Using Merge on a column and Index in Pandas

I have two separate dataframes that share a project number. In type_df, the project number is the index. In time_df, the project number is a column. I would like to count the number of rows in type_df that have a Project Type of 2. I am trying to do this with pandas.merge(). It works great when using both columns, but not indices. I'm not sure how to reference the index and if merge is even the right way to do this.

import pandas as pd
type_df = pd.DataFrame(data = [['Type 1'], ['Type 2']], 
                       columns=['Project Type'], 
                       index=['Project2', 'Project1'])
time_df = pd.DataFrame(data = [['Project1', 13], ['Project1', 12], 
                               ['Project2', 41]], 
                       columns=['Project', 'Time'])
merged = pd.merge(time_df,type_df, on=[index,'Project'])
print merged[merged['Project Type'] == 'Type 2']['Project Type'].count()

Error:

> Name 'Index' is not defined.

Desired Output:

2

If you want to use an index in your merge you have to specify left_index=True or right_index=True, and then use left_on or right_on. For you it should look something like this:

merged = pd.merge(type_df, time_df, left_index=True, right_on='Project')

Another solution is use DataFrame.join:

df3 = type_df.join(time_df, on='Project')

For version pandas 0.23.0+ the on, left_on, and right_on parameters may now refer to either column names or index level names:

left_index = pd.Index(['K0', 'K0', 'K1', 'K2'], name='key1')
left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                     'key2': ['K0', 'K1', 'K0', 'K1']},
                    index=left_index)
                    
right_index = pd.Index(['K0', 'K1', 'K2', 'K2'], name='key1')

right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
                     'D': ['D0', 'D1', 'D2', 'D3'],
                     'key2': ['K0', 'K0', 'K0', 'K1']},
                      index=right_index)
          
print (left)    
       A   B key2
key1             
K0    A0  B0   K0
K0    A1  B1   K1
K1    A2  B2   K0
K2    A3  B3   K1
        
print (right)
       C   D key2
key1             
K0    C0  D0   K0
K1    C1  D1   K0
K2    C2  D2   K0
K2    C3  D3   K1

---

df = left.merge(right, on=['key1', 'key2'])
print (df)
       A   B key2   C   D
key1                     
K0    A0  B0   K0  C0  D0
K1    A2  B2   K0  C1  D1
K2    A3  B3   K1  C3  D3

You must have the same column in each dataframe to merge on.

In this case, just make a 'Project' column for type_df, then merge on that:

type_df['Project'] = type_df.index.values
merged = pd.merge(time_df,type_df, on='Project', how='inner')
merged
#    Project  Time Project Type
#0  Project1    13       Type 2
#1  Project1    12       Type 2
#2  Project2    41       Type 1

print merged[merged['Project Type'] == 'Type 2']['Project Type'].count()
2