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Ultimate Visualization with Python
course content

Conteúdo do Curso

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

bookKDE Plot

Kernel density estimation (KDE) plot is a plot used to visualize the probability density function estimation. It is in a way similar to a histogram which we discussed in the previous section, however, the KDE plot is a continuous curve, not a set of bars, and is based on all of the data points rather than the intervals. Let’s have a look at an example of a KDE plot:

As you can see, here we have a histogram combined with a KDE plot (orange curve). This combination gives us a much clearer probability density function approximation than a single histogram.

With seaborn creating a KDE plot is as simple as it gets, since there is a special kdeplot() function. Its most important parameters data, x and y work the same way as in the countplot() function.

First Option

We can simply set only one of these parameters via passing a sequence of values. Here is an example to clarify everything:


              
123456789

            
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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 KDE plot setting only the data parameter
sns.kdeplot(data=weather_df['Seattle'], fill=True)
plt.show()
copy

We only set the value for the data parameter passing a Series object and use the fill parameter to fill in the area under the curve (it is not filled in by default).

Second Option

It is also possible to set a 2D object like a DataFrame for data and a column name (or a key if the data is a dictionary) for x (vertical orientation) or y (horizontal orientation):


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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)
# Creating a KDE plot setting both the data and x parameters
sns.kdeplot(data=weather_df, x='Seattle', fill=True)
plt.show()
copy

We achieved the same results passing the whole DataFrame as the data parameter and the column name for the x parameter.

By the way, the KDE plot we created has a characteristic bell curve and closely resembles the normal distribution with the mean of approximately 52°F.

In case you want to explore more about the kdeplot() function, feel free to refer to its documentation.

Tarefa

  1. Use the correct function to create a KDE plot.
  2. Use countries_df as the data for the plot (the first argument).
  3. Set 'GDP per capita' as the column to use and the orientation to horizontal via the second argument.
  4. Fill in the area under the curve via the third (rightmost) argument.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 5. Capítulo 4
toggle bottom row

bookKDE Plot

Kernel density estimation (KDE) plot is a plot used to visualize the probability density function estimation. It is in a way similar to a histogram which we discussed in the previous section, however, the KDE plot is a continuous curve, not a set of bars, and is based on all of the data points rather than the intervals. Let’s have a look at an example of a KDE plot:

As you can see, here we have a histogram combined with a KDE plot (orange curve). This combination gives us a much clearer probability density function approximation than a single histogram.

With seaborn creating a KDE plot is as simple as it gets, since there is a special kdeplot() function. Its most important parameters data, x and y work the same way as in the countplot() function.

First Option

We can simply set only one of these parameters via passing a sequence of values. Here is an example to clarify everything:


              
123456789

            
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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 KDE plot setting only the data parameter
sns.kdeplot(data=weather_df['Seattle'], fill=True)
plt.show()
copy

We only set the value for the data parameter passing a Series object and use the fill parameter to fill in the area under the curve (it is not filled in by default).

Second Option

It is also possible to set a 2D object like a DataFrame for data and a column name (or a key if the data is a dictionary) for x (vertical orientation) or y (horizontal orientation):


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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)
# Creating a KDE plot setting both the data and x parameters
sns.kdeplot(data=weather_df, x='Seattle', fill=True)
plt.show()
copy

We achieved the same results passing the whole DataFrame as the data parameter and the column name for the x parameter.

By the way, the KDE plot we created has a characteristic bell curve and closely resembles the normal distribution with the mean of approximately 52°F.

In case you want to explore more about the kdeplot() function, feel free to refer to its documentation.

Tarefa

  1. Use the correct function to create a KDE plot.
  2. Use countries_df as the data for the plot (the first argument).
  3. Set 'GDP per capita' as the column to use and the orientation to horizontal via the second argument.
  4. Fill in the area under the curve via the third (rightmost) argument.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 5. Capítulo 4
toggle bottom row

bookKDE Plot

Kernel density estimation (KDE) plot is a plot used to visualize the probability density function estimation. It is in a way similar to a histogram which we discussed in the previous section, however, the KDE plot is a continuous curve, not a set of bars, and is based on all of the data points rather than the intervals. Let’s have a look at an example of a KDE plot:

As you can see, here we have a histogram combined with a KDE plot (orange curve). This combination gives us a much clearer probability density function approximation than a single histogram.

With seaborn creating a KDE plot is as simple as it gets, since there is a special kdeplot() function. Its most important parameters data, x and y work the same way as in the countplot() function.

First Option

We can simply set only one of these parameters via passing a sequence of values. Here is an example to clarify everything:


              
123456789

            
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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 KDE plot setting only the data parameter
sns.kdeplot(data=weather_df['Seattle'], fill=True)
plt.show()
copy

We only set the value for the data parameter passing a Series object and use the fill parameter to fill in the area under the curve (it is not filled in by default).

Second Option

It is also possible to set a 2D object like a DataFrame for data and a column name (or a key if the data is a dictionary) for x (vertical orientation) or y (horizontal orientation):


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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)
# Creating a KDE plot setting both the data and x parameters
sns.kdeplot(data=weather_df, x='Seattle', fill=True)
plt.show()
copy

We achieved the same results passing the whole DataFrame as the data parameter and the column name for the x parameter.

By the way, the KDE plot we created has a characteristic bell curve and closely resembles the normal distribution with the mean of approximately 52°F.

In case you want to explore more about the kdeplot() function, feel free to refer to its documentation.

Tarefa

  1. Use the correct function to create a KDE plot.
  2. Use countries_df as the data for the plot (the first argument).
  3. Set 'GDP per capita' as the column to use and the orientation to horizontal via the second argument.
  4. Fill in the area under the curve via the third (rightmost) argument.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Kernel density estimation (KDE) plot is a plot used to visualize the probability density function estimation. It is in a way similar to a histogram which we discussed in the previous section, however, the KDE plot is a continuous curve, not a set of bars, and is based on all of the data points rather than the intervals. Let’s have a look at an example of a KDE plot:

As you can see, here we have a histogram combined with a KDE plot (orange curve). This combination gives us a much clearer probability density function approximation than a single histogram.

With seaborn creating a KDE plot is as simple as it gets, since there is a special kdeplot() function. Its most important parameters data, x and y work the same way as in the countplot() function.

First Option

We can simply set only one of these parameters via passing a sequence of values. Here is an example to clarify everything:


              
123456789

            
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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 KDE plot setting only the data parameter
sns.kdeplot(data=weather_df['Seattle'], fill=True)
plt.show()
copy

We only set the value for the data parameter passing a Series object and use the fill parameter to fill in the area under the curve (it is not filled in by default).

Second Option

It is also possible to set a 2D object like a DataFrame for data and a column name (or a key if the data is a dictionary) for x (vertical orientation) or y (horizontal orientation):


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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)
# Creating a KDE plot setting both the data and x parameters
sns.kdeplot(data=weather_df, x='Seattle', fill=True)
plt.show()
copy

We achieved the same results passing the whole DataFrame as the data parameter and the column name for the x parameter.

By the way, the KDE plot we created has a characteristic bell curve and closely resembles the normal distribution with the mean of approximately 52°F.

In case you want to explore more about the kdeplot() function, feel free to refer to its documentation.

Tarefa

  1. Use the correct function to create a KDE plot.
  2. Use countries_df as the data for the plot (the first argument).
  3. Set 'GDP per capita' as the column to use and the orientation to horizontal via the second argument.
  4. Fill in the area under the curve via the third (rightmost) argument.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 5. Capítulo 4
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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