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Ultimate Visualization with Python
course content

Course Content

Ultimate Visualization with Python

Ultimate Visualization with Python

1. Matplotlib Introduction
Plotting ApplicationsWhy Use Matplotlib?Creating a CanvasBasic Plotting
2. Creating Commonly Used Plots
Line PlotMultiple Line PlotsScatter PlotBar ChartStacked Bar ChartsGrouped Bar Charts
3. Plots Customization
Adding Title to the PlotAdding LegendAxes CustomizationColors and Transparency Customizing GridSubplots
4. More Statistical Plots
HistogramBox PlotPie Chart
5. Plotting with Seaborn
Seaborn IntroductionCountplotPlots DecorationKDE PlotJoint PlotPair PlotHeatmap

bookHistogram

Let’s start with a histogram. Histograms are used to represent frequency or probability distribution of a given variable (approximate distribution) using vertical bins of equal width (or we can call them bars).

pyplot module has a special function called hist to create a histogram. The first and the only required parameter is our data (called x) which can be either an array or a sequence of arrays. If a sequence of arrays is passed, the bins for each array are painted in different colors. Here is a simple example for you:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
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)
# Creating a histogram
plt.hist(weather_df['Seattle'])
plt.show()
copy

Intervals and Height

We passed a Series object, which contains average yearly temperatures in Seattle, in the hist() function. Our sample was divided into 10 equal intervals by default starting from the minimum value to the maximum value. There are, however, only 9 bins, since there are no values which belong to the second interval.

The height of each bin by default is equal to the frequency of the values in this interval (number of times they occur).

Number of Bins

Another important, yet optional parameter is bins which takes either the number of bins (integer) or a sequence of numbers specifying the edges of the bins or a string. Most of the time passing the number of bins is more than enough.

There several methods for determining the width of the bins (more on this here), but here we will use the Sturges' formula (written in Python): bins = 1+int(np.log2(n)) where n is the sample size (the size of the array).

Let’s see it in action:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
# Specifying the number of bins
plt.hist(weather_df['Seattle'], bins=1 + int(np.log2(len(weather_df))))
plt.show()
copy

The number of rows in the DataFrame is 26 (the size of the Series), so the resulting number of bins is 5.

Probability Density Approximation

That’s all fine, but what if we want to have a look at the probability density approximation? All we need is to set the parameter density to True.

Now the height of each bin will be the count of the values in the interval divided by the product of the total number of values (the size of the sample) and the bin width. As a result, the sum of the areas of the bins will be equal to 1, which is exactly what we need from a probability density function.

Let’s now modify our example:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
# Making a histogram a probability density function approximation
plt.hist(weather_df['Seattle'], bins=1 + int(np.log2(len(weather_df))), density=True)
plt.show()
copy

Now we have an approximation of the probability density function for our temperature data.

If you want to explore more about the hist() function parameters, you can refer to its documentation.

Task
test

Swipe to show code editor

Your task is to create an approximation of a probability density function using a sample from the standard normal distribution:

  1. Use the correct function for creating a histogram.
  2. Use normal_sample as the data for the histogram.
  3. Specify the number of bins as the second argument using the Sturges' formula.
  4. Make the histogram an approximation of a probability density function via correctly specifying the rightmost argument.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 4. Chapter 1
toggle bottom row

bookHistogram

Let’s start with a histogram. Histograms are used to represent frequency or probability distribution of a given variable (approximate distribution) using vertical bins of equal width (or we can call them bars).

pyplot module has a special function called hist to create a histogram. The first and the only required parameter is our data (called x) which can be either an array or a sequence of arrays. If a sequence of arrays is passed, the bins for each array are painted in different colors. Here is a simple example for you:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
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)
# Creating a histogram
plt.hist(weather_df['Seattle'])
plt.show()
copy

Intervals and Height

We passed a Series object, which contains average yearly temperatures in Seattle, in the hist() function. Our sample was divided into 10 equal intervals by default starting from the minimum value to the maximum value. There are, however, only 9 bins, since there are no values which belong to the second interval.

The height of each bin by default is equal to the frequency of the values in this interval (number of times they occur).

Number of Bins

Another important, yet optional parameter is bins which takes either the number of bins (integer) or a sequence of numbers specifying the edges of the bins or a string. Most of the time passing the number of bins is more than enough.

There several methods for determining the width of the bins (more on this here), but here we will use the Sturges' formula (written in Python): bins = 1+int(np.log2(n)) where n is the sample size (the size of the array).

Let’s see it in action:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
# Specifying the number of bins
plt.hist(weather_df['Seattle'], bins=1 + int(np.log2(len(weather_df))))
plt.show()
copy

The number of rows in the DataFrame is 26 (the size of the Series), so the resulting number of bins is 5.

Probability Density Approximation

That’s all fine, but what if we want to have a look at the probability density approximation? All we need is to set the parameter density to True.

Now the height of each bin will be the count of the values in the interval divided by the product of the total number of values (the size of the sample) and the bin width. As a result, the sum of the areas of the bins will be equal to 1, which is exactly what we need from a probability density function.

Let’s now modify our example:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
# Making a histogram a probability density function approximation
plt.hist(weather_df['Seattle'], bins=1 + int(np.log2(len(weather_df))), density=True)
plt.show()
copy

Now we have an approximation of the probability density function for our temperature data.

If you want to explore more about the hist() function parameters, you can refer to its documentation.

