application_vnd.jupyter.stderr_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1)

def info(train):
    pass

train.describe().T

cont_col = [i for (i, j) in zip(test.columns, test.dtypes) if j in ["int", "float"]]
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
    sns.kdeplot(
        ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
    )
    sns.kdeplot(
        ax=ax, data=test, x=cont_col[i], color="#00BFC4", label="Test", fill=True
    )
    ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()

fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
    sns.kdeplot(ax=ax, data=train, x=cont_col[i], color='#F8766D', label='Train', fill=True)
    sns.kdeplot(ax=ax, data=origin, x=cont_col[i], color='#00BFC4', label='Original', fill=True)
    ax.set_title(f'{cont_col[i]} distribution')
fig.tight_layout()
plt.legend()