I think need melt if want use barplot: data = df.melt(‘date’, var_name=”a”, value_name=”b”) print (data) date a b 0 2017-09-05 A 25 1 2017-09-06 A 261 2 2017-09-07 A 188 3 2017-09-08 A 200 4 2017-09-09 A 292 5 2017-09-05 B 261 6 2017-09-06 B 1519 7 2017-09-07 B 1545 8 2017-09-08 B 2110 9 … Read more
Using the new pandas release (0.14.0 or later) the below code will work. To create the two axis I have manually created two matplotlib axes objects (ax and ax2) which will serve for both bar plots. When plotting a Dataframe you can choose the axes object using ax=…. Also in order to prevent the two … Read more
A. using a list of colors The easiest solution to make sure to have the same colors for the same categories in both plots would be to manually specify the colors at plot creation. # First bar plot ax = sns.barplot(data=a, x=’Scenario’, y=’Duration’, hue=”Program”, ci=None, palette=[“C0”, “C1”, “k”]) # … # Second bar plot ax2 … Read more
It’s typically not required to use seaborn to plot grouped bars, it’s just a matter of shaping the dataframe, usually with .pivot or .pivot_table. See How to create a grouped bar plot for more examples. Using pandas.DataFrame.plot with a wide dataframe will be easier, in this case, than using a long dataframe with seaborn.barplot, because … Read more
Using pandas: import pandas as pd groups = [[23,135,3], [123,500,1]] group_labels = [‘views’, ‘orders’] # Convert data to pandas DataFrame. df = pd.DataFrame(groups, index=group_labels).T # Plot. pd.concat( [df.mean().rename(‘average’), df.min().rename(‘min’), df.max().rename(‘max’)], axis=1).plot.bar()
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
Pandas will show grouped bars by columns. Entries in each row but different columns will constitute a group in the resulting plot. Hence you need to “reshape” your dataframe to have the “group” as columns. In this case you can pivot like df.pivot(“column”, “group”, “val”) producing group g1 g2 column c1 10 8 c2 12 … Read more
Tested in python 3.11, pandas 1.5.1, matplotlib 3.6.2 Sample Data and Imports import pandas as pd import matplotlib.pyplot as plt import numpy as np np.random.seed(2022) # creates a consistent sample y = np.random.rand(10,4) y[:,0]= np.arange(10) df = pd.DataFrame(y, columns=[“X”, “A”, “B”, “C”]) X A B C 0 0.0 0.499058 0.113384 0.049974 1 1.0 0.486988 0.897657 … Read more
The seaborn.catplot organizing function returns a FacetGrid, which gives you access to the fig, the ax, and its patches. If you add the labels when nothing else has been plotted you know which bar-patches came from which variables. From @LordZsolt’s answer I picked up the order argument to catplot: I like making that explicit because … Read more