How to show label names in pandas groupby histogram plot

Question

I can plot multiple histograms in a single plot using pandas but there are few things missing:

1. How to give the label.
2. I can only plot one figure, how to change it to layout=(3,1) or something else.
3. Also, in figure 1, all the bins are filled with solid colors, and its kind of difficult to know which is which, how to fill then with different markers (eg. crosses,slashes,etc)?

Here is the MWE:

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('iris')

df.groupby('species')['sepal_length'].hist(alpha=0.7,label='species')
plt.legend()

Output:

To change layout I can use by keyword, but can't give them colors

HOW TO GIVE DIFFERENT COLORS?

df.hist('sepal_length',by='species',layout=(3,1))
plt.tight_layout()

Gives:

Answer 1

In pandas version 1.1.0 you can simply set the legend keyword to true.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('iris')

df.groupby('species')['sepal_length'].hist(alpha=0.7, legend = True)

output image

Answer 2

You can resolve to groupby:

fig,ax = plt.subplots()

hatches = ('\\', '//', '..')         # fill pattern
for (i, d),hatch in zip(df.groupby('species'), hatches):
    d['sepal_length'].hist(alpha=0.7, ax=ax, label=i, hatch=hatch)

ax.legend()

Output:

Answer 3

It's more code, but using pure matplotlib will always give you more control over the plots. For your second case:

import matplotlib.pyplot as plt
import numpy as np
from itertools import zip_longest

# Dictionary of color for each species
color_d = dict(zip_longest(df.species.unique(), 
                           plt.rcParams['axes.prop_cycle'].by_key()['color']))

# Use the same bins for each
xmin = df.sepal_length.min()
xmax = df.sepal_length.max()
bins = np.linspace(xmin, xmax, 20)

# Set up correct number of subplots, space them out. 
fig, ax = plt.subplots(nrows=df.species.nunique(), figsize=(4,8))
plt.subplots_adjust(hspace=0.4)

for i, (lab, gp) in enumerate(df.groupby('species')):
    ax[i].hist(gp.sepal_length, ec='k', bins=bins, color=color_d[lab])
    ax[i].set_title(lab)

    # same xlim for each so we can see differences
    ax[i].set_xlim(xmin, xmax)