Pandas Wrap Display into Multiple Columns-CodePudding

All,

I have a pandas dataframe with ~30 rows and 1 column. When I display it in Jupyter, all 30 rows are displayed in one long list. I am looking for a way to wrap the rows into multiple displayed columns, such as below:

Example dataframe:

df = pd.DataFrame([
  'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j',
  'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',
  'u', 'v', 'w', 'x', 'y', 'z', 'aa', 'ab', 'ac', 'ad'],
  columns=['value'])

Example output

    value       value       value
0   a       10  k       20  u
1   b       11  l       21  v
2   c       12  m       22  w
3   d       13  n       23  x
4   e       14  o       24  y
5   f       15  p       25  z
6   g       16  q       26  aa
7   h       17  r       27  ab
8   i       18  s       28  ac
9   j       19  t       29  ad

CodePudding user response：

Try

df[['col1', 'col2']] = df['col'].str.split(' ', 1, expand=True)

CodePudding user response：

You can use this helper function:

def reshape(df, rows=10):
    length = len(df)
    cols = np.ceil(length / rows).astype(int)
    df = df.assign(rows=np.tile(np.arange(rows), cols)[:length], 
                   cols=np.repeat(np.arange(cols), rows)[:length]) \
           .pivot('rows', 'cols', df.columns.tolist()) \
           .sort_index(level=1, axis=1).droplevel(level=1, axis=1).rename_axis(None)
    return df

Output

>>> reshape(df)
  value value value
0     a     k     u
1     b     l     v
2     c     m     w
3     d     n     x
4     e     o     y
5     f     p     z
6     g     q    aa
7     h     r    ab
8     i     s    ac
9     j     t    ad