Data analytics with Python

Data analysis try to understand data by placing it in a visual context, use programming tools to analyze complex data in distinct scenarios in the real world.

In this article, we will use python libraries to create plots and analysis using:

Matplotlib ( https://matplotlib.org/ )

Pandas (https://pandas.pydata.org/docs/ )

Plotly (https://plotly.com/python/)

Using pandas read_csv 

• import pandas as pd

• iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'])

• print(iris.head())

Scatter Plot in matplotlib

• # create color dictionary
• colors = {'Iris-setosa':'r', 'Iris-versicolor':'g', 'Iris-virginica':'b'}
• # create a figure and axis
• fig, ax = plt.subplots()
• # plot each data-point
• for i in range(len(iris['sepal_length'])):
• ax.scatter(iris['sepal_length'][i], iris['sepal_width'][i],color=colors[iris['class'][i]])
• # set a title and labels
• ax.set_title('Iris Dataset')
• ax.set_xlabel('sepal_length')
• ax.set_ylabel('sepal_width') 

•  # get columns to plot  
• columns = iris.columns.drop(['class'])
• # create x data
• x_data = range(0, iris.shape[0])
• # create figure and axis
• fig, ax = plt.subplots() 
• # plot each column 
•  for column in columns:
•        ax.plot(x_data, iris[column], label=column)
• # set title and legend
  
• ax.set_title('Iris Dataset') 
  
• ax.legend() 

• #Multiple Histograms
• iris.plot.hist(subplots=True, layout=(2,2), figsize=(10, 10), bins=20)

• import numpy as np
• # get correlation matrix
• corr = iris.corr()
• fig, ax = plt.subplots()
• # create heatmap
• im = ax.imshow(corr.values)
• # set labels
• ax.set_xticks(np.arange(len(corr.columns)))
• ax.set_yticks(np.arange(len(corr.columns)))
• ax.set_xticklabels(corr.columns)
• ax.set_yticklabels(corr.columns)
• # Rotate the tick labels and set their alignment.
• plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
• rotation_mode="anchor") 

• # get correlation matrix
• corr = iris.corr()
• fig, ax = plt.subplots()
• # create heatmap
• im = ax.imshow(corr.values)
• # set labels
• ax.set_xticks(np.arange(len(corr.columns)))
• ax.set_yticks(np.arange(len(corr.columns)))
• ax.set_xticklabels(corr.columns)
• ax.set_yticklabels(corr.columns)
• # Rotate the tick labels and set their alignment.
• plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
• rotation_mode="anchor")
• # Loop over data dimensions and create text annotations.
• for i in range(len(corr.columns)):
• for j in range(len(corr.columns)):
• text = ax.text(j, i, np.around(corr.iloc[i, j], decimals=2),
• ha="center", va="center", color="black") 

In this article, we looked at Matplotlib visualization with Python and The code is available in github in: 

https://github.com/Tomas10000/data_analytics_python/blob/master/data_analytics_in_python.ipynb