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Safari File Edit View History Bookmarks Develop Window Help ()) 53% Fri 5:44:52 PM Q E ... datahub.ucsd.edu ABP Course Hero A5-Experimentation - Jupyter Notebook Home Page - Select or create a notebook CL7-ScientificComputing - Jupyter Notebook jupyter A5-Experimentation Last Checkpoint: 3 minutes ago (unsaved changes) Logout Control Panel File Edit View Insert Cell Kernel Widgets Help Not Trusted Python 3 0 Run C Markdown Validate Show Usage Part 2: pandas While numpy revolutionized how to work with matrices in Python, pandas took things one step further in its introduction of the DataFrame. Here, you'll begin working with a dataset that stores heterogeneous information to gain practice with pandas . Q5 - Data (0.5 pts) Note: Make sure that you have a directory called data in the same directory as your Jupyter notebook and that your data directory contains the file random_guess . csv . This should already be the case if you're working with the assignment in Datahub. One very helpful function in pandas allows users to read datasets into Python from a CSV file and store them as a DataFrame. Look up documentation for pandas online and figure out how to read in a CSV file into Python as a DataFrame using read_csv ( ) . Use the default settings for read_csv here. The data you will read into this notebook are in data/random_guess . csv . Assign this DataFrame to the variable df . In [ ] : # YOUR CODE HERE raise NotImplementedError ( ) In [ ] : # check to ensure that what you read in is a pandas dataframe assert (isinstance (df, pd . DataFrame) ) In [ ]: assert (df . shape == (272, 2) )Safari File Edit View History Bookmarks Develop Window Help () 53% Fri 5:45:43 PM Q E .. . datahub.ucsd.edu ABP Course Hero A5-Experimentation - Jupyter Notebook Home Page - Select or create a notebook CL7-ScientificComputing - Jupyter Notebook jupyter A5-Experimentation Last Checkpoint: 4 minutes ago (autosaved) Logout Control Panel File Edit View Insert Cell Kernel Widgets Help Not Trusted Python 3 0 + Run C Markdown Validate Show Usage One very helpful function in pandas allows users to read datasets into Python from a CSV file and store them as a DataFrame. Look up documentation for pandas online and figure out how to read in a CSV file into Python as a DataFrame using read_csv ( ) . Use the default settings for read_csv here. The data you will read into this notebook are in data/ random_guess . csv . Assign this DataFrame to the variable df . In [ ] : # YOUR CODE HERE raise NotImplementedError ( ) In [ ]: # check to ensure that what you read in is a pandas dataframe assert (isinstance (df, pd. DataFrame) ) In [ ]: assert(df . shape == (272, 2) ) Look at the data Run the code below to get a sense of the data you've just read in: In [ ]: # see the first few rows of data frame you read in df . head ( ) In this dataset, each row is data collected from a student. While we only see the first five rows, there are 272 individuals who participated in this experiment. The first column includes the experimental ID for the student. The second column includes their random guess. We'll be working with these data throughout the rest of the assignment
