<jupyter_start><jupyter_text>___ DTA 447 (Online) - Data ScienceWeek 1 Assignment - Basic Python Programming Skills using Jupyter Notebook ___  Welcome to Jupyter notebook!You will run all your python codes using jupyter notebook, so it is important that you get introduced to jupyter notebook and demostrate some basic programming skills.Notebooks will let you run codes and add block of texts to provide information related to what you are doing.  **Double click on this text. You should get a markdown cell that you can edit.****Add your information:**  We can also run python code in notebooks. Run the following cell.<jupyter_code># Set the string 'Hello World' as a variable named s.
s = "Hello world" 
# use the built in python function print() to print the content of the variable.
print (s)
# Reverse and print out the output
print (s[::-1])<jupyter_output>dlrow olleH<jupyter_text>If there is a problem with your code, error messages will be displayed.  You will see python reported an SyntaxError message and gave you suggestion on how to fix it. ___ **You will be expected to understand basic navigation of the notebook. You are encouraged to add cells and try out different pieces of codes. For more information on using notebooks you are highly encouraged to go through the [tutorial](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html) available online.** ___  Import some data science tools (libraries) needed<jupyter_code># Pandas for managing datasets
import pandas as pd

# Matplotlib for additional customization
import matplotlib.pyplot as plt

# Seaborn for plotting and styling
import seaborn as sns<jupyter_output><empty_output><jupyter_text>**Show datasets that are included in Seaborn package**<jupyter_code>sns.get_dataset_names()
# Load the tips dataset into a dataframe and display the first 5 rows
tips = sns.load_dataset('tips')

tips.head(5)
# plot the number of tips by weekday
sns.countplot(x='day', data=tips, hue='sex')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06336633663366337, 'fraction_numerical': 0.0034653465346534654, 'mean_word_length': 5.315625, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '396151', 'license': 'MIT', 'n_tokens_mistral': 580, 'n_tokens_neox': 563, 'n_words': 266, 'path': 'welcome-jupyter.ipynb', 'repo_name': 'oasanusi/welcome-jupyter', 'size': '2020'}