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python實(shí)現(xiàn)股票歷史數(shù)據(jù)可視化分析案例

更新時(shí)間：2021年06月10日 11:27:30 作者：榮仔！最靚的仔！

股票交易數(shù)據(jù)分析可直觀股市走向，對(duì)于如何把握股票行情，快速解讀股票交易數(shù)據(jù)有不可替代的作用，感興趣的可以了解一下

1 數(shù)據(jù)預(yù)處理

1.1 股票歷史數(shù)據(jù)csv文件讀取

import pandas as pd
import csv

df = pd.read_csv("/home/kesci/input/maotai4154/maotai.csv")

1.2 關(guān)鍵數(shù)據(jù)——在csv文件中選擇性提取“列”

df_high_low = df[['date','high','low']]

1.3 數(shù)據(jù)類(lèi)型轉(zhuǎn)換

df_high_low_array = np.array(df_high_low)
df_high_low_list =df_high_low_array.tolist()

1.4 數(shù)據(jù)按列提取并累加性存入列表

price_dates, heigh_prices, low_prices = [], [], []
for content in zip(df_high_low_list):
    price_date = content[0][0]
    heigh_price = content[0][1]
    low_price = content[0][2]
    price_dates.append(price_date)
    heigh_prices.append(heigh_price)
    low_prices.append(low_price)

2 pyecharts實(shí)現(xiàn)數(shù)據(jù)可視化

2.1 導(dǎo)入庫(kù)

import pyecharts.options as opts
from pyecharts.charts import Line

2.2 初始化畫(huà)布

Line(init_opts=opts.InitOpts(width="1200px", height="600px"))

2.3 根據(jù)需要傳入關(guān)鍵性數(shù)據(jù)并畫(huà)圖

    .add_yaxis(
        series_name="最低價(jià)",
        y_axis=low_prices,
        markpoint_opts=opts.MarkPointOpts(
            data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)]
        ),
        markline_opts=opts.MarkLineOpts(
            data=[
                opts.MarkLineItem(type_="average", name="平均值"),
                opts.MarkLineItem(symbol="none", x="90%", y="max"),
                opts.MarkLineItem(symbol="circle", type_="max", name="最高點(diǎn)"),
            ]
        ),
    )

tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=True)

2.4 將生成的文件形成HTML代碼并下載

.render("HTML名字填這里.html")

2.5 完整代碼展示

import pyecharts.options as opts
from pyecharts.charts import Line
 
(
    Line(init_opts=opts.InitOpts(width="1200px", height="600px"))
    .add_xaxis(xaxis_data=price_dates)
    .add_yaxis(
        series_name="最高價(jià)",
        y_axis=heigh_prices,
        markpoint_opts=opts.MarkPointOpts(
            data=[
                opts.MarkPointItem(type_="max", name="最大值"),
                opts.MarkPointItem(type_="min", name="最小值"),
            ]
        ),
        markline_opts=opts.MarkLineOpts(
            data=[opts.MarkLineItem(type_="average", name="平均值")]
        ),
    )
    .add_yaxis(
        series_name="最低價(jià)",
        y_axis=low_prices,
        markpoint_opts=opts.MarkPointOpts(
            data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)]
        ),
        markline_opts=opts.MarkLineOpts(
            data=[
                opts.MarkLineItem(type_="average", name="平均值"),
                opts.MarkLineItem(symbol="none", x="90%", y="max"),
                opts.MarkLineItem(symbol="circle", type_="max", name="最高點(diǎn)"),
            ]
        ),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="茅臺(tái)股票歷史數(shù)據(jù)可視化", subtitle="日期、最高價(jià)、最低價(jià)可視化"),
        tooltip_opts=opts.TooltipOpts(trigger="axis"),
        toolbox_opts=opts.ToolboxOpts(is_show=True),
        xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=True),
    )
    .render("everyDayPrice_change_line_chart2.html")
)

3 結(jié)果展示

到此這篇關(guān)于python實(shí)現(xiàn)股票歷史數(shù)據(jù)可視化分析案例的文章就介紹到這了,更多相關(guān)python股票數(shù)據(jù)可視化內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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