Python Pandas数据框：使用列中的值创建新列

Question

I've searched several books and sites and I can't find anything that quite matches what I'm trying to do. I would like to create itemized lists from a dataframe and reconfigure the data like so:

      A     B                A     B     C     D  
0     1     aa          0    1     aa  
1     2     bb          1    2     bb  
2     3     bb          2    3     bb    aa  
3     3     aa     --\  3    4     aa    bb    dd  
4     4     aa     --/  4    5     cc  
5     4     bb  
6     4     dd  
7     5     cc  

I've experimented with grouping, stacking, unstacking, etc. but nothing that I've attempted has produced the desired result. If it's not obvious, I'm very new to python and a solution would be great but an understanding of the process I need to follow would be perfect.

Thanks in advance

Answer 1

Using pandas you can query all results eg where A=4.

A crude but working method would be to iterate through the various index values and gather all 'like' results into a numpy array and convert this into a new dataframe.

Pseudo code to demonstrate my example: (will need rewriting to actually work)

l= [0]*df['A'].max() 
for item in xrange(df['A'].max() ):
    l[item] = df.loc[df['A'].isin(item)]

df = pd.DataFrame(l)
# or something of the sort

I hope that helps.

Update from comments:

animal_list=[]

for animal in ['cat','dog'...]:
    newdf=df[[x.is('%s'%animal) for x in df['A']]]

    body=[animal]    
    for item in newdf['B']
        body.append(item)

    animal_list.append(body)

df=pandas.DataFrame(animal_list)

Answer 2

A quick and dirty method that will work with strings. Customize the column naming as per needs.

data =  {'A': [1, 2, 3, 3, 4, 4, 4, 5],
         'B': ['aa', 'bb', 'bb', 'aa', 'aa', 'bb', 'dd', 'cc']}
df = pd.DataFrame(data)

maxlen = df.A.value_counts().values[0]  # this helps with creating 
                                    # lists of same size

newdata = {}
for n, gdf in df.groupby('A'):
    newdata[n]= list(gdf.B.values) + [''] * (maxlen - len(gdf.B)) 

# recreate DF with Col 'A' as index; experiment with other orientations
newdf = pd.DataFrame.from_dict(newdict, orient='index') 

# customize this section
newdf.columns = list('BCD')
newdf['A'] = newdf.index
newdf.index = range(len(newdf))
newdf = newdf.reindex_axis(list('ABCD'), axis=1) # to set the desired order

print newdf

The result is:

A   B   C   D
0  1  aa        
1  2  bb        
2  3  bb  aa    
3  4  aa  bb  dd
4  5  cc