Summarizing and Presenting EDA Results
After completing your exploratory data analysis, your next step is to communicate the most important findings in a way that is clear, relevant, and actionable for your audience.
Effective EDA summaries help decision-makers quickly understand what matters in the data and what steps they might take next. To achieve this, focus on the following principles:
Key Principles for Communicating EDA Results
- Clarity: avoid jargon, use straightforward language, and highlight only the most meaningful results;
- Relevance: choose findings that directly relate to business objectives, such as sales trends, customer segments, or anomalies in product performance;
- Actionable insights: instead of just listing statistics, interpret what they mean and suggest possible actions or questions for further analysis.
By applying these principles, you ensure your EDA summary is easy to understand and supports informed decision-making.
12345678910111213141516171819202122232425import pandas as pd import matplotlib.pyplot as plt # Simulate retail sales data data = { "Product": ["A", "B", "C", "D", "E"], "Units Sold": [120, 340, 230, 150, 90], "Revenue": [2400, 6800, 4600, 3000, 1800], "Returns": [4, 10, 7, 3, 2] } df = pd.DataFrame(data) # Create summary statistics summary = df[["Units Sold", "Revenue", "Returns"]].agg(["mean", "median", "max", "min"]) print("Summary Table:") print(summary) # Visualize total units sold by product plt.figure(figsize=(6,4)) plt.bar(df["Product"], df["Units Sold"], color="skyblue") plt.title("Units Sold by Product") plt.xlabel("Product") plt.ylabel("Units Sold") plt.tight_layout() plt.show()
Suppose you are presenting these results to a retail management team. You might summarize the findings as follows:
Key Insights:
- Product B is the top performer: it has significantly higher units sold and revenue compared to other products;
- Products C and D also perform well, while Products A and E lag behind;
- Median units sold across all products is 150, but the wide range suggests areas for potential inventory or marketing adjustments;
- Returns are low overall, with Product B having the highest number of returns, possibly due to its higher sales volume.
Recommendations:
- Prioritize promotion for lower-performing products (A and E);
- Investigate the reasons behind Product B's higher number of returns to identify possible improvements.
Merci pour vos commentaires !
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After completing your exploratory data analysis, your next step is to communicate the most important findings in a way that is clear, relevant, and actionable for your audience.
Effective EDA summaries help decision-makers quickly understand what matters in the data and what steps they might take next. To achieve this, focus on the following principles:
Key Principles for Communicating EDA Results
- Clarity: avoid jargon, use straightforward language, and highlight only the most meaningful results;
- Relevance: choose findings that directly relate to business objectives, such as sales trends, customer segments, or anomalies in product performance;
- Actionable insights: instead of just listing statistics, interpret what they mean and suggest possible actions or questions for further analysis.
By applying these principles, you ensure your EDA summary is easy to understand and supports informed decision-making.
12345678910111213141516171819202122232425import pandas as pd import matplotlib.pyplot as plt # Simulate retail sales data data = { "Product": ["A", "B", "C", "D", "E"], "Units Sold": [120, 340, 230, 150, 90], "Revenue": [2400, 6800, 4600, 3000, 1800], "Returns": [4, 10, 7, 3, 2] } df = pd.DataFrame(data) # Create summary statistics summary = df[["Units Sold", "Revenue", "Returns"]].agg(["mean", "median", "max", "min"]) print("Summary Table:") print(summary) # Visualize total units sold by product plt.figure(figsize=(6,4)) plt.bar(df["Product"], df["Units Sold"], color="skyblue") plt.title("Units Sold by Product") plt.xlabel("Product") plt.ylabel("Units Sold") plt.tight_layout() plt.show()
Suppose you are presenting these results to a retail management team. You might summarize the findings as follows:
Key Insights:
- Product B is the top performer: it has significantly higher units sold and revenue compared to other products;
- Products C and D also perform well, while Products A and E lag behind;
- Median units sold across all products is 150, but the wide range suggests areas for potential inventory or marketing adjustments;
- Returns are low overall, with Product B having the highest number of returns, possibly due to its higher sales volume.
Recommendations:
- Prioritize promotion for lower-performing products (A and E);
- Investigate the reasons behind Product B's higher number of returns to identify possible improvements.
Merci pour vos commentaires !
