<jupyter_start><jupyter_text><jupyter_code>#import the libraries
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import scipy.stats as stats
#1: Load the data file
df = pd.read_csv("/content/chidata_for_assignment.csv")
df.head()
#2: Display the columns
df.columns
#3print and display the column information
df.shape
#4display the column information
df.info()
#5print out the first 10 records
df.head(10)
#Generates a seaborn count plot of gender
gender_count = pd.DataFrame(df)
gender_Count = sns.countplot(x="Gender", data=df)
gender_Count.set_title("Categorical Gender")
#Adding appropriate Chart Title
gender_Count.set_title('Categorical Gender difference in health')
#sns.regplot(x="Gender", y="Insurance", data=df)
#Generate a count plot for insurance
gender_count = pd.DataFrame(df)
gender_count = sns.countplot(x="Insurance", data=df)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10077519379844961, 'fraction_numerical': 0.009966777408637873, 'mean_word_length': 7.071428571428571, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '613041', 'license': 'MIT', 'n_tokens_mistral': 308, 'n_tokens_neox': 297, 'n_words': 90, 'path': 'Assignment_2.ipynb', 'repo_name': 'gndede/python', 'size': '903'}