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.

Working with Text Files

To review the concepts covered in this step, please refer to the Importing Text Files module of the Importing Text Files in Python course.

Knowing how to work with text files forms the basis of data processing in Python. This step will help you understand how to open, read, and close text files in Python.

In this step, we will practice opening, reading, and closing text files in Python. We will use the open function to open a file, read its contents, and then close it. We will also explore the different methods for reading a text file, such as read, readline, and readlines. The goal of this step is to familiarize you with the process of working with text files in Python.

Task 1.1: Opening a Text File

Open the 'lyrics.txt' file using the open function in Python. Assign the opened file to a variable named 'file'. Print a message indicating the file has been opened, using the 'name' attribute of the file object to display the name of the file.

file = open('lyrics.txt', 'r')
print(f"File '{file.name}' opened successfully")

Task 1.2: Reading a Text File

Read the contents of the 'file' using the read method and assign the result to a variable named 'data'. Print the content of 'data'. Use the 'mode' attribute of the file object to indicate the mode in which the file is opened.

data = file.read()
print(f"Reading file in '{file.mode}' mode:")
print(data)

Task 1.3: Closing a Text File

Close the 'file' using the close method in Python. Print a message indicating the file has been closed, using the 'closed' attribute of the file object to check if the file is closed.

file.close()
print(f"File closed: {file.closed}")

Task 1.4: Reading a Text File Line by Line with file.readline()

Open the 'lyrics.txt' file again and read it line by line using the readline method. Print each line as you read it. Use a with statement to automatically handle file closing.

with open('lyrics.txt', 'r') as file:
    print(f"File '{file.name}' opened for line-by-line reading")
    line = file.readline()
    while line:
        print(line)
        line = file.readline()

Task 1.5: Reading All Lines at Once

Open the 'lyrics.txt' file again and read all lines at once using the readlines method. Assign the result to a variable named 'lines'. Print the 'lines' variable. Use a with statement for this task.

with open('lyrics.txt', 'r') as file:
    print(f"File '{file.name}' opened for reading all lines at once")
    lines = file.readlines()
    print(lines)

Task 1.6: Iterating Over a File with a For Loop

Open the 'lyrics.txt' file once more and use a for loop to iterate over each line in the file. Print each line as you iterate over it. Utilize a with statement to manage the file opening and closing.

with open('lyrics.txt', 'r') as file:
    print(f"File '{file.name}' opened for iteration using a for loop")
    for line in file:
        print(line)

Loading and Reading Flat Files

To review the concepts covered in this step, please refer to the Loading and Reading Flat Files module of the Importing Text Files in Python course.

Knowing how to load and read flat files allows you to work with structured data in Python. This step will help you understand the structure of flat files and how to read them using the CSV Reader module.

In this step, we will practice loading and reading flat files in Python. We will explore the structure of flat files, the different types of flat files, and how to read them using the CSV Reader module. The goal of this step is to familiarize you with the process of working with flat files in Python.

Task 2.1: Importing the CSV Reader Module

Before we can start working with flat files, we need to import the necessary module. In this task, you will import Python's built-in CSV module, which is designed for reading and writing CSV files.

import csv

Task 2.2: Opening the CSV File

Now that we have imported the CSV module, we can open our CSV file. In this task, you will use Python's built-in open() function to open the 'World_Bank-Fictional.csv' file.

file = open('World_Bank-Fictional.csv', 'r')

Task 2.3: Reading the CSV File Using CSV Reader

After opening the file, we will use the CSV Reader to read the file. In this task, you will create a CSV reader object and iterate over the rows of the CSV file, displaying them. Close the file after you have printed the rows.

csv_reader = csv.reader(file)
for row in csv_reader:
    print(row)
file.close()

Task 2.4: Analyzing the Structure and Content of the CSV Data

In this task, you will analyze the structure and content of the data in the 'World_Bank-Fictional.csv' file. You'll use the CSV Reader to read the file and then write code to understand the type of data it contains. Use a with statement to handle opening and closing of the file. Determine the number and name of columns, and count the number of rows. Print the data in the first row of each column.

