How to Fix “ValueError” While Merging DataFrames in Pandas [All Method]️
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The step-by-step guide on this page will show you How to Fix “ValueError” While Merging DataFrames in Pandas. What should you do if you come across a code error! Let’s get started on fixing it.
Question: What is the best solution for this problem? Answer: This blog code can help you solve errors How to Fix “ValueError” While Merging DataFrames in Pandas. Question: What is causing this error and what can be done to fix it? Answer: Check out this blog for a solution to your problem.
Suppose we’re trying to merge multiple DataFrames.
Example Scenario
We have DataFrame df1
:
year col1
0 2010 1
1 2011 2
2 2012 3
3 2013 4
4 2014 5
And DataFrame df2
:
year col2
0 2010 6
1 2011 7
2 2012 8
3 2013 9
4 2014 10
We’ll try merging.
merged_df = df1.merge(df2, on=['year'], how='inner')
We might end up with an error message like this.
ValueError: You are trying to merge on object and int64 columns.
If you wish to proceed you should use pd.concat
Change the column type
The error message is telling us that there’s a type error on the column we are merging over.
The first step would be to check the on=[]
list in our call to merge()
, and check the dtypes
for those columns.
print(df1.dtypes)
print(df2.dtypes)
In our scenario, maybe year
is an int
in df1
, but in df2
, year
is a str
.
We can first cast that column to some common datatype, and then merge.
df1['year'] = df1['year'].astype(int)
df2['year'] = df2['year'].astype(int)
Note that we would need to check each column we’re merging over.
Now you learned, How you can use & How to Fix “ValueError” While Merging DataFrames in Pandas.
If you have any questions or get stuck, please dont hesitate to reach out to me for help.