🗂️ Visualizations

• ⚙️ Setup
• 📊 Basic Visualizations
  • 📊 Bar Chart
  • 📈 Histogram
  • 📉 Line Chart
  • ⚫ Scatter Plot
  • 🔥 Heatmap
• 🧱 Subplots
• 💾 Save to Disk
  • 🖼️ Save to Image
  • 📄 Save Plots to PDF

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⚙️ Setup

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

import numpy as np
import matplotlib.pyplot as plt

# %matplotlib notebook

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv")
df.dropna(inplace=True)
df.head(20)
df.shape

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	MALE
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	FEMALE
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	FEMALE
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	FEMALE
5	Adelie	Torgersen	39.3	20.6	190.0	3650.0	MALE
6	Adelie	Torgersen	38.9	17.8	181.0	3625.0	FEMALE
7	Adelie	Torgersen	39.2	19.6	195.0	4675.0	MALE
12	Adelie	Torgersen	41.1	17.6	182.0	3200.0	FEMALE
13	Adelie	Torgersen	38.6	21.2	191.0	3800.0	MALE
14	Adelie	Torgersen	34.6	21.1	198.0	4400.0	MALE
15	Adelie	Torgersen	36.6	17.8	185.0	3700.0	FEMALE
16	Adelie	Torgersen	38.7	19.0	195.0	3450.0	FEMALE
17	Adelie	Torgersen	42.5	20.7	197.0	4500.0	MALE
18	Adelie	Torgersen	34.4	18.4	184.0	3325.0	FEMALE
19	Adelie	Torgersen	46.0	21.5	194.0	4200.0	MALE
20	Adelie	Biscoe	37.8	18.3	174.0	3400.0	FEMALE
21	Adelie	Biscoe	37.7	18.7	180.0	3600.0	MALE
22	Adelie	Biscoe	35.9	19.2	189.0	3800.0	FEMALE
23	Adelie	Biscoe	38.2	18.1	185.0	3950.0	MALE
24	Adelie	Biscoe	38.8	17.2	180.0	3800.0	MALE

(333, 7)

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---

📊 Basic Visualizations

📊 Bar Chart

counts = df["species"].value_counts()
_ = plt.bar(counts.index, counts.values)
plt.title("Count of Penguins per Species");
plt.xlabel("Species");
plt.ylabel("Count");
plt.show();

📈 Histogram

plt.hist(df["body_mass_g"], bins=20, color="skyblue", edgecolor="black");
plt.title("Distribution of Body Mass (g)");
plt.xlabel("Body Mass (g)");
plt.ylabel("Frequency");
plt.show();

📉 Line Chart

avg = df.groupby("species")["body_mass_g"].mean()
plt.plot(avg.index, avg.values, marker='o');
plt.title("Average Body Mass per Species");
plt.xlabel("Species");
plt.ylabel("Avg Body Mass (g)");
plt.show();

⚫ Scatter Plot

species = df["species"].unique()
for sp in species:
    subset = df[df["species"] == sp]
    plt.scatter(subset["bill_length_mm"], subset["flipper_length_mm"], label=sp);
plt.title("Bill Length vs. Flipper Length");
plt.xlabel("Bill Length (mm)");
plt.ylabel("Flipper Length (mm)");
plt.legend();
plt.show();

🔥 Heatmap

import numpy as np

corr = df.select_dtypes(include="number").corr()
fig, ax = plt.subplots();
cax = ax.matshow(corr, cmap="coolwarm");
fig.colorbar(cax);
ticks = np.arange(len(corr.columns));
plt.xticks(ticks, corr.columns, rotation=90);
plt.yticks(ticks, corr.columns);
plt.title("Correlation Heatmap", pad=20);
plt.show();

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---

🧱 Subplots

fig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()
subplot_index = 0

def next_ax():
    global subplot_index
    plt.sca(axes[subplot_index])
    subplot_index += 1

# --- Paste starts here ---
# --- Plot 1 ---
next_ax()
counts = df["species"].value_counts()
_ = plt.bar(counts.index, counts.values)
plt.title("Count of Penguins per Species");
plt.xlabel("Species");
plt.ylabel("Count");

# --- Plot 2 ---
next_ax()
plt.hist(df["body_mass_g"], bins=20, color="skyblue", edgecolor="black");
plt.title("Distribution of Body Mass (g)");
plt.xlabel("Body Mass (g)");
plt.ylabel("Frequency");

# --- Plot 3 ---
next_ax()
avg = df.groupby("species")["body_mass_g"].mean()
plt.plot(avg.index, avg.values, marker='o');
plt.title("Average Body Mass per Species");
plt.xlabel("Species");
plt.ylabel("Avg Body Mass (g)");

# --- Plot 4 ---
next_ax()
species = df["species"].unique()
for sp in species:
    subset = df[df["species"] == sp]
    plt.scatter(subset["bill_length_mm"], subset["flipper_length_mm"], label=sp);
plt.title("Bill Length vs. Flipper Length");
plt.xlabel("Bill Length (mm)");
plt.ylabel("Flipper Length (mm)");
plt.legend();

# --- Paste ends here ---
plt.tight_layout();
plt.show();

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---

💾 Save to Disk

🖼️ Save to Image

counts = df["species"].value_counts()
_ = plt.bar(counts.index, counts.values)
plt.title("Count of Penguins per Species");
plt.xlabel("Species");
plt.ylabel("Count");
plt.savefig('output.png'); # or output.pdf or output.png

📄 Save Plots to PDF

from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt

rows, cols = 2, 3
plots_per_page = rows * cols

plots = [
    lambda: plt.bar(df["species"].value_counts().index, df["species"].value_counts().values),
    lambda: plt.hist(df["body_mass_g"], bins=20),
    lambda: plt.plot(df.groupby("species")["body_mass_g"].mean()),
    lambda: plt.scatter(df["bill_length_mm"], df["flipper_length_mm"]),
    lambda: plt.hist(df["flipper_length_mm"]),
    lambda: plt.boxplot(df["body_mass_g"]),
    lambda: plt.plot(df["bill_depth_mm"].rolling(5).mean()),
    lambda: plt.hist(df["body_mass_g"], bins=20),
]

with PdfPages("output.pdf") as pdf:
    for i in range(0, len(plots), plots_per_page):
        fig, axes = plt.subplots(rows, cols, figsize=(cols * 4, rows * 3))

        # Ensure axes is always a list
        if plots_per_page == 1:
            axes = [axes]
        else:
            axes = axes.flatten()

        for j in range(plots_per_page):
            plot_idx = i + j
            if plot_idx >= len(plots):
                fig.delaxes(axes[j])
            else:
                plt.sca(axes[j])
                plots[plot_idx]();  # works fine

        plt.tight_layout()
        pdf.savefig(fig)
        plt.close(fig)

<BarContainer object of 3 artists>

(array([ 3., 11., 13., 29., 31., 38., 35., 17., 24., 17., 18., 21., 15.,
        12., 10., 17., 11.,  5.,  5.,  1.]),
 array([2700., 2880., 3060., 3240., 3420., 3600., 3780., 3960., 4140.,
        4320., 4500., 4680., 4860., 5040., 5220., 5400., 5580., 5760.,
        5940., 6120., 6300.]),
 <BarContainer object of 20 artists>)

[<matplotlib.lines.Line2D at 0x15eb59e10>]

<matplotlib.collections.PathCollection at 0x15d606e50>

(array([ 3., 20., 51., 77., 44., 15., 42., 38., 28., 15.]),
 array([172. , 177.9, 183.8, 189.7, 195.6, 201.5, 207.4, 213.3, 219.2,
        225.1, 231. ]),
 <BarContainer object of 10 artists>)

{'whiskers': [<matplotlib.lines.Line2D at 0x15d55c590>,
  <matplotlib.lines.Line2D at 0x15d55e210>],
 'caps': [<matplotlib.lines.Line2D at 0x15d28d650>,
  <matplotlib.lines.Line2D at 0x15d28e390>],
 'boxes': [<matplotlib.lines.Line2D at 0x15d55ff90>],
 'medians': [<matplotlib.lines.Line2D at 0x15d28f710>],
 'fliers': [<matplotlib.lines.Line2D at 0x15d28e990>],
 'means': []}

[<matplotlib.lines.Line2D at 0x15ccc6910>]

(array([ 3., 11., 13., 29., 31., 38., 35., 17., 24., 17., 18., 21., 15.,
        12., 10., 17., 11.,  5.,  5.,  1.]),
 array([2700., 2880., 3060., 3240., 3420., 3600., 3780., 3960., 4140.,
        4320., 4500., 4680., 4860., 5040., 5220., 5400., 5580., 5760.,
        5940., 6120., 6300.]),
 <BarContainer object of 20 artists>)

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