Hands-on Exploratory Data Analysis in Python + ChatGPT 3.5

Load your dataset in a Jupyter notebook by installing pandas, importing it as pd, and using read_excel to load an Excel file, then view the first five rows with head.

Identify and impute missing values in a dataframe using python, pandas, and simple imputer with ChatGPT guidance, applying median for numeric data and most frequent for categorical data.

Identify and fix inconsistent values to assign correct data types in Python data frames. Use pandas to inspect dtypes, detect non-numeric costs, and convert to float, with ChatGPT-assisted code.

Learn to clean a pandas data frame by removing rows with inconsistent values and assign correct data types (float 64, date time 64) to ensure compatibility.

Identify duplicates with the duplicated function and remove them using the drop_duplicates method to yield a clean data frame ready for analysis.

Sort datasets by numeric values with a simple Python pandas code, explore ascending or descending orders for age or cost, and use ChatGPT to generate the code.

Master data filtering techniques in Python by selecting dataframes with single and multiple conditions. Explore filtering by country France and product category games, with ChatGPT-assisted Python code.

Merge the extra data by adding the refund variable to the cleaned data using order ID as the key with pandas merge in Python.

learn to append data by rows with pandas pd.concat to extend a dataset. load extra_data.xls as extra_data, concatenate with masked_df to form final_df and increase rows to about 1007.

Identify the primary country of residence through frequency analysis of a categorical country variable, then visualize results with a bar chart in Python using pandas and ChatGPT-generated code.

compute category frequencies to reveal the product category with the highest sales, visualize results, and identify the top five customers, the top five sales representatives, and the top sales manager.

Discover how to analyze categorical data by computing percentages from value counts and visualizing with a pie chart, revealing that 78.3% of purchases occur on PC.

Explore numeric measures in descriptive analysis with Python code, using mean, median, mode, percentiles, and standard deviation to summarize numeric variables such as order value, cost, and refund.

Analyze the distribution of three numeric variables: order value, cost, and refund, by generating Python code with ChatGPT to plot histograms and box plots, revealing medians, percentiles, and outliers.

Apply the group by method to mix categorical and numeric data, identify top product categories by median order value and median cost.

Use the groupby method to compute median order value and median cost by customer_name, revealing the top three profitable and highest-expense customers. Also rank sales reps by median refunds.

Learn to build and visualize pivot tables in Python using ChatGPT to summarize loyalty by customers, using repeat purchase, order value, and cost to identify the most profitable customers.

Engage in hands-on exploratory data analysis by building and sorting pivot tables to analyze median sales and median cost by customers, and identify top ten loyal customers by purchase frequency.

Explore cross-tabulation to measure the relationship between two categorical variables, visualize with heatmaps, and identify which sales managers drive sales by product category and by country.

Apply correlation analysis in Python to reveal relationships among numeric variables like order value, cost, and refund using a correlation matrix and heatmap.