This is a continuation of the data analysis project using L.A. city's data 'Great Streets Pedestrian Survey 2015'.
The data shown in the process of data wrangling and cleaned which is described on my other repo.

This project is to demonstrate how to use prepare and merging multiple data sources into a single dataset for analysis.

Identifying gaps in data such as errors in street names from human input and data validation.

Screenshots

Zipcode, Birth Year, Col Attributes	Create New Features from Question 1

Geo Coordinates

• pandas
• numpy
• regular expressions
• Google Maps API
• https://data.lacity.org/Community-Economic-Development/Great-Streets-Pedestrian-Survey-2015/6yv9-t9gq/data

• pip install pandas
• pip install numpy
• pip install regex

Install the dependencies and setup your google maps API.

    category_no_punct = re.sub(r'[^\w\s]', '', category)
    question_no_punct = re.sub(r'[^\w\s]', '', row['What brings you to this neighborhood today?'])

    data['status'] == 'OK' and  data['results'][0]['geometry']['location_type'] == 'GEOMETRIC_CENTER':

def similar(a, b):
    return SequenceMatcher(None, a, b).ratio()

...
    if similar(category_words, result) == 1.0 or similar(category_words, result) >= 9.0:
         return True

See the open issues for a list of proposed features (and known issues).

• Top Feature Requests (Add your votes using the 👍 reaction)
• Top Bugs (Add your votes using the 👍 reaction)
• Newest Bugs

• ykimdeveloper - message me for any questions

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The original setup of this repository is by Yosep Kim.

For a full list of all authors and contributors, see the contributors page.

DATA-WRANGLING-Great-Streets-Survey-ykimdeveloper follows good practices of security, but 100% security cannot be assured. DATA-WRANGLING-Great-Streets-Survey-ykimdeveloper is provided "as is" without any warranty. Use at your own risk.

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This project is licensed under the MIT license.

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