Understanding MultiIndex and DateTime Index Columns in Pandas DataFrames

====================================================================================

In this article, we will delve into the world of Pandas data frames with MultiIndex columns. Specifically, we’ll explore how to set value in rows meeting a condition when one index column is a DateTime.

Introduction to MultiIndex DataFrames

A Pandas DataFrame can have multiple index levels, which allows for more complex and flexible data structures than traditional single-indexed data frames. In this article, we will focus on MultiIndex data frames with two or more levels.

Setting Value in Rows Meeting Condition

The question presented is a common one when working with Pandas data frames that contain DateTime index columns and multiple index levels. The scenario involves setting the value of a specific column to a certain value for rows meeting a condition specified by another column.

Reviewing the Original Post’s Problem

In the original post, the author presents a DataFrame df with two index columns: ‘symbol’ and ’timestamp’. The ’timestamp’ column is of type DateTime. The DataFrame also contains other columns such as ’trade_count’ and ‘foo’.

The problem presented involves setting the value of the ‘foo’ column to 123.4 for rows where the ’trade_count’ column exceeds a certain threshold (1E5, or 10,000).

Reviewing Possible Solutions

The original post presents several attempts at solving this problem:

• Using boolean indexing with the & operator.
• Trying to assign values using the .loc attribute of the DataFrame.
• Assigning values directly to a new column ('asdf') in an attempt to modify the existing ‘foo’ column.

However, each attempt resulted in the original value of the ‘foo’ column remaining unchanged. We will now examine each solution and determine why they failed.

Understanding Boolean Indexing

The key concept behind the first proposed solution is boolean indexing using the & operator. This operation allows us to combine multiple conditions using logical operators (e.g., &, |, ~) to filter the DataFrame accordingly.

Here’s how we can implement this approach:

m = (df.index.get_level_values(0) == 'ABC') & (df['trade_count'] > 1E5)
df.loc[m, 'foo'] = 123.4

In this code snippet:

• df.index.get_level_values(0) retrieves the values of the first index level (‘symbol’).
• The resulting Series is compared to 'ABC' using the equality operator (==). This condition filters out rows where the ‘symbol’ is not equal to 'ABC'.
• The second condition (df['trade_count'] > 1E5) checks if the value in the ’trade_count’ column exceeds 10,000.
• The two conditions are combined using the & operator (&). This produces a new boolean Series where each element is True only when both conditions are met.
• Finally, we use .loc[m] to select rows from the DataFrame where the condition m is true. We then assign the value 123.4 to the ‘foo’ column in these selected rows.

This solution correctly updates the ‘foo’ values for the specified rows while leaving other values unchanged.

Data Creation

To demonstrate this concept, we need a sample DataFrame with MultiIndex columns. Here’s how you can create it:

import pandas as pd

data = {'trade_count': {('ABC',
   pd.Timestamp('2023-01-19 00:00:00-0500', tz='UTC-05:00')): 113381.0,
  ('ABC', pd.Timestamp('2023-01-20 00:00:00-0500', tz='UTC-05:00')): 79924.0,
  ('XYZ', pd.Timestamp('2024-08-08 00:00:00-0400', tz='UTC-04:00')): 92877.0,
  ('XYZ', pd.Timestamp('2024-08-09 00:00:00-0400', tz='UTC-04:00')): 156009.0},
 'foo': {('ABC', pd.Timestamp('2023-01-19 00:00:00-0500', tz='UTC-05:00')): 0.0,
  ('ABC', pd.Timestamp('2023-01-20 00:00:00-0500', tz='UTC-05:00')): 0.0,
  ('XYZ', pd.Timestamp('2024-08-08 00:00:00-0400', tz='UTC-04:00')): 0.0,
  ('XYZ', pd.Timestamp('2024-08-09 00:00:00-0400', tz='UTC-04:00')): 0.0}}

df = pd.DataFrame(data)
df.index.names = ['symbol', 'timestamp']

In this code snippet, we create a dictionary with the required data, including values for both index levels (‘symbol’ and ’timestamp’). The pd.Timestamp function is used to specify the DateTime values.

After defining the DataFrame, we set the index names using df.index.names = ['symbol', 'timestamp'].

This DataFrame will serve as our sample dataset for demonstrating the concept of boolean indexing with MultiIndex data frames.

Last modified on 2024-03-13