Extending dates for values in a dataframe Python
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I have data that looks like:
Year Month Region Value1 Value2
2016 1 west 2 3
2016 1 east 4 5
2016 1 north 5 3
2016 2 west 6 4
2016 2 east 7 3
.
.
2016 12 west 2 3
2016 12 east 3 7
2016 12 north 6 8
2017 1 west 2 3
.
.
2018 7 west 1 1
2018 7 east 9 9
2018 7 north 5 1
I want to extend my values into Year 2021 for each Month but keep the previous values from the final month in the set (Month 7 of Year 2018).
The desired output would be attached to the ends of each set by Region, Month, and Year like:
2018 7 west 1 1
2018 7 east 9 9
2018 7 north 5 1
2018 8 west 1 1
2018 8 east 9 9
2018 8 north 5 1
2018 9 west 1 1
2018 9 east 9 9
2018 9 north 5 1
.
.
2019 7 west 1 1
2019 7 east 9 9
2019 7 north 5 1
.
.
2021 7 west 1 1
2021 7 east 9 9
2021 7 north 5 1
What would the best way to approach this be?
python python-3.x pandas python-2.7 dataframe
asked Nov 21 '18 at 23:23
HelloToEarthHelloToEarth
532215
add a comment |
0
I have data that looks like:
Year Month Region Value1 Value2
2016 1 west 2 3
2016 1 east 4 5
2016 1 north 5 3
2016 2 west 6 4
2016 2 east 7 3
.
.
2016 12 west 2 3
2016 12 east 3 7
2016 12 north 6 8
2017 1 west 2 3
.
.
2018 7 west 1 1
2018 7 east 9 9
2018 7 north 5 1
I want to extend my values into Year 2021 for each Month but keep the previous values from the final month in the set (Month 7 of Year 2018).
The desired output would be attached to the ends of each set by Region, Month, and Year like:
2018 7 west 1 1
2018 7 east 9 9
2018 7 north 5 1
2018 8 west 1 1
2018 8 east 9 9
2018 8 north 5 1
2018 9 west 1 1
2018 9 east 9 9
2018 9 north 5 1
.
.
2019 7 west 1 1
2019 7 east 9 9
2019 7 north 5 1
.
.
2021 7 west 1 1
2021 7 east 9 9
2021 7 north 5 1
What would the best way to approach this be?
python python-3.x pandas python-2.7 dataframe
asked Nov 21 '18 at 23:23
HelloToEarthHelloToEarth
532215
add a comment |
0
0
0
0
I have data that looks like:
Year Month Region Value1 Value2
2016 1 west 2 3
2016 1 east 4 5
2016 1 north 5 3
2016 2 west 6 4
2016 2 east 7 3
.
.
2016 12 west 2 3
2016 12 east 3 7
2016 12 north 6 8
2017 1 west 2 3
.
.
2018 7 west 1 1
2018 7 east 9 9
2018 7 north 5 1
I want to extend my values into Year 2021 for each Month but keep the previous values from the final month in the set (Month 7 of Year 2018).
The desired output would be attached to the ends of each set by Region, Month, and Year like:
2018 7 west 1 1
2018 7 east 9 9
2018 7 north 5 1
2018 8 west 1 1
2018 8 east 9 9
2018 8 north 5 1
2018 9 west 1 1
2018 9 east 9 9
2018 9 north 5 1
.
.
2019 7 west 1 1
2019 7 east 9 9
2019 7 north 5 1
.
.
2021 7 west 1 1
2021 7 east 9 9
2021 7 north 5 1
What would the best way to approach this be?
python python-3.x pandas python-2.7 dataframe
asked Nov 21 '18 at 23:23
HelloToEarthHelloToEarth
532215
I have data that looks like:
Year Month Region Value1 Value2
2016 1 west 2 3
2016 1 east 4 5
2016 1 north 5 3
2016 2 west 6 4
2016 2 east 7 3
.
.
2016 12 west 2 3
2016 12 east 3 7
2016 12 north 6 8
2017 1 west 2 3
.
.
2018 7 west 1 1
2018 7 east 9 9
2018 7 north 5 1
I want to extend my values into Year 2021 for each Month but keep the previous values from the final month in the set (Month 7 of Year 2018).
