pandas- drop column with duplicated header after performing 'asfreq'
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I have data like
DATE COUNT
2018-01-01 1
2018-01-02 1
2018-01-06 1
2018-01-07 1
I use df=df.asfreq('D', fill_value=0) to fill in the missing dates:
DATE COUNT
DATE
2018-01-01 2018-01-01 1
2018-01-02 2018-01-02 1
2018-01-03 0
2018-01-04 0
2018-01-05 0
2018-01-06 2018-01-06 1
2018-01-07 2018-01-07 1
How would I delete the original DATE featuring missing dates?
python-3.x pandas
asked Nov 26 '18 at 21:26
machumpmachump
397116
add a comment |
I have data like
DATE COUNT
2018-01-01 1
2018-01-02 1
2018-01-06 1
2018-01-07 1
I use df=df.asfreq('D', fill_value=0) to fill in the missing dates:
DATE COUNT
DATE
2018-01-01 2018-01-01 1
2018-01-02 2018-01-02 1
2018-01-03 0
2018-01-04 0
2018-01-05 0
2018-01-06 2018-01-06 1
2018-01-07 2018-01-07 1
How would I delete the original DATE featuring missing dates?
python-3.x pandas
asked Nov 26 '18 at 21:26
machumpmachump
397116
You can usedf.drop("DATE", axis=1). This will leave theindexDATEin place.
– Evan
Nov 26 '18 at 21:38
1
I assumed this question was a duplicate but couldn't find one. Pandas docs here: pandas.pydata.org/pandas-docs/stable/generated/…
– Evan
Nov 26 '18 at 21:38
3
df.set_index('DATE').asfreq('D', fill_value=0), won't have the duplicate column. Do you set your index toDATEsomewhere without dropping the column?
– user3483203
Nov 26 '18 at 21:39
add a comment |
I have data like
DATE COUNT
2018-01-01 1
2018-01-02 1
2018-01-06 1
2018-01-07 1
I use df=df.asfreq('D', fill_value=0) to fill in the missing dates:
DATE COUNT
DATE
2018-01-01 2018-01-01 1
2018-01-02 2018-01-02 1
2018-01-03 0
2018-01-04 0
2018-01-05 0
2018-01-06 2018-01-06 1
2018-01-07 2018-01-07 1
How would I delete the original DATE featuring missing dates?
python-3.x pandas
asked Nov 26 '18 at 21:26
machumpmachump
397116
I have data like
DATE COUNT
2018-01-01 1
2018-01-02 1
2018-01-06 1
2018-01-07 1
I use df=df.asfreq('D', fill_value=0) to fill in the missing dates:
DATE COUNT
DATE
2018-01-01 2018-01-01 1
2018-01-02 2018-01-02 1
2018-01-03 0
2018-01-04 0
2018-01-05 0
2018-01-06 2018-01-06 1
2018-01-07 2018-01-07 1
How would I delete the original DATE featuring missing dates?
python-3.x pandas
python-3.x pandas
asked Nov 26 '18 at 21:26
machumpmachump
397116
asked Nov 26 '18 at 21:26
machumpmachump
397116
asked Nov 26 '18 at 21:26
machumpmachump
397116
asked Nov 26 '18 at 21:26
machumpmachump
397116
asked Nov 26 '18 at 21:26
machumpmachump
397116
397116
You can usedf.drop("DATE", axis=1). This will leave theindexDATEin place.
– Evan
Nov 26 '18 at 21:38
1
I assumed this question was a duplicate but couldn't find one. Pandas docs here: pandas.pydata.org/pandas-docs/stable/generated/…
– Evan
Nov 26 '18 at 21:38
3
df.set_index('DATE').asfreq('D', fill_value=0), won't have the duplicate column. Do you set your index toDATEsomewhere without dropping the column?
– user3483203
Nov 26 '18 at 21:39
add a comment |
You can usedf.drop("DATE", axis=1). This will leave theindexDATEin place.
– Evan
Nov 26 '18 at 21:38
1
I assumed this question was a duplicate but couldn't find one. Pandas docs here: pandas.pydata.org/pandas-docs/stable/generated/…
– Evan
Nov 26 '18 at 21:38
3
df.set_index('DATE').asfreq('D', fill_value=0), won't have the duplicate column. Do you set your index toDATEsomewhere without dropping the column?
– user3483203
Nov 26 '18 at 21:39
You can use
df.drop("DATE", axis=1). This will leave the index DATE in place.– Evan
Nov 26 '18 at 21:38
You can use
df.drop("DATE", axis=1). This will leave the index DATE in place.– Evan
Nov 26 '18 at 21:38
1
1
I assumed this question was a duplicate but couldn't find one. Pandas docs here: pandas.pydata.org/pandas-docs/stable/generated/…
– Evan
Nov 26 '18 at 21:38
I assumed this question was a duplicate but couldn't find one. Pandas docs here: pandas.pydata.org/pandas-docs/stable/generated/…
– Evan
Nov 26 '18 at 21:38
3
3
df.set_index('DATE').asfreq('D', fill_value=0), won't have the duplicate column. Do you set your index to DATE somewhere without dropping the column?– user3483203
Nov 26 '18 at 21:39
df.set_index('DATE').asfreq('D', fill_value=0), won't have the duplicate column. Do you set your index to DATE somewhere without dropping the column?– user3483203
Nov 26 '18 at 21:39
add a comment |
1 Answer
1
active
oldest
votes
Assuming that the index is datetime, you could also resample by mean to the original sample rate of this dataframe. This would keep the original data and put NaNs when data is missing.
df = df.resample('D').mean()
If you want to fill the missing date's with 0's you can do:
df = df.resample('D').mean().fillna(0)
answered Nov 26 '18 at 22:44
deKeijzerdeKeijzer
8712
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Assuming that the index is datetime, you could also resample by mean to the original sample rate of this dataframe. This would keep the original data and put NaNs when data is missing.
df = df.resample('D').mean()
If you want to fill the missing date's with 0's you can do:
df = df.resample('D').mean().fillna(0)
answered Nov 26 '18 at 22:44
deKeijzerdeKeijzer
8712
add a comment |
Assuming that the index is datetime, you could also resample by mean to the original sample rate of this dataframe. This would keep the original data and put NaNs when data is missing.
df = df.resample('D').mean()
If you want to fill the missing date's with 0's you can do:
df = df.resample('D').mean().fillna(0)
answered Nov 26 '18 at 22:44
deKeijzerdeKeijzer
8712
add a comment |
Assuming that the index is datetime, you could also resample by mean to the original sample rate of this dataframe. This would keep the original data and put NaNs when data is missing.
df = df.resample('D').mean()
If you want to fill the missing date's with 0's you can do:
df = df.resample('D').mean().fillna(0)
answered Nov 26 '18 at 22:44
deKeijzerdeKeijzer
8712
Assuming that the index is datetime, you could also resample by mean to the original sample rate of this dataframe. This would keep the original data and put NaNs when data is missing.
df = df.resample('D').mean()
If you want to fill the missing date's with 0's you can do:
df = df.resample('D').mean().fillna(0)
answered Nov 26 '18 at 22:44
deKeijzerdeKeijzer
8712
answered Nov 26 '18 at 22:44
deKeijzerdeKeijzer
8712
answered Nov 26 '18 at 22:44
deKeijzerdeKeijzer
8712
answered Nov 26 '18 at 22:44
deKeijzerdeKeijzer
8712
8712
add a comment |
add a comment |
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You can use
df.drop("DATE", axis=1). This will leave theindexDATEin place.– Evan
Nov 26 '18 at 21:38
1
I assumed this question was a duplicate but couldn't find one. Pandas docs here: pandas.pydata.org/pandas-docs/stable/generated/…
– Evan
Nov 26 '18 at 21:38
3
df.set_index('DATE').asfreq('D', fill_value=0), won't have the duplicate column. Do you set your index toDATEsomewhere without dropping the column?– user3483203
Nov 26 '18 at 21:39