Скачать или смотреть How to Append New Prices to a List in a Pandas DataFrame Based on Conditions

This guide explains how to manage prices in a Pandas DataFrame by appending new prices under specific conditions, helping you keep track of your investments effectively.
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Managing Price Lists in Pandas DataFrames

Managing financial data can be tricky, especially when you need to keep, update, or reset certain values based on a set of conditions. In this guide, we’ll tackle a common problem faced by data analysts: how to append new prices to a list in a Pandas DataFrame, while maintaining flexibility to hold or sell when necessary.

The Problem

Imagine you have a DataFrame containing gold prices and a trading strategy defined by a column labeled Long that represents:

1: Buy (long position held)

0: Do nothing (hold)

-1: Sell (liquidate)

You want to create a new column, LongPrice, which will keep track of all the prices at which you bought gold. When holding or doing nothing, the LongPrice should remain the same. But when selling, the entry in the LongPrice should become NaN. Here's the initial structure of your DataFrame:

DateOpenHighLowCloseLong20HighLongPricex/x/x569.80575.30568.00572.501575.30NaN........................As you process each row, you want to generate an output that looks like this:

DateOpenHighLowCloseLong20HighLongPricex/x/x569.80575.30......1575.30[575.30]...............0575.30[575.30, 575.30]x/x/x............-1575.30NaN...............1577.00[577.00]The Solution

The solution to achieve this involves using Pandas functionalities to conditionally modify the LongPrice column based on the values in the Long column. We'll use the shift function combined with cumulative sum to accurately manage the price lists.

Step-by-Step Implementation

Here's a simple yet effective way to accomplish your goal:

Create a condition to identify sell signals.

Create a new column that holds the latest price formatted as a list.

Group the entries based on the changes in the Long status and use cumulative sums to build the LongPrice lists.

Here is a sample code to demonstrate this:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code

Boolean Mask: We use df["Long"].ne(-1) to create a mask s that distinguishes holding positions from sell signals.

Price Formatting: The line s2 = df["20High"].apply(lambda x: [x]) * df["Long"] generates a list of prices conditioned on the Long value.

Grouping and Cumulative Sum: The code groupby(s.ne(s.shift()).cumsum()) helps to manage the transitions between states (buying, holding, or selling). The cumulative sum aggregates the lists properly based on these transitions.

Output

When you run the provided code on your DataFrame, you should see the LongPrice column populated as intended. Holding positions will retain the last price recorded, while selling will reset the entry to NaN.

Conclusion

Managing a list of prices in a Pandas DataFrame based on trading conditions can streamline performance tracking and analysis. By leveraging functions like shift, cumsum, and groupby, you can implement effective solutions for your financial data. Try out this method in your project to enhance your data management capabilities.



Feel free to replace the placeholder DataFrame in the example with your actual DataFrame, and adjust the code as necessary for your specific case. Happy data analyzing!