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# Pandas Basics

Before solving these basic Pandas exercises you should have read Series and Data Frames.

For these exercises we use a dataset describing used cars obtained from kaggle.com. Licences: Open Data Commons Database Contents License (DbCL) v1.0 and Open Data Commons Open Database License (ODbL) .

    import pandas as pd

    data = pd.read_csv('cars.csv')

## First Look

### Basic Information

Print the following information about the data frame `data`:

- first 10 rows,

- number of rows,

- basic statistical information,

- column labels, data types, memory usage.

Solution:

    # your solution

### Missing Values

Are there missing values in `data`?

Solution:

    # your answer

### Value Counts

Use `DataFrame.nunique` to get the number of different values per column.

Solution:

    # your solution

### Unique Car Models

Use `DataFrame.value_counts` to get the number of unique `'name'`-`'year'` combinations.

Solution:

    # your solution

## Restructure Columns

### New Columns

Append a column `'manual_trans'` containing `True` where column `'transmission'` shows `'Manual'`, else `False`.

Append a column `'age'` showing a car’s age (now minus `'year'`).

Solution:

    # your solution

### Remove Columns

Remove columns `'seller_type'`, `'transmission'`, and `'owner'`.

Solution:

    # your solution

## Mean Price

### Series with String Index

Create a Pandas series `price` with column `'name'` as index and column `'selling_price'` as data.

Solution:

    # your solution

### Mean

Calculate mean price for model `'Maruti Swift Dzire VDI'`.

Solution:

    # your solution

## Kilometers per Year

### Boolean Indexing

Use boolean row indexing to get a data frame `one_model` with columns `'km_driven'` and `'age'` containing only rows with `'name'` equal to `'Maruti Swift Dzire VDI'`.

Solution:

    # your solution

### New Column

Add a column `'km_per_year'` to the `one_model` data frame containing kilometers per year.

Solution:

    # your solution

### Mean

Get the mean of column `'km_per_year'` in `one_model`.

Solution:

    # your solution

## Oldest Car

Find the oldest car in `data` and print its name and manufacturing year. Have a look at Pandas’ documentation for suitable functions.

Solution:

    # your solution