18CSE301J_RA2011003010493

• Home
• About

My Python Visualizations

import pandas as pd
import numpy as np

import warnings
warnings.filterwarnings('ignore')

# for data visualizations
import matplotlib.pyplot as plt
import seaborn as sn
%matplotlib inline

inputdata_df = pd.read_csv('Energy Use Emissions.csv')

inputdata_df

	Domain Code	Domain	Area Code (M49)	Area	Element Code	Element	Item Code	Item	Year Code	Year	Unit	Value	Flag	Flag Description
0	GN	Energy Use	4	Afghanistan	7225	Emissions (CH4)	6801	Gas-Diesel oil	2013	2013	kilotonnes	0.0100	E	Estimated value
1	GN	Energy Use	4	Afghanistan	7225	Emissions (CH4)	6801	Gas-Diesel oil	2014	2014	kilotonnes	0.0084	E	Estimated value
2	GN	Energy Use	4	Afghanistan	7225	Emissions (CH4)	6801	Gas-Diesel oil	2015	2015	kilotonnes	0.0111	E	Estimated value
3	GN	Energy Use	4	Afghanistan	7225	Emissions (CH4)	6801	Gas-Diesel oil	2016	2016	kilotonnes	0.0085	E	Estimated value
4	GN	Energy Use	4	Afghanistan	7225	Emissions (CH4)	6801	Gas-Diesel oil	2017	2017	kilotonnes	0.0057	E	Estimated value
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
24502	GN	Energy Use	716	Zimbabwe	7273	Emissions (CO2)	6807	Electricity	2015	2015	kilotonnes	424.3909	X	Figure from international organizations
24503	GN	Energy Use	716	Zimbabwe	7273	Emissions (CO2)	6807	Electricity	2016	2016	kilotonnes	213.9419	X	Figure from international organizations
24504	GN	Energy Use	716	Zimbabwe	7273	Emissions (CO2)	6807	Electricity	2017	2017	kilotonnes	187.7450	X	Figure from international organizations
24505	GN	Energy Use	716	Zimbabwe	7273	Emissions (CO2)	6807	Electricity	2018	2018	kilotonnes	218.3081	X	Figure from international organizations
24506	GN	Energy Use	716	Zimbabwe	7273	Emissions (CO2)	6807	Electricity	2019	2019	kilotonnes	201.9786	E	Estimated value

24507 rows × 14 columns

inputdata_df.shape

(24507, 14)

inputdata_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24507 entries, 0 to 24506
Data columns (total 14 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   Domain Code       24507 non-null  object 
 1   Domain            24507 non-null  object 
 2   Area Code (M49)   24507 non-null  int64  
 3   Area              24507 non-null  object 
 4   Element Code      24507 non-null  int64  
 5   Element           24507 non-null  object 
 6   Item Code         24507 non-null  int64  
 7   Item              24507 non-null  object 
 8   Year Code         24507 non-null  int64  
 9   Year              24507 non-null  int64  
 10  Unit              24507 non-null  object 
 11  Value             24507 non-null  float64
 12  Flag              24507 non-null  object 
 13  Flag Description  24507 non-null  object 
dtypes: float64(1), int64(5), object(8)
memory usage: 2.6+ MB

inputdata_df.columns

Index(['Domain Code', 'Domain', 'Area Code (M49)', 'Area', 'Element Code',
       'Element', 'Item Code', 'Item', 'Year Code', 'Year', 'Unit', 'Value',
       'Flag', 'Flag Description'],
      dtype='object')

inputdata_df.head()
df = pd.DataFrame(inputdata_df)
 
name = df['Item']
price = df['Value']
 
# Figure Size
fig, ax = plt.subplots(figsize =(16, 9))
 
# Horizontal Bar Plot
ax.barh(name, price)
 
# Remove axes splines
for s in ['top', 'bottom', 'left', 'right']:
    ax.spines[s].set_visible(False)
 
# Remove x, y Ticks
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
 
# Add padding between axes and labels
ax.xaxis.set_tick_params(pad = 5)
ax.yaxis.set_tick_params(pad = 10)
 
# Add x, y gridlines
ax.grid(b = True, color ='grey',
        linestyle ='-.', linewidth = 0.5,
        alpha = 0.2)
 
# Show top values
ax.invert_yaxis()
 
# Add Plot Title
ax.set_title('Emissions based on fuel:',
             loc ='left', )
 
# Add Text watermark
fig.text(0.9, 0.15, 'Ayush Abhigyan', fontsize = 12,
         color ='grey', ha ='right', va ='bottom',
         alpha = 0.7)
 
# Show Plot
plt.show()

import plotly.express as px
 
fig = px.pie(df, values=df['Value'], names=df['Item'],
             title='Emissions based on fuel')
fig.show()

fig = px.choropleth(data_frame=df,
                    locations='Area',
                    locationmode='country names',
                    color='Value',
                    color_continuous_scale=px.colors.sequential.Plasma,
                    projection='natural earth')

# Show the map
fig.show()

fig = px.line(df, x='Year', y='Value', color='Element')
fig.show()