تصور بيانات بايثون 2026: ماتبلوتليب، سيبورن، بلوتلي وستريمليت

import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np

# Figure and axes
fig, ax = plt.subplots(figsize=(10, 6))

# Line plot
x = np.linspace(0, 10, 200)
ax.plot(x, np.sin(x), label='sin(x)', linewidth=2, color='#0066CC')
ax.plot(x, np.cos(x), label='cos(x)', linewidth=2, color='#FF6600', linestyle='--')

# Styling
ax.set_title('Trigonometric Functions', fontsize=16, fontweight='bold', pad=15)
ax.set_xlabel('x', fontsize=12)
ax.set_ylabel('f(x)', fontsize=12)
ax.legend(fontsize=11)
ax.grid(True, alpha=0.3)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.savefig('trig.png', dpi=300, bbox_inches='tight')
plt.show()

# Subplots
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
fig.suptitle('Multiple Plots', fontsize=16, fontweight='bold')

axes[0,0].hist(np.random.normal(0, 1, 1000), bins=30, color='steelblue', edgecolor='white')
axes[0,0].set_title('Normal Distribution')

axes[0,1].scatter(np.random.rand(50), np.random.rand(50), alpha=0.6, s=80)
axes[0,1].set_title('Scatter Plot')

x = ['A', 'B', 'C', 'D', 'E']
y = [23, 45, 12, 67, 34]
axes[1,0].bar(x, y, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7'])
axes[1,0].set_title('Bar Chart')

axes[1,1].boxplot([np.random.normal(0, std, 100) for std in [1, 2, 3]])
axes[1,1].set_title('Box Plot')

plt.tight_layout()
plt.show()

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

# Load sample dataset
tips = sns.load_dataset('tips')

# Set theme
sns.set_theme(style='whitegrid', palette='muted', font_scale=1.2)

# Distribution plots
fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Histogram + KDE
sns.histplot(tips['total_bill'], kde=True, ax=axes[0], color='steelblue')
axes[0].set_title('Total Bill Distribution')

# Box plot by category
sns.boxplot(data=tips, x='day', y='total_bill', hue='sex', ax=axes[1])
axes[1].set_title('Bills by Day and Gender')

# Violin plot
sns.violinplot(data=tips, x='time', y='tip', hue='smoker',
               split=True, inner='quart', ax=axes[2])
axes[2].set_title('Tips Distribution')
plt.tight_layout()
plt.show()

# Correlation heatmap
corr = tips.select_dtypes(include='number').corr()
plt.figure(figsize=(8, 6))
sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm',
            center=0, square=True, linewidths=0.5)
plt.title('Correlation Matrix')
plt.show()

# Pair plot
sns.pairplot(tips, hue='sex', plot_kws={'alpha': 0.6})
plt.suptitle('Pair Plot', y=1.02)
plt.show()

# FacetGrid — multiple plots by category
g = sns.FacetGrid(tips, col='time', row='smoker', height=4)
g.map_dataframe(sns.scatterplot, x='total_bill', y='tip', alpha=0.6)
g.add_legend()
g.set_titles(col_template='{col_name}', row_template='{row_name}')
plt.show()

import plotly.express as px
import plotly.graph_objects as go
import pandas as pd

# Simple line chart
df = px.data.gapminder().query("continent == 'Asia'")
fig = px.line(df, x='year', y='lifeExp', color='country',
              title='Life Expectancy in Asia',
              labels={'lifeExp': 'Life Expectancy', 'year': 'Year'})
fig.update_layout(
    plot_bgcolor='white',
    paper_bgcolor='white',
    font_family='Inter',
    showlegend=True,
)
fig.show()  # opens in browser

# Animated scatter plot
df = px.data.gapminder()
fig = px.scatter(df, x='gdpPercap', y='lifeExp',
                 animation_frame='year',
                 animation_group='country',
                 size='pop', color='continent',
                 hover_name='country',
                 log_x=True,
                 size_max=55,
                 range_x=[100, 100000],
                 range_y=[25, 90],
                 title='World Health and Wealth (1952-2007)')
fig.show()

# Custom chart with graph_objects
fig = go.Figure()
x = ['Q1', 'Q2', 'Q3', 'Q4']

fig.add_trace(go.Bar(name='2025', x=x, y=[23, 45, 56, 78], marker_color='#4C72B0'))
fig.add_trace(go.Bar(name='2026', x=x, y=[34, 52, 61, 89], marker_color='#DD8452'))
fig.add_trace(go.Scatter(name='Target', x=x, y=[30, 50, 65, 85],
                          mode='lines+markers', line=dict(color='red', dash='dash')))

fig.update_layout(
    title='Quarterly Revenue 2025 vs 2026',
    xaxis_title='Quarter',
    yaxis_title='Revenue ($M)',
    barmode='group',
    hovermode='x unified'
)
fig.show()

# Export to HTML
fig.write_html('chart.html')

# Export static image (requires kaleido)
fig.write_image('chart.png', width=1200, height=600, scale=2)

pip install streamlit plotly pandas

# dashboard.py
import streamlit as st
import plotly.express as px
import pandas as pd

st.set_page_config(page_title='Sales Dashboard', layout='wide')
st.title('Sales Analytics Dashboard')

# Sidebar filters
with st.sidebar:
    st.header('Filters')
    year = st.selectbox('Year', [2024, 2025, 2026])
    region = st.multiselect('Region', ['North', 'South', 'East', 'West'],
                             default=['North', 'South'])

# KPI metrics
col1, col2, col3, col4 = st.columns(4)
col1.metric('Total Revenue', '$2.4M', '+12%')
col2.metric('Orders', '1,847', '+8%')
col3.metric('Avg Order Value', '$1,299', '+4%')
col4.metric('Satisfaction', '4.7/5', '+0.2')

# Charts in two columns
c1, c2 = st.columns(2)
with c1:
    fig = px.line(get_monthly_data(year), x='month', y='revenue',
                  title='Monthly Revenue')
    st.plotly_chart(fig, use_container_width=True)

with c2:
    fig = px.pie(get_regional_data(region), values='revenue',
                 names='region', title='Revenue by Region')
    st.plotly_chart(fig, use_container_width=True)

# Data table
st.subheader('Recent Orders')
df = get_orders(year, region)
st.dataframe(df, use_container_width=True)

# Run: streamlit run dashboard.py