Python可视化分析
关键词
Matplotlib、Pyecharts、Seaborn、Plotly、Bokeh
Pyecharts
简介 - pyecharts - A Python Echarts Plotting Library built with love.
https://pyecharts.org/#/zh-cn/introPython ❤️ ECharts = pyecharts
# 一次性导入所有图表
from pyecharts.charts import *
# pyecharts的配置项
from pyecharts import options as opts
from pyecharts.components import Table
from pyecharts.commons.utils import JsCode
'''直角坐标系'''
chart = Bar() #柱状图
chart = Line(init_opts=opts.InitOpts(width="1000px",height="500px")) #折线图
chart = Scatter() #散点图
chart.add_xaxis(x_data) ##需要转换为list
chart.add_yaxis('', y_data, #data可以传入ChartItems
areastyle_opts=opts.AreaStyleOpts(opacity=0.5), #区域颜色填充
label_opts=opts.LabelOpts(is_show=True), #节点标签
category_gap="70%", # 柱子宽度设置
yaxis_index=0, #可选，当使用第二个y坐标轴时
)
chart.extend_axis( #添加额外的坐标轴
yaxis = opts.AxisOpts(
name="uv",
type_="value",
min_=0,
max_=1.6,
interval=0.4,
axislabel_opts=opts.LabelOpts(formatter="{value} 万人"), #坐标轴刻度
)
# xy轴翻转
chart.reversal_axis()
# overlap层叠多个图
chart_1.overlap(chart_2)
#全局设置 tooltip交互
chart.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True,trigger="axis", # 触发类型
trigger_on='mousemove|click', # 触发条件，点击或者悬停均可出发
axis_pointer_type="cross" # 指示器类型，鼠标移动到图表区可以查看效果
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=True, # 两边不显示间隔
axisline_opts=opts.AxisLineOpts(is_show=True), # 轴不显示
axispointer_opts=opts.AxisPointerOpts(is_show=True,type_="shadow"),
axislabel_opts=opts.LabelOpts(rotate=15) # 坐标轴标签配置
),
yaxis_opts=opts.AxisOpts(
name="pv",
type_="value",
min_=0,
max_=100,
interval=20,
axislabel_opts=opts.LabelOpts(formatter="{value} 万次"), # 坐标轴标签配置
axistick_opts=opts.AxisTickOpts(is_show=True), # 刻度显示
splitline_opts=opts.SplitLineOpts(is_show=True), # 分割线
),
title_opts=opts.TitleOpts(title="pv与uv趋势图",subtitle),
)
'''Grid整合图'''
ggrid = (
Grid()
.add(bar, grid_opts=opts.GridOpts(pos_bottom="60%"))
.add(line, grid_opts=opts.GridOpts(pos_top="60%"))
)
ggrid.render_notebook()
'''饼图'''
chart = Pie()
chart.set_series_opts(label_opts=opts.LabelOpts(
formatter="{b}: {c}", #{a}系列名称，{b}数据名称，{c}数值名称
font_size = '15',
font_style = 'oblique',
font_weight = 'bolder'
)
chart.add('',data_pair, #[['Apple', 123], ['Huawei', 153]]
radius=['50%', '70%'] #圆环效果
rosetype='area' #扇形的花瓣 'area'或'radius'
center=['25%', '50%'] #多个饼图 指定显示位置
)
'''漏斗图'''
chart = Funnel()
chart.add("",data_pair, #[['访问', 30398], ['注册', 15230]]
gap=2, #间隔距离
sort_ = "descending", #ascending, none
tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a}
{b} : {c}%",is_show=True),
label_opts=opts.LabelOpts(is_show=True, position="ourside"),
itemstyle_opts=opts.ItemStyleOpts(border_color="#fff", border_width=1),
)
'''地理坐标系'''
chart = Map(init_opts=opts.InitOpts(
bg_color='#080b30', # 设置背景颜色
theme='dark', # 设置主题
width='980px', # 设置图的宽度
height='700px', # 设置图的高度
) #区域地图
# 添加自定义坐标点
chart.add_coordinate('x', 116.397428, 39.90923)
chart.add_coordinate('y', 112.398615, 29.91659)
#添加数据
chart.add("label",data, #data键值对列表
maptype='china', # 必须的参数，指定地图类型
is_map_symbol_show=False, # 不显示红点
)
# 全局配置项
chart.set_global_opts(
# 视觉组件是必须的，需要通过视觉组件的颜色来展示数据大小
visualmap_opts=opts.VisualMapOpts(max_=100),
# 标题设置
title_opts=opts.TitleOpts(
