Pandas merge unexpectedly produces suffixes

Question

I am merging two Pandas DataFrames together and am getting "_x" and "_y" suffixes. Easy to replicate example below. I tried adding , suffixes=(False, False) into the merge, but it returns an error: ValueError: columns overlap but no suffix specified: Index(['f1', 'f2', 'f3'], dtype='object'). I must be missing something obvious here? I understand why this would occur using join, but I didn't expect it for merge.

Please ignore the copy slice error. I can't figure out why it doesn't throw this error on Line 10, but does throw it on Line 17. (If you know, there's an open question here on it!)

System details: Windows 10
conda 4.8.2
Python 3.8.3
pandas 1.0.5 py38he6e81aa_0 conda-forge

import pandas as pd

#### Build an example DataFrame for easy-to-replicate example ####
myid = [1, 1, 1, 2, 2]
myorder = [3, 2, 1, 2, 1]
y = [3642, 3640, 3632, 3628, 3608]
x = [11811, 11812, 11807, 11795, 11795]
df = pd.DataFrame(list(zip(myid, myorder, x, y)), 
                  columns =['myid', 'myorder', 'x', 'y']) 
df.sort_values(by=['myid', 'myorder'], inplace=True) #Line10
df.reset_index(drop=True, inplace=True)
display(df.style.hide_index())

### Typical analysis on existing DataFrame, Error occurs in here ####
for id in df.myid.unique():
    tempdf = df[mygdf.myid == id]
    tempdf.sort_values(by=['myid', 'myorder'], inplace=True) #Line17
    tempdf.reset_index(drop=True, inplace=True)
    for i, r in tempdf.iloc[1:].iterrows():
        ## in reality, calling a more complicated function here
        ## this is just a simple example
        tempdf.loc[i, 'f1'] = tempdf.x[i-1] - tempdf.x[i]
        tempdf.loc[i, 'f2'] = tempdf.y[i-1] - tempdf.y[i]
        tempdf.loc[i, 'f3'] = tempdf.y[i] +2
   
    what_i_care_about = ['myid', 'myorder', 'f1', 'f2', 'f3']

    df = pd.merge(df, tempdf[what_i_care_about], 
                  on=['myid', 'myorder'], how='outer')
    del tempdf

display(df.style.hide_index())

Answer 1

Your problem is that there are columns you are not merging on that are common to both source DataFrames. Pandas needs a way to say which one came from where, so it adds the suffixes, the defaults being '_x' on the left and '_y' on the right.

If you have a preference on which source data frame to keep the columns from, then you can set the suffixes and filter accordingly, for example if you want to keep the clashing columns from the left:

# Label the two sides, with no suffix on the side you want to keep
df = pd.merge(
    df, 
    tempdf[what_i_care_about], 
    on=['myid', 'myorder'], 
    how='outer',
    suffixes=('', '_delme')  # Left gets no suffix, right gets something identifiable
)
# Discard the columns that acquired a suffix
df = df[[c for c in df.columns if not c.endswith('_delme')]]

Alternatively, you can drop one of each of the clashing columns prior to merging, then Pandas has no need to assign a suffix.