utilsviz.py

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


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)


# Distribution graphs (histogram/bar graph) of column data
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
    nunique = df.nunique()
    # For displaying purposes, pick columns that have between 1 and 50 unique values
    df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
    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()


# In[4]:


# Correlation matrix
def plotCorrelationMatrix(df, graphWidth):
    filename = ""
    df = df.dropna('columns')  # drop columns with NaN
    # keep columns where there are more than 1 unique values
    df = df[[col for col in df if df[col].nunique() > 1]]
    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()


# In[5]:


# 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')
    # keep columns where there are more than 1 unique values
    df = df[[col for col in df if df[col].nunique() > 1]]
    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()