Lesson 1: Introduction to Data Analysis with Python
3.1.1 Importance of Data in SEO Data-driven decisions lead to more effective SEO strategies. By analysing data, SEOs can identify trends, measure performance, and optimise their efforts. Python offers powerful tools for data analysis, making it easier to interpret and act on complex data sets.
3.1.2 Overview of Libraries: Pandas, NumPy, Matplotlib
- Pandas: A library for data manipulation and analysis. It provides data structures and functions needed to work with structured data seamlessly.
- NumPy: A library for numerical operations. It supports large multi-dimensional arrays and matrices.
- Matplotlib: A plotting library for creating static, animated, and interactive visualisations.
Installing Libraries: To install these libraries, use pip:
bashCopy codepip install pandas numpy matplotlib
Real-World Example: For a practical example of using Python to analyse SEO data, refer to my blog post on using Python to understand the overlap in SERPs data to perfectly map keywords to landing pages.
Lesson 2: Data Manipulation with Pandas
3.2.1 Reading Data from Various Sources Pandas make it easy to read data from various sources, such as CSV files, Excel files, and SQL databases.
python
import pandas as pd
# Reading data from a CSV file
df = pd.read_csv("seo_data.csv")
print(df.head())
Explanation:
pd.read_csv("seo_data.csv"): Reads a CSV file into a DataFrame.df.head(): Displays the first five rows of the DataFrame.
3.2.2 Data Cleaning and Preprocessing Data often needs cleaning and preprocessing before analysis. This can include handling missing values, converting data types, and normalising data.
python
# Example: Data Cleaning
df.dropna(inplace=True) # Removing missing values
df['keyword'] = df['keyword'].str.lower() # Converting to lowercase
df['search_volume'] = df['search_volume'].astype(int) # Converting to integer
Explanation:
df.dropna(inplace=True): Removes rows with missing values.df['keyword'].str.lower(): Converts the ‘keyword’ column to lowercase.df['search_volume'].astype(int): Converts the ‘search_volume’ column to integer.
3.2.3 Data Aggregation and Summarisation Pandas allows you to aggregate and summarise data to extract meaningful insights.
python
# Example: Data Aggregation
grouped_df = df.groupby('keyword').sum()
print(grouped_df)
Explanation:
df.groupby('keyword').sum(): Groups the DataFrame by the ‘keyword’ column and calculates the sum of each group.
Lesson 3: Visualising SEO Data
3.3.1 Creating Plots with Matplotlib Visualising data helps in understanding trends and patterns. Matplotlib is a versatile library for creating various types of plots.
python
import matplotlib.pyplot as plt
# Example: Line Plot
plt.plot(df['date'], df['traffic'])
plt.xlabel('Date')
plt.ylabel('Traffic')
plt.title('Traffic Over Time')
plt.show()
Explanation:
plt.plot(df['date'], df['traffic']): Creates a line plot with ‘date’ on the x-axis and ‘traffic’ on the y-axis.plt.xlabel('Date'): Sets the label for the x-axis.plt.ylabel('Traffic'): Sets the label for the y-axis.plt.title('Traffic Over Time'): Sets the title of the plot.plt.show(): Displays the plot.
3.3.2 Visualising Trends and Patterns Visualising data can help you identify trends and patterns that are not immediately obvious from raw data.
python
# Example: Bar Chart
plt.bar(df['keyword'], df['search_volume'])
plt.xlabel('Keyword')
plt.ylabel('Search Volume')
plt.title('Search Volume by Keyword')
plt.show()
Explanation:
plt.bar(df['keyword'], df['search_volume']): Creates a bar chart with ‘keyword’ on the x-axis and ‘search_volume’ on the y-axis.plt.xlabel('Keyword'): Sets the label for the x-axis.plt.ylabel('Search Volume'): Sets the label for the y-axis.plt.title('Search Volume by Keyword'): Sets the title of the chart.plt.show(): Displays the chart.
Lesson 4: Using APIs for SEO Data
3.4.1 Introduction to APIs APIs (Application Programming Interfaces) allow you to connect to various services and extract data programmatically. Many SEO tools offer APIs to access their data.
3.4.2 Connecting to Popular SEO APIs
- Google Analytics API: Provides access to Google Analytics data.
- Google Search Console API: Provides access to Google Search Console data.
- Moz API: Provides access to Moz’s SEO data.
- Ahrefs API: Provides access to Ahrefs’ SEO data.
3.4.3 Extracting and Processing API Data
Example: Connecting to Google Search Console API
To connect to the Google Search Console API, you need to set up credentials and install the google-api-python-client library.
bash
pip install google-api-python-client
Example Code:
python
from googleapiclient.discovery import build
from google.oauth2 import service_account
# Authentication and building the service
SCOPES = ['https://www.googleapis.com/auth/webmasters.readonly']
SERVICE_ACCOUNT_FILE = 'path/to/your/service-account-file.json'
credentials = service_account.Credentials.from_service_account_file(
SERVICE_ACCOUNT_FILE, scopes=SCOPES)
service = build('searchconsole', 'v1', credentials=credentials)
# Requesting data from the API
site_url = 'https://www.example.com'
request = {
'startDate': '2023-01-01',
'endDate': '2023-01-31',
'dimensions': ['query']
}
response = service.searchanalytics().query(siteUrl=site_url, body=request).execute()
# Processing the response
for row in response['rows']:
print(f"Query: {row['keys'][0]}, Clicks: {row['clicks']}, Impressions: {row['impressions']}")
Explanation:
service_account.Credentials.from_service_account_file(): Authenticates using a service account file.build('searchconsole', 'v1', credentials=credentials): Builds the Search Console service.service.searchanalytics().query(): Sends a request to the Search Console API.response['rows']: Processes the response data.
Module 3 Summary
By the end of Module 3, you will have a strong understanding of how to manipulate and analyse SEO data using Python. You will also learn how to visualise data to identify trends and patterns. Additionally, you will be able to connect to various SEO APIs to extract and process data programmatically. For a practical example of using Python to analyse SEO data, check out my blog post on comparing SERP similarity for keywords at scale.