Course Content

Ultimate Visualization with Python

1. Matplotlib Introduction

Plotting Applications Why Use Matplotlib?Creating a Canvas Basic Plotting

2. Creating Commonly Used Plots

Line Plot Multiple Line Plots Scatter Plot Bar Chart Stacked Bar Charts Grouped Bar Charts

3. Plots Customization

Adding Title to the Plot Adding Legend Axes Customization Colors and Transparency Customizing Grid Subplots

4. More Statistical Plots

Histogram Box Plot Pie Chart

5. Plotting with Seaborn

Seaborn Introduction Countplot Plots Decoration KDE Plot Joint Plot Pair Plot Heatmap

Multiple Line Plots

It is often the case when we need to create more than one line plot on one Axes object. For instance, we may want to compare two or more graphs which represent certain dynamics, trends, etc. There are two possible ways to accomplish this. Let's start with the first one.

Here is a sample of our data:


              12345
            
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
# Loading the dataset with the average yearly temperatures in Boston and Seattle
weather_df = pd.read_csv(url, index_col=0)
print(weather_df.head())

We will use two line plots to compare average yearly temperatures of Seattle and Boston. Here is the first option:


              1234567
            
import matplotlib.pyplot as plt
import pandas as pd
weather_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv', index_col=0)
# Calling the plot() function for each of the line plots
plt.plot(weather_df['Boston'], '-o')
plt.plot(weather_df['Seattle'], '-o')
plt.show()

First Option

Here we use the plot() function twice to plot two line separate line plots on one Axes object. Remember, indices of the pandas Series are used (in our example we have years as indices) for x-axis values.

The second option is the following:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
weather_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv', index_col=0)
# Calling the plot() function once for two line plots
plt.plot(weather_df['Boston'], '-o', weather_df['Seattle'], '-o')
plt.show()

Second Option

Here we use the plot() function only once, however, since we are specifying the markers twice, matplotlib understands that these are two separate plots (and their indices are used).

However, without specifying the markers this option creates only one plot (using the leftmost pandas Series for x-axis and the right one for y-axis).

As a side note, feel free to explore even more about line plots with their documentation.

Task

Use the correct function to create a line plot on the fifth line.
Pass data_linear as an argument in the function on the sixth line, do not use any markers.
Use the correct function to create a line plot on the eighth line.
Pass data_squared as an argument in the function on the fifth line, use 'o' markers with solid line.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 2

Multiple Line Plots

Here is a sample of our data:


              12345
            
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
# Loading the dataset with the average yearly temperatures in Boston and Seattle
weather_df = pd.read_csv(url, index_col=0)
print(weather_df.head())

We will use two line plots to compare average yearly temperatures of Seattle and Boston. Here is the first option:


              1234567
            
import matplotlib.pyplot as plt
import pandas as pd
weather_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv', index_col=0)
# Calling the plot() function for each of the line plots
plt.plot(weather_df['Boston'], '-o')
plt.plot(weather_df['Seattle'], '-o')
plt.show()

First Option

The second option is the following:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
weather_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv', index_col=0)
# Calling the plot() function once for two line plots
plt.plot(weather_df['Boston'], '-o', weather_df['Seattle'], '-o')
plt.show()

Second Option

Here we use the plot() function only once, however, since we are specifying the markers twice, matplotlib understands that these are two separate plots (and their indices are used).

However, without specifying the markers this option creates only one plot (using the leftmost pandas Series for x-axis and the right one for y-axis).

As a side note, feel free to explore even more about line plots with their documentation.

Task

Use the correct function to create a line plot on the fifth line.
Pass data_linear as an argument in the function on the sixth line, do not use any markers.
Use the correct function to create a line plot on the eighth line.
Pass data_squared as an argument in the function on the fifth line, use 'o' markers with solid line.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 2

Multiple Line Plots

Here is a sample of our data:


              12345
            
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
# Loading the dataset with the average yearly temperatures in Boston and Seattle
weather_df = pd.read_csv(url, index_col=0)
print(weather_df.head())

We will use two line plots to compare average yearly temperatures of Seattle and Boston. Here is the first option:


              1234567
            
import matplotlib.pyplot as plt
import pandas as pd
weather_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv', index_col=0)
# Calling the plot() function for each of the line plots
plt.plot(weather_df['Boston'], '-o')
plt.plot(weather_df['Seattle'], '-o')
plt.show()

First Option

The second option is the following:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
weather_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv', index_col=0)
# Calling the plot() function once for two line plots
plt.plot(weather_df['Boston'], '-o', weather_df['Seattle'], '-o')
plt.show()

Second Option

Here we use the plot() function only once, however, since we are specifying the markers twice, matplotlib understands that these are two separate plots (and their indices are used).

However, without specifying the markers this option creates only one plot (using the leftmost pandas Series for x-axis and the right one for y-axis).

As a side note, feel free to explore even more about line plots with their documentation.

Task

Use the correct function to create a line plot on the fifth line.
Pass data_linear as an argument in the function on the sixth line, do not use any markers.
Use the correct function to create a line plot on the eighth line.
Pass data_squared as an argument in the function on the fifth line, use 'o' markers with solid line.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Here is a sample of our data:


              12345
            
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
# Loading the dataset with the average yearly temperatures in Boston and Seattle
weather_df = pd.read_csv(url, index_col=0)
print(weather_df.head())

We will use two line plots to compare average yearly temperatures of Seattle and Boston. Here is the first option:


              1234567
            
import matplotlib.pyplot as plt
import pandas as pd
weather_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv', index_col=0)
# Calling the plot() function for each of the line plots
plt.plot(weather_df['Boston'], '-o')
plt.plot(weather_df['Seattle'], '-o')
plt.show()

First Option

The second option is the following:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
weather_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv', index_col=0)
# Calling the plot() function once for two line plots
plt.plot(weather_df['Boston'], '-o', weather_df['Seattle'], '-o')
plt.show()

Second Option

Here we use the plot() function only once, however, since we are specifying the markers twice, matplotlib understands that these are two separate plots (and their indices are used).

However, without specifying the markers this option creates only one plot (using the leftmost pandas Series for x-axis and the right one for y-axis).

As a side note, feel free to explore even more about line plots with their documentation.

Task

Use the correct function to create a line plot on the fifth line.
Pass data_linear as an argument in the function on the sixth line, do not use any markers.
Use the correct function to create a line plot on the eighth line.
Pass data_squared as an argument in the function on the fifth line, use 'o' markers with solid line.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 2. Chapter 2

Switch to desktop for real-world practiceContinue from where you are using one of the options below