Create time series of market value
You can now use the number of shares to calculate the total market capitalization for each component and trading date from the historical price series.
The result will be the key input to construct the value-weighted stock index, which you will complete in the next exercise.
Cet exercice fait partie du cours
Manipulating Time Series Data in Python
Instructions
We have already imported pandas as pd and matplotlib.pyplot as plt for you. We have also created the variables components and stock_prices that you worked with in the last exercises.
- Select the
'Number of Shares'fromcomponents, assign tono_shares, and print the result, sorted in the default (ascending) order. - Multiply
stock_pricesbyno_sharesto create a time series of market cap per ticker, and assign it tomarket_cap. - Select the first and the last row of
market_capand assign these tofirst_valueandlast_value. - Use
pd.concat()to concatenatefirst_valueandlast_valuealongaxis=1and plot the result as horizontal bar chart.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Select the number of shares
no_shares = ____
print(____)
# Create the series of market cap per ticker
market_cap = ____
# Select first and last market cap here
first_value = ____
last_value = ____
# Concatenate and plot first and last market cap here