python - 使用时间戳索引将行添加到数据框

Question

我正在尝试向 Pandas 数据框动态添加新行。索引是一个时间戳，我无法弄清楚如何在不弄乱索引的情况下插入新行。代码的第一部分创建数据框:

data = {'time_stamp': ['2014-05-01 18:47:05.069', '2014-05-01 18:47:05.119', '2014-05-02 18:47:05.230',],
        'col_a': [34, 25, 26],
        'col_b' : [21,32,43]}
df = pd.DataFrame(data, columns = ['time_stamp', 'col_a', 'col_b'])
df['time_stamp'] = pd.to_datetime(df['time_stamp'], format="%Y-%m-%d %H:%M:%S.%f")
df.index = df['time_stamp'] # Make time_stamp the index
del df['time_stamp']        # Drop the initial time_stamp column
print df

结果:

                             col_a  col_b
time_stamp                           
2014-05-01 18:47:05.069     34     21
2014-05-01 18:47:05.119     25     32
2014-05-02 18:47:05.230     26     43

尝试使用 concat 添加一行(与 append 相同的问题):

#Insert new row (corresponding to an incoming update message with a time stamp an a new value on col_a
ts = pd.to_datetime("2014-05-04 18:47:05.487", format="%Y-%m-%d %H:%M:%S.%f")
new_row = pd.DataFrame([[11]], columns = ["col_a"])
df = pd.concat([df, pd.DataFrame(new_row)], ignore_index=False)
print df

结果:

                            col_a  col_b
2014-05-01 18:47:05.069000     34   21.0
2014-05-01 18:47:05.119000     25   32.0
2014-05-02 18:47:05.230000     26   43.0
0                              11    NaN

如果我用名为“time_frame”的列和相应的时间戳扩展“new_row”，它将创建一个名为“time_stamp”的新列，而不是在索引列中插入新值。

                            col_a  col_b              time_stamp
2014-05-01 18:47:05.069000     34   21.0                     NaT
2014-05-01 18:47:05.119000     25   32.0                     NaT
2014-05-02 18:47:05.230000     26   43.0                     NaT
0                              11    NaN 2014-05-04 18:47:05.487

任何想法将不胜感激。

Answer 1

让我们尝试在 pd.DataFrame 构造中使用 index 参数。

ts = pd.to_datetime("2014-05-04 18:47:05.487", format="%Y-%m-%d %H:%M:%S.%f")
new_row = pd.DataFrame([[11]], columns = ["col_a"], index=[ts])
df1 = pd.concat([df, pd.DataFrame(new_row)], ignore_index=False)
print(df1)

输出:

                        col_a  col_b
2014-05-01 18:47:05.069     34   21.0
2014-05-01 18:47:05.119     25   32.0
2014-05-02 18:47:05.230     26   43.0
2014-05-04 18:47:05.487     11    NaN