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Data Analysis Foundations with Python

Chapter 16: Case Study 1: Sales Data Analysis

16.2 EDA and Visualization

After defining the problem, the next logical step is Exploratory Data Analysis (EDA) and Visualization. This phase helps us understand the nature of our data, identify patterns, and even spot irregularities that could impact the quality of any predictive models we might build later on. 

In this section, we will go through various stages of EDA and data visualization related to our Sales Data Analysis case study. We'll touch upon data cleaning, data transformation, and data visualization to get a good grasp of what our sales data looks like and how it behaves. So let's dive in! 

16.2.1 Importing the Data

First, let's read the sales_data.csv file into a Pandas DataFrame. This will allow us to start exploring its contents.

# Import sales_data.csv
df_sales = pd.read_csv('sales_data.csv')

# Show first five rows
df_sales.head()

Download here the sales_data.csv file

16.2.2 Data Cleaning

Before we start any analysis, let's make sure our data is clean. We'll check for missing values and duplicate entries.

# Check for missing values
print(df_sales.isnull().sum())

# Check for duplicate entries
print(df_sales.duplicated().sum())

If there are missing or duplicated entries, you'll have to handle them appropriately (e.g., remove or impute the missing values).

16.2.3 Basic Statistical Insights

Let's also take a look at some basic statistics.

# Descriptive statistics
df_sales.describe()

16.2.4 Data Visualization

Sales Trend Analysis

We want to know how sales have been trending over time. Let's plot the monthly sales.

# Convert 'OrderDate' to datetime type
df_sales['OrderDate'] = pd.to_datetime(df_sales['OrderDate'])

# Aggregate data by month
df_monthly_sales = df_sales.resample('M', on='OrderDate').sum()

# Plotting
plt.figure(figsize=(10,6))
plt.plot(df_monthly_sales.index, df_monthly_sales['Quantity'])
plt.title('Monthly Sales Trend')
plt.xlabel('Month')
plt.ylabel('Total Sales')
plt.show()

Customer Segmentation

To get an idea of customer behavior, let's plot a histogram showing the frequency of order quantities.

# Histogram of Order Quantities
plt.figure(figsize=(10,6))
plt.hist(df_sales['Quantity'], bins=50, edgecolor='black')
plt.title('Customer Segmentation by Order Quantity')
plt.xlabel('Order Quantity')
plt.ylabel('Frequency')
plt.show()

These are just the starting steps, but they should give you a good sense of what's happening with your sales data. In the following sections, we'll dive deeper into specific analyses and even build predictive models based on this data.

And there you have it! With EDA and visualization, you're making the first steps toward understanding your sales data inside and out. Trust us; this information will be golden when you're making data-driven decisions! 

16.2 EDA and Visualization

After defining the problem, the next logical step is Exploratory Data Analysis (EDA) and Visualization. This phase helps us understand the nature of our data, identify patterns, and even spot irregularities that could impact the quality of any predictive models we might build later on. 

In this section, we will go through various stages of EDA and data visualization related to our Sales Data Analysis case study. We'll touch upon data cleaning, data transformation, and data visualization to get a good grasp of what our sales data looks like and how it behaves. So let's dive in! 

16.2.1 Importing the Data

First, let's read the sales_data.csv file into a Pandas DataFrame. This will allow us to start exploring its contents.

# Import sales_data.csv
df_sales = pd.read_csv('sales_data.csv')

# Show first five rows
df_sales.head()

Download here the sales_data.csv file

16.2.2 Data Cleaning

Before we start any analysis, let's make sure our data is clean. We'll check for missing values and duplicate entries.

# Check for missing values
print(df_sales.isnull().sum())

# Check for duplicate entries
print(df_sales.duplicated().sum())

If there are missing or duplicated entries, you'll have to handle them appropriately (e.g., remove or impute the missing values).

16.2.3 Basic Statistical Insights

Let's also take a look at some basic statistics.

# Descriptive statistics
df_sales.describe()

16.2.4 Data Visualization

Sales Trend Analysis

We want to know how sales have been trending over time. Let's plot the monthly sales.

# Convert 'OrderDate' to datetime type
df_sales['OrderDate'] = pd.to_datetime(df_sales['OrderDate'])

# Aggregate data by month
df_monthly_sales = df_sales.resample('M', on='OrderDate').sum()

# Plotting
plt.figure(figsize=(10,6))
plt.plot(df_monthly_sales.index, df_monthly_sales['Quantity'])
plt.title('Monthly Sales Trend')
plt.xlabel('Month')
plt.ylabel('Total Sales')
plt.show()

Customer Segmentation

To get an idea of customer behavior, let's plot a histogram showing the frequency of order quantities.

# Histogram of Order Quantities
plt.figure(figsize=(10,6))
plt.hist(df_sales['Quantity'], bins=50, edgecolor='black')
plt.title('Customer Segmentation by Order Quantity')
plt.xlabel('Order Quantity')
plt.ylabel('Frequency')
plt.show()

These are just the starting steps, but they should give you a good sense of what's happening with your sales data. In the following sections, we'll dive deeper into specific analyses and even build predictive models based on this data.

And there you have it! With EDA and visualization, you're making the first steps toward understanding your sales data inside and out. Trust us; this information will be golden when you're making data-driven decisions! 

