concat

问题 How can I "concat" a specific column from many Python Pandas dataframes, WHERE another column in each of the many dataframes meets a certain condition (colloquially termed condition "X" here). In SQL this would be simple using JOIN clause with WHERE df2.Col2 = "X" and df3.Col2 = "X" and df4.col2 = "X"... etc (which can be run dynamically). In my case, I want to create a big dataframe with all the "Col1"s from each of the many dataframes, but only include the Col1 row values WHERE the

来源： https://stackoverflow.com/questions/63810209/how-do-i-concatenate-only-if-not-null-in-sql-server

问题 I am trying to concat all my columns into a new column. The concatenated values should be stored in a list. My dataframe: df = pd.DataFrame({'A': ['1', '2', nan], 'B': [nan, '5', nan], 'C': ['7', nan, '9']}) desired output: df: A B C concat_col 1 nan 7 [1,7] 2 5 nan [2,5] nan nan 9 [9] What i tried: df['concat'] = pd.Series(df.fillna('').values.tolist()).str.join(',') Output i got: A B C concat_col 1 nan 7 1,,7 2 5 nan 2,5,, nan nan 9 ,,9 回答1: The following code should work: df['concat_col']

问题 This question already has answers here : Pandas Merging 101 (2 answers) Closed 2 years ago . I have Three dataframes. All of them have a common column and I need to merge them based on the common column without missing any data Input >>>df1 0 Col1 Col2 Col3 1 data1 3 4 2 data2 4 3 3 data3 2 3 4 data4 2 4 5 data5 1 4 >>>df2 0 Col1 Col4 Col5 1 data1 7 4 2 data2 6 9 3 data3 1 4 >>>df3 0 Col1 Col6 Col7 1 data2 5 8 2 data3 2 7 3 data5 5 3 Expected Output >>>df 0 Col1 Col2 Col3 Col4 Col5 Col6 Col7

问题 This question already has answers here : Pandas Merging 101 (2 answers) Closed 2 years ago . I have Three dataframes. All of them have a common column and I need to merge them based on the common column without missing any data Input >>>df1 0 Col1 Col2 Col3 1 data1 3 4 2 data2 4 3 3 data3 2 3 4 data4 2 4 5 data5 1 4 >>>df2 0 Col1 Col4 Col5 1 data1 7 4 2 data2 6 9 3 data3 1 4 >>>df3 0 Col1 Col6 Col7 1 data2 5 8 2 data3 2 7 3 data5 5 3 Expected Output >>>df 0 Col1 Col2 Col3 Col4 Col5 Col6 Col7

问题 I do as below: data1 = pd.DataFrame({ 'b' : [1, 1, 1], 'a' : [2, 2, 2]}) data2 = pd.DataFrame({ 'b' : [1, 1, 1], 'a' : [2, 2, 2]}) frames = [data1, data2] data = pd.concat(frames) data a b 0 2 1 1 2 1 2 2 1 0 2 1 1 2 1 2 2 1 The data column order is in alphabet order. Why is it so? and how to keep the original order? 回答1: You are creating DataFrames out of dictionaries. Dictionaries are a unordered which means the keys do not have a specific order. So d1 = {'key_a': 'val_a', 'key_b': 'val_b'}

问题 Those who use, know, how useful automation tool AHK is.. The AHK has function StringSplit or StrSplit() which does very fast split string into array elements. This is very useful if you want to manipulate some parts of well formed string, but unfortunately it appears there is no way around! I spend time searching and there was a mess of samples with old syntax which just does not work. All I wanted is Final_Concatenated_String := StrConcat(My_Array_Of_Strings, "\") which obviously does not

问题 I am trying to convert a sql query to python. The sql statement is as follows: select * from table 1 union select * from table 2 union select * from table 3 union select * from table 4 Now I have those tables in 4 dataframe df1, df2, df3, df4 and I would like to union 4 pandas dataframe which would match the result as the same as sql query. I am confused of what operation to be used which is equivalent to sql union? Thanks in advance!! Note: The column name for all the dataframes are the same

问题 I am trying to convert a sql query to python. The sql statement is as follows: select * from table 1 union select * from table 2 union select * from table 3 union select * from table 4 Now I have those tables in 4 dataframe df1, df2, df3, df4 and I would like to union 4 pandas dataframe which would match the result as the same as sql query. I am confused of what operation to be used which is equivalent to sql union? Thanks in advance!! Note: The column name for all the dataframes are the same

问题 I am trying to convert a sql query to python. The sql statement is as follows: select * from table 1 union select * from table 2 union select * from table 3 union select * from table 4 Now I have those tables in 4 dataframe df1, df2, df3, df4 and I would like to union 4 pandas dataframe which would match the result as the same as sql query. I am confused of what operation to be used which is equivalent to sql union? Thanks in advance!! Note: The column name for all the dataframes are the same