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Python Data Science Toolbox (Part 1)
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    Python Data Science Toolbox (Part 1)

    Run the hidden code cell below to import the data used in this course.

    Take Notes

    Add notes about the concepts you've learned and code cells with code you want to keep.

    Add your notes here

    # Add your code snippets here
    # Define count_entries()
    def count_entries(df, col_name):
        """Return a dictionary with counts of 
        occurrences as value for each key."""
    
        # Initialize an empty dictionary: langs_count
        langs_count = {}
        
        # Extract column from DataFrame: col
        col = df[col_name]
        
        # Iterate over lang column in DataFrame
        for entry in col:
    
            # If the language is in langs_count, add 1
            if entry in langs_count.keys():
                lang_count[entry] +=1
            # Else add the language to langs_count, set the value to 1
            else:
                lang_count[entry] = 1
    
        # Return the langs_count dictionary
        return langs_count
    
    # Call count_entries(): result
    result = count_entries(tweet_df,'lang')
    
    # Print the result
    print(result)

    Explore Datasets

    Use the DataFrame imported in the first cell to explore the data and practice your skills!

    • Write a function that takes a timestamp (see column timestamp_ms) and returns the text of any tweet published at that timestamp. Additionally, make it so that users can pass column names as flexible arguments (*args) so that the function can print out any other columns users want to see.
    • In a filter() call, write a lambda function to return tweets created on a Tuesday. Tip: look at the first three characters of the created_at column.
    • Make sure to add error handling on the functions you've created!