Group Bar Chart With Seaborn/matplotlib

My goal is to create a grouped bar chart like the one below, using a pandas DataFrame that is grouped by two variables 'Alpha' and 'Beta.' xl2 = xl.groupby(['Alpha','Beta']).media

Solution 1:

You can use ggplot for this

from ggplot import *
import pandas as pd
import numpy as np

df = pd.DataFrame({
    "x": np.random.choice(range(2001, 2008), 250),
    "w": np.random.uniform(50, 400, 250),
    "cat": np.random.choice(["A", "B", "C", "D", "E"], 250)
})

print ggplot(df, aes(x='x', weight='w', fill='cat')) + geom_bar() + theme_bw()

Solution 2:

is that what you want?

In [167]: df
Out[167]:
    a  b  c
022113312221323043225332612271228023932310220112121221013121140231503316312170111802219010

In [168]: plot = df.groupby(['a','b']).mean()

In [169]: plot
Out[169]:
            c
a b
010.50000022.66666733.000000121.666667211.00000020.66666730.000000312.00000022.50000031.500000

In [170]: sns.barplot(x=plot.index, y=plot.c)

PS if you need something different, please provide a sample data set and expected grouped resulting DF (both in text/dict/JSON/CSV form)

PPS you may also want to check this answer

Solution 3:

Altair can be helpful in such cases. Here is the plot produced by the following code.

Imports

import pandas as pd
import numpy as np
from altair import *

Generating dataset

np.random.seed(0)
df = pd.DataFrame({
    "x": np.random.choice(range(0, 5), 250),
    "w": np.random.uniform(50, 400, 250),
    "cat": np.random.choice(["A", "B", "C", "D", "E"], 250)
})

Plotting

Chart(df).mark_bar().encode(x=X('cat', axis=False),  
                            y=Y('median(w)', axis=Axis(grid=False)),
                            color='cat',
                            column=Column('x', axis=Axis(axisWidth=1.0, offset=-8.0, orient='bottom'),scale=Scale(padding=30.0)),
                        ).configure_facet_cell( strokeWidth=0.0).configure_cell(width=200, height=200)

The key things in the altair code are:

1. X values are categories ('cat' in the df)
2. Color is by category
3. Y values are by median of the variable
4. Different columns represent different years