<jupyter_start><jupyter_text>Exploring Datasets with *pandas* and MatplotlibToolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's websiteThe dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data.  Downloading and Prepping data<jupyter_code>import numpy as np #useful for scientific computing
import pandas as pd # primary data structures library
import image
from PIL.Image import core as _imaging<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07094017094017094, 'fraction_numerical': 0.011111111111111112, 'mean_word_length': 5.691428571428571, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '379630', 'license': 'MIT', 'n_tokens_mistral': 324, 'n_tokens_neox': 294, 'n_words': 134, 'path': 'Data Visualization/Practice_Waffle-Charts_Word-Clouds_and_Regression-Plots.ipynb', 'repo_name': 'KarthikCsv/data-science', 'size': '1170'}