Question: Jupyter Notebooks/Python Organize Data to make 3-5 correlations between state level (Delaware) store data for Walmart's foot traffic (dailiys)and Foot traffic for a single restaurant

Jupyter Notebooks/Python

Organize Data to make 3-5 correlations between state level (Delaware) store data for Walmart's foot traffic (dailiys)and Foot traffic for a single restaurant Applebee's located in West Virginia (weekly)

Show graphs. Clean Data. Normalize if possible. Provide 3 to 5 correlations supported by graphs and statistics

Conclude with why the correlations of the foot traffic between the 2 are beneficial.

id name type time_frame start_date end_date region_type region_name lat lng company_name foottraffic panel_visits home_distance_estimated_foottraffic_2 home_distance_estimated_foottraffic_3 home_distance_percentage_0.3
58ef6e6c173f5601f82d8f28 Walmart chain daily 01/01/2017 01/01/2017 state Delaware Walmart 37408 402 6316 8545 0.227
58ef6e6c173f5601f82d8f28 Walmart chain daily 02/01/2017 02/01/2017 state Delaware Walmart 50048 518 8615 13780 1.187
58ef6e6c173f5601f82d8f28 Walmart chain daily 03/01/2017 03/01/2017 state Delaware Walmart 35548 355 8315 11577 1.148
58ef6e6c173f5601f82d8f28 Walmart chain daily 04/01/2017 04/01/2017 state Delaware Walmart 38006 395 8398 12017 1.781
58ef6e6c173f5601f82d8f28 Walmart chain daily 05/01/2017 05/01/2017 state Delaware Walmart 42741 439 7210 10824 0.339
23ce9592612e6c33808f2d6d Applebee's venue weekly 02/01/2017 08/01/2017 venue West Virginia 38.8908221 -79.845166 Applebee's 3668 24 1242 1662 0
23ce9592612e6c33808f2d6d Applebee's venue weekly 09/01/2017 15/01/2017 venue West Virginia 38.8908221 -79.845166 Applebee's 3210 22 182 548 0
23ce9592612e6c33808f2d6d Applebee's venue weekly 16/01/2017 22/01/2017 venue West Virginia 38.8908221 -79.845166 Applebee's 4765 35 708 1252 0

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