Plotting with Pandas Matplotlib Seaborn and Numpy

Here are the helper functions for plotting datasets.

Charting functions with matplotlib, numpy, pandas, and seaborn

Change the footnote with:

export PLOT_FOOTNOTE="custom footnote on images"

analysis_engine.charts.plot_df(log_label, title, column_list, df, xcol='date', xlabel='Date', ylabel='Pricing', linestyle='-', color='blue', show_plot=True, dropna_for_all=True)

Parameters:

• log_label – log identifier
• title – title of the plot
• column_list – list of columns in the df to show
• df – initialized pandas.DataFrame
• xcol – x-axis column in the initialized pandas.DataFrame
• xlabel – x-axis label
• ylabel – y-axis label
• linestyle – style of the plot line
• color – color to use
• show_plot – bool to show the plot
• dropna_for_all – optional - bool to toggle keep None’s in the plot df (default is drop them for display purposes)

analysis_engine.charts.dist_plot(log_label, df, width=10.0, height=10.0, title='Distribution Plot', style='default', xlabel='', ylabel='', show_plot=True, dropna_for_all=True)

Show a distribution plot for the passed in dataframe: df

Parameters:

• log_label – log identifier
• df – initialized pandas.DataFrame
• width – width of figure
• height – height of figure
• style – style to use
• xlabel – x-axis label
• ylabel – y-axis label
• show_plot – bool to show plot or not
• dropna_for_all – optional - bool to toggle keep None’s in the plot df (default is drop them for display purposes)

analysis_engine.charts.show_with_entities(log_label, xlabel, ylabel, title, ax, fig, legend_list=None, show_plot=True)

Helper for showing a plot with a legend and a footnoe

Parameters:

• log_label – log identifier
• xlabel – x-axis label
• ylabel – y-axis label
• title – title of the plot
• ax – axes
• fig – figure
• legend_list – list of legend items to show
• show_plot – bool to show the plot

analysis_engine.charts.plot_overlay_pricing_and_volume(log_label, ticker, df, xlabel=None, ylabel=None, high_color='#CC1100', close_color='#3498db', volume_color='#2ECC71', date_format='%Y-%m-%d %I:%M:%S %p', show_plot=True, dropna_for_all=True)

Plot pricing (high, low, open, close) and volume as an overlay off the x-axis

Here is a sample chart from the Stock Analysis Jupyter Intro Notebook

Parameters:

• log_label – log identifier
• ticker – ticker name
• df – timeseries pandas.DateFrame
• xlabel – x-axis label
• ylabel – y-axis label
• high_color – optional - high plot color
• close_color – optional - close plot color
• volume_color – optional - volume color
• data_format – optional - date format string this must be a valid value for the df['date'] column that would work with: datetime.datetime.stftime(date_format)
• show_plot – optional - bool to show the plot
• dropna_for_all – optional - bool to toggle keep None’s in the plot df (default is drop them for display purposes)

analysis_engine.charts.plot_hloc_pricing(log_label, ticker, df, title, show_plot=True, dropna_for_all=True)

Plot the high, low, open and close columns together on a chart

Parameters:

• log_label – log identifier
• ticker – ticker
• df – initialized pandas.DataFrame
• title – title for the chart
• show_plot – bool to show the plot
• dropna_for_all – optional - bool to toggle keep None’s in the plot df (default is drop them for display purposes)

analysis_engine.charts.add_footnote(fig=None, xpos=0.9, ypos=0.01, text=None, color='#888888', fontsize=8)

Add a footnote based off the environment key: PLOT_FOOTNOTE

Parameters:

• fig – add the footnote to this figure object
• xpos – x-axes position
• ypos – y-axis position
• text – text in the footnote
• color – font color
• fontsize – text size for the footnote text

analysis_engine.charts.set_common_seaborn_fonts()

Set the font and text style

analysis_engine.charts.send_final_log(log_label, fn_name, result)

Parameters:

• log_label – log identifier
• fn_name – function name
• result – dictionary result