title=title, # 主标题
subtitle=subtitle, # 副标题
pos_left='center', # 标题展示位置
title_textstyle_opts=dict(color='#fff') # 设置标题字体颜色
),
# 图例设置
legend_opts=opts.LegendOpts(
is_show=True, # 是否显示图例
pos_left='right', # 图例显示位置
pos_top='3%', #图例距离顶部的距离
orient='horizontal' # 图例水平布局
),
)
chart = Geo() #点地图（GEO）
chart.add_schema(maptype='china')
chart.add("", data,
type_='scatter' #Scatter，effectScatter（带涟漪效果的散点），Line（流向图），HeatMap
)
#timeline
tl = Timeline()
tl.add_schema(
is_auto_play = True, # 是否自动播放
play_interval = 1500, # 播放速度
is_loop_play = True, # 是否循环播放
)
tl.add(label, bar)
#展示
chart.render_notebook()
'''词云'''
from pyecharts.charts import WordCloud
from pyecharts.globals import SymbolType
word1 = WordCloud(init_opts=opts.InitOpts(width='750px', height='750px'))
word1.add("", [*zip(df_ticai.index.tolist(), df_ticai.values.tolist())],
word_size_range=[20, 200],
shape=SymbolType.DIAMOND)
word1.set_global_opts(title_opts=opts.TitleOpts('标题关键词分布'),
toolbox_opts=opts.ToolboxOpts())
word1.render_notebook()Plotly
Plotly Python Graphing LibraryPlotly'shttps://plotly.com/python/
- plotly是一个基于javascript的绘图库，plotly绘图种类丰富，效果美观；
- 易于保存与分享plotly的绘图结果，并且可以与Web无缝集成；
- ploty默认的绘图结果，是一个HTML网页文件，通过浏览器可以直接查看；
1. Plotly绘图原理
ployly常用的两个绘图模块：graph_objs（go）和express（px）
- 直接使用px调用某个绘图方法时，会自动创建画布，并画出图形。
2. 代码
import plotly as py
import plotly.graph_objs as go
import plotly.express as px
from plotly import tools
#散点图
fig = px.scatter(
tips,
x="total_bill",
y="tip",
color="sex", # 颜色和标记来同时区分
symbol="smoker",
facet_col="time", # 切面图的列元素
facet_col_wrap=2, # 每行最多子图
# 改变图例名称
labels={"sex": "Gender", "smoker": "Smokes"}
)
fig.show()
#柱状图
fig = px.bar(
tips, # 数据框
x="day", # xy轴
y="total_bill",
color="smoker", # 颜色
barmode="stack", # 柱状图模式
facet_col="sex", # 切面图的列元素
category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], # 自定义顺序
"smoker": ["Yes", "No"],
"sex": ["Male", "Female"]})
fig.update_layout(legend_traceorder="reversed") # 设置顺序
fig.show()2.1 改变图例位置
#图例legend位置设置
fig.update_layout(legend=dict(
yanchor="top", # y轴顶部
y=0.99,
xanchor="left", # x轴靠左
x=0.01,
orientation="h", # 开启水平显示
))2.2 布局调整代码
fig.update_layout(title='GDP per capita (happy vs less happy countries)',
xaxis_title='GDP per capita',
titlefont={'size': 24},
width=600,
height=400,
template="plotly_dark",
showlegend=True,
paper_bgcolor="lightgray",
plot_bgcolor='lightgray',
font=dict(
color ='black',
)
)2.3 trendlines
#demo
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", facet_col="smoker", color="sex",
log_x=False, #为True时对x变量log变化
trendline="ols", #线性拟合-最小二乘
trendline_options=dict(log_x=True), #log拟合
trendline_scope="overall", #全局
trendline_color_override="black",
)
fig.show()
'''
Locally Weighted Scatterplot Smoothing (LOWESS)
'''
df = px.data.stocks(datetimes=True)
fig = px.scatter(df, x="date", y="GOOG", trendline="lowess", trendline_options=dict(frac=0.1))
fig.show()
'''
Moving Averages
'''
fig1 = px.scatter(df, x="date", y="GOOG", trendline="rolling", trendline_options=dict(window=5),
title="5-point moving average")
#
fig2 = px.scatter(df, x="date", y="GOOG", trendline="ewm", trendline_options=dict(halflife=2),
