Question

我的数据框 df1 包含以下信息：

        Eng      Install date  Car
0       eng1       3/6/2010    car1
1       eng1     10/25/2010    car1
2       eng1       1/1/2014    car2
3       eng2       1/1/2011    car3
4       eng3       1/1/2014    car1
5       eng4       1/1/2015    car1
6       eng4       1/5/2016    car3
7       eng4       5/8/2017    car2
8       eng5       2/3/2010    car1
9       eng5       1/1/2011    car4

我想绘制条形间隔，显示一个汽车历史记录（选定汽车内的发动机旋转时间范围）

这是car1的示例历史记录：

start       end         Engine No.
2/3/2010    3/6/2010    eng5
3/6/2010    1/1/2014    eng1
1/1/2014    1/1/2015    eng3
1/1/2015    Today       eng4

Answer 1

这不是你要求的，但希望这足够了：

df = pd.DataFrame({'Eng':['eng1','eng1','eng1','eng2','eng3','eng4','eng4','eng4','eng5','eng5'],
'Install date':['3/6/2010','10/25/2010','1/1/2014','1/1/2011','1/1/2014','1/1/2015','1/5/2016','5/8/2017','2/3/2010','1/1/2011'],
'Car':['car1','car1','car2','car3','car1','car1','car3','car2','car1','car4']})

df
Out[47]:
    Eng Install date   Car
0  eng1     3/6/2010  car1
1  eng1   10/25/2010  car1
2  eng1     1/1/2014  car2
3  eng2     1/1/2011  car3
4  eng3     1/1/2014  car1
5  eng4     1/1/2015  car1
6  eng4     1/5/2016  car3
7  eng4     5/8/2017  car2
8  eng5     2/3/2010  car1
9  eng5     1/1/2011  car4
df['Install date'] = pd.to_datetime(df['Install date'])
for car in df.Car.unique():
    most_recent_eng = df.loc[df.Car == car].groupby('Install date').max().tail(1).Eng.iloc[0]
    new_df = df.loc[df.Car == car].append(pd.DataFrame({'Car':[car],'Eng':[most_recent_eng],'Install date':[pd.to_datetime('now')]}), sort=False)
    new_df.pivot_table(values='Car',columns='Eng',index='Install date', aggfunc='count').resample('1d').ffill().plot(kind='line',linewidth=10, title=car)

结果：

Answer 2

这是有点不同的方法。对于标签，请使用ax.annotate。我将标签定为日期，但不知道您是否要使用这些日期作为标签或发动机编号。我确定您是从这里得到的：

import pandas as pd
df = pd.DataFrame({'Eng':['eng1','eng1','eng1','eng2','eng3','eng4','eng4','eng4','eng5','eng5'],
'Install date':['3/6/2010','10/25/2010','1/1/2014','1/1/2011','1/1/2014','1/1/2015','1/5/2016','5/8/2017','2/3/2010','1/1/2011'],
'Car':['car1','car1','car2','car3','car1','car1','car3','car2','car1','car4']})

# df
# Out[47]:
#     Eng Install date   Car
# 0  eng1     3/6/2010  car1
# 1  eng1   10/25/2010  car1
# 2  eng1     1/1/2014  car2
# 3  eng2     1/1/2011  car3
# 4  eng3     1/1/2014  car1
# 5  eng4     1/1/2015  car1
# 6  eng4     1/5/2016  car3
# 7  eng4     5/8/2017  car2
# 8  eng5     2/3/2010  car1
# 9  eng5     1/1/2011  car4
df['Install date'] = pd.to_datetime(df['Install date'])
for car in df.Car.unique():
    most_recent_eng = df.loc[df.Car == car].groupby('Install date').max().tail(1).Eng.iloc[0]
    new_df = df.loc[df.Car == car].append(pd.DataFrame({'Car':[car],'Eng':[most_recent_eng],'Install date':[pd.to_datetime('now')]}), sort=False)
    piv = new_df.pivot_table(values='Car',columns='Eng',index='Install date', aggfunc='count')
    piv = piv.reindex(columns=piv.ffill().sum().sort_values(ascending=False).index).ffill()
    ax = piv.plot(marker='o', linestyle='-',linewidth=2, title=car, ms=4)
    j = 0.005
    i = 1
    for date, eng in zip(new_df[:-1]['Install date'].tolist(),new_df[:-1]['Eng'].tolist()):
        ax.annotate(date.strftime("%Y-%m-%d"), (date, 1.00), xycoords='data',xytext=(date, 1.00+i*j), textcoords='data',
            arrowprops=dict(arrowstyle="->"),
            )
        j+=0.005
        i = -1*i

制作：

如何在python中绘制时间间隔数据？

2 个答案: