Creating rows as per duration using datetime pandas
1
I am facing an issue to write a code using datetime. I had created a scenario I am working on. Can someone help me out on the code.
Input:
Name, Channel, Duration, Start_time
John, A, 2, 16:00:00
Joseph, B, 3, 15:05:00
Output:
Name, Channel, Duration, Start_time
John, A, 2, 16:00:00
John, A, 2, 16:01:00
Joseph, B, 3, 15:05:00
Joseph, B, 3, 15:06:00
Joseph, B, 3, 15:07:00
Thank you in advance.
enter image description here
python pandas datetime timedelta
edited Nov 19 '18 at 6:40
Vivek Kalyanarangan
5,0361827
asked Nov 19 '18 at 6:39
Srikanth AyithySrikanth Ayithy
83
add a comment |
1
I am facing an issue to write a code using datetime. I had created a scenario I am working on. Can someone help me out on the code.
Input:
Name, Channel, Duration, Start_time
John, A, 2, 16:00:00
Joseph, B, 3, 15:05:00
Output:
Name, Channel, Duration, Start_time
John, A, 2, 16:00:00
John, A, 2, 16:01:00
Joseph, B, 3, 15:05:00
Joseph, B, 3, 15:06:00
Joseph, B, 3, 15:07:00
Thank you in advance.
enter image description here
python pandas datetime timedelta
edited Nov 19 '18 at 6:40
Vivek Kalyanarangan
5,0361827
asked Nov 19 '18 at 6:39
Srikanth AyithySrikanth Ayithy
83
add a comment |
1
1
1
I am facing an issue to write a code using datetime. I had created a scenario I am working on. Can someone help me out on the code.
Input:
Name, Channel, Duration, Start_time
John, A, 2, 16:00:00
Joseph, B, 3, 15:05:00
Output:
Name, Channel, Duration, Start_time
John, A, 2, 16:00:00
John, A, 2, 16:01:00
Joseph, B, 3, 15:05:00
Joseph, B, 3, 15:06:00
Joseph, B, 3, 15:07:00
Thank you in advance.
enter image description here
python pandas datetime timedelta
edited Nov 19 '18 at 6:40
Vivek Kalyanarangan
5,0361827
asked Nov 19 '18 at 6:39
Srikanth AyithySrikanth Ayithy
83
I am facing an issue to write a code using datetime. I had created a scenario I am working on. Can someone help me out on the code.
Input:
Name, Channel, Duration, Start_time
John, A, 2, 16:00:00
Joseph, B, 3, 15:05:00
Output:
Name, Channel, Duration, Start_time
John, A, 2, 16:00:00
John, A, 2, 16:01:00
Joseph, B, 3, 15:05:00
Joseph, B, 3, 15:06:00
Joseph, B, 3, 15:07:00
Thank you in advance.
enter image description here
python pandas datetime timedelta
python pandas datetime timedelta
edited Nov 19 '18 at 6:40
Vivek Kalyanarangan
5,0361827
asked Nov 19 '18 at 6:39
Srikanth AyithySrikanth Ayithy
83
edited Nov 19 '18 at 6:40
Vivek Kalyanarangan
5,0361827
asked Nov 19 '18 at 6:39
Srikanth AyithySrikanth Ayithy
83
edited Nov 19 '18 at 6:40
Vivek Kalyanarangan
5,0361827
edited Nov 19 '18 at 6:40
Vivek Kalyanarangan
5,0361827
edited Nov 19 '18 at 6:40
Vivek Kalyanarangan
5,0361827
5,0361827
asked Nov 19 '18 at 6:39
Srikanth AyithySrikanth Ayithy
83
asked Nov 19 '18 at 6:39
Srikanth AyithySrikanth Ayithy
83
asked Nov 19 '18 at 6:39
Srikanth AyithySrikanth Ayithy
83
83
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
0
Use:
df['Start_time'] = pd.to_timedelta(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 16:00:00
1 John A 2 16:01:00
2 Joseph B 3 15:05:00
3 Joseph B 3 15:06:00
4 Joseph B 3 15:07:00
Explanation:
- Firt convert column
Start_timeto_timedelta
- Then
repeatvalues of index by columnDurationand repeat rows byloc
- Create counter by
cumcountper index values and convert it to 1 minute timedeltas, which are added to new repeated columnStart_time
- Last
reset_indexwith parameterdrop=Truefor avoid duplicated index values
EDIT:
If want datetimes in output solution is same, only first convert values to_datetime:
df['Start_time'] = pd.to_datetime(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 2018-11-19 16:00:00
1 John A 2 2018-11-19 16:01:00
2 Joseph B 3 2018-11-19 15:05:00
3 Joseph B 3 2018-11-19 15:06:00
4 Joseph B 3 2018-11-19 15:07:00
answered Nov 19 '18 at 6:42
jezraeljezrael
329k23271350
Brilliant. This worked like a stunner. Thank you very much man..... :)
– Srikanth Ayithy
Nov 19 '18 at 9:23
@SrikanthAyithy - You are welcome! If my answer was helpful, don't forget accept it. Thanks.
