Achieving Parallel Indexing in Pandas Panels for Efficient Data Analysis

Parallel Indexing in Pandas Panels

In this article, we will explore how to achieve parallel indexing in pandas panels. A panel is a data structure that can store data with multiple columns (or items) and multiple rows (or levels). This allows us to easily perform operations on data with different characteristics.

Parallel indexing refers to the ability to use multiple indices to access specific data points in a panel. In this case, we want to use two time series as indices, where each time series represents the start and end timestamps of a recording.

Understanding the Problem

Let’s consider an example of how we can structure our data using pandas panels. We have three recordings at two distances from an antenna: 1m and 3m. The data for each recording consists of temperature and density measurements. We also have two time series representing the start and end timestamps of each recording.

import pandas as pd
from datetime import datetime
np.random.seed(0)
data = {'temp': np.random.randint(15,25,9),
        'dens': np.random.randint(900,1100,9)}

We can create a panel using the pd.Panel function:

panel_data = pd.Panel(data={'1m': temp, '3m': dens},
                       index=pd.MultiIndex.from_product([[1, 2], [1, 2]], names=['item', 'level']),
                       columns=pd.Index(range(9), name='item'))

However, we cannot directly use the two time series as indices. Instead, we need to create separate dataframes for each recording and then merge them together.

Solution

One way to achieve parallel indexing is by creating separate dataframes for each recording and merging them together using the merge function:

start_rec = pd.TimeSeries([datetime(2013, 11, 11, 15), datetime(2013, 11, 12, 15),
                           datetime(2013, 11, 13, 15)], name='start')
end_rec = pd.TimeSeries([datetime(2013, 11, 11, 16), datetime(2013, 11, 12, 16),
                        datetime(2013, 11, 13, 16)], name='end')

data1m = pd.DataFrame({'temp': np.random.randint(15,25,9), 'dens': np.random.randint(900,1100,9)}, columns=['temp', 'dens'])
data1m['start'] = start_rec
data1m['end'] = end_rec

data3m = pd.DataFrame({'temp': np.random.randint(15,25,9), 'dens': np.random.randint(900,1100,9)}, columns=['temp', 'dens'])
data3m['start'] = start_rec
data3m['end'] = end_rec

data1m.set_index(['start', 'end'], inplace=True)
data3m.set_index(['start', 'end'], inplace=True)

panel_data = pd.Panel({'1m': data1m, '3m': data3m})

Filtering the Data

Once we have created our panel, we can use the loc function to filter the data based on specific conditions:

panel_data.loc['3m'].select(lambda row: row['start'] < pd.Timestamp('2013-11-12') or 
                             row['end'] < pd.Timestamp('2013-11-13'))

This will return a new panel that only includes the data for recordings where the start timestamp is before November 12th, 2013, and/or the end timestamp is before November 13th, 2013.

Conclusion

In this article, we explored how to achieve parallel indexing in pandas panels. We created separate dataframes for each recording and merged them together using the merge function. Finally, we filtered the data using the loc function based on specific conditions. This approach allows us to easily perform operations on data with different characteristics.

Full Code

import pandas as pd
from datetime import datetime, timedelta

np.random.seed(0)

# Create time series for start and end recordings
start_rec = pd.TimeSeries([datetime(2013, 11, 11, 15), datetime(2013, 11, 12, 15),
                           datetime(2013, 11, 13, 15)], name='start')
end_rec = pd.TimeSeries([datetime(2013, 11, 11, 16), datetime(2013, 11, 12, 16),
                        datetime(2013, 11, 13, 16)], name='end')

# Create dataframes for 1m and 3m recordings
data1m = pd.DataFrame({'temp': np.random.randint(15,25,9), 'dens': np.random.randint(900,1100,9)}, columns=['temp', 'dens'])
data1m['start'] = start_rec
data1m['end'] = end_rec

data3m = pd.DataFrame({'temp': np.random.randint(15,25,9), 'dens': np.random.randint(900,1100,9)}, columns=['temp', 'dens'])
data3m['start'] = start_rec
data3m['end'] = end_rec

# Set indices for dataframes
data1m.set_index(['start', 'end'], inplace=True)
data3m.set_index(['start', 'end'], inplace=True)

# Create panel with 1m and 3m recordings
panel_data = pd.Panel({'1m': data1m, '3m': data3m})

# Filter data based on conditions
filtered_panel = panel_data.loc['3m'].select(lambda row: row['start'] < pd.Timestamp('2013-11-12') or 
                                                 row['end'] < pd.Timestamp('2013-11-13'))

print(filtered_panel)

Last modified on 2023-09-17