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【代码+论文】通过ML、Time Series模型学习股价行为

=(0, 1, 2), seasonal_order=(0, 1, 2, 12),...']['2009':'2012'] mmm_arima_train = company_price_df['MMM']['2009':'2012'] chipotle_arima_train = company_price_df...['CMG']['2009':'2012'] duke_arima_train = company_price_df['DUK']['2009':'2012'] companies_train = [...companies_train: model = sm.tsa.statespace.SARIMAX(company_price, order...=(0, 1, 2), seasonal_order=(0, 1, 2, 12),

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绘图系列(2):利用 seaborn 绘制箱线图等图形

sns.set(style='darkgrid', color_codes = True) data = pd.read_csv(r'F:\SPC\stormevents\legacy\Stormdata_2012...plt.subplots(2, 2, figsize = (16, 8)) # 绘制箱线图 sns.boxplot(x = tora.TOR_F_SCALE, y = tora.TOR_WIDTH, order...=["EF0", "EF1", "EF2", "EF3", "EF4"], palette='vlag', ax = ax[0, 0]) # 类似箱线图，但是将每个点绘制到图中 sns.swarmplot...sns.violinplot(x = tora.TOR_F_SCALE, y = tora.TOR_LENGTH, order=["EF0", "EF1", "EF2", "EF3", "EF4"],...palette='vlag', ax = ax [1, 0]) plt.scatter(tora.TOR_LENGTH, tora.TOR_WIDTH, s = 12， axes = ax[1, 1

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Zipline 3.0 中文文档（一）

(ax=ax1) ax1.set_ylabel('Portfolio Value') ax2 = plt.subplot(212, sharex=ax1) perf.AAPL.plot(ax=ax2)...(ax=ax1) ax1.set_ylabel('Portfolio Value') ax2 = plt.subplot(212, sharex=ax1) perf.AAPL.plot(ax=ax2)...(ax=ax1) ax1.set_ylabel('Portfolio Value') ax2 = plt.subplot(212, sharex=ax1) perf.AAPL.plot(ax=ax2)...2012-01-04,58.57143,59.240002,58.468571,59.062859,65005500,0.0,1.0 2012-01-05,59.278572,59.792858,58.952858,59.718571,67817400,0.0,1.0...2012-01-04,58.57143,59.240002,58.468571,59.062859,65005500,0.0,1.0 2012-01-05,59.278572,59.792858,58.952858,59.718571,67817400,0.0,1.0

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数据科学 IPython 笔记本 8.12 文本和注解

=10, color='gray') ax.text('2012-1-1', 3950, "New Year's Day", **style) ax.text('2012-7-4', 4250, "Independence...Day", ha='center', **style) ax.text('2012-9-4', 4850, "Labor Day", ha='center', **style) ax.text('2012...-10-31', 4600, "Halloween", ha='right', **style) ax.text('2012-11-25', 4450, "Thanksgiving", ha='center...', **style) ax.text('2012-12-25', 3850, "Christmas ", ha='right', **style) # 标记轴域 ax.set(title='USA...='offset points') ax.annotate('', xy=('2012-9-1', 4850), xytext=('2012-9-7', 4850), xycoords

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使用 EarthPy 堆叠和裁剪tif栅格数据

RainStormChina/RainStormChina/2002/StormPeak2002.tif', '/home/mw/input/precip7227/RainStormChina/RainStormChina/2012.../StormPeak2012.tif', '/home/mw/input/precip7227/RainStormChina/RainStormChina/2003/StormPeak2003.tif...In [17]: fig, ax = plt.subplots(figsize=(12, 12)) ep.plot_rgb( array, ax=ax, stretch=True...In [20]: fig, ax = plt.subplots(figsize=(12, 12)) ep.plot_rgb( array_nodata, ax=ax, stretch...In [45]: # Open Landsat image as a Rasterio object in order to crop it stack_band_paths = glob.glob('

