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

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:

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.

# 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()

# 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()

# 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

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:

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

plotPerColumnDistribution(df1, 10, 5)

Let's check 2nd file: /kaggle/input/Senators.csv

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:

df2.head(5)

</style>

	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

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

Building from Source for Developers

git clone https://github.com/TralahM/parliamet-2017-dataset.git
cd parliamet-2017-dataset

Contributing

See the Contributing File

See the Pull Request File

LICENCE

Read the license here