Working with Hierarchical Indexing in Pandas for Adding Values to a Subcolumn

Understanding the Problem and its Context

In this blog post, we will explore how to add values to a subcolumn in a pandas DataFrame. The question arises when we want to add new columns based on certain conditions, but instead of adding them directly to the existing DataFrame, we need to create a new column that is calculated from other columns within the same group.

The provided Stack Overflow post presents an example where the user wants to calculate the simple moving average (SMA) for each stock in the stock_list. However, the code given results in an error message indicating that a value is trying to be set on a copy of a slice from the DataFrame. This happens because when we try to add new values directly to a subcolumn without specifying the row index, pandas creates a copy of the slice instead of modifying it.

Introduction to Hierarchical Indexing

Hierarchical indexing (MultiIndex) in pandas is a powerful feature that allows us to create DataFrames with multiple levels of indexing. This can be especially useful when working with grouped data or datasets where we need to perform operations on different subsets of rows and columns simultaneously.

Creating Hierarchical Indexes

To start, we will create an array of tuples representing the hierarchical index for each group in our DataFrame. In this case, we want to add a new column called sma50 that represents the SMA of the close values for each stock.

index = [('TSLA', 'open'), ('TSLA', 'high'),
         ('TSLA', 'low'), ('TSLA', 'close'),('XOM', 'open'), ('XOM', 'high'),
         ('XOM', 'low'), ('XOM', 'close')]

Next, we will create an array of values that correspond to each group in the hierarchical index.

values = [283.50, 285.16, 277.25, 280.690, 75.8561, 77.2335, 75.1625,
          77.1171, 160.8679, 163.6377, 160.6602, 161.8868, 28382084,
         278.75, 285.79, 276.50, 285.480, 77.2141, 78.4751, 77.1462,
          78.4363, 14554442, 162.3518, 163.9444, 161.6098, 162.4507,
         27963014, 285.37, 294.47, 283.83, 294.075, 74.6435, 76.2926,
          74.1876, 75.4584, 16346833, 162.2330, 162.5595, 158.8994,
         160.5711, 35655839, 293.61, 298.73, 292.50, 293.900, 4228172,
          75.5748, 76.2053, 75.4099, 75.4196, 15028835, 160.3834, 165.4579,
         160.0963, 163.4795, 42427424]

Now, we can use the pd.MultiIndex.from_tuples() function to create a hierarchical index from our array of tuples.

index = pd.MultiIndex.from_tuples(index)

Creating the DataFrame with Hierarchical Index

Next, we need to create the DataFrame with the desired structure. We will pass in the values and the hierarchical index as arguments to the pd.DataFrame() function.

data = pd.DataFrame(data=values,index=index).reset_index()

Here, .reset_index() is used to reset the index of the resulting DataFrame so that it becomes a regular column with integer indices.

Adding New Columns

Finally, we can add new columns to our DataFrame using standard pandas operations. In this case, we want to calculate the SMA values for each stock.

data['sma50'] = data.groupby(level=0)['close'].transform(lambda x: x.rolling(window=50).mean())

In this code snippet, .groupby(level=0) groups the DataFrame by the first level of the hierarchical index (i.e., the stocks), and .transform() applies a function to each group.

The lambda function calculates the rolling mean of the close values for each stock with a window size of 50. The result is assigned back to the sma50 column using assignment.

Example Use Case

Here’s an example use case that demonstrates how to add new columns to a DataFrame based on certain conditions:

import pandas as pd

# Create a sample DataFrame
data = {
    'stock': ['TSLA', 'GOOG', 'MSFT'],
    'open': [100, 500, 200],
    'high': [150, 550, 220],
    'low': [90, 450, 180],
    'close': [120, 520, 210]
}
df = pd.DataFrame(data)

# Create a new column for SMA
index = [('TSLA', 'open'), ('TSLA', 'high'),
         ('TSLA', 'low'), ('TSLA', 'close'),('GOOG', 'open'), ('GOOG', 'high'),
         ('GOOG', 'low'), ('GOOG', 'close'),('MSFT', 'open'), ('MSFT', 'high'),
         ('MSFT', 'low'), ('MSFT', 'close')]
values = [100, 150, 90, 120, 500, 550, 450, 520, 200, 220, 180,
          210]
index = pd.MultiIndex.from_tuples(index)
data = pd.DataFrame(data=values,index=index)

# Assign the DataFrame to a variable
df = pd.Dataframe(data).reset_index()

# Create new columns for SMA
df['sma50'] = df.groupby(level=0)['close'].transform(lambda x: x.rolling(window=50).mean())

This example demonstrates how to create a new column called sma50 that represents the SMA of the close values for each stock using hierarchical indexing.

By following this approach, you can easily add new columns to your DataFrames and perform operations on different subsets of rows and columns simultaneously.

Last modified on 2024-01-24