Sort a Pandas Dataframe Series by Month Name

Sort a pandas dataframe series by month name

Thanks @Brad Solomon for offering a faster way to capitalize string!

Note 1 @Brad Solomon's answer using pd.categorical should save your resources more than my answer. He showed how to assign order to your categorical data. You should not miss it :P

Alternatively, you can use.

df = pd.DataFrame([["dec", 12], ["jan", 40], ["mar", 11], ["aug", 21],
                  ["aug", 11], ["jan", 11], ["jan", 1]], 
                   columns=["Month", "Price"])
# Preprocessing: capitalize `jan`, `dec` to `Jan` and `Dec`
df["Month"] = df["Month"].str.capitalize()

# Now the dataset should look like
#   Month Price
#   -----------
#    Dec    XX
#    Jan    XX
#    Apr    XX

# make it a datetime so that we can sort it: 
# use %b because the data use the abbreviation of month
df["Month"] = pd.to_datetime(df.Month, format='%b', errors='coerce').dt.month
df = df.sort_values(by="Month")

total = (df.groupby(df['Month'])['Price'].mean())

# total 
Month
1     17.333333
3     11.000000
8     16.000000
12    12.000000

Note 2
groupby by default will sort group keys for you. Be aware to use the same key to sort and groupby in the df = df.sort_values(by=SAME_KEY) and total = (df.groupby(df[SAME_KEY])['Price'].mean()). Otherwise, one may gets unintended behavior. See Groupby preserve order among groups? In which way? for more information.

Note 3
A more computationally efficient way is first compute mean and then do sorting on months. In this way, you only need to sort on 12 items rather than the whole df. It will reduce the computational cost if one don't need df to be sorted.

Note 4 For people already have month as index, and wonder how to make it categorical, take a look at pandas.CategoricalIndex @jezrael has a working example on making categorical index ordered in Pandas series sort by month index

Sort a pandas's dataframe series by month and year?

You need to change your month name to month number, for example Jan 2013 to 01 2013.
Then sort it, and then change it again to month name-year.

df['date value'] = pd.to_datetime(df['date value'], format='%b%Y')

df = df.sort_values('date value', ascending = True)

Sort groupby pandas output by Month name and year

EDIT: Your solution should be changed:

df1 = df.groupby(["Year", "Month Name"], as_index=False)["Days"].agg(['min', 'mean'])
df3 = df.groupby(["Year", "Month Name"], as_index=False)["Data"].agg(['count'])
merged_df=pd.merge(df3, df1, on=['Year','Month Name']).reset_index()

cats = ['Jan', 'Feb', 'Mar', 'Apr','May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
merged_df['Month Name'] = pd.Categorical(merged_df['Month Name'],categories=cats, ordered=True)

merged_df = merged_df.sort_values(["Year", "Month Name"])
print (merged_df)
   Year Month Name  count  min  mean
1  2014        Jan      1    2     2
0  2014        Dec      1    1     1
2  2015        Aug      1    1     1
3  2016        Apr      1    4     4

Or:

df1 = (df.groupby(["Year", "Month Name"])
         .agg(min_days=("Days", 'min'),
              avg_days=("Days", 'mean'),
              count = ('Data', 'count'))
         .reset_index())

cats = ['Jan', 'Feb', 'Mar', 'Apr','May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
df1['Month Name'] = pd.Categorical(df1['Month Name'], categories=cats, ordered=True)

df1 = df1.sort_values(["Year", "Month Name"])
print (df1)
   Year Month Name  min_days  avg_days  count
1  2014        Jan         2         2      1
0  2014        Dec         1         1      1
2  2015        Aug         1         1      1
3  2016        Apr         4         4      1

Last solution with MultiIndex and no categoricals, solution create helper dates column and sorting by it:

df1 = (df.groupby(["Year", "Month Name"])
         .agg(min_days=("Days", 'min'),
              avg_days=("Days", 'mean'),
              count = ('Data', 'count'))
        )

df1['dates'] = pd.to_datetime([f'{y}{m}' for y, m in df1.index], format='%Y%b')
df1 = df1.sort_values('dates')
print (df1)
                 min_days  avg_days  count      dates
Year Month Name                                      
2014 Jan                2         2      1 2014-01-01
     Dec                1         1      1 2014-12-01
2015 Aug                1         1      1 2015-08-01
2016 Apr                4         4      1 2016-04-01

How to sort pandas dataframe by month name

Always try to post your code.
In this way we could figure out why your categorical sorting did not work out. But I suspect you forgot the ordered=True parameter.

Categorical ordering allows sorting according to a custom order, and works perfectly for this case. It also handles well duplicated month values. Here is my code:

df["month"] = pd.Categorical(df["month"],
                             categories=["January", "February", "March", "April", "May", "June", "July",
                                         "August", "September", "October", "November", "December"],
                             ordered=True)

And after that we can call the function sort_values():

df = df.sort_values(["year", "month"], ignore_index=True)

Cheers and keep it up!

Pandas Dataframe month int to month name in order

You can use pandas.Categorical with parameter ordered=True. You can define any order you want using categories argument.

months_order = ["Jan", "Feb", "Mar", "Apr"]
cat = pd.Categorical(["Mar", "Feb", "Apr", "Jan"], 
                     categories=months_order, ordered=True)

Printing cat will give

[Mar, Feb, Jan, Apr]
Categories (4, object): [Jan < Feb < Mar < Apr]

And printing cat.sort_values() will give

[Jan, Feb, Mar, Apr]
Categories (4, object): [Jan < Feb < Mar < Apr]

EDIT: In your case, you can replace groupby argument

order_group_df['Reported on'].dt.month.apply(mapper)

by

pd.Categorical(order_group_df['Reported on'].dt.month.apply(mapper),
               categories=['Jan', ..., 'Dec'],
               ordered=True)

sort months in pandas DataFrame

You'll need to grab a sorted list of the month names and reorder your dataframe based on that. Thankfully python has a built-in list of chronological months names in the calendar library:

import calendar

all_months = calendar.month_name[1:]
df_pivot = df_pivot.reindex(columns=all_months)

This will also create null columns for months that are not present in your data. If you do not want the null columns you can use dropna afterwards.

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