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How to filter or select rows that contain only dates in a pandas column
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I have a pandas data frame, with a column as follows:
df["Date"]
2015-04-11 00:00:00
2015-03-11 00:00:00
NaN
2014-11-15 00:00:00
its not available
2017-01-27 00:00:00
2016-05-21 00:00:00
was not detected
2015-09-16 00:00:00
incomplete
...
I would like to filter out only those rows that contain the dates.
df["Date"]
2015-04-11 00:00:00
2015-03-11 00:00:00
2014-11-15 00:00:00
2017-01-27 00:00:00
2016-05-21 00:00:00
2015-09-16 00:00:00
....
Please let me know if there is a way to filter the dates. Thank you
python pandas datetime
asked Mar 8 at 20:20
user9463814user9463814
606
add a comment |
I have a pandas data frame, with a column as follows:
df["Date"]
2015-04-11 00:00:00
2015-03-11 00:00:00
NaN
2014-11-15 00:00:00
its not available
2017-01-27 00:00:00
2016-05-21 00:00:00
was not detected
2015-09-16 00:00:00
incomplete
...
I would like to filter out only those rows that contain the dates.
df["Date"]
2015-04-11 00:00:00
2015-03-11 00:00:00
2014-11-15 00:00:00
2017-01-27 00:00:00
2016-05-21 00:00:00
2015-09-16 00:00:00
....
Please let me know if there is a way to filter the dates. Thank you
python pandas datetime
asked Mar 8 at 20:20
user9463814user9463814
606
add a comment |
I have a pandas data frame, with a column as follows:
df["Date"]
2015-04-11 00:00:00
2015-03-11 00:00:00
NaN
2014-11-15 00:00:00
its not available
2017-01-27 00:00:00
2016-05-21 00:00:00
was not detected
2015-09-16 00:00:00
incomplete
...
I would like to filter out only those rows that contain the dates.
df["Date"]
2015-04-11 00:00:00
2015-03-11 00:00:00
2014-11-15 00:00:00
2017-01-27 00:00:00
2016-05-21 00:00:00
2015-09-16 00:00:00
....
Please let me know if there is a way to filter the dates. Thank you
python pandas datetime
asked Mar 8 at 20:20
user9463814user9463814
606
I have a pandas data frame, with a column as follows:
df["Date"]
2015-04-11 00:00:00
2015-03-11 00:00:00
NaN
2014-11-15 00:00:00
its not available
2017-01-27 00:00:00
2016-05-21 00:00:00
was not detected
2015-09-16 00:00:00
incomplete
...
I would like to filter out only those rows that contain the dates.
df["Date"]
2015-04-11 00:00:00
2015-03-11 00:00:00
2014-11-15 00:00:00
2017-01-27 00:00:00
2016-05-21 00:00:00
2015-09-16 00:00:00
....
Please let me know if there is a way to filter the dates. Thank you
python pandas datetime
python pandas datetime
asked Mar 8 at 20:20
user9463814user9463814
606
asked Mar 8 at 20:20
user9463814user9463814
606
asked Mar 8 at 20:20
user9463814user9463814
606
asked Mar 8 at 20:20
user9463814user9463814
606
asked Mar 8 at 20:20
user9463814user9463814
606
606
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
Using to_datetime + errors='coerce' with notna
df=df.loc[pd.to_datetime(df.Date,errors='coerce').notna()].copy()
df
Out[925]:
Date
0 2015-04-11 00:00:00
1 2015-03-11 00:00:00
3 2014-11-15 00:00:00
5 2017-01-27 00:00:00
6 2016-05-21 00:00:00
8 2015-09-16 00:00:00
answered Mar 8 at 20:21
Wen-BenWen-Ben
127k83872
++ for also adding the copy() to avoid possible SettingWithCopyWarning later.
– cs95
Mar 8 at 20:51
add a comment |
I'm assuming because they are mixed dates and strings that the column is full of object and not datetime datatype. Are there no actual times in your dataframe? If not (meaning they are all 00:00:00) you can do a partial string search for the 0's.
df[df['Date'].str.contains('00:00:00')]
answered Mar 8 at 20:24
55thSwiss55thSwiss
171111
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Using to_datetime + errors='coerce' with notna
df=df.loc[pd.to_datetime(df.Date,errors='coerce').notna()].copy()
df
Out[925]:
Date
0 2015-04-11 00:00:00
1 2015-03-11 00:00:00
3 2014-11-15 00:00:00
5 2017-01-27 00:00:00
6 2016-05-21 00:00:00
8 2015-09-16 00:00:00
answered Mar 8 at 20:21
Wen-BenWen-Ben
127k83872
++ for also adding the copy() to avoid possible SettingWithCopyWarning later.
