Using Pandas and sqlite3

Question

Try to implement the privote_table of pandas to produce a table for each of party and each state shows how much the party receievd in total contributions from the state.

Is this the right way to do or i has to get into the data base and get fectched out. However the code below gives error.

party_and_state = candidates.merge(contributors, on='id')
party_and_state.pivot_table(df,index=["party","state"],values=["amount"],aggfunc=[np.sum])

The expected result could be something like the table below. The first coulmn is the state name then the party D underneath the party D is the total votes from each state, the same applies with the party R

+-----------------+---------+--------+
|     state       | D       | R      |
+-----------------+---------+--------+
|      AK         | 500     | 900    |
|      IL         | 600     | 877    |
|      FL         | 200     | 400    |
|      UT         | 300     | 300    |
|      CA         | 109     | 90     |
|      MN         | 800     | 888    |

Answer 1

Consider the generalized pandas merge with pd as qualifier instead of a dataframe since the join fields are differently named hence requiring left_on and right_on args. Additionally, do not pass in df if running pivot_table as method of a dataframe since the called df is passed into the function.

Below uses the contributors and contributors_with_candidates text files. Also, per your desired results, you may want to use the values arg of pivot_table:

import numpy as np
import pandas as pd

contributors = pd.read_table('contributors_with_candidate_id.txt', sep="|")
candidates = pd.read_table('candidates.txt', sep="|")

party_and_state = pd.merge(contributors, candidates, 
                           left_on=['candidate_id'], right_on=['id'])
party_and_state.pivot_table(index=["party", "state"],
                            values=["amount"], aggfunc=np.sum)    
#                amount
# party state          
# D     CA      1660.80
#       DC       200.09
#       FL      4250.00
#       IL       200.00
#       MA       195.00
# ...
# R     AK      1210.00
#       AR     14200.00
#       AZ       120.00
#       CA     -6674.53
#       CO     -5823.00

party_and_state.pivot_table(index=["state"], columns=["party"],
                            values=["amount"], aggfunc=np.sum)
#         amount          
# party        D         R
# state                   
# AK         NaN   1210.00
# AR         NaN  14200.00
# AZ         NaN    120.00
# CA     1660.80  -6674.53
# CO         NaN  -5823.00
# CT         NaN   2300.00

Do note, you can do the merge as an inner join in SQL with read_sql:

party_and_state = pd.read_sql("SELECT c.*, n.* FROM contributors c " +
                              "INNER JOIN candidates n ON c.candidate_id = n.id", 
                              con = db)

party_and_state.pivot_table(index=["state"], columns=["party"],
                            values=["amount"], aggfunc=np.sum)