I am not sure if this is the prettiest way to do it, but it does fix it with one line: import matplotlib.pyplot as plt import seaborn as sns import numpy as np import pandas as pd np.random.seed(0) fig = plt.figure() ax1 = fig.add_subplot(111) ax1.plot(pd.Series(np.random.uniform(0, 1, size=10))) ax2 = ax1.twinx() ax2.plot(pd.Series(np.random.uniform(10, 20, size=10)), color=”r”) # … Read more
I had a problem with the answer by @mwaskorn, namely that g.set_xticklabels(rotation=30) fails, because this also requires the labels. A bit easier than the answer by @Aman is to just add plt.xticks(rotation=45)
Seaborn 0.12 With seaborn 0.12, the FutureWarning from seaborn 0.11 is now an TypeError. Only data may be specified as the first positional argument for seaborn plots. All other arguments must use keywords (e.g. x= and y=). This applies to all seaborn plotting functions. sns.*plot(data=penguins, x=”bill_length_mm”, y=”bill_depth_mm”) or sns.*plot(penguins, x=”bill_length_mm”, y=”bill_depth_mm”) sns.*plot(data=penguins.bill_length_mm) or sns.*plot(penguins.bill_length_mm) See … Read more
Imports and Data import numpy import pandas import matplotlib.pyplot as plt import seaborn as sns seaborn.set(style=”ticks”) numpy.random.seed(0) N = 37 _genders= [‘Female’, ‘Male’, ‘Non-binary’, ‘No Response’] df = pandas.DataFrame({ ‘Height (cm)’: numpy.random.uniform(low=130, high=200, size=N), ‘Weight (kg)’: numpy.random.uniform(low=30, high=100, size=N), ‘Gender’: numpy.random.choice(_genders, size=N) }) Update August 2021 With seaborn 0.11.0, it’s recommended to use new figure … Read more
Imports and Sample DataFrame import matplotlib.pyplot as plt import pandas as pd import seaborn as sns # for sample data from matplotlib.lines import Line2D # for legend handle # DataFrame used for all options df = sns.load_dataset(‘diamonds’) carat cut color clarity depth table price x y z 0 0.23 Ideal E SI2 61.5 55.0 326 … Read more
You can get what handles/labels should exist in the legend before you actually draw the legend itself. You then draw the legend only with the specific ones you want. import matplotlib.pyplot as plt import seaborn as sns import pandas as pd tips = sns.load_dataset(“tips”) sns.stripplot(x=”day”, y=”total_bill”, hue=”smoker”, data=tips, jitter=True, palette=”Set2″, dodge=True, linewidth=1, edgecolor=”gray”) # Get … Read more
In the latest seaborn, you can use the countplot function: seaborn.countplot(x=’reputation’, data=df) To do it with barplot you’d need something like this: seaborn.barplot(x=df.reputation.value_counts().index, y=df.reputation.value_counts()) You can’t pass ‘reputation’ as a column name to x while also passing the counts in y. Passing ‘reputation’ for x will use the values of df.reputation (all of them, not … Read more
Updating slightly, with seaborn 0.11.1: Seaborn’s relplot function creates a FacetGrid and gives each subplot its own explanatory title. You can add a title over the whole thing: import seaborn as sns tips = sns.load_dataset(‘tips’) rp = sns.relplot(data=tips, x=’total_bill’, y=’tip’, col=”sex”, row=’smoker’, kind=’scatter’) # rp is a FacetGrid; # relplot is a nice organized way … Read more
Usually, grid.map is the tool for this, as shown in many examples in the mutli-plot grids tutorial. But you are using relplot to combine lineplot with a FacetGrid as it is suggested in the docs (last example) which lets you use some extra styling parameters. Because relplot processes the data a bit differently than if … Read more
There is line shadow showing the confidence interval, because the dataset contains multiple y(team_strikerate) values for each x(season) value. By default, sns.lineplot() will estimate the mean by aggregating over multiple y values at each x value. After aggregation, the mean of y values at each x value will be plotted as a line. The line … Read more