数据框 - 合并 csv 和 excel 文件中的列

Question

Hi there stack overflow community,

I have the following dataframe in an excel:

sparte  sparten      status stati       gesellschaft    gesellschaften
10      Krankenvoll     B   beantragt       0          - Allgemein -
11      Reisekranken    A   aktiv         10000        nordinvest
12      Krankenkasse    N   beitragsfrei  M552D        SV SparkassenVersicherung

and the folliwing column for merging in a csv:

   sparten    status    gesellschaft
    10           B          0
    11           A        10000
    12           N        M552D

to merge some columns from an excel and a csv file I'm using the following code:

df1 = pd.read_csv(r'path', sep=',').drop(columns = ['risiko'])
df2 = pd.read_excel(r'path')

df3 = pd.merge(df1,df2[['status','stati']],on='status', how='left').drop(columns = ['status'])
df4 = df3.merge(df2[['sparte','sparten']],on='sparte', how='left').drop(columns = ['sparte'])

It works fine for me, but now i want to me merge the following column:

    df4 = df3.merge(df2[['gesellschaft','gesellschaften']],on='gesellschaft', how='left')
    print(df4)

...and it does not work. It merges only the cells with this format M552D , but leaves the cells with numbers untouched. I don't understand what I'm doing wrong. If I try to put how='right' the merge works, but the other columns disappear.

Maybe someone has an idea what is happening here! Thanks for any hint!

Answer 1

The problem is that the geselschaft column contains only strings in df1 which is loaded with read_csv , because the column is not fully numeric. But in df2 which is loaded with read_excel , it contains a mix of int and string values. And at Pandas level and int and a string cannot be equal.

A possible workaround is to force a string conversion at merge time:

df4 = df3.merge(df2[['gesellschaft','gesellschaften']], left_on='gesellschaft',
  right_on = df2['gesellschaft'].astype('str'), how='left')