Pandas Capstone Project: Analyzing Apple and Tesla stocks

Read the stock data CSV files for Apple and Tesla, assign them to apple_df and tesla_df respectively, and ensure that the dates are parsed correctly.

Select the appropriate shape for the apple_df and tesla_df dataframes.

Create a new dataframe called tesla_column_df by reading only the Date and Close columns from the Tesla stock data CSV file.

Read Apple stock data for March 2022 and store it in apple_row_df dataframe.

Using apple_df, what is the mean Close price rounded to the nearest integer? Example, if the value is 90.182, enter 90. If it's 90.871, enter 91.

Select the correct mean for apple_df and tesla_df dataframes.

Find the maximum and minimum values of the Close column for both Apple and Tesla stock data and select all correct answers.

Select the correct average and median values of the Close column for both Apple and Tesla stocks in this activity.

Create a new column called High_Low_Diff in both the Apple and Tesla data to calculate the difference between the "High" and "Low" values.

Rename the columns in the Apple and Tesla data to make them more readable as follows:

For the Apple data dataset, the column:

{'Open': 'Apple_Open', 'High': 'Apple_High', 'Low': 'Apple_Low', 'Close': 'Apple_Close', 'Adj Close': 'Apple_Adj_Close', 'Volume': 'Apple_Volume'}

For the Tesla data dataset, the column:

{'Open': 'Tesla_Open', 'High': 'Tesla_High', 'Low': 'Tesla_Low', 'Close': 'Tesla_Close', 'Adj Close': 'Tesla_Adj_Close', 'Volume': 'Tesla_Volume'}

Drop all the rows from apple_df and tesla_df that correspond to the month of May 2022.

Add new rows to the Apple and Tesla data for a new day in this activity.

Apple dataset new row details:

{'Date': pd.to_datetime('2023-02-18'), 'Apple_Open': 300.0, 'Apple_High': 305.0, 'Apple_Low': 295.0, 'Apple_Close': 303.0, 'Apple_Adj_Close': 303.0, 'Apple_Volume': 50000000, 'High_Low_Diff': 10.0}

Tesla dataset new row details:

{'Date': pd.to_datetime('2023-02-18'), 'Tesla_Open': 700.0, 'Tesla_High': 710.0, 'Tesla_Low': 690.0, 'Tesla_Close': 702.0, 'Tesla_Adj_Close': 702.0, 'Tesla_Volume': 20000000, 'High_Low_Diff': 20.0}

Select all the correct options.

Write month name for both Apple and Tesla dataset sperated by comma like this: January, April.

Write month name for both Apple and Tesla dataset sperated by comma like this: January, April.

Write mean value for both Apple and Tesla dataset sperated by comma like this: 2683.919982, 4016.179992.

In this activity, we will filter the Apple and Tesla data to select only the rows that meet a specific criteria. We will filter the data to select rows where the closing price is greater than the opening price for both stocks. The results for Apple and Tesla will be stored in apple_pos and tesla_pos dataframes, respectively.

Write in this format: (120, 12)

Write in this format: (120, 12)

In this activity, we will filter the data for Apple and Tesla stocks based on multiple criteria. We will filter the data to select rows where the volume is greater than 50 million and the closing price is greater than the opening price for both stocks. The results for Apple and Tesla will be stored in apple_filtered and tesla_filtered dataframes, respectively.

In this activity, we will filter the data for Apple and Tesla stocks based on the values in a specific column. We will filter the data for both stocks where the closing price is greater than or equal to 280. The results for Apple and Tesla will be stored in apple_filtered_close and tesla_filtered_close dataframes, respectively.

In this activity, we will re-read the apple.csv and tesla.csv stock files and filter them for plotting. We will ensure that the dates are parsed correctly. The filtered data will be stored in the apple_pos and tesla_pos dataframes. We will filter the data from August 2022 onwards to select rows where the closing price is greater than the opening price for both Apple and Tesla stocks.

Plot the Close price in apple_df using Date as its x axis and Close as its y axis. Plot only the prices starting in August 1st, 2022 (inclusive).

• The title of the chart must be "Apple Close Price from August 2022"
• The color of the line must be "black"

Make sure you're storing the results of .plot in the variable ax. It should look something like:

In this activity, we will plot the line chart for the filtered dataframes apple_pos and tesla_pos using the plot function.

In this activity, we will plot the line chart for the filtered dataframes apple_pos and tesla_pos using the plot function. Use the Date column for the x-axis and Volume column for the y-axis. Which of the following statements are correct?

In this activity, we will plot the bar chart for the filtered dataframes apple_pos and tesla_pos using the plot function. Use the Date column for the x-axis and Volume column for the y-axis. Which of the following statements are correct?

In this activity, we will plot the bar chart for the filtered dataframes apple_pos and tesla_pos using the plot function. Use the Date column for the x-axis and Close column for the y-axis. Which of the following statements are correct?

In this activity, we will plot the pie chart for the filtered dataframes apple_pos and tesla_pos using the plot function. Use the Date column for the x-axis and Close column for the y-axis. Which of the following statements are correct?

In this activity, we will plot the pie chart for the filtered dataframes apple_pos and tesla_pos using the plot function. Use the Date column for the x-axis and Volume column for the y-axis. Which of the following statements are correct?

In this activity, we will plot the scatter plot for the filtered dataframes apple_pos and tesla_pos using the plot function. Use the Date column for the x-axis and Close column for the y-axis. Which of the following statements are correct?