import csv

# Open the CSV file
with open('World_Bank-Fictional.csv', 'r') as file:
    csv_reader = csv.reader(file)

    # Reading the headers
    headers = next(csv_reader)
    print("Headers:", headers)

    # Initializing a counter for rows
    row_count = 0

    # Analyzing the data in each row
    for row in csv_reader:
        row_count += 1

        # Print first row data
        if row_count == 1:
            print("First row data:", row)

    print("Total number of rows:", row_count)

# Outputs headers, first row data, and row count.

Importing Text Files with Numpy

To review the concepts covered in this step, please refer to the Import Text File with Numpy module of the Importing Text Files in Python course.

Importing text files with Numpy is important because it allows you to work with numerical data in Python. This step will help you understand how to load data from a file using Numpy and how to specify the data type of the values loaded using dtype.

In this step, we will practice importing text files with Numpy. We will explore how to load data from a file using Numpy, how to specify the data type of the values loaded using dtype, and how to load specific columns using usecols. The goal of this step is to familiarize you with the process of importing text files with Numpy.

Task 3.1: Import Numpy

Before we can start working with Numpy, we need to import it. Import the Numpy library using the standard alias 'np'.

import numpy as np

Task 3.2: Load Data from CSV File

Now that we have Numpy imported, we can use it to load data from our CSV file. Use the np.genfromtxt function to load the data from the 'World_Bank-Fictional.csv' file. For now, just load the data without specifying any data types or columns. Print the data. You'll see some NaN values and everything will be of dtype float.

data = np.genfromtxt('World_Bank-Fictional.csv', delimiter=',', skip_header=1)
print(data)

Task 3.3: Specify Data Types

Specify the data types for each column when loading data from a CSV file, using the dtype argument in the np.genfromtxt() function. Print the data after it has been loaded. Notice the difference from Task 3.2.

The table below outlines the columns and their corresponding data types in order:

| Column Name | Data Type | |-------------------|-----------| | Country | object | | Year | int64 | | Population | int64 | | GDP | int64 | | GDP_Growth | float64 | | Inflation | float64 | | Life_Expectancy | float64 | | Literacy_Rate | float64 | | Unemployment_Rate | float64 |

dtype=['object', 'int64', 'int64', 'int64', 'float64', 'float64', 'float64', 'float64', 'float64']

# Load the data with specified data types
data = np.genfromtxt('World_Bank-Fictional.csv', delimiter=',', skip_header=1,
                     dtype=['object', 'int64', 'int64', 'int64', 'float64', 
                            'float64', 'float64', 'float64', 'float64'])

# Display the data
print(data)

Task 3.4: Load Specific Columns

Sometimes we only want to load specific columns from our data. Reload the data from the CSV file, but this time only load the 'Country', 'Year', and 'Population' columns. Use the dtpye argument to load the strings and numbers correctly. Print the data.

data = np.genfromtxt('World_Bank-Fictional.csv', delimiter=',', skip_header=1, usecols=[0, 1, 2], dtype=['object', 'int64', 'int64'])
print(data)

Importing Text Files into Pandas

To review the concepts covered in this step, please refer to the Import Text File into Pandas module of the Importing Text Files in Python course.

Importing text files into Pandas allows you to work with tabular data in Python. This step will help you understand how to load data from a file using Pandas and how to handle missing values in a dataframe using na_filter.

In this step, we will practice importing text files into Pandas. We will explore how to load data from a file using Pandas, how to handle missing values in a dataframe using na_filter, and how to parse dates in a dataframe using parse_dates. The goal of this step is to familiarize you with the process of importing text files into Pandas.

Task 4.1: Importing Pandas Library

Import the pandas library as pd.

import pandas as pd

Task 4.2: Loading Data from a CSV File

Load the data from the 'World_Bank-Fictional.csv' file into a pandas DataFrame and display the DataFrame.

df = pd.read_csv('World_Bank-Fictional.csv')
df

Task 4.3: Parsing Dates

Load the data once more but this time, parse the 'Year' column as dates and display the DataFrame.

df = pd.read_csv('World_Bank-Fictional.csv', parse_dates=['Year'])
df