Merge 3 dataframes with different timesteps (10min and 15min and 30min) using pandas

Question

The goal is to merge three different dataframes having different timesteps (10min, 15min and 30min. The code must recognize what timestep to consider firstly and identify the next available next timestep. in This example 2019/04/02 10:40:00 does not exist in the dataframes dataset. Therefore the next timestep to consider after 2019/04/02 10:30:00 would be 2019/04/02 10:45:00.

df1:

Timestamp	data1
2019/04/02 10:00:00	1
2019/04/02 10:10:00	1
2019/04/02 10:20:00	1
2019/04/02 10:30:00	1

df2:

Timestamp	data2
2019/04/02 10:00:00	2
2019/04/02 10:15:00	22
2019/04/02 10:30:00	222
2019/04/02 10:45:00	2222
2019/04/02 11:00:00	22222

df3:

Timestamp	data3
2019/04/02 10:00:00	3
2019/04/02 10:30:00	33
2019/04/02 11:00:00	333
2019/04/02 11:30:00	3333

desired result:

Timestamp	data1	data2	data3
2019/04/02 10:00:00	1	2	3
2019/04/02 10:10:00	1	NaN	NaN
2019/04/02 10:15:00	NaN	22	NaN
2019/04/02 10:20:00	1	NaN	NaN
2019/04/02 10:30:00	1	222	33
2019/04/02 10:45:00	NaN	2222	NaN
2019/04/02 11:00:00	NaN	22222	333
2019/04/02 11:30:00	NaN	NaN	3333

I used the python concat function and the merge function but did not deliver he desired result.

Answer 1

# Convert Timestamp columns to datetime
df1['Timestamp'] = pd.to_datetime(df1['Timestamp'])
df2['Timestamp'] = pd.to_datetime(df2['Timestamp'])
df3['Timestamp'] = pd.to_datetime(df3['Timestamp'])

# Sort the DataFrames based on Timestamp
df1 = df1.sort_values('Timestamp')
df2 = df2.sort_values('Timestamp')
df3 = df3.sort_values('Timestamp')

# Merge using merge_asof
result = pd.merge_asof(df1, df2, on='Timestamp', direction='nearest')
result = pd.merge_asof(result, df3, on='Timestamp', direction='nearest')

EDIT: IF YOU WANT TO KEEP NAN VALUES

# Convert Timestamp columns to datetime
df1['Timestamp'] = pd.to_datetime(df1['Timestamp'])
df2['Timestamp'] = pd.to_datetime(df2['Timestamp'])
df3['Timestamp'] = pd.to_datetime(df3['Timestamp'])

# Merge using merge with how='outer'
result = pd.merge(df1, df2, on='Timestamp', how='outer')
result = pd.merge(result, df3, on='Timestamp', how='outer')

# Sort the result based on Timestamp
result = result.sort_values('Timestamp')