Jupyter Guide

To get started, open the file on the right entitled "Step 1...". You'll complete each task for Step 1 in that Jupyter Notebook file. Remember, you must run the cells (Ctrl/Cmd(⌘) + Enter) for each task before moving onto the next task in the Jupyter Notebook. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Creating a Basic Line Plot

To review the concepts covered in this step, please refer to the Introducing the Plotting Ecosystem module of the Plotting Data with Pandas course.

Creating a basic line plot is important because it is one of the most common types of plots and can be used to represent trends over time. In this step, you will use the plot function to create a line graph.

Let's get our toes wet and create a basic line plot! The goal of this step is to use the plot function to create a line graph that visualizes some basic data point. You will create some data points with lists of integers and you'll plot the data points using the matplotlib.pyplot.plot function.

Task 1.1: Importing Required Libraries

Before we can start plotting, we need to import the necessary libraries. In this task, you will import matplotlib's pyplot for plotting.

import matplotlib.pyplot as plt

Task 1.2: Creating Sample Data

In this task, you will create some sample data for plotting. Let's define two lists, x representing the x-axis values and y representing the y-axis values.

x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

Task 1.3: Creating a Line Plot

Now that we have our data, let's create a line plot. In this task, you will create a line plot using the sample data we defined in the previous task.

plt.plot(x, y)
plt.show()

Creating a Scatter Plot

To review the concepts covered in this step, please refer to the Plotting Using Pandas module of the Plotting Data with Pandas course.

Let's move on to another type of plot - the scatter plot! Creating a scatter plot is important because it can be used to visualize the relationship between two variables. In this step, you will use pandas to create a scatter plot.

The goal of this step is to create a scatter plot that visualizes the relationship between a country's GDP and its life expectancy. You will use the 'GDP' column as the x-axis and the 'Life_Expectancy' column as the y-axis.

Task 2.1: Import Necessary Libraries

Import the pandas and matplotlib libraries, which will be used for data manipulation and visualization respectively.

import pandas as pd
import matplotlib.pyplot as plt

Task 2.2: Load the Data

Load the 'World_Bank-Fictional.csv' file into a pandas DataFrame.

df = pd.read_csv('World_Bank-Fictional.csv')

Task 2.3: Inspect the Data

Inspect the first 5 rows of the DataFrame to understand its structure and contents.

df.head()

Task 2.4: Create a Scatter Plot

Create a scatter plot using the 'GDP' column as the x-axis and the 'Life_Expectancy' column as the y-axis.

df.plot(kind='scatter', x='GDP', y='Life_Expectancy')
plt.show()

Creating a Bar Plot

To review the concepts covered in this step, please refer to the Plotting Using Pandas module of the Plotting Data with Pandas course.

Next up is the bar plot! Creating a bar plot is important because it can be used to compare the values of different categories. In this step, you will create a bar chart in pandas.

The goal of this step is to create a bar chart that compares the literacy rates of different countries. You will use the 'Country' column as the x-axis and the 'Literacy_Rate' column as the y-axis. Don't forget to add a title and labels to your chart!

Task 3.1: Import Necessary Libraries

Import the pandas and matplotlib libraries which will be used for data manipulation and plotting respectively.

import pandas as pd
import matplotlib.pyplot as plt

Task 3.2: Load the Data

Load the 'World_Bank-Fictional.csv' file into a pandas DataFrame.

df = pd.read_csv('World_Bank-Fictional.csv')

Task 3.3: Inspect the Data

Inspect the first 5 rows of the DataFrame to understand the structure of the data.

df.head()

Task 3.4: Create a Grouped Bar Plot

Create a grouped bar plot where each country is a group, and within each group, there are bars for different years. Use the provided code to create the pivot table we will plot. Call the plot(kind='bar') method on the pivot_df object.

The x-axis should represent the years, and the y-axis should be the 'Literacy_Rate'. Add a title and labels to your chart.

# Provided code
pivot_df = df.pivot_table(index='Year', columns='Country', values='Literacy_Rate')

# Create bar plot
pivot_df.plot(kind='bar', edgecolor='black')
plt.title('Literacy Rate by Country Over Years')
plt.xlabel('Year')
plt.ylabel('Literacy Rate')
plt.show()

Creating a Histogram

To review the concepts covered in this step, please refer to the Plotting Using Pandas module of the Plotting Data with Pandas course.

Now let's visualize some frequency data with a histogram! Creating a histogram is important because it can be used to visualize the distribution of a dataset. In this step, you will generate a histogram using Pandas.

The goal of this step is to create a histogram that shows the distribution of the 'Population' column. Remember, a histogram represents frequency data and will help you visualize the distribution of the population data.

Task 4.1: Import Necessary Libraries

Import the necessary libraries for data manipulation and visualization. In this case, you will need pandas and matplotlib.

import pandas as pd
import matplotlib.pyplot as plt

Task 4.2: Load the Dataset

Load the 'World_Bank-Fictional.csv' dataset using pandas and assign it to a variable named df.

df = pd.read_csv('World_Bank-Fictional.csv')

Task 4.3: Inspect the Dataset

Inspect the first 5 rows of the dataset using the head() function.

df.head()

Task 4.4: Plot the Histogram

Plot a histogram of the 'Population' column using pandas. Add a title to your plot.

df['Population'].plot(kind="hist", title="Distribution of Populations")
plt.show()

Creating an Area Chart

To review the concepts covered in this step, please refer to the Plotting Using Pandas module of the Plotting Data with Pandas course.

Last but not least, let's create an area chart! Area charts are useful because they can be used to visualize the change in one or more quantities over time. In this step, you will create an area chart in pandas.

The goal of this step is to create a stacked area plot that visualizes the change in GDP and population over time for each country. You will use the 'Year' column as the x-axis and the 'GDP' column pivoted on the 'Country' column as the y-axis.

Task 5.1: Import Necessary Libraries

Import the pandas and matplotlib libraries which will be used throughout this lab.

import pandas as pd
import matplotlib.pyplot as plt

Task 5.2: Load the Data

Load the 'World_Bank-Fictional.csv' file into a pandas DataFrame.

df = pd.read_csv('World_Bank-Fictional.csv')

Task 5.3: Inspect the Data

Inspect the first 5 rows of the DataFrame to understand its structure.

df.head()

Task 5.4: Pivot the Data

Pivot the DataFrame so that each country's GDP is a separate column. Display the first 5 rows of the pivoted data.

df_pivot = df.pivot(index='Year', columns='Country', values='GDP')
df_pivot.head()

Task 5.5: Plot the Data

Create a stacked area plot of the pivoted DataFrame.

df_pivot.plot(kind='area', stacked=True)
plt.title('GDP Over Time')
plt.ylabel('GDP')
plt.show()