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Date variables are entered to specify wanted date range\n2. CSV is loaded into dataframe\n3. dataframe is trimmed to be between wanted dates\n4. unwanted transactions taken from \"unwanted_transactions.txt\" are removed from the dataframe\n5. A daily expenses is created and saved to .csv\n6. Daily expenses dataframe is visualised on a graph\n7. Avarage daily spend is printed to terminal\n\n## Example Graph Output \n\nThis script generates an interactive graph which looks like this:\n\n\u003cimg width=\"1783\" alt=\"example_graph\" src=\"https://user-images.githubusercontent.com/32711718/124261402-a6e2fa80-db28-11eb-85e4-7c936c472ade.png\"\u003e\n\n## Example Terminal Output\n\n    The average daily spend between 25/06/2021 and 01/07/2021 is £23.09\n    \n## Requirements\n\nThis script was last executed using Pyhton 3.8.5 and requires pandas and plotly modules. Use the following command to install needed modules:\n\n    pip3 install -r requirements.txt\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgowhale%2Fdaily-spend-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgowhale%2Fdaily-spend-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgowhale%2Fdaily-spend-analysis/lists"}