Merging 2 or more data frames and transposing the result - pandas

Question

I have several DFs derived from a Panda binning process using the below code;
df2 = df.resample(rule=timedelta(milliseconds=250))[('diffA')].mean().dropna()
df3 = df.resample(rule=timedelta(milliseconds=250))[('diffB')].mean().dropna()
.. etc
Every DF will have column containing 'time' in Datetime format( example:2019-11-22 13:18:00.000 ) and second column containing a number (i.e. 0.06 ). Different DFs will have different 'time' bins. I am trying to concatenate all DFs into one , where certain elements of the resulting DF may contain 'NaN'.
The Datetime format of the DFs give an error when using;
method 1) df4=pd.merge(df2,df3,left_on='time',right_on='time')
method 2) pd.pivot_table(df2, values = 'diffA', index=['time'], columns = 'time').reset_index()
When DFs have been combined , I also want to transpose the resulting DF, where:
Rows: are 'DiffA','DiffB'..etc
Columns: are time bins accordingly.
Have tried the transpose() method with individual DFs, just to try, but getting an error as my time /index is in 'Datetime' format..
Once that is in place, I am looking for a method to extract rows from the resulting transposed DF as individual data series.
Please advise how I can achieve the above with some guidance, appreciate any feedback ! thank you so much for your help.
Data frames ( 2 - for example )
time DiffA
2019-11-25 08:18:01.250 0.06
2019-11-25 08:18:01.500 0.05
2019-11-25 08:18:01.750 0.04
2019-11-25 08:18:02.000 0
2019-11-25 08:18:02.250 0.22
2019-11-25 08:18:02.500 0.06
time DiffB
2019-11-26 08:18:01.250 0.2
2019-11-27 08:18:01.500 0.05
2019-11-25 08:18:01.000 0.6
2019-11-25 08:18:02.000 0.01
2019-11-25 08:18:02.250 0.8
2019-11-25 08:18:02.500 0.5
resulting merged DF should be as follows ( text only);
time ( first row )
2019-11-25 08:18:01.000,
2019-11-25 08:18:01.250,
2019-11-25 08:18:01.500,
2019-11-25 08:18:01.750,
2019-11-25 08:18:02.000,
2019-11-25 08:18:02.250,
2019-11-25 08:18:02.500,
2019-11-26 08:18:01.250,
2019-11-27 08:18:01.500
(second row)
diffA nan 0.06 0.05 0.04 0 0.22 0.06 nan nan
(third row)
diffB 0.6 nan nan nan 0.01 0.8 0.5 0.2 0.05

Answer 1

Solution
The core logic: You need to use outer-join on the column 'time' to merge each of the sampled-dataframes together to achieve your objective. Finally resetting the index to the column time completes the solution.
I will use the dummy data I created below to create a reproducible solution.
Note: I have used df as the final dataframe and df0 as the original dataframe. My df0 is your df.
df = pd.DataFrame()
for i, column_name in zip(range(5), column_names):
if i==0:
df = df0.sample(n=10, random_state=i).rename(columns={'data': f'df{column_name}'})
else:
df_other = df0.sample(n=10, random_state=i).rename(columns={'data': f'df{column_name}'})
df = pd.merge(df, df_other, on='time', how='outer')
print(df.set_index('time').T)
Output:
Dummy Data
import pandas as pd
# dummy data:
df0 = pd.DataFrame()
df0['time'] = pd.date_range(start='2020-02-01', periods=15, freq='D')
df0['data'] = np.random.randint(0, high=9, size=15)
print(df0)
Output:
time data
0 2020-02-01 6
1 2020-02-02 1
2 2020-02-03 7
3 2020-02-04 0
4 2020-02-05 8
5 2020-02-06 8
6 2020-02-07 1
7 2020-02-08 6
8 2020-02-09 2
9 2020-02-10 6
10 2020-02-11 8
11 2020-02-12 3
12 2020-02-13 0
13 2020-02-14 1
14 2020-02-15 0