The desired output would be attached to the ends of each set by Region, Month, and Year like:
2018 7 west 1 1
2018 7 east 9 9
2018 7 north 5 1
2018 8 west 1 1
2018 8 east 9 9
2018 8 north 5 1
2018 9 west 1 1
2018 9 east 9 9
2018 9 north 5 1
.
.
2019 7 west 1 1
2019 7 east 9 9
2019 7 north 5 1
.
.
2021 7 west 1 1
2021 7 east 9 9
2021 7 north 5 1
What would the best way to approach this be?
python python-3.x pandas python-2.7 dataframe
python python-3.x pandas python-2.7 dataframe
asked Nov 21 '18 at 23:23
HelloToEarthHelloToEarth
532215
asked Nov 21 '18 at 23:23
HelloToEarthHelloToEarth
532215
asked Nov 21 '18 at 23:23
HelloToEarthHelloToEarth
532215
asked Nov 21 '18 at 23:23
HelloToEarthHelloToEarth
532215
asked Nov 21 '18 at 23:23
HelloToEarthHelloToEarth
532215
532215
add a comment |
add a comment |
1 Answer
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I would create a function that uses pd.date_range with a freq of months:
This functions assumes that you have three regions but can be modified for more.
def myFunction(df, periods, freq='M'):
# find the last date in the df
last = pd.to_datetime(df.Year*10000+df.Month*100+1,format='%Y%m%d').max()
# create new date range based on n periods with a freq of months
newDates = pd.date_range(start=last, periods=periods+1, freq=freq)
newDates = newDates[newDates>last]
newDates = newDates[:periods+1]
new_df = pd.DataFrame({'Date':newDates})[1:]
# convert Date to year and month columns
new_df['Year'] = new_df['Date'].dt.year
new_df['Month'] = new_df['Date'].dt.month
new_df.drop(columns='Date', inplace=True)
# add your three regions and ffill values
west = df[:-2].append([new_df], sort=False, ignore_index=True).ffill()
east = df[:-1].append([new_df], sort=False, ignore_index=True).ffill()
north = df.append([new_df], sort=False, ignore_index=True).ffill()
# append you three region dfs and drop duplicates
new = west.append([east,north], sort=False, ignore_index=True).drop_duplicates()
return new.sort_values(['Year', 'Month']).reset_index().drop(columns='index')
myFunction(df,3)
with periods set to three this will return the next three months...
Year Month Region Value1 Value2
0 2016 1 west 2.0 3.0
1 2016 1 east 4.0 5.0
2 2016 1 north 5.0 3.0
3 2016 2 west 6.0 4.0
4 2016 2 east 7.0 3.0
5 2016 12 west 2.0 3.0
6 2016 12 east 3.0 7.0
7 2016 12 north 6.0 8.0
8 2017 1 west 2.0 3.0
9 2018 7 west 1.0 1.0
10 2018 7 east 9.0 9.0
11 2018 7 north 5.0 1.0
12 2018 8 west 1.0 1.0
13 2018 8 east 9.0 9.0
14 2018 8 north 5.0 1.0
15 2018 9 west 1.0 1.0
16 2018 9 east 9.0 9.0
17 2018 9 north 5.0 1.0
18 2018 10 west 1.0 1.0
19 2018 10 east 9.0 9.0
20 2018 10 north 5.0 1.0
answered Nov 22 '18 at 3:19
ChrisChris
3,2082523
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
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1
I would create a function that uses pd.date_range with a freq of months:
This functions assumes that you have three regions but can be modified for more.
def myFunction(df, periods, freq='M'):
# find the last date in the df
last = pd.to_datetime(df.Year*10000+df.Month*100+1,format='%Y%m%d').max()
# create new date range based on n periods with a freq of months
newDates = pd.date_range(start=last, periods=periods+1, freq=freq)
newDates = newDates[newDates>last]
newDates = newDates[:periods+1]
new_df = pd.DataFrame({'Date':newDates})[1:]
# convert Date to year and month columns
new_df['Year'] = new_df['Date'].dt.year
new_df['Month'] = new_df['Date'].dt.month
new_df.drop(columns='Date', inplace=True)
# add your three regions and ffill values
west = df[:-2].append([new_df], sort=False, ignore_index=True).ffill()
east = df[:-1].append([new_df], sort=False, ignore_index=True).ffill()
north = df.append([new_df], sort=False, ignore_index=True).ffill()
# append you three region dfs and drop duplicates
new = west.append([east,north], sort=False, ignore_index=True).drop_duplicates()
return new.sort_values(['Year', 'Month']).reset_index().drop(columns='index')
myFunction(df,3)
with periods set to three this will return the next three months...
