Pandas Styling: 판다스 스타일링

import pandas as pd
import numpy as np

np.random.seed(88)
df = pd.DataFrame({'A': np.linspace(1,10,10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10,4), columns = list('BCDE'))],
              axis= 1)
df.iloc[3,3] = np.nan
df.iloc[0,2] = np.nan

Styling the DataFrame

1. Highlight: 하이라이트

Highlight Min-Max values

• For highlighting maximum values : chain '.highlight_max()' function to the styler object
• Additionally, you can also specify the axis for which you want to highlight the values

1.1.Highlight Max values

• Without Styling

print(df)

• With Styling

df.style.highlight_max()

• default는 axis =0
• axis= 0 은 raw들의 같은 위치값을 가지는 수 끼리 비교하여 max 값을 highlight.
• 행렬의 개념으로 생각하면 됨

df.style.highlight_max(axis=1)

• axis =1 은 columns 의 같은 위치 값을 가지는 수 끼리 비교하여 가장 큰 값을 highlight

1.2. Highlight Min values

df.style.highlight_min()

df.style.highlight_min(axis=1)

1.3. Highlight Null values

• set_na_rep() : along with highlighting the missing values, they may be represented as 'nan'. You can change the representation of these missing values using the set_na_rep() function
• This function can also be chained with any styler function but chaining it with highlight_null will provide more details

df.style.highlight_null()

df.style.highlight_null(null_color='green')

df.style.set_na_rep("OutofScope").highlight_null(null_color="orange")

2. Create Heatmap within dataframe: 히트맵

• Heatmaps are used to represent values with the color shades
• The higher is the color shade, the larger is the value present.
• These color shades represent the intensity of values as compared to other values.
• To plot such a mapping in the dataframe itself, there is no direct function but the 'styler.background_gradient()' workaround does the work

df.style.background_gradient()

• There are few parameters you can pass to this function to further customize the output generated
  • 1. cmap : by default, the 'PuBu' colormap is selected by pandas. You can create a custom matplotlib colormap and pass it to the cmap parameter.
  • 2. axis : generating heat via rows or columns criteria, by default: columns
  • 3. text_color_threshold : controls text visibility across varying background colors

3. Table Properties: 테이블 속성

• The dataframe presented in the Jupyter notebooks( google colab) is a table rendered using HTML and CSS
• The table properties can be controlled using the 'set_properties' method
• This method is used to set one or more data-independent properties → modifications are done purely based on visual appearance and no significance as such.
• This method takes in the properties to be set as a dictionary

df.style.set_properties(**{'border': '1.3px solid green',
                          'color': 'magenta'})

4. Create Bar Charts: 차트 바

• Just as the heat map, the bar charts can also be plotted within the dataframe itself
• The bars as plotted in each cell depending upon the axis selected
• By default, the axis=0 and the plot color are also fixed by pandas but it is configurable.
• To plot these bars, you simply need to chain the '.bar()' function to the styler object

df.style.bar()

df.style.bar(colors='green')

5. Control Precision: 소수점 자리 설정

• Current values of the dataframe have float values and their decimals have no boundary condition
• Even the column 'A', which had to hold a single value is having too many decimal places
• To control this behavior, you can use the '.set_precision()' function and pass the values for maximum decimals to be allowed

df.style.set_precision(2)

6. Add Captions: 캡션 넣기

• Like every image has a caption that defines the post text, you can add captions to your data frames
• This text will depict what the dataframe results talks about
• They may be sort of summary statistics like pivot tables

df.style.set_caption("This is Dataframe styling demo").set_precision(2).background_gradient()

7. Hiding Index or Column: 행렬 숨기기

• You can hide the index or any particular column from the dataframe
• Hiding index from the dataframe can be useful in cases when index doesn't convey anything significant about the data
• The column hiding depends on whether it is useful or not

df.style.hide_index()

df.style.hide_columns('B')

8. Control display values: 값 설정

• Using the styler object's '.format()' function, you can distinguish between the actual values held by the dataframe and the values you present
• The 'format' function takes in the format spec string that defines how individual values are presented
• You can directly specify the specification which will apply to the whole dataset or you can pass the specific column on which you want to control the display values

df.style.format("{:.3%}")

• the missing values have also been marked by the format function → This can be skipped and substituted with a different value using the 'na_rep' (na replacement) parameter

df.style.format("{:.3%}", na_rep="&&")

9. Table Styles: 테이블 스타일

• These are styles that apply to the table as a whole, but you don't look at the data
• It is very similar to the set_properties function but here, in the table styles, you can customize all web elements more easily
• The function of concern here is the 'set_table_styles' that takes in the list of dictionaries for defining the elements
• The dictionary needs to have the selector (HTML tag or CSS class) and its corresponding props (attributes or properties of the element)
• The props need to be a list of tuples of properties for that selector

styles = [
    dict(selector="tr:hover",
        props=[("background", "#f4f4f4")]),
    dict(selector="th", props=[("color", "#fff"),
                              ("border", "1px solid #eee"),
                              ("padding", "12px 35px"),
                              ("border-collapse", "collapse"),
                              ("background", "#00cccc"),
                              ("text-tramsform", "uppercase"),
                              ("font-size", 18px)
                              ]),
    dict(selector="td", props=[("color", "#999"),
                              ("border", "1px solid #eee"),
                              ("padding", "12px 35px"),
                              ("border-collapse", "collapse"),
                              ("font-size", 15px)
                              ]),
    dict(selector="table", props=[("font-family", 'Arial'),
                                 ("margin", "25px auto"),
                                 ("border-collapse", "collapse"),
                                 ("border", "1px solid #eee"),
                                 ("border-bottom", "2px solid #00cccc"),
                                 ]),
    dict(selector="caption", props=[("caption-side", "bottom")])
]

df.style.set_table_styles(styles).set_caption("Image by Author (Made in Pandas)").highlight_max().highlight_null(null_color='red')

10. Export to Excel: 엑셀에 저장

• You can store all the styling you have done on your dataframe in an excel file
• The '.to_excel' function on the styler object makes it possible
• This function needs two parameters : the name of the file to be saved (with extension 'xlsx') and the 'engine' parameter should be 'openpyxl'

df.style.set_precision(2).background_gradient().hide_index().to_excel('styled.xlsx', engine='openpyxl')