plotting.py 文件源码

def _plotMonthlyStats(stats, columns, groupBy=True):
    dataToPlot = stats.copy()
    # Group by month and rename date column
    if groupBy:
        dataToPlot = dataToPlot.groupby(stats['date'].dt.month).mean()
        dataToPlot = dataToPlot.reset_index().rename(columns={'date': 'month'})

    # change stats from columns to row attribute
    dataToPlot = pd.melt(dataToPlot, id_vars=['month'], value_vars=columns,
                         var_name='stats', value_name='val')
    # Rename stats and weekdays
    dataToPlot['stats'].replace(NAMES, inplace=True)
    dataToPlot['month'].replace(months, inplace=True)
    order = [m for m in monthsOrder if m in dataToPlot['month'].unique()]
    # Plot
    g = sns.factorplot(data=dataToPlot, x="month", y="val", col="stats", order=order, kind="bar", sharey=False)
    g.set_xticklabels(rotation=45)
    g.set(xlabel='')
    return g
    #sns.plt.show()

# def _plotMonthlyStats(stats, columns):
#     """
#     Plot aggregated (mean) stats by month
#     :param stats: data to plot
#     :param columns: columns from stats to plot
#     """
#     MEASURE_NAME = 'month'
#     months={1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', 7:'Jul', 8:'Aug',
#             9:'Sep', 10:'Oct', 11:'Nov', 12:'Dec'}
#     order = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
#     stats[MEASURE_NAME] = stats[MEASURE_NAME].map(months)
#
#     order = [m for m in order if m in stats[MEASURE_NAME].unique()]
#
#     f, axes = getAxes(2,2)
#     for i, c in enumerate(columns):
#         if c in NAMES:
#             c = NAMES[c]
#         g = sns.barplot(x=MEASURE_NAME, y=c, data=stats, order=order, ax=axes[i])
#         g.set_xlabel('')
#     sns.plt.show()