Tourism Trends

Note

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Description: §

You’re a Data Analyst at a tourism board, tasked with analyzing visitor patterns for popular destinations. Your goal is to prepare and transform the data for insights.

Tasks: §

• Task 1: Data Melting
  Transform the dataset from wide format to long format, pivoting on the ‘Quarter’ column.
• Task 2: Visitor Type Categories
  Create a new column ‘Visitor_Category’ by mapping ‘Destination’ to ‘Local’ (Domestic) or ‘Foreign’ (International).
• Task 3: Destination Groups
  Create a new column ‘Region’ by grouping destinations into continents: Asia (Tokyo, Bangkok) Europe (Paris) Americas (New York) Oceania (Sydney)

# import libraries
import pandas as pd
import numpy as np
import sys
 
print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)
print('Numpy version ' + np.__version__)

Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]
Pandas version 2.2.1
Numpy version 1.26.4

The Data §

The dataset consists of 10,000 entries, representing tourist visits to popular destinations.

Columns: §

• Destination: City visited (Paris, Tokyo, New York, Sydney, Bangkok)
• Age_Group: Visitor age range (18-24, 25-34, 35-44, 45-54, 55+)
• Q1, Q2, Q3, Q4: Visitors per quarter
• Revenue: Revenue generated

# set the seed
np.random.seed(0)
 
# destination options
destinations = ['Paris', 'Tokyo', 'New York', 'Sydney', 'Bangkok']
 
# age groups
age_groups = ['18-24', '25-34', '35-44', '45-54', '55+']
 
# quarter options
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
 
# generate data
data = {
    'Destination': np.random.choice(destinations, size=10000),
    'Age_Group': np.random.choice(age_groups, size=10000),
    'Q1': np.random.randint(1, 100, size=10000),
    'Q2': np.random.randint(1, 100, size=10000),
    'Q3': np.random.randint(1, 100, size=10000),
    'Q4': np.random.randint(1, 100, size=10000),
    'Revenue': np.random.randint(100, 1000, size=10000)
}
 
df = pd.DataFrame(data)
 
df.head()

	Destination	Age_Group	Q1	Q2	Q3	Q4	Revenue
0	Bangkok	55+	76	75	85	97	679
1	Paris	25-34	71	92	69	83	900
2	Sydney	55+	51	19	55	12	555
3	Sydney	35-44	20	40	69	49	213
4	Sydney	25-34	74	66	68	46	227

Let us take a quick look at the data types.

We can see we have out 10,000 rows, no null values, and only the “Destination” and “Age_Group” columns are of type string.

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 7 columns):
 #   Column       Non-Null Count  Dtype 
---  ------       --------------  ----- 
 0   Destination  10000 non-null  object
 1   Age_Group    10000 non-null  object
 2   Q1           10000 non-null  int32 
 3   Q2           10000 non-null  int32 
 4   Q3           10000 non-null  int32 
 5   Q4           10000 non-null  int32 
 6   Revenue      10000 non-null  int32 
dtypes: int32(5), object(2)
memory usage: 351.7+ KB

Task 1: Data Melting §

Transform the dataset from wide format to long format, pivoting on the ‘Quarter’ column

This task is asking us to transform the Q1, Q2, Q3, Q4 columns into one column we can call “visitors”. In order to accomplish this, pandas provides us with a method called pd.melt().

Parameters:

• id_vars - all of the columns to leave alone
• value_vars - all of the columns you want to melt/pivot
• var_name - what do you want to call the columns you are pivoting
• value_name - what do the values under the pivoting columns represent.

# columns to keep
keep_cols = ["Destination","Age_Group","Revenue"]
 
new_df = pd.melt(df, id_vars=keep_cols, var_name="Quarter", value_name="Visitors")
new_df.head()

	Destination	Age_Group	Revenue	Quarter	Visitors
0	Bangkok	55+	679	Q1	76
1	Paris	25-34	900	Q1	71
2	Sydney	55+	555	Q1	51
3	Sydney	35-44	213	Q1	20
4	Sydney	25-34	227	Q1	74

Task 2: Visitor Type Categories §

Create a new column ‘Visitor_Category’ by mapping ‘Destination’ to ‘Local’ (Domestic) or ‘Foreign’ (International).

I will first define what countries are Domestic or International.

• Domestic = ‘New York’
• International = ‘Paris’, ‘Tokyo’, ‘Sydney’, ‘Bangkok’

# map the values
mapping = {**dict.fromkeys(['Paris', 'Tokyo', 'Sydney', 'Bangkok'], 'International'), 
           **dict.fromkeys(['New York'], 'Domestic')}
 
# apply the map to the Destination column
new_df['Visitor_Category'] = new_df['Destination'].map(mapping)
new_df.head(8)

	Destination	Age_Group	Revenue	Quarter	Visitors	Visitor_Category
0	Bangkok	55+	679	Q1	76	International
1	Paris	25-34	900	Q1	71	International
2	Sydney	55+	555	Q1	51	International
3	Sydney	35-44	213	Q1	20	International
4	Sydney	25-34	227	Q1	74	International
5	Tokyo	45-54	438	Q1	52	International
6	Sydney	55+	921	Q1	60	International
7	New York	18-24	354	Q1	98	Domestic

Task 3: Destination Groups §

Create a new column ‘Region’ by grouping destinations into continents:

• Asia (Tokyo, Bangkok)
• Europe (Paris)
• Americas (New York)
• Oceania (Sydney)

We can use the same strategy from Task #2.

# map the values
mapping = {**dict.fromkeys(['Tokyo', 'Bangkok'], 'Asia'), 
           **dict.fromkeys(['Paris'], 'Europe'),
           **dict.fromkeys(['New York'], 'Americas'),
           **dict.fromkeys(['Sydney'], 'Oceania')}
 
# apply the map to the Destination column
new_df['Region'] = new_df['Destination'].map(mapping)
new_df.head()

	Destination	Age_Group	Revenue	Quarter	Visitors	Visitor_Category	Region
0	Bangkok	55+	679	Q1	76	International	Asia
1	Paris	25-34	900	Q1	71	International	Europe
2	Sydney	55+	555	Q1	51	International	Oceania
3	Sydney	35-44	213	Q1	20	International	Oceania
4	Sydney	25-34	227	Q1	74	International	Oceania

Summary §

You just went through a Pandas tutorial that walked you through data analysis of tourism trends. It covered transforming data from wide to long format, creating visitor categories like Domestic and International, and grouping destinations by continent like Asia, Europe, Americas, and Oceania. The tutorial used Pandas functions like pd.melt() and map() to get the job done, giving you insights into tourist visit patterns.

Key Takeaways §

• Data transformation using pd.melt().
• Creating new columns using mapping.
• Data categorization using the map() function.