使用python和pandas將時間戳列拆分為CSV中的兩個新列

Question

我有一個超過210000行的大型CSV文件。 我是python和pandas的新手。 我想有效地遍歷timestamp列，將timestamp列拆分為2個新列（日期和時間），然后將新的日期列格式化為％Y％m％d並刪除新的time列。即，只寫回CSV文件新格式的日期列。 你怎么做到這一點 ？

輸入文件樣本：

   minit,timestamp,open,high,low,close
   0,2009-02-23 17:32:00,1.2708,1.2708,1.2706,1.2706
   1,2009-02-23 17:33:00,1.2708,1.2708,1.2705,1.2706
   2,2009-02-23 17:34:00,1.2706,1.2707,1.2702,1.2702
   3,2009-02-23 17:35:00,1.2704,1.2706,1.27,1.27
   4,2009-02-23 17:36:00,1.2701,1.2706,1.2698,1.2703
   5,2009-02-23 17:37:00,1.2703,1.2703,1.27,1.2702
   6,2009-02-23 17:38:00,1.2701,1.2701,1.2696,1.2697

輸出文件樣本：

   minit,date,open,high,low,close
   0,20090223,1.2708,1.2708,1.2706,1.2706
   1,20090223,1.2708,1.2708,1.2705,1.2706
   2,20090223,1.2706,1.2707,1.2702,1.2702
   3,20090223,1.2704,1.2706,1.27,1.27
   4,20090223,1.2701,1.2706,1.2698,1.2703
   5,20090223,1.2703,1.2703,1.27,1.2702
   6,20090223,1.2701,1.2701,1.2696,1.2697

我在Google搜索后開始編寫示例代碼來完成此操作：

     import csv
     import itertools
     import operator
     import time
     import datetime
     import pandas as pd
     from pandas import DataFrame, Timestamp
     from numpy import *

     def datestring_to_timestamp(str):
         return time.mktime(time.strptime(str, "%Y-%m-%d %H:%M:%S"))

     def timestamp_to_datestring(timestamp):
        return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp))

     def timestamp_to_float(str):
        return float(datetime.datetime.strptime(str, '%Y-%m-%d %H:%M:%S').strftime("%s"))

     def timestamp_to_intstring(str):
        return datetime.datetime.strptime(str, '%Y-%m-%d %H:%M:%S').strftime("%s")

    def timestamp_to_int(str):
        return int(datetime.datetime.strptime(str, '%Y-%m-%d %H:%M:%S').strftime("%s"))

    with open("inputfile.csv", 'rb') as input, open('outputfile.csv', 'wb') as output:
       reader = csv.reader(input, delimiter = ',')
       writer = csv.writer(output, delimiter = ',')

    # Need to process loop or process the timestamp column 

Answer 1

您可以在to_csv的參數中指定日期格式字符串，該字符串to_csv您的喜好輸出日期，而無需提取/轉換/添加新列等。

因此，使用read_csv加載數據：

df = pd.read_csv('mydata.csv', parse_dates=['timestamp']

In [15]:

df
Out[15]:
   minit           timestamp    open    high     low   close
0      0 2009-02-23 17:32:00  1.2708  1.2708  1.2706  1.2706
1      1 2009-02-23 17:33:00  1.2708  1.2708  1.2705  1.2706
2      2 2009-02-23 17:34:00  1.2706  1.2707  1.2702  1.2702
3      3 2009-02-23 17:35:00  1.2704  1.2706  1.2700  1.2700
4      4 2009-02-23 17:36:00  1.2701  1.2706  1.2698  1.2703
5      5 2009-02-23 17:37:00  1.2703  1.2703  1.2700  1.2702
6      6 2009-02-23 17:38:00  1.2701  1.2701  1.2696  1.2697

您可以根據需要在此階段重命名該列，然后可以將參數date_format='%Y%m%d' to to_csv`，這只會將日期部分輸出到csv，我們可以重新加載它並顯示什么它保存了：

In [19]:

df.rename(columns={'timestamp':'date'},inplace=True)
df.to_csv(r'c:\data\date.csv', date_format='%Y%m%d')
df1 = pd.read_csv(r'C:\data\date.csv', index_col=[0])
df1
Out[19]:
   minit      date    open    high     low   close
0      0  20090223  1.2708  1.2708  1.2706  1.2706
1      1  20090223  1.2708  1.2708  1.2705  1.2706
2      2  20090223  1.2706  1.2707  1.2702  1.2702
3      3  20090223  1.2704  1.2706  1.2700  1.2700
4      4  20090223  1.2701  1.2706  1.2698  1.2703
5      5  20090223  1.2703  1.2703  1.2700  1.2702
6      6  20090223  1.2701  1.2701  1.2696  1.2697