– jezrael
Nov 19 '18 at 9:24
Thank you very much for the previous answer. Need one more small help. If I have to summarize the input data i mentioned into half an hour bands. Like my output looks-like, Name, Channel, Duration, Timeband, Duration, count John, A, 2, 16:00:00-16:30:00 1 Joseph, B, 3, 15:00:00-15:30:00 1................... We may face an issue when we summarize a row with duration 10 watching which will split between bands like 5 minutes in 15:00:00-15:30:00 and other 5 minutes in 15:30:00-16:00:00.
– Srikanth Ayithy
Nov 19 '18 at 10:02
@SrikanthAyithy - Only one answer should be accepting, can you check it?
– jezrael
Nov 19 '18 at 10:15
Done! I accepted your answer.
– Srikanth Ayithy
Nov 19 '18 at 10:21
|
show 1 more comment
0
Use -
df['dates'] = df.apply(lambda x: list(pd.date_range(start=x['Start_time'], periods=x['Duration'], freq='1min')), axis=1)
df.set_index(['Name','Channel','Duration', 'Start_time'])['dates'].apply(pd.Series).stack().reset_index().drop(['level_4','Start_time'],1).rename(columns={0:'Start_time'})
Output
Name Channel Duration Start_time
0 John A 3 2018-11-19 16:00:00
1 John A 3 2018-11-19 16:01:00
2 John A 3 2018-11-19 16:02:00
3 Joseph B 4 2018-11-19 15:05:00
4 Joseph B 4 2018-11-19 15:06:00
5 Joseph B 4 2018-11-19 15:07:00
6 Joseph B 4 2018-11-19 15:08:00
Explanation
- Apply
pd.date_range()toStart_timeandDuration
- explode that into a
dfwith the second line
answered Nov 19 '18 at 7:11
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
This is working too. However, results are going into one row.
– Srikanth Ayithy
Nov 19 '18 at 10:21
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
0
Use:
df['Start_time'] = pd.to_timedelta(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 16:00:00
1 John A 2 16:01:00
2 Joseph B 3 15:05:00
3 Joseph B 3 15:06:00
4 Joseph B 3 15:07:00
Explanation:
- Firt convert column
Start_timeto_timedelta
- Then
repeatvalues of index by columnDurationand repeat rows byloc
- Create counter by
cumcountper index values and convert it to 1 minute timedeltas, which are added to new repeated columnStart_time
- Last
reset_indexwith parameterdrop=Truefor avoid duplicated index values
EDIT:
If want datetimes in output solution is same, only first convert values to_datetime:
df['Start_time'] = pd.to_datetime(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 2018-11-19 16:00:00
1 John A 2 2018-11-19 16:01:00
2 Joseph B 3 2018-11-19 15:05:00
3 Joseph B 3 2018-11-19 15:06:00
4 Joseph B 3 2018-11-19 15:07:00
answered Nov 19 '18 at 6:42
jezraeljezrael
329k23271350
Brilliant. This worked like a stunner. Thank you very much man..... :)
– Srikanth Ayithy
Nov 19 '18 at 9:23
@SrikanthAyithy - You are welcome! If my answer was helpful, don't forget accept it. Thanks.
– jezrael
Nov 19 '18 at 9:24
Thank you very much for the previous answer. Need one more small help. If I have to summarize the input data i mentioned into half an hour bands. Like my output looks-like, Name, Channel, Duration, Timeband, Duration, count John, A, 2, 16:00:00-16:30:00 1 Joseph, B, 3, 15:00:00-15:30:00 1................... We may face an issue when we summarize a row with duration 10 watching which will split between bands like 5 minutes in 15:00:00-15:30:00 and other 5 minutes in 15:30:00-16:00:00.
– Srikanth Ayithy
Nov 19 '18 at 10:02
@SrikanthAyithy - Only one answer should be accepting, can you check it?
– jezrael
Nov 19 '18 at 10:15
Done! I accepted your answer.
