pandas

问题 I have two csv files.Depending upon the value of a cell in csv file 1 I should be able to search that value in a column of csv file 2 and get he corresponding value from other column in csv file 2. I am sorry if this very confusing.It will probably get clear by illustration CSV file 1 Car Mileage A 8 B 6 C 10 CSV file 2 Score Mileage(Min) Mileage(Max) 1 1 3 2 4 6 3 7 9 4 10 12 5 13 15 And my desired output CSV file is something like this Car Mileage Score A 8 3 B 6 2 C 10 4 Car A is given a

问题 I have two csv files.Depending upon the value of a cell in csv file 1 I should be able to search that value in a column of csv file 2 and get he corresponding value from other column in csv file 2. I am sorry if this very confusing.It will probably get clear by illustration CSV file 1 Car Mileage A 8 B 6 C 10 CSV file 2 Score Mileage(Min) Mileage(Max) 1 1 3 2 4 6 3 7 9 4 10 12 5 13 15 And my desired output CSV file is something like this Car Mileage Score A 8 3 B 6 2 C 10 4 Car A is given a

问题 I have two csv files.Depending upon the value of a cell in csv file 1 I should be able to search that value in a column of csv file 2 and get he corresponding value from other column in csv file 2. I am sorry if this very confusing.It will probably get clear by illustration CSV file 1 Car Mileage A 8 B 6 C 10 CSV file 2 Score Mileage(Min) Mileage(Max) 1 1 3 2 4 6 3 7 9 4 10 12 5 13 15 And my desired output CSV file is something like this Car Mileage Score A 8 3 B 6 2 C 10 4 Car A is given a

问题 Here is the data - Account_Number Dummy_Account 1050080713252 ACC0000000000001 1050223213427 ACC0000000000002 1050080713252 ACC0000000169532 1105113502309 ACC0000000123005 1100043521537 ACC0000000000004 1100045301840 ACC0000000000005 1105113502309 ACC0000000000040 Rows 1,3 have duplicate values in Account_Number . So do rows 4,7 . I need to replace the duplicate values in Account_Number with the same values in Dummy_Account . So for 1050080713252 , both rows 1,3 should have same dummy values

问题 I am researching prescription habits and have large dataframes of sold products. I am trying to transform purchases of medications into courses of the drugs by calculating how long the product would have lasted and adding a 5 day fudge factor for compliance, starting delays, etc to calculate an end date for the purchase. I then want to combine prescriptions with overlapping date windows but I'm struggling to find an efficient way to do this. I was hoping a groupby would be possible but I can

问题 I am researching prescription habits and have large dataframes of sold products. I am trying to transform purchases of medications into courses of the drugs by calculating how long the product would have lasted and adding a 5 day fudge factor for compliance, starting delays, etc to calculate an end date for the purchase. I then want to combine prescriptions with overlapping date windows but I'm struggling to find an efficient way to do this. I was hoping a groupby would be possible but I can

问题 I am researching prescription habits and have large dataframes of sold products. I am trying to transform purchases of medications into courses of the drugs by calculating how long the product would have lasted and adding a 5 day fudge factor for compliance, starting delays, etc to calculate an end date for the purchase. I then want to combine prescriptions with overlapping date windows but I'm struggling to find an efficient way to do this. I was hoping a groupby would be possible but I can

问题 I am researching prescription habits and have large dataframes of sold products. I am trying to transform purchases of medications into courses of the drugs by calculating how long the product would have lasted and adding a 5 day fudge factor for compliance, starting delays, etc to calculate an end date for the purchase. I then want to combine prescriptions with overlapping date windows but I'm struggling to find an efficient way to do this. I was hoping a groupby would be possible but I can

问题 I have two dataframes: df_1: Letters Boolean a Nan b Nan c Nan df_2: a b d 2.7 1.2 3.6 1 2 3 How do I check if df_1['letters'] is present in df_2.keys(). If it is present, I want boolean to take the value 'x': Something like: Letters Boolean a x b x c Nan I tried using this code: for x in df_1['letters']: if x in df_2.keys(): df_1['Boolean']='x' 回答1: Use numpy.where with isin: df1['Boolean'] = np.where(df1['Letters'].isin(df2.columns), 'x', np.nan) 回答2: You need : df1['Boolean']=df1.Letters

问题 I am trying to output a pandas dataframe with colored cells to the console using the pandas Styler feature. When I run code examples in Spyder 3.2.6 running python 2.7, instead of seeing the dataframe as expected, the console outputs "...pandas.core.style.Styler at 0x1d0d7cf8>..."....this is what I would expect see with high-dimensional objects that ie need to be subset in order to view properly in the console I've also tried calling .render() on the dataframe but this outputs a lot of