Question

DataFrame，其中Date为日期时间：

   Column   |       Date             
:-----------|----------------------:
    A       |   2018-08-05 17:06:01 
    A       |   2018-08-05 17:06:02 
    A       |   2018-08-05 17:06:03 
    B       |   2018-08-05 17:06:07 
    B       |   2018-08-05 17:06:09 
    B       |   2018-08-05 17:06:11

返回表为；

   Column   |       Date            
:-----------|----------------------:
    A       |   2018-08-05 17:06:02 
    B       |   2018-08-05 17:06:09

Answer 1

准备示例数据框：

# Initiate dataframe
date_var = "date"
df = pd.DataFrame(data=[['A', '2018-08-05 17:06:01'],
                        ['A', '2018-08-05 17:06:02'],
                        ['A', '2018-08-05 17:06:03'],
                        ['B', '2018-08-05 17:06:07'],
                        ['B', '2018-08-05 17:06:09'],
                        ['B', '2018-08-05 17:06:11']],
                  columns=['column', date_var])

# Convert date-column to proper pandas Datetime-values/pd.Timestamps
df[date_var] = pd.to_datetime(df[date_var])

提取所需的平均时间戳值：

# Extract the numeric value associated to each timestamp (epoch time)
# NOTE: this is being accomplished via accessing the .value - attribute of each Timestamp in the column
In:
[tsp.value for tsp in df[date_var]]
Out:
[
    1533488761000000000, 1533488762000000000, 1533488763000000000,
    1533488767000000000, 1533488769000000000, 1533488771000000000
]

# Use this to calculate the mean, then convert the result back to a timestamp
In:
pd.Timestamp(np.nanmean([tsp.value for tsp in df[date_var]]))
Out:
Timestamp('2018-08-05 17:06:05.500000')

Answer 2

例如。

您的数据：

df = pd.DataFrame(data=[['A', '2018-08-05 17:06:01'],
                   ['A', '2018-08-05 17:06:02'],
                   ['A', '2018-08-05 17:06:03'],
                   ['B', '2018-08-05 17:06:07'],
                   ['B', '2018-08-05 17:06:09'],
                   ['B', '2018-08-05 17:06:11']],
            columns = ['column', 'date'])

解决方案：

df.date = pd.to_datetime(df.date).values.astype(np.int64)

df = pd.DataFrame(pd.to_datetime(df.groupby('column').mean().date))

输出：

                      date
column                    
A      2018-08-05 17:06:02
B      2018-08-05 17:06:09

我希望这会有所帮助。

熊猫约会时间平均值

2 个答案: