<jupyter_start><jupyter_text>_Lambda School Data Science_ Join datasetsObjectives- concatenate data with pandas- merge data with pandasLinks- [Pandas Cheat Sheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)  - Combine Data Sets: Standard Joins- Python Data Science Handbook  - [Chapter 3.6](https://jakevdp.github.io/PythonDataScienceHandbook/03.06-concat-and-append.html), Combining Datasets: Concat and Append  - [Chapter 3.7](https://jakevdp.github.io/PythonDataScienceHandbook/03.07-merge-and-join.html), Combining Datasets: Merge and Join  Download dataWe’ll work with a dataset of [3 Million Instacart Orders, Open Sourced](https://tech.instacart.com/3-million-instacart-orders-open-sourced-d40d29ead6f2)!<jupyter_code>!wget https://s3.amazonaws.com/instacart-datasets/instacart_online_grocery_shopping_2017_05_01.tar.gz
!tar --gunzip --extract --verbose --file=instacart_online_grocery_shopping_2017_05_01.tar.gz
%cd instacart_2017_05_01<jupyter_output>/content/instacart_2017_05_01<jupyter_text>Goal: Reproduce this exampleThe first two orders for user id 1:<jupyter_code>from IPython.display import display, Image
url = 'https://cdn-images-1.medium.com/max/1600/1*vYGFQCafJtGBBX5mbl0xyw.png'
example = Image(url=url, width=600)

display(example)<jupyter_output><empty_output><jupyter_text>Load dataHere's a list of all six CSV filenames<jupyter_code>!ls -lh<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1125, 'fraction_numerical': 0.04513888888888889, 'mean_word_length': 6.705882352941177, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, 'https://': 6, 'lorem ipsum': 0, 'www.': 0, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '397724', 'license': 'MIT', 'n_tokens_mistral': 580, 'n_tokens_neox': 522, 'n_words': 90, 'path': 'module2-join-datasets/LS_DS_122_Join_datasets.ipynb', 'repo_name': 'connorpheraty/DS-Unit-1-Sprint-2-Data-Wrangling', 'size': '1440'}