Python Pandas Group Dataframe按列/ Sum Integer列按String列

Question

I have been stuck all day and have been through numerous SO articles and am still stuck on my last final piece. I imported a CSV into a massive dataframe, then eventually got the smaller dataframe below: (Note: My df is indexed on 'Name' right now, which is what I need to base the group or sum off of)

  Name          Classification       Value 1     Value 2
Company 1   Classification Code 1      5000       8000      
Company 1   Classification Code 1      6000       2000
Company 2   Classification Code 1      2000       3000    
Company 2   Classification Code 1      1000       4500     
Company 3   Classification Code 2      15000      10000      
Company 3   Classification Code 2      20000      32000     
Company 4   Classification Code 3      7500       10000    
Company 4   Classification Code 3      7000       1500     

What I am struggling with now is how to sum the two values based on the company (I have mainly been using groupby and sum() but have been stuck for hours. I know there are a lot of SO articles talking about summing things in pandas but I have had no luck for hours. ANY help would be greatly appreciated. Thanks so much.

Edit: The output I am looking for is the following

Company 1    Classification Code 1    11,000    10,000
Company 2    Classification Code 1    3,000      7,500
Company 3    Classification Code 2    35,000    42,000
Company 4    Classification Code 3    14,500    11,500

Answer 1

Option 1
set_index then groupby
This assumes that the 'Classification' column is the same across Company

df.set_index('Classification', append=True) \
    .groupby(level=[0, 1]).sum().reset_index(1)

                  Classification  Value 1  Value 2
Name                                              
Company 1  Classification Code 1    11000    10000
Company 2  Classification Code 1     3000     7500
Company 3  Classification Code 2    35000    42000
Company 4  Classification Code 3    14500    11500

Option 2
groupby then agg
This doesn't make any assumptions about uniqueness of 'Classification' across 'Company' but will just grab the first 'Classification' per 'Company'

df.groupby(level=0).agg(
    {'Classification': 'first', 'Value 1': 'sum', 'Value 2': 'sum'})

                  Classification  Value 1  Value 2
Name                                              
Company 1  Classification Code 1    11000    10000
Company 2  Classification Code 1     3000     7500
Company 3  Classification Code 2    35000    42000
Company 4  Classification Code 3    14500    11500

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Response to Comments
In regards to concatenation
Check dtypes with df.dtypes . If you see object instead of int then yes, you need to convert to numeric .

You can do this simply with

df.apply(pd.to_numeric, errors='ignore').groupby(level=0).agg(
    {'Classification': 'first', 'Value 1': 'sum', 'Value 2': 'sum'})

Or more manually

df['Value 1'] = df['Value 1'].astype(int)
df['Value 2'] = df['Value 2'].astype(int)

Then proceed to prior suggestions.

In regards to placement of columns
You can always reorder your columns

d1 = df.apply(pd.to_numeric, errors='ignore').groupby(level=0).agg(
    {'Classification': 'first', 'Value 1': 'sum', 'Value 2': 'sum'})

d1[df.columns]

Or

d1 = df.apply(pd.to_numeric, errors='ignore').groupby(level=0).agg(
    {'Classification': 'first', 'Value 1': 'sum', 'Value 2': 'sum'})

d1.reindex_axis(df.columns, 1)