Automating presentations using python

Presentations are a crucial component of communicating data-driven insights to stakeholders. Analysts often find themselves in a constant battle, striving to extract and transform data, generate meaningful visualizations, and interpret the findings for their stakeholders. The repetitive nature of these tasks, coupled with unrealistic stakeholder requests, can make the process tedious and time-consuming. However, with the power of automation and Python, analysts can streamline and automate the entire presentation process, saving time and enabling them to focus on deeper analysis and insights.

The Challenges Analysts Face

Analysts are familiar with the arduous process of collecting data from various sources, performing data transformations, and presenting it in a visually appealing manner. This involves creating charts, graphs, and tables that accurately represent the underlying data. The struggle is compounded when dealing with frequent presentations, each requiring different data sets and analyses. Furthermore, stakeholders often have specific formatting requirements that need to be met, down to the smallest details like font selection. This can create a daunting situation where even the slightest oversight can lead to justified scrutiny.

The Importance of Presentations

Despite the challenges, presentations play a pivotal role in engaging stakeholders and empowering them to make informed decisions. Well-crafted presentations serve as reliable documents that spark conversations, inspire ideas, and instigate action. The ability to convey complex information in a clear and concise manner is a valuable skill for analysts, and their presentations can significantly influence the success of projects, strategies, and business outcomes.

Automation with Python

Thankfully, automation comes to the rescue, enabling analysts to streamline their presentation preparation. Python, a powerful programming language for data analysis, provides an array of libraries and tools that simplify the end-to-end process. By leveraging Python, analysts can automate data extraction from multiple sources, perform necessary transformations, and load the data into charts, tables, and graphs.

Creating Presentation Templates

One approach to automation involves using a single presentation template and replacing the data within it for different segments, products, or customers. Analysts can use Python libraries such as Pandas and Matplotlib to extract and transform data, generate visualizations, and populate the presentation template. By automating this process, analysts can generate multiple copies of the presentation with updated data, saving considerable time and effort.

Seamless ETL (Extract, Transform, Load) Process

Python’s ecosystem offers a wide range of tools and libraries that facilitate the entire ETL process. Analysts can utilize libraries like Pandas, NumPy, and SQLalchemy to extract data from various sources, apply transformations, and load it into the desired format. These tools provide efficient data manipulation capabilities, enabling analysts to handle large datasets and perform complex computations with ease.

Integrating Visualization Libraries

Python’s visualization libraries, such as Matplotlib, Seaborn, and Plotly, empower analysts to create stunning visualizations to communicate insights effectively. These libraries offer a wide range of customizable chart types and styles, allowing analysts to tailor their visualizations to stakeholder preferences. By automating the visualization process, analysts can generate consistent and visually appealing charts and graphs for their presentations.

Automating the presentation preparation process through Python offers analysts a powerful solution to the challenges they face regularly. By leveraging Python’s capabilities, analysts can streamline data extraction, transformation, and visualization, while ensuring consistency and accuracy in their presentations.

Automation empowers analysts to focus on deep analysis and deriving valuable insights, while also reducing the time and effort invested in repetitive tasks. By presenting data in a clear and visually engaging manner, analysts can inspire stakeholders, foster informed decision-making, and drive positive business outcomes.

Here is a sample python code

import pandas as pd
from pptx import Presentation

# Step 1: Fetch data from MySQL database
# Assuming you have a connection to the database already established
customers_query = "SELECT * FROM customers"
sales_query = "SELECT * FROM sales"
orders_query = "SELECT * FROM orders"

customers_df = pd.read_sql(customers_query, your_db_connection)
sales_df = pd.read_sql(sales_query, your_db_connection)
orders_df = pd.read_sql(orders_query, your_db_connection)

# Step 2: Join and transform the data
# Replace this with your specific data transformation logic
merged_df = pd.merge(customers_df, sales_df, on='customer_id', how='inner')
final_df = pd.merge(merged_df, orders_df, on='customer_id', how='inner')

