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https://github.com/tralahm/parliament-2017-dataset
Concise, Clean data sets of the 2017 Kenyan General Election results for the Members of the Senate and National Assembly Composition
https://github.com/tralahm/parliament-2017-dataset
csv-parsing data-analysis data-visualization datasets election-data ipynb-jupyter-notebook kaggle-dataset kenya-constituencies kenya-counties matplotlib python3 tralahtek
Last synced: 3 days ago
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Concise, Clean data sets of the 2017 Kenyan General Election results for the Members of the Senate and National Assembly Composition
- Host: GitHub
- URL: https://github.com/tralahm/parliament-2017-dataset
- Owner: TralahM
- License: mit
- Created: 2020-01-06T22:45:31.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-06T23:32:11.000Z (almost 5 years ago)
- Last Synced: 2024-11-05T05:03:48.972Z (3 days ago)
- Topics: csv-parsing, data-analysis, data-visualization, datasets, election-data, ipynb-jupyter-notebook, kaggle-dataset, kenya-constituencies, kenya-counties, matplotlib, python3, tralahtek
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/tralahm/members-of-parliament-ke-2017
- Size: 262 KB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.rst
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
[](https://travis-ci.com/TralahM/parliamet-2017-dataset)
[](https://opensource.org/licenses/MIT)
[](https://github.com/TralahTek)
[](https://www.python.org/)
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[](http://inch-ci.org/github/TralahM/parliamet-2017-dataset)
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[](https://github.com/TralahM/pull/)
[](https://gitHub.com/TralahM/parliamet-2017-dataset/pull/)
[](https://github.com/TralahM/parliamet-2017-dataset)
# parliamet-2017-dataset.
## Introduction
This is a brief analysis of the structure of the data contained herein.
## Exploratory Analysis
To begin this exploratory analysis, first import libraries and define functions for plotting the data using `matplotlib`.
**Importing some libraries to facilitate this exercise**
```python
from mpl_toolkits.mplot3d import Axes3D
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import os # accessing directory structure
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
```
There are 2 csv files in the current version of the dataset:
```python
for dirname, _, filenames in os.walk('./kaggle/input'):
for filename in filenames:
if filename.endswith(".csv"):
print(os.path.join(dirname, filename))
```
./kaggle/input/MPs.csv
./kaggle/input/Senators.csv
Next we define functions for plotting data.
```python
# Distribution graphs (histogram/bar graph) of column data
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values
nRow, nCol = df.shape
columnNames = list(df)
nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow
plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k')
for i in range(min(nCol, nGraphShown)):
plt.subplot(nGraphRow, nGraphPerRow, i + 1)
columnDf = df.iloc[:, i]
if (not np.issubdtype(type(columnDf.iloc[0]), np.number)):
valueCounts = columnDf.value_counts()
valueCounts.plot.bar()
else:
columnDf.hist()
plt.ylabel('counts')
plt.xticks(rotation = 90)
plt.title(f'{columnNames[i]} (column {i})')
plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0)
plt.show()
```
```python
# Correlation matrix
def plotCorrelationMatrix(df, graphWidth):
filename = df.dataframeName
df = df.dropna('columns') # drop columns with NaN
df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values
if df.shape[1] < 2:
print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')
return
corr = df.corr()
plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum = 1)
plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.gca().xaxis.tick_bottom()
plt.colorbar(corrMat)
plt.title(f'Correlation Matrix for {filename}', fontsize=15)
plt.show()
```
```python
# Scatter and density plots
def plotScatterMatrix(df, plotSize, textSize):
df = df.select_dtypes(include =[np.number]) # keep only numerical columns
# Remove rows and columns that would lead to df being singular
df = df.dropna('columns')
df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values
columnNames = list(df)
if len(columnNames) > 10: # reduce the number of columns for matrix inversion of kernel density plots
columnNames = columnNames[:10]
df = df[columnNames]
ax = pd.plotting.scatter_matrix(df, alpha=0.75, figsize=[plotSize, plotSize], diagonal='kde')
corrs = df.corr().values
for i, j in zip(*plt.np.triu_indices_from(ax, k = 1)):
ax[i, j].annotate('Corr. coef = %.3f' % corrs[i, j], (0.8, 0.2), xycoords='axes fraction', ha='center', va='center', size=textSize)
plt.suptitle('Scatter and Density Plot')
plt.show()
```
Now we're ready to read in the data and use the plotting functions to visualize the data.
