Let's read them and concatenate them to make a single file:
import pandas as pd
import os os.chdir(' ')
ts1=pd.read_csv('datatraining.txt')
ts2=pd.read_csv('datatest.txt')
ts3=pd.read_csv('datatest2.txt')
ts=pd.concat([ts1,ts2,ts3]
Before using the date column as an index, we will convert it to a datetime format and drop the actual date column:
ts['datetime'] = pd.to_datetime(ts['date'])
ts = ts.set_index('datetime')
ts.drop(['date'], axis=1, inplace=True)
Once the new datetime column is set to an index, it can be used for subsetting. For example, for filtering all the records for a particular day, we can just enclose the data inside the subsetting (square, []) brackets:
ts['2015-02-05']
The output is similar to the following screenshot:
Filtering all records for a particular day
To filter all the records for a particular hour across all days, the following snippet will do the job:
ts[ts.index.hour==4]
The following is the output:
Filtering all records for a particular hour
We can also filter out all the records between two timestamps by using the following snippet:
ts['2015-02-05':'2015-02-06']
The following is the output:
Filtering all records between two timestamps