Find Average of Every Three Columns in Pandas dataframe

Question

I am new to Python and Pandas. I have a panda dataframe with monthly columns ranging from 2000 (2000-01) to 2016 (2016-06).

I want to find the average of every three months and assign it to a new quarterly column (2000q1). I know I can do the following:

df['2000q1'] = df[['2000-01', '2000-02', '2000-03']].mean(axis=1)
df['2000q2'] = df[['2000-04', '2000-05', '2000-06']].mean(axis=1)
    .
    .
    .
df['2016-02'] = df[['2016-04', '2016-05', '2016-06']].mean(axis=1)

But, this is very tedious. I appreciate it if someone helps me find a better way.

Answer 1

You can use groupby on columns:

df.groupby(np.arange(len(df.columns))//3, axis=1).mean()

Or, those can be converted to datetime. You can use resample:

df.columns = pd.to_datetime(df.columns)
df.resample('Q', axis=1).mean()

---

Here's a demo:

cols = pd.date_range('2000-01', '2000-06', freq='MS')
cols = cols.strftime('%Y-%m')
cols
Out: 
array(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06'], 
      dtype='<U7')

df = pd.DataFrame(np.random.randn(10, 6), columns=cols)

df
Out: 
    2000-01   2000-02   2000-03   2000-04   2000-05   2000-06
0 -1.263798  0.251526  0.851196  0.159452  1.412013  1.079086
1 -0.909071  0.685913  1.394790 -0.883605  0.034114 -1.073113
2  0.516109  0.452751 -0.397291 -0.050478 -0.364368 -0.002477
3  1.459609 -1.696641  0.457822  1.057702 -0.066313 -0.910785
4 -0.482623  1.388621  0.971078 -0.038535  0.033167  0.025781
5 -0.016654  1.404805  0.100335 -0.082941 -0.418608  0.588749
6  0.684735 -2.007105  0.552615  1.969356 -0.614634  0.021459
7  0.382475  0.965739 -1.826609 -0.086537 -0.073538 -0.534753
8  1.548773 -0.157250  0.494819 -1.631516  0.627794 -0.398741
9  0.199049  0.145919  0.711701  0.305382 -0.118315 -2.397075

First alternative:

df.groupby(np.arange(len(df.columns))//3, axis=1).mean()
Out: 
          0         1
0 -0.053692  0.883517
1  0.390544 -0.640868
2  0.190523 -0.139108
3  0.073597  0.026868
4  0.625692  0.006805
5  0.496162  0.029067
6 -0.256585  0.458727
7 -0.159465 -0.231609
8  0.628781 -0.467487
9  0.352223 -0.736669

Second alternative:

df.columns = pd.to_datetime(df.columns)
df.resample('Q', axis=1).mean()

Out: 
   2000-03-31  2000-06-30
0   -0.053692    0.883517
1    0.390544   -0.640868
2    0.190523   -0.139108
3    0.073597    0.026868
4    0.625692    0.006805
5    0.496162    0.029067
6   -0.256585    0.458727
7   -0.159465   -0.231609
8    0.628781   -0.467487
9    0.352223   -0.736669

You can assign this to a DataFrame:

res = df.resample('Q', axis=1).mean()

Change column names as you like:

res = res.rename(columns=lambda col: '{}q{}'.format(col.year, col.quarter))

res
Out: 
     2000q1    2000q2
0 -0.053692  0.883517
1  0.390544 -0.640868
2  0.190523 -0.139108
3  0.073597  0.026868
4  0.625692  0.006805
5  0.496162  0.029067
6 -0.256585  0.458727
7 -0.159465 -0.231609
8  0.628781 -0.467487
9  0.352223 -0.736669

And attach this to your current DataFrame by:

pd.concat([df, res], axis=1)