I have a dataframe like this: df = pd.DataFrame({"year": [2000,2000,2000,2001,2001,2001], "A": [1,1,0,0,1,0], "B": [4,4,6,10,10,10]}) df year A B 0 2000 1 4 1 2000 1 4 2 2000 0 6 3 2001 0 10 4 2001 1 10 5 2001 0 10 I would like to create a unique id number for each combination of… Read More create id number for unique combination of column values
I’ve a data frame which contains one column. Below is the example Questionsbysortorder Q1-4,Q2-3,Q3-2,Q4-3,Q5-3 Q1-1,Q2-2,Q3-1,Q4-1 Q1-5,Q2-3,Q3-3 I’m trying to explode the columns with the help of already given row values. Like below is the example Questionsbysortorder Q1 Q2 Q3 Q4 Q5 Q1-4,Q2-3,Q3-2,Q4-3,Q5-3 4 3 2 3 3 Q1-1,Q2-2,Q3-1,Q4-1 1 2 1 1 NA Q1-5,Q2-3,Q5-3 5… Read More Unable to split the column into multiple columns based on the first column value
df person time_bought product 42 abby 10min fruit 12 abby 5min fruit 10 abby 10min other 3 barry 12min fruit … How could I convert the lines below into a a generalisable function, since I’m using groupby all the time. ref = df.groupby(‘person’)[‘time_bought’].shift() m1 = df.loc[df.product=="fruit", ‘time_bought’].groupby(df[‘person’]).diff().gt(ref) m2 = df[‘product’].ne(‘fruit’) df[‘new_group’] = m1.groupby(df[‘person’]).cumsum().add(1).mask(m2) # gives… Read More Mask using groupby apply function
So I have a dataframe like this df = pd.DataFrame({ ‘A’: [1,1,2,2,3,3,3], ‘B’: [1,3,1,3,1,2,1], ‘C’: [1,3,5,3,7,7,1]}) A B C 0 1 1 1 1 1 3 3 2 2 1 5 3 2 3 3 4 3 1 7 5 3 2 7 6 3 1 1 I want to create a binning of column… Read More dataframe groupby aggregation count function with condition for binning purpose
I have a dataframe like as shown below sub_id,teacher,div,pid,pos_date 1,ABC,SCIENCE,A1,12/10/2021 1,ABC,SCIENCE,A1,22/06/2019 1,ABC,SCIENCE,A1,12/12/2018 1,ABC,SCIENCE,A1,27/11/2020 1,DEF,CHEMISTRY,A1,12/10/2021 1,DEF,CHEMISTRY,A2,11/11/2018 1,DEF,CHEMISTRY,A2,12/10/2021 1,ABC,SCIENCE,A2,12/10/2019 1,ABC,SCIENCE,A2,12/10/2020 1,ABC,SCIENCE,A3,12/11/2021 1,ABC,SCIENCE,A3,22/03/2022 1,ABC,SCIENCE,A4,22/10/2021 1,ABC,SCIENCE,A4,12/04/2021 df = pd.read_clipboard() I would like to do the below a) Group by sub_id,teacher,div and pid b) For each group, compute the below 1) Max(pos_date) 2) Average gap between each pos_date 3) Median… Read More Compute avg gap between dates and max date for each group using pandas
I have a data frame data data_ = {‘ID’: [777, 777, 777,777,777,777],’Month’:[1,1,1,2,2,2], ‘Salary’: [130,170,50,140,180,60], ‘O’: ["AC","BR","BR","AC","BR","BR"], ‘D’:["LF","AC","LF","LF","AC","LF"], ‘B’:[True,True,False,True,True,False]} data = pd.DataFrame(data=data_) for each subgroup of this data frame: Subgroup=data.groupby(["ID","Month"]) I would like to add a new column NEW_Salary filled with the values of Salary where B is false in each subgroup as show in the… Read More Create new column from a row value in a grouped data frame?
How to add a new column of aggregated data I want to create 03 new columns in a dataframe Column 01: unique_list Create a new column in the dataframe of unique values of cfop_code for each key Column 02: unique_count A column that check the number of unique values that shows in unique_list Column 03:… Read More How to add new columns in Pandas for unique values of certain key (problem agregate)
I have a dataframe like as below name,id,AL A,1,22 A,2,22 B,5,21 B,5,23 B,4,24 C,6,21 I would like to do the below a) Groupby name b) get the unique count (nunique) of id and PL for each name With the help of this post, I managed to do the below cols = [‘id’,’PL’] for col in… Read More use pandas col to store % of values in dict format
I have a dataframe in pandas that gives you the below table: I would like to get the below results by summing up the calls and getting the type in one single line. (you can observe the yellow rows) >Solution : Try: # aggregate by Key # keep the first No, join types, and sum… Read More Combine multiple rows to a single line in pandas data frame
I have a dataframe like as shown below key, values_list 1, {‘ABC’:100} 2, {‘DEF’:100} 3, {‘ASE’:95,’ABC’:5} 4, {‘ABC’:55,’ASE’:40,’DEF’:5} 5, {‘DEF’:90,’ABC’:5,’ASE’:2.5,’XYZ’:2.5} I would like to do the below a) Convert dict values to string and include % symbol at the end of each string So, I tried the below df[‘values_list’].str.replace(r'[0-9]+’, ‘[0-9]%’) # Approach 1 np.where(df[‘values_list’].str.isdigit(),df[‘values_list’]+’%’,df[‘values_list’]) #Approach… Read More Append % symbol to dict numeric values in a dataframe column