapping turned on. In these cases Excel will automatically calculate a row height based on the text when it loads the file. Since this row height isn’t available to XlsxWriter when it creates the file the object may appear as if it is sized incorrectly. For example:

import xlsxwriter

workbook = xlsxwriter.Workbook('image.xlsx')
worksheet = workbook.add_worksheet()
wrap_format = workbook.add_format({'text_wrap' True})

worksheet.write('A9', 'Some text that wraps', wrap_format)

worksheet.insert_image('B2', 'logo.png')
worksheet.insert_image('B8', 'logo.png')

workbook.close()

Conditional Formatting Examples

Highlight cells greater than an integer value:

worksheet.conditional_format('A1:F10', {'type'     'cell',
                                        'criteria' 'greater than',
                                        'value'    5,
                                        'format'   format1})

Highlight cells greater than a value in a reference cell:

worksheet.conditional_format('A1:F10', {'type'     'cell',
                                        'criteria' 'greater than',
                                        'value'    'H1',
                                        'format'   format1})

Highlight cells more recent (greater) than a certain date:

date = datetime.datetime.strptime('2011-01-01', "%Y-%m-%d")

worksheet.conditional_format('A1:F10', {'type'     'date',
                                        'criteria' 'greater than',
                                        'value'    date,
                                        'format'   format1})

Highlight cells with a date in the last seven days:

worksheet.conditional_format('A1:F10', {'type'     'time_period',
                                        'criteria' 'last 7 days',
                                        'format'   format1})

Highlight cells with strings starting with the letter :

worksheet.conditional_format('A1:F10', {'type'     'text',
                                        'criteria' 'begins with',
                                        'value'    'b',
                                        'format'   format1})

Highlight cells that are 1 standard deviation above the average for the range:

worksheet.conditional_format('A1:F10', {'type'   'average',
                                        'format' format1})

Highlight duplicate cells in a range:

worksheet.conditional_format('A1:F10', {'type'   'duplicate',
                                        'format' format1})

Highlight unique cells in a range:

worksheet.conditional_format('A1:F10', {'type'   'unique',
                                        'format' format1})

Highlight the top 10 cells:

worksheet.conditional_format('A1:F10', {'type'   'top',
                                        'value'  10,
                                        'format' format1})

Highlight blank cells:

worksheet.conditional_format('A1:F10', {'type'   'blanks',
                                        'format' format1})

Set traffic light icons in 3 cells:

worksheet.conditional_format('B3:D3', {'type' 'icon_set',
                                       'icon_style' '3_traffic_lights'})

See also .

Textbox formatting: Fonts

The following font properties can be set for the entire textbox:

name
size
bold
italic
underline
color

These properties correspond to the equivalent Worksheet cell Format object properties. See the section for more details about Format properties and how to set them.

The font properties are:

  • : Set the font name:

    {'font'  {'name' 'Arial'}}
    
  • : Set the font size:

    {'font'  {'name' 'Arial', 'size' 7}}
    
  • : Set the font bold property:

    {'font'  {'bold' True}}
    
  • : Set the font italic property:

    {'font'  {'italic' True}}
    
  • : Set the font underline property:

    {'font'  {'underline' True}}
    
  • : Set the font color property. Can be a color index, a color name or HTML style RGB color:

    {'font' {'color' 'red' }}
    {'font' {'color' '#92D050'}}
    

Here is an example of Font formatting in a textbox:

worksheet.insert_textbox('B2', 'Some font formatting',
                         {'font' {'bold' True,
                                   'italic' True,
                                   'underline' True,
                                   'name' 'Arial',
                                   'color' 'red',
                                   'size' 14}})

Accessing XlsxWriter from Pandas

In order to apply XlsxWriter features such as Charts, Conditional Formatting and Column Formatting to the Pandas output we need to access the underlying and objects. After that we can treat them as normal XlsxWriter objects.