– cs95
Mar 8 at 20:51
add a comment |
Using to_datetime + errors='coerce' with notna
df=df.loc[pd.to_datetime(df.Date,errors='coerce').notna()].copy()
df
Out[925]:
Date
0 2015-04-11 00:00:00
1 2015-03-11 00:00:00
3 2014-11-15 00:00:00
5 2017-01-27 00:00:00
6 2016-05-21 00:00:00
8 2015-09-16 00:00:00
answered Mar 8 at 20:21
Wen-BenWen-Ben
127k83872
++ for also adding the copy() to avoid possible SettingWithCopyWarning later.
– cs95
Mar 8 at 20:51
add a comment |
Using to_datetime + errors='coerce' with notna
df=df.loc[pd.to_datetime(df.Date,errors='coerce').notna()].copy()
df
Out[925]:
Date
0 2015-04-11 00:00:00
1 2015-03-11 00:00:00
3 2014-11-15 00:00:00
5 2017-01-27 00:00:00
6 2016-05-21 00:00:00
8 2015-09-16 00:00:00
answered Mar 8 at 20:21
Wen-BenWen-Ben
127k83872
Using to_datetime + errors='coerce' with notna
df=df.loc[pd.to_datetime(df.Date,errors='coerce').notna()].copy()
df
Out[925]:
Date
0 2015-04-11 00:00:00
1 2015-03-11 00:00:00
3 2014-11-15 00:00:00
5 2017-01-27 00:00:00
6 2016-05-21 00:00:00
8 2015-09-16 00:00:00
answered Mar 8 at 20:21
Wen-BenWen-Ben
127k83872
answered Mar 8 at 20:21
Wen-BenWen-Ben
127k83872
answered Mar 8 at 20:21
Wen-BenWen-Ben
127k83872
answered Mar 8 at 20:21
Wen-BenWen-Ben
127k83872
127k83872
++ for also adding the copy() to avoid possible SettingWithCopyWarning later.
– cs95
Mar 8 at 20:51
add a comment |
++ for also adding the copy() to avoid possible SettingWithCopyWarning later.
– cs95
Mar 8 at 20:51
++ for also adding the copy() to avoid possible SettingWithCopyWarning later.
– cs95
Mar 8 at 20:51
++ for also adding the copy() to avoid possible SettingWithCopyWarning later.
– cs95
Mar 8 at 20:51
add a comment |
I'm assuming because they are mixed dates and strings that the column is full of object and not datetime datatype. Are there no actual times in your dataframe? If not (meaning they are all 00:00:00) you can do a partial string search for the 0's.
df[df['Date'].str.contains('00:00:00')]
answered Mar 8 at 20:24
55thSwiss55thSwiss
171111
add a comment |
I'm assuming because they are mixed dates and strings that the column is full of object and not datetime datatype. Are there no actual times in your dataframe? If not (meaning they are all 00:00:00) you can do a partial string search for the 0's.
df[df['Date'].str.contains('00:00:00')]
answered Mar 8 at 20:24
55thSwiss55thSwiss
171111
add a comment |
I'm assuming because they are mixed dates and strings that the column is full of object and not datetime datatype. Are there no actual times in your dataframe? If not (meaning they are all 00:00:00) you can do a partial string search for the 0's.
df[df['Date'].str.contains('00:00:00')]
answered Mar 8 at 20:24
55thSwiss55thSwiss
171111
I'm assuming because they are mixed dates and strings that the column is full of object and not datetime datatype. Are there no actual times in your dataframe? If not (meaning they are all 00:00:00) you can do a partial string search for the 0's.
df[df['Date'].str.contains('00:00:00')]
answered Mar 8 at 20:24
55thSwiss55thSwiss
171111
answered Mar 8 at 20:24
55thSwiss55thSwiss
171111
answered Mar 8 at 20:24
55thSwiss55thSwiss
171111
answered Mar 8 at 20:24
55thSwiss55thSwiss
171111
171111
add a comment |
add a comment |
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