Đệm ô excel

Gần đây Taimienphi có nhận được câu hỏi từ phía các bạn độc giả rằng làm cách nào để xem xét độ rộng, căn lề văn bản trong ô, bảng, ô, bảng trong Word. Bài viết dưới đây sẽ giải đáp thắc mắc này của quý độc giả

Việc kẻ bảng, xét độ rộng, căn lề văn bản trong ô, bảng, ô, bảng trong word sẽ làm cho văn bản của chúng ta trở nên trực quan, đẹp mắt, dễ quan sát hơn đúng không nào. Tuy nhiên không phải ai cũng biết chức năng chỉnh sửa căn tự động mà thông thường hay căn chỉnh bằng tay dẫn đến các ô trong bảng sẽ không được đồng đều. hôm nay, Taimienphi. vn sẽ hướng dẫn các bạn cách xác định độ rộng, căn lề văn bản trong ô, bảng, ô, bảng từ một cách chi tiết. Thân mời các bạn tham khảo

CÁCH XÉT ĐỘ RỘNG, CĂN LỀ VĂN BẢN TRONG Ô, BẢNG, Ô, BẢNG TỪ

Chúng ta có một ví dụ như bên dưới, chúng ta sẽ tiến hành căn cứ để điều chỉnh độ rộng bằng cách sử dụng chức năng của Thuộc tính bảng

Xét độ rộng

Bước 1. Các bạn tô đen các ô cần căn chỉnh độ rộng

Bước 2. Tiếp theo chuột phải vào và chọn Table Properties

Bước 3. You select to the tab Column, in the Preferred width you can to edit the length at here, đơn vị tính có thể chỉnh sửa trong phần Measure in. Sau đó nhấn Ok để hoàn thành

Căn lề văn bản trong ô

Bước 1. Các bạn chọn ô muốn căn lề văn bản rồi chuột phải và chọn Thuộc tính bảng

Bước 2. The you select to the Cell - Options

Bước 3. Trong phần Cell Options các bạn có thể căn chỉnh lề, trên, dưới, trái, phải

Việc căn giữa ô trong word cũng rất đơn giản, tuy nhiên, nếu bạn là người ít kinh nghiệm thì khi thực hiện thao tác căn giữa ô trong word sẽ gặp một chút khó khăn, đừng lo, trên thuthuat. taimienphi. vn đã có đầy đủ các bước hướng dẫn việc căn giữa ô trong từ áp dụng với mọi phiên bản từ trước đến nay để các bạn áp dụng và làm theo

Mô hình. Cây quyết địnhRegressionRandomPredicted. Khối uKhông phải khối uKhối uKhông phải khối uKhối uKhông phải khối uNhãn thực tế. Khối u (Dương tính)38. 0000002. 00000018. 00000022. 00000021nanKhông phải khối u (Âm tính)19. 000000439. 0000006. 000000452. 000000226232. 000000

Đầu ra ở trên trông rất giống với biểu diễn HTML DataFrame tiêu chuẩn. Nhưng HTML ở đây đã gắn sẵn một số lớp CSS vào từng ô, ngay cả khi chúng ta chưa tạo bất kỳ kiểu nào. Chúng ta có thể xem những thứ này bằng cách gọi . to_html() , trả về HTML thô dưới dạng chuỗi, hữu ích cho việc xử lý thêm hoặc thêm vào tệp - đọc tiếp trong. Dưới đây chúng tôi sẽ chỉ ra cách chúng tôi có thể sử dụng những thứ này để định dạng DataFrame để giao tiếp nhiều hơn. Ví dụ làm thế nào chúng ta có thể xây dựng

[2]:

06.

[4]:

[4]:

Ma trận nhầm lẫn cho nhiều mô hình dự đoán ung thư. Mô hình. Cây Quyết ĐịnhHồi QuyDự Đoán. Khối uKhông phải khối uKhối uKhông phải khối uNhãn thực tế. Khối u (Dương tính)3821822Không phải khối u (Âm tính)194396452

Định dạng hiển thị

Giá trị định dạng

Trước khi thêm kiểu, bạn nên chỉ ra rằng Trình tạo kiểu có thể phân biệt giá trị hiển thị với giá trị thực, ở cả giá trị dữ liệu và chỉ mục hoặc . Để kiểm soát giá trị hiển thị, văn bản được in trong mỗi ô dưới dạng chuỗi và chúng ta có thể sử dụng . định dạng() và . các phương thức format_index() để thao tác điều này theo a hoặc một khả năng gọi được nhận một giá trị và trả về một chuỗi. Có thể xác định điều này cho toàn bộ bảng hoặc chỉ mục hoặc cho các cột riêng lẻ hoặc các cấp độ MultiIndex.

Additionally, the format function has a precision argument to specifically help formatting floats, as well as decimal and thousands separators to support other locales, an na_rep argument to display missing data, and an escape argument to help displaying safe-HTML or safe-LaTeX. The default formatter is configured to adopt pandas’

[2]:

07 option, controllable using

[2]:

[5]:

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

[5]:

Mô hình. Cây quyết địnhRegressionRandomPredicted. TumourNon-TumourTumourNon-TumourTumourNon-TumourActual Label. Tumour (Positive)38. 00218$ -22 000 000. 021MISSINGNon-Tumour (Negative)19. 004396$ -452 000 000. 0226232

Using Styler to manipulate the display is a useful feature because maintaining the indexing and datavalues for other purposes gives greater control. You do not have to overwrite your DataFrame to display it how you like. Here is an example of using the formatting functions whilst still relying on the underlying data for indexing and calculations

[6]:

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

[6]:

TokyoBeijing2021-01-013. 9200964. 8130012021-01-021. 6910510. 5484212021-01-032. 2681930. 2964682021-01-044. 5373443. 6914102021-01-052. 6360430. 1083262021-01-064. 6141570. 6888762021-01-071. 0912042. 1688092021-01-084. 1085514. 6729752021-01-093. 3126122. 2303772021-01-103. 0145121. 240717

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Điều kiện thời tiết TokyoBắc KinhThứ HaiMưa toMưa toThứ BaMưaKhôThứ tưMưa nặng hạtKhôThứ nămKhôMưaThứ sáuMưa toMưa to

Hiding Data

The index and column headers can be completely hidden, as well subselecting rows or columns that one wishes to exclude. Both these options are performed using the same methods

The index can be hidden from rendering by calling . hide() without any arguments, which might be useful if your index is integer based. Similarly column headers can be hidden by calling . hide(axis=“columns”) without any further arguments.

Specific rows or columns can be hidden from rendering by calling the same . hide() method and passing in a row/column label, a list-like or a slice of row/column labels to for the

[2]:

09 argument.

Hiding does not change the integer arrangement of CSS classes, e. g. hiding the first two columns of a DataFrame means the column class indexing will still start at

[2]:

10, since

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11 and

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12 are simply ignored

We can update our

[2]:

13 object from before to hide some data and format the values

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Người mẫu. Cây Quyết ĐịnhHồi QuyDự Đoán. TumourNon-TumourTumourNon-TumourActual Label. Tumour (Positive)3821822Non-Tumour (Negative)194396452

Methods to Add Styles

There are 3 primary methods of adding custom CSS styles to Styler .

