<jupyter_start><jupyter_text>Senate Data Clean Up<jupyter_code>import pandas as pd
import numpy as np
## free file from https://simplemaps.com/data/us-cities.

df = pd.read_csv('../data/raw/uscities.csv')

df[:5]
    
### basic visualizations
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])

counts = df['state_id'].value_counts() 
#print(counts)
states = np.array([x for x in counts.axes])
c = [c for c in counts]



print(states)
print(c)


plt.figure(figsize=(120, 7)) 
ax.set_xticklabels(states[0], fontsize=7)
ax.bar(states[0], c, align='center')
### basic visualizations
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])

plt.pie(c,labels=states[0])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16733601070950468, 'fraction_numerical': 0.022757697456492636, 'mean_word_length': 5.3931623931623935, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, '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': '51647', 'license': 'MIT', 'n_tokens_mistral': 309, 'n_tokens_neox': 299, 'n_words': 63, 'path': 'notebooks/Untitled.ipynb', 'repo_name': 'TylerMamrot/bookClassifier', 'size': '747'}