<jupyter_start><jupyter_text>Intro to pandas - Notebook for Episode 12016/11/17<jupyter_code>import pandas as pd
articles_df = pd.read_csv('articles.csv')
articles_df
type(articles_df)
my_string = "jkflsdjaljfld;"
type(my_string)
articles_df.columns
articles_df.shape
articles_df.columns.values
articles_df.Title
articles_df.Month.head(5)
pd.unique(articles_df['Month'])
articles_df.Month.unique()
pub_count = len(articles_df.ISSNs.unique())
pub_count
print(pub_count)
articles_df.Citation_Count.describe()
articles_df['Citation_Count'].describe()

articles_df.describe()
byLang = articles_df.groupby('LanguageId')
byLang.describe()<jupyter_output><empty_output><jupyter_text>Summarize author counts for each publication (ISSNs) in your data. HINT: you can use the following syntax to only create summary statistics for one column in your data by_ISSNs['Author_Count'].describe()<jupyter_code>by_ISSNs = articles_df.groupby('ISSNs')
by_ISSNs['Citation_Count'].describe()<jupyter_output><empty_output><jupyter_text>count the number of samples by publisherarticle_counts = articles_df.groupby('ISSNs')['Title'].count()<jupyter_code>by_ISSNs['Title'].count()

by_ISSNs['Title'].count()['2056-9890']
articles_df['double_count'] = articles_df.Author_Count * 2
articles_df<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12268518518518519, 'fraction_numerical': 0.014660493827160493, 'mean_word_length': 9.808333333333334, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '38708', 'license': 'CC-BY-4.0', 'n_tokens_mistral': 489, 'n_tokens_neox': 441, 'n_words': 76, 'path': 'notebooks/Episode1.ipynb', 'repo_name': 'lgatto/2016-11-17-cam', 'size': '1296'}