如何用Python數(shù)據(jù)可視化來分析用戶留存率
關(guān)于“漏斗圖”
漏斗圖常用于用戶行為的轉(zhuǎn)化率分析,例如通過漏斗圖來分析用戶購買流程中各個環(huán)節(jié)的轉(zhuǎn)化率。當然在整個分析過程當中,我們會把流程優(yōu)化前后的漏斗圖放在一起,進行比較分析,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稍顯簡單與粗糙,制作的原理也比較簡單,先繪制出水平方向的直方圖,然后利用plot.barh()
當中的“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
當然我們用plotly
來繪制的話則會更加的簡單一些,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()
到此這篇關(guān)于如何用Python
數(shù)據(jù)可視化來分析用戶留存率的文章就介紹到這了,更多相關(guān)用Python
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