如何從沒有列名或行名的 Dataframe 中刪除字符串值

Question

我有這個 csv 文件“flagged_dates.csv”，其中包含字符串值。 其中一些是日期，而另一些的值為零。 我想擺脫零，但我正在努力尋找解決方案。 我想過使用類似 str.rstrip 的東西，但我需要我沒有的列名。 你能提出什么建議嗎？ 先感謝您：）

這是 dataframe 的示例：

    flagged_dates = pd.read_csv('/content/drive/MyDrive/shared/data/flag_raster.csv')
    print(flagged_dates.iloc[:10, :10].to_csv(index=False)) #The entire dataframe contains 100 rows and columns

Unnamed: 0,0,1,2,3,4,5,6,7,8
0,0,2019-10-10 21:59:17.074007,2015-10-13 00:55:55.544607,2017-05-24 06:00:15.959202,0,2016-12-07 09:01:04.729686,0,0,2019-05-29 11:16:44.130063
1,0,0,0,2019-02-21 07:15:12.114444,2017-04-29 17:44:49.584567,2017-06-28 11:26:30.686198,2019-03-25 10:18:28.397219,2019-05-01 01:27:21.282324,0
2,0,0,2016-09-22 04:08:48.025359,0,0,2016-09-24 17:35:26.833975,0,0,0
3,0,0,0,2015-07-12 21:13:44.182608,0,0,2017-10-03 22:17:52.257038,2019-01-04 08:20:07.684796,0
4,0,0,0,0,0,2016-03-04 10:12:21.341795,0,0,0
5,2016-08-23 09:22:21.965876,2018-05-01 09:12:19.511673,2017-12-12 07:00:04.313859,0,0,2016-10-23 15:30:11.193316,2016-03-01 01:22:12.548658,2015-06-14 21:36:41.142947,2018-08-19 23:37:49.534862
6,0,0,2019-01-29 16:23:27.070208,0,0,0,2016-08-08 01:13:21.147689,0,0
7,0,0,2017-12-04 22:51:46.265644,0,0,2016-05-13 05:11:55.885217,0,0,0
8,0,0,2018-03-16 03:45:21.555053,0,0,0,0,2019-12-04 04:04:20.600046,0
9,0,0,0,0,2018-01-10 08:28:51.902587,0,0,0,2015-05-05 06:25:43.124125

Answer 1

如果您沒有列名，您可以簡單地重命名您的列（它們將按照您在列表中的 state 的順序重命名）。

對於 4 列 dataframe：

df.columns = ['col1', 'col2', 'col3', 'col4']

我敢打賭，在某些時候您將不得不處理它，因此在解決列名問題的情況下開始數據爭論是一個好習慣。

Answer 2

如果您只對日期感興趣，您可以執行以下操作以獲取不包括0的日期列表

import pandas as pd

df1 = pd.read_csv('myCsv.csv')
my_list = df1.values.flatten()
my_list =  my_list[my_list!='0']

print(my_list) # my_list is an numpy.ndarray

輸入 myCsv.csv

0,2019-10-10 21:59:17.074007,2015-10-13 00:55:55.544607,2017-05-24 06:00:15.959202,0,2016-12-07 09:01:04.729686,0,0,2019-05-29 11:16:44.130063
0,0,0,2019-02-21 07:15:12.114444,2017-04-29 17:44:49.584567,2017-06-28 11:26:30.686198,2019-03-25 10:18:28.397219,2019-05-01 01:27:21.282324,0
0,0,2016-09-22 04:08:48.025359,0,0,2016-09-24 17:35:26.833975,0,0,0
0,0,0,2015-07-12 21:13:44.182608,0,0,2017-10-03 22:17:52.257038,2019-01-04 08:20:07.684796,0
0,0,0,0,0,2016-03-04 10:12:21.341795,0,0,0
2016-08-23 09:22:21.965876,2018-05-01 09:12:19.511673,2017-12-12 07:00:04.313859,0,0,2016-10-23 15:30:11.193316,2016-03-01 01:22:12.548658,2015-06-14 21:36:41.142947,2018-08-19 23:37:49.534862
0,0,2019-01-29 16:23:27.070208,0,0,0,2016-08-08 01:13:21.147689,0,0
0,0,2017-12-04 22:51:46.265644,0,0,2016-05-13 05:11:55.885217,0,0,0
0,0,2018-03-16 03:45:21.555053,0,0,0,0,2019-12-04 04:04:20.600046,0
0,0,0,0,2018-01-10 08:28:51.902587,0,0,0,2015-05-05 06:25:43.124125

Output

['2019-02-21 07:15:12.114444' '2017-04-29 17:44:49.584567'
 '2017-06-28 11:26:30.686198' '2019-03-25 10:18:28.397219'
 '2019-05-01 01:27:21.282324' '2016-09-22 04:08:48.025359'
 '2016-09-24 17:35:26.833975' '2015-07-12 21:13:44.182608'
 '2017-10-03 22:17:52.257038' '2019-01-04 08:20:07.684796'
 '2016-03-04 10:12:21.341795' '2016-08-23 09:22:21.965876'
 '2018-05-01 09:12:19.511673' '2017-12-12 07:00:04.313859'
 '2016-10-23 15:30:11.193316' '2016-03-01 01:22:12.548658'
 '2015-06-14 21:36:41.142947' '2018-08-19 23:37:49.534862'
 '2019-01-29 16:23:27.070208' '2016-08-08 01:13:21.147689'
 '2017-12-04 22:51:46.265644' '2016-05-13 05:11:55.885217'
 '2018-03-16 03:45:21.555053' '2019-12-04 04:04:20.600046'
 '2018-01-10 08:28:51.902587' '2015-05-05 06:25:43.124125']