<jupyter_start><jupyter_text>`EDAhelper.column_stats`Obtain summary statistics of column(s) including count, mean, median, mode, Q1, Q3, variance, standard deviation, correlation, and covariance in table format.<jupyter_code># import function
from EDAhelper.column_stats import column_stats
import pandas as pd
import numpy as np
import statistics
# load data
data = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/iris.data")
columns = ['SepalLength', 'SepalWidth']

data.head()<jupyter_output><empty_output><jupyter_text>Generate summary table, correlation matrix, and covariance matrix`column_stats` takes two arguments; a data set, and a list of column names as strings<jupyter_code>column_stats(data, columns)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0967741935483871, 'fraction_numerical': 0.0025806451612903226, 'mean_word_length': 7.818181818181818, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '505322', 'license': 'MIT', 'n_tokens_mistral': 245, 'n_tokens_neox': 237, 'n_words': 67, 'path': 'docs/example_column_stats.ipynb', 'repo_name': 'suuuuperNOVA/EDAhelper', 'size': '775'}