How to group date-indexed data and extract timeseries information
Working with simplified student sample data that looks like this:
Date | Loc | SID | Test | Score
----------------------------------------------
2018-03-01 L1 S1 T1 3
2018-03-01 L1 S1 T1 5
2018-03-01 L2 S3 T1 3
2018-03-03 L2 S3 T2 4
2018-03-03 L1 S2 T1 1
2018-03-03 L1 S1 T2 5
2018-03-03 L1 S1 T1 4
2018-03-03 L1 S2 T3 7
2018-03-03 L2 S1 T1 5
2018-03-05 L1 S2 T2 3
2018-03-05 L2 S1 T1 1
2018-03-05 L1 S3 T2 5
2018-03-05 L1 S2 T1 8
2018-03-05 L1 S1 T1 6
2018-03-05 L2 S1 T1 3
2018-03-05 L2 S3 T3 5
2018-03-08 L2 S2 T2 4
2018-03-08 L2 S1 T2 2
2018-03-09 L1 S3 T1 6
2018-03-09 L2 S3 T1 5
2018-03-09 L1 S1 T3 8
2018-03-09 L1 S1 T3 6
2018-03-11 L1 S3 T2 6
2018-03-11 L2 S3 T1 9
2018-03-11 L1 S3 T2 3
2018-03-11 L1 S1 T1 5
2018-03-11 L2 S1 T1 4
2018-03-11 L1 S1 T3 9
2018-03-14 L2 S2 T1 3
2018-03-14 L1 S2 T1 3
Would like to groupby (Loc, SID, Test) and calculate the Average Score and Weighted Average Score based on a weekly re-sample so it looks something like the following (not complete, only showing Week 1):
| # Times Test Taken | Avg. Score | Wgtd Avg. Score
------------|------------------------------------------------------
Week 1| L1 S1 T1 | 4 | 4.50 |
T2 | 1 | 5.00 |
S2 T1 | 2 | 4.50 |
T2 | 1 | 3.00 |
T3 | 1 | 7.00 |
S3 T2 | 1 | 5.00 |
L2 S1 T1 | 3 | 3.00 |
S3 T1 | 1 | 4.00
So far I've:
import pandas as pd
df = pd.read_csv(TheData)
df2 = df.copy()
df2.Date = pd.to_datetime(df2.Date)
df2.set_index('Date', inplace=True)
df3 = df2.copy()
df3.groupby(['Loc', 'SID', 'Test']).resample('W')['Score'].count()
# df3.groupby(['Loc', 'SID', 'Test']).resample('W').count()
df3.groupby(['Loc', 'SID', 'Test']).resample('W').mean()
I believe I have the correct info for "# Times Test Taken" and "Average Score". How can I feed this info into new columns into the same dataframe?
For the weighted avg. score, I'm open to suggestions on how to calculate it such that it can reflect differences in Test Type (T1-T3) as it pertains to score. I'm not even sure that I'm even thinking about this metric the right way.
Will continue to update as I make progress. Any feedback is greatly appreciated.
python pandas dataframe time-series weighted-average
edited Nov 13 at 6:39
dmitriys
15119
asked Nov 12 at 22:26
jarwal
155
add a comment |
Working with simplified student sample data that looks like this:
Date | Loc | SID | Test | Score
----------------------------------------------
2018-03-01 L1 S1 T1 3
2018-03-01 L1 S1 T1 5
2018-03-01 L2 S3 T1 3
2018-03-03 L2 S3 T2 4
2018-03-03 L1 S2 T1 1
2018-03-03 L1 S1 T2 5
2018-03-03 L1 S1 T1 4
2018-03-03 L1 S2 T3 7
2018-03-03 L2 S1 T1 5
2018-03-05 L1 S2 T2 3
2018-03-05 L2 S1 T1 1
2018-03-05 L1 S3 T2 5
2018-03-05 L1 S2 T1 8
2018-03-05 L1 S1 T1 6
2018-03-05 L2 S1 T1 3
2018-03-05 L2 S3 T3 5
2018-03-08 L2 S2 T2 4
2018-03-08 L2 S1 T2 2
2018-03-09 L1 S3 T1 6
2018-03-09 L2 S3 T1 5
2018-03-09 L1 S1 T3 8
2018-03-09 L1 S1 T3 6
2018-03-11 L1 S3 T2 6
2018-03-11 L2 S3 T1 9
2018-03-11 L1 S3 T2 3
2018-03-11 L1 S1 T1 5
2018-03-11 L2 S1 T1 4
2018-03-11 L1 S1 T3 9
2018-03-14 L2 S2 T1 3
2018-03-14 L1 S2 T1 3
