Get the ticker of the largest consumer services company

Instead of indexing your data with a conditional expression, you can also filter by certain values with .loc[row_selector, column_selector]. Additionally, you can use .set_index() to set a particular column with unique values as the index of a DataFrame, and .idxmax() to return the index of the maximum value.

In this exercise, you will apply these methods of selecting companies to find the most valuable consumer services company on any of the three exchanges, and use its ticker to plot its stock price trend. DataReader, date, pandas as pd, and matplotlib.pyplot as plt have been imported, as has the listings DataFrame from the last exercise.

This exercise is part of the course

Importing and Managing Financial Data in Python

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Exercise instructions

Use .set_index() to set the 'Stock Symbol' column as the index for listings, assigning it to listings_ss.
Use .loc[] to filter rows where 'Sector' is equal to 'Consumer Services', select the column 'Market Capitalization', and apply .idxmax() to assign the ticker of the largest Consumer Services company to ticker.
Using date(), set start to January 1, 2015.
Use DataReader() to extract the stock data for the ticker from 'yahoo' since start and store in data.
Plot the 'close' and 'volume' values in data, with arguments secondary_y='volume' and title=ticker.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Set the index of listings to Stock Symbol
listings_ss = listings.____(____)

# Get ticker of the largest Consumer Services company
ticker = listings_ss.____[____, ____].____()

# Set the start date
start = ____

# Import the stock data
data = ____

# Plot close and volume
data[['close', 'volume']].plot(secondary_y=____, title=____)

# Show the plot
plt.show()

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