Combining two Series into a DataFrame in pandas

Question

I have two Series s1 and s2 with the same  non-consecutive  indices  How do I combine s1 and s2 to being two columns in a DataFrame and keep one of the indices as a third column

User · Answer

Pandas will automatically align these passed in series and create the joint index They happen to be the same here  reset index moves the index to a column   In  2   s1   Series randn 5  index  1 2 4 5 6    In  4   s2   Series randn 5  index  1 2 4 5 6    In  8   DataFrame dict s1   s1  s2   s2   reset index   Out 8       index        s1        s2 0      1 -0 176143  0 128635 1      2 -1 286470  0 908497 2      4 -0 995881  0 528050 3      5  0 402241  0 458870 4      6  0 380457  0 072251

User · Answer

I used pandas to convert my numpy array or iseries to an dataframe then added and additional the additional column by key as  prediction   If you need dataframe converted back to a list then use values tolist    output pd DataFrame X test  output  prediction   y pred  list output values tolist

User · Answer

Example code   a   pd Series  1 2 3 4   index  7 2 8 9   b   pd Series  5 6 7 8   index  7 2 8 9   data   pd DataFrame   a   a  b  b   idx col  a index     Pandas allows you to create a DataFrame from a dict with Series as the values and the column names as the keys  When it finds a Series as a value  it uses the Series index as part of the DataFrame index  This data alignment is one of the main perks of Pandas  Consequently  unless you have other needs  the freshly created DataFrame has duplicated value  In the above example  data  idx col   has the same data as data index

User · Answer

If I may answer this   The fundamentals behind converting series to data frame is to understand that  1  At conceptual level  every column in data frame is a series   2  And  every column name is a key name that maps to a series   If you keep above two concepts in mind  you can think of many ways to convert series to data frame  One easy solution will be like this   Create two series here  import pandas as pd  series 1   pd Series list range 10     series 2   pd Series list range 20 30      Create an empty data frame with just desired column names  df   pd DataFrame columns     Column name 1    Column name 1      Put series value inside data frame using mapping concept  df  Column name 1     series 1  df  Column name 2     series 2   Check results now  df head 5

User · Answer

I think concat is a nice way to do this  If they are present it uses the name attributes of the Series as the columns  otherwise it simply numbers them    In  1   s1   pd Series  1  2   index   A    B    name  s1    In  2   s2   pd Series  3  4   index   A    B    name  s2    In  3   pd concat  s1  s2   axis 1  Out 3      s1  s2 A   1   3 B   2   4  In  4   pd concat  s1  s2   axis 1  reset index   Out 4     index  s1  s2 0     A   1   3 1     B   2   4   Note  This extends to more than 2 Series

User · Answer

Why don t you just use  to frame if both have the same indexes      v0 23  a to frame   join b     lt  v0 23  a to frame   join b to frame

User · Answer

If you are trying to join Series of equal length but their indexes don t match  which is a common scenario   then concatenating them will generate NAs wherever they don t match  x   pd Series   a  1  b  2    y   pd Series   d  4  e  5   pd concat  x y  axis 1    Output  I ve added column names for clarity  Index   x    y a      1 0  NaN b      2 0  NaN d      NaN  4 0 e      NaN  5 0  Assuming that you don t care if the indexes match  the solution is to reindex both Series before concatenating them   If drop False  which is the default  then Pandas will save the old index in a column of the new dataframe  the indexes are dropped here for simplicity   pd concat  x reset index drop True  y reset index drop True   axis 1    Output  column names added   Index   x   y 0       1   4 1       2   5

User · Answer

Not sure I fully understand your question  but is this what you want to do   pd DataFrame data dict s1 s1  s2 s2   index s1 index     index s1 index is not even necessary here

User · Answer

A simplification of the solution based on join     df   a to frame   join b

[python] Combining two Series into a DataFrame in pandas

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