#!/usr/bin/env python
# coding: utf-8

# # Nigerian Presidential Election Result Analysis - 2015
# 
# <b>Author:</b> <a href="http://umaryusuf.com/">Umar Yusuf</a><br />
# <b>Blog post:</b> http://umar-yusuf.blogspot.com.ng/2016/09/Analysis-of-Nigerian-Presidential-Election-Result-2015-using-Python-Programming-Language.html
# 
# ### Data Source
# The data was gathered from the <a href="http://www.inecnigeria.org">Independent National Electoral Commission (INEC)</a> official website.
# 
# The Data (in .csv format) used is available for <a href="http://www.inecnigeria.org/?inecnews=2015-presidential-election-result-2">download here</a>. Download it and save it at thesame location with this notebook.
# 
# Am going to use these three main python programming packages <a href="http://pandas.pydata.org/">pandas</a> with <a href="http://matplotlib.org/">matplotlib</a> embedded to analyse the 2015 Presidential Election Result.

# <img src="https://3.bp.blogspot.com/-Ooph3sgMzSs/V8CxTqP8-kI/AAAAAAAABGw/sqZ__txePmE0nBIIY2tiWtyurw4tQQhHwCLcB/s1600/map-of-nigeria-2015.png" />

# # Introduction
# Nigeria has 36 states and 1 federal capital territory. The 2015 presidential election was held in the 37 territories within the country.
# 
# Fourteen (14) political parties representing fourteen (14) candidates participated in the 2015 presidential elections. The parties are as follow: AA, ACPN, AD, ADC, APA, APC, CPP, HOPE, KOWA, NCP, PDP, PPN, UDP and UPP. See the result table below:-
# 
# <img src="https://1.bp.blogspot.com/-nNvpzMakXEA/V-TpXczIWxI/AAAAAAAABKc/ovdSUMHBmKw3mvHznanChFxXjMWZtBohgCLcB/s1600/inec-result.bmp" />
# 
# Even though the battle was between the two biggest parties (APC and PDP). The dataset we will explore will contain all the parties.
# 
# The dataset contains the numeric values by states for:-<br />
# 1~ Vote scored by each political party<br />
# 2~ Number_of_Registered_Voters<br />
# 3~ Number_of_Accredited_Voters<br />
# 4~ Number_of_Valid_Votes<br />
# 5~ Number_of_Rejected_Votes<br />
# 6~ Total_Votes_Cast<br />
# 7~ Population<br />
# 8~ Population_Rank<br />
# 9~ Number_of_LGA<br />
# 
# #### I will attempt to answer the following questions through this analysis:-
# a) What are the minimum and maximum votes for each party?<br />
# b) Is winning in top states with highest numbers of voters’ turnout, registered voters, total votes cast, and population related to winning the general election?<br />
# c) Is there any odd case where "Population" of a state is lower than "Number_of_Registered_Voters" and "Number_of_Accredited_Voters"?<br />
# d) Which state voted most for the lowest rank party?<br />

# #### Import libraries and load in the dataset

# In[1]:


# Lets import the packages
import pandas as pd

# Lets enable our plot to display inline within notebook
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


inec_table = pd.read_csv("INEC 2015 Presidential Election Results.csv")
inec_table.head()


# ### Statistical summary of all the columns
# 
# This will show us the minimum and maximum votes for each party.

# In[3]:


inec_table.describe()


# ### Turnout of Voters for the election
# We can see the ratio of voters turnout for the election by dividing "Number_of_Reg_Voters" by "Total_Votes_Cast" for each state

# In[4]:


inec_table["Voters Turnout"] = inec_table["Total_Votes_Cast"] / inec_table["Number_of_Reg_Voters"]

inec_table[["State", "Voters Turnout"]][:11]


# In[5]:


inec_table.plot(x="State", y='Voters Turnout', figsize=(20, 5), kind="line", grid=1)


# ### Five top states with the highest "Number_of_Reg_Voters"

# In[6]:


inec_table.sort_values("Number_of_Reg_Voters", ascending=False)[:5]


# #### Which party got the highest vote among the top states with the highest "Number_of_Reg_Voters"

# In[7]:


win1 = inec_table.sort_values("Number_of_Reg_Voters", ascending=False)[:5]

win1.plot(x="State", y=['AA', 'ACPN', 'AD', 'ADC', 'APA', 'APC', 'CPP', 'HOPE', 'KOWA', 'NCP', 'PDP', 'PPN', 'UDP', 'UPP'], 
          figsize=(20, 5), kind="bar", grid=1)


# ### Five top states with the highest number of "Total_Votes_Cast"

# In[8]:


inec_table.sort_values("Total_Votes_Cast", ascending=False)[:5]


# #### Which party got the highest vote among the top states with the highest "Total_Votes_Cast"

# In[9]:


win2 = inec_table.sort_values("Total_Votes_Cast", ascending=False)[:5]

win2.plot(x="State", y=['AA', 'ACPN', 'AD', 'ADC', 'APA', 'APC', 'CPP', 'HOPE', 'KOWA', 'NCP', 'PDP', 'PPN', 'UDP', 'UPP'], 
          figsize=(20, 5), kind="bar", grid=1)


# ### Five top states with the highest "Population"

# In[10]:


inec_table.sort_values("Population", ascending=False)[:5]


