pandas-groupby

This would be useful so I know how many unique groups I have to perform calculations on. Thank you. Suppose groupby object is called dfgroup . As documented , you can get the number of groups with len(dfgroup) . As of v0.23, there are a multiple options to use. First, the setup, df = pd.DataFrame({'A': list('aabbcccd'), 'B': 'x'}) df A B 0 a x 1 a x 2 b x 3 b x 4 c x 5 c x 6 c x 7 d x g = df.groupby(['A']) 1) ngroups Newer versions of the groupby API provide this (undocumented) attribute which stores the number of groups in a GroupBy object. g.ngroups # 6 Note that this is different from

Want to output a Pandas groupby dataframe to CSV. Tried various StackOverflow solutions but they have not worked. Python 3.6.1, Pandas 0.20.1 groupby result looks like: id month year count week 0 9066 82 32142 895 1 7679 84 30112 749 2 8368 126 42187 872 3 11038 102 34165 976 4 8815 117 34122 767 5 10979 163 50225 1252 6 8726 142 38159 996 7 5568 63 26143 582 Want a csv that looks like week count 0 895 1 749 2 872 3 976 4 767 5 1252 6 996 7 582 Current code: week_grouped = df.groupby('week') week_grouped.sum() #At this point you have the groupby result week_grouped.to_csv('week_grouped.csv')

I have the following dataset, Day Element Data_Value 6786 01-01 TMAX 112 9333 01-01 TMAX 101 9330 01-01 TMIN 60 11049 01-01 TMIN 0 6834 01-01 TMIN 25 11862 01-01 TMAX 113 1781 01-01 TMAX 115 11042 01-01 TMAX 105 1110 01-01 TMAX 111 651 01-01 TMIN 44 11350 01-01 TMIN 83 1798 01-02 TMAX 70 4975 01-02 TMAX 79 12774 01-02 TMIN 0 3977 01-02 TMIN 60 2485 01-02 TMAX 73 4888 01-02 TMIN 31 11836 01-02 TMIN 26 11368 01-02 TMAX 71 2483 01-02 TMIN 26 I want to group by the Day and then find the overall min of TMIN an the max of TMAX and put these in to a data frame, so I get an output like... Day DayMin

I want to ask a quick question related to regression analysis in python pandas. So, assume that I have the following datasets: Group Y X 1 10 6 1 5 4 1 3 1 2 4 6 2 2 4 2 3 9 My aim is to run regression; Y is dependent and X is independent variable. The issue is I want to run this regression by Group and print the coefficients in a new data set. So, the results should be like: Group Coefficient 1 0.25 (lets assume that coefficient is 0.25) 2 0.30 I hope I can explain my question. Many thanks in advance for your help. I am not sure about the type of regression you need, but this is how you do an

I have a time series object grouped of the type <pandas.core.groupby.SeriesGroupBy object at 0x03F1A9F0> . grouped.sum() gives the desired result but I cannot get rolling_sum to work with the groupby object. Is there any way to apply rolling functions to groupby objects? For example: x = range(0, 6) id = ['a', 'a', 'a', 'b', 'b', 'b'] df = DataFrame(zip(id, x), columns = ['id', 'x']) df.groupby('id').sum() id x a 3 b 12 However, I would like to have something like: id x 0 a 0 1 a 1 2 a 3 3 b 3 4 b 7 5 b 12 Note: as identified by @kekert, the following pandas pattern has been deprecated. See