Using Pandas to Download/Load Zipped CSV File from URL

As a data scientist or analyst, working with large datasets is an essential part of our job. One common challenge we face is dealing with zipped CSV files that contain the actual data. In this article, we will explore how to use Python and its popular data analysis library Pandas to download and load these zipped CSV files from URLs.

Introduction

Pandas is a powerful library in Python for data manipulation and analysis. It provides an efficient way to handle structured data, including tabular data such as spreadsheets and SQL tables. However, sometimes the dataset we need might be stored in a compressed format, such as a zip file. In this article, we will show you how to download and load these zipped CSV files using Python and Pandas.

Prerequisites

Before we dive into the solution, let’s assume that:

You have Python installed on your system.
You have Pandas installed. If not, you can install it using pip: pip install pandas
You have the urllib.request module available in Python, which is used to download files from URLs.

Problem Description

The problem described in the Stack Overflow post is a common issue when working with zipped CSV files. The error message “pandas.io.common.CParserError: Error tokenizing data. C error: Expected 3 fields in line 5, saw 62” indicates that Pandas is unable to parse the CSV file because it expects three fields per row but finds something else.

Solution

The problem can be solved by using the skiprows parameter when reading the CSV file. The skiprows parameter allows us to skip a specified number of rows at the beginning of the file, which in this case is 4 (assuming that the header row starts from the fifth line).

Here’s an example code snippet that demonstrates how to download and load a zipped CSV file using Python and Pandas:

import pandas as pd

# URL of the zipped CSV file
url = "http://api.worldbank.org/v2/en/indicator/NY.GDP.MKTP.CD?downloadformat=csv"

# Download the zip file from the URL
import urllib.request
urllib.request.urlretrieve(url, "GDP.zip")

# Open the zip file and extract the CSV file
import zipfile
compressed_file = zipfile.ZipFile('GDP.zip')
csv_file = compressed_file.open('API_NY.GDP.MKTP.CD_DS2_en_csv_v2.csv')

# Read the CSV file into a Pandas DataFrame, skipping 4 rows at the beginning
GDP = pd.read_csv(csv_file, skiprows=4)

# Print the first few rows of the DataFrame to verify that it's been loaded correctly
print(GDP.head())

Explanation

Here’s a step-by-step explanation of what happens in this code:

We import the pandas library and assign it the alias pd.
We define the URL of the zipped CSV file we want to download.
We use the urllib.request.urlretrieve() function to download the zip file from the specified URL and save it to a local file named GDP.zip.
We open the GDP.zip file using the zipfile.ZipFile() class and extract the CSV file by calling the open() method.
We read the CSV file into a Pandas DataFrame using the read_csv() function, specifying the skiprows parameter to skip 4 rows at the beginning of the file.

Conclusion

In this article, we showed you how to use Python and Pandas to download and load zipped CSV files from URLs. By using the skiprows parameter when reading the CSV file, we can easily overcome the issue of zipped CSV files and get access to the actual data.

Last modified on 2023-09-04