Continuing on from the above example we do that as follows:

import pandas as pd

# Create a Pandas dataframe from the data.
df = pd.DataFrame({'Data' 10, 20, 30, 20, 15, 30, 45]})

# Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd.ExcelWriter('pandas_simple.xlsx', engine='xlsxwriter')

# Convert the dataframe to an XlsxWriter Excel object.
df.to_excel(writer, sheet_name='Sheet1')

# Get the xlsxwriter objects from the dataframe writer object.
workbook  = writer.book
worksheet = writer.sheets'Sheet1'

This is equivalent to the following code when using XlsxWriter on its own:

workbook  = xlsxwriter.Workbook('filename.xlsx')
worksheet = workbook.add_worksheet()

Выжать максимум

Скриптинг на Python — всегда гарантия хорошей расширяемости приложения. Взять хотя бы редактор Sublime Text: плагины для него есть уже на любой случай жизни. Впрочем, можно не ходить далеко за примером. Один из создателей DataNitro в качестве иллюстрации возможностей своей разработки написал на Excel’е Twitter-клиент (подробнее можно прочитать здесь: bit.ly/Res2mZ). Причем минимальными усилиями благодаря питоновской обертке для Twitter API — tweepy. Конечно, это всего лишь Proof-of-Concept, но зато хорошая иллюстрация того, что интеграция с Python с его огромным количеством готовых модулей на любой случай жизни может быть очень полезной. Кстати, сделать такую фишку не так уж и сложно, о чем пишется в официальной документации Python. А освоить основы языка можно за полчаса. К слову, интерактивная школа по программированию Codeacademy как раз выпустила неплохой курс (http://www.codecademy.com/tracks/python).

Chart series option: Error Bars

Error bars can be added to a chart series to indicate error bounds in the data. The error bars can be vertical (the most common type) or horizontal (for Bar and Scatter charts only).

The following properties can be set for error bars in a chart series:

type
value        (for all types except standard error and custom)
plus_values  (for custom only)
minus_values (for custom only)
direction
end_style
line

The property sets the type of error bars in the series:

chart.add_series({
    'values'       '=Sheet1!$A$1:$A$6',
    'y_error_bars' {'type' 'standard_error'},
})

The available error bars types are available:

fixed
percentage
standard_deviation
standard_error
custom

All error bar types, except for and must also have a value associated with it for the error bounds:

chart.add_series({
    'values' '=Sheet1!$A$1:$A$6',
    'y_error_bars' {
        'type' 'percentage',
        'value' 5,
    },
})

The error bar type must specify and which should either by a type range formula or a list of values:

chart.add_series({
    'categories' '=Sheet1!$A$1:$A$6',
    'values'     '=Sheet1!$B$1:$B$6',
    'y_error_bars' {
        'type'         'custom',
        'plus_values'  '=Sheet1!$C$1:$C$6',
        'minus_values' '=Sheet1!$D$1:$D$6',
    },
})

# or

chart.add_series({
    'categories' '=Sheet1!$A$1:$A$6',
    'values'     '=Sheet1!$B$1:$B$6',
    'y_error_bars' {
        'type'         'custom',
        'plus_values'  1, 1, 1, 1, 1],
        'minus_values' 2, 2, 2, 2, 2],
    },
})

Note, as in Excel the items in the do not need to be negative.

The property sets the direction of the error bars. It should be one of the following:

plus   # Positive direction only.
minus  # Negative direction only.
both   # Plus and minus directions, The default.

The property sets the style of the error bar end cap. The options are 1 (the default) or 0 (for no end cap):

chart.add_series({
    'values' '=Sheet1!$A$1:$A$6',
    'y_error_bars' {
        'type' 'fixed',
        'value' 2,
        'end_style' ,
        'direction' 'minus'
    },
})

Filter data in an autofilter

The defines the cell range that the filter applies to and creates drop-down selectors in the heading row. In order to filter out data it is necessary to apply some criteria to the columns using either the or methods.

The method is used to filter columns in a autofilter range based on simple criteria:

worksheet.filter_column('A', 'x > 2000')
worksheet.filter_column('B', 'x > 2000 and x < 5000')

It isn’t sufficient to just specify the filter condition. You must also hide any rows that don’t match the filter condition. Rows are hidden using the parameter. cannot filter rows automatically since this isn’t part of the file format.

