This article explains Pair Plots (Scatter Matrices) in Python for exploratory data analysis, showing pairwise relationships between numerical variables using scatter plots and distribution plots.
The article provides the following Python code using `seaborn` and `matplotlib` to create a pair plot:
```python
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Create some random data
data = np.random.rand(100, 4)
df = pd.DataFrame(data, columns= 'A', 'B', 'C', 'D' » )
# Create the pair plot
sns.pairplot(df)
# Show the plot
plt.show()
```
The article provides the following Python code using `seaborn` and `matplotlib` to create a pair plot:
```python
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Create some random data
data = np.random.rand(100, 4)
df = pd.DataFrame(data, columns= 'A', 'B', 'C', 'D' » )
# Create the pair plot
sns.pairplot(df)
# Show the plot
plt.show()
```
The author describes building a personal, open-source computational engine using Python libraries SymPy, NumPy, pandas, SciPy, statsmodels, Pingouin, Matplotlib, and Seaborn, effectively replicating the functionality of Wolfram Mathematica at no cost.
