<jupyter_start><jupyter_text>Introduction<jupyter_code>%matplotlib inline 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_excel('2011_Current_Table1.xlsx')
df.shape
df.head()
airports = pd.concat([df['citymarketid_1'], df['citymarketid_2']])
len(np.unique(airports))
from_nyc = df[df['city1'].str.contains('NY')]
from_nyc_to_or = from_nyc[from_nyc['city2'].str.contains('OR')]
plt.figure();
df.fare.plot(kind='bar'); plt.axhline(0, color='k')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17288801571709234, 'fraction_numerical': 0.019646365422396856, 'mean_word_length': 9.2, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '550551', 'license': 'MIT', 'n_tokens_mistral': 209, 'n_tokens_neox': 206, 'n_words': 30, 'path': 'air-fares/.ipynb_checkpoints/Air Fares-checkpoint.ipynb', 'repo_name': 'noppanit/machine-learning', 'size': '509'}