Jupyter Guide

To get started, open the file on the right entitled "Step 1...". You'll complete each task for Step 1 in that Jupyter Notebook file. Remember, you must run the cells (ctrl/cmd + Enter) for each task before moving onto the next task in the Jupyter Notebook. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

File Handling in Python

To review the concepts covered in this step, please refer to the Opening and Modifying Files module of the Reading and Writing CSV Files in Python course.

File handling is important because it allows us to read, write, and modify data stored in files. In this step, we will practice opening a file, reading its content, and writing new data to it.

In this step, we will practice the basics of file handling in Python. We will use the open function to open a file, read its content using the read method, and write new data using the write method. We will also explore the use of different modes such as read mode (r), write mode (w), and create mode (x). Finally, we'll print a file one line at a time using readlines and a for loop. We'll be working with the example_data.txt file.

Task 1.1: Opening a File

Open the example_data.txt file in read mode and print its content. Close the file after printing the data.

file = open('example_data.txt', 'r')
print(file.read())
file.close()

Task 1.2: Writing to a File

Open the example_data.txt file in append mode and write 'Hello, World!' to it. Be sure to close the file to save your changes.

file = open('example_data.txt', 'a')
file.write('Hello, World!')
file.close()

Task 1.3: Creating a File

Create a new file named new_file.txt and write 'This is a new file.' to it. Be sure to close the file to save your changes.

file = open('new_file.txt', 'x')
file.write('This is a new file.')
file.close()

Task 1.4: Reading a File Line by Line

Open the example_data.txt file in read mode and print its content line by line with a line number before each line. Close the file after reading each line. Use a for loop with a counter that increments with each line to get the line number.

counter = 1
for foo in bar:
    print(counter)
    print(foo)
    counter += 1

file = open('example_data.txt', 'r')
counter = 1
for line in file.readlines():
    print(counter)
    print(line)
    counter += 1
file.close()

Reading and Writing Files

To review the concepts covered in this step, please refer to the Opening and Modifying Files module of the Reading and Writing CSV Files in Python course.

Understanding the OS module is important because it provides a way of using operating system dependent functionality. In this step, we will practice checking if a file exists and deleting it using the OS module.

In this step, we will practice using the OS module to interact with the file system. We will use the os.path.exists() function to check if a file exists (comma_separated.csv) and the os.remove() function to delete a file (delete_me.csv). We will also handle errors when trying to delete a file that doesn't exist.

Task 2.1: Import the OS module

To start working with the OS module, you first need to import it. Import the OS module in the code cell below.

import os

Task 2.2: Check if a file exists

Use the os.path.exists() function to check if a file named comma_separated.csv exists in the current directory.

os.path.exists('comma_separated.csv')

Task 2.3: Delete a file

Use the os.remove() function to delete a file named delete_me.csv in the current directory. Only delete the file if it exists. If the file exists, print deleting file!, and if the file can't be found, print file missing!. Run your code twice to print both messages.

# First Run
if os.path.exists('delete_me.csv'):
    print('deleting file!')
    os.remove('delete_me.csv')
else:
    print('file missing!')

# Second Run
if os.path.exists('delete_me.csv'):
    print('deleting file!')
    os.remove('delete_me.csv')
else:
    print('file missing!')

Working with CSV Files

To review the concepts covered in this step, please refer to the Read CSVs with Python’s CSV Package module of the Reading and Writing CSV Files in Python course.

Knowing how to read and write CSV files is important because CSV is a common data format that is widely used for data storage and transmission. In this step, we will practice reading data from a CSV file and writing data to a CSV file using Python's built-in CSV module.

In this step, we will practice reading and writing CSV files using Python's built-in CSV module. We will use the csv.reader, csv.DictReader and csv.writer methods to read and write data from the comma_separated.csv file.

Task 3.1: Import the CSV Module

First, we need to import the CSV module to use its functions. Import the CSV module in Python.

import csv

Task 3.2: Open and Read a CSV File

Now, let's open and read a CSV file. Open the comma_separated.csv file and read its contents using the csv.reader method.

with open(filepath, 'r') as file:
    #do stuff here

with open('comma_separated.csv', 'r') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

Task 3.3: Read a CSV File as a Dictionary

Sometimes, it's more convenient to read a CSV file as a dictionary. Use the csv.DictReader method to read the comma_separated.csv file as a list of dictionaries.

with open('comma_separated.csv', 'r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row)

Task 3.4: Write to a CSV File

Finally, let's practice writing to a CSV file. Write the following data to a new CSV file named output.csv: ['Name', 'Age'], ['Alice', '20'], and ['Bob', '25']. Use the Jupyter UI to confirm that output.csv was created with comma separated rows.

with open('output.csv', 'w') as file:
    writer = csv.writer(file)
    writer.writerow(['Name', 'Age'])
    writer.writerow(['Alice', '20'])
    writer.writerow(['Bob', '25'])

Importing Data with Pandas and NumPy

To review the concepts covered in this step, please refer to the Importing with Pandas and NumPy module of the Reading and Writing CSV Files in Python course.

Understanding how to use Pandas and NumPy for data manipulation is important because they provide powerful tools for data analysis and manipulation. In this step, we will practice reading CSV files with Pandas and NumPy, and perform basic data manipulations.

In this step, we will practice using Pandas and NumPy to read CSV files and manipulate data. We will explore how to use Pandas to handle CSV files with different separators, and how to concatenate multiple data frames. There will be a tab_separated.csv and a comma separated version,comma_separated.csv, to read and concat. We will also practice using NumPy's loadtxt function to load data from a CSV file.

Task 4.1: Importing Necessary Libraries

Import the necessary libraries for data manipulation, which are pandas and numpy.

import pandas as pd
import numpy as np

Task 4.2: Reading CSV Files with Pandas

Read the comma_separated.csv and tab_separated.csv files using pandas and assign them to variables df_comma and df_tab respectively. Print the shape of the resulting data frames.

df_comma = pd.read_csv('comma_separated.csv')
df_tab = pd.read_csv('tab_separated.csv', sep='\t')
print(df_comma.shape)
print(df_tab.shape)

Task 4.3: Concatenating Data Frames

Concatenate df_comma and df_tab into a single data frame df_concat. Print the shape of the resulting data frame.

df_concat = pd.concat([df_comma, df_tab])
print(df_concat.shape)

Task 4.4: Loading CSV Files with NumPy

Load the comma_separated.csv file using numpy's loadtxt function and assign it to the variable data_loadtxt. Print the data.

data_loadtxt = np.loadtxt(fname='comma_separated.csv', delimiter=',', skiprows=1, dtype=object)
print(data_loadtxt)