Iterate through each subplot / FacetGrid with for ax in g.axes.flat:. ax.patches contains matplotlib.patches.Rectangle and matplotlib.patches.PathPatch, so the correct ones must be used. Caveat: all hues must appear for each group in each Facet, otherwise the patches and hatches will not match. In this case, manual or conditional code will probably be required to correctly … Read more
distplot has been removed: removed in a future version of seaborn. Therefore, alternatives are to use histplot and displot. sns.histplot import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np df = pd.DataFrame(np.random.randn(100, 4), columns=list(‘ABCD’)) X = df[‘A’] sns.histplot(X, kde=True, bins=20) plt.show() sns.displot import pandas as pd import matplotlib.pyplot … Read more
In my case, I didn’t have to define a custom function to change the width as suggested above (which btw didn’t work for me as all the bars were unaligned). I simply added the attribute dodge=False to the argument of the seaborn plotting function and this made the trick! e.g. sns.countplot(x=’x’, hue=”y”, data=data, dodge=False); See … Read more
You can use coefficients of linear fit to make a legend like in this example: import seaborn as sns import matplotlib.pyplot as plt from scipy import stats tips = sns.load_dataset(“tips”) # get coeffs of linear fit slope, intercept, r_value, p_value, std_err = stats.linregress(tips[‘total_bill’],tips[‘tip’]) # use line_kws to set line label for legend ax = sns.regplot(x=”total_bill”, … Read more
The lmplot function returns a FacetGrid instance. This object has a method called set, to which you can pass key=value pairs and they will be set on each Axes object in the grid. Secondly, you can set only one side of an Axes limit in matplotlib by passing None for the value you want to … Read more
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
As a quick example (using a slightly cleaner method than the potentially duplicate question): import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111) ax.plot(range(10)) ax.set_xlabel(‘X-axis’) ax.set_ylabel(‘Y-axis’) ax.spines[‘bottom’].set_color(‘red’) ax.spines[‘top’].set_color(‘red’) ax.xaxis.label.set_color(‘red’) ax.tick_params(axis=”x”, colors=”red”) plt.show() Alternatively [t.set_color(‘red’) for t in ax.xaxis.get_ticklines()] [t.set_color(‘red’) for t in ax.xaxis.get_ticklabels()]
There isn’t a clear and quick answer to this at the top of search engine results so I provide simple examples here: .1e = scientific notation with 1 decimal point (standard form) .2f = 2 decimal places .3g = 3 significant figures .4% = percentage with 4 decimal places A more detailed explanation on the … Read more
We create the figure with the subplots: f, axes = plt.subplots(1, 2) Where axes is an array with each subplot. Then we tell each plot in which subplot we want them with the argument ax. sns.boxplot( y=”b”, x= “a”, data=df, orient=”v” , ax=axes[0]) sns.boxplot( y=”c”, x= “a”, data=df, orient=”v” , ax=axes[1]) And the result is:
Yes you need to reshape the DataFrame: df = pd.melt(df, id_vars=”class”, var_name=”sex”, value_name=”survival rate”) df Out: class sex survival rate 0 first men 0.914680 1 second men 0.300120 2 third men 0.118990 3 first woman 0.667971 4 second woman 0.329380 5 third woman 0.189747 6 first children 0.660562 7 second children 0.882608 8 third children … Read more