如何在Plotly中重新創建自定義Matplotlib圖？

Matplotlib 是一個用于 Python 的跨平臺、資料可視化和圖形繪圖庫，并且是高度可定制的。

Matplotlib具有定制化的巨大優勢。下面是自定義的直方圖matploltlib

import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.ticker as ticker
from matplotlib.patches import Rectangle
from matplotlib.gridspec import GridSpec
from matplotlib.patches import Polygon
from matplotlib.patches import ConnectionPatch

def customized_Histogram(df, j):
    """
    Generate Histogram

    Parameters:
    ****************

    df:
        pandas dataframe

    j(str):
        column name in str

    ****************
    Generates Advanced Histogram
    """

    # Colours for different percentiles
    perc_25_colour = 'gold'
    perc_50_colour = 'mediumaquamarine'
    perc_75_colour = 'deepskyblue'
    perc_95_colour = 'peachpuff'

    # Plot the Histogram from the random data
    fig, ax = plt.subplots(figsize=(14,8))

    # '''
    # counts  = numpy.ndarray of count of data ponts for each bin/column in the histogram
    # bins    = numpy.ndarray of bin edge/range values
    # patches = a list of Patch objects.
    #         each Patch object contains a Rectnagle object. 
    #         e.g. Rectangle(xy=(-2.51953, 0), width=0.501013, height=3, angle=0)
    # '''
    counts, bins, patches = ax.hist(df[j], facecolor=perc_50_colour, edgecolor='gray')


    # Set the ticks to be at the edges of the bins.
    ax.set_xticks(bins.round(2))
    plt.xticks(rotation=70)

    # Set the graph title and axes titles
    plt.title(f'Distribution of {j}', fontsize=20)
    plt.ylabel('Count', fontsize=15)
    plt.xlabel(j, fontsize=15)

    # Change the colors of bars at the edges
    twentyfifth, seventyfifth, ninetyfifth = np.percentile(df[j], [25, 75, 95])

    for patch, leftside, rightside in zip(patches, bins[:-1], bins[1:]):

        if rightside < twentyfifth:
            patch.set_facecolor(perc_25_colour)
        elif leftside > ninetyfifth:
            patch.set_facecolor(perc_95_colour)
        elif leftside > seventyfifth:
            patch.set_facecolor(perc_75_colour)

    # Calculate bar centre to display the count of data points and %
    bin_x_centers = 0.5 * np.diff(bins)   bins[:-1]
    bin_y_centers = ax.get_yticks()[1] * 0.25

    # Display the the count of data points and % for each bar in histogram
    for i in range(len(bins)-1):
        bin_label = "{0:,}".format(counts[i])   "  ({0:,.2f}%)".format((counts[i]/counts.sum())*100)
        plt.text(bin_x_centers[i],
                  bin_y_centers, 
                  bin_label, 
                  rotation=90, 
                  rotation_mode='anchor')

    # Annotation for bar values
    ax.annotate('Each bar shows count and percentage of total',
                xy=(.80,.30), 
                xycoords='figure fraction',
                horizontalalignment='center', 
                verticalalignment='bottom',
                fontsize=10, 
                bbox=dict(boxstyle="round", 
                          fc="white"),
                rotation=-90)

    #create legend
    handles = [Rectangle((0,0),1,1,color=c,ec="k") for c in [
                                                             perc_25_colour, 
                                                             perc_50_colour, 
                                                             perc_75_colour, 
                                                             perc_95_colour
                                                             ]
                ]
    labels= ["0-25 Percentile","25-50 Percentile", "50-75 Percentile", ">95 Percentile"]
    plt.legend(handles, labels, bbox_to_anchor=(0.5, 0., 0.80, 0.99))


    # fig.savefig("filename.jpg",dpi=150, bbox_inches='tight')
    plt.show()

import seaborn as sns
tips = sns.load_dataset("tips")

customized_Histogram(tips, "total_bill")

自定義直方圖

如何使用Plotly?

uj5u.com熱心網友回復：

• plotly可以以相同的方式構建自定義圖形。從根本上講，它是了解 API 和功能（就像matplotlib解決方案的情況）
• matplotlib hist()和numpy histogram()基本相同。回傳計數和垃圾箱
  轉載請註明出處，本文鏈接：https://www.uj5u.com/ruanti/359781.html

  標籤：Python matplotlib 情节地 数据可视化

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