data-visualization

Matplotlib Basics

```python

import matplotlib.pyplot as plt

# Line plot

plt.figure(figsize=(10, 6))

plt.plot(x, y, marker='o', linestyle='-', color='blue', label='Series 1')

plt.xlabel('X Label')

plt.ylabel('Y Label')

plt.title('Title')

plt.legend()

plt.grid(True, alpha=0.3)

plt.show()

# Bar chart

plt.bar(categories, values, color='skyblue', edgecolor='black')

plt.xlabel('Categories')

plt.ylabel('Values')

plt.xticks(rotation=45)

plt.tight_layout()

plt.show()

```

Seaborn for Statistical Plots

```python

import seaborn as sns

# Set style

sns.set_style("whitegrid")

# Distribution

sns.histplot(data=df, x='value', kde=True, bins=30)

# Box plot

sns.boxplot(data=df, x='category', y='value')

# Violin plot

sns.violinplot(data=df, x='category', y='value')

# Heatmap

corr = df.corr()

sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)

# Pairplot

sns.pairplot(df, hue='target', diag_kind='kde')

```

```python

# Quick overview

df.info()

df.describe()

# Missing values

df.isnull().sum()

# Value counts

df['category'].value_counts().plot(kind='bar')

# Distribution

df.hist(figsize=(12, 10), bins=30)

plt.tight_layout()

plt.show()

# Correlation matrix

plt.figure(figsize=(10, 8))

sns.heatmap(df.corr(), annot=True, cmap='coolwarm',

center=0, square=True)

plt.title('Correlation Matrix')

plt.show()

```

```python

import plotly.express as px

import plotly.graph_objects as go

# Interactive scatter

fig = px.scatter(df, x='feature1', y='target',

color='category', size='value',

hover_data=['name', 'date'],

title='Interactive Scatter Plot')

fig.show()

# Time series

fig = px.line(df, x='date', y='value', color='category',

title='Time Series')

fig.update_xaxes(rangeslider_visible=True)

fig.show()

# 3D scatter

fig = px.scatter_3d(df, x='x', y='y', z='z',

color='category', size='value')

fig.show()

```

```python

import dash

from dash import dcc, html

from dash.dependencies import Input, Output

app = dash.Dash(__name__)

app.layout = html.Div([

html.H1('Sales Dashboard'),

dcc.Dropdown(

id='category-dropdown',

options=[{'label': cat, 'value': cat}

for cat in df['category'].unique()],

value=df['category'].unique()[0]

),

dcc.Graph(id='sales-graph'),

dcc.RangeSlider(

id='year-slider',

min=df['year'].min(),

max=df['year'].max(),

value=[df['year'].min(), df['year'].max()],

marks={str(year): str(year)

for year in df['year'].unique()}

)

])

@app.callback(

Output('sales-graph', 'figure'),

[Input('category-dropdown', 'value'),

Input('year-slider', 'value')]

)

def update_graph(selected_category, year_range):

filtered_df = df[

(df['category'] == selected_category) &

(df['year'] >= year_range[0]) &

(df['year'] <= year_range[1])

]

fig = px.line(filtered_df, x='date', y='sales')

return fig

if __name__ == '__main__':

app.run_server(debug=True)

```

```python

fig, axes = plt.subplots(2, 2, figsize=(12, 10))

# Top left

axes[0, 0].hist(data1, bins=30)

axes[0, 0].set_title('Histogram')

# Top right

axes[0, 1].scatter(x, y)

axes[0, 1].set_title('Scatter')

# Bottom left

axes[1, 0].plot(x, y)

axes[1, 0].set_title('Line Plot')

# Bottom right

axes[1, 1].boxplot([data1, data2, data3])

axes[1, 1].set_title('Box Plot')

plt.tight_layout()

plt.show()

```

1. Choose the right chart type:

- Comparison: Bar chart

- Distribution: Histogram, box plot

- Relationship: Scatter plot

- Time series: Line chart

- Composition: Pie chart, stacked bar

1. Design principles:

- Clear labels and titles

- Appropriate color schemes

- Remove chart junk

- Consistent formatting

- Accessibility (color-blind friendly)

1. Common pitfalls to avoid:

- Misleading axes (non-zero baseline)

- Too many colors

- 3D charts (distort perception)

- Pie charts with many categories

- Dual y-axes (confusing)

```python

# Seaborn palettes

sns.color_palette("viridis", as_cmap=True)

sns.color_palette("coolwarm", as_cmap=True)

sns.color_palette("Set2")

# Custom colors

colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#FFA07A']

```

```python

# High-resolution PNG

plt.savefig('figure.png', dpi=300, bbox_inches='tight')

# Vector format (PDF, SVG)

plt.savefig('figure.pdf', bbox_inches='tight')

plt.savefig('figure.svg', bbox_inches='tight')

```