Contenido del Curso
Ultimate Visualization with Python
Ultimate Visualization with Python
KDE 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:
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()
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):
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()
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.
Swipe to show code editor
- Use the correct function to create a KDE plot.
- Use
countries_dfas the data for the plot (the first argument). - Set
'GDP per capita'as the column to use and the orientation to horizontal via the second argument. - Fill in the area under the curve via the third (rightmost) argument.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
KDE 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:
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()
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):
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()
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.
Swipe to show code editor
- Use the correct function to create a KDE plot.
- Use
countries_dfas the data for the plot (the first argument). - Set
'GDP per capita'as the column to use and the orientation to horizontal via the second argument. - Fill in the area under the curve via the third (rightmost) argument.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
KDE 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:
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()
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):
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()
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.
Swipe to show code editor
- Use the correct function to create a KDE plot.
- Use
countries_dfas the data for the plot (the first argument). - Set
'GDP per capita'as the column to use and the orientation to horizontal via the second argument. - Fill in the area under the curve via the third (rightmost) argument.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
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:
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()
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):
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()
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.
Swipe to show code editor
- Use the correct function to create a KDE plot.
- Use
countries_dfas the data for the plot (the first argument). - Set
'GDP per capita'as the column to use and the orientation to horizontal via the second argument. - Fill in the area under the curve via the third (rightmost) argument.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opcionesCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones