Define the plot order of the x-axis, when using sort_values and pandas/pyplot

Question

I am plotting a bar chart of 'Time zone (UTC)' Vs 'Frequency', using value_counts() for column 'UTC_Hrs', in a pandas dataframe of twitter posts.

UTC_Hrs
-5.0
11.0
Nan
0.0

etc

I want the x axis to be ordered from, -11.0 to + 13.0, but I can't work out how to do it.

order = ["-11.0", "-10.0", "-9.0", "-8.0", "-7.0", "-6.0",  "-5.0", "-4.0",
         "-3.5", "-3.0", "0.0", "1.0" , "2.0","3.0", "4.0", "5.0",                
         "5.5","6.0","7.0", "8.0", "9.0", "10.0", "11.0", "13.0"]

I've found out how to order it as it occurs in the df by using, value_counts(sort=False):

df['UTC_Hrs'].value_counts(sort=False).plot(kind='bar')

I also found out how to pretend I had done it using ;-):

set_xticklabels(order)

I've tried other multiple variations inspired by other answers and the pandas docs but nothing has worked. The most complex thing and maybe closest I got was to to use a dictionary and mapping but couldn't work out how to call this on value_counts, thinking this was the wrong approach.

UTC_Hrs = df['UTC_Hrs'] 
mapping = {UTC_Hrs: i for i, UTC_Hrs in enumerate(order)}
key = df['UTC_Hrs'].map(mapping)
dfx = df.iloc[key.argsort()]
dfx.plot(kind='bar')??? value_counts by referencing dict?

Any help and advice much appreciated, Thanks

jezrael · Accepted Answer · 2018-02-25 21:12:40Z

1

You can use reindex with intersection order and s.index, but is necessary same types - both strings or both floats:

s = df['UTC_Hrs'].astype(str).value_counts()
s.reindex(np.intersect1d(order, s.index)).plot.bar()

s = df['UTC_Hrs'].value_counts()
s.reindex(np.intersect1d(np.array(order).astype(float), s.index)).plot.bar()

edited Feb 25, 2018 at 21:12

answered Feb 25, 2018 at 20:16

jezrael

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1 Comment

scatter Over a year ago

thanks for coming to the rescue for the second day in a row, much appreciated

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