– Srikanth Ayithy
Nov 19 '18 at 10:21
|
show 1 more comment
0
Use:
df['Start_time'] = pd.to_timedelta(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 16:00:00
1 John A 2 16:01:00
2 Joseph B 3 15:05:00
3 Joseph B 3 15:06:00
4 Joseph B 3 15:07:00
Explanation:
- Firt convert column
Start_timeto_timedelta
- Then
repeatvalues of index by columnDurationand repeat rows byloc
- Create counter by
cumcountper index values and convert it to 1 minute timedeltas, which are added to new repeated columnStart_time
- Last
reset_indexwith parameterdrop=Truefor avoid duplicated index values
EDIT:
If want datetimes in output solution is same, only first convert values to_datetime:
df['Start_time'] = pd.to_datetime(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 2018-11-19 16:00:00
1 John A 2 2018-11-19 16:01:00
2 Joseph B 3 2018-11-19 15:05:00
3 Joseph B 3 2018-11-19 15:06:00
4 Joseph B 3 2018-11-19 15:07:00
answered Nov 19 '18 at 6:42
jezraeljezrael
329k23271350
Brilliant. This worked like a stunner. Thank you very much man..... :)
– Srikanth Ayithy
Nov 19 '18 at 9:23
@SrikanthAyithy - You are welcome! If my answer was helpful, don't forget accept it. Thanks.
– jezrael
Nov 19 '18 at 9:24
Thank you very much for the previous answer. Need one more small help. If I have to summarize the input data i mentioned into half an hour bands. Like my output looks-like, Name, Channel, Duration, Timeband, Duration, count John, A, 2, 16:00:00-16:30:00 1 Joseph, B, 3, 15:00:00-15:30:00 1................... We may face an issue when we summarize a row with duration 10 watching which will split between bands like 5 minutes in 15:00:00-15:30:00 and other 5 minutes in 15:30:00-16:00:00.
– Srikanth Ayithy
Nov 19 '18 at 10:02
@SrikanthAyithy - Only one answer should be accepting, can you check it?
– jezrael
Nov 19 '18 at 10:15
Done! I accepted your answer.
– Srikanth Ayithy
Nov 19 '18 at 10:21
|
show 1 more comment
0
0
0
Use:
df['Start_time'] = pd.to_timedelta(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 16:00:00
1 John A 2 16:01:00
2 Joseph B 3 15:05:00
3 Joseph B 3 15:06:00
4 Joseph B 3 15:07:00
Explanation:
- Firt convert column
Start_timeto_timedelta
- Then
repeatvalues of index by columnDurationand repeat rows byloc
- Create counter by
cumcountper index values and convert it to 1 minute timedeltas, which are added to new repeated columnStart_time
- Last
reset_indexwith parameterdrop=Truefor avoid duplicated index values
EDIT:
If want datetimes in output solution is same, only first convert values to_datetime:
df['Start_time'] = pd.to_datetime(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 2018-11-19 16:00:00
1 John A 2 2018-11-19 16:01:00
2 Joseph B 3 2018-11-19 15:05:00
3 Joseph B 3 2018-11-19 15:06:00
4 Joseph B 3 2018-11-19 15:07:00
answered Nov 19 '18 at 6:42
jezraeljezrael
329k23271350
Use:
df['Start_time'] = pd.to_timedelta(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 16:00:00
1 John A 2 16:01:00
2 Joseph B 3 15:05:00
3 Joseph B 3 15:06:00
4 Joseph B 3 15:07:00
Explanation:
- Firt convert column
Start_timeto_timedelta
- Then
repeatvalues of index by columnDurationand repeat rows byloc
- Create counter by
cumcountper index values and convert it to 1 minute timedeltas, which are added to new repeated columnStart_time
- Last
reset_indexwith parameterdrop=Truefor avoid duplicated index values
EDIT:
If want datetimes in output solution is same, only first convert values to_datetime:
df['Start_time'] = pd.to_datetime(df['Start_time'])
df = df.loc[df.index.repeat(df['Duration'])]
td = pd.to_timedelta(df.groupby(level=0).cumcount() * 60, unit='s')
df['Start_time'] = df['Start_time'] + td
df = df.reset_index(drop=True)
print (df)
Name Channel Duration Start_time
0 John A 2 2018-11-19 16:00:00
1 John A 2 2018-11-19 16:01:00
2 Joseph B 3 2018-11-19 15:05:00
3 Joseph B 3 2018-11-19 15:06:00
4 Joseph B 3 2018-11-19 15:07:00
answered Nov 19 '18 at 6:42
jezraeljezrael
329k23271350
edited Nov 19 '18 at 7:16
answered Nov 19 '18 at 6:42
jezraeljezrael
329k23271350
answered Nov 19 '18 at 6:42
jezraeljezrael
329k23271350
answered Nov 19 '18 at 6:42
jezraeljezrael
329k23271350
329k23271350
Brilliant. This worked like a stunner. Thank you very much man..... :)
– Srikanth Ayithy
Nov 19 '18 at 9:23
@SrikanthAyithy - You are welcome! If my answer was helpful, don't forget accept it. Thanks.
