Intro to Pandas for Data Analysis

# Practicing Series Filtering with S&P500 and Census Data

In this project we'll practice filtering and selection of Pandas series with two datasets: one containing S&P500 returns and one containing Census Data. Your job will be to create different query expressions to gather insights from the data that's stored in Pandas series.
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## 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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### Rename the series accordingly

Rename both series with the names specified below, given their variables:

• `age_marriage`: should be named "Age of First Marriage"
• `sp500`: should be named "S&P500 Returns 90s"
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### What's the maximum Age of marriage?

What's the maximum value in `age_marriage`?

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### What's the median Age of Marriage?

Enter a whole number (an integer).

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### What's the minimum return from S&P500?

Enter the value with up 2 decimals of precision. Example, if the value is `-11.8718`, enter only `-11.87`.

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### How many Women marry at age 21?

`21` is the most common age for marriage (you can check that using the `.mode()` method). How many women married at that age?

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### How many positive S&P500 returns are there?

That is, a return greater than `0`.

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### Select all women below 20 or above 39

Perform a selection of all the values in `age_marriage` that are below `20` or above `39`. Store your results in the variable `age_20_39`.

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### Select all women whose ages are **even**, and are older than 30 y/o

Perform a selection of all the values that are greater than `30` and even. Store your result in the variable `age_30_even`.

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### Select the S&P500 returns between 1.5 and 3

Select all the S&P500 returns that are greater than `1.5` and lower than `3`. Store your results in the variable `sp_15_to_3`.

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#### Santiago Basulto

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## Intro to Pandas for Data Analysis

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