<jupyter_start><jupyter_text>Series<jupyter_code>ys = [[0,1,2,3,4],[4,3,2,1,0]]
x_ax = [0,1,2,3,4]
for y_ax in ys:
    ts = pd.Series(y_ax,index=x_ax)
    ts.plot(kind='bar', figsize=(15,5))
    plt.show()
ts = pd.Series(np.random.randint(0, 10, size=(100,)), index=range(0, 100))
ts.plot(kind='line', figsize=(15,5))
plt.show()<jupyter_output><empty_output><jupyter_text>DataFrame<jupyter_code>index_date = pd.date_range('20170801', periods=100)
df = pd.DataFrame(np.random.randn(100, 4), index=ts.index, columns=['apple', 'google', 'tesla', 'nvidia']).cumsum()
df
df.plot(figsize=(20,10))
plt.show()
df.describe()
df.sort_index(axis=1, ascending=True).T
df['apple'][3:5]
df.mean()
df.apply(np.cumsum).plot(figsize=(20, 10))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2304635761589404, 'fraction_numerical': 0.07549668874172186, 'mean_word_length': 8.108433734939759, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '387374', 'license': 'MIT', 'n_tokens_mistral': 375, 'n_tokens_neox': 343, 'n_words': 42, 'path': 'python/playing_pandas.ipynb', 'repo_name': 'marceljanerfont/myLab', 'size': '755'}