Unlocking Insights: Analyzing Scraped Data with Python Libraries Matplotlib and Seaborn

Data analysis:

Data analysis is the process of examining data to uncover hidden patterns, relationships, and trends. It is an essential step in the data science process and enables us to make informed decisions based on the insights we uncover. Once you have scraped data from websites, the next step is to clean and process it before analyzing it using various techniques.

Introduction:

In this blog post, we'll explore two popular Python libraries for data visualization, Matplotlib and Seaborn, and demonstrate how they can be used to analyze scraped data.

Matplotlib:

Matplotlib is a popular data visualization library that provides a wide range of plotting functions for creating charts, graphs, and other visualizations. Seaborn is a higher-level data visualization library that is built on top of Matplotlib and provides more advanced functions for creating more complex and aesthetically pleasing visualizations.

What we are trying to do???:

Before we dive into the code examples, let's take a look at the data we'll be working with. For this example, we'll be scraping data from a website that lists the top 100 grossing movies of all time. The data includes the movie title, the studio that produced it, the year it was released, and the total gross earnings. We have scraped this data and cleaned it using Pandas, a popular data manipulation library in Python.

Code Examples:

Now, let's move on to the code examples.

First, we'll start with Matplotlib. Matplotlib provides a wide range of plotting functions, including scatter plots, line plots, bar plots, and more. In this example, we'll create a scatter plot to visualize the relationship between the year the movie was released and its total gross earnings.

import matplotlib.pyplot as plt
import pandas as pd

# Load the cleaned data
df = pd.read_csv('movies.csv')

# Create a scatter plot
plt.scatter(df['Year'], df['Gross Earnings'])

# Add labels and title
plt.xlabel('Year')
plt.ylabel('Gross Earnings')
plt.title('Relationship between Year and Gross Earnings')

# Show the plot
plt.show()

This code loads the cleaned data from a CSV file and creates a scatter plot using the scatter() function in Matplotlib. We then add labels and a title to the plot using the xlabel(), ylabel(), and title() functions. Finally, we use the show() function to display the plot.

The resulting scatter plot shows a clear upward trend between the year the movie was released and its total gross earnings, indicating that movies tend to earn more as time goes on.

Seaborn:

Next, let's move on to Seaborn. Seaborn provides more advanced plotting functions, including heatmaps, violin plots, and box plots. In this example, we'll create a box plot to visualize the distribution of gross earnings by studio.

import seaborn as sns
import pandas as pd

# Load the cleaned data
df = pd.read_csv('movies.csv')

# Create a box plot
sns.boxplot(x='Studio', y='Gross Earnings', data=df)

# Add labels and title
plt.xlabel('Studio')
plt.ylabel('Gross Earnings')
plt.title('Distribution of Gross Earnings by Studio')

# Show the plot
plt.show()

This code loads the cleaned data from a CSV file and creates a box plot using the boxplot() function in Seaborn. We specify the x-axis as 'Studio', the y-axis as 'Gross Earnings', and the data as the cleaned dataframe. We then add labels and a title to the plot using the xlabel(), ylabel(), and title() functions from Matplotlib. Finally, we use the show() function to display the plot.

The resulting box plot shows the distribution of gross earnings by studio, with the boxes indicating the interquartile range (IQR) and the whiskers indicating the minimum and maximum values. The plot provides insights into which studios tend to have higher gross earnings and which studios have more variability in their earnings.

Calculate summary statistics and other metrics:

In addition to creating visualizations, we can also use Python libraries to calculate summary statistics and other metrics to gain further insights into our scraped data. For example, we can use Pandas to calculate the mean, median, and standard deviation of the gross earnings.

import pandas as pd

# Load the cleaned data
df = pd.read_csv('movies.csv')

# Calculate summary statistics
mean = df['Gross Earnings'].mean()
median = df['Gross Earnings'].median()
std = df['Gross Earnings'].std()

# Print the results
print(f"Mean gross earnings: {mean}")
print(f"Median gross earnings: {median}")
print(f"Standard deviation of gross earnings: {std}")

This code loads the cleaned data from a CSV file and uses Pandas to calculate the mean, median, and standard deviation of the gross earnings. We then print the results to the console.

Conclusion:

Analyzing scraped data is an essential step in gaining insights and making informed decisions. In this blog post, we explored how to use Python libraries like Matplotlib and Seaborn to create visualizations that provide insights into our scraped data. We also demonstrated how to use Pandas to calculate summary statistics and other metrics.

By combining web scraping with data analysis, we can gain valuable insights into a wide range of topics, from movie gross earnings to stock prices to social media sentiment. The possibilities are endless, and the only limit is our imagination.