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Python Data Science Toolbox (Part 1)

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

1 hidden cell

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!