作者：俊欣

來(lái)源：關(guān)于數(shù)據(jù)分析與可視化

今天和大家來(lái)分享一些數(shù)據(jù)可視化方向的干貨，我們來(lái)嘗試用Python來(lái)繪制一下“漏斗圖”，但愿大家在看完本篇文章之后會(huì)有所收獲。

關(guān)于“漏斗圖”

漏斗圖常用于用戶行為的轉(zhuǎn)化率分析，例如通過(guò)漏斗圖來(lái)分析用戶購(gòu)買流程中各個(gè)環(huán)節(jié)的轉(zhuǎn)化率。當(dāng)然在整個(gè)分析過(guò)程當(dāng)中，我們會(huì)把流程優(yōu)化前后的漏斗圖放在一起，進(jìn)行比較分析，得出相關(guān)的結(jié)論，今天小編就用“matplotlib”、“plotly”以及“pyecharts”這幾個(gè)模塊來(lái)為大家演示一下怎么畫出好看的漏斗圖

首先我們先要導(dǎo)入需要用到的模塊以及數(shù)據(jù)，

import matplotlib.pyplot as plt import pandas as pd
df = pd.DataFrame({"環(huán)節(jié)": ["環(huán)節(jié)一", "環(huán)節(jié)二", "環(huán)節(jié)三", "環(huán)節(jié)四", "環(huán)節(jié)五"], "人數(shù)": [1000, 600, 400, 250, 100], "總體轉(zhuǎn)化率": [1.00, 0.60, 0.40, 0.25, 0.1]})

需要用到的數(shù)據(jù)如下圖所示

用matplotlib來(lái)制作漏斗圖，制作出來(lái)的效果可能會(huì)稍顯簡(jiǎn)單與粗糙，制作的原理也比較簡(jiǎn)單，先繪制出水平方向的直方圖，然后利用plot.barh()當(dāng)中的“l(fā)eft”參數(shù)將直方圖向左移，便能出來(lái)類似于漏斗圖的模樣

y = [5,4,3,2,1] x = [85,75,58,43,23] x_max = 100 x_min = 0 for idx, val in enumerate(x): plt.barh(y[idx], x[idx], left = idx+5) plt.xlim(x_min, x_max)

而要繪制出我們想要的想要的漏斗圖的模樣，代碼示例如下

from matplotlib import font_manager as fm # funnel chart y = [5,4,3,2,1]
labels = df["環(huán)節(jié)"].tolist()
x = df["人數(shù)"].tolist()
x_range = 100
font = fm.FontProperties(fname="KAITI.ttf")

fig, ax = plt.subplots(1, figsize=(12,6)) for idx, val in enumerate(x):
    left = (x_range - val)/2 plt.barh(y[idx], x[idx], left = left, color='#808B96', height=.8, edgecolor='black') # label plt.text(50, y[idx]+0.1, labels[idx], ha='center',
             fontproperties=font, fontsize=16, color='#2A2A2A') # value plt.text(50, y[idx]-0.3, x[idx], ha='center',
             fontproperties=font, fontsize=16, color='#2A2A2A') if idx != len(x)-1:
        next_left = (x_range - x[idx+1])/2 shadow_x = [left, next_left, 100-next_left, 100-left, left]
        shadow_y = [y[idx]-0.4, y[idx+1]+0.4, 
                    y[idx+1]+0.4, y[idx]-0.4, y[idx]-0.4]
        plt.plot(shadow_x, shadow_y)
plt.xlim(x_min, x_max)
plt.axis('off')
plt.title('每個(gè)環(huán)節(jié)的流失率', fontproperties=font, loc='center', fontsize=24, color='#2A2A2A')
plt.show()

繪制出來(lái)的漏斗圖如下圖所示

當(dāng)然我們用plotly來(lái)繪制的話則會(huì)更加的簡(jiǎn)單一些，代碼示例如下

import plotly.express as px data = dict(values=[80,73,58,42,23],
            labels=['環(huán)節(jié)一', '環(huán)節(jié)二', '環(huán)節(jié)三', '環(huán)節(jié)四', '環(huán)節(jié)五'])
fig = px.funnel(data, y='labels', x='values')
fig.show()

最后我們用pyecharts模塊來(lái)繪制一下，當(dāng)中有專門用來(lái)繪制“漏斗圖”的方法，我們只需要調(diào)用即可

from pyecharts.charts import Funnel
from pyecharts import options as opts
from pyecharts.globals import ThemeType c = ( Funnel(init_opts=opts.InitOpts(width="900px", height="600px",theme = ThemeType.INFOGRAPHIC ))
    .add( "環(huán)節(jié)",
        df[["環(huán)節(jié)","總體轉(zhuǎn)化率"]].values,
        sort_="descending",
        label_opts=opts.LabelOpts(position="inside"),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="Pyecharts漏斗圖", pos_bottom = "90%", pos_left = "center"))
) c.render_notebook()

我們將數(shù)據(jù)標(biāo)注上去之后

c = (
    Funnel(init_opts=opts.InitOpts(width="900px", height="600px",theme = ThemeType.INFOGRAPHIC ))
    .add( "商品",
        df[["環(huán)節(jié)","總體轉(zhuǎn)化率"]].values,
        sort_="descending",
        label_opts=opts.LabelOpts(position="inside"),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="Pyecharts漏斗圖", pos_bottom = "90%", pos_left = "center"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter=":{c}"))
)
c.render_notebook()