Установить цвет фона фрейма данных Pandas и цвет шрифта в Python

Опубликовано: 27 Марта, 2022

As we know, the basic idea behind styling is to make more impactful for the end-user readability. We can make changes like the color and format of the data visualized in order to communicate insight more efficiently. For the more impactful visualization on the pandas DataFrame, generally, we DataFrame.style property, which returns styler object having a number of useful methods for formatting and visualizing the data frames.

Using DataFrame.style property

df.style.set_properties : By using this, we can use inbuilt functionality to manipulate data frame styling from font color to background color.

Python3

# Importing the necessary libraries -->
import pandas as pd
import numpy as np
  
# Seeding random data from numpy
np.random.seed(24)
  
# Making the DatFrame
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)
  
# DataFrame without any styling
print("Original DataFrame:
")
print(df)
print("
Modified Stlying DataFrame:")
df.style.set_properties(**{"background-color": "black",
                           "color": "green"})

Output:

df.style.set_properties

df.style.highlight_null : With the help of this, we can highlight the missing or null values inside the data frame.

Python3

# Replacing the locating value by NaN (Not a Nummber)
df.iloc[0, 3] = np.nan
df.iloc[2, 3] = np.nan
df.iloc[4, 2] = np.nan
df.iloc[7, 4] = np.nan
  
# Highlight the NaN values in DataFrame
print("
Modified Stlying DataFrame:")
df.style.highlight_null(null_color="red")

Выход:

df.style.highlight_min: для выделения минимального значения в каждом столбце во всем фрейме данных.

Python3

# Highlight the Min values in each column 
print("
Modified Stlying DataFrame:")
df.style.highlight_min(axis=0)

Output:

df.style.highlight_min

df.style.highlight_max : For highlighting the maximum value in each column throughout the data frame.

Python3

# Highlight the Max values in each column 
print("
Modified Stlying DataFrame:")
df.style.highlight_max(axis=0)

Output:

df.style.highlight_max

Using User-defined Function

We can modify DataFrame using a user-defined function: With the help of this function, we can customizing the font color of positive data values inside the data frame.

Python3

# function for set text color of positive
# values in Dataframes
def color_positive_green(val):
    """
    Takes a scalar and returns a string with
    the css property `"color: green"` for positive
    strings, black otherwise.
    """
    if val > 0:
        color = "green"
    else:
        color = "black"
    return "color: %s" % color
  
df.style.applymap(color_positive_green)

Output:

User-Defined Function

Using Seaborn Library

Using color palette for gradient fill in DataFrame: By importing the light palette of colors from the seaborn library, we can map the color gradient for the background of the data frame.

Python3

# Import seaborn library
import seaborn as sns
  
# Declaring the cm variable by the 
# color palette from seaborn
cm = sns.light_palette("green", as_cmap=True)
  
# Visualizing the DataFrame with set precision
print("
Modified Stlying DataFrame:")
df.style.background_gradient(cmap=cm).set_precision(2)

Output:

Seaborn Color Palette

Using color palette with highlight null or missing values: Here, we highlight the NaN values in red color with gradient color palette of seaborn.

Python3

# Highlight the NaN values in DataFrame 
# using seaborn color palette
print("
Modified Stlying DataFrame:")
df.style.background_gradient(cmap=cm).set_precision(2).highlight_null("red")

Output:

Seaborn Color Palette with highlight_null

Assemble Seaborn properties with DataFrame.style property: Customizing the seaborn color palette with highlight properties of a data frame for more impactful data visualization.

Python3

# Highlight the NaN values in DataFrame 
# using seaborn color palette as well as
# min("lighblue") and max("blue") values 
# in each column
print("
Modified Stlying DataFrame:")
df.style.background_gradient(cmap=cm).set_precision(2).highlight_null("red").highlight_min(axis=0, color="lightblue").highlight_max(axis=0, color="blue")

Выход:

Цветовая палитра Seaborn с разн. выделить свойства

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Python

Установить цвет фона фрейма данных Pandas и цвет шрифта в Python

Using DataFrame.style property

Python3

Python3

Python3

Python3

Using User-defined Function

Python3

Using Seaborn Library

Python3

Python3

Python3

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