Using . set_table_styles() to control broader areas of the table with specified internal CSS. Although table styles allow the flexibility to add CSS selectors and properties controlling all individual parts of the table, they are unwieldy for individual cell specifications. Also, note that table styles cannot be exported to Excel.
Using . set_td_classes() to directly link either external CSS classes to your data cells or link the internal CSS classes created by . set_table_styles() . See . These cannot be used on column header rows or indexes, and also won’t export to Excel.
Using the . apply() and . applymap() functions to add direct internal CSS to specific data cells. See . As of v1. 4. 0 there are also methods that work directly on column header rows or indexes; . apply_index() and . applymap_index() . Note that only these methods add styles that will export to Excel. These methods work in a similar way to DataFrame. apply() and DataFrame. applymap() .

Table Styles

Table styles are flexible enough to control all individual parts of the table, including column headers and indexes. However, they can be unwieldy to type for individual data cells or for any kind of conditional formatting, so we recommend that table styles are used for broad styling, such as entire rows or columns at a time

Table styles are also used to control features which can apply to the whole table at once such as creating a generic hover functionality. Bộ chọn giả

[2]:

14, cũng như các bộ chọn giả khác, chỉ có thể được sử dụng theo cách này

To replicate the normal format of CSS selectors and properties (attribute value pairs), e. g

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

the necessary format to pass styles to . set_table_styles() is as a list of dicts, each with a CSS-selector tag and CSS-properties. Properties can either be a list of 2-tuples, or a regular CSS-string, for example.

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

[2]:

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Người mẫu. Cây Quyết ĐịnhHồi QuyDự Đoán. TumourNon-TumourTumourNon-TumourActual Label. Tumour (Positive)3821822Non-Tumour (Negative)194396452

Next we just add a couple more styling artifacts targeting specific parts of the table. Be careful here, since we are chaining methods we need to explicitly instruct the method not to

[2]:

15 the existing styles

[2]:

[2]:

[2]:

Người mẫu. Cây Quyết ĐịnhHồi QuyDự Đoán. TumourNon-TumourTumourNon-TumourActual Label. Tumour (Positive)3821822Non-Tumour (Negative)194396452

As a convenience method (since version 1. 2. 0) we can also pass a dict to . set_table_styles() which contains row or column keys. Behind the scenes Styler just indexes the keys and adds relevant

[2]:

16 or

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17 classes as necessary to the given CSS selectors.

[2]:

[2]:

[2]:

Người mẫu. Cây Quyết ĐịnhHồi QuyDự Đoán. TumourNon-TumourTumourNon-TumourActual Label. Tumour (Positive)3821822Non-Tumour (Negative)194396452

Setting Classes and Linking to External CSS

If you have designed a website then it is likely you will already have an external CSS file that controls the styling of table and cell objects within it. You may want to use these native files rather than duplicate all the CSS in python (and duplicate any maintenance work)

Table Attributes

It is very easy to add a

[2]:

18 to the main

[2]:

03 using . set_table_attributes() . This method can also attach inline styles - read more in .

[2]:

[2]:

[4]:

Data Cell CSS Classes

New in version 1. 2. 0

The . set_td_classes() method accepts a DataFrame with matching indices and columns to the underlying Styler ’s DataFrame. That DataFrame will contain strings as css-classes to add to individual data cells. the

[2]:

20 elements of the

[2]:

03. Rather than use external CSS we will create our classes internally and add them to table style. We will save adding the borders until the .

[4]:

[4]:

[4]:

Người mẫu. Cây Quyết ĐịnhHồi QuyDự Đoán. TumourNon-TumourTumourNon-TumourActual Label. Tumour (Positive)3821822Non-Tumour (Negative)194396452

Styler Functions

Acting on Data

We use the following methods to pass your style functions. Both of those methods take a function (and some other keyword arguments) and apply it to the DataFrame in a certain way, rendering CSS styles

. applymap() (elementwise). accepts a function that takes a single value and returns a string with the CSS attribute-value pair.
. apply() (column-/row-/table-wise). chấp nhận một hàm lấy Sê-ri hoặc DataFrame và trả về Sê-ri, DataFrame hoặc mảng có nhiều mảng có hình dạng giống hệt nhau trong đó mỗi phần tử là một chuỗi có cặp giá trị-thuộc tính CSS. This method passes each column or row of your DataFrame one-at-a-time or the entire table at once, depending on the
```
[2]:
```
22 keyword argument. For columnwise use
```
[2]:
```
23, rowwise use
```
[2]:
```
24, and for the entire table at once use
```
[2]:
```
25.

This method is powerful for applying multiple, complex logic to data cells. We create a new DataFrame to demonstrate this

[4]:

[4]:

[4]:

ABCD01. 7640520. 4001570. 9787382. 24089311. 867558-0. 9772780. 950088-0. 1513572-0. 1032190. 4105990. 1440441. 45427430. 7610380. 1216750. 4438630. 33367441. 494079-0. 2051580. 313068-0. 8540965-2. 5529900. 6536190. 864436-0. 74216562. 269755-1. 4543660. 045759-0. 18718471. 5327791. 4693590. 1549470. 3781638-0. 887786-1. 980796-0. 3479120. 15634991. 2302911. 202380-0. 387327-0. 302303

For example we can build a function that colors text if it is negative, and chain this with a function that partially fades cells of negligible value. Since this looks at each element in turn we use

[2]:

[4]:

[4]:

[4]:

Chúng ta cũng có thể xây dựng một hàm làm nổi bật giá trị tối đa trên các hàng, cột và DataFrame cùng một lúc. In this case we use

[2]:

27. Below we highlight the maximum in a column

We can use the same function across the different axes, highlighting here the DataFrame maximum in purple, and row maximums in pink

This last example shows how some styles have been overwritten by others. In general the most recent style applied is active but you can read more in the . You can also apply these styles to more granular parts of the DataFrame - read more in section on

It is possible to replicate some of this functionality using just classes but it can be more cumbersome. See

Debugging Tip. If you’re having trouble writing your style function, try just passing it into

[2]:

28. Internally,

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29 uses

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28 so the result should be the same, and with

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28 you will be able to inspect the CSS string output of your intended function in each cell

Acting on the Index and Column Headers

Similar application is achieved for headers by using

. applymap_index() (elementwise). accepts a function that takes a single value and returns a string with the CSS attribute-value pair.
. apply_index() (level-wise). accepts a function that takes a Series and returns a Series, or numpy array with an identical shape where each element is a string with a CSS attribute-value pair. This method passes each level of your Index one-at-a-time. To style the index use
```
[2]:
```
23 and to style the column headers use
```
[2]:
```
24.

