目錄1 數(shù)據(jù)預(yù)處理1.1 股票歷史數(shù)據(jù)csv文件讀取1.2 關(guān)鍵數(shù)據(jù)——在csv文件中選擇性提取“列”1.3 數(shù)據(jù)類型轉(zhuǎn)換1.4 數(shù)據(jù)按列提取并累加性存入列表2 pyecharts實(shí)現(xiàn)數(shù)據(jù)可視化2.1 導(dǎo)入庫2.2 初始化畫布2.3 根據(jù)需要傳入關(guān)鍵性數(shù)據(jù)并畫圖2.4 將生成的文件形成HTML代碼并下載2.5 完整代碼展示3 結(jié)果展示

投資有風(fēng)險(xiǎn)，選擇需謹(jǐn)慎。 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！

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 pdimport 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

import pyecharts.options as optsfrom pyecharts.charts import Line2.2 初始化畫布

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 optsfrom 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

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