DataFrame.join left_index right_index inverted

Code Sample, a copy-pastable example if possible

import numpy as np
import pandas as pd

df_left = pd.DataFrame(data=['X'],columns=['C'],index=[22])
df_right = pd.DataFrame(data=['X'],columns=['C'],index=[999])
merge = pd.merge(df_left,df_right,on=['C'], left_index=True)

print merge.index

Problem description

The copied code print a DataFrame where the key is 999. As I understand from the documentation where left_index=True the keys from the left DataFrame should be used as join keys. My output: Int64Index([999], dtype=‘int64’) Expected output: Int64Index([22], dtype=‘int64’)

INSTALLED VERSIONS ------------------ commit: None python: 2.7.12.final.0 python-bits: 64 OS: Linux OS-release: 4.15.0-32-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None

pandas: 0.23.3 pytest: None pip: 18.0 setuptools: 20.7.0 Cython: None numpy: 1.15.0 scipy: None pyarrow: None xarray: None IPython: 5.8.0 sphinx: None patsy: None dateutil: 2.7.3 pytz: 2018.5 blosc: None bottleneck: None tables: None numexpr: None feather: None matplotlib: None openpyxl: None xlrd: 1.1.0 xlwt: None xlsxwriter: 1.0.5 lxml: None bs4: None html5lib: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None

[TColl]

I think we’re not quite on the same page here.

on allows us to specify the merge columns to use in both dataframes via one argument, in this application we’re using on instead of left_on and/or right_on.

In this situation, if left_index or right_index are included (but left_on and right_on are excluded), the behaviour mentioned in this issue occurs.

here’s an example:

import pandas as pd

age_data = {
    'Name':['Ash','Bob','Charlie'],
    'ID':[1,2,3],
    'Age':[18,80,55]
}
height_data = {
    'Name':['Ash','Charlie','Derek'],
    'ID':[1,3,4],
    'Height':[140,162,180]
}

ages = pd.DataFrame(data=age_data, index=[1,2,3])
heights = pd.DataFrame(data=height_data, index=[91,92,93])

common_columns = ['Name', 'ID']
               
common_records_left_index = pd.merge(ages, heights, how='inner', on=common_columns, left_index=True)
common_records_right_index = pd.merge(ages, heights, how='inner', on=common_columns, right_index=True)

Here, we should end up with two dataframes which both contain the combined age and height data for Ash and Charlie (as they’re the only records with both an age and a height provided), with index values as follows:

• common_records_left_index should have index keys of 1 and 3 (preserved from the ages dataframe), and
• common_records_right_index should have index keys of 91 and 93 (preserved from the heights dataframe)

However, the opposite case is true - left_index=True preserves the keys from the right dataframe during the merge, and right_index=True preserves the keys from the left dataframe.

[jreback]

would take a PR for a test that replicates the OP