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[isaiahoh]

• Reading a CSV
• Display with matplotlib
• Statistics question
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[Napwillcode8848]

READING A CSV FILE WITH MATPLOTLIB

Content:

• CSV file introduction
• How to open a CSV file
• How to read a CSV file introduction
• Matplotlib introduction
• A step by step guide of how to read a file
• Application of a CSV file
• How to select columns
• How to predict a value based on data on a CSV file

Introduction

• CSV (Comma-Separated Values) files are important because they provide a simple, universally accepted, lightweight, and efficient way to store and transfer structured data. Their ease of use, compatibility across platforms and software, and suitability for both human-readable and machine-readable formats make them indispensable in fields like data science, business analysis, and software development.
• Example of a CSV file: IMAGE WILL BE INSERTED HERE

How to open a CSV file in Python

• Before you can read data from a file, you must learn how to open it. Note that the file must be located in the same folder as the current program you are running.

  with open(filename) as my_file: with open('filename.csv') as my_file:

How to read a file

• There are so many ways of reading a file. For instance, importing a CSV module to visualise the file opened. This line creates a CSV reader, which is a Python object that can read CSV data from a given file, splitting up the file into rows.

  with open('marriage_data.csv') as file: reader = csv.reader(file)

  • However, for this project we will use Matplotlib.

Matplotlib Introduction

• Integration with Pandas: Pandas is for reading while Matplotlib is for visualising.
• It offers a wide range of customizable options for plots, including line charts, bar charts, scatter plots, histograms, and more. You can customise everything from axis labels to colours, grid lines, titles, and markers.

A Step by Step Guide

• Import pandas and matplotlib

  import pandas as pd import matplotlib.pyplot as plt

  • Load CSV file in: You can load your CSV file using Pandas' read_csv() method.

Load the CSV file

data = pd.read_csv('filename.csv')

Display the first few rows of the dataframe

print(filename.head())

   * Visualise it with some basic methods:  

• plt.plot() is used to create the line plot.
• We customise the plot by adding labels and titles using plt.xlabel(), plt.ylabel(), and plt.title().
• plt.xticks(rotation=45) rotates the x-axis labels (dates) by 45 degrees to make them more readable.
  • For example, the file contains 2 columns: Date and Sales

Plot the data

plt.plot(data['Date'], data['Sales'])

Add labels and title

plt.xlabel('Date') plt.ylabel('Sales') plt.title('Sales Over Time')

Show the plot

plt.xticks(rotation=45) # Rotate date labels for better readability plt.show()

• Customization:

  Create a plot with customizations

  plt.plot(data['Date'], data['Sales'], color='red', marker='o', linestyle='--')

  Adding grid lines

  plt.grid(True)

  Customise axes

  plt.xlabel('Date', fontsize=12) plt.ylabel('Sales', fontsize=12) plt.title('Sales Over Time with Custom Style', fontsize=15)

  Display the plot

  plt.xticks(rotation=45) plt.show()

• Save the plot as image: If you want to save the plot as an image, use the savefig() function.

  plt.plot(data['Date'], data['Sales']) plt.xlabel('Date') plt.ylabel('Sales') plt.title('Sales Over Time')

  Save the plot as a PNG file

  plt.savefig('sales_plot.png')

Application of the CSV file with a house sale example!

With the given example CSV file, we will explore how to “select columns” and “predict the price of a house based on the data!”

• CSV Example: (house_data.csv)

  House_Size,Price,Bedrooms 1000,200000,3 1500,300000,4 2000,400000,3 2500,500000,4 3000,600000,5

How to Select a Column?

• The data[['House_Size', 'Price']] selects two specific columns (House_Size and Price) from the DataFrame. This creates a new DataFrame (selected_columns) that contains only these two columns.

  selected_columns = data[['House_Size', 'Price']]

  • We use Matplotlib’s plt.plot() method to create a plot of House_Size vs Price. plt.plot(selected_columns['House_Size'], selected_columns['Price'], marker='o', linestyle='-', color='blue')
  • The plt.show() command displays the figure. plt.show()
  • Now, try to put them all together. import pandas as pd import matplotlib.pyplot as plt

    Step 1: Load the CSV file into a Pandas DataFrame

    data = pd.read_csv('house_data.csv')

    Step 2: Select the columns 'House_Size' and 'Price' for plotting

    selected_columns = data[['House_Size', 'Price']]

    Print selected columns (optional, to verify the selection)

    print(selected_columns)

    Step 3: Plot the data using Matplotlib

    plt.plot(selected_columns['House_Size'], selected_columns['Price'], marker='o', linestyle='-', color='blue')

    Add labels and title

    plt.xlabel('House Size (sqft)') plt.ylabel('Price ($)') plt.title('House Size vs Price')

    Display the plot

    plt.show()

  • With the following code, this is what you should get: Selected Columns:

 House_Size (sqft)   Price ($)

0 1000 200000 1 1500 300000 2 2000 400000 3 2500 500000 4 3000 600000

Now, let’s try to predict how much it would cost if I had this size of a house.

Here is a step breakdown:

• Extracting Columns: We extract the House_Size and Price columns to use them for both visualization and model fitting.
• Plotting the Data: We create a scatter plot to visualize the relationship between house size and price using Matplotlib.
• Linear Regression Model: We fit a simple linear regression model using the LinearRegression class from sklearn, which helps us predict the price for a house based on its size.
• Predicting the Price: Finally, we predict the cost of a house for a specified size (e.g., 2200 sqft).
• Here is a code example:

import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression import numpy as np

Step 1: Load the CSV file into a Pandas DataFrame

data = pd.read_csv('house_data.csv')

Step 2: Extract the 'House_Size' and 'Price' columns

house_size = data['House_Size (sqft)'].values.reshape(-1, 1) # Reshaping for sklearn price = data['Price ($)'].values

Step 3: Visualize the data using a scatter plot

plt.scatter(house_size, price, color='blue') plt.xlabel('House Size (sqft)') plt.ylabel('Price ($)') plt.title('House Size vs Price') plt.show()

Step 4: Fit a linear regression model to predict the price of a house based on its size

model = LinearRegression() model.fit(house_size, price)

Predict the price of a house for a given size, e.g., 2200 sqft

house_size_to_predict = np.array([[2200]]) predicted_price = model.predict(house_size_to_predict)

print(f'The predicted price for a house of size 2200 sqft is: ${predicted_price[0]:,.2f}')

• This is what you should get from the following code: The predicted price for a house of size 2200 sqft is: $440,000.00