# Import Pipeline
from quantopian.pipeline import Pipeline, CustomFactor
from quantopian.research import run_pipeline

# Import filter/screen
from quantopian.pipeline.filters import QTradableStocksUS, Q500US

# Import data
from quantopian.pipeline.data.morningstar import Fundamentals

# Import numpy and pandas
import numpy as np
import pandas as pd

# Create a pipeline function
# It's not neccessary to define a pipeline with a function but it is convenient.
# This way the function can be easily copied and pasted into an algo if needed.
def make_pipeline():
    symbol_as_of_pipe = Fundamentals.symbol.latest
    name_as_of_pipe = Fundamentals.short_name.latest
    
    return Pipeline(
        columns={
            'symbol_as_of_pipe': symbol_as_of_pipe,
            'name_as_of_pipe': name_as_of_pipe,
        },
        screen=Q500US(),
    )

# Run the pipeline and display the money flow index values
pipe_df = run_pipeline(make_pipeline(), '1-1-2008', '1-10-2018')
pipe_df.head(10)

# First, use the pandas 'map' method 
pipe_df['symbol_current_via_map'] = pipe_df.index.get_level_values(level=1).map(lambda asset: asset.symbol)

# Next use python lists
assets = pipe_df.index.get_level_values(level=1)
pipe_df['symbol_current_via_list'] = [asset.symbol for asset in assets]

# Add the name just for convenience
pipe_df['name_current'] = pipe_df.index.get_level_values(level=1).map(lambda asset: asset.asset_name)

# See what we have
pipe_df.head(10)

different_symbols = pipe_df.query('symbol_as_of_pipe != symbol_current_via_map').drop_duplicates('symbol_current_via_map')
different_symbols