Sorting the order of bars in pandas/matplotlib bar plots

Question

What is the Pythonic/pandas way of sorting 'levels' within a column in pandas to give a specific ordering of bars in bar plot.

For example, given:

import pandas as pd df = pd.DataFrame({     'group': ['a', 'a', 'a', 'a', 'a', 'a', 'a',                'b', 'b', 'b', 'b', 'b', 'b', 'b'],     'day': ['Mon', 'Tues', 'Fri', 'Thurs', 'Sat', 'Sun', 'Weds',             'Fri', 'Sun', 'Thurs', 'Sat', 'Weds', 'Mon', 'Tues'],     'amount': [1, 2, 4, 2, 1, 1, 2, 4, 5, 3, 4, 2, 1, 3]}) dfx = df.groupby(['group']) dfx.plot(kind='bar', x='day') 

I can generate the following pair of plots:

The order of the bars follows the row order.

What's the best way of reordering the data so that the bar charts have bars ordered Mon-Sun?

UPDATE: this rubbish solution works - but it's far from elegant in the way it uses an extra sorting column:

df2 = pd.DataFrame({     'day': ['Mon', 'Tues', 'Weds', 'Thurs', 'Fri', 'Sat', 'Sun'],     'num': [0, 1, 2, 3, 4, 5, 6]}) df = pd.merge(df, df2, on='day') df = df.sort_values('num') dfx = df.groupby(['group']) dfx.plot(kind='bar', x='day') 

FURTHER GENERALISATION:

Is there a solution that also fixes the order of bars in a 'dodged' bar plot:

df.pivot('day', 'group', 'amount').plot(kind='bar') 

Answer 1

You'll have to provide a mapping to specify how to order the day names. (If they were stored as proper dates, there would be other ways to do this.)

Updated:

Build the key. You could write out a dictionary explicitly or use something clever like this dict comprehension.

weekdays = ['Mon', 'Tues', 'Weds', 'Thurs', 'Fri', 'Sat', 'Sun'] mapping = {day: i for i, day in enumerate(weekdays)} key = df['day'].map(mapping) 

And the sorting is simple:

df.iloc[key.argsort()] 

Answer 2

I know this response is late, but a simplistic solution to the two cases presented, without use of a dictionary/mappings would be something like I've posted below.

Setting 'day' as an index enables you to use .loc to select data in a specific order

1) For the two separate plots

df=pd.DataFrame({'group':['a','a','a','a','a','a','a','b','b','b','b','b','b','b'],      'day':['Mon','Tues','Fri','Thurs','Sat','Sun','Weds','Fri','Sun','Thurs','Sat','Weds','Mon','Tues'],      'amount':[1,2,4,2,1,1,2,4,5,3,4,2,1,3]})  order = ['Mon', 'Tues', 'Weds','Thurs','Fri','Sat','Sun']` df.set_index('day').loc[order].groupby('group').plot(kind='bar') 

2) For the pivot example with the dodged plot:

order = ['Mon', 'Tues', 'Weds','Thurs','Fri','Sat','Sun'] df.pivot('day','group','amount').loc[order].plot(kind='bar') 

note that pivot results in day being in the index already so you can use .loc here again.

Edit: it is best practice to use .loc instead of .ix in these solutions, .ix will be deprecated and can have weird results when column names and indexes are numbers.