  Intro to Pandas for Data Analysis

# Series Practice with World Bank's data

Practice the fundamental concepts of Pandas Series using series extracted from World Bank's data on economic, political, and social indicators for countries around the world. You will practice data manipulation and access in series, perform basic statistical operations and sorting.

## Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

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### What is the value of the element with index 29 in the `literacy_rate` series

Write answer in the form of a number with two decimal places. For example, if the answer is 78.868623, write 78.87.

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### What is the mean of the `internet_users` series

Write answer in the form of a number with two decimal places. For example, if the answer is 78.868623, write 78.87.

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### What is the standard deviation of the `internet_users` series

Write answer with two decimal places. For example, if the answer is 45.5432, write 45.54.

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### What is the median of the `exports` series

Write complete answer. For example, if the answer is 45.5432, write 45.5432.

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### What is the average literacy rate of all countries

Write complete answer. For example, if the answer is 45.5432, write 45.5432.

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### Sort the series in ascending order

Sort the `country_name` series in ascending order and assign the result to a new variable called `country_name_sorted`.

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### Sort multiple series at once

Both the series `country_name` and `literacy_rate` have the same number of elements and the elements are in the same order with respect to index number. Arrange the country name as per ascending order of literacy rate. Assign the result of country name to new variable called `country_name_sorted_by_literacy_rate` and the result of literacy rate to new variable called `literacy_rate_sorted`.

Example: If the country name is `['India', 'China', 'Japan']` and literacy rate is `[80, 90, 70]`, then the result should be `['Japan', 'India', 'China']` and `[70, 80, 90]`.  Author

#### Anurag Verma

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## Intro to Pandas for Data Analysis

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