Mixing
Pd.DF dropping rows on datetime hourly data
I have the following issue (probably an easy one but I'm still learning).
My data has a time interval of 15minutes and runs for a couple of days.
Can anyone help me how I can drop all the rows except for the 15 minutes starting at 17:00 each day?
python
edited Nov 21 '18 at 19:35
m0nhawk
15.5k83160
asked Nov 21 '18 at 19:29
BartBart
1
add a comment |
I have the following issue (probably an easy one but I'm still learning).
My data has a time interval of 15minutes and runs for a couple of days.
Can anyone help me how I can drop all the rows except for the 15 minutes starting at 17:00 each day?
python
edited Nov 21 '18 at 19:35
m0nhawk
15.5k83160
asked Nov 21 '18 at 19:29
BartBart
1
add a comment |
I have the following issue (probably an easy one but I'm still learning).
My data has a time interval of 15minutes and runs for a couple of days.
Can anyone help me how I can drop all the rows except for the 15 minutes starting at 17:00 each day?
python
edited Nov 21 '18 at 19:35
m0nhawk
15.5k83160
asked Nov 21 '18 at 19:29
BartBart
1
I have the following issue (probably an easy one but I'm still learning).
My data has a time interval of 15minutes and runs for a couple of days.
Can anyone help me how I can drop all the rows except for the 15 minutes starting at 17:00 each day?
python
python
edited Nov 21 '18 at 19:35
m0nhawk
15.5k83160
asked Nov 21 '18 at 19:29
BartBart
1
edited Nov 21 '18 at 19:35
m0nhawk
15.5k83160
asked Nov 21 '18 at 19:29
BartBart
1
edited Nov 21 '18 at 19:35
m0nhawk
15.5k83160
edited Nov 21 '18 at 19:35
m0nhawk
15.5k83160
edited Nov 21 '18 at 19:35
m0nhawk
15.5k83160
15.5k83160
asked Nov 21 '18 at 19:29
BartBart
1
asked Nov 21 '18 at 19:29
BartBart
1
asked Nov 21 '18 at 19:29
BartBart
1
1
add a comment |
add a comment |
1 Answer
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Found the answer myself and it might be usefull for others.
The code I used is the following.
df= df.drop(df[df.index.hour != 17].index)
df= df.drop(df[df.index.minute != 00].index)
This drops all the rows which are not 17:00
I'm aware that this possibly isn't the cleanest code but it does the job, cleaner alternatives are still welcome though.
answered Nov 22 '18 at 10:27
BartBart
1
add a comment |
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1 Answer
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active
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Found the answer myself and it might be usefull for others.
The code I used is the following.
df= df.drop(df[df.index.hour != 17].index)
df= df.drop(df[df.index.minute != 00].index)
This drops all the rows which are not 17:00
I'm aware that this possibly isn't the cleanest code but it does the job, cleaner alternatives are still welcome though.
answered Nov 22 '18 at 10:27
BartBart
1
add a comment |
Found the answer myself and it might be usefull for others.
The code I used is the following.
df= df.drop(df[df.index.hour != 17].index)
df= df.drop(df[df.index.minute != 00].index)
This drops all the rows which are not 17:00
I'm aware that this possibly isn't the cleanest code but it does the job, cleaner alternatives are still welcome though.
answered Nov 22 '18 at 10:27
BartBart
1
add a comment |
Found the answer myself and it might be usefull for others.
The code I used is the following.
df= df.drop(df[df.index.hour != 17].index)
df= df.drop(df[df.index.minute != 00].index)
This drops all the rows which are not 17:00
I'm aware that this possibly isn't the cleanest code but it does the job, cleaner alternatives are still welcome though.
answered Nov 22 '18 at 10:27
BartBart
1
Found the answer myself and it might be usefull for others.
The code I used is the following.
df= df.drop(df[df.index.hour != 17].index)
df= df.drop(df[df.index.minute != 00].index)
This drops all the rows which are not 17:00
I'm aware that this possibly isn't the cleanest code but it does the job, cleaner alternatives are still welcome though.
answered Nov 22 '18 at 10:27
BartBart
1
answered Nov 22 '18 at 10:27
BartBart
1
answered Nov 22 '18 at 10:27
BartBart
1
answered Nov 22 '18 at 10:27
BartBart
1
1
add a comment |
add a comment |
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