Basic Plot Creation in Matplotlib: Complete Guide

Data visualization ka foundation hain yeh 8 chart types. Line chart se lekar Box plot tak — har chart ka basic version, professional styled version, aur har parameter ki detail explanation ke saath.

📑 Is Guide Mein 8 Chart Types Cover Honge:

• Line Chart: plt.plot() — Trends over time dikhana
• Bar Chart: plt.bar() — Category comparison
• Horizontal Bar: plt.barh() — Readable category bars
• Scatter Plot: plt.scatter() — Relationship between 2 variables
• Histogram: plt.hist() — Data distribution
• Pie Chart: plt.pie() — Percentage breakdown
• Box Plot: plt.boxplot() — Spread & outliers
• Area Chart: plt.stackplot() — Cumulative trends

🔍 Kya Hai: Line chart data points ko straight lines se connect karta hai — time ke saath trends dikhane ke liye sabse common chart hai.

💻 Basic Version:

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
revenue = [45000, 52000, 48000, 61000, 55000, 67000]

plt.plot(months, revenue)
plt.title("Monthly Revenue")
plt.xlabel("Month")
plt.ylabel("Revenue (₹)")
plt.show()

💻 Professional Styled Version:

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
revenue_2023 = [45000, 52000, 48000, 61000, 55000, 67000]
revenue_2024 = [50000, 58000, 53000, 69000, 62000, 75000]

fig, ax = plt.subplots(figsize=(10, 6))

ax.plot(months, revenue_2023,
        color="#3498db",          # line color (hex code)
        linewidth=2.5,             # line thickness
        linestyle="-",              # solid line ("-", "--", "-.", ":")
        marker="o",                # data point marker ("o","s","^","D","*")
        markersize=8,              # marker size
        markerfacecolor="white",   # marker fill color
        markeredgecolor="#3498db", # marker border color
        markeredgewidth=2,         # marker border thickness
        label="2023",               # legend label
        alpha=0.9,                 # transparency (0=invisible, 1=solid)
        zorder=2                   # layer order (higher=front)
)

ax.plot(months, revenue_2024,
        color="#e74c3c",
        linewidth=2.5,
        linestyle="--",
        marker="s",
        markersize=8,
        markerfacecolor="white",
        markeredgecolor="#e74c3c",
        markeredgewidth=2,
        label="2024",
        alpha=0.9,
        zorder=2
)

# Title & Labels
ax.set_title("Monthly Revenue Comparison", fontsize=16, fontweight="bold", pad=15)
ax.set_xlabel("Month", fontsize=12, labelpad=10)
ax.set_ylabel("Revenue (₹)", fontsize=12, labelpad=10)

# Grid
ax.grid(True, linestyle="--", alpha=0.3, zorder=0)

# Legend
ax.legend(fontsize=11, loc="upper left", framealpha=0.9)

# Spine styling
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig("line_chart.png", dpi=150, bbox_inches="tight")
plt.show()

📋 All Parameters Explained:

🔍 Kya Hai: Bar chart categories ko rectangular bars se compare karta hai — har bar ki height value represent karti hai. Category comparison ka king hai.

💻 Basic Version:

departments = ["IT", "HR", "Finance", "Marketing", "Operations"]
avg_salary = [82500, 55800, 75200, 62400, 48200]

plt.bar(departments, avg_salary)
plt.title("Average Salary by Department")
plt.ylabel("Salary (₹)")
plt.show()

💻 Professional Styled Version (with Data Labels):

departments = ["IT", "HR", "Finance", "Marketing", "Operations"]
avg_salary = [82500, 55800, 75200, 62400, 48200]

# Color gradient — highest=dark, lowest=light
colors = ["#1a5276", "#7fb3d8", "#2980b9", "#5dade2", "#aed6f1"]

fig, ax = plt.subplots(figsize=(10, 6))

bars = ax.bar(departments, avg_salary,
    color=colors,                 # individual bar colors
    width=0.6,                    # bar width (0 to 1)
    edgecolor="#2c3e50",           # bar border color
    linewidth=1.2,                 # bar border thickness
    alpha=0.9,                     # transparency
    zorder=2                       # layer order
)

