Trying to merge 2 dataframes but get ValueError

It happens when common column in both table are of different data type.

Example: In table1, you have date as string whereas in table2 you have date as datetime. so before merging,we need to change date to common data type.

Additional: when you save df to .csv format, the datetime (year in this specific case) is saved as object, so you need to convert it into integer (year in this specific case) when you do the merge. That is why when you upload both df from csv files, you can do the merge easily, while above error will show up if one df is uploaded from csv files and the other is from an existing df. This is somewhat annoying, but have an easy solution if kept in mind.

@Arnon Rotem-Gal-Oz answer is right for the most part. But I would like to point out the difference between df['year']=df['year'].astype(int) and df.year.astype(int). df.year.astype(int) returns a view of the dataframe and doesn’t not explicitly change the type, atleast in pandas 0.24.2. df['year']=df['year'].astype(int) explicitly change the type because it’s an assignment. I would argue that this is the safest way to permanently change the dtype of a column.

Example:

df = pd.DataFrame({'Weed': ['green crack', 'northern lights', 'girl scout
cookies'], 'Qty':[10,15,3]})
df.dtypes

Weed object,
Qty int64

df['Qty'].astype(str)
df.dtypes

Weed object,
Qty int64

Even setting the inplace arg to True doesn’t help at times. I don’t know why this
happens though. In most cases inplace=True equals an explicit assignment.

df['Qty'].astype(str, inplace = True)
df.dtypes

Weed object,
Qty int64

Now the assignment,

df['Qty'] = df['Qty'].astype(str)
df.dtypes

Weed object,
Qty object

At first check the type of columns which you want to merge. You will see one of them is string where other one is int. Then convert it to int as following code:

df["something"] = df["something"].astype(int)

merged = df.merge[df1, on="something"]

I found that my dfs both had the same type column (str) but switching from join to merge solved the issue.

this simple solution works for me

    final = pd.concat([df, rankingdf], axis=1, sort=False)

but you may need to drop some duplicate column first.

In my case, it happened because I was trying to merge columns that where not the index, so in order to fix this I used this code that I found in the documentation:

df.set_index('key').join(other.set_index('key'))

Documentation:
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.join.html
set key to be the index

My 2 cents: I had the same issue and could not see why I was getting that because when doing
data.head() I saw the exact same values in ds (time column).

The error was fixed when I added parse_dates in the pd.read_csv() function. This way:

data = pd.read_csv(('source.csv'), sep=';', parse_dates=['Date'], encoding= 'unicode_escape')

I was also facing the same issue as I was only trying to merge 2 data-frames. In my scenario, both datasets are almost identical, except 2 extra columns.
Hence I was following your solution.

def merge_dataframe(df1, df2, mode, column_name: any, col_chng: str):
    """
    :param df1: dataframe as str
    :param df2: dataframe as str
    :param mode: str
    :param column_name: any
    :param col_chng:
    :return:
    """
    df1 = pd.DataFrame(df1)
    df2 = pd.DataFrame(df2)

    os.chdir(output_dir)
    if column_name is None:
        new_df = df1.merge(df2, how=mode)
    else:
        df1[column_name] = df2[column_name].astype(col_chng)
        new_df = df1.merge(df2, on=column_name, how=mode)
    new_df.to_excel('new_df.xlsx', index=False)

Hope, this will help others. Kindly correct me, if I’m missing something.