Do two tables share an ID column that Pandas refuses to join on?
left = pd.DataFrame({'id': [1, 2], 'x': ['a', 'b']})
right = pd.DataFrame({'id': ['1', '2'], 'y': ['c', 'd']})
left.merge(right, on='id')
ValueError: You are trying to merge on int64 and str columns for key 'id'. If you wish to proceed you should use pd.concat
Printed side by side the two columns look the same. One holds the integer 1, the other the text '1', and those are different keys. Pandas used to merge them and quietly match nothing, which was worse; now it stops and says so.
Convert one side and the merge works:
left.merge(right.astype({'id': 'int64'}), on='id')
id x y
0 1 a c
1 2 b d
Which side to convert
Convert toward whatever the ID genuinely is, not toward whichever is easier.
Convert to integer when the values are counts or database keys and nothing depends on their formatting. Convert to string when the ID has leading zeros, is longer than about 15 digits, or is a code that happens to contain only digits — zip codes, account numbers, ISBNs, national IDs. Turning those into integers strips leading zeros irreversibly, and past 2^53 it starts losing precision on the values themselves.
When in doubt, text is the safer direction, because an ID is an identifier rather than a quantity. You are never going to add two customer numbers together.
Where the mismatch comes from
Almost always from two different sources. A CSV read with default settings types a digits-only column as integer; the same column pulled from an API arrives as JSON strings; a database column declared VARCHAR comes back as text. Each is behaving correctly in isolation.
The durable fix is at load time rather than at merge time:
pd.read_csv('customers.csv', dtype={'id': str})
That way every frame in the pipeline agrees on what an ID is, and the merge needs no repair. It also prevents the quieter version of this bug, where a zip code column loads as an integer and 01234 becomes 1234 before you ever get to a join.
The suggestion in the message is usually wrong
The error recommends pd.concat, which is right only if you meant to stack the two frames rather than join them. If you wanted rows from both tables matched on a key, concat is not a substitute — it will put them side by side or end to end without matching anything. Take the advice as "if you were not actually merging" rather than as a fix.
The mistakes I see most often
Converting with astype(int) on a column containing nulls. That raises about non-finite values. Convert the other side to string instead, or handle the missing values first.
Fixing the dtype after the merge. By then the rows are gone.
Trusting head(). Both columns print without quotes in a frame, so the display gives nothing away. df.dtypes is the check.
Practice
See merge in Pandas for joining frames, astype in Pandas for converting dtypes, and read_csv in Pandas for setting them at load time.