# Data Labels — har bar ke upar value dikhana
for bar, val in zip(bars, avg_salary):
    ax.text(
        bar.get_x() + bar.get_width() / 2,  # x position (center of bar)
        bar.get_height() + 1000,              # y position (slightly above bar)
        f"₹{val:,}",                            # formatted text
        ha="center",                            # horizontal alignment
        va="bottom",                            # vertical alignment
        fontsize=10,                             # text size
        fontweight="bold",                       # text weight
        color="#2c3e50"                         # text color
    )

ax.set_title("Average Salary by Department", fontsize=16, fontweight="bold", pad=15)
ax.set_xlabel("Department", fontsize=12)
ax.set_ylabel("Average Salary (₹)", fontsize=12)
ax.grid(axis="y", linestyle="--", alpha=0.3, zorder=0)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig("bar_chart.png", dpi=150, bbox_inches="tight")
plt.show()

📋 All Parameters Explained:

🔍 Kya Hai: Horizontal bar chart category names lambe hone par vertical bars se zyada readable hota hai. Ranking dikhane ke liye best hai — longest bar = highest value clearly dikhai deta hai.

💻 Professional Styled Version:

products = ["Samsung Galaxy S24", "iPhone 15 Pro", "OnePlus 12", 
            "Google Pixel 8", "Nothing Phone 2"]
sales = [15200, 18500, 12800, 8900, 6500]

# Sort for ranking effect
sorted_data = sorted(zip(sales, products))
sales_sorted = [x[0] for x in sorted_data]
products_sorted = [x[1] for x in sorted_data]

fig, ax = plt.subplots(figsize=(10, 6))

bars = ax.barh(products_sorted, sales_sorted,
    color="#2ecc71",             # bar color
    height=0.5,                  # bar height (barh mein width ki jagah height)
    edgecolor="#27ae60",         # border color
    linewidth=1,                 # border thickness
    alpha=0.85                   # transparency
)

# Data labels — bar ke end mein value
for bar, val in zip(bars, sales_sorted):
    ax.text(
        bar.get_width() + 200,     # x position (right of bar)
        bar.get_y() + bar.get_height() / 2,  # y position (center)
        f"{val:,} units",
        ha="left", va="center",
        fontsize=10, fontweight="bold"
    )

ax.set_title("Top Selling Smartphones", fontsize=16, fontweight="bold", pad=15)
ax.set_xlabel("Units Sold", fontsize=12)
ax.grid(axis="x", linestyle="--", alpha=0.3)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig("horizontal_bar.png", dpi=150, bbox_inches="tight")
plt.show()

🔍 Kya Hai: Scatter plot do continuous variables ke beech relationship dikhata hai — har dot ek data point hai. Correlation, clusters, outliers — sab ek glance mein visible hote hain.

💻 Professional Styled Version:

import numpy as np

np.random.seed(42)
experience = np.random.uniform(1, 20, 100)
salary = experience * 5000 + np.random.normal(0, 8000, 100) + 30000
performance = np.random.uniform(1, 5, 100)

fig, ax = plt.subplots(figsize=(10, 7))

scatter = ax.scatter(experience, salary,
    c=performance,                # color mapped to 3rd variable
    cmap="RdYlGn",               # colormap (Red-Yellow-Green)
    s=performance * 40,           # size mapped to variable (bubble chart!)
    alpha=0.7,                    # transparency
    edgecolors="#2c3e50",         # dot border color
    linewidth=0.5,                # dot border thickness
    zorder=2                      # layer order
)

# Colorbar — 3rd variable ka legend
cbar = plt.colorbar(scatter, ax=ax, shrink=0.8, label="Performance Rating")

# Trend line
z = np.polyfit(experience, salary, 1)
p = np.poly1d(z)
ax.plot(sorted(experience), p(sorted(experience)), 
        "--", color="red", linewidth=1.5, alpha=0.7, label="Trend Line")

ax.set_title("Experience vs Salary (Bubble = Performance)", fontsize=15, fontweight="bold")
ax.set_xlabel("Experience (Years)", fontsize=12)
ax.set_ylabel("Salary (₹)", fontsize=12)
ax.legend(fontsize=10)
ax.grid(True, linestyle="--", alpha=0.3)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig("scatter_plot.png", dpi=150, bbox_inches="tight")
plt.show()

📋 Scatter-Specific Parameters:

🔍 Kya Hai: Histogram continuous data ki frequency distribution dikhata hai — data ko bins (ranges) mein divide karke har bin mein kitne values hain yeh bars se represent karta hai. Skewness, outliers, distribution shape — sab dikhta hai.

