plotly笔记--常见平面图形的绘制（Scatter、Bar、Histogram、Pie）

浅浅的花香味﹌ 2023-07-24 13:08 12阅读 0赞

# plotly笔记–简单平面图形的绘制 #

### 文章目录 ###

*  plotly笔记--简单平面图形的绘制
 *   *  前期准备
     *  1、Scatter
     *   *  lines
         *  line\_shape
         *  markers
         *  markers+lines型
     *  2、Bar
     *   *  单条bar
         *  多条bar
         *  层叠柱状图
     *  3、Histogram
     *  4、Pie

这篇文章将简单描述怎么使用plotly绘制常用的平面图形，文中使用到的数据是从天池下载的经典泰坦尼克训练数据集，需要此数据的小朋友们可以前往天池下载，我也会将文中使用到的数据和源代码放在Github上以便下载： [Github][]

## 前期准备 ##

# 前期准备
    #导入plotly
    import plotly
    # 查看库的版本
    plotly.__version__
    # 注意此处是两个"_"

![在这里插入图片描述][20200411202511627.png]

import pandas as pd
    import numpy as np
    import os
    # 查看当前路径
    os.getcwd()
    # 更改当前工作目录
    os.chdir("F:/天池数据集/泰坦尼克/")
    # 将目录里的内容输出为列表
    os.listdir()

![在这里插入图片描述][20200411202536848.png]

# 读入数据
    titain_data = pd.read_csv("titanic_train.csv")
    # 对数据情况大致了解一下
    titain_data.head()
    titain_data.info()
    titain_data.describe()
    # 观察数据类型，缺失值，最值等方面信息，对如何进行后面的数据处理整体把控

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70]

# 对Age项统计缺失值
    titain_data["Age"].isnull().sum()
    # 对Age的空值进行前向替换
    titain_data["Age"].fillna(method="ffill", inplace=True)
    # 再次查看缺失值状况
    titain_data["Age"].isnull().sum()
    # 对目标字段进行描述
    titain_data["Age"].describe()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 1]

# 对数据中年龄进行分段统计，并使用plotly绘制出散点图/线形图
    w = [0, 15, 30, 45, 60, 100]
    titain_data["Age_bin"] = pd.cut(
        titain_data["Age"],
        bins=w,
        labels=['0-15岁', '15-30岁', '30-45岁', '45-60岁', '60岁以上'],
        right=False)
    Age_x = titain_data["Age_bin"].value_counts(sort=False)
    # titain_data.head()
    Age_x
    x = list(Age_x.index)
    y = Age_x.values

下面开始绘图

## 1、Scatter ##

### lines ###

# Scatter
    # mode:定义图形的类型，线形或者散点或线+点
    import plotly.graph_objects as go
    # lines型
    trace0 = Scatter(x=x, y=y, mode="lines", name="年龄分布")
    fig = go.Figure(trace)
    fig.update_layout(title="Age", xaxis_title="Age", yaxis_title="number")
    fig.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 2]

### line\_shape ###

几种不同类型的线条画法：linear、spline、vh、hv、vhv、hvh

import plotly.graph_objects as go
    import numpy as np
    
    x = np.array([1, 2, 3, 4, 5])
    y = np.array([1, 3, 2, 3, 1])
    
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=x, y=y, name="linear",
                        line_shape='linear'))
    fig.add_trace(go.Scatter(x=x, y=y + 5, name="spline",
                        text=["tweak line smoothness<br>with 'smoothing' in line object"],
                        hoverinfo='text+name',
                        line_shape='spline'))
    fig.add_trace(go.Scatter(x=x, y=y + 10, name="vhv",
                        line_shape='vhv'))
    fig.add_trace(go.Scatter(x=x, y=y + 15, name="hvh",
                        line_shape='hvh'))
    fig.add_trace(go.Scatter(x=x, y=y + 20, name="vh",
                        line_shape='vh'))
    fig.add_trace(go.Scatter(x=x, y=y + 25, name="hv",
                        line_shape='hv'))
    
    fig.update_traces(hoverinfo='text+name', mode='lines+markers')
    fig.update_layout(legend=dict(y=0.5, traceorder='reversed', font_size=16))
    
    fig.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 3]

### markers ###

# markers型
    trace = Scatter(x=x,
                    y=y,
                    mode="markers",
                    name="年龄分布",
                    marker=dict(size=8,
                                color=np.random.randn(5),
                                colorscale="Viridis",
                                showscale=True))
    fig = go.Figure(trace)
    fig.update_layout(title="Age", xaxis_title="Age", yaxis_title="number")
    fig.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 4]

### markers+lines型 ###

# markers+lines型
    trace = Scatter(x=x,
                    y=y,
                    mode="markers+lines",
                    name="年龄分布",
                    line=dict(width=2, color="indianred"))
    fig = go.Figure(trace)
    fig.update_layout(title="Age", xaxis_title="Age", yaxis_title="number")
    fig.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 5]

## 2、Bar ##

### 单条bar ###

# bar
    bar1 = go.Bar(
        x=x,
        y=y,
        text=y,
        textposition='outside',
        width=0.6,
        marker=dict(color=["indianred", "yellow", "blue", "darkgray", "darkgreen"],
                    opacity=0.39))     # opacity设置透明度
    fig = go.Figure(bar1)
    fig.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 6]

### 多条bar ###

# 多条bar
    # 凭空想像一个y1
    y1 = y * 2
    bar1 = go.Bar(x=x, y=y, text=y, textposition='outside',name="真实数据")
    bar2 = go.Bar(x=x, y=y1, text=y1, textposition='outside',name="我瞎掰的值")
    layout = go.Layout(title="年龄分布图",
                       xaxis=dict(title="年龄"),
                       yaxis=dict(title="数量"))
    fig = go.Figure(data=[bar1, bar2], layout=layout)
    fig.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 7]

### 层叠柱状图 ###

# 层叠柱状图
    bar1 = go.Bar(
        x=x,
        y=y,
        text=y,
        textposition='outside',
        name="真实数据",
        marker=dict(pacity=0.4))
    bar2 = go.Bar(
        x=x,
        y=y1,
        text=y1,
        textposition='outside',
        name="我瞎掰的值",
        marker=dict(pacity=0.6))
    layout = go.Layout(title="年龄分布图",
                       xaxis=dict(title="年龄"),
                       yaxis=dict(title="数量"),
                       barmode="stack")
    fig = go.Figure(data=[bar1, bar2], layout=layout)
    fig.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 8]

## 3、Histogram ##

# Histogram
    hist = go.Histogram(x=titain_data["Age"],
                        histnorm='probability',
                        marker=dict(color="darkgreen", opacity=0.39),
                        xbins={ 'size': 10})
    fig = go.Figure(hist)
    fig.update_layout(bargap=0.1)
    fig.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 9]

## 4、Pie ##

# Pie
    pie = go.Pie(labels=Age_x.index,
                 values=Age_x.values,
                 hole=0.3,
                 textfont=dict(size=12, color='white'))  # hole为中间部分洞的大小
    layout = go.Layout(title="不同年龄比例")
    fig = go.Figure(data=pie, layout=layout)
    fig.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDM5MDQ2Mg_size_16_color_FFFFFF_t_70 10]

更多内容大家可以查看官网教程：[官网链接][Link 1]

[Github]: https://github.com/GraceYaa/Data-Visualisation
[20200411202511627.png]: /images/20230528/f8f868b950c3477f82076e523c574f93.png
[20200411202536848.png]: /images/20230528/452316d50e344b01ac2ee1342b8dbac1.png
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[Link 1]: https://plot.ly/python/