如何根據列值（0/1）在Pandas中的分組時間序列資料中顯示不同的水平條顏色？

我有以下來自一百萬行的采樣資料框。它將顯示例外行的值計數、只有例外行的資料框和繪圖。

輸入資料：

df_sample = pd.DataFrame({
    'AbsoluteTopImpressionPercentage': [0.0,  1.0,  1.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  1.0,  0.75,  1.0,  1.0,  0.0,  0.0,  0.0,  1.0,  1.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.5,  0.0,  0.5,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  1.0,  1.0,  0.0,  0.0,  0.5,  0.0,  0.0,  0.0,  1.0,  1.0], 
    'AdFormat': ['TEXT',  'TEXT',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'TEXT',  'TEXT',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'TEXT',  'UNKNOWN',  'TEXT',  'TEXT',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'TEXT',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'TEXT',  'UNKNOWN',  'UNKNOWN',  'TEXT',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'UNKNOWN',  'TEXT',  'UNKNOWN',  'TEXT',  'UNKNOWN',  'UNKNOWN'], 
    'AdNetworkType1': ['SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH'], 
    'AdNetworkType2': ['SEARCH',  'SEARCH',  'SEARCH',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH',  'SEARCH_PARTNERS',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH_PARTNERS',  'SEARCH_PARTNERS',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH',  'SEARCH'], 
    'AllConversionRate': [0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0], 
    'Clicks': [0,  1,  0,  0,  0,  0,  0,  0,  0,  1,  4,  0,  0,  0,  0,  0,  0,  1,  1,  1,  1,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  10,  0,  1,  0,  0,  1,  0], 
    'ConversionRate': [0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0], 
    'Ctr': [0.0,  1.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  1.0,  0.2105,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  1.0,  0.3333,  1.0,  0.25,  0.0,  0.5,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.0,  0.2326,  0.0,  1.0,  0.0,  0.0,  1.0,  0.0], 
    'Date': ['2021-04-09 00:00:00 00:00',  '2021-04-19 00:00:00 00:00',  '2022-08-24 00:00:00 00:00',  '2021-07-25 00:00:00 00:00',  '2022-07-29 00:00:00 00:00',  '2022-05-27 00:00:00 00:00',  '2022-05-18 00:00:00 00:00',  '2022-07-24 00:00:00 00:00',  '2021-02-05 00:00:00 00:00',  '2021-04-30 00:00:00 00:00',  '2021-07-11 00:00:00 00:00',  '2021-05-24 00:00:00 00:00',  '2022-05-22 00:00:00 00:00',  '2021-07-01 00:00:00 00:00',  '2021-07-11 00:00:00 00:00',  '2022-03-24 00:00:00 00:00',  '2021-05-12 00:00:00 00:00',  '2022-06-14 00:00:00 00:00',  '2021-04-27 00:00:00 00:00',  '2021-01-29 00:00:00 00:00',  '2022-09-08 00:00:00 00:00',  '2021-06-07 00:00:00 00:00',  '2022-05-28 00:00:00 00:00',  '2022-03-26 00:00:00 00:00',  '2021-04-09 00:00:00 00:00',  '2022-05-22 00:00:00 00:00',  '2021-05-28 00:00:00 00:00',  '2022-05-22 00:00:00 00:00',  '2021-07-06 00:00:00 00:00',  '2021-07-14 00:00:00 00:00',  '2021-06-24 00:00:00 00:00',  '2021-03-24 00:00:00 00:00',  '2021-06-03 00:00:00 00:00',  '2022-05-30 00:00:00 00:00',  '2021-03-15 00:00:00 00:00',  '2022-08-05 00:00:00 00:00',  '2021-07-06 00:00:00 00:00',  '2022-03-30 00:00:00 00:00',  '2022-09-07 00:00:00 00:00',  '2021-05-27 00:00:00 00:00',  '2021-06-04 00:00:00 00:00',  '2022-04-16 00:00:00 00:00',  '2022-05-22 00:00:00 00:00',  '2021-07-08 00:00:00 00:00',  '2022-05-26 00:00:00 00:00',  '2021-02-09 00:00:00 00:00',  '2022-04-27 00:00:00 00:00',  '2021-05-06 00:00:00 00:00',  '2021-06-29 00:00:00 00:00',  '2022-06-01 00:00:00 00:00'], 
    'Anomaly': [0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0]
})

我的代碼：

df_sample = df_sample.sort_values(by='Date')
df_sample['Date'] = df_sample['Date'].apply(pd.to_datetime)
print(df_sample.Anomaly.value_counts())
print('')
display(df_sample.loc[df_sample['Anomaly'] == 1])

colors=[]
for val in df_sample['Anomaly']:
    if val == 1:
        colors.append('red')
    else:
        colors.append('blue')

# or
#colors = pd.cut(df['Sum'].tolist(), [-np.inf, 10, 20, np.inf],
#                labels=['green', 'orange', 'red'])

#ax = df['Sum'].plot(kind='barh',color=colors)

fig, axs = plt.subplots(figsize=(22, 12))
df_sample.groupby(df_sample['Date'].dt.date)["Clicks"].sum().plot(kind='barh', ax=axs, color=colors)
plt.xlabel("Clicks")
plt.ylabel("Dates")

every_nth = 7
for n, label in enumerate(axs.yaxis.get_ticklabels()):
    if n % every_nth != 0:
        label.set_visible(False)

輸出：

現在在輸出中，我希望那些 df_sample.Anomaly 值 == 1 的行在圖形水平條中顯示為紅色而不是藍色。任何想法？

uj5u.com熱心網友回復：

在不過多更改代碼的情況下，您需要在分組資料上創建顏色串列，稍后您將繪制這些顏色串列。

我創建的行out用作max列的聚合Anomaly。在每組的示例資料中，Anomaly只有 0 或只有 1。如果這些值不同，您可以在此處決定您想要做什么（例如取第一個或最后一個，將它們相加，最小值或最大值）

然后根據新資料構建顏色串列out['Anomaly']并根據out['Clicks']

df_sample['Date'] = pd.to_datetime(df_sample['Date'])
df_sample = df_sample.sort_values(by='Date')

out = df_sample.groupby(df_sample['Date'].dt.date).agg({'Clicks' : 'sum', 'Anomaly': 'max'})
print(out)

#collect colors based on that data
colors = ['red' if val==1 else 'blue' for val in out['Anomaly']]

fig, axs = plt.subplots(figsize=(10,8))
out['Clicks'].plot(kind='barh', ax=axs, color=colors)
axs.set_xlabel("Clicks")
axs.set_ylabel("Dates")

every_nth = 7
for n, label in enumerate(axs.yaxis.get_ticklabels()):
    if n % every_nth != 0:
        label.set_visible(False)

標籤：Python熊猫数据框matplotlib时间序列

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