本文的文字及圖片來源于網路,僅供學習、交流使用,不具有任何商業用途,著作權歸原作者所有,如有問題請及時聯系我們以作處理，
以下文章來源于python資料分析之禪 ，作者鳥哥
折線圖是排列在作業表的列或行中的資料可以繪制到折線圖中，折線圖可以顯示隨時間（根據常用比例設定）而變化的連續資料，因此非常適用于顯示在相等時間間隔下資料的趨勢，
下面我給大家介紹一下如何用pyecharts畫出各種折線圖
1.基本折線圖
import pyecharts.options as opts from pyecharts.charts import Line x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日'] y=[100,200,300,400,500,400,300] line=( Line() .set_global_opts( tooltip_opts=opts.TooltipOpts(is_show=False), xaxis_opts=opts.AxisOpts(type_="category"), yaxis_opts=opts.AxisOpts( type_="value", axistick_opts=opts.AxisTickOpts(is_show=True), splitline_opts=opts.SplitLineOpts(is_show=True), ), ) .add_xaxis(xaxis_data=x) .add_yaxis( series_name="基本折線圖", y_axis=y, symbol="emptyCircle", is_symbol_show=True, label_opts=opts.LabelOpts(is_show=False), ) ) line.render_notebook()
series_name：圖形名稱 y_axis：資料 symbol：標記的圖形，pyecharts提供的型別包括'circle', 'rect', 'roundRect', 'triangle', 'diamond', 'pin', 'arrow', 'none'，也可以通過 'image://url' 設定為圖片，其中 URL 為圖片的鏈接，is_symbol_show：是否顯示 symbol
2.連接空資料（折線圖）
有時候我們要分析的資料存在空缺值，需要進行處理才能畫出折線圖
import pyecharts.options as opts from pyecharts.charts import Line x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日'] y=[100,200,300,400,None,400,300] line=( Line() .add_xaxis(xaxis_data=x) .add_yaxis( series_name="連接空資料（折線圖）", y_axis=y, is_connect_nones=True ) .set_global_opts(title_opts=opts.TitleOpts(title="Line-連接空資料")) ) line.render_notebook()
3.多條折線重疊
import pyecharts.options as opts from pyecharts.charts import Line x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日'] y1=[100,200,300,400,100,400,300] y2=[200,300,200,100,200,300,400] line=( Line() .add_xaxis(xaxis_data=x) .add_yaxis(series_name="y1線",y_axis=y1,symbol="arrow",is_symbol_show=True) .add_yaxis(series_name="y2線",y_axis=y2) .set_global_opts(title_opts=opts.TitleOpts(title="Line-多折線重疊")) ) line.render_notebook()
4.平滑曲線折線圖
import pyecharts.options as opts from pyecharts.charts import Line x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日'] y1=[100,200,300,400,100,400,300] y2=[200,300,200,100,200,300,400] line=( Line() .add_xaxis(xaxis_data=x) .add_yaxis(series_name="y1線",y_axis=y1, is_smooth=True) .add_yaxis(series_name="y2線",y_axis=y2, is_smooth=True) .set_global_opts(title_opts=opts.TitleOpts(title="Line-多折線重疊")) ) line.render_notebook()
is_smooth：平滑曲線標志
5.階梯圖
import pyecharts.options as opts from pyecharts.charts import Line x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日'] y1=[100,200,300,400,100,400,300] line=( Line() .add_xaxis(xaxis_data=x) .add_yaxis(series_name="y1線",y_axis=y1, is_step=True) .set_global_opts(title_opts=opts.TitleOpts(title="Line-階梯圖")) ) line.render_notebook()
is_step：階梯圖引數
6.變換折線的樣式
import pyecharts.options as opts from pyecharts.charts import Line from pyecharts.faker import Faker x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日'] y1=[100,200,300,400,100,400,300] line = ( Line() .add_xaxis(xaxis_data=x) .add_yaxis( "y1", y1, symbol="triangle", symbol_size=30, linestyle_opts=opts.LineStyleOpts(color="red", width=4, type_="dashed"), itemstyle_opts=opts.ItemStyleOpts( border_width=3, border_color="yellow", color="blue" ), ) .set_global_opts(title_opts=opts.TitleOpts(title="Line-ItemStyle")) ) line.render_notebook()
linestyle_opts:折線樣式配置 color設定顏色，width設定寬度 type設定型別，有'solid', 'dashed', 'dotted'三種型別 itemstyle_opts：圖元樣式配置，border_width設定描邊寬度，border_color設定描邊顏色，color設定紋理填充顏色
7.折線面積圖
import pyecharts.options as opts from pyecharts.charts import Line x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日'] y1=[100,200,300,400,100,400,300] y2=[200,300,200,100,200,300,400] line=( Line() .add_xaxis(xaxis_data=x) .add_yaxis(series_name="y1線",y_axis=y1,areastyle_opts=opts.AreaStyleOpts(opacity=0.5)) .add_yaxis(series_name="y2線",y_axis=y2,areastyle_opts=opts.AreaStyleOpts(opacity=0.5)) .set_global_opts(title_opts=opts.TitleOpts(title="Line-多折線重疊")) ) line.render_notebook()