You can select a

[2]:

34 of a

[2]:

35 but currently no similar

[2]:

09 application is available for these methods

Tooltips and Captions

Table captions can be added with the . set_caption() method. You can use table styles to control the CSS relevant to the caption.

[4]:

Adding tooltips (since version 1. 3. 0) can be done using the . set_tooltips() method in the same way you can add CSS classes to data cells by providing a string based DataFrame with intersecting indices and columns. You don’t have to specify a

[2]:

37 name or any css

[2]:

38 for the tooltips, since there are standard defaults, but the option is there if you want more visual control.

[4]:

[4]:

[4]:

The only thing left to do for our table is to add the highlighting borders to draw the audience attention to the tooltips. We will create internal CSS classes as before using table styles. Setting classes always overwrites so we need to make sure we add the previous classes

[4]:

[4]:

[4]:

Finer Control with Slicing

The examples we have shown so far for the

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29 and

[2]:

40 functions have not demonstrated the use of the

[2]:

09 argument. This is a useful argument which permits a lot of flexibility. it allows you to apply styles to specific rows or columns, without having to code that logic into your

[2]:

42 function

The value passed to

[2]:

09 behaves similar to slicing a DataFrame;

A scalar is treated as a column label
Một danh sách (hoặc mảng Sê-ri hoặc NumPy) được coi là nhiều nhãn cột
A tuple is treated as
```
[2]:
```
44

Consider using

[2]:

45 to construct the tuple for the last one. We will create a MultiIndexed DataFrame to demonstrate the functionality

[4]:

[4]:

[4]:

c1c2c3c4Ar1-1. 048553-1. 420018-1. 7062701. 950775r2-0. 509652-0. 438074-1. 2527950. 777490Br1-1. 613898-0. 212740-0. 8954670. 386902r2-0. 510805-1. 180632-0. 0281820. 428332

We will use subset to highlight the maximum in the third and fourth columns with red text. We will highlight the subset sliced region in yellow

[5]:

[5]:

[5]:

c1c2c3c4Ar1-1. 048553-1. 420018-1. 7062701. 950775r2-0. 509652-0. 438074-1. 2527950. 777490Br1-1. 613898-0. 212740-0. 8954670. 386902r2-0. 510805-1. 180632-0. 0281820. 428332

If combined with the

[2]:

46 as suggested then it can index across both dimensions with greater flexibility

[5]:

[5]:

[5]:

c1c2c3c4Ar1-1. 048553-1. 420018-1. 7062701. 950775r2-0. 509652-0. 438074-1. 2527950. 777490Br1-1. 613898-0. 212740-0. 8954670. 386902r2-0. 510805-1. 180632-0. 0281820. 428332

This also provides the flexibility to sub select rows when used with the

[2]:

[5]:

[5]:

[5]:

c1c2c3c4Ar1-1. 048553-1. 420018-1. 7062701. 950775r2-0. 509652-0. 438074-1. 2527950. 777490Br1-1. 613898-0. 212740-0. 8954670. 386902r2-0. 510805-1. 180632-0. 0281820. 428332

There is also scope to provide conditional filtering

Suppose we want to highlight the maximum across columns 2 and 4 only in the case that the sum of columns 1 and 3 is less than -2. 0 (essentially excluding rows

[2]:

48)

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

c1c2c3c4Ar1-1. 048553-1. 420018-1. 7062701. 950775r2-0. 509652-0. 438074-1. 2527950. 777490Br1-1. 613898-0. 212740-0. 8954670. 386902r2-0. 510805-1. 180632-0. 0281820. 428332

Only label-based slicing is supported right now, not positional, and not callables

If your style function uses a

[2]:

09 or

[2]:

22 keyword argument, consider wrapping your function in a

[2]:

51, partialing out that keyword

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

Optimization

Generally, for smaller tables and most cases, the rendered HTML does not need to be optimized, and we don’t really recommend it. There are two cases where it is worth considering

If you are rendering and styling a very large HTML table, certain browsers have performance issues
If you are using
```
[2]:
```
13 to dynamically create part of online user interfaces and want to improve network performance

Here we recommend the following steps to implement

1. Remove UUID and cell_ids

Ignore the

[2]:

53 and set

[2]:

54 to

[2]:

55. This will prevent unnecessary HTML

This is sub-optimal

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

This is better

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

2. Use table styles

Use table styles where possible (e. g. for all cells or rows or columns at a time) since the CSS is nearly always more efficient than other formats

This is sub-optimal

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

df.style.format(precision=0, na_rep='MISSING', thousands=" ",
                formatter={('Decision Tree', 'Tumour'): "{:.2f}",
                           ('Regression', 'Non-Tumour'): lambda x: "$ {:,.1f}".format(x*-1e6)
                          })

01012134

This is better

[5]:

[5]:

[5]:

01012134

3. Set classes instead of using Styler functions

For large DataFrames where the same style is applied to many cells it can be more efficient to declare the styles as classes and then apply those classes to data cells, rather than directly applying styles to cells. It is, however, probably still easier to use the Styler function api when you are not concerned about optimization

This is sub-optimal

[5]:

[5]:

[5]:

This is better

[5]:

[5]:

[5]:

4. Don’t use tooltips

Tooltips require

[2]:

54 to work and they generate extra HTML elements for every data cell

5. If every byte counts use string replacement

You can remove unnecessary HTML, or shorten the default class names by replacing the default css dict. You can read a little more about CSS

[6]:

[6]:

[6]:

[6]:

[6]:

[6]:

01012134

Builtin Styles

Some styling functions are common enough that we’ve “built them in” to the

[2]:

13, so you don’t have to write them and apply them yourself. The current list of such functions is

. highlight_null . for use with identifying missing data.
. highlight_min và . highlight_max . for use with identifying extremeties in data.
. highlight_between and . highlight_quantile . for use with identifying classes within data.
. background_gradient . a flexible method for highlighting cells based on their, or other, values on a numeric scale.
. text_gradient . similar method for highlighting text based on their, or other, values on a numeric scale.
. bar . to display mini-charts within cell backgrounds.

