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Python Pandas: How to Fix "ValueError: You are trying to merge on int64 and object columns"

When merging or joining DataFrames in Pandas using functions like pd.merge() or DataFrame.join(), you might encounter the ValueError: You are trying to merge on int64 and object columns. If you wish to proceed you should use pd.concat. This error is a clear indicator that the key columns (or index levels) you're attempting to join on have incompatible data types—specifically, one is an integer type (int64) and the other is an object type (often representing strings). Pandas raises this error to prevent potentially incorrect merges due to type mismatches.

This guide will thoroughly explain why this ValueError occurs, demonstrate how to reproduce it, and provide robust solutions, primarily focusing on converting the data types of your join keys to be consistent using .astype() and addressing scenarios involving missing values with nullable integer types like 'Int64'.

Understanding the Error: The Importance of Matching Key Types​

Pandas' merge and join operations rely on matching values in specified key columns or indices to combine DataFrames. For these matches to be meaningful and accurate, the data types of these keys should be compatible. If you try to merge a column of integers (e.g., 2022, 2023) with a column of strings (e.g., '2022', '2023'), Pandas can not directly equate them because 2022 (as an integer) is different from '2022' (as a string). The "ValueError: You are trying to merge on int64 and object columns" specifically points out this int vs object (string) incompatibility in your join keys.

Scenario 1: Error with pd.merge() due to Mismatched dtype in Key Columns​

This is the most common scenario for this error.

Reproducing the Error​

Let's consider two DataFrames where the 'year' column, intended as the merge key, has different data types.

import pandas as pd

df_sales = pd.DataFrame({
    'year': [2022, 2023, 2024, 2025], # 'year' is int64
    'revenue': [100000, 120000, 150000, 130000],
    'region': ['North', 'North', 'South', 'South']
})

df_headcount = pd.DataFrame({
    'year': ['2022', '2023', '2024', '2025'], # 'year' is object (string)
    'employees': [50, 55, 65, 60],
    'office_location': ['CityA', 'CityA', 'CityB', 'CityB']
})

try:
    # ⛔️ Attempting to merge on 'year'. df_sales['year'] is int64, df_headcount['year'] is object.
    merged_df_error = pd.merge(df_sales, df_headcount, on='year', how='left')
    print(merged_df_error)
except ValueError as e:
    print(f"Error: {e}")
    # Output:
    # Error: You are trying to merge on int64 and object columns. If you wish to proceed you should use pd.concat

Output:

Error: You are trying to merge on int64 and object columns for key 'year'. If you wish to proceed you should use pd.concat

Verifying Column Data Types​

You can check the dtype of the columns:

import pandas as pd

# df_sales and df_headcount defined as above
df_sales = pd.DataFrame({
    'year': [2022, 2023, 2024, 2025], # 'year' is int64
    'revenue': [100000, 120000, 150000, 130000],
    'region': ['North', 'North', 'South', 'South']
})
df_headcount = pd.DataFrame({
    'year': ['2022', '2023', '2024', '2025'], # 'year' is object (string)
    'employees': [50, 55, 65, 60],
    'office_location': ['CityA', 'CityA', 'CityB', 'CityB']
})

print(f"dtype of df_sales['year']: {df_sales['year'].dtype}")           # Output: int64
print(f"dtype of df_headcount['year']: {df_headcount['year'].dtype}")   # Output: object

Output:

dtype of df_sales['year']: int64
dtype of df_headcount['year']: object

Solution: Convert Key Column(s) to a Consistent Type using .astype()​

Before merging, convert one of the 'year' columns to match the data type of the other. It's often best to convert the string/object column to numeric if the data represents numbers.

import pandas as pd

# df_sales and df_headcount defined as above
df_sales = pd.DataFrame({
    'year': [2022, 2023, 2024, 2025], # 'year' is int64
    'revenue': [100000, 120000, 150000, 130000],
    'region': ['North', 'North', 'South', 'South']
})
df_headcount = pd.DataFrame({
    'year': ['2022', '2023', '2024', '2025'], # 'year' is object (string)
    'employees': [50, 55, 65, 60],
    'office_location': ['CityA', 'CityA', 'CityB', 'CityB']
})

# ✅ Convert 'year' column in df_headcount to integer
df_headcount['year'] = df_headcount['year'].astype(int)

print(f"New dtype of df_headcount['year']: {df_headcount['year'].dtype}") # Output: int64

# Now merge the DataFrames (both 'year' columns are int64)
merged_df_correct = pd.merge(df_sales, df_headcount, on='year', how='left')

print("Successfully merged DataFrame:")
print(merged_df_correct)

Output:

New dtype of df_headcount['year']: int32
Successfully merged DataFrame:
   year  revenue region  employees office_location
0  2022   100000  North         50           CityA
1  2023   120000  North         55           CityA
2  2024   150000  South         65           CityB
3  2025   130000  South         60           CityB
note

Alternatively, you could convert df_sales['year'] to str if string comparison was intended, but for numerical keys like 'year', converting to a numeric type is usually more appropriate.

Handling Missing Values (None, NaN) with 'Int64' Nullable Type​

Standard integer columns (int64) in Pandas cannot hold missing values (NaN). If you try to convert an object column containing None or empty strings (that might become NaN) to int using astype(int), you'll get another error.

