来自两个变量列的Matplotlib条形图-熊猫数据操作（主要是）

Question

我有一个时间索引表，该表的BLIP列只有两个值“ XX”和“ YY”。目的是显示x轴以下的计数“ XX”和“ YY”，其中“ YY”。我正在尝试使用Wes McKenney关于数据分析的书（我认为是第26页）中的代码从熊猫表中创建正确的数据结构：df = base_df.drop(columns=dropcols).set_index('Created')group = ['f2','BLIP']df0 = df_minus.groupby(group)agg_counts = df0.size().unstack().fillna(0)indexer = agg_counts.sum(1).argsort()count_subset = agg_counts.take(indexer).copy()table = count_subset.groupby('BLIP').resample('MS').count().unstack('BLIP')['BLIP']chart = table.plot.bar(title = chart_title, x=None, color = ['green', 'red', 'grey']);线agg_counts = df0.size().unstack().fillna(0) 导致以下错误：TypeError: 'numpy.int32' object is not callable我在这里找到了一段代码的宝石，但是找不到解密它的文档。data['values'].plot(kind='bar', color=data.positive.map({True: 'g', False: 'r'}))这似乎是非常简单的，但是我对此颇为关注。大熊猫表格式类似于create_date f1 f2 f3 BLIP f5...dt_stamp    X  Y  Z  XX   K1dt_stamp    S  R  Y  YY   K3dt_stamp    P  P  T  XX   K1等等。我尝试过杰西的建议df_plus =df[df['BLIP']=='XX']df_minus=df[df['BLIP']=='YY']ax = plt.axes()ax.bar(df_plus.index, df_plus['BLIP'], width=0.4, color='g')ax.bar(df_neg.index, df_minus['BLIP'], width=0.4, color='r')ax.autoscale()plt.show()这导致ValueError: shape mismatch: objects cannot be broadcast to a single shape整体解决方案df = base_dfplt.clf()fig = plt.figure()width = 8height = 6fig.set_size_inches(width, height)chart_title = 'YTD CR Trend Summary'df_plus =df[df['BLIP'] == 'XX']df_minus=df[df['BLIP']== 'IYY']p =  df_plus.resample('MS').count()['BLIP'].fillna(0)n = df_minus.resample('MS').count()['BLIP'].apply(lambda x: int(-x)) print(chart_title, len(df), p.sum(), n.sum())plt.clf()fig = plt.figure()# ax = fig.add_subplot(1, 1, 1)ax = plt.axes(label=chart_title) #label suppresses warningif p.sum() != False:    ax.bar(p.index, p, width=10, color='g') if n.sum() != False:    ax.bar(n.index, n, width=10, color='r')plt.suptitle(chart_title, fontsize=11)filename = f'{graph_images_dir}{chart_title}.png'print(f'Saving {filename}')plt.savefig(filename,  bbox_inches='tight', pad_inches=0.5, dpi=200)plt.show()