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《Pandas 1.x Cookbook · 第二版》第05章探索性数据分析

, rug=True, ax=ax) >>> fig.savefig( ......ax=g.ax, ... ) >>> g.ax.figure.savefig( ... "c5-catbox2.png", dpi=300 ... ) ?...col_order=[2012, 2014, 2016, 2018], ... col_wrap=2, ... ) >>> g.axes[0].figure.savefig( ......hue_order=[2012, 2014, 2016, 2018], ... ) >>> g.ax.figure.savefig( ......col_order=["Ford", "Tesla"], ... ) >>> res.fig.savefig( ...

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Python-Seaborn 17个超好看图表绘制

分簇散点图:stripplot #语法 ''' seaborn.stripplot(x=None, y=None, hue=None, data=None, order=None, hue_order=...条形图常规条形图:barplot #语法 ''' seaborn.barplot(x=None, y=None, hue=None, data=None, order=None, hue_order=...计数条形图:countplot #语法 ''' seaborn.countplot(x=None, y=None, hue=None, data=None, order=None, hue_order=...=axes[0]) #调节order和hue_order参数，可以控制x轴展示的顺序,linewidth调节线宽 sns.boxplot(x='菜系',y='评分',hue='难度',data=df,...hue_order=['简单','一般','较难'],ax=axes[1]) ?

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我用Facebook开源神器Prophet，预测股市行情基于Python（系列2）

04 422 2010-08-12 492 2010-11-19 563 2011-03-04 633 2011-06-14 703 2011-09-22 774 2012...-01-04 844 2012-04-16 914 2012-07-25 984 2012-11-05 1055 2013-02-19 1125 2013-05-30 1195...m = Prophet(changepoints=['2009-03-09', '2010-07-02', '2011-09-26', '2012-03-20', '2010-04-06']) forecast...fig, ax1 = plt.subplots() ax1.plot(two_years.SP500) ax1.plot(two_years.yhat) ax1.plot(two_years.yhat_upper...fig, ax1 = plt.subplots() ax1.plot(full_df.SP500) ax1.plot(full_df.yhat, color='black', linestyle=':'

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数据导入与预处理-拓展-pandas时间数据处理03

数据下载地址：训练集为2012 年 8 月至 2014 年 8月的数据，需要用这些数据预测接下来 7 个月的乘客数量。...从 2012 年 8 月- 2013 年 12 月的数据中构造一个数据集。...= fig.add_subplot(311) ax1.plot(train['Count']) ax2 = fig.add_subplot(312) ax2.plot(train['Count_diff..._1']) ax3 = fig.add_subplot(313) ax3.plot(train['Count_diff_2']) ax1.set_title("原始") ax2.set_title("1...y_hat_avg = test.copy() fit1 = sm.tsa.statespace.SARIMAX(train.Count, order=(2, 1, 4),seasonal_order=

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课前准备--单细胞CNV分析与CNV聚类（封装版）

sc.pp.normalize_total(adata)adata.var['symbol'] = adata.var.indexadata.var.index = adata.var['gene_ids']gene_order...= pd.read_csv('/home/samples/DB/scRNA/inferCNV/gene_order.txt',sep = '\t',index_col = 0)gene_intersect...= list(set(adata.var.index) & set(gene_order.index))adata = adata[:,gene_intersect]gene_order = gene_order.loc...[gene_intersect,:]adata.var.index.name = 'gene'tmp = pd.merge(adata.var,gene_order,on = 'gene_ids')tmp.index...ax=ax2, show=False)cnv.pl.umap(adata, color="celltype", ax=ax3)plt.savefig(outdir + '/' + sample + '.

