seaborn.distplot has been DEPRECATED in seaborn 0.11 and is replaced with the following: displot(), a figure-level function with a similar flexibility over the kind of plot to draw. This is a FacetGrid, and does not have the ax parameter, so it will not work with matplotlib.pyplot.subplots. histplot(), an axes-level function for plotting histograms, including with … Read more
You can do it by changing your plotting line like this df.letters.value_counts().sort_values().plot(kind = ‘barh’)
This can be done by passing a dictionary with FacetGrid keyword arguments to relplot, like this: facet_kws={‘sharey’: False, ‘sharex’: True} (See seaborn.relplot documentation.)
To do this you can feed the regplot() function the scatter_kws arg like so: import seaborn as sns tips = sns.load_dataset(‘tips’) sns.regplot(x=’total_bill’, y=’tip’, data=tips, marker=”o”, color=”red”, scatter_kws={‘s’:2}) sns.regplot(x=’total_bill’, y=’tip’, data=tips, marker=”o”, color=”red”, scatter_kws={‘s’:20})
You have a couple of options here. You can tweak your use of the color parameter, or you can use the palette parameter. palette Using palette would arguably be the cleaner approach. Ideally, you would make only one call to lineplot when using palette–with the use of hue parameter: np.random.seed(42) y0 = pd.DataFrame(np.random.random(20), columns=[‘value’]) y1 … Read more
This now can be done using FacetGrid methods .map() or .map_dataframe(): import seaborn as sns import scipy as sp tips = sns.load_dataset(‘tips’) g = sns.lmplot(x=’total_bill’, y=’tip’, data=tips, row=’sex’, col=”time”, height=3, aspect=1) def annotate(data, **kws): r, p = sp.stats.pearsonr(data[‘total_bill’], data[‘tip’]) ax = plt.gca() ax.text(.05, .8, ‘r={:.2f}, p={:.2g}’.format(r, p), transform=ax.transAxes) g.map_dataframe(annotate) plt.show()
You can directly use pandas for plotting. pandas via groupby fig, ax = plt.subplots() df.groupby(“logic”).plot(x=”n_index”, y=”value”, marker=”o”, ax=ax) ax.legend([“False”,”True”]) The drawback here would be that the legend needs to be created manually. pandas via pivot df.pivot_table(“value”, “n_index”, “logic”).plot(marker=”o”) seaborn lineplot For seaborn lineplot it seems a single marker is enough to get the desired result. … Read more
This functionality is not built into seaborn.countplot as far as I know – the order parameter only accepts a list of strings for the categories, and leaves the ordering logic to the user. This is not hard to do with value_counts() provided you have a DataFrame though. For example, import pandas as pd import seaborn … Read more
Whenever you set the x-ticklabels manually, you should try to first set the corresponding ticks, and then specify the labels. In your case, therefore you should do g = sns.lineplot(data=df) g.set_xticks(range(len(df))) # <— set the ticks first g.set_xticklabels([‘2011′,’2012′,’2013′,’2014′,’2015′,’2016′,’2017′,’2018’])
I had faced the same problem. Restarting the notebook solved my problem. If that doesn’t solve the problem, you can try this pip install seaborn Edit As few people have posted in the comments, you can also use python -m pip install seaborn Plus, as per https://bugs.python.org/issue22295 it is a better way because in this … Read more