Would like to groupby (Loc, SID, Test) and calculate the Average Score and Weighted Average Score based on a weekly re-sample so it looks something like the following (not complete, only showing Week 1):
| # Times Test Taken | Avg. Score | Wgtd Avg. Score
------------|------------------------------------------------------
Week 1| L1 S1 T1 | 4 | 4.50 |
T2 | 1 | 5.00 |
S2 T1 | 2 | 4.50 |
T2 | 1 | 3.00 |
T3 | 1 | 7.00 |
S3 T2 | 1 | 5.00 |
L2 S1 T1 | 3 | 3.00 |
S3 T1 | 1 | 4.00
So far I've:
import pandas as pd
df = pd.read_csv(TheData)
df2 = df.copy()
df2.Date = pd.to_datetime(df2.Date)
df2.set_index('Date', inplace=True)
df3 = df2.copy()
df3.groupby(['Loc', 'SID', 'Test']).resample('W')['Score'].count()
# df3.groupby(['Loc', 'SID', 'Test']).resample('W').count()
df3.groupby(['Loc', 'SID', 'Test']).resample('W').mean()
I believe I have the correct info for "# Times Test Taken" and "Average Score". How can I feed this info into new columns into the same dataframe?
For the weighted avg. score, I'm open to suggestions on how to calculate it such that it can reflect differences in Test Type (T1-T3) as it pertains to score. I'm not even sure that I'm even thinking about this metric the right way.
Will continue to update as I make progress. Any feedback is greatly appreciated.
python pandas dataframe time-series weighted-average
edited Nov 13 at 6:39
dmitriys
15119
asked Nov 12 at 22:26
jarwal
155
add a comment |
Working with simplified student sample data that looks like this:
Date | Loc | SID | Test | Score
----------------------------------------------
2018-03-01 L1 S1 T1 3
2018-03-01 L1 S1 T1 5
2018-03-01 L2 S3 T1 3
2018-03-03 L2 S3 T2 4
2018-03-03 L1 S2 T1 1
2018-03-03 L1 S1 T2 5
2018-03-03 L1 S1 T1 4
2018-03-03 L1 S2 T3 7
2018-03-03 L2 S1 T1 5
2018-03-05 L1 S2 T2 3
2018-03-05 L2 S1 T1 1
2018-03-05 L1 S3 T2 5
2018-03-05 L1 S2 T1 8
2018-03-05 L1 S1 T1 6
2018-03-05 L2 S1 T1 3
2018-03-05 L2 S3 T3 5
2018-03-08 L2 S2 T2 4
2018-03-08 L2 S1 T2 2
2018-03-09 L1 S3 T1 6
2018-03-09 L2 S3 T1 5
2018-03-09 L1 S1 T3 8
2018-03-09 L1 S1 T3 6
2018-03-11 L1 S3 T2 6
2018-03-11 L2 S3 T1 9
2018-03-11 L1 S3 T2 3
2018-03-11 L1 S1 T1 5
2018-03-11 L2 S1 T1 4
2018-03-11 L1 S1 T3 9
2018-03-14 L2 S2 T1 3
2018-03-14 L1 S2 T1 3
Would like to groupby (Loc, SID, Test) and calculate the Average Score and Weighted Average Score based on a weekly re-sample so it looks something like the following (not complete, only showing Week 1):
| # Times Test Taken | Avg. Score | Wgtd Avg. Score
------------|------------------------------------------------------
Week 1| L1 S1 T1 | 4 | 4.50 |
T2 | 1 | 5.00 |
S2 T1 | 2 | 4.50 |
T2 | 1 | 3.00 |
T3 | 1 | 7.00 |
S3 T2 | 1 | 5.00 |
L2 S1 T1 | 3 | 3.00 |
S3 T1 | 1 | 4.00
So far I've:
import pandas as pd
df = pd.read_csv(TheData)
df2 = df.copy()
df2.Date = pd.to_datetime(df2.Date)
df2.set_index('Date', inplace=True)
df3 = df2.copy()
df3.groupby(['Loc', 'SID', 'Test']).resample('W')['Score'].count()
# df3.groupby(['Loc', 'SID', 'Test']).resample('W').count()
df3.groupby(['Loc', 'SID', 'Test']).resample('W').mean()
I believe I have the correct info for "# Times Test Taken" and "Average Score". How can I feed this info into new columns into the same dataframe?
