Transform a Pandas Dataframe Column and Index to Values

user_5618856 0
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I have a dataframe in a "yes/no" format like

    7   22
1   NaN t
25  t   NaN

where "t" stands for yes and I need to transform it to a X-Y table since the column name is the X coordinate and the index is the Y coordinate:

  X  Y
1 22  1
2  7 25

a pseudo-code like:

if a cell = "t":
     newdf.X = df.column(t)
     newdf.Y = df.index(t)
pandas transformation dataframe python
3 Answers

Try this:

# Use np.where to get the integer location of the 't's in the dataframe
r, c = np.where(df == 't')

# Use dataframe constructor with dataframe indexes to define X, Y
df_out = pd.DataFrame({'X':df.columns[c], 'Y':df.index[r]})
df_out

Output:

    X   Y
0  22   1
1   7  25

Update to address @RajeshC comment:

Given df,

      7   22
1   NaN    t
13  NaN  NaN
25    t  NaN

Then:

r, c = np.where(df == 't')
df_out = pd.DataFrame({'X':df.columns[c], 'Y':df.index[r]}, index=r)
df_out = df_out.reindex(range(df.shape[0]))
df_out

Output:

     X     Y
0   22   1.0
1  NaN   NaN
2    7  25.0
user_63615310

Another option with stack:

pd.DataFrame.from_records(
    df.stack().index.swaplevel(),
    columns=['X', 'Y'])

Output:

    X   Y
0  22   1
1   7  25
user_67927430

Based on the magic of stacking, my answer is a tweak of @Perl's answer (couldn't comment because reps < 50)-

df.stack().to_frame().reset_index().drop(0,axis = 1).rename(columns = {'level_0':"Y","level_1":"X"}).reindex(columns=["X","Y"])

PS - Thanks Kristian Canler for the edit.

user_100298090
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