– jezrael
Nov 19 '18 at 9:24
Thank you very much for the previous answer. Need one more small help. If I have to summarize the input data i mentioned into half an hour bands. Like my output looks-like, Name, Channel, Duration, Timeband, Duration, count John, A, 2, 16:00:00-16:30:00 1 Joseph, B, 3, 15:00:00-15:30:00 1................... We may face an issue when we summarize a row with duration 10 watching which will split between bands like 5 minutes in 15:00:00-15:30:00 and other 5 minutes in 15:30:00-16:00:00.
– Srikanth Ayithy
Nov 19 '18 at 10:02
@SrikanthAyithy - Only one answer should be accepting, can you check it?
– jezrael
Nov 19 '18 at 10:15
Done! I accepted your answer.
– Srikanth Ayithy
Nov 19 '18 at 10:21
|
show 1 more comment
Brilliant. This worked like a stunner. Thank you very much man..... :)
– Srikanth Ayithy
Nov 19 '18 at 9:23
@SrikanthAyithy - You are welcome! If my answer was helpful, don't forget accept it. Thanks.
– jezrael
Nov 19 '18 at 9:24
Thank you very much for the previous answer. Need one more small help. If I have to summarize the input data i mentioned into half an hour bands. Like my output looks-like, Name, Channel, Duration, Timeband, Duration, count John, A, 2, 16:00:00-16:30:00 1 Joseph, B, 3, 15:00:00-15:30:00 1................... We may face an issue when we summarize a row with duration 10 watching which will split between bands like 5 minutes in 15:00:00-15:30:00 and other 5 minutes in 15:30:00-16:00:00.
– Srikanth Ayithy
Nov 19 '18 at 10:02
@SrikanthAyithy - Only one answer should be accepting, can you check it?
– jezrael
Nov 19 '18 at 10:15
Done! I accepted your answer.
– Srikanth Ayithy
Nov 19 '18 at 10:21
Brilliant. This worked like a stunner. Thank you very much man..... :)
– Srikanth Ayithy
Nov 19 '18 at 9:23
Brilliant. This worked like a stunner. Thank you very much man..... :)
– Srikanth Ayithy
Nov 19 '18 at 9:23
@SrikanthAyithy - You are welcome! If my answer was helpful, don't forget accept it. Thanks.
– jezrael
Nov 19 '18 at 9:24
@SrikanthAyithy - You are welcome! If my answer was helpful, don't forget accept it. Thanks.
– jezrael
Nov 19 '18 at 9:24
Thank you very much for the previous answer. Need one more small help. If I have to summarize the input data i mentioned into half an hour bands. Like my output looks-like, Name, Channel, Duration, Timeband, Duration, count John, A, 2, 16:00:00-16:30:00 1 Joseph, B, 3, 15:00:00-15:30:00 1................... We may face an issue when we summarize a row with duration 10 watching which will split between bands like 5 minutes in 15:00:00-15:30:00 and other 5 minutes in 15:30:00-16:00:00.
– Srikanth Ayithy
Nov 19 '18 at 10:02
Thank you very much for the previous answer. Need one more small help. If I have to summarize the input data i mentioned into half an hour bands. Like my output looks-like, Name, Channel, Duration, Timeband, Duration, count John, A, 2, 16:00:00-16:30:00 1 Joseph, B, 3, 15:00:00-15:30:00 1................... We may face an issue when we summarize a row with duration 10 watching which will split between bands like 5 minutes in 15:00:00-15:30:00 and other 5 minutes in 15:30:00-16:00:00.
– Srikanth Ayithy
Nov 19 '18 at 10:02
@SrikanthAyithy - Only one answer should be accepting, can you check it?
– jezrael
Nov 19 '18 at 10:15
@SrikanthAyithy - Only one answer should be accepting, can you check it?
– jezrael
Nov 19 '18 at 10:15
Done! I accepted your answer.
– Srikanth Ayithy
Nov 19 '18 at 10:21
Done! I accepted your answer.
