Project Name Recycling Rates By Waste Type¶
Problem Statement The state of recycling in Singapore. Has it increased or decreased?¶
Project Goals¶
- Learn how to import a dataset with Pandas
- Learn how to clean and manipulate data
- Create data visualisations with the Matplotlib library
- Gain familiarity with Jupyter Notebook and the Markdown language
Notebook Setup¶
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%matplotlib inline
Import libraries¶
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Select background style¶
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plt.style.use('dark_background')
Data Exploration and Manipulation¶
Read from CSV File¶
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df = pd.read_csv('data/recycling-rate-by-waste-type.csv')
Display the size of the data¶
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df.shape # This will display the number of rows and columns
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df.columns # Displays the names of the columns
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df.dtypes # Displays the name and type of the columns
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Display the first five rows in the dataset¶
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df.head() # Displays the first five rows
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Display the last five rows in the dataset¶
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df.tail() # Displays the last five rows
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Check the dataset for missing values¶
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df.isnull().sum() # Displays columns with blank values
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Rename columns¶
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df.columns = ['year', 'wastetype', 'recyclingrate']
df.head()
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Summary of the dataset¶
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df.info()
List the years covered in the dataset¶
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df["year"].unique()
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Displays all unique wastetypes¶
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uniquewastetypes = df["wastetype"].unique() # This is placed in a variable
uniquewastetypes
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Total number of wastetypes¶
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len(uniquewastetypes) # Displays the total number of unique wastetypes in the dataset
totalwastetypes = len(uniquewastetypes)
totalwastetypes
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Categorise by the year 2015¶
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year2015 = df['year'] == 2015
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df[year2015]
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df['recyclingrate'] = df['recyclingrate'].astype(int) # Ensures that the recyclingrate column is an integer
Data Visualisation¶
Recycling Rates in 2015¶
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chart1 = df[year2015].plot.barh(y='recyclingrate', x="wastetype", color='pink', figsize=(8,5))
chart1.set_title("Recycling Rates in 2015", fontsize=20)
chart1.set_xlabel("Recycling Rate in %", fontsize=13)
chart1.set_ylabel("Type of Wastes", fontsize=13)
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year2000 = df['year'] == 2000
df[year2000]
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Recycling Rates in 2000¶
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chart2 = df[year2000].plot.barh(y='recyclingrate', x="wastetype", figsize=(8,4))
chart2.set_title("Recycling Rates in 2000", fontsize=20)
chart2.set_xlabel("Recycling Rate in %", fontsize=13)
chart2.set_ylabel("Type of Wastes", fontsize=13)
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Filter column by food waste¶
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foodwaste = df['wastetype'] == "Food"
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df[foodwaste]
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Recycling Trends from 2000 to 2015¶
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# df4 = df.pivot[]
# df.pivot(columns='var', values='val')
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chart3 = df[foodwaste].plot(x='year', y='recyclingrate', color='lime', figsize=(10,7))
chart3.set_title("Recycling from 2000 to 2015", fontsize=20)
chart3.set_xlabel("Year", fontsize=13)
chart3.set_ylabel("Recycling Rate in %", fontsize=13)
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