8.雙橫坐標折線圖
import pyecharts.options as opts from pyecharts.charts import Line from pyecharts.commons.utils import JsCode js_formatter = """function (params) { console.log(params); return '降水量 ' + params.value + (params.seriesData.length ? '：' + params.seriesData[0].data : ''); }""" line=( Line() .add_xaxis( xaxis_data=[ "2016-1", "2016-2", "2016-3", "2016-4", "2016-5", "2016-6", "2016-7", "2016-8", "2016-9", "2016-10", "2016-11", "2016-12", ] ) .extend_axis( xaxis_data=[ "2015-1", "2015-2", "2015-3", "2015-4", "2015-5", "2015-6", "2015-7", "2015-8", "2015-9", "2015-10", "2015-11", "2015-12", ], xaxis=opts.AxisOpts( type_="category", axistick_opts=opts.AxisTickOpts(is_align_with_label=True), axisline_opts=opts.AxisLineOpts( is_on_zero=False, linestyle_opts=opts.LineStyleOpts(color="#6e9ef1") ), axispointer_opts=opts.AxisPointerOpts( is_show=True, label=opts.LabelOpts(formatter=JsCode(js_formatter)) ), ), ) .add_yaxis( series_name="2015 降水量", is_smooth=True, symbol="emptyCircle", is_symbol_show=False, color="#d14a61", y_axis=[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3], label_opts=opts.LabelOpts(is_show=False), linestyle_opts=opts.LineStyleOpts(width=2), ) .add_yaxis( series_name="2016 降水量", is_smooth=True, symbol="emptyCircle", is_symbol_show=False, color="#6e9ef1", y_axis=[3.9, 5.9, 11.1, 18.7, 48.3, 69.2, 231.6, 46.6, 55.4, 18.4, 10.3, 0.7], label_opts=opts.LabelOpts(is_show=False), linestyle_opts=opts.LineStyleOpts(width=2), ) .set_global_opts( legend_opts=opts.LegendOpts(), tooltip_opts=opts.TooltipOpts(trigger="none", axis_pointer_type="cross"), xaxis_opts=opts.AxisOpts( type_="category", axistick_opts=opts.AxisTickOpts(is_align_with_label=True), axisline_opts=opts.AxisLineOpts( is_on_zero=False, linestyle_opts=opts.LineStyleOpts(color="#d14a61") ), axispointer_opts=opts.AxisPointerOpts( is_show=True, label=opts.LabelOpts(formatter=JsCode(js_formatter)) ), ), yaxis_opts=opts.AxisOpts( type_="value", splitline_opts=opts.SplitLineOpts( is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1) ), ), ) ) line.render_notebook()
9.用電量隨時間變化
import pyecharts.options as opts from pyecharts.charts import Line x_data = [ "00:00", "01:15", "02:30", "03:45", "05:00", "06:15", "07:30", "08:45", "10:00", "11:15", "12:30", "13:45", "15:00", "16:15", "17:30", "18:45", "20:00", "21:15", "22:30", "23:45", ] y_data = [ 300, 280, 250, 260, 270, 300, 550, 500, 400, 390, 380, 390, 400, 500, 600, 750, 800, 700, 600, 400, ] line=( Line() .add_xaxis(xaxis_data=x_data) .add_yaxis( series_name="用電量", y_axis=y_data, is_smooth=True, label_opts=opts.LabelOpts(is_show=False), linestyle_opts=opts.LineStyleOpts(width=2), ) .set_global_opts( title_opts=opts.TitleOpts(title="一天用電量分布", subtitle="純屬虛構"), tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"), xaxis_opts=opts.AxisOpts(boundary_gap=False), yaxis_opts=opts.AxisOpts( axislabel_opts=opts.LabelOpts(formatter="{value} W"), splitline_opts=opts.SplitLineOpts(is_show=True), ), visualmap_opts=opts.VisualMapOpts( is_piecewise=True, dimension=0, pieces=[ {"lte": 6, "color": "green"}, {"gt": 6, "lte": 8, "color": "red"}, {"gt": 8, "lte": 14, "color": "yellow"}, {"gt": 14, "lte": 17, "color": "red"}, {"gt": 17, "color": "green"}, ], pos_right=0, pos_bottom=100 ), ) .set_series_opts( markarea_opts=opts.MarkAreaOpts( data=[ opts.MarkAreaItem(name="早高峰", x=("07:30", "10:00")), opts.MarkAreaItem(name="晚高峰", x=("17:30", "21:15")), ] ) ) ) line.render_notebook()
這里給大家介紹幾個關鍵引數：
①visualmap_opts：視覺映射配置項，可以將折線分段并設定標簽（is_piecewise），將不同段設定顏色（pieces）；
②markarea_opts：標記區域配置項，data引數可以設定標記區域名稱和位置，
轉載請註明出處，本文鏈接：https://www.uj5u.com/houduan/37675.html
標籤：Python