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B2B营销中使用Python进行探索性数据分析

= fig.add_subplot(111)ax2 = ax.twinx()ax.bar(landing_page_2, percentage_lp)ax2.plot(landing_page_2,...= fig.add_subplot(111)ax2 = ax.twinx()ax.bar(segment_time.index, segment_time[‘sales cycle’])ax2.plot...#Figure 7fig = plt.figure(figsize = (10,4))ax = fig.add_subplot(111)ax2 = ax.twinx()ax.bar(lead_time.index...":"count"}) fig = plt.figure(figsize = (10,4))ax = fig.add_subplot(111)ax2 = ax.twinx()ax.bar(first_order_segment.index..., first_order_segment['first_order_time'])ax2.plot(first_order_segment.index, first_order_segment['mql_id

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Matplotlib数据分布型图表（3

基础语法： seaborn.boxenplot(x,y,hue,data,order,hue_order,orient,ax,**kwargs) x：x轴的数值列名（本实例中为season） y：y轴的数值列名...（本实例中为pm2_5） hue：分类显示的列名 data：采用的数据名称（本实例为df） order：x轴数值的顺序排列（列表） hue_order：分类显示的顺序排列 orient：排列方向，默认水平...= fig.add_subplot(131) sns.boxenplot(x = 'season', y = 'pm2_5', data = df, ax = ax1) ax1.set_title('..., ax = ax3, fliersize = 1, whis = 2) ax3.set_yticks([]) ax3.set_ylabel('') ax3.set_title('Boxplot 2*IQR...基础语法： seaborn.violinplot(x,y,data,hue,order,hue_order,orient,scale,inner,ax,**kwargs) 参数解释同增强箱型图一致，其他的参数解释

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Seaborn入门(一): 实现Boxplot

与之相关的函数seaborn.boxplot的具体参数为： seaborn.boxplot(x=None, y=None, hue=None, data=None, order=None, hue_order...order可以修改box的次序。 palatte给定调色板，也可以给自己的颜色列表。 orient给出box是是垂直的还是平行的，orient = 'h'是修改为平行的，'v'是垂直的。...default boxplot 对两列作图： ax = sns.boxplot(x="day", y="total_bill", data=tips) ?...grouping order参数修改box的次序： ax = sns.boxplot(x="time", y="tip", data=tips, order=["Dinner...horizontal.png sns.swarmplot显示出所有的点： ax = sns.boxplot(x="day", y="total_bill", data=tips) ax = sns.swarmplot

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数据挖掘从入门到放弃（五）seaborn 的数据可视化

=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, dodge=True, ax=None, *...','Male'], ax=axis2) sns.countplot(x='time', hue="sex", data=tips, order=['Dinner','Lunch'], ax=axis3...=axis1) sns.countplot(y='sex', hue="time", data=tips, order=['Female','Male'], ax=axis2) sns.countplot...(y='time', hue="sex", data=tips, order=['Dinner','Lunch'], ax=axis3) ?...(y='time', hue="sex", data=tips, order=['Dinner','Lunch'], ax=axis3) ?

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pandas入门3-2:识别异常值以及lambda 函数

1115.0 2012-12-10 NaN 1269.0 1269.0 2012-12-31 2000.0 NaN NaN 2013-12-...(1+Year.loc[2012,'YR_PCT_Change']) * Year.loc[2012,'Max'] 1566.8465510881595 数据可视化 --为每个state创建单独的图表。...':].fillna(method='pad').plot(ax=axes[0,0]) Daily.loc['GA']['CustomerCount']['2012':].fillna(method=...'pad').plot(ax=axes[0,1]) Daily.loc['TX']['CustomerCount']['2012':].fillna(method='pad').plot(ax=axes...[1,0]) Daily.loc['NY']['CustomerCount']['2012':].fillna(method='pad').plot(ax=axes[1,1]) #增加标题 axes

