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如何用Python數(shù)據(jù)可視化來分析用戶留存率,很多新手對(duì)此不是很清楚,為了幫助大家解決這個(gè)難題,下面小編將為大家詳細(xì)講解,有這方面需求的人可以來學(xué)習(xí)下,希望你能有所收獲。
漏斗圖常用于用戶行為的轉(zhuǎn)化率分析,例如通過漏斗圖來分析用戶購(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
來制作漏斗圖,制作出來的效果可能會(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()
繪制出來的漏斗圖如下圖所示
當(dāng)然我們用plotly
來繪制的話則會(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
模塊來繪制一下,當(dāng)中有專門用來繪制“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()
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