The individual documentation on each function often gives more examples of their arguments

Highlight Null

[6]:

[6]:

[6]:

ABCD01. 7640520. 400157nan2. 24089311. 867558-0. 9772780. 950088-0. 1513572-0. 1032190. 4105990. 1440441. 45427430. 7610380. 1216750. 4438630. 33367441. 494079-0. 2051580. 313068nan

Highlight Min or Max

[6]:

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

[6]:

ABCD01. 7640520. 400157nan2. 24089311. 867558-0. 9772780. 950088-0. 1513572-0. 1032190. 4105990. 1440441. 45427430. 7610380. 1216750. 4438630. 33367441. 494079-0. 2051580. 313068nan

Highlight Between

This method accepts ranges as float, or NumPy arrays or Series provided the indexes match

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

ABCD01. 7640520. 400157nan2. 24089311. 867558-0. 9772780. 950088-0. 1513572-0. 1032190. 4105990. 1440441. 45427430. 7610380. 1216750. 4438630. 33367441. 494079-0. 2051580. 313068nan

Highlight Quantile

Hữu ích để phát hiện các giá trị phần trăm cao nhất hoặc thấp nhất

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

ABCD01. 7640520. 400157nan2. 24089311. 867558-0. 9772780. 950088-0. 1513572-0. 1032190. 4105990. 1440441. 45427430. 7610380. 1216750. 4438630. 33367441. 494079-0. 2051580. 313068nan

Background Gradient and Text Gradient

You can create “heatmaps” with the

[2]:

58 and

[2]:

59 methods. These require matplotlib, and we’ll use Seaborn to get a nice colormap

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

ABCD01. 7640520. 400157nan2. 24089311. 867558-0. 9772780. 950088-0. 1513572-0. 1032190. 4105990. 1440441. 45427430. 7610380. 1216750. 4438630. 33367441. 494079-0. 2051580. 313068nan5-2. 5529900. 6536190. 864436-0. 74216562. 269755-1. 4543660. 045759-0. 18718471. 5327791. 4693590. 1549470. 3781638-0. 887786-1. 980796-0. 3479120. 15634991. 2302911. 202380-0. 387327-0. 302303

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

. background_gradient and . text_gradient have a number of keyword arguments to customise the gradients and colors. See the documentation.

Set properties

Use

[2]:

60 when the style doesn’t actually depend on the values. This is just a simple wrapper for

[2]:

61 where the function returns the same properties for all cells

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

ABCD01. 7640520. 400157nan2. 24089311. 867558-0. 9772780. 950088-0. 1513572-0. 1032190. 4105990. 1440441. 45427430. 7610380. 1216750. 4438630. 33367441. 494079-0. 2051580. 313068nan

Bar charts

You can include “bar charts” in your DataFrame

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Additional keyword arguments give more control on centering and positioning, and you can pass a list of

[2]:

62 to highlight lower and higher values or a matplotlib colormap

To showcase an example here’s how you can change the above with the new

[2]:

63 option, combined with setting

[2]:

64 and

[2]:

65 limits, the

[2]:

66 of the figure, and underlying css

[2]:

38 of cells, leaving space to display the text and the bars. We also use

[2]:

59 to color the text the same as the bars using a matplotlib colormap (although in this case the visualization is probably better without this additional effect)

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

ABCD01. 7640. 4002. 24111. 868-0. 9770. 950-0. 1512-0. 1030. 4110. 1441. 45430. 7610. 1220. 4440. 33441. 494-0. 2050. 3135-2. 5530. 6540. 864-0. 74262. 270-1. 4540. 046-0. 18771. 5331. 4690. 1550. 3788-0. 888-1. 981-0. 3480. 15691. 2301. 202-0. 387-0. 302

The following example aims to give a highlight of the behavior of the new align options

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

AlignAll NegativeBoth Neg and PosAll PositiveLarge Positiveleft-100-60-30-20-10-5090102050100100103101102right-100-60-30-20-10-5090102050100100103101102zero-100-60-30-20-10-5090102050100100103101102mid-100-60-30-20-10-5090102050100100103101102mean-100-60-30-20-10-509010205010010010310110299-100-60-30-20-10-5090102050100100103101102

Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with

[2]:

69, and import it on the second DataFrame with

[2]:

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

ABCD1. 7640520. 400157nan2. 2408931. 867558-0. 9772780. 950088-0. 151357-0. 1032190. 4105990. 1440441. 4542740. 7610380. 1216750. 4438630. 3336741. 494079-0. 2051580. 313068nan-2. 5529900. 6536190. 864436-0. 7421652. 269755-1. 4543660. 045759-0. 1871841. 5327791. 4693590. 1549470. 378163-0. 887786-1. 980796-0. 3479120. 1563491. 2302911. 202380-0. 387327-0. 302303

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

c1c2c3c4-1. 048553-1. 420018-1. 7062701. 950775-0. 509652-0. 438074-1. 2527950. 777490-1. 613898-0. 212740-0. 8954670. 386902-0. 510805-1. 180632-0. 0281820. 428332

Notice that you’re able to share the styles even though they’re data aware. The styles are re-evaluated on the new DataFrame they’ve been

[2]:

71d upon

Limitations

DataFrame only (use
```
[2]:
```
72)
The index and columns do not need to be unique, but certain styling functions can only work with unique indexes
No large repr, and construction performance isn’t great; although we have some
You can only apply styles, you can’t insert new HTML entities, except via subclassing

Other Fun and Useful Stuff

Here are a few interesting examples

Widgets

[2]:

13 interacts pretty well with widgets. If you’re viewing this online instead of running the notebook yourself, you’re missing out on interactively adjusting the color palette