title="Exponentially-weighted moving average (halflife of 2 points)")
fig3 = px.scatter(df, x="date", y="GOOG", trendline="expanding", trendline_options=dict(function="mean"), title="Expanding mean")
#自定义
fig4 = px.scatter(df, x="date", y="GOOG", trendline="rolling", trendline_options=dict(function="median", window=5),
title="Rolling Median")
'''高斯滑动'''
fig = px.scatter(df, x="date", y="GOOG", trendline="rolling",
trendline_options=dict(window=5, win_type="gaussian", function_args=dict(std=2)),
title="Rolling Mean with Gaussian Window")
#trendline统计学结果
results = px.get_trendline_results(fig)
print(results)
'''只显示trendlines'''
df = px.data.stocks(indexed=True, datetimes=True)
fig = px.scatter(df, trendline="rolling", trendline_options=dict(window=5),
title="5-point moving average")
fig.data = [t for t in fig.data if t.mode == "lines"]
fig.update_traces(showlegend=True) #trendlines have showlegend=False by default
fig.show()3. 工具函数
3.1 多张图导出到一个html文件
# 多张图导出到一个html文件
def plot_list(df_list):
fig_list =[]
from plotly.subplots import make_subplots
# Creation des figures
i=0
for df in df_list:
i+=1
fig = go.Figure()
fig.add_trace(go.Scatter(x=list(df.Date), y=list(df.Temp), name="Température"))
# Add figure title
fig.update_layout(
title_text=f"Cas n°{i}")
# Set x-axis title
fig.update_xaxes(title_text="Date")
fig_list.append(fig)
# Création d'un seul fichier HTML
filename=f"{os.path.join('output', 'full_list.html')}"
dashboard = open(filename, 'w')
dashboard.write("" + "\n")
include_plotlyjs = True
for fig in fig_list:
inner_html = fig.to_html(include_plotlyjs = include_plotlyjs).split('')[1].split('')[0]
dashboard.write(inner_html)
include_plotlyjs = False
dashboard.write("" + "\n")Matplotlib
教程 | Matplotlib 中文https://www.matplotlib.org.cn/tutorials/
1. 导入
import matplotlib.pyplot as plt2. 显示
#显示数组
plt.imshow(arr, origin='lower') #origin调整原点位置3. 图表
plt.spy() #This function plots a black dot for every nonzero entry of the array4. 功能函数
fig.tight_layout() #子图自动调整布局
bar=plt.bar()
plt.bar_label(bar) #柱状图数值
#子图
fig, ax = plt.subplots(nrow, ncol, sharex, sharey, figsize)
ax = ax.flatten() #用ax[0] ax[1] ... 对应图片位置
*pandas.DataFrame.plot
DataFrame.plot(x=None, y=None, kind='line', ax=None,
subplots=False, sharex=None, sharey=False, layout=None,
figsize=None, use_index=True, title=None, grid=None, legend=True,
style=None, logx=False, logy=False, loglog=False, xticks=None,
yticks=None, xlim=None, ylim=None, rot=None, fontsize=None,
colormap=None, position=0.5, table=False, yerr=None, xerr=None,
stacked=True/False, sort_columns=False, secondary_y=False, mark_right=True, **kwds)1. kind图表类型
‘line’ : line plot (default) # 折线图
‘bar’ : vertical bar plot # 条形图
‘barh’ : horizontal bar plot # 横向条形图
‘hist’ : histogram # 柱状图
‘box’ : boxplot #箱线图
‘kde’ : Kernel Density Estimation plot #密度估计图，主要对柱状图添加Kernel 概率密度线
‘density’ : same as ‘kde’
‘area’ : area plot #区域图
‘pie’ : pie plot #饼图
‘scatter’ : scatter plot #散点图 需要传入columns方向的索引
‘hexbin’ : hexbin plot #具有六边形单元的二维直方图Seaborn
User guide and tutorial — seaborn 0.12.0 documentation (pydata.org)https://seaborn.pydata.org/tutorial.html
详解可视化神器 seaborn，制作图形又快又美！ - 知乎
Bokeh
- Bokeh有自己的数据结构ColumnDataSource