The following is an example of how you might filter a data range to match an autofilter criteria:

Excel + Python

Один из первых найденных проектов —PyXLL позволял быстро писать на питоне функции, которые далее можно было использовать наравне со стандартными функциями Excel’я (вроде СУММА()). Я даже попробовал его в действии. Выглядит это так: ты пишешь Python-скрипт в соответствии с некоторыми правилами, реализуя необходимые действия с получаемыми данными, после чего через специальный аддон для Excel’я импортируешь написанные функции. Не так плохо, но хотелось прямо в коде Python обращаться к нужным ячейкам электронной таблицы. И это позволял другой найденный аддон DataNitro. После его установки в Excel’е появляется новая вкладка, откуда вызывается редактор. Интеграция выполнена очень удобно, поэтому можно, не сильно заморачиваясь, написать что-то вроде:

Далее запускаем сценарий с помощью кнопки на панели тулбара и получаем нужное значение в ячейке A1! Недолго думая, я стал наращивать функционал, который мне был нужен. Пробую импортировать библиотеки для работы с нужными форматами и прочитать данные — все работает. Запрашиваю через API информацию из нескольких онлайн-сервисов — все отлично агрегируется. Пишу простенькую приблуду для рассылки отчетов — все отлично отправляется. В итоге за тридцать минут удается сделать скрипт, который будет экономить два часа — те, что превращались для меня в пытку :). Сперва я даже начал выполнять в Python все расчеты, лишь позже вспомнив, что с этим справится сам Excel. Впрочем, если нужно посчитать что-то серьезное, то ничего не стоит подгрузить математический модуль NumPy и делать любые научные вычисления.

Вкладка аддона, с помощью которой реализуется интеграция DataNitro и Excel Другие статьи в выпуске:

OpenPGP Public Keys

Source and binary executables are signed by the release manager or binary builder using their OpenPGP key. Release files for currently supported releases are signed by the following:

  • Steve Dower (Windows binaries) (key id: FC62 4643 4870 34E5)
  • Łukasz Langa (3.8.x source files and tags) (key id: B269 95E3 1025 0568)
  • Ned Deily (macOS binaries, 3.7.x / 3.6.x source files and tags) (key ids: 2D34 7EA6 AA65 421D, FB99 2128 6F5E 1540, and Apple Developer ID DJ3H93M7VJ)
  • Larry Hastings (3.5.x source files and tags) (key id: 3A5C A953 F73C 700D)
  • Benjamin Peterson (2.7.z source files and tags) (key id: 04C3 67C2 18AD D4FF and A4135B38)

Release files for older releases which have now reached end-of-life may have been signed by one of the following:

  • Anthony Baxter (key id: 0EDD C5F2 6A45 C816)
  • Georg Brandl (key id: 0A5B 1018 3658 0288)
  • Martin v. Löwis (key id: 6AF0 53F0 7D9D C8D2)
  • Ronald Oussoren (key id: C9BE 28DE E6DF 025C)
  • Barry Warsaw (key ids: 126E B563 A74B 06BF, D986 6941 EA5B BD71, and ED9D77D5)

You can import a person’s public keys from a public keyserver network server you trust by running a command like:

or, in many cases, public keys can also be found at keybase.io. On the version-specific download pages, you should see a link to both the downloadable file and a detached signature file. To verify the authenticity of the download, grab both files and then run this command:

Note that you must use the name of the signature file, and you should use the one that’s appropriate to the download you’re verifying.

(These instructions are geared to GnuPG and Unix command-line users.)

columns

The parameter can be used to set properties for columns within the table.

The sub-properties that can be set are:

header
header_format
formula
total_string
total_function
total_value
format

The column data must be specified as a list of dicts. For example to override the default ‘Column n’ style table headers:

worksheet.add_table('B3:F7', {'data' data,
                              'columns' })

See the resulting image above.

If you don’t wish to specify properties for a specific column you pass an empty hash ref and the defaults will be applied:

...
columns, 
    {header, 'Product'},
    {header, 'Quarter 1'},
    {},                     # Defaults to 'Column 3'.
    {header, 'Quarter 3'},
    {header, 'Quarter 4'},

...

Column formulas can by applied using the column property:

formula = '=SUM(Table8:])'

worksheet.add_table('B3:G7', {'data' data,
                              'columns' })

The Excel 2007 style and Excel 2010 style structural references are supported within the formula. However, other Excel 2010 additions to structural references aren’t supported and formulas should conform to Excel 2007 style formulas. See the Microsoft documentation on Using structured references with Excel tables for details.

