One error you may encounter when using pandas is:

ValueError: You are trying to merge on int64 and object columns.
            If you wish to proceed you should use pd.concat

This error occurs when you attempt to merge two pandas DataFrames but the column you’re merging on is an object in one DataFrame and an integer in the other DataFrame.

The following example shows how to fix this error in practice.

How to Reproduce the Error

Suppose we create the following two pandas DataFrames:

import pandas as pd

#create DataFrame
df1 = pd.DataFrame({'year': [2015, 2016, 2017, 2018, 2019, 2020, 2021],
                    'sales': [500, 534, 564, 671, 700, 840, 810]})

df2 = pd.DataFrame({'year': ['2015', '2016', '2017', '2018', '2019', '2020', '2021'],
                    'refunds': [31, 36, 40, 40, 43, 70, 62]})

#view DataFrames
print(df1)

   year  sales
0  2015    500
1  2016    534
2  2017    564
3  2018    671
4  2019    700
5  2020    840
6  2021    810

print(df2)

   year  refunds
0  2015       31
1  2016       36
2  2017       40
3  2018       40
4  2019       43
5  2020       70
6  2021       62

Now suppose we attempt to merge the two DataFrames:

#attempt to merge two DataFrames
big_df = df1.merge(df2, on='year', how='left')

ValueError: You are trying to merge on int64 and object columns.
            If you wish to proceed you should use pd.concat

We receive a ValueError because the year variable in the first DataFrame is an integer but the year variable in the second DataFrame is an object.

How to Fix the Error

The easiest way to fix this error is to simply convert the year variable in the second DataFrame to an integer and then perform the merge.

The following syntax shows how to do so:

#convert year variable in df2 to integer
df2['year']=df2['year'].astype(int)

#merge two DataFrames
big_df = df1.merge(df2, on='year', how='left')

#view merged DataFrame
big_df

	year	sales	refunds
0	2015	500	31
1	2016	534	36
2	2017	564	40
3	2018	671	40
4	2019	700	43
5	2020	840	70
6	2021	810	62

Notice that we don’t receive any ValueError and we are able to successfully merge the two DataFrames into one.

Additional Resources

The following tutorials explain how to fix other common errors in Python: