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)