Learn CSV Files | Reading Files in Pandas

Since pandas is the go-to library for data analysis and manipulation, one of its key features is its ability to read and write various file types, including CSV files.

A CSV (Comma-Separated Values) file is a plain text file used to store tabular data, where each row represents a record, and columns are separated by commas.

A CSV file can contain the following data:

Numbers: integer or decimal values (e.g., 42, 3.14);
Text: strings or categorical data (e.g., John, Active);
Dates/Times: timestamps (e.g., 2023-12-30);
Booleans: logical values (True, False).

Each row must have the same number of columns, and the first row often contains column headers.

Functions like read_csv() and to_csv() come in handy for dealing with CSV data.

The basic syntax of read_csv() and key parameters are as follows:

pandas.read_csv(filepath_or_buffer, sep=',', header=0, names=None, usecols=None, ...)

filepath_or_buffer: path to the CSV file (string or URL);
sep: delimiter (default is a comma ,);
header: row number to use as the column headers (default is the first row);
names: List of column names to use;
usecols: olumns to read (subset of columns).


              12345
            
# Loading the CSV into a `DataFrame`
import pandas as pd

salary_data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a43d24b6-df61-4e11-9c90-5b36552b3437/Salary+Dataset.csv')
print(salary_data)

Note

Make sure that the dataset link is wrapped in quotation marks.

The basic syntax of to_csv() and key parameters are as follows:

pandas.DataFrame.to_csv(path_or_buf=None, sep=',', ..., columns=None, header=True, index=True, ...)

path_or_buf: file path or object where the CSV should be written;
sep: delimiter for separating values (default is a comma ,);
columns: subset of columns to write (default is all columns);
header: whether to include column names as the header (default is True);
index: whether to write row indices to the file (default is True).


              1234567
            
import pandas as pd

countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'], 'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'], 'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(countries_data)

countries.to_csv('countries.csv')
print('Done')

Task

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You are given a URL to a CSV file stored as a string in the file_url variable.

Read the CSV file from the given URL into a DataFrame named wine_data.

Solution

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Since pandas is the go-to library for data analysis and manipulation, one of its key features is its ability to read and write various file types, including CSV files.

A CSV (Comma-Separated Values) file is a plain text file used to store tabular data, where each row represents a record, and columns are separated by commas.

A CSV file can contain the following data:

Numbers: integer or decimal values (e.g., 42, 3.14);
Text: strings or categorical data (e.g., John, Active);
Dates/Times: timestamps (e.g., 2023-12-30);
Booleans: logical values (True, False).

Each row must have the same number of columns, and the first row often contains column headers.

Functions like read_csv() and to_csv() come in handy for dealing with CSV data.

The basic syntax of read_csv() and key parameters are as follows:

pandas.read_csv(filepath_or_buffer, sep=',', header=0, names=None, usecols=None, ...)

filepath_or_buffer: path to the CSV file (string or URL);
sep: delimiter (default is a comma ,);
header: row number to use as the column headers (default is the first row);
names: List of column names to use;
usecols: olumns to read (subset of columns).


              12345
            
# Loading the CSV into a `DataFrame`
import pandas as pd

salary_data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a43d24b6-df61-4e11-9c90-5b36552b3437/Salary+Dataset.csv')
print(salary_data)

Note

Make sure that the dataset link is wrapped in quotation marks.

The basic syntax of to_csv() and key parameters are as follows:

pandas.DataFrame.to_csv(path_or_buf=None, sep=',', ..., columns=None, header=True, index=True, ...)

path_or_buf: file path or object where the CSV should be written;
sep: delimiter for separating values (default is a comma ,);
columns: subset of columns to write (default is all columns);
header: whether to include column names as the header (default is True);
index: whether to write row indices to the file (default is True).


              1234567
            
import pandas as pd

countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'], 'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'], 'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(countries_data)

countries.to_csv('countries.csv')
print('Done')

Task

Swipe to start coding

You are given a URL to a CSV file stored as a string in the file_url variable.

Read the CSV file from the given URL into a DataFrame named wine_data.

Solution

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Section 2. Chapter 1

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CSV Files

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CSV Files

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