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Magnify

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Hover to magnify 012345678910111213141516171819202122232400. 231. 03-0. 84-0. 59-0. 96-0. 22-0. 621. 84-2. 050. 87-0. 92-0. 232. 15-1. 330. 08-1. 251. 20-1. 051. 06-0. 422. 29-2. 592. 820. 68-1. 581-1. 751. 56-1. 13-1. 101. 030. 00-2. 463. 45-1. 661. 27-0. 52-0. 021. 52-1. 09-1. 86-1. 13-0. 68-0. 810. 35-0. 061. 79-2. 822. 260. 780. 442-0. 653. 22-1. 760. 522. 20-0. 37-3. 003. 73-1. 872. 460. 21-0. 24-0. 10-0. 78-3. 02-0. 82-0. 21-0. 230. 86-0. 681. 45-4. 893. 031. 910. 613-1. 623. 71-2. 310. 434. 17-0. 43-3. 864. 16-2. 151. 080. 120. 60-0. 890. 27-3. 67-2. 71-0. 31-1. 591. 35-1. 830. 91-5. 802. 812. 110. 284-3. 354. 48-1. 86-1. 705. 19-1. 02-3. 814. 72-0. 721. 08-0. 180. 83-0. 22-1. 08-4. 27-2. 88-0. 97-1. 781. 53-1. 802. 21-6. 343. 342. 492. 095-0. 844. 23-1. 65-2. 005. 34-0. 99-4. 133. 94-1. 06-0. 941. 240. 09-1. 78-0. 11-4. 45-0. 85-2. 06-1. 350. 80-1. 631. 54-6. 512. 802. 143. 776-0. 745. 35-2. 11-1. 134. 20-1. 85-3. 203. 76-3. 22-1. 230. 340. 57-1. 820. 54-4. 43-1. 83-4. 03-2. 62-0. 20-4. 681. 93-8. 463. 342. 525. 817-0. 444. 69-2. 30-0. 215. 93-2. 63-1. 835. 46-4. 50-3. 16-1. 730. 180. 110. 04-5. 99-0. 45-6. 20-3. 890. 71-3. 950. 67-7. 262. 973. 396. 6680. 925. 80-3. 33-0. 655. 99-3. 19-1. 835. 63-3. 53-1. 30-1. 610. 82-2. 45-0. 40-6. 06-0. 52-6. 60-3. 48-0. 04-4. 600. 51-5. 853. 232. 405. 0890. 385. 54-4. 49-0. 807. 05-2. 64-0. 445. 35-1. 96-0. 33-0. 800. 26-3. 37-0. 82-6. 05-2. 61-8. 45-4. 450. 41-4. 711. 89-6. 932. 143. 005. 16102. 065. 84-3. 90-0. 987. 78-2. 49-0. 595. 59-2. 22-0. 71-0. 461. 80-2. 790. 48-5. 97-3. 44-7. 77-5. 49-0. 70-4. 61-0. 52-7. 721. 545. 025. 81111. 864. 47-2. 17-1. 385. 90-0. 490. 025. 78-1. 04-0. 600. 491. 96-1. 471. 88-5. 92-4. 55-8. 15-3. 42-2. 24-4. 33-1. 17-7. 901. 365. 315. 83123. 194. 22-3. 06-2. 275. 93-2. 640. 336. 72-2. 84-0. 201. 892. 63-1. 530. 75-5. 27-4. 53-7. 57-2. 85-2. 17-4. 78-1. 13-8. 992. 116. 425. 60132. 314. 45-3. 87-2. 056. 76-3. 25-2. 177. 99-2. 56-0. 800. 712. 33-0. 16-0. 46-5. 10-3. 79-7. 58-4. 000. 33-3. 67-1. 05-8. 712. 475. 876. 71143. 784. 33-3. 88-1. 586. 22-3. 23-1. 465. 57-2. 93-0. 33-0. 971. 723. 610. 29-4. 21-4. 10-6. 68-4. 50-2. 19-2. 43-1. 64-9. 363. 366. 117. 53155. 645. 31-3. 98-2. 265. 91-3. 30-1. 035. 68-3. 06-0. 33-1. 162. 194. 201. 01-3. 22-4. 31-5. 74-4. 44-2. 30-1. 36-1. 20-11. 272. 596. 695. 91164. 084. 34-2. 44-3. 306. 04-2. 52-0. 475. 28-4. 841. 580. 230. 105. 791. 80-3. 13-3. 85-5. 53-2. 97-2. 13-1. 15-0. 56-13. 132. 076. 164. 94175. 644. 57-3. 53-3. 766. 58-2. 58-0. 756. 58-4. 783. 63-0. 290. 565. 762. 05-2. 27-2. 31-4. 95-3. 16-3. 06-2. 430. 84-12. 573. 567. 364. 70185. 995. 82-2. 85-4. 157. 12-3. 32-1. 217. 93-4. 851. 44-0. 630. 357. 470. 87-1. 52-2. 09-4. 23-2. 55-2. 46-2. 891. 90-9. 743. 437. 074. 39194. 036. 23-4. 10-4. 117. 19-4. 10-1. 526. 53-5. 21-0. 240. 011. 166. 43-1. 97-2. 64-1. 66-5. 20-3. 25-2. 87-1. 651. 64-10. 662. 837. 483. 94

Sticky Headers

If you display a large matrix or DataFrame in a notebook, but you want to always see the column and row headers you can use the . set_sticky method which manipulates the table styles CSS.

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

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1712920. 323510-0. 545163-0. 668909-0. 1500310. 521620-0. 4289800. 6764630. 369081-0. 7248320. 7935421. 2374220. 4012752. 1415230. 2490120. 486755-0. 1632740. 592222-0. 292600-0. 5471680. 619104-0. 0136050. 7767340. 1314241. 189480-0. 666317-0. 9390361. 1055150. 6214521. 586605-0. 7609701. 6496460. 2831991. 275812-0. 4520120. 301361-0. 976951-0. 268106-0. 079255-1. 2583322. 216658-1. 175988-0. 863497-1. 653022-0. 5615140. 4507530. 4172000. 094676-2. 2310541. 316862-0. 4774410. 646654-0. 2002521. 074354-0. 0581760. 1209900. 222522-0. 1795070. 421655-0. 914341-0. 2341780. 741524130. 9327141. 423761-1. 2808350. 347882-0. 863171-0. 8525801. 0449332. 0945360. 8062060. 416201-1. 1095030. 145302-0. 9968710. 325456-0. 6050811. 1753261. 6450540. 293432-2. 7668221. 0328490. 079115-1. 4141321. 4633762. 3354860. 411951-0. 0485430. 159284-0. 651554-1. 0931281. 568390-0. 077807-2. 390779-0. 842346-0. 229675-0. 999072-1. 367219-0. 792042-1. 8785751. 4514521. 266250-0. 7343150. 2661520. 735523-0. 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It is also possible to stick MultiIndexes and even only specific levels

