如何在多个条件下对Pandas上的数据进行分组？

2019年7月28日 259次阅读

这是我的桌子

timestamp        date month  day   hour   price
0  2017-01-01 00:00  01/01/2017   Jan  Sun  00:00  60.23
1  2017-01-01 01:00  01/01/2017   Jan  Sun  01:00  60.73
2  2017-01-01 02:00  01/01/2017   Jan  Sun  02:00  75.99
3  2017-01-01 03:00  01/01/2017   Jan  Sun  03:00  60.76
4  2017-01-01 04:00  01/01/2017   Jan  Sun  04:00  49.01

我每天24小时都有数据,每天都有一整年的数据.

例如,我想将每个季节的数据分组为工作日和周末
Weekend_Winter =所有星期六和星期日的数据,分别为11月,12月,1月,2月

相当新手,所以任何帮助都会有用

最佳答案 如果想要按条件筛选数据,请使用
boolean indexing与比较
dayofweek创建的布尔掩码和
isin作为列表L中的检查成员资格：

#changed timestamp values only for better sample
print (df)
            timestamp        date month  day   hour  price
0 2017-01-01 00:00:00  01/01/2017   Jan  Sun  00:00  60.23
1 2017-01-03 00:00:00  01/01/2017   Jan  Sun  00:00  60.23
2 2017-02-01 01:00:00  01/01/2017   Jan  Sun  01:00  60.73
3 2017-02-05 01:00:00  01/01/2017   Jan  Sun  01:00  60.73
4 2017-03-01 02:00:00  01/01/2017   Jan  Sun  02:00  75.99
5 2017-04-01 03:00:00  01/01/2017   Jan  Sun  03:00  60.76
6 2017-11-01 04:00:00  01/01/2017   Jan  Sun  04:00  49.01

L = ['Nov','Dec','Jan','Feb']
mask = (df['timestamp'].dt.dayofweek > 4) & (df['month'].isin(L))
df1 = df[mask]
print (df1)
            timestamp        date month  day   hour  price
0 2017-01-01 00:00:00  01/01/2017   Jan  Sun  00:00  60.23
3 2017-02-05 01:00:00  01/01/2017   Jan  Sun  01:00  60.73
5 2017-04-01 03:00:00  01/01/2017   Jan  Sun  03:00  60.76

如果需要season的新列和日期类型：

df['season'] = (df['timestamp'].dt.month%12 + 3) // 3
df['state'] = np.where(df['timestamp'].dt.dayofweek > 4, 'weekend','weekdays')
print (df)
            timestamp        date month  day   hour  price  season     state
0 2017-01-01 00:00:00  01/01/2017   Jan  Sun  00:00  60.23       1   weekend
1 2017-01-03 00:00:00  01/01/2017   Jan  Sun  00:00  60.23       1  weekdays
2 2017-02-01 01:00:00  01/01/2017   Jan  Sun  01:00  60.73       1  weekdays
3 2017-02-05 01:00:00  01/01/2017   Jan  Sun  01:00  60.73       1   weekend
4 2017-03-01 02:00:00  01/01/2017   Jan  Sun  02:00  75.99       2  weekdays
5 2017-04-01 03:00:00  01/01/2017   Jan  Sun  03:00  60.76       2   weekend
6 2017-11-01 04:00:00  01/01/2017   Jan  Sun  04:00  49.01       4  weekdays

并且它可以用于具有聚合的groupby,例如总和：

df2 = df.groupby(['season','state'], as_index=False)['price'].sum()
print (df2)
   season     state   price
0       1  weekdays  120.96
1       1   weekend  120.96
2       2  weekdays   75.99
3       2   weekend   60.76
4       4  weekdays   49.01
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