As stated above the table parameter turns on the “Total” row in the table but it doesn’t populate it with any defaults. Total captions and functions must be specified via the property and the and sub properties:

options = {'data' data,
           'total_row' 1,
           'columns' :])',
                        'total_function' 'sum'
                        },
                       ]}

# Add a table to the worksheet.
worksheet.add_table('B3:G8', options)

The supported totals row functions are:

average
count_nums
count
max
min
std_dev
sum
var

User defined functions or formulas aren’t supported.

It is also possible to set a calculated value for the using the sub property. This is only necessary when creating workbooks for applications that cannot calculate the value of formulas automatically. This is similar to setting the optional property in :

options = {'data' data,
           'total_row' 1,
           'columns' :])',
                        'total_function' 'sum',
                        'total_value' 807}]}

Formatting can also be applied to columns, to the column data using and to the header using :

currency_format = workbook.add_format({'num_format' '$#,##0'})
wrap_format     = workbook.add_format({'text_wrap' 1})

worksheet.add_table('B3:D8', {'data' data,
                              'total_row' 1,
                              'columns' })

Scope of the target

An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. If this scope contains a nonlocal or global declaration for the target, the assignment expression honors that. A lambda (being an explicit, if anonymous, function definition) counts as a scope for this purpose.

There is one special case: an assignment expression occurring in a list, set or dict comprehension or in a generator expression (below collectively referred to as «comprehensions») binds the target in the containing scope, honoring a nonlocal or global declaration for the target in that scope, if one exists. For the purpose of this rule the containing scope of a nested comprehension is the scope that contains the outermost comprehension. A lambda counts as a containing scope.

The motivation for this special case is twofold. First, it allows us to conveniently capture a «witness» for an any() expression, or a counterexample for all(), for example:

if any((comment := line).startswith('#') for line in lines):
    print("First comment:", comment)
else:
    print("There are no comments")

if all((nonblank := line).strip() == '' for line in lines):
    print("All lines are blank")
else:
    print("First non-blank line:", nonblank)

Second, it allows a compact way of updating mutable state from a comprehension, for example:

# Compute partial sums in a list comprehension
total = 0
partial_sums = 
print("Total:", total)

However, an assignment expression target name cannot be the same as a for-target name appearing in any comprehension containing the assignment expression. The latter names are local to the comprehension in which they appear, so it would be contradictory for a contained use of the same name to refer to the scope containing the outermost comprehension instead.

For example, is invalid: the for i part establishes that i is local to the comprehension, but the i := part insists that i is not local to the comprehension. The same reason makes these examples invalid too:

 for j in range(5)] # INVALID
                       # INVALID
                      # INVALID

While it’s technically possible to assign consistent semantics to these cases, it’s difficult to determine whether those semantics actually make sense in the absence of real use cases. Accordingly, the reference implementation will ensure that such cases raise SyntaxError, rather than executing with implementation defined behaviour.

This restriction applies even if the assignment expression is never executed:

     # INVALID
  # INVALID

For the comprehension body (the part before the first «for» keyword) and the filter expression (the part after «if» and before any nested «for»), this restriction applies solely to target names that are also used as iteration variables in the comprehension. Lambda expressions appearing in these positions introduce a new explicit function scope, and hence may use assignment expressions with no additional restrictions.

Due to design constraints in the reference implementation (the symbol table analyser cannot easily detect when names are re-used between the leftmost comprehension iterable expression and the rest of the comprehension), named expressions are disallowed entirely as part of comprehension iterable expressions (the part after each «in», and before any subsequent «if» or «for» keyword):

                    # INVALID
  # INVALID
]       # INVALID
        # INVALID

A further exception applies when an assignment expression occurs in a comprehension whose containing scope is a class scope. If the rules above were to result in the target being assigned in that class’s scope, the assignment expression is expressly invalid. This case also raises SyntaxError:

class Example:
      # INVALID

(The reason for the latter exception is the implicit function scope created for comprehensions — there is currently no runtime mechanism for a function to refer to a variable in the containing class scope, and we do not want to add such a mechanism. If this issue ever gets resolved this special case may be removed from the specification of assignment expressions. Note that the problem already exists for using a variable defined in the class scope from a comprehension.)


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