如何合并多个数据帧并按时间戳对它们进行排序 - Pandas Python

Question

I open all the data files, I transform them into dataframe and I make a list of them. Each dataframe has the same structure.

Here the code :

path = r'/home/afdg/Documents/Partage_Ubuntu/XML/Results'
all_csv_files = glob.glob(path + "/*.csv")

list_df = []

for filename in all_csv_files :
  df = pd.read_csv(filename, usecols=['duration', 'begin', 'end', 'time' ])
  df_DisponibilityAlarm = df.copy()
  df_DisponibilityAlarm = df_DisponibilityAlarm.drop(['duration','time'], 1)
  list_df.append(df_DisponibilityAlarm)

I would like to merge all the dataframe into one and sort by timestamp. I tried this but it doesn't work. I don't know how to connect my list of dataframes with the sorting. :

df_DisponibilityAlarm = (df_DisponibilityAlarm.stack()
  .rename_axis([None, 'Flag'])
  .reset_index(level=1, name='Timestamp'))

df_DisponibilityAlarm = df_DisponibilityAlarm.sort_values(by=['Timestamp'])

When I use concat list I have more rows than the original files : The shape of the files :

(358, 2)                                                                                             
(1690, 2)                                                                                            
(508, 2)                                                                                             
(3872, 2)                                                                                            
(13129, 2)                                                                                           
(2, 2)                                                                                               
(46, 2)                                                                                              
(92, 2)   

Result : (19697,2) and after : (19708, 2)

11 rows in more

Can you help me please ?

Answer 1

您应该连接数据框列表：

df_all = pd.concat(list_df)