Seaborn ヒートマップ：Pythonでヒートマップを作成する完全ガイド

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
 
# Create sample data
data = np.random.rand(5, 7)
ax = sns.heatmap(data)
plt.title("Basic Seaborn Heatmap")
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
 
# Load a built-in dataset
df = sns.load_dataset("mpg").select_dtypes(include="number")
 
# Compute the correlation matrix
corr = df.corr()
 
# Plot the heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(
    corr,
    annot=True,
    fmt=".2f",
    cmap="coolwarm",
    center=0,
    square=True,
    linewidths=0.5
)
plt.title("Correlation Matrix - MPG Dataset")
plt.tight_layout()
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
 
data = np.random.rand(6, 6)
fig, axes = plt.subplots(1, 3, figsize=(18, 5))
 
cmaps = ["viridis", "coolwarm", "YlOrRd"]
for ax, cmap in zip(axes, cmaps):
    sns.heatmap(data, cmap=cmap, ax=ax, annot=True, fmt=".2f")
    ax.set_title(f'cmap="{cmap}"')
 
plt.tight_layout()
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
 
data = np.random.randint(0, 1000, size=(4, 5))
 
plt.figure(figsize=(8, 5))
sns.heatmap(
    data,
    annot=True,
    fmt="d",            # integer format
    cmap="Blues",
    annot_kws={"size": 14, "weight": "bold"}  # customize font
)
plt.title("Heatmap with Integer Annotations")
plt.show()

plt.figure(figsize=(12, 8))  # width=12, height=8 inches
sns.heatmap(data, cmap="viridis")
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
 
df = sns.load_dataset("mpg").select_dtypes(include="number")
corr = df.corr()
 
# Create a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
 
plt.figure(figsize=(10, 8))
sns.heatmap(
    corr,
    mask=mask,
    annot=True,
    fmt=".2f",
    cmap="coolwarm",
    center=0,
    square=True,
    linewidths=0.5
)
plt.title("Lower Triangle Correlation Heatmap")
plt.tight_layout()
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
 
df = sns.load_dataset("mpg").select_dtypes(include="number").dropna()
corr = df.corr()
 
g = sns.clustermap(
    corr,
    annot=True,
    fmt=".2f",
    cmap="vlag",
    center=0,
    linewidths=0.5,
    figsize=(8, 8),
    dendrogram_ratio=0.15
)
g.ax_heatmap.set_title("Clustered Correlation Heatmap", pad=60)
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
 
np.random.seed(42)
data = np.random.uniform(-1, 1, size=(8, 8))
 
plt.figure(figsize=(8, 6))
sns.heatmap(
    data,
    vmin=-1,
    vmax=1,
    center=0,
    cmap="RdBu_r",
    annot=True,
    fmt=".2f"
)
plt.title("Heatmap with Fixed Color Range (-1 to 1)")
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
 
# Train a quick model
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
    iris.data, iris.target, test_size=0.3, random_state=42
)
clf = RandomForestClassifier(random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
 
# Build and plot the confusion matrix
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(7, 5))
sns.heatmap(
    cm,
    annot=True,
    fmt="d",
    cmap="Blues",
    xticklabels=iris.target_names,
    yticklabels=iris.target_names
)
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix - Iris Classification")
plt.tight_layout()
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
 
np.random.seed(0)
hours = list(range(24))
days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
data = pd.DataFrame(
    np.random.poisson(lam=20, size=(7, 24)),
    index=days,
    columns=hours
)
 
plt.figure(figsize=(14, 5))
sns.heatmap(data, cmap="YlOrRd", linewidths=0.3, annot=False)
plt.xlabel("Hour of Day")
plt.ylabel("Day of Week")
plt.title("Activity Heatmap by Day and Hour")
plt.tight_layout()
plt.show()

pip install pygwalker

import pandas as pd
import pygwalker as pyg
 
df = pd.read_csv("your_data.csv")
walker = pyg.walk(df)