REK-proj-1/jupyter_test.ipynb

%matplotlib inline
%load_ext autoreload
%autoreload 2

import numpy as np
import pandas as pd
import torch
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import Markdown, display, HTML

# Fix the dying kernel problem (only a problem in some installations - you can remove it, if it works without it)
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'

a = np.array(
    [[1.0, 2.0, 3.0], 
     [4.0, 5.0, 6.0], 
     [7.0, 8.0, 9.0]]
)

print("Numpy array")
print(a)
print()

df = pd.DataFrame(a, columns=['A', 'B', 'C'])

print("Pandas DataFrame")
print(df)
print()

print("Pretty display of pandas DataFrame")
display(HTML(df.to_html(index=False)))
print()

tensor = torch.from_numpy(a)

print("PyTorch tensor")
print(tensor)
print()

# Matplotlib

print("Matplolib chart")

# Prepare the data
x = np.linspace(0, 10, 100)

# Plot the data
plt.plot(x, x, label='linear')

# Add a legend
plt.legend()

# Show the plot
plt.show()

# Seaborn

print("Seaborn chart")

sns.set_theme(style="darkgrid")

# Load the example Titanic dataset (the dataset may load some time)
df = sns.load_dataset("titanic")

# Make a custom palette with gendered colors
pal = dict(male="#6495ED", female="#F08080")

# Show the survival probability as a function of age and sex
g = sns.lmplot(x="age", y="survived", col="sex", hue="sex", data=df,
               palette=pal, y_jitter=.02, logistic=True, truncate=False)
g.set(xlim=(0, 80), ylim=(-.05, 1.05))

# Show the plot
plt.show()

Numpy array
[[1. 2. 3.]
 [4. 5. 6.]
 [7. 8. 9.]]

Pandas DataFrame
     A    B    C
0  1.0  2.0  3.0
1  4.0  5.0  6.0
2  7.0  8.0  9.0

Pretty display of pandas DataFrame

A	B	C
1.0	2.0	3.0
4.0	5.0	6.0
7.0	8.0	9.0

PyTorch tensor
tensor([[1., 2., 3.],
        [4., 5., 6.],
        [7., 8., 9.]], dtype=torch.float64)

Matplolib chart

Seaborn chart