<jupyter_start><jupyter_text>20-data-exploration: EDA on the cleaned dataThe purpose of this notebook is to perform any desired exploration of the properties of the data  Helpful packages and preliminaries<jupyter_code>#Data processing and evaluation
import pandas as pd
import numpy as np

#Graphical display
import matplotlib.pyplot as plt
import seaborn as sns

#Graphing magics
%matplotlib inline
#Constants/globals
cleaned_data_filename = ''<jupyter_output><empty_output><jupyter_text>Statistical assessment and visualization<jupyter_code>#load and view data
data = pd.read_csv(cleaned_data_filename)
data.head(10)
#view information about the data
data.info()
#view info about numerical data
data.describe()
#view info about categorical data
for x in df.columns:
    if df[x].dtype=='object':
        print('Col name: ', x, '\n', df[x].value_counts(), '\n')
#view correlation information
pp = sns.pairplot(data)
#view distribution about numerical columns
vp = sns.violinplot(data)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08669950738916256, 'fraction_numerical': 0.003940886699507389, 'mean_word_length': 6.309352517985611, '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': '640357', 'license': 'MIT', 'n_tokens_mistral': 320, 'n_tokens_neox': 310, 'n_words': 104, 'path': '20-data-exploration.ipynb', 'repo_name': 'Vanderbilt-PTY-Data-Science/ancient-artifacts-dl', 'size': '1015'}