For the weighted avg. score, I'm open to suggestions on how to calculate it such that it can reflect differences in Test Type (T1-T3) as it pertains to score. I'm not even sure that I'm even thinking about this metric the right way.
Will continue to update as I make progress. Any feedback is greatly appreciated.
python pandas dataframe time-series weighted-average
edited Nov 13 at 6:39
dmitriys
15119
asked Nov 12 at 22:26
jarwal
155
Working with simplified student sample data that looks like this:
Date | Loc | SID | Test | Score
----------------------------------------------
2018-03-01 L1 S1 T1 3
2018-03-01 L1 S1 T1 5
2018-03-01 L2 S3 T1 3
2018-03-03 L2 S3 T2 4
2018-03-03 L1 S2 T1 1
2018-03-03 L1 S1 T2 5
2018-03-03 L1 S1 T1 4
2018-03-03 L1 S2 T3 7
2018-03-03 L2 S1 T1 5
2018-03-05 L1 S2 T2 3
2018-03-05 L2 S1 T1 1
2018-03-05 L1 S3 T2 5
2018-03-05 L1 S2 T1 8
2018-03-05 L1 S1 T1 6
2018-03-05 L2 S1 T1 3
2018-03-05 L2 S3 T3 5
2018-03-08 L2 S2 T2 4
2018-03-08 L2 S1 T2 2
2018-03-09 L1 S3 T1 6
2018-03-09 L2 S3 T1 5
2018-03-09 L1 S1 T3 8
2018-03-09 L1 S1 T3 6
2018-03-11 L1 S3 T2 6
2018-03-11 L2 S3 T1 9
2018-03-11 L1 S3 T2 3
2018-03-11 L1 S1 T1 5
2018-03-11 L2 S1 T1 4
2018-03-11 L1 S1 T3 9
2018-03-14 L2 S2 T1 3
2018-03-14 L1 S2 T1 3
Would like to groupby (Loc, SID, Test) and calculate the Average Score and Weighted Average Score based on a weekly re-sample so it looks something like the following (not complete, only showing Week 1):
| # Times Test Taken | Avg. Score | Wgtd Avg. Score
------------|------------------------------------------------------
Week 1| L1 S1 T1 | 4 | 4.50 |
T2 | 1 | 5.00 |
S2 T1 | 2 | 4.50 |
T2 | 1 | 3.00 |
T3 | 1 | 7.00 |
S3 T2 | 1 | 5.00 |
L2 S1 T1 | 3 | 3.00 |
S3 T1 | 1 | 4.00
So far I've:
import pandas as pd
df = pd.read_csv(TheData)
df2 = df.copy()
df2.Date = pd.to_datetime(df2.Date)
df2.set_index('Date', inplace=True)
df3 = df2.copy()
df3.groupby(['Loc', 'SID', 'Test']).resample('W')['Score'].count()
# df3.groupby(['Loc', 'SID', 'Test']).resample('W').count()
df3.groupby(['Loc', 'SID', 'Test']).resample('W').mean()
I believe I have the correct info for "# Times Test Taken" and "Average Score". How can I feed this info into new columns into the same dataframe?
For the weighted avg. score, I'm open to suggestions on how to calculate it such that it can reflect differences in Test Type (T1-T3) as it pertains to score. I'm not even sure that I'm even thinking about this metric the right way.
Will continue to update as I make progress. Any feedback is greatly appreciated.
python pandas dataframe time-series weighted-average
python pandas dataframe time-series weighted-average
edited Nov 13 at 6:39
dmitriys
15119
asked Nov 12 at 22:26
jarwal
155
edited Nov 13 at 6:39
dmitriys
15119
asked Nov 12 at 22:26
jarwal
155
edited Nov 13 at 6:39
dmitriys
15119
edited Nov 13 at 6:39
dmitriys
15119
edited Nov 13 at 6:39
dmitriys
15119
15119
asked Nov 12 at 22:26
jarwal
155
asked Nov 12 at 22:26
jarwal
155
asked Nov 12 at 22:26
jarwal
155
155
add a comment |
add a comment |
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