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

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6001421. 929561-2. 346771-0. 669700-1. 1652580. 8147880. 444449-0. 5767580. 3530910. 4088930. 091391-2. 2943890. 485506-0. 081304-0. 716272-1. 6480101. 005361-1. 4896030. 3630980. 758602-1. 373847-0. 9720571. 9885370. 3198291. 1690600. 1465851. 0303881. 1659841. 3695630. 730984-1. 383696-0. 515189-0. 808927-1. 174651-1. 631502-1. 123414-0. 478155-1. 5830671. 4190741. 6687771. 5675170. 222103-0. 336040-1. 3520640. 251032-0. 4016950. 268413-0. 012299-0. 9189532. 921208-0. 5815880. 6728481. 2511361. 3822631. 4298971. 290990-1. 272673-0. 308611-0. 422988-0. 6756420. 8744411. 305736-0. 262585-1. 099395-0. 667101-0. 646737-0. 556338-0. 1965910. 119306-0. 266455-0. 5242672. 6509510. 097318-0. 9746970. 1899641. 141155-0. 0644341. 104971-1. 508908-0. 0318330. 803919-0. 6592210. 9391450. 214041-0. 5318050. 9560600. 2493280. 637903-0. 5101581. 850287-0. 3484072. 001376-0. 389643-0. 024786-0. 4709730. 8693390. 1706670. 5980621. 2172621. 2740131-0. 389981-0. 752441-0. 7348713. 517318-1. 173559-0. 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198418-1. 1493180. 586321-1. 171937-0. 6077312. 7537471. 479287-1. 136365-0. 0204850. 320444-1. 9557550. 660402-1. 5453710. 200519-0. 0172631. 6346860. 5992460. 4629890. 0237210. 2255460. 170972-0. 027496-0. 061233-0. 566411-0. 6695670. 6016180. 503656-0. 678253-2. 907108-1. 7171230. 3976311. 3001080. 215821-0. 593075-0. 225944-0. 9460571. 0003080. 3931601. 342074-0. 370687-0. 166413-0. 419814-0. 2559311. 7894780. 2823780. 742260-0. 0504981. 4153090. 838166-1. 400292-0. 937976-1. 4991480. 8018590. 2248240. 2835720. 643703-1. 1984650. 5272060. 2152020. 4370481. 3128680. 7412430. 0779880. 0061230. 1903700. 018007-1. 026036-2. 378430-1. 0699490. 8438221. 289216-1. 423369-0. 4628870. 197330-0. 9350760. 4412710. 414643-0. 377887-0. 5305150. 6215921. 0095720. 5697180. 175291-0. 656279-0. 112273-0. 392137-1. 043558-0. 467318-0. 384329-2. 00920730. 6585980. 101830-0. 6827810. 229349-0. 3056570. 4048770. 252244-0. 837784-0. 0396240. 3294570. 7516941. 469070-0. 1571991. 032628-0. 584639-0. 9255440. 342474-0. 9693630. 133480-0. 385974-0. 6002780. 2819390. 8685791. 129803-0. 0418980. 9611930. 131521-0. 792889-1. 2857370. 073934-1. 333315-1. 0441251. 2773381. 4922570. 4113791. 771805-1. 1111281. 123233-1. 0194491. 738357-0. 690764-0. 120710-0. 421359-0. 727294-0. 857759-0. 069436-0. 328334-0. 5581801. 063474-0. 519133-0. 4969021. 089589-1. 6158010. 080174-0. 229938-0. 498420-0. 6246150. 059481-0. 093158-1. 784549-0. 503789-0. 1405280. 002653-0. 4849300. 055914-0. 680948-0. 9942711. 2770520. 0376512. 155421-0. 4375890. 6964040. 417752-0. 5447851. 1906900. 9782620. 7521020. 5044720. 139853-0. 505089-0. 264975-1. 6031940. 7318470. 010903-1. 165346-0. 125195-1. 032685-0. 4655201. 5148080. 3047620. 7934140. 314635-1. 6382790. 111737-0. 7770370. 2517831. 126303-0. 8087980. 422064-0. 349264B00-0. 356362-0. 0892270. 6093730. 542382-0. 768681-0. 0480742. 015458-1. 5523510. 2515521. 4596350. 9497070. 339465-0. 0013721. 7985891. 5591630. 2317830. 423141-0. 3105300. 3537952. 173336-0. 196247-0. 375636-0. 8582210. 2584100. 6564300. 9608191. 1378931. 5534050. 038981-0. 632038-0. 132009-1. 834997-0. 242576-0. 297879-0. 441559-0. 7696910. 224077-0. 1530090. 519526-0. 6801880. 5358510. 671496-0. 1830640. 3012341. 288256-2. 478240-0. 3604030. 424067-0. 834659-0. 128464-0. 489013-0. 014888-1. 461230-1. 435223-1. 3198021. 0836750. 979140-0. 3752911. 110189-1. 0113510. 587886-0. 822775-1. 1838651. 4551731. 1343280. 239403-0. 837991-1. 1309320. 7831681. 8455201. 437072-1. 1984431. 3790982. 1291130. 260096-0. 0119750. 0433020. 7229411. 028152-0. 2358061. 145245-1. 3595980. 2321890. 503712-0. 614264-0. 530606-2. 435803-0. 255238-0. 0644230. 7846430. 2563460. 1280231. 414103-1. 1186590. 8773530. 5005610. 463651-2. 034512-0. 981683-0. 6919441-1. 113376-1. 1694020. 680539-1. 5342121. 653817-1. 295181-0. 5668260. 4770141. 4133710. 5171051. 401153-0. 8726850. 8309570. 181507-0. 1456160. 694592-0. 7512080. 3244440. 681973-0. 0549720. 917776-1. 024810-0. 206446-0. 6001130. 8528051. 455109-0. 0797690. 0760760. 207699-1. 850458-0. 124124-0. 610871-0. 8833620. 219049-0. 685094-0. 645330-0. 242805-0. 7756020. 2330702. 422642-1. 423040-0. 5824210. 968304-0. 701025-0. 1678500. 2772641. 3012310. 301205-3. 081249-0. 5628680. 192944-0. 6645920. 5656860. 190913-0. 841858-1. 856545-1. 0227771. 2959680. 4519210. 6599550. 065818-0. 3195860. 253495-1. 144646-0. 4834040. 5559020. 8070690. 7141960. 6611960. 0536670. 346833-1. 288977-0. 386734-1. 2621270. 477495-0. 494034-0. 9114141. 152963-0. 342365-0. 1601870. 470054-0. 853063-1. 387949-0. 257257-1. 030690-0. 1102100. 328911-0. 5559230. 987713-0. 5019572. 069887-0. 0675030. 316029-1. 5062322. 2016210. 492097-0. 085193-0. 9778221. 039147-0. 6539322-0. 405638-1. 402027-1. 1662421. 3061840. 856283-1. 236170-0. 646721-1. 4740640. 0829600. 090310-0. 1699770. 4063450. 915427-0. 9745030. 2716371. 539184-0. 098866-0. 5251491. 0639330. 085827-0. 1296220. 947959-0. 072496-0. 2375920. 0125491. 0657610. 996596-0. 1724812. 583139-0. 028578-0. 2548561. 328794-1. 5929512. 434350-0. 341500-0. 307719-1. 333273-1. 1008450. 2090971. 7347770. 6396320. 424779-0. 1293270. 905029-0. 4829091. 731628-2. 783425-0. 333677-0. 1108951. 212636-0. 2084120. 4271171. 3485630. 0438591. 772519-1. 4161060. 4011550. 8071570. 303427-1. 2462880. 178774-0. 066126-1. 8622881. 2412950. 377021-0. 822320-0. 7490141. 4636521. 602268-1. 0438771. 185290-0. 565783-1. 0768791. 360241-0. 1219910. 9910431. 0079520. 450185-0. 7443761. 388876-0. 316847-0. 841655-1. 056842-0. 5002260. 0969591. 176896-2. 9396521. 7922130. 3163400. 3032181. 024967-0. 590871-0. 453326-0. 795981-0. 393301-0. 374372-1. 2701991. 6183721. 197727-0. 9148633-0. 6252100. 2889110. 288374-1. 372667-0. 591395-0. 4789421. 335664-0. 459855-1. 615975-1. 1896760. 374767-2. 4887330. 586656-1. 4220080. 4960301. 911128-0. 560660-0. 499614-0. 372171-1. 8330690. 237124-0. 9444460. 9121400. 359790-1. 3592350. 166966-0. 047107-0. 279789-0. 594454-0. 739013-1. 5276450. 4016681. 791252-2. 7748480. 5238732. 2075850. 488999-0. 3392830. 1317110. 0184091. 186551-0. 4243181. 554994-0. 205917-0. 9349750. 654102-1. 227761-0. 461025-0. 421201-0. 058615-0. 5845630. 336913-0. 477102-1. 3814630. 757745-0. 2689680. 0348701. 2316860. 2366001. 234720-0. 0402470. 0295821. 0349050. 380204-0. 012108-0. 859511-0. 990340-1. 205172-1. 0301780. 4266760. 497796-0. 8768080. 9579630. 1730160. 131612-1. 003556-1. 069908-1. 7992071. 429598-0. 116015-1. 4549800. 2619170. 4444120. 2732900. 8441150. 218745-1. 033350-1. 1882950. 0583730. 800523-1. 6270680. 8616510. 871018-0. 003733-0. 2433540. 9472960. 5094060. 0445460. 2668961. 337165100. 699142-1. 9280330. 1053631. 0423220. 715206-0. 7637830. 098798-1. 1578980. 1341050. 0420410. 6748260. 165649-1. 622970-3. 1312740. 597649-1. 8803310. 663980-0. 256033-1. 5240580. 4927990. 2211630. 429622-0. 6595841. 264506-0. 032131-2. 114907-0. 2640430. 457835-0. 676837-0. 6290030. 489145-0. 5516860. 942622-0. 512043-0. 4558930. 021244-0. 178035-2. 498073-0. 1712920. 323510-0. 545163-0. 668909-0. 1500310. 521620-0. 4289800. 6764630. 369081-0. 7248320. 7935421. 2374220. 4012752. 1415230. 2490120. 486755-0. 1632740. 592222-0. 292600-0. 5471680. 619104-0. 0136050. 7767340. 1314241. 189480-0. 666317-0. 9390361. 1055150. 6214521. 586605-0. 7609701. 6496460. 2831991. 275812-0. 4520120. 301361-0. 976951-0. 268106-0. 079255-1. 2583322. 216658-1. 175988-0. 863497-1. 653022-0. 5615140. 4507530. 4172000. 094676-2. 2310541. 316862-0. 4774410. 646654-0. 2002521. 074354-0. 0581760. 1209900. 222522-0. 1795070. 421655-0. 914341-0. 2341780. 74152410. 9327141. 423761-1. 2808350. 347882-0. 863171-0. 8525801. 0449332. 0945360. 8062060. 416201-1. 1095030. 145302-0. 9968710. 325456-0. 6050811. 1753261. 6450540. 293432-2. 7668221. 0328490. 079115-1. 4141321. 4633762. 3354860. 411951-0. 0485430. 159284-0. 651554-1. 0931281. 568390-0. 077807-2. 390779-0. 842346-0. 229675-0. 999072-1. 367219-0. 792042-1. 8785751. 4514521. 266250-0. 7343150. 2661520. 735523-0. 4308600. 2298640. 850083-2. 2412411. 0638500. 289409-0. 3543600. 113063-0. 1730061. 3869981. 8862360. 587119-0. 9611330. 3992951. 4615600. 3108230. 280220-0. 879103-1. 3263480. 003337-1. 085908-0. 4367232. 1119260. 1060680. 6155972. 152996-0. 1961550. 025747-0. 0390610. 656823-0. 3471052. 5139791. 7580701. 288473-0. 739185-0. 691592-0. 098728-0. 2763860. 4899810. 516278-0. 8382580. 596673-0. 3310530. 521174-0. 1450230. 836693-1. 0921660. 361733-1. 1699810. 0467310. 655377-0. 7568521. 285805-0. 0950190. 3602531. 3706210. 08301020. 8888932. 288725-1. 0323320. 212273-1. 0918261. 6924981. 0253670. 5508540. 679430-1. 335712-0. 7983412. 265351-1. 0069382. 0597610. 420266-1. 1896570. 5066740. 260847-0. 5331450. 7272671. 4122761. 482106-0. 9962580. 588641-0. 412642-0. 920733-0. 8746910. 8390020. 501668-0. 342493-0. 533806-2. 146352-0. 5973390. 1157260. 850683-0. 7522390. 377263-0. 5619820. 262783-0. 356676-0. 3674620. 753611-1. 267414-1. 330698-0. 5364530. 840938-0. 763108-0. 268100-0. 6774241. 6068310. 151732-2. 0857011. 2192960. 4008630. 591165-1. 4852131. 5019791. 196569-0. 2141540. 339554-0. 0344461. 1764520. 546340-1. 255630-1. 309210-0. 4454370. 189437-0. 7374630. 843767-0. 605632-0. 0607770. 4093101. 285569-0. 6226381. 0181930. 8806800. 046805-1. 818058-0. 8098290. 8752240. 409569-0. 116621-1. 2389193. 305724-0. 024121-1. 7565001. 3289580. 507593-0. 866554-2. 240848-0. 661376-0. 6718240. 215720-0. 2963260. 4814020. 829645-0. 7210251. 2639140. 549047-1. 2349453-1. 9788380. 721823-0. 559067-1. 2352430. 420716-0. 5988450. 359576-0. 619366-1. 757772-1. 1562510. 7052120. 875071-1. 0203760. 394760-0. 1479700. 2302491. 3552031. 7944882. 678058-0. 153565-0. 460959-0. 098108-1. 407930-2. 4877021. 8230140. 099873-0. 517603-0. 509311-1. 833175-0. 9009060. 459493-0. 6554401. 466122-1. 531389-0. 4221060. 4214220. 5786150. 2597950. 018941-0. 1687261. 611107-1. 586550-1. 3849410. 8583771. 0332421. 7013431. 748344-0. 371182-0. 8435752. 089641-0. 345430-1. 7405560. 141915-2. 1971380. 689569-0. 1500250. 2874560. 654016-1. 521919-0. 918008-0. 5875280. 2306360. 2626370. 6156740. 600044-0. 494699-0. 7430890. 220026-0. 2422070. 528216-0. 328174-1. 536517-1. 476640-1. 162114-1. 2602221. 106252-1. 467408-0. 349341-1. 8412170. 031296-0. 076475-0. 3533830. 8075450. 779064-2. 398417-0. 2678281. 5497340. 8143970. 284770-0. 6593690. 761040-0. 7220670. 8103321. 5012951. 440865-1. 367459-0. 700301-1. 5406620. 159837-0. 625415