– Srikanth Ayithy
Nov 19 '18 at 10:21
|
show 1 more comment
0
Use -
df['dates'] = df.apply(lambda x: list(pd.date_range(start=x['Start_time'], periods=x['Duration'], freq='1min')), axis=1)
df.set_index(['Name','Channel','Duration', 'Start_time'])['dates'].apply(pd.Series).stack().reset_index().drop(['level_4','Start_time'],1).rename(columns={0:'Start_time'})
Output
Name Channel Duration Start_time
0 John A 3 2018-11-19 16:00:00
1 John A 3 2018-11-19 16:01:00
2 John A 3 2018-11-19 16:02:00
3 Joseph B 4 2018-11-19 15:05:00
4 Joseph B 4 2018-11-19 15:06:00
5 Joseph B 4 2018-11-19 15:07:00
6 Joseph B 4 2018-11-19 15:08:00
Explanation
- Apply
pd.date_range()toStart_timeandDuration
- explode that into a
dfwith the second line
answered Nov 19 '18 at 7:11
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
This is working too. However, results are going into one row.
– Srikanth Ayithy
Nov 19 '18 at 10:21
add a comment |
0
Use -
df['dates'] = df.apply(lambda x: list(pd.date_range(start=x['Start_time'], periods=x['Duration'], freq='1min')), axis=1)
df.set_index(['Name','Channel','Duration', 'Start_time'])['dates'].apply(pd.Series).stack().reset_index().drop(['level_4','Start_time'],1).rename(columns={0:'Start_time'})
Output
Name Channel Duration Start_time
0 John A 3 2018-11-19 16:00:00
1 John A 3 2018-11-19 16:01:00
2 John A 3 2018-11-19 16:02:00
3 Joseph B 4 2018-11-19 15:05:00
4 Joseph B 4 2018-11-19 15:06:00
5 Joseph B 4 2018-11-19 15:07:00
6 Joseph B 4 2018-11-19 15:08:00
Explanation
- Apply
pd.date_range()toStart_timeandDuration
- explode that into a
dfwith the second line
answered Nov 19 '18 at 7:11
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
This is working too. However, results are going into one row.
– Srikanth Ayithy
Nov 19 '18 at 10:21
add a comment |
0
0
0
Use -
df['dates'] = df.apply(lambda x: list(pd.date_range(start=x['Start_time'], periods=x['Duration'], freq='1min')), axis=1)
df.set_index(['Name','Channel','Duration', 'Start_time'])['dates'].apply(pd.Series).stack().reset_index().drop(['level_4','Start_time'],1).rename(columns={0:'Start_time'})
Output
Name Channel Duration Start_time
0 John A 3 2018-11-19 16:00:00
1 John A 3 2018-11-19 16:01:00
2 John A 3 2018-11-19 16:02:00
3 Joseph B 4 2018-11-19 15:05:00
4 Joseph B 4 2018-11-19 15:06:00
5 Joseph B 4 2018-11-19 15:07:00
6 Joseph B 4 2018-11-19 15:08:00
Explanation
- Apply
pd.date_range()toStart_timeandDuration
- explode that into a
dfwith the second line
answered Nov 19 '18 at 7:11
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
Use -
df['dates'] = df.apply(lambda x: list(pd.date_range(start=x['Start_time'], periods=x['Duration'], freq='1min')), axis=1)
df.set_index(['Name','Channel','Duration', 'Start_time'])['dates'].apply(pd.Series).stack().reset_index().drop(['level_4','Start_time'],1).rename(columns={0:'Start_time'})
Output
Name Channel Duration Start_time
0 John A 3 2018-11-19 16:00:00
1 John A 3 2018-11-19 16:01:00
2 John A 3 2018-11-19 16:02:00
3 Joseph B 4 2018-11-19 15:05:00
4 Joseph B 4 2018-11-19 15:06:00
5 Joseph B 4 2018-11-19 15:07:00
6 Joseph B 4 2018-11-19 15:08:00
Explanation
- Apply
pd.date_range()toStart_timeandDuration
- explode that into a
dfwith the second line
answered Nov 19 '18 at 7:11
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
answered Nov 19 '18 at 7:11
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
answered Nov 19 '18 at 7:11
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
answered Nov 19 '18 at 7:11
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
5,0361827
This is working too. However, results are going into one row.
– Srikanth Ayithy
Nov 19 '18 at 10:21
add a comment |
This is working too. However, results are going into one row.
– Srikanth Ayithy
Nov 19 '18 at 10:21
This is working too. However, results are going into one row.
– Srikanth Ayithy
Nov 19 '18 at 10:21
This is working too. However, results are going into one row.
– Srikanth Ayithy
Nov 19 '18 at 10:21
add a comment |
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