<jupyter_start><jupyter_text>import pandas as pdfrom matplotlib import pyplot as plt%matplotlib inline<jupyter_code>print('seom')
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
data_url = "https://elitedatascience.com/wp-content/uploads/2017/04/Pokemon.csv"
df = pd.read_csv('Pokemon.csv', index_col=0)
df.head()
sns.lmplot(x='Attack', y='Defense', data=df, hue='Stage', fit_reg=False)
plt.ylim(0, None)
plt.xlim(0, None)
stats_df = df.drop(['Total', 'Stage', 'Legendary'], axis=1)
sns.boxplot(data=stats_df)
sns.distplot(df.Attack)
sns.countplot(x='Type 1', data=df)
plt.xticks(rotation=-45)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15601783060921248, 'fraction_numerical': 0.019316493313521546, 'mean_word_length': 7.641025641025641, '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': '188973', 'license': 'MIT', 'n_tokens_mistral': 284, 'n_tokens_neox': 271, 'n_words': 52, 'path': 'Learning Basic Data Science/SeaBorn.ipynb', 'repo_name': 'thealphadollar/Machine-Learning-PC', 'size': '673'}