How to filter stocks in a large dataset using python?

@norberto 

To filter stocks in a large dataset using Python, you can follow these steps:

1. Import the necessary libraries:

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import pandas as pd

1. Load your stock dataset into a pandas DataFrame:

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df = pd.read_csv('stock_data.csv') # Make sure the dataset is in the same directory or provide the correct path

1. Explore the data to understand its structure and available columns:

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print(df.head())  # Display the first few rows to get an idea of the dataset
print(df.info())  # Get general information about the dataset, including column names and data types

1. Apply filters to the DataFrame based on desired criteria. For example, if you want to filter stocks that have a price greater than $50 and a volume greater than 100,000, you can use the following code:

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filtered_stocks = df[(df['Price'] > 50) & (df['Volume'] > 100000)]

1. Optionally, you can refine your filter criteria by adding multiple conditions or using other logical operators. For instance, filtering stocks from a specific sector:

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filtered_stocks = df[(df['Price'] > 50) & (df['Volume'] > 100000) & (df['Sector'] == 'Technology')]

1. Finally, you can examine the filtered stocks by printing the resulting DataFrame:

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print(filtered_stocks)

Remember to adjust the column names and filter criteria based on your specific dataset.