Pandas - 将列值拆分为新列

Question

I have a large dataframe and I am storing a lot of redundant values that are making it hard to handle my data. I have a dataframe of the form:

import pandas as pd

df = pd.DataFrame([["a","g","n1","y1"], ["a","g","n2","y2"], ["b","h","n1","y3"], ["b","h","n2","y4"]], columns=["meta1", "meta2", "name", "data"])

>>> df

  meta1 meta2 name data
    a     g   n1   y1
    a     g   n2   y2
    b     h   n1   y3
    b     h   n2   y4

where I have the names of the new columns I would like in name and the respective data in data .

I would like to produce a dataframe of the form:

df = pd.DataFrame([["a","g","y1","y2"], ["b","h","y3","y4"]], columns=["meta1", "meta2", "n1", "n2"])

>>> df

meta1 meta2  n1  n2
  a     g  y1  y2
  b     h  y3  y4

The columns called meta are around 15+ other columns that contain most of the data, and I don't think are particularly well suited to for indexing. The idea is that I have a lot of repeated/redundant data stored in meta at the moment and I would like to produce the more compact dataframe presented.

I have found some similar Qs but can't pinpoint what sort of operations I need to do: pivot, re-index, stack or unstack, etc.?

PS - the original index values are unimportant for my purposes.

Any help would be much appreciated.

Question I think is related:

I think the following Q is related to what I am trying to do, but I can't see how to apply it, as I don't want to produce more indexes.

• Python Pandas- how to unstack a pivot table with two values with each value becoming a new column?

Answer 1

If you group your meta columns into a list then you can do this:

metas = ['meta1', 'meta2']

new_df = df.set_index(['name'] + metas).unstack('name')
print new_df

            data    
name          n1  n2
meta1 meta2         
a     g       y1  y2
b     h       y3  y4

Which gets you most of the way there. Additional tailoring can get you the rest of the way.

print new_df.data.rename_axis([None], axis=1).reset_index()

  meta1 meta2  n1  n2
0     a     g  y1  y2
1     b     h  y3  y4

Answer 2

You can use pivot_table with reset_index and rename_axis (new in pandas 0.18.0 ):

print (df.pivot_table(index=['meta1','meta2'], 
                      columns='name', 
                      values='data', 
                      aggfunc='first')
         .reset_index()
         .rename_axis(None, axis=1))

  meta1 meta2  n1  n2
0     a     g  y1  y2
1     b     h  y3  y4

But better is use aggfunc join :

print (df.pivot_table(index=['meta1','meta2'], 
                      columns='name', 
                      values='data', 
                      aggfunc=', '.join)
         .reset_index()
         .rename_axis(None, axis=1))

  meta1 meta2  n1  n2
0     a     g  y1  y2
1     b     h  y3  y4

Explanation, why join is generally better as first :

If use first , you can lost all data which are not first in each group by index , but join concanecate them:

import pandas as pd

df = pd.DataFrame([["a","g","n1","y1"], 
                   ["a","g","n2","y2"], 
                   ["a","g","n1","y3"], 
                   ["b","h","n2","y4"]], columns=["meta1", "meta2", "name", "data"])

print (df)
  meta1 meta2 name data
0     a     g   n1   y1
1     a     g   n2   y2
2     a     g   n1   y3
3     b     h   n2   y4

print (df.pivot_table(index=['meta1','meta2'], 
                      columns='name', 
                      values='data', 
                      aggfunc='first')
         .reset_index()
         .rename_axis(None, axis=1))
  meta1 meta2    n1  n2
0     a     g    y1  y2
1     b     h  None  y4

print (df.pivot_table(index=['meta1','meta2'], 
                      columns='name', 
                      values='data', 
                      aggfunc=', '.join)
         .reset_index()
         .rename_axis(None, axis=1))

  meta1 meta2      n1  n2
0     a     g  y1, y3  y2
1     b     h    None  y4