Conditional mean of a dataframe based on datetime column names

Question

I'm new to python. I'm looking for a way to generate mean for row values based on column names(Column names are date series formats from January to December). I want to generate mean for every 10 days for over a period of an year. My dataframe is in the below format(2000 rows)

import pandas as pd
df= pd.DataFrame({'A':[81,80.09,83,85,88],
                  'B':[21.8,22.04,21.8,21.7,22.06],
                  '20210113':[0,0.05,0,0,0.433],
                  '20210122':[0,0.13,0,0,0.128],
                  '20210125':[0.056,0,0.043,0.062,0.16],
                  '20210213':[0.9,0.56,0.32,0.8,0],
                  '20210217':[0.7,0.99,0.008,0.23,0.56],
                  '20210219':[0.9,0.43,0.76,0.98,0.5]})

Expected Output:

In [2]: df
Out[2]: 
   A        B     c(Mean 20210111,..20210119 ) D(Mean of 20210120..20210129)..
0  81       21.8
1  80.09    22.04
2  83       21.8
3  85       21.7           
4  88       22.06

Answer 1

One way would be to isolate the date columns from the rest of the DF. Transpose it to be able to use normal grouping operations. Then transpose back and merge to the unaffected portion of the DataFrame.

import pandas as pd

df = pd.DataFrame({'A': [81, 80.09, 83, 85, 88],
                   'B': [21.8, 22.04, 21.8, 21.7, 22.06],
                   '20210113A.2': [0, 0.05, 0, 0, 0.433],
                   '20210122B.1': [0, 0.13, 0, 0, 0.128],
                   '20210125C.3': [0.056, 0, 0.043, 0.062, 0.16],
                   '20210213': [0.9, 0.56, 0.32, 0.8, 0],
                   '20210217': [0.7, 0.99, 0.008, 0.23, 0.56],
                   '20210219': [0.9, 0.43, 0.76, 0.98, 0.5]})

# Unaffected Columns Go Here
keep_columns = ['A', 'B']

# Get All Affected Columns
new_df = df.loc[:, ~df.columns.isin(keep_columns)]

# Strip Extra Information From Column Names
new_df.columns = new_df.columns.map(lambda c: c[0:8])

# Transpose
new_df = new_df.T

# Convert index to DateTime for easy use
new_df.index = pd.to_datetime(new_df.index, format='%Y%m%d')

# Resample every 10 Days on new DT index (Drop any rows with no values)
new_df = new_df.resample("10D").mean().dropna(how='all')

# Transpose and Merge Back on DF
df = df[keep_columns].merge(new_df.T, left_index=True, right_index=True)

# For Display
print(df.to_string())

Output:

       A      B  2021-01-13 00:00:00  2021-01-23 00:00:00  2021-02-12 00:00:00
0  81.00  21.80               0.0000                0.056             0.833333
1  80.09  22.04               0.0900                0.000             0.660000
2  83.00  21.80               0.0000                0.043             0.362667
3  85.00  21.70               0.0000                0.062             0.670000
4  88.00  22.06               0.2805                0.160             0.353333

---

new_df = df.loc[:, ~df.columns.isin(keep_columns)]

new_df

              0     1      2      3      4
20210113  0.000  0.05  0.000  0.000  0.433
20210122  0.000  0.13  0.000  0.000  0.128
20210125  0.056  0.00  0.043  0.062  0.160
20210213  0.900  0.56  0.320  0.800  0.000
20210217  0.700  0.99  0.008  0.230  0.560
20210219  0.900  0.43  0.760  0.980  0.500

---

new_df.index = pd.to_datetime(new_df.index, format='%Y%m%d')

new_df

                0     1      2      3      4
2021-01-13  0.000  0.05  0.000  0.000  0.433
2021-01-22  0.000  0.13  0.000  0.000  0.128
2021-01-25  0.056  0.00  0.043  0.062  0.160
2021-02-13  0.900  0.56  0.320  0.800  0.000
2021-02-17  0.700  0.99  0.008  0.230  0.560
2021-02-19  0.900  0.43  0.760  0.980  0.500

---

new_df = new_df.resample("10D").mean().dropna(how='all')

new_df

                   0     1         2      3         4
2021-01-13  0.000000  0.09  0.000000  0.000  0.280500
2021-01-23  0.056000  0.00  0.043000  0.062  0.160000
2021-02-12  0.833333  0.66  0.362667  0.670  0.353333

---

new_df.T

   2021-01-13  2021-01-23  2021-02-12
0      0.0000       0.056    0.833333
1      0.0900       0.000    0.660000
2      0.0000       0.043    0.362667
3      0.0000       0.062    0.670000
4      0.2805       0.160    0.353333