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 |
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)