Вивчайте Data Cleaning Essentials | Business Data Manipulation

Business datasets often contain data quality issues that can affect your analysis and decision-making. Some of the most common problems include missing values, where information such as sales amounts or customer names is absent; inconsistent formats, such as variations in capitalization or spelling in product names; and outliers, which are values that deviate significantly from the rest of the data and may indicate errors or unusual events. These issues can lead to inaccurate results if not properly addressed, so it is crucial to identify and clean your data before performing any analysis.


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import pandas as pd

# Sample sales dataset with missing values and inconsistent product name capitalization
data = {
    "Product": ["laptop", "Monitor", "LAPTOP", None, "keyboard", "Keyboard"],
    "Sales": [1200, 300, None, 450, None, 200]
}
df = pd.DataFrame(data)
print(df)

To ensure your business data is ready for analysis, you need to follow a systematic approach to cleaning. Start by filling missing values—numerical fields like sales amounts can often be filled with zeros or an average value, depending on your business context. Next, standardize text fields so that entries like laptop, LAPTOP, and Laptop are all formatted the same way, usually using title case or lower case. Finally, check for and remove invalid entries, such as rows with missing critical information (like a missing product name) or impossible values (like negative sales). Applying these strategies helps you create a reliable dataset for business insights.


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# Fill missing sales values with zero
df["Sales"] = df["Sales"].fillna(0)

# Standardize product names to title case and remove rows with missing product names
df["Product"] = df["Product"].str.title()
df = df.dropna(subset=["Product"])

print(df)

1. What is a common approach to handling missing values in business data?

2. Why is it important to standardize text fields (like product names) in business datasets?

3. Fill in the blanks: To replace missing values in a list of dictionaries, you can use a loop and the method.

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              123456789
            
import pandas as pd

# Sample sales dataset with missing values and inconsistent product name capitalization
data = {
    "Product": ["laptop", "Monitor", "LAPTOP", None, "keyboard", "Keyboard"],
    "Sales": [1200, 300, None, 450, None, 200]
}
df = pd.DataFrame(data)
print(df)


              12345678
            
# Fill missing sales values with zero
df["Sales"] = df["Sales"].fillna(0)

# Standardize product names to title case and remove rows with missing product names
df["Product"] = df["Product"].str.title()
df = df.dropna(subset=["Product"])

print(df)

1. What is a common approach to handling missing values in business data?

2. Why is it important to standardize text fields (like product names) in business datasets?

3. Fill in the blanks: To replace missing values in a list of dictionaries, you can use a loop and the method.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 2

Data Cleaning Essentials

1. What is a common approach to handling missing values in business data?

2. Why is it important to standardize text fields (like product names) in business datasets?

3. Fill in the blanks: To replace missing values in a list of dictionaries, you can use a ____ loop and the ____ method.

Data Cleaning Essentials

1. What is a common approach to handling missing values in business data?

2. Why is it important to standardize text fields (like product names) in business datasets?

3. Fill in the blanks: To replace missing values in a list of dictionaries, you can use a ____ loop and the ____ method.

3. Fill in the blanks: To replace missing values in a list of dictionaries, you can use a loop and the method.

3. Fill in the blanks: To replace missing values in a list of dictionaries, you can use a loop and the method.