Pandas Read csv — Python for Finance and Data Science

In my last few stories, we mostly retrieved financial data from an API. I would like to show you how to read CSV files in Python using Pandas read_csv since it is another useful way to read financial data for our analysis.

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Photo by Dominika Roseclay from Pexels

Why to use pandas read csv?

Pandas read csv method is super useful to load any data in CSV format to a Pandas DataFrame. For example, I have a CSV file containing the last year of historical prices. I have downloaded the file from Apple in Yahoo Finance where you can download historical prices in CSV format for any company. The name of the files is ‘ AAPL.csv ‘.

Would it not be great to load the data from a CSV file into a Pandas DataFrame? That way we would be able to use all Pandas capabilities to work, analyse and plot the data.

How to read csv files in Python Pandas?

Read a CSV file in Python cannot be easier thanks to Pandas. We can read a CSV file in Pandas with only three lines of codes as shown below:

  • First of all, import pandas
  • Use Pandas read_csv method and pass as argument the name of the file (Ensure that the file is in the same folder location that the python script)
  • Finally, pass the needed parameters
import pandas as pd
apple = pd.read_csv('AAPL.csv',index_col='Date')
print(apple)
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pandas read_cvs

What paramaters can we use with Pandas Read csv?

And just like that, we have our Pandas DataFrame containing Apple historical prices. Is it not faster using read_csv method in Pandas than open the file in Excel?

Certainly, reading csv into Python and Pandas is super fast. But this is not all, you may have observed that I passed the parameter index_col as an argument. Index_col let us select the column to use as the index of our DataFrame. Besides index_col, there are plenty of other arguments that can be used with Pandas read_csv method to handle the loaded data . A extended description can be found in the Pandas documentation.

Below are a few of the pandas read_csv arguments that I use the most:

  • ucsecols: Return a subset of the columns. For instance, using [0–4] would only load the first four columns included in the csv file
  • skiprows: Number of lines to skip from the csv file
  • nrows: Number of rows in the file to read
  • skip_blank_lines: If equal to True, blank lines are skipped instead of showing them as NaN values.
  • iIndex_col: Name a column to be used as index
  • parse_dates: If equal to True dates in the index will be parsed

Wrapping Up

Although it may be faster to use an API to read financial data into Pandas, read_csv method is very useful as well and it is worth to know. Specially, when we have access to tabular data in Excel. In just a few lines of codes, we have the data loaded into Python and Pandas ready for analysis.

If you have liked the article, have a look at some of the other Python for Finance articles.

Originally published at https://codingandfun.com on May 6, 2020.

Written by

Python for Finance. Learn step by step how to automate cool financial analysis tools. Writing at https://codingandfun.com/. Twitter: @CodingandF

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