<jupyter_start><jupyter_text>As we can see, the first data set seems to be correct<jupyter_code>cc2.head()
cc2.columns
cc2.describe()
cc2['btc_market_price'].plot('hist')
cc2.info()
cc3.head()
cc3.columns
cc3.info()
cc3.describe()
cc4.head()
cc4.columns
cc4.describe()
cc4.info()
cc5.head()
cc5.columns
cc5.describe()
cc5.info()
cc6.head()
cc6.columns
cc6.info()
cc6.describe()
cc6['Open'].plot('hist')
cc7.head()
cc7.columns
cc7.info()
cc7.describe()
cc7 = cc7.drop(columns="eth_chaindatasize")
cc7.info()
cc7.count
cc7['eth_ens_register'].fillna(0, inplace=True)
cc7['eth_ens_register']<jupyter_output><empty_output><jupyter_text>We swaped all the "NaN" values to 0.0"<jupyter_code>cc8.head()
cc8.describe()
cc8.info()
cc8.columns
cc8['Open'].plot('hist')
cc8.count()
cc9.head()
cc9.describe()
cc9.count()
cc9.info()
cc9.columns
cc10.head()
cc10.describe()
cc10.info()
cc10.count()
cc11.head()
cc11.count()
cc11.info()
cc11.columns
cc11.describe()
cc12.head()
cc12.info()
cc12.columns
cc12.count()
cc12.describe()
cc13.head()
cc13.info()
cc13.describe()
cc13.count()
cc13.columns
cc14.head()
cc14.info()
cc14.columns
cc14.count()
cc15.head()
cc15.info()
cc15.columns
cc15.describe()
cc15.count()
cc16.head()
cc16.info()
cc16.describe()
cc16.count
cc17.head()
cc17.describe()
cc17.info()
cc17.count()
cc17.describe()
cc18.head()
cc18.count()
cc18.describe()
cc18.info()
cc19.head()
cc19.describe()
cc19.columns
cc19.count()
cc19.info()<jupyter_output><class 'pandas.core.frame.DataFrame'>
RangeIndex: 461 entries, 0 to 460
Data columns (total 7 columns):
Date          461 non-null object
Open          461 non-null float64
High          461 non-null float64
Low           461 non-null float64
Close         461 non-null float64
Volume        461 non-null object
Market Cap    461 non-null object
dtypes: float64(4), object(3)
memory usage: 25.3+ KB
{'fraction_non_alphanumeric': 0.16756756756756758, 'fraction_numerical': 0.09837837837837837, 'mean_word_length': 4.970967741935484, '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': '391454', 'license': 'MIT', 'n_tokens_mistral': 853, 'n_tokens_neox': 755, 'n_words': 155, 'path': 'notebooks/CryptoCurrencies.ipynb', 'repo_name': 'malerax213/Basic-Datascience', 'size': '1850'}