Before diving into formal analysis with a dataset, it is often helpful to perform some initial investigations of the data through exploratory data analysis (EDA) to get a better sense of what you will be working with. Basic summary statistics and visualizations are important components of EDA as they allow us to condense a large amount of information into a small set of numbers or graphics that can be easily interpreted.

This lesson focuses on *univariate summaries*, where we explore each variable separately. This is useful for answering questions about each individual feature. Variables can typically be classified as *quantitative* (i.e., numeric) or *categorical* (i.e., discrete). Depending on its type, we may want to choose different summary metrics and visuals to use.

Let’s say we have the following dataset on New York City rental listings imported into a `pandas`

DataFrame (subsetted from the StreetEasy dataset):

import pandas as pd # Import dataset rentals = pd.read_csv('streeteasy.csv') # Preview first 5 rows print(rentals.head())

rent | size_sqft | borough |
---|---|---|

2550 | 480 | Manhattan |

11500 | 2000 | Manhattan |

3000 | 1000 | Queens |

4500 | 916 | Manhattan |

4795 | 975 | Manhattan |

As seen, we have two quantitative variables (`rent`

and `size_sqft`

) and one categorical variable (`borough`

). The `pandas`

library offers a handy method `.describe()`

for displaying some of the most common summary statistics for the columns in a DataFrame. By default, the result only includes numeric columns, but we can specify `include='all'`

to the method to display categorical ones as well:

# Display summary statistics for all columns print(rentals.describe(include='all'))

rent | size_sqft | borough | |
---|---|---|---|

count | 5000.000000 | 5000.000000 | 5000 |

unique | NaN | NaN | 3 |

top | NaN | NaN | Manhattan |

freq | NaN | NaN | 3539 |

mean | 4536.920800 | 920.101400 | NaN |

std | 2929.838953 | 440.150464 | NaN |

min | 1250.000000 | 250.000000 | NaN |

25% | 2750.000000 | 633.000000 | NaN |

50% | 3600.000000 | 800.000000 | NaN |

75% | 5200.000000 | 1094.000000 | NaN |

max | 20000.000000 | 4800.000000 | NaN |

This is a great way to get an overview of all the variables in a dataset. Notice how different statistics are displayed depending on the variable type. In the rest of the lesson, we’ll look more closely at the common ways to summarize and visualize quantitative and categorical variables.

### Instructions

**1.**

In **script.py**, we’ve imported a dataset containing information on the budget and earnings of movies from various genres into a DataFrame called `movies`

.

Start by inspecting the first 5 rows of `movies`

using the `.head()`

method and print the result.

How many quantitative and categorical variables do you see?

**2.**

Use the `.describe()`

method to display the summary statistics for `movies`

and print the result. Make sure to show statistics for all columns in the DataFrame.

What kinds of metrics are displayed for quantitative columns versus categorical columns?