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I have moved a Pandas script I wrote from one computer to another. When running it on the new computer I am getting this error but am unsure what is causing it.

dfm = master_df dfa = pd.read_csv(path) dfa["Size"] = pd.cut(dfa["NOMSIZE_IN_MM_U"],bins=[0,300,600,float('inf')]) dfa["Depth"] = pd.cut(dfa["DEPTH_U"],bins=[0,2,4,6,float('inf')]) dfm['Size'] = pd.cut(dfm['NOMSIZE_IN_MM'], bins = [0,300,600,float('inf')]) dfm['Depth'] = pd.cut(dfm['AVE_DEPTH'], bins = [0,2,4,6,float('inf')]) master_df = dfm.join(dfa.set_index(['Size', 'Depth'])['REPAIR_DURATION'],on=['Size', 'Depth'])

Returns:

Traceback (most recent call last): File "s:/!AMD Share/Julian D - Student/LARM Gravity/Python Scripts/LARM3_GS.py", line 442, in <module> master_df = dfm.join(dfa.set_index(['Size', 'Depth'])['REPAIR_DURATION'],on=['Size', 'Depth']) File "C:\Users\DITTHAJ0\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py", line 4767, in join rsuffix=rsuffix, sort=sort) File "C:\Users\DITTHAJ0\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py", line 4782, in _join_compat suffixes=(lsuffix, rsuffix), sort=sort) File "C:\Users\DITTHAJ0\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\reshape\merge.py", line 54, in merge return op.get_result() File "C:\Users\DITTHAJ0\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\reshape\merge.py", line 569, in get_result join_index, left_indexer, right_indexer = self._get_join_info() File "C:\Users\DITTHAJ0\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\reshape\merge.py", line 726, in _get_join_info sort=self.sort) File "C:\Users\DITTHAJ0\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\reshape\merge.py", line 1353, in _left_join_on_index _get_multiindex_indexer(join_keys, right_ax, sort=sort) File "C:\Users\DITTHAJ0\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\reshape\merge.py", line 1304, in _get_multiindex_indexer rlab, llab, shape = map(list, zip(* map(fkeys, index.levels, join_keys))) File "C:\Users\DITTHAJ0\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\reshape\merge.py", line 1390, in _factorize_keys lk.is_dtype_equal(rk)): AttributeError: 'CategoricalIndex' object has no attribute 'is_dtype_equal'

Where dfa:

 NOMSIZE_IN_MM_U DEPTH_U REPAIR_DURATION 0 300 2 1 1 300 4 1 2 300 6 2 3 300 8 3 4 600 2 2 5 600 4 2 6 600 6 2 7 600 8 5 8 900 2 4 9 900 4 4 10 900 6 5 11 900 8 10

Master Data:

 ID AVE_DEPTH NOMSIZE_IN_MM 1 0 3.985 915 2 1 2.655 915 3 2 4.200 915
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1 Answer 1

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  • Code tested in pandas 1.2.1 - update with pip or conda, depending on your environment.
  • Also review Pandas Merging 101 for a full breakdown of merging and joining dataframes.

Sample DataFrames and Setup

import pandas as pd # test dataframes dfm = pd.DataFrame({'ID': [0, 1, 2], 'AVE_DEPTH': [3.985, 2.655, 4.200], 'NOMSIZE_IN_MM': [915, 915, 915]}) dfa = pd.DataFrame({'NOMSIZE_IN_MM_U': [300, 300, 300, 300, 600, 600, 600, 600, 900, 900, 900, 900], 'DEPTH_U': [2, 4, 6, 8, 2, 4, 6, 8, 2, 4, 6, 8], 'REPAIR_DURATION': [1, 1, 2, 3, 2, 2, 2, 5, 4, 4, 5, 10]}) # add bins dfa["Size"] = pd.cut(dfa["NOMSIZE_IN_MM_U"],bins=[0,300,600,float('inf')]) dfa["Depth"] = pd.cut(dfa["DEPTH_U"],bins=[0,2,4,6,float('inf')]) dfm['Size'] = pd.cut(dfm['NOMSIZE_IN_MM'], bins = [0,300,600,float('inf')]) dfm['Depth'] = pd.cut(dfm['AVE_DEPTH'], bins = [0,2,4,6,float('inf')]) # join or merge the dataframes

.join

  • Combine on indices
# set index - it's better to be explicit dfm.set_index(['Size', 'Depth'], inplace=True) dfa.set_index(['Size', 'Depth'], inplace=True) # join dataframes df = dfm.join(dfa.REPAIR_DURATION) # display(df) ID AVE_DEPTH NOMSIZE_IN_MM REPAIR_DURATION Size Depth (600.0, inf] (2.0, 4.0] 0 3.985 915 4 (2.0, 4.0] 1 2.655 915 4 (4.0, 6.0] 2 4.200 915 5

.merge

  • Combine on a combination of index and columns
# merge dataframes df = dfm.merge(dfa[['Size', 'Depth', 'REPAIR_DURATION']], on=['Size', 'Depth']) # display(df) ID AVE_DEPTH NOMSIZE_IN_MM Size Depth REPAIR_DURATION 0 0 3.985 915 (600.0, inf] (2.0, 4.0] 4 1 1 2.655 915 (600.0, inf] (2.0, 4.0] 4 2 2 4.200 915 (600.0, inf] (4.0, 6.0] 5
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