Task
test

Swipe to show code editor

Your task is to create an approximation of a probability density function using a sample from the standard normal distribution:

  1. Use the correct function for creating a histogram.
  2. Use normal_sample as the data for the histogram.
  3. Specify the number of bins as the second argument using the Sturges' formula.
  4. Make the histogram an approximation of a probability density function via correctly specifying the rightmost argument.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 4. Chapter 1
toggle bottom row

bookHistogram

Let’s start with a histogram. Histograms are used to represent frequency or probability distribution of a given variable (approximate distribution) using vertical bins of equal width (or we can call them bars).

pyplot module has a special function called hist to create a histogram. The first and the only required parameter is our data (called x) which can be either an array or a sequence of arrays. If a sequence of arrays is passed, the bins for each array are painted in different colors. Here is a simple example for you:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
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)
# Creating a histogram
plt.hist(weather_df['Seattle'])
plt.show()
copy

Intervals and Height

We passed a Series object, which contains average yearly temperatures in Seattle, in the hist() function. Our sample was divided into 10 equal intervals by default starting from the minimum value to the maximum value. There are, however, only 9 bins, since there are no values which belong to the second interval.

The height of each bin by default is equal to the frequency of the values in this interval (number of times they occur).

Number of Bins

Another important, yet optional parameter is bins which takes either the number of bins (integer) or a sequence of numbers specifying the edges of the bins or a string. Most of the time passing the number of bins is more than enough.

There several methods for determining the width of the bins (more on this here), but here we will use the Sturges' formula (written in Python): bins = 1+int(np.log2(n)) where n is the sample size (the size of the array).

Let’s see it in action:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
# Specifying the number of bins
plt.hist(weather_df['Seattle'], bins=1 + int(np.log2(len(weather_df))))
plt.show()
copy

The number of rows in the DataFrame is 26 (the size of the Series), so the resulting number of bins is 5.

Probability Density Approximation

That’s all fine, but what if we want to have a look at the probability density approximation? All we need is to set the parameter density to True.

Now the height of each bin will be the count of the values in the interval divided by the product of the total number of values (the size of the sample) and the bin width. As a result, the sum of the areas of the bins will be equal to 1, which is exactly what we need from a probability density function.

Let’s now modify our example:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
# Making a histogram a probability density function approximation
plt.hist(weather_df['Seattle'], bins=1 + int(np.log2(len(weather_df))), density=True)
plt.show()
copy

Now we have an approximation of the probability density function for our temperature data.

If you want to explore more about the hist() function parameters, you can refer to its documentation.

Task
test

Swipe to show code editor

Your task is to create an approximation of a probability density function using a sample from the standard normal distribution:

  1. Use the correct function for creating a histogram.
  2. Use normal_sample as the data for the histogram.
  3. Specify the number of bins as the second argument using the Sturges' formula.
  4. Make the histogram an approximation of a probability density function via correctly specifying the rightmost argument.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Let’s start with a histogram. Histograms are used to represent frequency or probability distribution of a given variable (approximate distribution) using vertical bins of equal width (or we can call them bars).

pyplot module has a special function called hist to create a histogram. The first and the only required parameter is our data (called x) which can be either an array or a sequence of arrays. If a sequence of arrays is passed, the bins for each array are painted in different colors. Here is a simple example for you:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
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)
# Creating a histogram
plt.hist(weather_df['Seattle'])
plt.show()
copy

Intervals and Height

We passed a Series object, which contains average yearly temperatures in Seattle, in the hist() function. Our sample was divided into 10 equal intervals by default starting from the minimum value to the maximum value. There are, however, only 9 bins, since there are no values which belong to the second interval.

The height of each bin by default is equal to the frequency of the values in this interval (number of times they occur).

Number of Bins

Another important, yet optional parameter is bins which takes either the number of bins (integer) or a sequence of numbers specifying the edges of the bins or a string. Most of the time passing the number of bins is more than enough.

There several methods for determining the width of the bins (more on this here), but here we will use the Sturges' formula (written in Python): bins = 1+int(np.log2(n)) where n is the sample size (the size of the array).

Let’s see it in action:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
# Specifying the number of bins
plt.hist(weather_df['Seattle'], bins=1 + int(np.log2(len(weather_df))))
plt.show()
copy

The number of rows in the DataFrame is 26 (the size of the Series), so the resulting number of bins is 5.

Probability Density Approximation

That’s all fine, but what if we want to have a look at the probability density approximation? All we need is to set the parameter density to True.

Now the height of each bin will be the count of the values in the interval divided by the product of the total number of values (the size of the sample) and the bin width. As a result, the sum of the areas of the bins will be equal to 1, which is exactly what we need from a probability density function.

Let’s now modify our example:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)
# Making a histogram a probability density function approximation
plt.hist(weather_df['Seattle'], bins=1 + int(np.log2(len(weather_df))), density=True)
plt.show()
copy

Now we have an approximation of the probability density function for our temperature data.

If you want to explore more about the hist() function parameters, you can refer to its documentation.

Task
test

Swipe to show code editor

Your task is to create an approximation of a probability density function using a sample from the standard normal distribution:

  1. Use the correct function for creating a histogram.
  2. Use normal_sample as the data for the histogram.
  3. Specify the number of bins as the second argument using the Sturges' formula.
  4. Make the histogram an approximation of a probability density function via correctly specifying the rightmost argument.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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