Sort pandas dataframe rows according to a string value column

Question

I have the following dataframe :

        month       price
0       April  102.478015
1      August   94.868053
2    December   97.278205
3    February  100.114510
4     January   99.419109
5        July   93.402928
6        June   96.114224
7       March  101.297762
8         May  102.905340
9    November   97.952169
10    October   95.606478
11  September   94.226803

I would like to have the months in a coherent order (January in the first row until December in the 12th row). How please could I do ?

If necessary, you can copy this dataframe and then execute

pd.read_clipboard(sep='\s\s+')

to have the dataframe on your jupyter notebook

jezrael · Accepted Answer · 2020-02-03 10:27:41Z

3

Convert values to ordered categoricals, so possible use DataFrame.sort_values:

cats = ['January','February','March','April','May','June',
        'July','August','September','October','November','December']
df['month'] = pd.CategoricalIndex(df['month'], ordered=True, categories=cats)
#alternative
#df['month'] = pd.Categorical(df['month'], ordered=True, categories=cats)
df = df.sort_values('month')
print (df)
        month       price
4     January   99.419109
3    February  100.114510
7       March  101.297762
0       April  102.478015
8         May  102.905340
6        June   96.114224
5        July   93.402928
1      August   94.868053
11  September   94.226803
10    October   95.606478
9    November   97.952169
2    December   97.278205

answered Feb 3, 2020 at 10:27

jezrael

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3 Comments

Corentin Limier Over a year ago

and you can use calendar.month_name to generate cats variable

yatu Over a year ago

Why pd.CategoricalIndex instead of pd.Categorical @jez?

jezrael Over a year ago

@yatu - Because in some oldier pandas version Categorical failed, only working CategoricalIndex

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