Groupby Nth Row as an Item

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In this post, we will explore how to get the groupby nth row directly in the row as an item. We’ll discuss the concepts behind groupby operations and provide a step-by-step solution using Python.

Introduction

Groupby operations are a powerful tool for data analysis. When working with grouped data, you often need to perform calculations or extract specific values from each group. In this post, we will focus on how to get the nth row of a group by directly inserting it into another column in the original dataframe.

Background

Before diving into the solution, let’s understand some key concepts:

Groupby: A groupby operation groups a dataset by one or more columns. The resulting object has one index and one column of labels for each group.
Nth row: The nth row refers to the row with index n in the grouped data.

Problem Statement

Given a dataframe df with columns ‘Date’, ‘Time’, ‘Open’, ‘High’, ‘Low’, and ‘Close’ (all on minute basis), we want to create a new column that contains the price of the second row of each day. This means for each row in the original dataframe, we need to insert the price from the previous day’s second row.

Step 1: Convert Date to Datetime64 and Set it as Index

To perform the groupby operation efficiently, we first convert the ‘Date’ column to datetime64 format and set it as the index of the dataframe. This step is crucial for performing date-based grouping.

# Convert Date to datetime64 and set it as index
df = df.assign(Date=pd.to_datetime(df['Date'], dayfirst=True)).set_index('Date')

Step 2: Find Second Value for Each Day

Next, we find the second value (price) for each day using the groupby operation.

# Find second value for each day
prices = df.groupby(level=0)['Open'].nth(1).squeeze()

In this step, groupby(level=0) groups the dataframe by the index (which is the ‘Date’ column), and ['Open'] specifies that we are interested in the ‘Open’ column. nth(1) selects the second value for each group, and .squeeze() removes any unnecessary whitespace from the resulting series.

Step 3: Find Last Row for Each Day

To get the last row of each day, we use the duplicated method to find duplicates in the index (which are the previous days) and then select the last occurrence using the keep='last' argument.

# Find last row for each day
mask = ~df.index.duplicated(keep='last')

This step is necessary because we want to insert values from the second row of the previous day’s group into our new dataframe.

Step 4: Create New Columns

Now that we have the prices and the last rows, we can create two new columns in our original dataframe (df).

# Create new columns
df.loc[mask, 'price at yesterday'] = prices.shift(1)
df.loc[mask, 'price 2d ago'] = prices.shift(2)

In this step, .loc[mask] selects the rows where mask is True (i.e., for each day). Then, we assign values from the prices series to these new columns using the shift method. The first shift moves us up one row in the prices series, and the second shift moves us two rows up.

Example Use Case

To demonstrate this approach, let’s create a sample dataframe df with the required structure:

# Setup a MRE:
df = pd.DataFrame({'Date': ['09-01-2015', '09-01-2015', '09-01-2015',
                            '10-01-2015', '10-01-2015', '10-01-2015',
                            '11-01-2015', '11-01-2015', '11-01-2015'],
                   'Open': [1, 2, 3, 4, 5, 6, 7, 8, 9]})

When you run the code snippet provided in the question, it will produce the same output as shown below:

# Output:
            Open  price at yesterday  price 2d ago
Date                                              
2015-01-09     1                 NaN           NaN
2015-01-09     2                 NaN           NaN
2015-01-09     3                 NaN           NaN
2015-01-10     4                 NaN           NaN
2015-01-10     5                 NaN           NaN
2015-01-10     6                 2.0           NaN
2015-01-11     7                 NaN           NaN
2015-01-11     8                 NaN           NaN
2015-01-11     9                 NaN           NaN

Note that the new columns price at yesterday and price 2d ago now contain the expected values.

By following these steps, you can effectively get the nth row of a group by directly inserting it into another column in the original dataframe. This technique is particularly useful when working with grouped data or performing calculations that require access to previous rows within each group.

Last modified on 2023-12-14