Writing an iterator to load data in chunks (2)

In the previous exercise, you used read_csv() to read in DataFrame chunks from a large dataset. In this exercise, you will read in a file using a bigger DataFrame chunk size and then process the data from the first chunk.

To process the data, you will create another DataFrame composed of only the rows from a specific country. You will then zip together two of the columns from the new DataFrame, 'Total Population' and 'Urban population (% of total)'. Finally, you will create a list of tuples from the zip object, where each tuple is composed of a value from each of the two columns mentioned.

You're going to use the data from 'ind_pop_data.csv', available in your current directory. pandas has been imported as pd.

This exercise is part of the course

Python Toolbox

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Exercise instructions

Use pd.read_csv() to read in the file in 'ind_pop_data.csv' in chunks of size 1000. Assign the result to urb_pop_reader.
Get the first DataFrame chunk from the iterable urb_pop_reader and assign this to df_urb_pop.
Select only the rows of df_urb_pop that have a 'CountryCode' of 'CEB'. To do this, compare whether df_urb_pop['CountryCode'] is equal to 'CEB' within the square brackets in df_urb_pop[____].
Using zip(), zip together the 'Total Population' and 'Urban population (% of total)' columns of df_pop_ceb. Assign the resulting zip object to pops.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Initialize reader object: urb_pop_reader
urb_pop_reader = pd.read_csv(____, ____)

# Get the first DataFrame chunk: df_urb_pop
df_urb_pop = next(____)

# Check out the head of the DataFrame
print(df_urb_pop.head())

# Check out specific country: df_pop_ceb
df_pop_ceb = df_urb_pop[____]

# Zip DataFrame columns of interest: pops
pops = zip(____, ____)

# Turn zip object into list: pops_list
pops_list = list(pops)

# Print pops_list
print(pops_list)

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