💻 Professional Styled Version:

np.random.seed(42)
salaries = np.random.normal(65000, 15000, 1000)

fig, ax = plt.subplots(figsize=(10, 6))

n, bins_edges, patches = ax.hist(salaries,
    bins=25,                       # number of bins (ranges)
    color="#3498db",               # bar color
    edgecolor="white",             # bar border
    linewidth=1,                   # border thickness
    alpha=0.8,                     # transparency
    density=False,                 # True = probability, False = count
    histtype="bar",                # "bar","step","stepfilled","barstacked"
    rwidth=0.9,                    # bar width relative to bin
    zorder=2
)

# Mean & Median reference lines
ax.axvline(np.mean(salaries), color="red", linewidth=2, linestyle="--", label=f"Mean: ₹{np.mean(salaries):,.0f}")
ax.axvline(np.median(salaries), color="green", linewidth=2, linestyle="-.", label=f"Median: ₹{np.median(salaries):,.0f}")

ax.set_title("Salary Distribution", fontsize=16, fontweight="bold")
ax.set_xlabel("Salary (₹)", fontsize=12)
ax.set_ylabel("Number of Employees", fontsize=12)
ax.legend(fontsize=11)
ax.grid(axis="y", linestyle="--", alpha=0.3)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig("histogram.png", dpi=150, bbox_inches="tight")
plt.show()

📋 Histogram-Specific Parameters:

🔍 Kya Hai: Pie chart whole ka parts dikhata hai — percentage breakdown visualize karne ke liye. Circle ko slices mein divide karta hai jahan har slice ek category ki proportion represent karta hai.

💻 Professional Styled Version:

departments = ["IT", "HR", "Finance", "Marketing", "Operations"]
emp_count = [3200, 2400, 2000, 1500, 900]
colors = ["#3498db", "#2ecc71", "#e74c3c", "#f39c12", "#9b59b6"]
explode = (0.05, 0, 0, 0, 0)  # IT slice slightly pulled out

fig, ax = plt.subplots(figsize=(8, 8))

wedges, texts, autotexts = ax.pie(emp_count,
    labels=departments,            # slice labels
    colors=colors,                 # slice colors
    autopct="%1.1f%%",             # percentage format inside slices
    startangle=90,                # rotation start angle
    explode=explode,               # pull out specific slices
    shadow=True,                   # 3D shadow effect
    textprops={"fontsize": 11},   # label font size
    pctdistance=0.75,             # percentage text position (0=center, 1=edge)
    labeldistance=1.1,            # label position from center
    wedgeprops={                   # slice styling
        "edgecolor": "white",
        "linewidth": 2
    }
)

# Style percentage text
for autotext in autotexts:
    autotext.set_color("white")
    autotext.set_fontweight("bold")

ax.set_title("Employee Distribution by Department", fontsize=16, fontweight="bold", pad=20)

plt.tight_layout()
plt.savefig("pie_chart.png", dpi=150, bbox_inches="tight")
plt.show()

📋 Pie-Specific Parameters:

🍩 Donut Chart Variation:

# Donut Chart — center mein hole
fig, ax = plt.subplots(figsize=(8, 8))
wedges, texts, autotexts = ax.pie(emp_count,
    labels=departments, colors=colors,
    autopct="%1.1f%%", startangle=90,
    wedgeprops={"width": 0.4, "edgecolor": "white", "linewidth": 2},  # width=0.4 makes donut!
    pctdistance=0.8
)
# Center text
ax.text(0, 0, f"Total\n{sum(emp_count):,}", ha="center", va="center", fontsize=16, fontweight="bold")
ax.set_title("Employee Distribution (Donut)", fontsize=15, fontweight="bold")
plt.savefig("donut_chart.png", dpi=150, bbox_inches="tight")
plt.show()

🔍 Kya Hai: Box plot data ki 5-number summary (min, Q1, median, Q3, max) ek box mein dikhata hai. Outliers dots ke roop mein separately dikhte hain. Distribution spread, skewness, aur outliers ek chart mein — EDA ka essential tool.