Problem with astype(int) and Missing Values​

import pandas as pd
import numpy as np

df_personnel = pd.DataFrame({
    'employee_id': ['101', '102', None, '104'], # Contains None
    'name': ['Alice', 'Bob', 'Charlie', 'David']
})

df_performance = pd.DataFrame({
    'employee_id': [101, 102, 103, 104], # int64
    'rating': [5, 4, 5, 3]
})

try:
    # ⛔️ This will cause an error because None cannot be converted to int
    df_personnel['employee_id'] = df_personnel['employee_id'].astype(int)
except TypeError as e: # Or ValueError depending on Pandas version and content
    print(f"Error converting object with None to int: {e}")
    # Example error: "TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'"
    # Or "ValueError: cannot convert NA to integer"

Output:

Error converting object with None to int: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'

Solution: Use astype('Int64') for Integers with Missing Values​

Pandas provides nullable integer types (capital 'I' in 'Int64', 'Int32', etc.) that can hold pd.NA (Pandas' missing value marker).

import pandas as pd

# df_personnel and df_performance defined as above
df_personnel = pd.DataFrame({
    'employee_id': ['101', '102', None, '104'], # Contains None
    'name': ['Alice', 'Bob', 'Charlie', 'David']
})

df_performance = pd.DataFrame({
    'employee_id': [101, 102, 103, 104], # int64
    'rating': [5, 4, 5, 3]
})

# ✅ Convert 'employee_id' in df_personnel to nullable integer type 'Int64'
df_personnel['employee_id'] = df_personnel['employee_id'].astype('Int64')
print(f"dtype of df_personnel['employee_id']: {df_personnel['employee_id'].dtype}") # Output: Int64

# Ensure df_performance['employee_id'] is also compatible or convert it too
# df_performance['employee_id'] = df_performance['employee_id'].astype('Int64') # Good practice if it might also have NAs

# Now merge (assuming df_performance['employee_id'] is also Int64 or compatible int64)
merged_nullable_df = pd.merge(df_performance, df_personnel, on='employee_id', how='left')

print("Merged DataFrame with 'Int64' key:")
print(merged_nullable_df)

Output:

dtype of df_personnel['employee_id']: Int64
Merged DataFrame with 'Int64' key:
   employee_id  rating   name
0          101       5  Alice
1          102       4    Bob
2          103       5    NaN
3          104       3  David
note

Using 'Int64' allows the column to contain both integers and pd.NA, making merges more robust when missing key values are possible.

Scenario 2: Error with DataFrame.join() due to Mismatched Key/Index Types​

The DataFrame.join() method is primarily used for joining DataFrames based on their indices. However, it can also join a DataFrame's column(s) with another DataFrame's index using the on parameter.

Understanding DataFrame.join(on=...)​

When you use df_left.join(df_right, on='key_col_in_df_left'), Pandas attempts to:

  1. Use the values from df_left['key_col_in_df_left'] as the left join key.
  2. Use the index of df_right as the right join key. If df_left['key_col_in_df_left'] has one dtype (e.g., object) and df_right.index has another (e.g., int64), the "merge on int64 and object columns" error can occur.

Reproducing the Error with join​

import pandas as pd

df_left = pd.DataFrame({
    'join_key_col': ['0', '1', '2'], # object type (strings)
    'data_left': ['L0', 'L1', 'L2']
})

# df_right has an integer index (RangeIndex: 0, 1, 2)
df_right = pd.DataFrame({
    'data_right': ['R0', 'R1', 'R2']
}, index=[0, 1, 2]) # Index is int64

print(f"dtype of df_left['join_key_col']: {df_left['join_key_col'].dtype}") # object
print(f"dtype of df_right.index: {df_right.index.dtype}")                   # int64

try:
    # ⛔️ Joining df_left['join_key_col'] (object) with df_right.index (int64)
    joined_df_error = df_left.join(df_right, on='join_key_col', how='left')
    print(joined_df_error)
except ValueError as e:
    print(f"Error with join: {e}")

Output:

dtype of df_left['join_key_col']: object
dtype of df_right.index: int64

Error with join: You are trying to merge on object and int64 columns for key 'join_key_col'. If you wish to proceed you should use pd.concat

Solution: Ensure Type Consistency or Prefer pd.merge() for Column-on-Column Joins​

  • Convert Types: You would need to make df_left['join_key_col'] and df_right.index compatible (e.g., both int or both object). 
    # Example: Convert df_left['join_key_col'] to int
    df_left['join_key_col'] = df_left['join_key_col'].astype(int)
    joined_df_fixed = df_left.join(df_right, on='join_key_col', how='left')
  • Prefer pd.merge(): For joining based on columns from both DataFrames (column-on-column join), pd.merge() is generally more explicit and often preferred. If you intended to join df_left['join_key_col'] with a column in df_right (not its index), merge is the tool: 
    # If df_right had a 'key_col_in_df_right' you wanted to join on:
    merged_df = pd.merge(df_left, df_right, left_on='join_key_col', right_on='key_col_in_df_right')

Conclusion​

The ValueError: You are trying to merge on int64 and object columns is Pandas' way of alerting you to a potentially problematic type mismatch in your join keys. The most reliable solution is to:

  1. Inspect the dtype of the columns or indices you intend to join on in both DataFrames.
  2. Convert one of the keys using .astype() to match the type of the other. For numerical keys stored as strings, astype(int) or astype(float) is common.
  3. If your integer key column contains or might contain missing values (None, NaN), use the nullable integer type astype('Int64') (or 'Int32', etc.) to prevent conversion errors.
  4. When using DataFrame.join(on='left_col'), remember it joins left_col with the index of the right DataFrame; ensure their types are compatible or consider if pd.merge() is more appropriate for your specific join logic.

By ensuring type consistency in your join keys, you can perform accurate and error-free merges and joins in Pandas.

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