Question:
How can I transform a CSV file containing financial data, into a structured Pandas DataFrame for data analysis?
Answer:
To import a CSV file into a Pandas DataFrame, the pandas.read_csv() function comes into play. Here's how you can do it step-by-step:
import pandas as pd # Read the CSV file into a DataFrame df = pd.read_csv("data.csv") # Print the DataFrame to view its contents print(df)
This code will create a DataFrame named df that contains all the data from your CSV file. The output will look like this:
Date price factor_1 factor_2 0 2012-06-11 1600.20 1.255 1.548 1 2012-06-12 1610.02 1.258 1.554 2 2012-06-13 1618.07 1.249 1.552 3 2012-06-14 1624.40 1.253 1.556 4 2012-06-15 1626.15 1.258 1.552 5 2012-06-16 1626.15 1.263 1.558 6 2012-06-17 1626.15 1.264 1.572
With your CSV data loaded into a Pandas DataFrame, you can effortlessly explore and manipulate it, leveraging Pandas' extensive data analysis capabilities.
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