<jupyter_start><jupyter_text>Data Pipelines<jupyter_code>%matplotlib inline 
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
import seaborn as sns
import warnings
import random
from datetime import datetime
random.seed(datetime.now())
warnings.filterwarnings('ignore')

# Make plots larger
plt.rcParams['figure.figsize'] = (10, 6)<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '187382', 'license': 'MIT', 'n_tokens_mistral': 138, 'n_tokens_neox': 133, 'n_words': 39, 'path': "Prof. Nik Brown's Notes/D09. MISC/NBB_data_pipelines.ipynb", 'repo_name': 'PratikMahajan/Data-Science-Reference', 'size': '407'}