HTML Escaping

Suppose you have to display HTML within HTML, that can be a bit of pain when the renderer can’t distinguish. You can use the

[2]:

74 formatting option to handle this, and even use it within a formatter that contains HTML itself

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

0120"&other"

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

0120

"&other"

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

0120

"&other"

Export to Excel

Some support (since version 0. 20. 0) is available for exporting styled

[2]:

75 to Excel worksheets using the

[2]:

76 or

[2]:

77 engines. CSS2. 2 properties handled include

```
[2]:
```
78
```
[2]:
```
79 properties
```
[2]:
```
80 properties
```
[2]:
```
81 properties
```
[2]:
```
82
```
[2]:
```
83
```
[2]:
```
84
```
[2]:
```
85
```
[2]:
```
86
```
[2]:
```
87
```
[2]:
```
88
```
[2]:
```
89
Shorthand and side-specific border properties are supported (e. g.
```
[2]:
```
79 and
```
[2]:
```
91) as well as the
```
[2]:
```
92 shorthands for all sides (
```
[2]:
```
93) or specified sides (
```
[2]:
```
94). Using a
```
[2]:
```
92 shorthand will override any border properties set before it (See for more details)
Only CSS2 named colors and hex colors of the form
```
[2]:
```
96 or
```
[2]:
```
97 are currently supported
The following pseudo CSS properties are also available to set Excel specific style properties
- ```
[2]:
```
  98
- ```
[2]:
```
  79 (for Excel-specific styles. “hair”, “mediumDashDot”, “dashDotDot”, “mediumDashDotDot”, “dashDot”, “slantDashDot”, or “mediumDashed”)

Table level styles, and data cell CSS-classes are not included in the export to Excel. individual cells must have their properties mapped by the

[2]:

29 and/or

[2]:

40 methods

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

A screenshot of the output

Export to LaTeX

There is support (since version 1. 3. 0) to export

[2]:

13 to LaTeX. The documentation for the . to_latex method gives further detail and numerous examples.

