pandas-groupby

问题 Simple DataFrame: df = pd.DataFrame({'A': [1,1,2,2], 'B': [0,1,2,3], 'C': ['a','b','c','d']}) df A B C 0 1 0 a 1 1 1 b 2 2 2 c 3 2 3 d I wish for every value ( groupby ) of column A, to get the value of column C, for which column B is maximum. For example for group 1 of column A, the maximum of column B is 1, so I want the value "b" of column C: A C 0 1 b 1 2 d No need to assume column B is sorted, performance is of top priority, then elegance. 回答1: Check with sort_values + drop_duplicates df

问题 I have a dataframe that gives upper and lower values of each indicator as follows df = pd.DataFrame( {'indicator': ['indicator 1', 'indicator 1', 'indicator 2', 'indicator 2'], 'year':[2014,2014,2015,2015], 'value type': ['upper', 'lower', 'upper', 'lower'], 'value':[12.3, 10.2, 15.4, 13.2] }, index=[1,2,3,4]) I want to remove the upper and lower values and replace that with the mean of two values. How can I do that? 回答1: You could groupby and transform by mean . df['value'] = df.groupby(

问题 I want to fill the value of the nan in the given value as following: df = pd.DataFrame({'A' : ['aa', 'bb', 'cc', 'aa'], 'B': ['xx', 'yy', 'zz','xx'], 'C': ['2', '3','8', np.nan]}) print (df) A B C aa xx 2 bb yy 3 cc zz 8 aa xx NaN Expected Output: A B C aa xx 2 bb yy 3 cc zz 8 aa xx 2 Since column A and B have value 2 in the third column, therefore last row should also have 2 in the C column. 回答1: Use GroupBy.ffill with DataFrame.sort_values and DataFrame.sort_index for NaN s to end of groups

问题 What is pivot? How do I pivot? Is this a pivot? Long format to wide format? I've seen a lot of questions that ask about pivot tables. Even if they don't know that they are asking about pivot tables, they usually are. It is virtually impossible to write a canonical question and answer that encompasses all aspects of pivoting.... ... But I'm going to give it a go. The problem with existing questions and answers is that often the question is focused on a nuance that the OP has trouble

问题 What is pivot? How do I pivot? Is this a pivot? Long format to wide format? I've seen a lot of questions that ask about pivot tables. Even if they don't know that they are asking about pivot tables, they usually are. It is virtually impossible to write a canonical question and answer that encompasses all aspects of pivoting.... ... But I'm going to give it a go. The problem with existing questions and answers is that often the question is focused on a nuance that the OP has trouble

问题 I have a data frame as shown below. B_ID No_Show Session slot_num Patient_count 1 0.4 S1 1 1 2 0.3 S1 2 1 3 0.8 S1 3 1 4 0.3 S1 3 2 5 0.6 S1 4 1 6 0.8 S1 5 1 7 0.9 S1 5 2 8 0.4 S1 5 3 9 0.6 S1 5 4 12 0.9 S2 1 1 13 0.5 S2 1 2 14 0.3 S2 2 1 15 0.7 S2 3 1 20 0.7 S2 4 1 16 0.6 S2 5 1 17 0.8 S2 5 2 19 0.3 S2 5 3 From the above I would like to find the cumulative No_show by Session df['Cum_No_show'] = df.groupby(['Session'])['No_Show'].cumsum() No we get B_ID No_Show Session slot_num Patient_count

问题 I have a pandas dataframe that looks like this: c y 0 9 0 1 8 0 2 3 1 3 6 2 4 1 3 5 2 3 6 5 3 7 4 4 8 0 4 9 7 4 I'd like to groupby y and get the min and max of c so that my new dataframe would look like this: c y min max 0 9 0 8 9 1 8 0 8 9 2 3 1 3 3 3 6 2 6 6 4 1 3 1 5 5 2 3 1 5 6 5 3 1 5 7 4 4 0 7 8 0 4 0 7 9 7 4 0 7 I tried using df['min'] = df.groupby(['y'])['c'].min() but that gave me some weird results. The first 175 rows were populated in the min column but then it went to NaN for all

问题 I have the following dataframe:- traffic_type date region total_views desktop 01/04/2018 aug 50 mobileweb 01/04/2018 aug 60 total 01/04/2018 aug 100 desktop 01/04/2018 world 20 mobileweb 01/04/2018 world 30 total 01/04/2018 world 40 I need to group by traffic_type, date, region, and filter the rows with traffic type total and in the same row create a desktop_share column which is total_views of traffic_type==desktop / total views of the traffic_type ==total the rest of the rows are blank for

问题 I am having a very slow performance when calling stack, groupby and apply for a large dataframe in Pandas (1498829 rows). The code gives the differences of pairs (with this I mean the difference of xx's for all i2 at every i1). The part of the code that is running slow is: def get_diff(x): teams = x.index.get_level_values(1) tmp = pd.DataFrame(x[:,None]-x[None,:], columns = teams.values, index=teams.values).stack() return tmp[tmp.index.get_level_values(0)!=tmp.index.get_level_values(1)] new

问题 I am having a very slow performance when calling stack, groupby and apply for a large dataframe in Pandas (1498829 rows). The code gives the differences of pairs (with this I mean the difference of xx's for all i2 at every i1). The part of the code that is running slow is: def get_diff(x): teams = x.index.get_level_values(1) tmp = pd.DataFrame(x[:,None]-x[None,:], columns = teams.values, index=teams.values).stack() return tmp[tmp.index.get_level_values(0)!=tmp.index.get_level_values(1)] new