💻 Professional Styled Version:

np.random.seed(42)
it_salary = np.random.normal(82000, 15000, 200)
hr_salary = np.random.normal(55000, 10000, 200)
fin_salary = np.random.normal(75000, 12000, 200)
mkt_salary = np.random.normal(62000, 18000, 200)

data = [it_salary, hr_salary, fin_salary, mkt_salary]
labels = ["IT", "HR", "Finance", "Marketing"]

fig, ax = plt.subplots(figsize=(10, 6))

bp = ax.boxplot(data,
    labels=labels,                # x-axis labels
    patch_artist=True,            # fill boxes with color (IMPORTANT!)
    notch=True,                   # confidence interval notch
    showmeans=True,               # show mean marker
    meanprops={"marker": "D", "markerfacecolor": "red", "markersize": 8},
    showfliers=True,              # show outlier dots
    flierprops={"marker": "o", "markerfacecolor": "#e74c3c", "markersize": 5},
    whiskerprops={"linewidth": 1.5},
    capprops={"linewidth": 1.5},
    medianprops={"color": "#2c3e50", "linewidth": 2}
)

# Color each box
box_colors = ["#3498db", "#2ecc71", "#e74c3c", "#f39c12"]
for patch, color in zip(bp["boxes"], box_colors):
    patch.set_facecolor(color)
    patch.set_alpha(0.7)

ax.set_title("Salary Distribution by Department", fontsize=16, fontweight="bold")
ax.set_ylabel("Salary (₹)", fontsize=12)
ax.grid(axis="y", linestyle="--", alpha=0.3)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig("box_plot.png", dpi=150, bbox_inches="tight")
plt.show()

📋 BoxPlot-Specific Parameters:

🔍 Kya Hai: Stacked area chart multiple categories ki cumulative trend dikhata hai. Har layer ek category represent karti hai aur total height combined value dikhata hai. Revenue breakdown by product category over time — iske liye perfect hai.

💻 Professional Styled Version:

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
electronics = [30000, 35000, 28000, 42000, 38000, 45000]
fashion     = [20000, 22000, 25000, 28000, 30000, 35000]
grocery     = [15000, 16000, 14000, 18000, 17000, 20000]

fig, ax = plt.subplots(figsize=(10, 6))

ax.stackplot(months, electronics, fashion, grocery,
    labels=["Electronics", "Fashion", "Grocery"],  # legend labels
    colors=["#3498db", "#e74c3c", "#2ecc71"],       # layer colors
    alpha=0.8,                                       # transparency
    edgecolor="white",                                # layer borders
    linewidth=0.5                                     # border thickness
)

ax.set_title("Revenue by Category (Stacked)", fontsize=16, fontweight="bold")
ax.set_xlabel("Month", fontsize=12)
ax.set_ylabel("Revenue (₹)", fontsize=12)
ax.legend(loc="upper left", fontsize=10)
ax.grid(axis="y", linestyle="--", alpha=0.3)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig("stacked_area.png", dpi=150, bbox_inches="tight")
plt.show()

💻 Simple Area Chart (Single Series — fill_between):

# Single area chart
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(months, electronics, color="#3498db", linewidth=2)
ax.fill_between(months, electronics,
    color="#3498db",     # fill color
    alpha=0.3            # transparency (0.2-0.4 best for area)
)
ax.set_title("Electronics Revenue Trend", fontsize=15, fontweight="bold")
plt.savefig("area_chart.png", dpi=150, bbox_inches="tight")
plt.show()

Next Post: Part 2

Next part mein hum cover karenge: Styling, Annotations & Layout — titles, labels, data labels, annotations, subplots, themes aur chart ko professional level tak polish karne ke complete tools.

Happy Visualizing! 📊🚀