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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def plot_summary_data(ax, df, info_df, color_list, symbol_list, is_charge=False, plot_style=None):
 """creates a plot of the selected df-data in the given axes. Typical usage: standard_fig, (ce_ax, cap_ax, ir_ax) = plt.subplots(nrows=3, ncols=1, sharex=True) list_of_lines, plot_style = plot_summary_data(ce_ax, ce_df, info_df=info_df, color_list=color_list, symbol_list=symbol_list, is_charge=False, plot_style=plot_style) the ce_df is a pandas.DataFrame with ce-values for all your selected cells. the color_list and the symbol_list are both list with colors and symbols to use when plotting to ensure that if you have several subplots (axes), then the lines and symbols match up for each given cell. Args: ax: the matplotlib axes to plot on df: DataFrame with the data to plot info_df: DataFrame with info for the data color_list: List of colors to use symbol_list: List of symbols to use is_charge: plots open symbols if True plot_style: selected style of the plot Returns: list of the matplotlib lines (convenient to have if you are adding a custom legend) the plot style (dictionary with matplotlib plotstyles) """


    logger.debug("trying to plot summary data")
    if plot_style is None:
        logger.debug("no plot_style given, using default")
        plot_style = DEFAULT_PLOT_STYLE
    else:
        logger.debug("plot_style given")

    list_of_lines = list()

    for datacol in df.columns:
        group = info_df.get_value(datacol[0], "groups")
        sub_group = info_df.get_value(datacol[0], "sub_groups")
        color = color_list[group - 1]
        marker = symbol_list[sub_group - 1]
        plot_style["marker"] = marker
        plot_style["markeredgecolor"] = color
        plot_style["color"] = color
        plot_style["markerfacecolor"] = 'none'
        logger.debug("selecting color for group: " + str(color))

        if not is_charge:
            plot_style["markerfacecolor"] = color
        lines = ax.plot(df[datacol], **plot_style)
        list_of_lines.extend(lines)

    return list_of_lines, plot_style