16.2 EDA and Visualization

After defining the problem, the next logical step is Exploratory Data Analysis (EDA) and Visualization. This phase helps us understand the nature of our data, identify patterns, and even spot irregularities that could impact the quality of any predictive models we might build later on. 

In this section, we will go through various stages of EDA and data visualization related to our Sales Data Analysis case study. We'll touch upon data cleaning, data transformation, and data visualization to get a good grasp of what our sales data looks like and how it behaves. So let's dive in! 

16.2.1 Importing the Data

First, let's read the sales_data.csv file into a Pandas DataFrame. This will allow us to start exploring its contents.

# Import sales_data.csv
df_sales = pd.read_csv('sales_data.csv')

# Show first five rows
df_sales.head()

Download here the sales_data.csv file

16.2.2 Data Cleaning

Before we start any analysis, let's make sure our data is clean. We'll check for missing values and duplicate entries.

# Check for missing values
print(df_sales.isnull().sum())

# Check for duplicate entries
print(df_sales.duplicated().sum())

If there are missing or duplicated entries, you'll have to handle them appropriately (e.g., remove or impute the missing values).

16.2.3 Basic Statistical Insights

Let's also take a look at some basic statistics.

# Descriptive statistics
df_sales.describe()

16.2.4 Data Visualization

Sales Trend Analysis

We want to know how sales have been trending over time. Let's plot the monthly sales.

# Convert 'OrderDate' to datetime type
df_sales['OrderDate'] = pd.to_datetime(df_sales['OrderDate'])

# Aggregate data by month
df_monthly_sales = df_sales.resample('M', on='OrderDate').sum()

# Plotting
plt.figure(figsize=(10,6))
plt.plot(df_monthly_sales.index, df_monthly_sales['Quantity'])
plt.title('Monthly Sales Trend')
plt.xlabel('Month')
plt.ylabel('Total Sales')
plt.show()

Customer Segmentation

To get an idea of customer behavior, let's plot a histogram showing the frequency of order quantities.

# Histogram of Order Quantities
plt.figure(figsize=(10,6))
plt.hist(df_sales['Quantity'], bins=50, edgecolor='black')
plt.title('Customer Segmentation by Order Quantity')
plt.xlabel('Order Quantity')
plt.ylabel('Frequency')
plt.show()

These are just the starting steps, but they should give you a good sense of what's happening with your sales data. In the following sections, we'll dive deeper into specific analyses and even build predictive models based on this data.

And there you have it! With EDA and visualization, you're making the first steps toward understanding your sales data inside and out. Trust us; this information will be golden when you're making data-driven decisions! 

16.2 EDA and Visualization

After defining the problem, the next logical step is Exploratory Data Analysis (EDA) and Visualization. This phase helps us understand the nature of our data, identify patterns, and even spot irregularities that could impact the quality of any predictive models we might build later on. 

In this section, we will go through various stages of EDA and data visualization related to our Sales Data Analysis case study. We'll touch upon data cleaning, data transformation, and data visualization to get a good grasp of what our sales data looks like and how it behaves. So let's dive in! 

16.2.1 Importing the Data

First, let's read the sales_data.csv file into a Pandas DataFrame. This will allow us to start exploring its contents.

# Import sales_data.csv
df_sales = pd.read_csv('sales_data.csv')

# Show first five rows
df_sales.head()

Download here the sales_data.csv file

16.2.2 Data Cleaning

Before we start any analysis, let's make sure our data is clean. We'll check for missing values and duplicate entries.

# Check for missing values
print(df_sales.isnull().sum())

# Check for duplicate entries
print(df_sales.duplicated().sum())

If there are missing or duplicated entries, you'll have to handle them appropriately (e.g., remove or impute the missing values).

16.2.3 Basic Statistical Insights

Let's also take a look at some basic statistics.

# Descriptive statistics
df_sales.describe()

16.2.4 Data Visualization

Sales Trend Analysis

We want to know how sales have been trending over time. Let's plot the monthly sales.

# Convert 'OrderDate' to datetime type
df_sales['OrderDate'] = pd.to_datetime(df_sales['OrderDate'])

# Aggregate data by month
df_monthly_sales = df_sales.resample('M', on='OrderDate').sum()

# Plotting
plt.figure(figsize=(10,6))
plt.plot(df_monthly_sales.index, df_monthly_sales['Quantity'])
plt.title('Monthly Sales Trend')
plt.xlabel('Month')
plt.ylabel('Total Sales')
plt.show()

Customer Segmentation

To get an idea of customer behavior, let's plot a histogram showing the frequency of order quantities.

# Histogram of Order Quantities
plt.figure(figsize=(10,6))
plt.hist(df_sales['Quantity'], bins=50, edgecolor='black')
plt.title('Customer Segmentation by Order Quantity')
plt.xlabel('Order Quantity')
plt.ylabel('Frequency')
plt.show()

These are just the starting steps, but they should give you a good sense of what's happening with your sales data. In the following sections, we'll dive deeper into specific analyses and even build predictive models based on this data.

And there you have it! With EDA and visualization, you're making the first steps toward understanding your sales data inside and out. Trust us; this information will be golden when you're making data-driven decisions!