### Let's check 1st file: /kaggle/input/MPs.csv
```python
nRowsRead = None # specify 'None' if want to read whole file
# MPs.csv may have more rows in reality, but we are only loading/previewing the first 1000 rows
df1 = pd.read_csv('./kaggle/input/MPs.csv', delimiter=',', nrows = nRowsRead)
df1.dataframeName = 'MPs.csv'
nRow, nCol = df1.shape
print(f'There are {nRow} rows and {nCol} columns')
```
There are 351 rows and 6 columns
Let's take a quick look at what the data looks like:
```python
df1.head(5)
```
Member of Parliament
Photo
County
Constituency
Party
Status
0
Hon. (Dr.) Keynan, Wehliye Adan, CBS, MP
http://www.parliament.go.ke/sites/default/file...
Wajir
Eldas
JP
Elected
1
Hon. Abdi, Yusuf Hassan
http://www.parliament.go.ke/sites/default/file...
Nairobi
Kamukunji
JP
Elected
2
Hon. Abdullah, Bashir Sheikh
http://www.parliament.go.ke/index.php/sites/de...
Mandera
Mandera North
JP
Elected
3
Hon. Abuor, Paul
http://www.parliament.go.ke/sites/default/file...
Migori
Rongo
ODM
Elected
4
Hon. Adagala, Beatrice Kahai
http://www.parliament.go.ke/sites/default/file...
Vihiga
Vihiga
ANC
Elected
Distribution graphs (histogram/bar graph) of sampled columns:
**Political Party and Election Status Distributions**
```python
plotPerColumnDistribution(df1, 10, 5)
```
### Let's check 2nd file: /kaggle/input/Senators.csv
```python
nRowsRead = None # specify 'None' if want to read whole file
# Senators.csv may have more rows in reality, but we are only loading/previewing the first 1000 rows
df2 = pd.read_csv('./kaggle/input/Senators.csv', delimiter=',', nrows = nRowsRead)
df2.dataframeName = 'Senators.csv'
nRow, nCol = df2.shape
print(f'There are {nRow} rows and {nCol} columns')
```
There are 67 rows and 5 columns
Let's take a quick look at what the data looks like:
```python
df2.head(5)
```
Senator
Photo
County
Party
Status
0
Sen. (Dr.) Ali Abdullahi Ibrahim
http://www.parliament.go.ke/sites/default/file...
Wajir
JP
Elected
1
Sen. (Dr.) Inimah Getrude Musuruve
http://www.parliament.go.ke/sites/default/file...
N\/A
ODM
Nominated
2
Sen. (Dr.) Langat Christopher Andrew
http://www.parliament.go.ke/sites/default/file...
Bomet
JP
Elected
3
Sen. (Dr.) Milgo Alice Chepkorir
http://www.parliament.go.ke/sites/default/file...
N\/A
JP
Nominated
4
Sen. (Dr.) Zani Agnes Philomena
http://www.parliament.go.ke/sites/default/file...
N\/A
N\/A
Nominated
Distribution graphs (histogram/bar graph) of sampled columns:
**Political Party and Election Status Distributions**
```python
plotPerColumnDistribution(df2[["Party","Status"]], 10, 5)
```
## Conclusion
* Jubilee has the tyranny of numbers in the current parliament
* ODM comes second
* Wiper third
* Some political party data is missing or the candindate was *independent*
```python
```
## Building from Source for Developers
```Bash
git clone https://github.com/TralahM/parliamet-2017-dataset.git
cd parliamet-2017-dataset
```
# Contributing
[See the Contributing File](CONTRIBUTING.rst)
[See the Pull Request File](PULL_REQUEST_TEMPLATE.md)
# LICENCE
[Read the license here](LICENSE)