With Seaborn, histograms are made using the histplot function. You can call the function with default values, what already gives a nice chart. Though, do not forget to play with the number of bins using the ‘bins’ argument. Indeed, a pattern can be hidden under the hood that one would not be able to detect with default bins values.
Drawing a simple histogram with default parameters
# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above)
sns.set(style="darkgrid")
df = sns.load_dataset("iris")
sns
sns.histplot(data=df, x="sepal_length")
plt.show()
You can add a kde curve to a histogram by setting the kde argument to True.
# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above)
sns.set(style="darkgrid")
df = sns.load_dataset("iris")
sns.histplot(data=df, x="sepal_length", kde=True)
plt.show()
Another way of drawing a histogram with Seaborn is by using the distplot function. In versions before 0.11.0, it automatically added a kdeplot-like smooth curve. Note that this function will be deprecated soon. Refer to the new distplot function documentation for future use.
# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above)
sns.set(style="darkgrid")
df = sns.load_dataset("iris")
sns.distplot(df["sepal_length"])
# in the next version of the distplot function, one would have to write:
# sns.distplot(data=df, x="sepal_length", kind='hist') # note that 'kind' is 'hist' by default
plt.show()
Controlling for the number of bins
# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above)
sns.set(style="darkgrid")
df = sns.load_dataset("iris")
sns.histplot(data=df, x="sepal_length", bins=20)
plt.show()
