import matplotlib.pyplot as plt from matplotlib.dates import date2num import datetime x = [ datetime.datetime(2011, 1, 4, 0, 0), datetime.datetime(2011, 1, 5, 0, 0), datetime.datetime(2011, 1, 6, 0, 0) ] x = date2num(x) y = [4, 9, 2] z = [1, 2, 3] k = [11, 12, 13] ax = plt.subplot(111) ax.bar(x-0.2, y, width=0.2, color=”b”, align=’center’) … Read more
Firstly freq_series.plot returns an axis not a figure so to make my answer a little more clear I’ve changed your given code to refer to it as ax rather than fig to be more consistent with other code examples. You can get the list of the bars produced in the plot from the ax.patches member. … Read more
Use: + scale_y_continuous(labels = scales::percent) Or, to specify formatting parameters for the percent: + scale_y_continuous(labels = scales::percent_format(accuracy = 1)) (the command labels = percent is obsolete since version 2.2.1 of ggplot2)
Seaborn’s barplot returns an axis-object (not a figure). This means you can do the following: import pandas as pd import seaborn as sns import matplotlib.pyplot as plt fake = pd.DataFrame({‘cat’: [‘red’, ‘green’, ‘blue’], ‘val’: [1, 2, 3]}) ax = sns.barplot(x = ‘val’, y = ‘cat’, data = fake, color=”black”) ax.set(xlabel=”common xlabel”, ylabel=”common ylabel”) plt.show()