# #### Which party got the highest vote among the top states with the highest "Population"

# In[11]:


win3 = inec_table.sort_values("Population", ascending=False)[:5]

win3.plot(x="State", y=['AA', 'ACPN', 'AD', 'ADC', 'APA', 'APC', 'CPP', 'HOPE', 'KOWA', 'NCP', 'PDP', 'PPN', 'UDP', 'UPP'], 
          figsize=(20, 5), kind="bar", grid=1)


# ### Five top states with the highest "Number_of_LGA"

# In[12]:


inec_table.sort_values("Number_of_LGA", ascending=False)[:5]


# #### Which party got the highest vote among the top states with the highest "Number_of_LGA"

# In[13]:


win4 = inec_table.sort_values("Number_of_LGA", ascending=False)[:5]

win4.plot(x="State", y=['AA', 'ACPN', 'AD', 'ADC', 'APA', 'APC', 'CPP', 'HOPE', 'KOWA', 'NCP', 'PDP', 'PPN', 'UDP', 'UPP'], 
          figsize=(20, 5), kind="bar", grid=1)


# ### Lets extract the following columns out to form a separate dataframe from the dataset
# 1~ Number_of_Registered_Voters<br />
# 2~ Number_of_Accredited_Voters<br />
# 3~ Number_of_Valid_Votes<br />
# 4~ Number_of_Rejected_Votes<br />
# 5~ Total_Votes_Cast<br />
# 6~ Population<br />
# 7~ Population_Rank<br />
# 8~ Number_of_LGA<br />

# In[14]:


voters_table = inec_table[['State', 'Number_of_Reg_Voters', 'Number_of_Accr_Voters', 'Number_of_Valid_Votes', 'Number_of_Rejected_Votes', 
                         'Number_of_Rejected_Votes', 'Total_Votes_Cast', 'Population', 'Population_Rank', 'Number_of_LGA']]
voters_table


# ### Summary statistics of voters_table

# In[15]:


voters_table.describe()


# ### Graph "Number_of_Registered_Voters" Vs "Number_of_Accredited_Voters" Vs "Population"
# Naturally, "Number_of_Registered_Voters" should be higher than "Number_of_Accredited_Voters". Likewise, "Population" should be higher than both "Number_of_Registered_Voters" and "Number_of_Accredited_Voters". Lets see if there is any odd case in any particular state?

# In[16]:


voters_table.plot(x='State', y=['Number_of_Reg_Voters', 'Number_of_Accr_Voters', 'Population'], kind='bar', figsize=(20, 5), title='Bar Plot', grid=1)


# ### Lets extract the parties columns out to form a separate dataframe from the dataset

# In[17]:


parties_table = inec_table[['State', 'AA', 'ACPN', 'AD', 'ADC', 'APA', 'APC', 'CPP', 'HOPE', 'KOWA', 'NCP', 'PDP', 'PPN', 'UDP', 'UPP']] 

parties_table


# ### Summary statistics of parties_table

# In[18]:


parties_table.describe()


# ### Sum of Votes gotten by each party

# In[19]:


vote_sum = parties_table[['AA', 'ACPN', 'AD', 'ADC', 'APA', 'APC', 'CPP', 'HOPE', 'KOWA', 'NCP', 'PDP', 'PPN', 'UDP', 'UPP']].sum()

vote_sum


# ### Visualize the total votes by party

# In[20]:


vote_sum.plot(kind='bar', figsize=(20, 5), grid=1)


# #### As you can see, votes gotten by "APC" and "PDP" far outweighs that of other parties. So lets focus on these two biggest parties...

# ### Visualize votes of "APC" and "PDP" by states

# In[21]:


parties_table.plot(x="State", y=["APC", "PDP"], figsize=(10,25), kind="barh", grid=100)


# ### States with lowest votes
# 
# Lets see what the bottom states with lowest number of votes have to offer

# In[22]:


low_vote_states = vote_sum.sort_values()[:11]
low_vote_states


# In[23]:


low_vote_states.plot(kind="bar", figsize=(15, 5), grid=100)


# ## HOPE Party
# Lets see the state that voted most for the lowest rank party - HOPE

# In[24]:


hope_party = parties_table[['State', 'HOPE']]
hope_party.plot(x='State', y='HOPE', kind='bar', figsize=(15, 5))


# As seen above, the states that voted most for lowest rank party (HOPE) are Ebonyi, Oyo and Rivers.

# # What next?
# You can do more with this dataset, but for me that is it on analysing Nigeria 2015 presidential election result with python.
# 
# Next, I will do a spatial analysis on thesame election result dataset with <b>QGIS (http://qgis.org/)</b> and <b>Tableau (http://tableau.com/)</b>. Note that there are excellent python packages that supports spatial analysis, namely: GeoPandas, PySAL, Pyshp, Shapely, ArcPy, PyQGIS, Fiona, Rasterio, GDAL/OGR etc
# 
# So if you are interested in the spatial analysis, click on the link below:-

# ~1~ <a href="http://umar-yusuf.blogspot.com/2016/09/Spatial-Analysis-of-Nigeria-2015-Presidential-Election-Result-Using-QGIS.html">Spatial Analysis of Nigeria 2015 Presidential Election Result Using QGIS</a> - <b>Desktop Visualization</b>
# 
# ~2~ <a href="http://umar-yusuf.blogspot.com/2016/09/Spatial-Analysis-of-Nigeria-2015-Presidential-Election-Result-Using-Tableau.html">Spatial Analysis of Nigeria 2015 Presidential Election Result Using Tableau</a> - <b>Web-based Visualization</b>