Year Month Region Value1 Value2
0 2016 1 west 2.0 3.0
1 2016 1 east 4.0 5.0
2 2016 1 north 5.0 3.0
3 2016 2 west 6.0 4.0
4 2016 2 east 7.0 3.0
5 2016 12 west 2.0 3.0
6 2016 12 east 3.0 7.0
7 2016 12 north 6.0 8.0
8 2017 1 west 2.0 3.0
9 2018 7 west 1.0 1.0
10 2018 7 east 9.0 9.0
11 2018 7 north 5.0 1.0
12 2018 8 west 1.0 1.0
13 2018 8 east 9.0 9.0
14 2018 8 north 5.0 1.0
15 2018 9 west 1.0 1.0
16 2018 9 east 9.0 9.0
17 2018 9 north 5.0 1.0
18 2018 10 west 1.0 1.0
19 2018 10 east 9.0 9.0
20 2018 10 north 5.0 1.0
answered Nov 22 '18 at 3:19
ChrisChris
3,2082523
add a comment |
1
I would create a function that uses pd.date_range with a freq of months:
This functions assumes that you have three regions but can be modified for more.
def myFunction(df, periods, freq='M'):
# find the last date in the df
last = pd.to_datetime(df.Year*10000+df.Month*100+1,format='%Y%m%d').max()
# create new date range based on n periods with a freq of months
newDates = pd.date_range(start=last, periods=periods+1, freq=freq)
newDates = newDates[newDates>last]
newDates = newDates[:periods+1]
new_df = pd.DataFrame({'Date':newDates})[1:]
# convert Date to year and month columns
new_df['Year'] = new_df['Date'].dt.year
new_df['Month'] = new_df['Date'].dt.month
new_df.drop(columns='Date', inplace=True)
# add your three regions and ffill values
west = df[:-2].append([new_df], sort=False, ignore_index=True).ffill()
east = df[:-1].append([new_df], sort=False, ignore_index=True).ffill()
north = df.append([new_df], sort=False, ignore_index=True).ffill()
# append you three region dfs and drop duplicates
new = west.append([east,north], sort=False, ignore_index=True).drop_duplicates()
return new.sort_values(['Year', 'Month']).reset_index().drop(columns='index')
myFunction(df,3)
with periods set to three this will return the next three months...
Year Month Region Value1 Value2
0 2016 1 west 2.0 3.0
1 2016 1 east 4.0 5.0
2 2016 1 north 5.0 3.0
3 2016 2 west 6.0 4.0
4 2016 2 east 7.0 3.0
5 2016 12 west 2.0 3.0
6 2016 12 east 3.0 7.0
7 2016 12 north 6.0 8.0
8 2017 1 west 2.0 3.0
9 2018 7 west 1.0 1.0
10 2018 7 east 9.0 9.0
11 2018 7 north 5.0 1.0
12 2018 8 west 1.0 1.0
13 2018 8 east 9.0 9.0
14 2018 8 north 5.0 1.0
15 2018 9 west 1.0 1.0
16 2018 9 east 9.0 9.0
17 2018 9 north 5.0 1.0
18 2018 10 west 1.0 1.0
19 2018 10 east 9.0 9.0
20 2018 10 north 5.0 1.0
answered Nov 22 '18 at 3:19
ChrisChris
3,2082523
add a comment |
1
1
1
I would create a function that uses pd.date_range with a freq of months:
This functions assumes that you have three regions but can be modified for more.