# Step 3: Read the PowerPoint template and replace data
template_pptx = "path/to/your/template.pptx"
presentation = Presentation(template_pptx)

customers = ['A', 'B', 'C']

for customer in customers:
filtered_df = final_df[final_df['customer_name'] == customer] # Apply customer filter

for slide in presentation.slides:
for shape in slide.shapes:
if shape.has_text_frame:
text_frame = shape.text_frame
for paragraph in text_frame.paragraphs:
for run in paragraph.runs:
# Replace placeholders with transformed data
if "" in run.text:
run.text = run.text.replace("", filtered_df.groupby('customer_name').sum().to_string())
if "" in run.text:
run.text = run.text.replace("", filtered_df.groupby('product_name').sum().to_string())
if "" in run.text:
run.text = run.text.replace("", filtered_df.groupby('geographical_location').sum().to_string())

if shape.has_chart:
chart = shape.chart
chart_data = pd.DataFrame({
'Category': filtered_df['customer_name'],
'Value': filtered_df['sales_amount']
chart.replace_data(chart_data['Category'].tolist(), chart_data['Value'].tolist())

# Step 4: Add insights slide based on amount change compared with the previous month
insights_slide = presentation.slides.add_slide(presentation.slide_layouts[1])
insights_text = insights_slide.shapes.add_textbox(left=100, top=100, width=600, height=400).text_frame
insights_text.text = f"Insights for Customer {customer}:\n"

filtered_df['previous_month_sales'] = filtered_df.groupby('month')['sales_amount'].shift(1)

for index, row in filtered_df.iterrows():

if pd.isnull(row['previous_month_sales']):
insights_text.text += f"- No previous month data available for {row['month']}.\n"
if row['sales_amount'] > row['previous_month_sales']:
insights_text.text += f"- Sales in {row['month']} increased compared to the previous month.\n"
elif row['sales_amount'] < row['previous_month_sales']:
insights_text.text += f"- Sales in {row['month']} decreased compared to the previous month.\n"
insights_text.text += f"- Sales in {row['month']} remained stable compared to the previous month.\n"

# Step 5: Save the presentation for each customer
output_path = f"path/to/output/{customer}_presentation.pptx"

print("Presentation generation complete.")




Check for indentation in your IDE. you should understand that this code serves as an inspiration and should not be copied verbatim. Each business has its unique data requirements, transformations, and presentation styles. Therefore, it is essential to adapt the code to your specific needs and customize it accordingly.

The provided code showcases an example of how you can automate the process of preparing presentations for stakeholders. It follows a step-by-step approach to fetch data from a MySQL database, perform transformations, and make the data presentation-ready.

Additionally, it demonstrates how to replace data in charts, tables, and text boxes within a PowerPoint template, and create copies of the presentation.

It is crucial to note that the code assumes a specific database schema and template structure. However, you should tailor it to match your own data schema and template design.

For instance, you can read all the shapes available in your template and replace the text according to your business requirements. By doing so, you can automate a significant portion of the presentation creation process.

To further enhance the automation, you can append visualizations generated using Python to the presentation. For example, you can utilize libraries such as Matplotlib or Seaborn to create compelling visualizations and add them to a new slide in the presentation. This enables you to showcase data insights visually and seamlessly integrate them into the automated presentation generation process.

Please exercise your creative thinking and apply your domain knowledge to automate presentation tasks effectively. While the provided code serves as a starting point, it is crucial to extract, transform, and load the data according to your specific business rules and requirements. This customization will ensure that the generated presentations accurately represent your data and deliver meaningful insights to stakeholders.

Remember, the code snippet provided is meant to inspire and guide you in automating your presentation tasks. It is crucial to review and validate the generated presentations to ensure accuracy and coherence with your business goals.

Embrace your creativity and leverage the power of automation to streamline the process of creating presentations. With the right customization and utilization of your domain knowledge, you can efficiently generate insightful presentations that empower your stakeholders to make informed decisions.