python - pandas: sort each column individually

Question

My dataframe looks something like this, only much larger.

d = {'Col_1' : pd.Series(['A', 'B']),
 'Col_2' : pd.Series(['B', 'A', 'C']),
 'Col_3' : pd.Series(['B', 'A']),
 'Col_4' : pd.Series(['C', 'A', 'B', 'D']),
 'Col_5' : pd.Series(['A', 'C']),}
df = pd.DataFrame(d)

Col_1  Col_2  Col_3  Col_4  Col_5
  A      B      B      C      A
  B      A      A      A      C
  NaN    C      NaN    B      NaN
  NaN    NaN    NaN    D      NaN

First, I'm trying to sort each column individually. I've tried playing around with something like: df.sort([lambda x: x in df.columns], axis=1, ascending=True, inplace=True) however have only ended up with errors. How do I sort each column individually to end up with something like:

Col_1  Col_2  Col_3  Col_4  Col_5
  A      A      A      A      A
  B      B      B      B      C
  NaN    C      NaN    C      NaN
  NaN    NaN    NaN    D      NaN

Second, I'm looking to concatenate the rows within the columns

 df = pd.concat([df,pd.DataFrame(df.sum(axis=0),columns=['Concatenation']).T])

I can combine everything with the line above after replacing np.nan with '', but the result comes out smashed ('AB') together and would require an additional step to clean (into something like 'A:B').

Answer 1

Here is one way:

>>> pandas.concat([df[col].order().reset_index(drop=True) for col in df], axis=1, ignore_index=True)
11:      0    1    2  3    4
0    A    A    A  A    A
1    B    B    B  B    C
2  NaN    C  NaN  C  NaN
3  NaN  NaN  NaN  D  NaN

[4 rows x 5 columns]

However, what you're doing is somewhat strange. DataFrames aren't just collections of unrelated columns. In a DataFrame, each row represents a record, so the value in one column is semantically linked to the values in other columns in that same row. By sorting the columns independently, you're discarding this information, so the rows are now meaningless. That's why the reset_index is needed in my example. Also, because of this, there's no way to do this in-place, which your example suggests you want.