Python Basics

This topic explains the basics of python like data ingestion, exploration, and visualization using basic plots.

Data ingestion

Python library is a collection of functions and methods that allows you to perform many actions without writing your code. To make use of the functions in a module, you’ll need to import the module with an import statement

# Import libraries for basic python operation

import numpy as np # for multi-dimensional arrays and matrices operations
import scipy.stats # for scientific computing and technical computing
import pandas as pd # data manipulation and analysis
import matplotlib # for plotting
import matplotlib.pyplot as plt
%matplotlib inline
# Read the csv file using pandas
data = pd.read_csv('petroleum.csv')

Download the petroleum.csv

# Display the basic table information
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 216 entries, 0 to 215
Data columns (total 5 columns):
Year             216 non-null int64
Geography        216 non-null object
Import           216 non-null float64
Export           216 non-null float64
CO2 Emissions    216 non-null float64
dtypes: float64(3), int64(1), object(1)
memory usage: 8.5+ KB

Display the sample data table information

# Display first 5 rows
data.head(5)

result:

Year Geography Import Export CO2 Emissions
0 1980 Africa 618.184 5428.078 525.605046
1 1981 Africa 609.270 3964.097 519.408287
2 1982 Africa 557.209 3458.547 558.221545
3 1983 Africa 477.787 3394.148 586.002081
4 1984 Africa 507.619 3629.964 612.150112

Data Visualization

Scatter Plot

The Scatter Diagram graphs pairs of numerical data to look for a relationship between them.

plt.figure(figsize=(10,10))
data[data.Geography == 'Asia'].plot.scatter('Import','CO2 Emissions',c = 'red')
plt.xlabel('Petroleum import', fontweight="bold",fontsize=15)
plt.ylabel('CO2 Emissions', fontweight="bold",fontsize=15)
plt.title('scatter Plot',fontweight="bold",fontsize = 20)
plt.xticks(fontweight="bold",fontsize = 10)
plt.yticks(fontweight="bold",fontsize = 10)
plt.show()

Describe basic statistics summary of a feature or variable

data[data.Geography == 'Asia'].Import.describe()

result:

count       36.000000
mean     11928.644624
std       4830.261052
min       5710.417000
25%       7001.003250
50%      11717.250500
75%      16120.587750
max      20838.615000
Name: Import, dtype: float64

Box Plot

A Box and Whisker Plot (or Box Plot) is a convenient way of visually displaying groups of numerical data through their quartiles.

# Plot box plot to find out the outliers using a single feature or variable

plt.figure(figsize=(10,5))
plt.subplot(1,2,1);
data.Import.plot(kind='box')
plt.title('Petroleum import',fontweight = 'bold',fontsize = 15 )
plt.xticks(fontweight="bold",fontsize = 10)
plt.yticks(fontweight="bold",fontsize = 10)
plt.ylabel('barrels per day',fontweight="bold",fontsize = 10)

plt.subplot(1,2,2);
data.Export.plot(kind='box')
plt.title('Petroleum export',fontweight = 'bold',fontsize = 15 )
plt.xticks(fontweight="bold",fontsize = 10)
plt.yticks(fontweight="bold",fontsize = 10)
plt.ylabel('barrels per day',fontweight="bold",fontsize = 10)

plt.subplots_adjust(wspace=1)
plt.show()

Histogram

A histogram is an accurate representation of the distribution of numerical data

# Plot histogram

plt.figure(figsize=(10,5))
data.Import.plot(kind='hist',histtype='step',bins=50)
plt.axvline(data.Import.mean(),c='red',label = 'Mean')
plt.axvline(data.Import.median(),c='green',linestyle='--',label = 'Median')
plt.axvline(data.Import.quantile(0.25),c='blue',linestyle=':',label = '0.25 quantile')
plt.axvline(data.Import.quantile(0.75),c='blue',linestyle=':',label = '0.75 quantile')
plt.axis(xmin=-100,xmax=25000)
plt.title('Petroleum Import',fontweight="bold",fontsize = 20)
plt.xlabel('barrels per day',fontweight="bold",fontsize = 15)
plt.ylabel('Frequency',fontweight="bold",fontsize = 15)
plt.xticks(fontweight="bold",fontsize = 10)
plt.yticks(fontweight="bold",fontsize = 10)
plt.legend(loc=1, prop={'size': 15})
plt.show()

Pie chart

A pie chart is a circular statistical graphic, which is divided into slices to illustrate numerical proportion

plt.figure(figsize=(15,7.5))
plt.subplot(1,2,1);
data[data.Year == 2015].Import.plot(kind='pie',startangle=90,autopct='%1.1f%%',colors=['C0','C1','C2','C3','C4','C5'],labels = ['Africa', 'Asia', 'South America', 'Europe', 'Middle East',
       'North America'],textprops={'fontweight':'bold','fontsize': 12});
plt.legend(loc=3,fontsize=10)
plt.ylabel('')
plt.title('Petroleum Import',fontweight="bold",fontsize = 20)
plt.axis('equal')

plt.subplot(1,2,2);
data[data.Year == 2015].Export.plot(kind='pie',startangle=90,autopct='%1.1f%%',colors=['C0','C1','C2','C3','C4','C5'],labels = ['Africa', 'Asia', 'South America', 'Europe', 'Middle East',
       'North America'],textprops={'fontweight':'bold','fontsize': 12});
plt.legend(loc=3,fontsize=10)
plt.ylabel('')
plt.title('Petroleum Export',fontweight="bold",fontsize = 20)
plt.axis('equal')

plt.subplots_adjust(wspace=1)
plt.show()

Bar chart

A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent.

plt.figure(figsize=(10,5))
N = 6
ind = np.arange(N)    # the x locations for the groups
data[data.Year == 2015].Export.plot(kind='bar')
plt.title('Bar Chart',fontweight="bold",fontsize = 20)
plt.ylabel('Peroleum Export',fontweight="bold",fontsize = 15)
plt.xlabel('Geography',fontweight="bold",fontsize = 15)
plt.xticks(ind, ('Africa', 'Asia', 'South America', 'Europe', 'Middle East',
       'North America'),fontweight="bold",fontsize = 10)
plt.yticks(fontweight="bold",fontsize = 10)
plt.show()

References :

  1. https://www.eia.gov/
  2. https://stackoverflow.com/

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