Python:Pandas merger results in NaN

I’m trying to perform a merge with Pandas. These two files have a public key (“KEY_PLA”), which I tried to use with the left join. But unfortunately, all columns transferred from the second file to the first file have NaN values.

Here’s what I’ve done so far:

df_1 = pd.read_excel(path1, skiprows=1)
df_2 = pd.read_excel(path2, skiprows=1)

df_1.columns = ["Index", "KEY", "KEY_PLA", "INFO1", "INFO2"]
df_2.columns = ["Index", "KEY_PLA", "INFO4"]

df_1.drop(["Index"], axis=1, inplace=True)
df_2.drop(["Index"], axis=1, inplace=True)

# Merge all dataframes
df_merge = pd. DataFrame()

df_merge = df_1.merge(df_2, left_on="KEY_PLA", right_on="KEY_PLA", how="left")
print(df_merge)

Here is the result:

This is the excel file:

Excel1
Excel2

What’s wrong with the code? I also checked the type and even converted the column to a string. But nothing works.

I think the problem is different types for concatenated column KEY_PLA, obviously one is an integer and the other is a string.

The solution is converted to the same, for example to ints:

print (df_1['KEY_PLA'].dtype)
object

print (df_2['KEY_PLA'].dtype)
int64

df_1['KEY_PLA'] = df_1['KEY_PLA'].astype(int)