pandas-groupby

I am very new to pandas and trying to use groupby. I have a df with multiple columns. I want to groupby a particular column and then sort each group based on a different column. I am getting the following error AttributeError: Cannot access callable attribute 'reset_index' of 'DataFrameGroupBy' objects, try using the 'apply' method . Any help would be much appreciated! Thanks! col1 | col2 | col3 | col4 | col5 ================================= A | A1 | A2 | A3 | DATE1 A | B1 | B2 | B3 | DATE2 I want to groupby col1 and then sort each group by col5 and then do reset_index to get all rows of the

I need to groupby-transform a dataframe by a datetime column AND another str(object) column to apply a function by group and asign the result to each of the row members of the group. I understand the groupby workflow but cannot make a pandas.Grouper for both conditions at the same time. Thus: How to use pandas.Grouper on multiple columns? Use the DataFrame.groupby with a list of pandas.Grouper as the by argument like this: df['result'] = df.groupby([ pd.Grouper('dt', freq='D'), pd.Grouper('other_column') ]).transform(foo) 来源： https://stackoverflow.com/questions/52187943/how-to-use-pandas

I am trying to create a boolean column using GroupBy.transform on a df like this, id type 1 1.00000 1 1.00000 2 2.00000 2 3.00000 3 2.00000 the code is like, df['has_two'] = df.groupby('id')['type'].transform(lambda x: x == 2) but instead of boolean values, has_two has float values, e.g. 0.0 . I am wondering why is that. UPDATE I created a test case, df = pd.DataFrame({'id':['1', '1', '2', '2', '3'], 'type':[1.0, 1.0, 2.0, 1.0, 2.0]}) df['has_2'] = df.groupby('id')['type'].transform(lambda x: x == 2) this gave me, id type has_2 0 1 1.0 0.0 1 1 1.0 0.0 2 2 2.0 1.0 3 2 1.0 0.0 4 3 2.0 1.0 if I

I have a DF such as the following: df = vid pos value sente 1 a A 21 2 b B 21 3 b A 21 3 a A 21 1 d B 22 1 a C 22 1 a D 22 2 b A 22 3 a A 22 Now I want to combine all rows with the same value for sente and vid into one row with the values for value joined by an " " df2 = vid pos value sente 1 a A 21 2 b B 21 3 b a A A 21 1 d a a B C D 22 2 b A 22 3 a A 22 I suppose a modification of this should do the trick: df2 = df.groupby["sente"].agg(lambda x: " ".join(x)) But I can't seem to figure out how to add the second column to the statement. Groupers can be passed as lists. Furthermore, you can

I have a data frame like this lvl1=['l1A','l1A','l1B','l1C','l1D'] lvl2=['l2A','l2A','l2A','l26','l27'] wgt=[.2,.3,.15,.05,.3] lvls=[lvl1,lvl2] df=pd.DataFrame(wgt, lvls).reset_index() df.columns = ['lvl' + str(i) for i in range(1,3)] + ['wgt'] df lvl1 lvl2 wgt 0 l1A l2A 0.20 1 l1A l2A 0.30 2 l1B l2A 0.15 3 l1C l26 0.05 4 l1D l27 0.30 I want to get the average weight at each level and add them as a separate column to this data frame. pd.concat([df, df.groupby('lvl1').transform('mean').add_suffix('_l1avg'), df.groupby('lvl2').transform('mean').add_suffix('_l2avg')], axis=1) lvl1 lvl2 wgt wgt

Suppose we have a dataframe that looks like this: start stop duration 0 A B 1 1 B A 2 2 C D 2 3 D C 0 What's the best way to construct a list of: i) start/stop pairs; ii) count of start/stop pairs; iii) avg duration of start/stop pairs? In this case, order should not matter: (A,B)=(B,A) . Desired output: [[start,stop,count,avg duration]] In this example: [[A,B,2,1.5],[C,D,2,1]] sort the first two columns (you can do this in-place, or create a copy and do the same thing; I've done the former), then groupby and agg : df[['start', 'stop']] = np.sort(df[['start', 'stop']], axis=1) (df.groupby([

I want to be able to concat dataframe results to memory as they go through a function and end up with a whole new dataframe with just the results. How do I do this without having a dataframe all ready created before the function? For example: import pandas as pd import numpy as np rand_df = pd.DataFrame({'A': [ 'x','x','y','y','z','z','z'],'B': np.random.randn(7)}) def myFuncOnDF(df, row): df = df.groupby(['A']).get_group(row).describe() myFuncOnDF(rand_df, 'x') myFuncOnDF(rand_df, 'y') myFuncOnDF(rand_df, 'z') How would I concat the results of myFuncOnDF() to a new dataframe that doesn't