操作列和行

Manipulating columns and rows

def Resample_10mins(df, ZTD_station):
# ensure the time column is in the right format
  df['Date'] = pd.to_datetime(df.Date)

# round to the nearest 10 minute interval
# if you want to floor / ceil the time, you may use 
#`dt.floor` or `dt.ceil` instead of `dt.round`
  df['rounded_to_nearest_10_min'] = df.Date.dt.round('10min')

# to get the mean of all columns
  df = df.groupby('rounded_to_nearest_10_min').agg('mean')

# to get the mean of a specific column
  df = df.groupby('rounded_to_nearest_10_min').agg({ZTD_station: 'mean'})

# Rename date column
  df = df.rename(columns={df.columns[0]: 'Date' })
  # df.rename(columns={'rounded_to_nearest_10_min': 'Date'}, inplace=True)

  return df

我有以下代码,我用它来以 30 秒到 10 分钟的速率对我的数据帧进行重新采样。但是,我注意到列和行结构发生了变化(比较第二个和第三个数据帧)我想要第二个而不是第三个的结构。

Date  GNSS_BIEL
0  2011-01-01 00:00:00   2.247777
1  2011-01-01 00:00:30   2.246933
2  2011-01-01 00:01:00   2.245638
3  2011-01-01 00:01:30   2.244568
4  2011-01-01 00:02:00   2.243413
                               Date
rounded_to_nearest_10_min          
2011-01-01 00:00:00        2.244251
2011-01-01 00:10:00        2.242808
2011-01-01 00:20:00        2.242657
2011-01-01 00:30:00        2.243564
2011-01-01 00:40:00        2.249966

您可以使用内置的重采样方法:

df.resample('10min', on='Date').mean()

更多详情,see this tutorial