
import pandas
from datetime import datetime


def clean(data):
    """
    Take in data and return cleaned version.
    """
    # Remove Date Values column
    data = data.drop(["Date Value"], axis=1)

    # Determine what values are missing
    # empty = data.apply(lambda col: pandas.isnull(col))


def main():
    """

    """
    # Connect to file.
    data = pandas.read_csv("data/Residential_Water_Usage_Zip_Code_on_Top.csv")

    clean(data)


if __name__ == '__main__':
    main()


import pandas
from datetime import datetime


def clean(data):
    """
    Take in data and return cleaned version.
    """
    # Remove Date Values column
    data = data.drop(["Date Value"], axis=1)

    # Determine what values are missing
    # empty = data.apply(lambda col: pandas.isnull(col))

    return data


def find_low_water_use(data):
    """

    """
    under100 = data[(data["90012"] <= 100) & (data["90013"] <= 100)]
    print(under100)
    under25 = data[(data["90012"] <= 25) & (data["90013"] <= 25)]
    print(under25)


def main():
    """

    """
    # Connect to file.
    data = pandas.read_csv("data/Residential_Water_Usage_Zip_Code_on_Top.csv")

    cleaned_data = clean(data)
    find_low_water_use(cleaned_data)


if __name__ == '__main__':
    main()

Add filter for water use in downtown LA zipcodes.
Add filter for water use in downtown LA zipcodes.

  
  import pandas
  from datetime import datetime
  
  
  def clean(data):
      """
      Take in data and return cleaned version.
      """
      # Remove Date Values column
      data = data.drop(["Date Value"], axis=1)
  
      # Determine what values are missing
      # empty = data.apply(lambda col: pandas.isnull(col))
  
+     return data
+ 
+ 
+ def find_low_water_use(data):
+     """
+ 
+     """
+     under100 = data[(data["90012"] <= 100) & (data["90013"] <= 100)]
+     print(under100)
+     under25 = data[(data["90012"] <= 25) & (data["90013"] <= 25)]
+     print(under25)
+ 
  
  def main():
      """
  
      """
      # Connect to file.
      data = pandas.read_csv("data/Residential_Water_Usage_Zip_Code_on_Top.csv")
  
-     clean(data)
+     cleaned_data = clean(data)
+     find_low_water_use(cleaned_data)
  
  
  if __name__ == '__main__':
      main()
  
Add filter for water use in downtown LA zipcodes.
## Code Before:

import pandas
from datetime import datetime


def clean(data):
    """
    Take in data and return cleaned version.
    """
    # Remove Date Values column
    data = data.drop(["Date Value"], axis=1)

    # Determine what values are missing
    # empty = data.apply(lambda col: pandas.isnull(col))


def main():
    """

    """
    # Connect to file.
    data = pandas.read_csv("data/Residential_Water_Usage_Zip_Code_on_Top.csv")

    clean(data)


if __name__ == '__main__':
    main()

## Instruction:
Add filter for water use in downtown LA zipcodes.
## Code After:

import pandas
from datetime import datetime


def clean(data):
    """
    Take in data and return cleaned version.
    """
    # Remove Date Values column
    data = data.drop(["Date Value"], axis=1)

    # Determine what values are missing
    # empty = data.apply(lambda col: pandas.isnull(col))

    return data


def find_low_water_use(data):
    """

    """
    under100 = data[(data["90012"] <= 100) & (data["90013"] <= 100)]
    print(under100)
    under25 = data[(data["90012"] <= 25) & (data["90013"] <= 25)]
    print(under25)


def main():
    """

    """
    # Connect to file.
    data = pandas.read_csv("data/Residential_Water_Usage_Zip_Code_on_Top.csv")

    cleaned_data = clean(data)
    find_low_water_use(cleaned_data)


if __name__ == '__main__':
    main()
