Python Data Analysis and Visualization Examples Using Pandas, Matplotlib, Seaborn, ReportLab, and Plotly/Dash

1. Basic Statistical Report – Generate a report containing count, mean, standard deviation, etc., for a dataset.

import pandas as pd # Load data df = pd.read_csv('data.csv') # Generate descriptive statistics report report_stats = df.describe() print(report_stats)

2. Pivot Table – Use a pivot table to summarize data across different dimensions.

pivot_table = pd.pivot_table(df, values='Sales', index=['Year', 'Category'], aggfunc=np.sum) print(pivot_table)

3. Bar Chart for Categorical Data – Compare sales volumes of different products.

import matplotlib.pyplot as plt product_sales = df['Product'].value_counts() product_sales.plot(kind='bar') plt.title('Product Sales Comparison') plt.xlabel('Product') plt.ylabel('Sales Count') plt.show()

4. Pie Chart for Proportion – Show the distribution of customer source channels.

channel_counts = df['Source_Channel'].value_counts(normalize=True) channel_counts.plot(kind='pie', autopct='%1.1f%%') plt.title('Customer Source Channel Distribution') plt.show()

5. Line Chart for Trend – Visualize monthly sales trends.

df['Month'] = df['Date'].dt.month monthly_sales = df.groupby('Month')['Sales'].sum() monthly_sales.plot(kind='line') plt.title('Monthly Sales Trend') plt.xlabel('Month') plt.ylabel('Total Sales') plt.show()

6. Heatmap for Variable Relationships – Visualize a correlation matrix.

import seaborn as sns correlation_matrix = df.corr() sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm') plt.title('Correlation Matrix Heatmap') plt.show()

7. Boxplot for Outlier Detection – Detect abnormal values in sales data.

sns.boxplot(x=df['Sales']) plt.title('Sales Data Outlier Detection') plt.show()

8. Grouped Bar Chart – Compare sales of each product category across different years.

sns.catplot(x='Category', y='Sales', hue='Year', data=df, kind='bar') plt.title('Annual Category Sales Comparison') plt.show()

9. Custom PDF Report – Generate a PDF that includes charts and tables using ReportLab.

from reportlab.lib.pagesizes import letter, landscape from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image, Table, TableStyle doc = SimpleDocTemplate('report.pdf', pagesize=landscape(letter)) story = [] story.append(Paragraph('Sales Report', style)) img_data = open('chart.png', 'rb').read() img = Image(img_data, width=400, height=300) story.append(img) table_data = [df.columns.tolist()] + df.values.tolist() t = Table(table_data) t.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), '#CCCCCC'), ('TEXTCOLOR',(0,0),(-1,0), '#000000'), ('ALIGN',(0,0),(-1,-1),'CENTER'), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,0), 14) ])) story.append(t) doc.build(story)

10. Interactive Dashboard (Plotly/Dash) – Build a web dashboard to explore multi‑dimensional sales data.

import plotly.express as px import dash import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output app = dash.Dash(__name__) app.layout = html.Div([ dcc.Dropdown(id='year-selector', options=[{'label': i, 'value': i} for i in df['Year'].unique()], value=df['Year'].min()), dcc.Graph(id='sales-graph') ]) @app.callback(Output('sales-graph', 'figure'), Input('year-selector', 'value')) def update_graph(selected_year): filtered_df = df[df['Year'] == selected_year] fig = px.bar(filtered_df, x='Category', y='Sales', title=f'Sales by Category in {selected_year}') return fig if __name__ == '__main__': app.run_server(debug=True)