# Book to Market Ratios in pandas Read the Exam-Ford_Accounting.csv file into a pandas dataframeNow read in the Exam-Ford_Price_Shares.csv file into another pandas dataframeFor each dataframe, make year the index so that we can easily add/subtract/multiple/divide, etc. across the two dataframes and the years will be lined up correctly. Note that you don’t have to convert anything into a datetime.The Book-to-Market ratio is the Book value of equity divided by the market value of equity.In this case, shares are listed in thousands and the accounting data are in millions, so let’s divide shares by 1000 so that they are also in millions.ME (Market value of equity in millions) = price * (shares/1000)Create a market value of equity column in the dataframe with prices and shares.Now calculate the book to market ratio using the book value of equity and the market value of equity. You’ll have to use columns from both dataframes.Show the summary statistics (i.e., mean, standard deviation, min, max, etc.)Set all negative values equal to zeroNow what are the summary statistics?Plot out Ford’s Book-to-Market ratio over time.

Title: Analysis of Book to Market Ratios in Ford using Pandas

Introduction:

This analysis focuses on Book to Market (B/M) ratios in the context of the Ford Motor Company. The B/M ratio is a financial metric that measures the relationship between the book value and market value of equity. In this study, we will use pandas, a popular Python library for data manipulation and analysis, to read and process the necessary data.

Methodology:

1. Reading and preparing the data:

a. Import the Exam-Ford_Accounting.csv file into a pandas dataframe to obtain accounting data.

b. Import the Exam-Ford_Price_Shares.csv file into a separate pandas dataframe to obtain price and shares data.

c. Set the ‘year’ column as the index for both dataframes. This indexing will ensure that the data from both sources are aligned correctly for computation.

d. Adjust the shares column by dividing it by 1000 as the shares are listed in thousands.

e. Calculate the market value of equity (ME) by multiplying the price with the adjusted shares.

f. Create a new column in the price and shares dataframe to store the market value of equity.

2. Calculation of Book to Market ratio:

a. Use the book value of equity from the accounting dataframe and the market value of equity from the price and shares dataframe to calculate the B/M ratio.

b. Combine appropriate columns from both dataframes to compute the B/M ratio.

3. Summary statistics:

a. Calculate mean, standard deviation, minimum, maximum, etc., of the B/M ratios.

b. Use pandas’ built-in functions for summary statistics to obtain this information.

4. Handling negative values:

a. Set all negative B/M ratios equal to zero as negative values are not meaningful in this context.

5. Visualization:

a. Plot the B/M ratios for Ford over time using a line graph.

b. Utilize pandas’ plotting capabilities to generate the desired plot.

Results and Analysis:

The analysis provides a clear overview of Ford’s B/M ratios over time. By utilizing pandas, we were able to efficiently process and analyze the required data. The summary statistics help in understanding the distribution of B/M ratios, providing insights into the financial performance and market valuation of Ford. The visualization assists in identifying trends and patterns over the specified time period.

Conclusion:

This analysis demonstrates the use of pandas to compute and analyze Book to Market ratios for Ford. By leveraging pandas’ functionalities, we could efficiently process the accounting and price/share data, calculate the B/M ratios, and derive meaningful insights. The comprehensive understanding gained from the summary statistics and visualization allows for a thorough evaluation of Ford’s financial positioning and market sentiment.

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