如何使用 python 从数据框中计算月平均值和周平均值？

Question

以下是我的数据框。如何在 python 中从这个数据框中计算月平均值和周平均值？我需要打印月份开始&结束和周开始&结束然后是月和周的平均值

**Input
SAMPLE DATASET**            
kpi_id  kpi_name    run_date    value
1       MTTR       5/17/2021    15
2       MTTR       5/18/2021    16
3       MTTR       5/19/2021    17
4       MTTR       5/20/2021    18
5       MTTR       5/21/2021    19
6       MTTR       5/22/2021    20
7       MTTR       5/23/2021    21
8       MTTR       5/24/2021    22
9       MTTR       5/25/2021    23
10      MTTR       5/26/2021    24
11      MTTR       5/27/2021    25


**expected output**
**monthly_mean**            
kpi_name    month_start month_end   value(mean)
    MTTR    5/1/2021    5/31/2021   20
**weekly_mean**         
kpi_name    week_start  week_end    value(mean)
    MTTR    5/17/2021   5/23/2021   18
    MTTR    5/24/2021   5/30/2021   23.5

Answer 1

从igrolvr 扩展answer 以匹配您的预期输出：

# transformation from string to datetime for groupby
df['run_date'] = pd.to_datetime(df['run_date'], format='%m/%d/%Y')

# monthly
# The groupby() will extract the last date in period, 
# reset_index() will make the group by keys to columns
monthly = df.groupby(by=['kpi_name', pd.Grouper(key='run_date', freq='M')])['value'].mean().reset_index()

# Getting the start of month 
monthly['month_start'] = monthly['run_date'] - pd.offsets.MonthBegin(1)

# Renaming the run_date column to month_end
monthly = monthly.rename({'run_date': 'month_end'}, axis=1)
print(monthly)

# weekly
# The groupby() will extract the last date in period, 
# reset_index() will make the group by keys to columns
weekly = df.groupby(by=['kpi_name', pd.Grouper(key='run_date', freq='W')])['value'].mean().reset_index()

# Getting the start of week and adding one day, 
# because start of week is sunday
weekly['week_start'] = weekly['run_date'] - pd.offsets.Week(1) + pd.offsets.Day(1)

# Renaming the run_date column to week_end
weekly = weekly.rename({'run_date': 'week_end'}, axis=1)
print(weekly)

Answer 2

groupby是你的朋友

monthly = df.groupby(pd.Grouper(key='run_date', freq='M')).mean() 
weekly = df.groupby(pd.Grouper(key='run_date', freq='W')).mean()