Welcome to the first step in our course. This lesson demonstrates how to load and explore the Airline dataset using Python, showcasing its basic structure and notable features.

Understanding the dataset you're working with is the first key step in any data science project. Exploring the dataset helps detect trends, outliers, incorrect data, and much more. As a data scientist, it is essential to understand which questions your data can answer and which it cannot. Let's dive in and explore!

Our dataset, called the "Flights" dataset, belongs to the Seaborn library. This dataset provides a monthly tally of airline passengers from 1949 to 1960.

The Flights dataset comprises three distinct columns:

• year: Represents the year in which the count of passengers was taken.
• month: Points towards the month in which the passenger count was gathered.
• passengers: Indicates the number of passengers that traveled in that month of a particular year.

Let's load the dataset in Python. You can easily load this dataset, along with other inbuilt Seaborn datasets, using the load_dataset() method as follows:

Python
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1import seaborn as sns
2
3# Load the Flights dataset
4flights_df = sns.load_dataset('flights')
5
6# Display the first five records
7print(flights_df.head())
8"""
9   year month  passengers
100  1949   Jan         112
111  1949   Feb         118
122  1949   Mar         132
133  1949   Apr         129
144  1949   May         121
15"""
16
17# Display the first 10 records
18print(flights_df.head(10))
19
20# Display the last five records
21print(flights_df.tail())

Running the above script will load the "Flights" dataset into a pandas DataFrame and display the first five records, the first ten, and the last 5 records, respectively. As you will see from the output, the dataset contains rows representing individual months over several years, with columns specifying the year, month, and number of passengers.

Now, let's delve a little deeper into the structure of our data. Our DataFrame flights_df has a specific shape, i.e., it contains a certain number of rows and columns. You can retrieve this shape using the shape attribute. This attribute returns a tuple representing the dimensionality of the DataFrame. It is used to get the current shape of DataFrame, i.e., (number of rows and columns).

Additionally, you can use the info() method to get a quick description of the data, including the total number of non-null entries and the column data types.

Python
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1# Get the dimensions of the dataset
2print('Shape of the dataset:', flights_df.shape)
3# Output: Shape of the dataset: (144, 3)
4
5# Get more information about the dataset
6flights_df.info()
7"""
8<class 'pandas.core.frame.DataFrame'>
9RangeIndex: 144 entries, 0 to 143
10Data columns (total 3 columns):
11 #   Column      Non-Null Count  Dtype   
12---  ------      --------------  -----   
13 0   year        144 non-null    int64   
14 1   month       144 non-null    category
15 2   passengers  144 non-null    int64   
16dtypes: category(1), int64(2)
17memory usage: 2.9 KB
18"""

This will print out the number of entries, columns, column names, their data types, and the count of non-null entries per column, telling us whether our data has any missing entries. In this case, our dataset is complete and contains no missing values.

We always want more! It is time we dig a little deeper into the dataset. A quick way to get a summary of the numerical fields in your dataset is to use the describe() command. This command provides a statistical summary for numerical columns.

Python
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1# Explore basic statistics of the dataset
2print(flights_df.describe())
3"""
4              year  passengers
5count   144.000000  144.000000
6mean   1954.500000  280.298611
7std       3.464102  119.966317
8min    1949.000000  104.000000
925%    1951.750000  180.000000
1050%    1954.500000  265.500000
1175%    1957.250000  360.500000
12max    1960.000000  622.000000
13"""

This command will generate a precise summary of the respective statistics of the DataFrame. You will see from the output that the years range from 1949 to 1960, and the median number of passengers, denoted by the 50% quantile, is around 265.5 - quite insightful already, isn't it?

Congratulations on completing your first exploration of the Flights dataset! You now have a better understanding of the structure of your data, its overall shape, and important statistical insights. You've successfully loaded the Airline dataset and done an initial exploration.

Throughout this lesson, we have covered:

• Loading the Airline dataset using the load_dataset() function in Seaborn.
• Getting dataset shape and summary with the describe() and info() attributes.
• Applying basic descriptive statistics to understand your data better, using the describe() function.

By doing this, we're laying a foundation for the subsequent steps: cleaning and manipulating this data, then visualizing and modeling it. The initial exploration of the data makes us better prepared for what lies ahead: visualizing and uncovering trends in air travel!

Are you ready to delve deeper? In the following practice session, you will have a chance to practice your skills and explore the dataset further. Use the knowledge you gained in this lesson to uncover more insights and expand your understanding of the dataset. Let's get to it!