def myFunction(df, periods, freq='M'):
# find the last date in the df
last = pd.to_datetime(df.Year*10000+df.Month*100+1,format='%Y%m%d').max()
# create new date range based on n periods with a freq of months
newDates = pd.date_range(start=last, periods=periods+1, freq=freq)
newDates = newDates[newDates>last]
newDates = newDates[:periods+1]
new_df = pd.DataFrame({'Date':newDates})[1:]
# convert Date to year and month columns
new_df['Year'] = new_df['Date'].dt.year
new_df['Month'] = new_df['Date'].dt.month
new_df.drop(columns='Date', inplace=True)
# add your three regions and ffill values
west = df[:-2].append([new_df], sort=False, ignore_index=True).ffill()
east = df[:-1].append([new_df], sort=False, ignore_index=True).ffill()
north = df.append([new_df], sort=False, ignore_index=True).ffill()
# append you three region dfs and drop duplicates
new = west.append([east,north], sort=False, ignore_index=True).drop_duplicates()
return new.sort_values(['Year', 'Month']).reset_index().drop(columns='index')
myFunction(df,3)
with periods set to three this will return the next three months...
Year Month Region Value1 Value2
0 2016 1 west 2.0 3.0
1 2016 1 east 4.0 5.0
2 2016 1 north 5.0 3.0
3 2016 2 west 6.0 4.0
4 2016 2 east 7.0 3.0
5 2016 12 west 2.0 3.0
6 2016 12 east 3.0 7.0
7 2016 12 north 6.0 8.0
8 2017 1 west 2.0 3.0
9 2018 7 west 1.0 1.0
10 2018 7 east 9.0 9.0
11 2018 7 north 5.0 1.0
12 2018 8 west 1.0 1.0
13 2018 8 east 9.0 9.0
14 2018 8 north 5.0 1.0
15 2018 9 west 1.0 1.0
16 2018 9 east 9.0 9.0
17 2018 9 north 5.0 1.0
18 2018 10 west 1.0 1.0
19 2018 10 east 9.0 9.0
20 2018 10 north 5.0 1.0
answered Nov 22 '18 at 3:19
ChrisChris
3,2082523
I would create a function that uses pd.date_range with a freq of months:
This functions assumes that you have three regions but can be modified for more.
def myFunction(df, periods, freq='M'):
# find the last date in the df
last = pd.to_datetime(df.Year*10000+df.Month*100+1,format='%Y%m%d').max()
# create new date range based on n periods with a freq of months
newDates = pd.date_range(start=last, periods=periods+1, freq=freq)
newDates = newDates[newDates>last]
newDates = newDates[:periods+1]
new_df = pd.DataFrame({'Date':newDates})[1:]
# convert Date to year and month columns
new_df['Year'] = new_df['Date'].dt.year
new_df['Month'] = new_df['Date'].dt.month
new_df.drop(columns='Date', inplace=True)
# add your three regions and ffill values
west = df[:-2].append([new_df], sort=False, ignore_index=True).ffill()
east = df[:-1].append([new_df], sort=False, ignore_index=True).ffill()
north = df.append([new_df], sort=False, ignore_index=True).ffill()
# append you three region dfs and drop duplicates
new = west.append([east,north], sort=False, ignore_index=True).drop_duplicates()
return new.sort_values(['Year', 'Month']).reset_index().drop(columns='index')
myFunction(df,3)
with periods set to three this will return the next three months...
Year Month Region Value1 Value2
0 2016 1 west 2.0 3.0
1 2016 1 east 4.0 5.0
2 2016 1 north 5.0 3.0
3 2016 2 west 6.0 4.0
4 2016 2 east 7.0 3.0
5 2016 12 west 2.0 3.0
6 2016 12 east 3.0 7.0
7 2016 12 north 6.0 8.0
8 2017 1 west 2.0 3.0
9 2018 7 west 1.0 1.0
10 2018 7 east 9.0 9.0
11 2018 7 north 5.0 1.0
12 2018 8 west 1.0 1.0
13 2018 8 east 9.0 9.0
14 2018 8 north 5.0 1.0
15 2018 9 west 1.0 1.0
16 2018 9 east 9.0 9.0
17 2018 9 north 5.0 1.0
18 2018 10 west 1.0 1.0
19 2018 10 east 9.0 9.0
20 2018 10 north 5.0 1.0
answered Nov 22 '18 at 3:19
ChrisChris
3,2082523
answered Nov 22 '18 at 3:19
ChrisChris
3,2082523
answered Nov 22 '18 at 3:19
ChrisChris
3,2082523
answered Nov 22 '18 at 3:19
ChrisChris
3,2082523
3,2082523
add a comment |
add a comment |
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