More About CSS and HTML

Ngôn ngữ Cascading Style Sheet (CSS), được thiết kế để tác động đến cách trình duyệt hiển thị các phần tử HTML, có những đặc thù riêng. It never reports errors. it just silently ignores them and doesn’t render your objects how you intend so can sometimes be frustrating. Here is a very brief primer on how

[2]:

13 creates HTML and interacts with CSS, with advice on common pitfalls to avoid

CSS Classes and Ids

The precise structure of the CSS

[2]:

18 attached to each cell is as follows

Cells with Index and Column names include
```
[4]:
```
05 and
```
[4]:
```
06 where
```
[4]:
```
07 is its level in a MultiIndex
Index label cells include
- ```
[4]:
```
  08
- ```
[4]:
```
  06 where
```
[4]:
```
  07 is the level in a MultiIndex
- ```
[4]:
```
  11 trong đó
```
[4]:
```
  12 là vị trí số của hàng
Column label cells include
- ```
[4]:
```
  13
- ```
[4]:
```
  06 where
```
[4]:
```
  07 is the level in a MultiIndex
- ```
[4]:
```
  16 where
```
[4]:
```
  17 is the numeric position of the column
Data cells include
- ```
[4]:
```
  18
- ```
[4]:
```
  11, where
```
[4]:
```
  12 is the numeric position of the cell
- ```
[4]:
```
  16, where
```
[4]:
```
  17 is the numeric position of the cell
Blank cells include
```
[4]:
```
23
Trimmed cells include
```
[4]:
```
24 or
```
[4]:
```
25

The structure of the

[4]:

26 is

[4]:

27 where

[4]:

06 is used only on headings, and headings will only have either

[4]:

11 or

[4]:

16 whichever is needed. By default we’ve also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn’t collide with the styling from another within the same notebook or page. You can read more about the use of UUIDs in

We can see example of the HTML by calling the . to_html() method.

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

CSS Hierarchies

The examples have shown that when CSS styles overlap, the one that comes last in the HTML render, takes precedence. So the following yield different results

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

00text

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

00text

This is only true for CSS rules that are equivalent in hierarchy, or importance. You can read more about CSS specificity here but for our purposes it suffices to summarize the key points

A CSS importance score for each HTML element is derived by starting at zero and adding

1000 for an inline style attribute
100 for each ID
10 for each attribute, class or pseudo-class
1 for each element name or pseudo-element

Let’s use this to describe the action of the following configurations

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

00text

This text is red because the generated selector

[4]:

31 is worth 101 (ID plus element), whereas

[4]:

32 is only worth 100 (ID), so is considered inferior even though in the HTML it comes after the previous

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

00text

In the above case the text is blue because the selector

[4]:

33 is worth 110 (ID plus class), which takes precedence

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

00text

Now we have created another table style this time the selector

[4]:

34 (ID plus element plus class) gets bumped up to 111

If your style fails to be applied, and its really frustrating, try the

[4]:

35 trump card

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

00text

Cuối cùng cũng có dòng chữ xanh đó

Extensibility

The core of pandas is, and will remain, its “high-performance, easy-to-use data structures”. With that in mind, we hope that

[2]:

04 accomplishes two goals

Provide an API that is pleasing to use interactively and is “good enough” for many tasks
Provide the foundations for dedicated libraries to build on

If you build a great library on top of this, let us know and we’ll link to it

phân lớp

Nếu mẫu mặc định không phù hợp với nhu cầu của bạn, bạn có thể phân lớp Styler và mở rộng hoặc ghi đè mẫu. Chúng tôi sẽ hiển thị một ví dụ về việc mở rộng mẫu mặc định để chèn tiêu đề tùy chỉnh trước mỗi bảng

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Chúng tôi sẽ sử dụng mẫu sau

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Bây giờ chúng ta đã tạo một mẫu, chúng ta cần thiết lập một lớp con của

[2]:

13 biết về nó

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Lưu ý rằng chúng tôi bao gồm trình tải ban đầu trong trình tải của môi trường của chúng tôi. Đó là bởi vì chúng tôi mở rộng mẫu ban đầu, vì vậy môi trường Jinja cần có thể tìm thấy nó

Bây giờ chúng ta có thể sử dụng trình tạo kiểu tùy chỉnh đó. Đó là

[4]:

38 lấy một DataFrame

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Bảng của tôi c1c2c3c4Ar1-1. 048553-1. 420018-1. 7062701. 950775r2-0. 509652-0. 438074-1. 2527950. 777490Br1-1. 613898-0. 212740-0. 8954670. 386902r2-0. 510805-1. 180632-0. 0281820. 428332

Mẫu tùy chỉnh của chúng tôi chấp nhận từ khóa

[4]:

39. Chúng tôi có thể cung cấp giá trị trong phương pháp

[4]:

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Ví dụ mở rộng c1c2c3c4Ar1-1. 048553-1. 420018-1. 7062701. 950775r2-0. 509652-0. 438074-1. 2527950. 777490Br1-1. 613898-0. 212740-0. 8954670. 386902r2-0. 510805-1. 180632-0. 0281820. 428332

Để thuận tiện, chúng tôi cung cấp phương thức

[4]:

41 giống như lớp con tùy chỉnh

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),
                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))
df.style

Tiêu đề khác c1c2c3c4Ar1-1. 048553-1. 420018-1. 7062701. 950775r2-0. 509652-0. 438074-1. 2527950. 777490Br1-1. 613898-0. 212740-0. 8954670. 386902r2-0. 510805-1. 180632-0. 0281820. 428332

Cấu trúc mẫu

Đây là cấu trúc mẫu cho cả mẫu tạo kiểu và mẫu tạo bảng

programming excel

Đệm ô excel

Định dạng hiển thị

Giá trị định dạng

Hiding Data

Methods to Add Styles

Table Styles

Setting Classes and Linking to External CSS

Table Attributes

Data Cell CSS Classes

Styler Functions

Acting on Data

Acting on the Index and Column Headers

Tooltips and Captions

Finer Control with Slicing

Optimization

1. Remove UUID and cell_ids

2. Use table styles

3. Set classes instead of using Styler functions

4. Don’t use tooltips

5. If every byte counts use string replacement

Builtin Styles

Highlight Null

Highlight Min or Max

Highlight Between

Highlight Quantile

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