在python中按条件过滤并绘制批处理图

Question

我有一个数据集df，如下图：

    id         timestamp        data     group_id
99  265 2019-11-28 15:44:34.027  22.5         1
100 266 2019-11-28 15:44:34.027  23.5         2
101 267 2019-11-28 15:44:34.027  27.5         3
102 273 2019-11-28 15:44:38.653  22.5         1
104 275 2019-11-28 15:44:38.653  22.5         2

我已经为按特定 group_id 和日期分组的数据块绘制了一个图表，例如。 group_id ==3，date =2020-01-01，使用以下代码：

df['timestamp'] = pd.to_datetime(df['timestamp'])

GROUP_ID = 2
df = df[df['group_id'] == GROUP_ID]

df['Date'] = [datetime.datetime.date(d) for d in df['timestamp']] 
df = df[df['Date'] == pd.to_datetime('2020-01-01')]      

df.plot(x='timestamp', y='data', figsize=(42, 16)) 
plt.axhline(y=40, color='r', linestyle='-')
plt.axhline(y=25, color='b', linestyle='-')

df['top_lim'] = 40
df['bottom_lim'] = 25

plt.fill_between(df['timestamp'], df['bottom_lim'], df['data'],
                where=(df['data'] >= df['bottom_lim'])&(df['data'] <= df['top_lim']),
                facecolor='orange', alpha=0.3)

mask = (df['data'] <= df['top_lim'])&(df['data'] >= df['bottom_lim'])
plt.scatter(df['timestamp'][mask], df['data'][mask], marker='.', color='black')

cumulated_time = df['timestamp'][mask].diff().sum()
plt.gcf().subplots_adjust(left = 0.3)

plt.xlabel('Timestamp')
plt.ylabel('Data')
plt.show()

现在我想为每个date 绘制每个group_id 的图表。我该怎么做？有没有办法按这两个条件对数据进行分组并绘制图表？还是使用for-loop 更好？

Answer 1

使用 for 循环可以采取以下方法。假设每个组有 2 个日期，一个很好的绘图方法是有 2 列，行等于组数

rows=len(groups) #set the desired number of rows
cols=2 #set the desired number of columns

fig, ax = plt.subplots(rows, cols, figsize=(13,8),sharex=False,sharey=False) # if you want to turn off sharing axis.
g=0 #to iterate over rows/cols
d=0 #to iterate over rows/cols
for group in groups:
    for date in dates:
        GROUP_ID = group
        df = df[df['group_id'] == GROUP_ID]
        df['Date'] = [datetime.datetime.date(d) for d in df['timestamp']] 
        df = df[df['Date'] == date]      
        df.plot(x='timestamp', y='data', figsize=(42, 16)) 
        ax[g][d].axhline(y=40, color='r', linestyle='-')
        ax[g][d].axhline(y=25, color='b', linestyle='-')
        df['top_lim'] = 40
        df['bottom_lim'] = 25
        ax[g][d].fill_between(df['timestamp'], df['bottom_lim'], df['data'],
                        where=(df['data'] >= df['bottom_lim'])&(df['data'] <= df['top_lim']),
                        facecolor='orange', alpha=0.3)
        mask = (df['data'] <= df['top_lim'])&(df['data'] >= df['bottom_lim'])
        ax[g][d].scatter(df['timestamp'][mask], df['data'][mask], marker='.', color='black')
        cumulated_time = df['timestamp'][mask].diff().sum()

        d=d+1
        if d==1:
            g=g
        else:
            g=g+1
            d=0


fig.text(0.5, -0.01, 'Timestamp', ha='center', va='center',fontsize=20)
fig.text(-0.01, 0.5, 'Data', ha='center', va='center', rotation='vertical',fontsize=20)
plt.subplots_adjust(left = 0.3)