Labeling Weeks in a Pandas DataFrame: A Guide to Daily and Weekly Change Labeling Methods

Labeling Weeks in a Pandas DataFrame

Introduction

In this article, we’ll explore how to label each week in a pandas DataFrame according to whether the opening price for that week was greater or lesser than the closing price. We’ll cover both daily and weekly change labeling methods.

Daily Change Labeling

To label each day ‘green’ or ‘red’ according to its daily return, we can use the following code:

import numpy as np

# assuming df is your DataFrame with Open and Close columns
df['labels'] = np.where(df['Open'] < df['Close'], 'green', 'red')

This code uses np.where to create a new column called labels in the DataFrame. It compares each row’s Open value to its Close value using <, and assigns 'green' if the opening price is lower, and 'red' otherwise.

Weekly Change Labeling

To label each week ‘green’ or ‘red’ according to its weekly return, we need to group by the Week_Number column, calculate the first and last values for both Open and Close, and then compare these values. We can do this using the following code:

# assuming df is your DataFrame with Week_Number, Open, and Close columns

temp = df.groupby('Week_Number')[['Open', 'Close']].agg({'Open': lambda x: x.iloc[0], 'Close': lambda x: x.iloc[-1]})
temp['label'] = np.where(temp['Open'] < temp['Close'], 'green', 'red')
temp = temp.merge(df, on='Week_Number')

# assign the labels to the original DataFrame
df['labels'] = temp['label']

This code first groups the DataFrame by Week_Number and calculates the first (Open_x) and last (Close_x, Close_y) values for both Open and Close. It then creates a new column called label using the same logic as before. Finally, it merges this temporary DataFrame back with the original one, assigning the label column to the original DataFrame.

Example Use Cases

Here’s an example of how you might use these labeling methods:

# assuming df is your DataFrame with Open and Close columns

print(df.head())  # show the first few rows of the DataFrame
print(df['labels'].head())  # show the labels for the first few rows

This code prints the first few rows of the original DataFrame, followed by the corresponding labels.

Tips and Variations

To label each day based on its daily return, you can use a similar approach to the one described above.
To label each week based on its weekly return, you can use a similar approach to the one described above. You might also want to consider using more sophisticated labeling methods, such as those that take into account the cumulative returns over multiple weeks.

Conclusion

In this article, we’ve explored how to label each week in a pandas DataFrame according to whether the opening price for that week was greater or lesser than the closing price. We’ve covered both daily and weekly change labeling methods, and provided examples of how to use these methods in practice.

Last modified on 2024-12-04