Default behavior of plot.bar changed to plot a different color for each bar

[Deborah-Digges]

Code Sample, a copy-pastable example if possible

BEFORE

df_india.groupby('sector').id.count().sort_values().plot.bar();

would result in a bar plot with all bars having the same color

AFTER version 0.20

df_india.groupby('sector').id.count().sort_values().plot.bar(color='cornflowerblue');

I need to explicitly provide a color, else each bar is colored differently

Minimal reproducible code:

import pandas as pd
df = pd.DataFrame({'account-start': ['2017-02-03', '2017-03-03', '2017-01-01'],
                   'client': ['Alice Anders', 'Bob Baker', 'Charlie Chaplin'],
                   'balance': [-1432.32, 10.43, 30000.00],
                   'db-id': [1234, 2424, 251],
                   'proxy-id': [525, 1525, 2542],
                   'rank': [52, 525, 32],
                   })
df.client.value_counts().plot.bar()

Output:

Problem description

Why was the behavior of Series.plot.bar changed to plot bars with different color? Visually these colors add nothing to the plot as different colors should only be used when they correspond to differences of meaning in the data.

Why is the default behavior to provide an unnecessarily visually overwhelming graph? It took me some time to realize why my bars suddenly started acting strangely. Now, I pass color='cornflowerblue' to get all my bars the same pleasant hue.

Reference for visual appeal and the use of colors: http://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf

Would it be possible to revert to the pre-0.20 behavior?

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]

INSTALLED VERSIONS

commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-5-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: C.UTF-8
LANG: C.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: 3.2.1
pip: 9.0.1
setuptools: 38.5.2
Cython: 0.26.1
numpy: 1.14.2
scipy: 1.0.0
pyarrow: 0.8.0
xarray: 0.10.1
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2018.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: 0.4.0
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.6.0

[TomAugspurger]

Do you have a reproducible example? This sounds like something that's been fixed, but it's hard to say without an example http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports

[TomAugspurger]

Looks like a duplicate of #18394

[Deborah-Digges]

Hi @TomAugspurger ! Thanks for replying. It's reproducible on version 0.22.0 of pandas. Is it fixed in a later version?

Minimal reproducible code:

import pandas as pd
df = pd.DataFrame({'account-start': ['2017-02-03', '2017-03-03', '2017-01-01'],
                   'client': ['Alice Anders', 'Bob Baker', 'Charlie Chaplin'],
                   'balance': [-1432.32, 10.43, 30000.00],
                   'db-id': [1234, 2424, 251],
                   'proxy-id': [525, 1525, 2542],
                   'rank': [52, 525, 32],
                   })
df.client.value_counts().plot.bar()

Output:

[Deborah-Digges]

@TomAugspurger This isn't really a duplicate of the mentioned issue. The other one was caused as a result of this, but is not complaining of the same aesthetic appeal issue I refer to above. It is more complaining about functionality (logy=true) not working due to this new change. Hence, I believe it is a different issue

[Deborah-Digges]

I think they are related, but not the same issue. The issue I raise here is really one of visual design rather than broken functionality

[TomAugspurger]

Sorry about that, wasn't reading closely enough.

Thanks for the example. Could you edit your original post to include the example?

Are you interested in submitting a pull request to fix this?