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探索性数据分析，Seaborn必会的几种图

0]) sns.boxplot(x="time", y="tip", hue="smoker", data=tips, palette="Set2", linewidth=2.5, order=["Dinner...", "Lunch"], hue_order = ["No","Yes"], ax=ax[1][1]) ?...order, hue_order：字符串列表，用于控制分类变量（对应的条形图）的绘制顺序，默认系统推断顺序。..., size_order, hue_order 可以根据这三个参数来设置展示顺序。...lmplot 线性拟合绘图函数，用于建立两个连续型变量的线性关系，参数order>1时，可以高阶拟合，默认是线性拟合。

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利用条形图绘制人口金字塔

/raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv") df.head() # 布局 fig, ax...= plt.subplots(figsize=(4, 8)) # 分组变量 group_col = 'Gender' # 按阶段排序 order_of_bars = df.Stage.unique...y='Stage', data=df.loc[df[group_col] == group, :], # 筛选数据 order...=order_of_bars, color=color, label=group, ax=ax,...# 指定轴 ) # 自定义轴 ax.set_xlabel("Users") ax.set_ylabel("Stage of Purchase") ax.set_title

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分布（三）利用python绘制箱线图

"], y=df["sepal_length"], linewidth=5, ax=ax[0]) ax_sub.set_title('自定义线宽') # 添加缺口 ax_sub = sns.boxplot...(x=df["species"], y=df["sepal_length"], notch=True, ax=ax[1]) ax_sub.set_title('添加缺口') # 自定义箱体 ax_sub..., y='sepal_length', data=df, order=["versicolor", "virginica", "setosa"], ax=ax[0]) ax_sub.set_title(...'指定顺序') # 按统计量降序：中位数 my_order = df.groupby(by=["species"])["sepal_length"].median().iloc[::-1].index...ax_sub = sns.boxplot(x='species', y='sepal_length', data=df, order=my_order, ax=ax[1]) ax_sub.set_title

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业务数据分析最佳案例！旅游业数据分析！⛵

= sns.countplot(x = "hotel", data = df, order = order, edgecolor = "black", palette = colors)for rect...= sns.countplot(x = "market_segment", data = df, order = order, edgecolor = "black",)for rect in ax.patches...= sns.countplot(x = "distribution_channel", data = df, order = order, edgecolor = "black", palette =...= sns.countplot(x = "meal", data = df, order = order, edgecolor = "black", palette = colors)for rect...= sns.countplot(x = "customer_type", data = df, order = order, edgecolor = "black")for rect in ax.patches

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seaborn从入门到精通03-绘图功能实现01-关系绘图

=None, col_order=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm...=None, markers=None, dashes=None, style_order=None, legend='auto', kind='scatter', height=5, aspect=...) ax.figure.set_size_inches(5,5) plt.title("3-hue-style-相同的离散值") ax=sns.relplot( data=tips,...=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, markers=True, style_order=None, legend...=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, dashes=True, markers=None, style_order

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【代码+论文】通过ML、Time Series模型学习股价行为

绘图系列(2):利用 seaborn 绘制箱线图等图形

Zipline 3.0 中文文档（一）

数据科学 IPython 笔记本 8.12 文本和注解

使用 EarthPy 堆叠和裁剪tif栅格数据

《Pandas 1.x Cookbook · 第二版》第05章探索性数据分析

Python-Seaborn 17个超好看图表绘制

我用Facebook开源神器Prophet，预测股市行情基于Python（系列2）

数据导入与预处理-拓展-pandas时间数据处理03

课前准备--单细胞CNV分析与CNV聚类（封装版）

B2B营销中使用Python进行探索性数据分析

Matplotlib数据分布型图表（3

Seaborn入门(一): 实现Boxplot

数据挖掘从入门到放弃（五）seaborn 的数据可视化

pandas入门3-2:识别异常值以及lambda 函数

探索性数据分析，Seaborn必会的几种图

利用条形图绘制人口金字塔

分布（三）利用python绘制箱线图

业务数据分析最佳案例！旅游业数据分析！⛵

seaborn从入门到精通03-绘图功能实现01-关系绘图

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